Marchant, Christian C.
RETRIEVAL OF AEROSOL MASS CONCENTRATION FROM ELASTIC LIDAR
DATA
by
Christian C. Marchant
A dissertation submitted in partial fulfillment
of the requirements for the degree
of
DOCTOR OF PHILOSOPHY
in
Electrical Engineering
Approved:
Dr. Todd K. Moon
Major Professor
Dr. Jacob H. Gunther
Committee Member
Dr. Scott E. Budge
Committee Member
Dr. Vladimir V. Zavyalov
Committee Member
Dr. Gail E. Bingham
Committee Member
Dr. Byron R. Burnham
Dean of Graduate Studies
UTAH STATE UNIVERSITY
Logan, Utah
2010
ii
Copyright
c Christian C. Marchant 2010
All Rights Reserved
iii
Abstract
Retrieval of Aerosol Mass Concentration from Elastic Lidar Data
by
Christian C. Marchant, Doctor of Philosophy
Utah State University, 2010
Major Professor: Dr. Todd K. Moon
Department: Electrical and Computer Engineering
Agricultural aerosol sources can contribute significantly to air pollution in many regions
of the country. Characterization of the aerosol emissions of agricultural operations is required
to establish a scientific basis for crafting regulations concerning agricultural aerosols. A new
lidar instrument for measuring aerosol emissions is described, as well as two new algorithms
for converting lidar measurements into aerosol concentration data. The average daily aerosol
emission rate is estimated from a dairy using lidar.
The Aglite Lidar is a portable scanning lidar for mapping the concentration of particulate
matter from agricultural and other sources. The instrument is described and performance
and lidar sensitivity data are presented. Its ability to map aerosol plumes is demonstrated,
as well as the ability to extract wind-speed information from the lidar data.
An iterative least-squares method is presented for estimating the solution to the lidar
equation. The method requires a priori knowledge of aerosol relationships from point sensors.
The lidar equation is formulated and solved in vector form. The solution is stable for signals
with extremely low signal-to-noise ratios and for signals at ranges far beyond the boundary
point.
Another lidar algorithm is also presented as part of a technique for estimating aerosol
concentration and particle-size distribution. This technique uses a form of the extended
iv
Kalman filter, wherein the target aerosol is represented as a linear combination of basisaerosols. For both algorithms, the algorithm is demonstrated using both synthetic test data
and field measurements of biological aerosol simulants. The estimated particle size distribution allows straightforward calculation of parameters such as volume-fraction concentration
and effective radius.
Particulate matter emission rates from a dairy in the San Joaquin Valley of California
were investigated during June 2008. Vertical particulate matter concentration profiles were
measured both upwind and downwind of the facility using lidar, and a mass balance technique
was used to estimate the average emission rate. Emission rates were also estimated using an
inverse modeling technique coupled with the filter-based measurements. The concentrations
measured by lidar and inverse modeling are of similar magnitude to each other, as well as to
those from studies with similar conditions.
(158 pages)
v
To my wonderful wife....
vi
Acknowledgments
I would like to acknowledge the invaluable guidance of Drs. Gail Bingham, Vladimir
Zavyalov, Thomas Wilkerson, Randal Martin, and Michael Wojcik, whose support was
essential in performing this research. This dissertation is a product of the Aglite program of
the Energy Dynamics Laboratory, part of the Utah State University Research Foundation,
under USDA Agreement number 58-3625-4-121 with Dr. Jerry Hatfield, the director of the
National Soil Tilth Laboratory in Ames, Iowa, who provided valuable direction to the Aglite
development team, along with Richard Pfeiffer and John Prueger. Any opinions, findings,
conclusions, or recommendations expressed in this publication are those of the authors and
do not necessarily reflect the view of the USDA.
I would further like to thank the faculty of the USU Department of Electrical and
Computer Engineering. In particular, I would like to recognize my advisor, Dr. Todd Moon,
as well as Drs. Jacob Gunther and Scott Budge. They provided much valuable education
and advice to me in my educational career and in the completion of this dissertation. It
would be ungrateful not to mention the contributions given by my colleagues Kori Moore,
Doug Ahlstrom, William Bradford, Derek Jones, Tanner Jones, Richard Larsen, Jan Marie
Andersen, Cordell Wright, and Scott Cornelsen.
Finally, I must extend deserved recognition to my family, and in particular my loving
wife, for their encouragement and support. Without them, this dissertation would not have
been possible. My parents have provided me with valuable advice and support, and I am so
grateful for my loving wife, Hanna. I am entirely dependent on her support and patience.
Christian C. Marchant
vii
Contents
Page
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
iii
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
vi
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ix
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
x
Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
1 Introduction . . . . . . . . . . . . . . . . . . . . . .
1.1 Project Background and Relevance . .
1.2 Literature Survey . . . . . . . . . . . .
1.3 Research Overview . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . .
.....
. . . .
. . . .
. . . .
. . . .
....
. . .
. . .
. . .
. . .
....
. . .
. . .
. . .
. . .
.
.
.
.
.
....
. . .
. . .
. . .
. . .
2 Aglite Description and Operation . . . . . . . . . . . . . . . . . . . . .
2.1 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Aglite Lidar Description . . . . . . . . . . . . . . . . . . . . .
2.2.1 Hardware Design . . . . . . . . . . . . . . . . . . . . .
2.2.2 Eye-Safety . . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Lidar Data and Calibration . . . . . . . . . . . . . . . . . . .
2.4 Example Data and Discussion . . . . . . . . . . . . . . . . . .
2.4.1 Aerosol Detection Capability . . . . . . . . . . . . . .
2.4.2 Fixed Direction Lidar Measurement . . . . . . . . . .
2.4.3 Crosswind Scans for Aerosol Flux Measurements . . .
2.5 Aerosol Traced Wind Motion . . . . . . . . . . . . . . . . . .
2.5.1 Wind Velocity from Lidar “Stares” . . . . . . . . . . .
2.5.2 Vertical Scans for Cross-Sections of Moving Plumes .
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
....
. . .
. . .
. . .
. . .
....
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
....
. . .
. . .
. . .
. . .
.. 1
. . 1
.
3
.
10
.
14
....
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
. . .
..
.
. .
.
.
.
.
.
.
.
.
.
.
. .
.
19
19
21
22
26
28
32
32
34
35
36
36
38
41
43
3 An Iterative Least Square Approach to Elastic-Lidar Retrievals for WellCharacterized Aerosols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
48
3.2 Well Characterized Local Aerosol . . . . . . . . . . . . . . . . . . . . . . . .
53
3.3 State Vector Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
57
3.4 Linearization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
59
3.5 Laser Pulse Shape and Geometric Form Factor . . . . . . . . . . . . . . . .
64
3.6 Solution Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
66
3.7 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
viii
3.7.1 Background Retrieval
3.7.2 Plume Retrieval . . .
3.8 Experimental Data . . . . . .
3.9 Conclusion . . . . . . . . . .
References . . . . . . . . . . . . . .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4 Estimation of Aerosol Effective Radius by Multi-Wavelength Elastic
dar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 EKF Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.1 Forward Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.2 State-Space Signal Model . . . . . . . . . . . . . . . . . . . . . . .
4.2.3 State-Vector Augmentation . . . . . . . . . . . . . . . . . . . . . .
4.3 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Experimental Measurements . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
.
70
. . 71
.
73
.
77
.
79
Li..
. .
.
.
.
.
.
.
.
81
81
82
82
84
87
89
94
99
5 Estimation of Dairy PM Emission Rates by Lidar and Inverse Modeling 101
5.1 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.2.1 Site Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.2.2 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.2.3 Inverse Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.2.4 Elastic Lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.3 Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.3.1 Point Samplers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.3.2 Aglite Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
5.3.3 Calculated Emission Rates . . . . . . . . . . . . . . . . . . . . . . . 113
5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . .
6.1 Elastic Lidar Applications . . . . . . . . . . . . . .
6.2 Lidar Design Tradeoffs . . . . . . . . . . . . . . . .
6.3 Wavelength and Component Aerosol Selection . . .
6.4 Information Retrieval from Lidar Returns . . . . .
6.5 Research Context . . . . . . . . . . . . . . . . . . .
6.6 Future Work . . . . . . . . . . . . . . . . . . . . .
Appendices
Appendix
Appendix
Appendix
....
. . .
. . .
. . .
. . .
. . .
. . .
.....................................
A Construction of Lidar Equation Matrices . .
B Derivation of Covariance Matrices . . . . . .
C Publication Release Forms . . . . . . . . . .
.
.
.
.
.....
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
....
. . .
. . .
. . .
....
. . .
. . .
. . .
. . .
. . .
. . .
....
. . .
. . .
. . .
....
. . .
. . .
. . .
. . .
. . .
. . .
.....
. . . .
. . . .
. . . .
. . 119
. 119
. 120
. . 121
. 122
. 123
. 124
..
.
.
.
126
127
130
133
Vita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
ix
List of Tables
Table
Page
2.1
Aglite lidar system parameters. . . . . . . . . . . . . . . . . . . . . . . . . .
27
2.2
Representative eye-safety distances for Aglite lidar. . . . . . . . . . . . . . .
28
3.1
Aerosol optical and mass characteristics. . . . . . . . . . . . . . . . . . . . .
70
3.2
Average retrieval error comparison. . . . . . . . . . . . . . . . . . . . . . . .
73
4.1
Simulant properties and conditions. . . . . . . . . . . . . . . . . . . . . . . .
96
5.1
Average calculated mass conversion factors (±95% CI) for each measurement
period, June 2008. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
113
Average temperature, wind velocity, estimated emission rates, and ±95%
confidence interval (g/day/AU) for each period, estimated by inverse modeling
and lidar. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
115
Emission rates estimated by inverse modeling, lidar measurements, and
previous studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
117
5.2
5.3
x
List of Figures
Figure
Page
2.1
Aglite lidar instrument in deployment configuration. . . . . . . . . . . . . .
23
2.2
Aglite lidar conceptual arrangement and layout of optical bench components. 23
2.3
Aglite geometric form factor as a function of range. . . . . . . . . . . . . . .
2.4
Example return power signal and signal variance from Aglite. . . . . . . . . . 31
2.5
Two examples of SNR values observed by Aglite under field conditions. . . . 31
2.6
Typical “stare” image of range-normalized IR channel intensity. . . . . . . .
33
2.7
Example retrieval showing measured signal, estimated backscatter, mode
radius, and PM10 concentration. . . . . . . . . . . . . . . . . . . . . . . . .
35
2.8
Examples of typical horizontal and staple scans by Aglite. . . . . . . . . . .
37
2.9
Local wind patterns shown in a time series of lidar returns at different ranges. 39
30
2.10 Returns from an 8-minute horizontal stare at 20 m height during light and
variable wind conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
2.11 Series of 30-second vertical scans showing the movement of plumes toward
the lidar. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
40
2.12 Distortion of the aerosol image due to simultaneous motion of wind and lidar
scan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
42
2.13 Wind-blown plumes of clay dust in Nevada. . . . . . . . . . . . . . . . . . .
42
3.1
Illustration of Aglite measuring an atmosphere that consists of a baseline
component plus a mixture of varying components. . . . . . . . . . . . . . . . 51
3.2
Comparison of synthesized data with the estimated solution for measured
power (left column) and PM10 concentration (right column) of (a,b) average
continental, (c,d) polluted continental, and (e,f) urban type homogeneous
atmospheres. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
62
Flowchart illustrating the operation of the iterative least-squares method. .
63
3.3
xi
3.4
3.5
(a) Laser pulse shape convolved with the integration window, and (b) its
frequency response. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
66
Estimated PM10 mean concentration and standard deviation compared to
actual values for synthesized data representing (a,b) average continental, (c,d)
polluted continental, (e,f) and urban type plumes. . . . . . . . . . . . . . .
72
3.6
Estimated values for (a,b) returned power, (c,d) emission component amplitude, and (e,f) PM concentration from swine facility measurement, assuming
one component aerosol (left column) and two component aerosols (right column). 76
3.7
Comparison of the stability of
and Klett methods under the
point backscatter coefficients,
values. . . . . . . . . . . . . .
estimated PM10 concentration from the NLS
conditions of (a) 25% error in the boundary
and (b) 50% error in the baseline extinction
. . . . . . . . . . . . . . . . . . . . . . . . . .
78
4.1
Backscatter coefficients of a (a) single-component, and (b) two-component
aerosol as a function of range. (c) The particle volume distributions of the
two components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.2
Illustration of the performance of an EKF filter on a synthetic data set, including (a) the estimated returned power, (b) SNR, (c) component amplitude,
(d) retrieved component bias, (e) volume concentration, and (f) effective radius. 93
4.3
Images of the estimated (a) PM10 volume-fraction concentration, and (b)
effective radius for the MS2 simulant. . . . . . . . . . . . . . . . . . . . . .
97
Measured (symbols) and estimated (lines) average return power and estimated
effective radius for (a,b) Btk, (c,d) ARD, and (e,f) MS2 type simulants. . .
98
5.1
Map of instrumentation locations. . . . . . . . . . . . . . . . . . . . . . . .
107
5.2
Average PM10 concentration (µg/m3 ) of (a) a vertical scan measured by
lidar at 94 and 98 degrees azimuth, and (b) a horizontal map estimated by
AERMOD, overlaid on the dairy footprint. . . . . . . . . . . . . . . . . . .
114
4.4
xii
Acronyms
AERMOD
American Meteorological Society Regulatory Model
APD
avalanche photo-diode
AU
animal unit
CMP
conservation management practice
EKF
extended Kalman filter
EKS
extended Kalman smoother
EPA
Environmental Protection Agency
FOV
field of view
GFF
geometric form factor
IR
infrared lidar channel (1064 nm)
ISCST3
Industrial Source Complex Short Term Model, Ver. 3
MCF
mass conversion factor
NAAQS
national ambient air quality standards
ND
neutral density
NLS
nonlinear least squares
OPC
optical particle counter
PM
particulate matter
PMT
photo-multiplier tube
PSD
particle-size distribution
PST
Pacific Standard Time
SNR
signal-to-noise ratio
TSP
total suspended particulate
UDP
user datagram protocol
USDA
United Stated Department of Agriculture
UV
ultra-violet lidar channel (355 nm)
V
visible lidar channel (532 nm)
1
Chapter 1
Introduction
1.1
Project Background and Relevance
The impact of agricultural operations on air-quality is a subject of interest to society
and an area of active research. Economic growth, increased demand for food and energy,
and the movement of urban populations into rural areas have resulted in increasing exposure
of people to emissions from these operations, which generate aerosols that impact both the
environment and human health.
Elastic lidar operates using the radar principle to detect Rayleigh and Mie scattering
from atmospheric gas and aerosols. These types of scattering are characterized by the
deflection of a photon by a gas molecule or dust particle in an elastic collision, meaning that
the energy of the photon is conserved. It has the ability to map aerosol concentration in the
atmosphere and to determine aerosol particle size [1]. This makes lidar an enormously useful
tool for investigating air-quality, both generally and in the context of agricultural operations
in particular. In fact, the use of lidar to map particulate matter (PM) concentration and
estimate aerosol emission rates from an agricultural facility has been demonstrated previously,
and lidar has been proven to be a versatile tool for investigating atmospheric aerosols and
a useful means of characterizing and monitoring the air-quality impact of industrial and
agricultural operations [2].
This dissertation is generated as part of the Ag program of the Energy Dynamics
Laboratory of the Utah State University Research Foundation, whose mission is to measure
the air-quality impact of industrial and agricultural facilities. The Ag program uses the
portable Aglite lidar with supporting instruments for mapping aerosol concentration from
agricultural sources.
This dissertation includes four papers, two of which have already been peer-reviewed
2
and published, and the other two have been submitted for publication. Chapter 2 comes
from “Aglite Lidar: A Portable Elastic Lidar System for Investigating Aerosol and Wind
Motions at or Around Agricultural Production Facilities” [3], which describes the hardware
and operational procedure of the Aglite instrument, as well as demonstrating its application
to measuring wind speed by tracking dust plume movement and image morphology. My
specific contributions to this paper included writing the majority of the text, performing
the software modeling of the expected performance of the instrument the development, and
coding the retrieval algorithm based on Klett’s method. In addition, I developed the process
for calibrating lidar signals using point sensors, assisted in the data collection, and performed
all of the data processing.
Chapter 3 comes from “An Iterative Least Square Approach to Elastic-Lidar Retrievals
for Well-Characterized Aerosols” [4]. This paper describes the minimum least-squares
algorithm outlined above, and demonstrates its performance using both synthetic and
experimental data. In addition to writing nearly all of the text of the paper, my contributions
included developing the method of representing an aerosol as a linear combination of basis
aerosols, applying Newton’s method to iteratively linearize and solve the lidar equation, and
incorporating image-restoration into the retrieval process. Additionally, I wrote the software
code to implement the algorithm, created the synthetic data used to verify it, and performed
all of the data processing.
Chapter 4 has been submitted for publication by the journal IEEE Transactions on
Geoscience and Remote Sensing as “Estimation of Aerosol Effective Radius by MultiWavelength Elastic Lidar,” which describes a novel form of the extended Kalman filter
(EKF) estimator, where the filter is applied to the lidar signal over range and directly
estimates lidar concentration, whereas other lidar EKF estimators were applied over time
and only retrieved optical coefficients. The algorithm uses an augmented state vector, making
it a type of EKF called an extended Kalman smoother (EKS). It also demonstrates the
estimation of effective particle radius from an aerosol release experiment at Dugway Proving
Grounds [5]. I also wrote nearly all of the text in this paper and my other contributions
3
included the formulation of the extended Kalman filter as applied to lidar data and the
development of a systematic method for forming basis aerosols. Additionally, I wrote the
software code to implement the algorithm, created the synthetic data used to verify it, and
performed all of the data processing.
Chapter 5 has been submitted for publication by the journal Transactions of the
ASABE as “Estimation of Dairy Particulate Matter Emission Rates by LIDAR and Inverse
Modeling,” and describes the application of the EKS lidar retrieval method to estimate the
PM10 emission rate from a dairy facility [6]. This paper demonstrates the first application
of lidar to estimate aerosol emissions from a dairy and adds to the very small set of existing
baseline data on dairy aerosol emission rates in the literature. My specific contributions to
this paper included writing the majority of the text, processing all of the lidar data, and
assisting in developing the algorithm used by the software models for estimating emission
rates.
1.2
Literature Survey
The most common early use of lidar for meteorological purposes was to measure
cloud and vertical aerosol profiles of the atmosphere. Early examples of applying the
optical radar principle to meteorological measurements used pulses of incoherent white
light to measure cloud heights as early as 1938 [7, 8]. The development of lidar, or laser
radar, shortly followed the invention of the Q-switched laser in 1962 [9]. The first use
of a laser transmitter, with its highly collimated beam, narrow spectral bandwidth, and
narrow temporal pulse width, enabled a dramatic increase in sensitivity and resolution. The
initial application of lidar to meteorological measurements was performed by Fiocco and
Smullin in 1963 to investigate aerosol layers in the upper stratosphere [10]. Other work by
Fiocco investigated residual stratospheric aerosol from meteors, and the upper boundary
layer of the troposphere [11, 12]. Important early work was also performed by Collis, and
included probing clouds, stratospheric aerosol layers, mapping dust plume morphology
generated by subterranean nuclear detonations, and studying waves in the lower troposphere
aerosol boundary layer [13–16]. Collis also published an important early paper outlining the
4
new emerging field of lidar and demonstrating the earliest analytical technique: the slope
method [17].
Early applications of elastic lidar to the problem of air quality used lidar to validate
parametric emission models by mapping emission plume size and height, including work by
Hamilton to capture vertical profiles of emissions from power plant smokestacks. Whereas
earlier work in probing the atmosphere with lidar normally used a stationary 90 degree
vertical stare, this work used a vertical scan composed of a series of measurements at varying
elevation angles, enabling the lidar to capture the morphology of an emission plume in a
2-D cross-section. The measured height of the plume was used to verify predictions of a
parametric model of the emission process [18, 19]. Hamilton also described the use of a
remarkable real-time 2-D analog display of these vertical scans of the emission profile intensity
in 1969 [20]. Barrett and Ben-Dov used lidar to probe atmospheric pollutants, including
a smoke plume and smog layer, and notably demonstrated the retrieval of quantitative
particulate mass concentration values from this data [21]. Johnson demonstrated the
measurement of morphology and dynamics of emission plumes from a smokestack [22, 23].
As pointed out by Reagan, lidar’s potential is greatest when used in conjunction with
supplemental instruments, such as point sensors [24]. These instruments can be used
both to calibrate the lidar and to constrain estimates made using lidar data, enabling
quantitative analysis of lidar data. Fernald demonstrated the use of coincident data from a
solar radiometer in the form of total optical thickness to constrain the analysis of lidar data
and retrieve both backscatter and extinction coefficients from a single channel [25]. Grams
demonstrated a method for retrieving complex index of refraction from vertical profile lidar
measurements of ash in the atmosphere by constraining it with independent measurements of
particle size distribution (PSD), which was measured using a jet impactor to collect particles
onto microscope slides, which were analyzed using a micro-photographic technique [26, 27].
Reagan performed a comparison of aerosol mass concentration measurements between an
ordinary monostatic elastic lidar, a bistatic elastic lidar, and in-situ instruments mounted on
an aircraft. The measurements from the monostatic lidar were constrained using data from
5
the bistatic lidar and a solar radiometer. The estimated aerosol mass concentration from
the monostatic lidar data was compared with simultaneously measured in-situ data from
an aircraft, whose instruments included an optical particle counter (OPC), an integrating
nephelometer, a particle mass monitor, a condensation nucleus counter, and a cascade
impactor [28].
An elastic lidar can also be significantly enhanced through the addition of a non-elastic
Raman scattering channel. A Raman channel enables independent measurement of extinction
alone, which can then be used as a priori information for retrieving backscatter values from
the elastic channel [29]. Bistatic and multiple-field-of-view lidars are also able to give more
information than a standard monostatic elastic lidar by measuring aerosol sidescatter in
addition to backscatter [30, 31].
Lidar has also been applied specifically to investigating agricultural activities. Cooper
used a Raman lidar capable of measuring spatially coherent water vapor structures in order
to investigate the mass-energy exchange between the ground and the atmosphere over the
canopy of an orchard [32]. Stoughton used an elastic lidar to map the dispersion of pesticide
applied by an airplane over a section of forest, with both horizontal and vertical lidar
scanning patterns. These lidar measurements tracked the drift range of the pesticide plumes
and were used to verify the accuracy of two different parametric spray drift models [33].
Holmén demonstrated the measurement of mass concentration values of emissions from
tillage activities. This lidar data was compared with mass concentration values measured by
point samplers in order to verify plume height and dispersion coefficients from a parametric
model [34–36]. Hiscox also demonstrated the use of an elastic lidar to map particulate
mass concentration from tillage activities, and compared them with mass concentration
measurements from point-sensors [37]. Eichinger used an elastic lidar to estimate heat flux
emitted from crop-fields by measuring the height and width of the entrainment zone, the
boundary layer between aerosols near the surface of the ground and cleaner air above. These
estimates were input into a boundary layer model, which generated an estimate of surface
heat flux [38].
6
Initial techniques for quantitative analysis of lidar data focused on retrieving the aerosol
optical properties of backscatter and extinction. This is a difficult problem both because
the equation describing an elastic lidar signal is nonlinear and also because the problem is
ill-posed; two optical coefficients must be estimated for every single channel. Many of these
techniques employed for processing lidar data are analytical in nature, attempting to solve
the partial differential form of the lidar equation. There are also some examples of algorithms
for analyzing lidar data from an optimization perspective. The task of quantitative analysis
of lidar data is complicated by the difficulty in achieving accurate absolute calibration of a
lidar instrument. Many early attempts at quantitative analysis relied on representing the
lidar signal as relative backscatter magnitude by range-correcting the observed signal and
normalizing by its minimum value [14]. If the following assumptions are satisfied, that the
minimum value corresponds to a section of the atmosphere with negligible aerosol scattering,
and if the extinction in the other portions of the signal are also negligible (i.e. optically
thin), then a measured range-corrected signal is proportional to the ratio of backscatter from
aerosols to backscatter from atmospheric gases. In this case, the supposedly pure section of
atmosphere essentially calibrates the lidar signal. Barrett extended this method to estimating
aerosol mass concentration by assuming a PSD for the aerosol and numerically calculating a
linear relationship between backscatter and mass concentration [21]. Collis proposed the
slope method, an analytical method for measuring extinction without calibrating the lidar
signal [17]. This method relies on the assumption that the atmosphere is homogeneous
over some range. In this case, the slope of the logarithmic range-corrected returned power
is determined by the extinction coefficient of the atmosphere. Another related method of
analyzing lidar signals is the slant method proposed by Sandford [39]. This method assumes
that the backscatter and extinction coefficients of the atmosphere are constant at any given
height. The lidar takes a series of measurements at different pointing elevation angles,
and uses the measured power from a given height as a function of angle to determine the
extinction coefficient at that height. Methods were also proposed that relied on a priori
estimates of extinction, either as a function of range [40] or of total transmittance [41].
7
There is a group of analytical solutions for retrieving backscatter as a function of
range from the lidar signal that rely on the partial differential equation form of the lidar
equation, which is of the same form as the Bernoulli equation [25, 42–46]. These methods all
have similar analytical form and rely on a known fixed relationship between extinction and
backscatter, as well as a calibration point, which is the boundary condition at some range
where the extinction value is known. These analytical solutions are the most popular means
of retrieving extinction and backscatter from lidar data, and most techniques in use today
for quantitative analysis of lidar data use them as an intermediate step, after which further
algorithms are used to convert backscatter and extinction coefficients of an aerosol to its
PSD. The analytical solution has two serious drawbacks. First, it has a pole located on the
far side of the calibration point, which makes the solution unstable at far ranges. Second,
the method has no means of accounting for noise in the signal when calculating the solution.
Although less popular than analytical methods, algorithms based on the principle of
optimization have also been proposed. The minimum cross-entropy method is an important
and under-recognized method proposed by Yee for estimating extinction coefficients [47].
This method uses the ellipsoid algorithm, an iterative optimization algorithm that seeks
to calculate the minimum norm solution that corresponds to a lidar signal within a given
distance of the measured signal. Several algorithms have been proposed that are forms of the
EKF. Rocadenbosch proposed a formulation of the EKF that estimates both extinction and
backscatter coefficients as a function of range using a single channel [48,49]. The state vector
consists of down-sampled backscatter and extinction coefficients, allowing the algorithm
to trade spatial resolution for the ability to independently recover both backscatter and
extinction from a single channel. For this formulation, the state vector represents the state
of the atmosphere over all measured range values at any given time and the computational
cost of propagating the state vector can become quite large. Dias proposed a form of the
extended Kalman smoother that describes the lidar signal using a semi-causal filter [50]. The
state vector propagates over range and is composed of only a small number of extinction and
optical thickness coefficients of neighboring data points. An algorithm was also proposed
8
by Warren for use with multi-wavelength lidar data that consists of two estimators coupled
together. The first is a Kalman filter for estimating particle concentration, whose output is
fed into a maximum-likelihood estimator of backscatter coefficients [51].
A lidar retrieval algorithm is typically used to retrieve the intermediate values of
extinction and backscatter. However, often the parameter of interest is the PSD of the
aerosol or other parameters that can be derived from it, such as mass concentration or
mode radius. Much work has been done to demonstrate methods of estimating PSDs from
multi-wavelength backscatter and extinction data. A simple method for discriminating
aerosol types from multiple backscatter channels was demonstrated by Sasano [52]. A
power-law relationship is assumed between the backscatter values of the different channels.
The exponent parameter is measured from lidar data and compared to a pre-computed table
of known aerosols. Retrieval of the PSD has also been demonstrated by assuming the PSD is
of a known form, i.e. a power-law distribution or a log-normal distribution, and estimating
the parameters of the distribution [53–55].
More sophisticated techniques for estimating the PSD from multi-wavelength measurements of an unknown aerosol involve approximating it as a linear combination of component
functions. Heintzenberg proposed an iterative least-square estimator that represented the
PSD as a discrete set of rectangle functions and approximates the measured optical properties as linear combinations of the particle size distribution component functions [56]. It
calculates the optimal amplitude of each individual component function one at a time,
while using a condition check to enforce constraints of smoothness and non-negativity. This
allows it to estimate a PSD function using a greater number of components than measured
channels. Qing suggested a similar approach that uses smoother B-spline functions instead
of the rectangle component functions [57]. Significantly, he also suggested minimizing a cost
function that included a Lagrange multiplier with a smoothing matrix. Donovan proposed
choosing the component functions of the PSD as orthogonal functions determined using
principal component analysis [58]. He also proposed that since the PSD could be calculated
as a linear function of measured optical coefficients, parameters such as total volume or
9
surface area concentration could be calculated directly as linear functions of the optical
coefficients, which he demonstrated using data from a 4 channel lidar, including one Raman
channel. Böckmann proposed a retrieval method that uses B-spline component functions
and projection onto a subspace by means of truncated singular value decomposition in order
to estimate a smooth PSD using only four channels [59].
There are important deficiencies in the previously demonstrated algorithms for converting lidar data into aerosol concentration and estimating aerosol size concentration. Many
of these algorithms, including the popular Klett method [44, 46], give unstable results and
in general these methods fail to perform adequately under low signal-to-noise ratio (SNR)
conditions or under conditions of significant calibration error, which are common under field
conditions.
With lidar, there is an inherent trade-off between both the spatial and temporal
resolution and the SNR of a measurement. At the same time, due to the range-squared falloff
of a lidar signal, it is difficult to construct a lidar instrument that can measure over a wide
span of range values at good SNR levels. As a result of these limitations, there is naturally
a strong desire by lidar operators to operate in a low SNR regime, either to increase the
resolution of a measurement or to extend the range over which data can be collected. The
algorithms described in this dissertation allow the retrieval of aerosol concentration from
lidar measurements with low SNR values, which in turn enables the instrument to scan
the atmosphere at a much higher sample rate and over greater distances than would be
otherwise possible.
Aglite is regularly used under novel and non-ideal conditions, as often occur during field
campaigns. In previous work mapping aerosol concentration with Aglite, it was found that
existing elastic lidar retrieval algorithms were not sufficiently robust under these conditions.
This motivated the development of new algorithms for retrievals. Aglite has been used to
measure emission rates from various agricultural operations, and to compare the effectiveness
of conservation practices.
10
1.3
Research Overview
This dissertation presents two novel algorithms. Previous algorithms only estimate
optical coefficients and have had unstable performance under low SNR conditions. In
addition to optical coefficients, the new algorithms are able to estimate aerosol PSD and give
stable results under low SNR conditions. They estimate both the PSD and concentration of
aerosols from elastic lidar data. This dissertation also describes the estimation of aerosol
emission rates from an entire dairy facility using lidar, which has never been done before. In
connection with this, it also presents a description of the Aglite lidar and other supporting
instruments, the procedure for using them to make measurements, and example results of
mapping aerosols and estimating net emission rates from a facility.
The ability of the techniques to generate accurate estimates has been verified using
computer-generated data. Additionally, the performance of the techniques on real data
has been demonstrated using measurements from field campaigns. Finally, estimation of
aerosol emission rates from a dairy has been demonstrated using one of the techniques,
and displayed alongside emission rates estimated using conventional sensors and an inverse
modeling technique.
The first algorithm operates as an iterative minimum least-squares estimator, and
demonstrates its stability and functionality in retrieving aerosol concentration from both
synthetic and real lidar signals [4]. The technique formulates the lidar equation as a
nonlinear matrix operation and the aerosol concentration as a vector over the entire range
of interest. The lidar equation is iteratively linearized and a least-squares solution to the
aerosol concentration vector is calculated using the Newton-Raphson method. This method
converges quickly if the initial guess is close to the desired solution, but it has poor global
convergence. Consequently, for atmospheres with large optical depths, other methods may
be more effective, such as a modified form of Newton’s method or the Levenberg-Marquardt
method [60]. By choosing an appropriate weighting matrix, the solution approximates a
maximum-likelihood solution to the lidar equation for the case of Gaussian noise.
Both algorithms describe the atmosphere as a linear combination of assumed aerosol
11
components, and retrieves the coefficients of these components in the form of an estimated
state vector x. The lidar equation describes the measured returned power P as a function
of range z, as well as two optical coefficients, backscatter β(z) and extinction σ(z), also
functions of range.


Zz
β(z)
P (z) = f {β(z), σ(z)} = K 2 exp −2 σ(z 0 ) dz 0 
z
(1.1)
0
Estimation of both of these optical coefficients is normally an ill-posed problem, unless
a relationship between them is assumed. Most currently popular algorithms assume a
power-law relationship between backscatter β and extinction σ coefficients of each individual
lidar channel.
β = kσ r
(1.2)
The expressions k and r are constants. Unfortunately, this approach ignores any
dependency that may exist between channels of a multi-wavelength lidar. Since the ultimate
science goal of an elastic lidar is often the estimation and characterization of aerosols
themselves, rather than their optical properties, it is reasonable to formulate both backscatter
and extinction as functions of aerosol concentration n. For the case of a multi-wavelength
lidar measuring an atmosphere with multiple components, this can be represented in vector
form by (1.3).
β z = fβ {n(z)}
σ z = fσ {n(z)}
(1.3)
The elements of β z and σ z are the optical coefficients of the individual wavelengths
and the elements of n are the amplitudes of the individual component aerosols at range z.
As a result, the measured returned power over multiple wavelengths can be modeled as a
nonlinear function of multiple aerosol components.
Pz = f {n(z)}
(1.4)
12
Formulating the lidar equation as a function of multiple aerosol components both
prevents the problem from being ill-posed, and allows the retrieval algorithms described
in this dissertation to take full advantage of any dependency between the measured lidar
channels.
In practice, lidar signals have measurement noise added to them and the lidar equation
is nonlinear. The algorithms formulate a linear approximation of the lidar equation using
Newton’s method. The linear approximation of (1.4) is shown by (1.5).
f {n(z)} ≈ (∇f {n(z)} − ∇f {n0 (z)}) n0 (z) + f {n0 (z)}
(1.5)
The linearization requires an a priori estimate of the solution. By iteratively using
(1.5) to linearize the lidar equation, applying the standard weighted minimum least-squares
solution [61], and feeding it back into the linearization equation, the retrieved solution will
converge to a point close to the desired solution. The weighting matrix should be chosen to
account for the noise levels of the individual measurement channels.
The second algorithm is an example of an EKS [5]. This algorithm is demonstrated to
estimate the effective particle radius from both synthetic and real lidar signals. The algorithm
is similar to the algorithm proposed by Dias [50]. By selecting an appropriate state vector, the
lidar signal can be represented by a state-update matrix and a nonlinear observation function.
Additionally, this technique assumes that the change in aerosol concentration as a function
of range is a random variable, whose covariance is known beforehand. This technique has
also been developed from the perspective of optimization, while also incorporating additional
a priori knowledge and is of lower order computational complexity than the first technique.
Like the previous algorithm, this algorithm composes the state vector using component
aerosol amplitude. Inspection of (1.1) shows that the measured power of a lidar signal at
a given range is a function of the backscatter at that range and the sum of the extinction
coefficients between the lidar and that range. However, it is possible to represent the lidar
signal as a Markov process by substituting the cumulative extinction with a new variable.
Dias composes the state vector x in the form shown in (1.6).
13


 βz 
xz = 

γz
γz =
z
X
σz 0
(1.6)
z 0 =0
Whereas (1.6) describes the state vector using optical coefficients for a single wavelength,
in the multi-wavelength case an alternative form for the state vector can be composed of
two vectors, describing both the current component aerosol amplitude and the cumulative
component aerosol amplitude.


 nz 
xz = 

γz
γz =
z
X
nz 0
(1.7)
z 0 =0
Using this state vector, the process of the lidar measurement can be described using a system
including a state-update equation and an observation equation.


 0 0 
xz+1 = 
 xz + wt
1 1


 cov(nz ) 0 
cov(wz ) = 

0
0
(1.8)
P(z) = k ·
fβ (nz )
· exp (−2∆z fσ (γ z )) + ν z
z2
cov(ν z ) = R
These equations may be implemented as an extended Kalman filter by linearizing the
observation equation using Newton’s method. This algorithm has much better computational
performance than the previously described one due to its much reduced state vector size.
Whereas the state vector of the first technique is proportional to the number of range bins
in the lidar signal, the state vector of the second technique is proportional to the lidar pulse
width in bins. Furthermore, it can be implemented as a fixed-lag EKS by augmenting the
state vector with aerosol concentration coefficients over a set window size.
14
References
[1] C. C. Marchant, “Algorithm development of the Aglite-lidar instrument,” Master’s
thesis, Utah State University, Logan, UT, 2008.
[2] G. E. Bingham, C. C. Marchant, V. V. Zavyalov, D. Ahlstrom, K. D. Moore, D. Jones,
T. D. Wilkerson, L. Hipps, R. S. Martin, J. L. Hatfield, J. H. Prueger, and R. L.
Pfeiffer, “Lidar based emissions measurement at the whole facility scale: Method and
error analysis,” Journal of Applied Remote Sensing, vol. 3, p. 033510, 2009.
[3] C. C. Marchant, T. D. Wilkerson, G. E. Bingham, V. V. Zavyalov, J. M. Andersen,
C. B. Wright, S. S. Cornelsen, R. S. Martin, P. J. Silva, and J. L. Hatfield, “Aglite
lidar: a portable elastic lidar system for investigating aerosol and wind motions at or
around agricultural production facilities,” Journal of Applied Remote Sensing, vol. 3, p.
033511, 2009.
[4] C. C. Marchant, T. K. Moon, and J. H. Gunther, “An iterative least square approach to
elastic-lidar retrievals for well-characterized aerosols,” IEEE Transactions on Geoscience
and Remote Sensing, vol. 48, pp. 2430–2444, 2010.
[5] C. C. Marchant, M. D. Wojcik, and W. J. Bradford, “Estimation of aerosol effective
radius by multi-wavelength elastic lidar,” IEEE Transactions on Geoscience and Remote
Sensing, submitted for publication, 2010.
[6] C. C. Marchant, K. D. Moore, M. D. Wojcik, R. S. Martin, J. L. Hatfield, J. H. Prueger,
and R. L. Pfeiffer, “Estimation of dairy particulate matter emission rates by lidar and
inverse modeling,” Transactions of the ASAE, submitted for publication, 2010.
[7] R. Bureau, Météorologie, vol. 3, p. 292, 1946.
[8] S. S. Friedland, J. Katzenstein, and M. R. Zatzick, “Pulsed searchlighting the atmosphere,” Journal of Geophysical Research, vol. 61, pp. 415–434, 1956.
[9] F. J. McClung and R. W. Hellwarth, “Giant optical pulsations from ruby,” Journal of
Applied Physics, vol. 33, pp. 828–829, 1962.
[10] G. Fiocco and L. D. Smullin, “Detection of scattering layers in the upper atmosphere
(60-140 km) by optical radar,” Nature, vol. 199, pp. 1275–1276, 1963.
[11] G. Fiocco and G. Colombo, “Optical radar results and meteoric fragmentation,” Journal
of Geophysical Research, vol. 69, pp. 1795–1803, 1964.
[12] G. Fiocco and G. Grams, “Observations of the aerosol layer at 20 km by optical radar,”
Journal of Atmospheric Science, vol. 21, pp. 323–324, 1964.
[13] R. T. H. Collis, “Lidar observation of cloud,” Science, vol. 149, pp. 978–981, 1965.
[14] R. T. H. Collis and M. G. H. Ligna, “Note on lidar observations of particulate matter
in the stratosphere,” Journal of Atmospheric Science, vol. 23, p. 255, 1966.
15
[15] R. T. H. Collis and J. Oblanas, “Project Pre-Gondola II. Airborne lidar observations,”
Stanford Research Institute Aerophysics Lab., 1968.
[16] R. T. H. Collis, F. G. Fernald, and J. E. Alder, “Lidar observation of Sierra-wave
conditions,” Journal of Applied Meteorology, vol. 7, pp. 227–233, 1968.
[17] R. T. H. Collis, “Lidar: A new atmospheric probe,” Quarterly Journal of the Royal
Meteorological Society, vol. 92, pp. 220–230, 1966.
[18] P. M. Hamilton, “The use of lidar in air pollution studies,” Air and Water Pollution,
vol. 10, pp. 427–434, 1966.
[19] P. M. Hamilton, “Plume height measurements at northfleet and tilbury power stations,”
Atmospheric Environment, vol. 1, p. 379, 1967.
[20] P. M. Hamilton, “The application of a pulsed-light rangefinder (lidar) to the study of
chimney plumes,” Philosophical Transactions of the Royal Society of London, vol. 265,
pp. 153–172, 1969.
[21] E. W. Barrett and O. Ben-Dov, “Appliation of the lidar to air pollution measurements,”
Journal of Applied Meteorology, vol. 6, pp. 500–515, 1967.
[22] W. B. Johnson, “Lidar observations and the diffusion and rise of stack plumes,” Journal
of Applied Meteorology, vol. 8, pp. 443–449, 1969.
[23] W. B. Johnson, “Lidar applications in air pollution research and control,” Journal of
the Air Pollution Control Association, vol. 19, pp. 176–180, 1969.
[24] J. A. Reagan, M. P. McCormick, and J. D. Spinhirne, “Lidar sensing of aerosols and
clouds in the troposphere and stratosphere,” Proceedings of the IEEE, vol. 77, pp.
433–448, 1989.
[25] F. G. Fernald, B. M. Herman, and J. A. Reagan, “Determination of aerosol height
distributions by lidar,” Journal of Applied Meteorology, vol. 11, pp. 482–489, 1972.
[26] G. W. Grams, J. I. H. Blifford, B. G. Schuster, and J. J. D. Luisi, “Complex index of
refraction of airborne fly ash determined by laser radar and collection of particles at 13
km,” Journal of Atmospheric Science, vol. 29, pp. 900–905, 1972.
[27] G. W. Grams, J. I. H. Blifford, D. A. Gillette, and P. B. Russell, “Complex index
of refraction of airborne soil particles,” Journal of Applied Meteorology, vol. 13, pp.
459–471, 1974.
[28] J. A. Reagan, J. D. Spinhirne, and D. M. Byrne, “Atmospheric particulate properties
inferred from lidar and solar radiometer observations compared with simultaneous in
situ aircraft measurements: A case study,” Journal of Applied Meteorology, vol. 16, pp.
911–928, 1977.
[29] A. Ansmann, M. Riebesell, and C. Weitkamp, “Measurement of atmospheric aerosol
extinction profiles with a Raman lidar,” Optics Letters, vol. 15, pp. 746–748, 1990.
16
[30] J. A. Reagan, D. M. Byrne, and B. M. Herman, “Bistatic LIDAR: A tool for characterizing atmospheric particulates: Part II - The inverse problem,” IEEE Transactions on
Geoscience and Remote Sensing, vol. GE-20, pp. 236–243, 1982.
[31] L. R. Bissonnette and D. L. Hutt, “Multiply scattered aerosol lidar returns: Inversion
method and comparison with in situ measurements,” Applied Optics, vol. 34, pp.
6959–6975, 1995.
[32] D. I. Cooper, W. E. Eichinger, D. E. Hof, D. Seville-Jones, R. C. Quick, and J. Tiee,
“Observations of coherent structures from a scanning lidar over an irrigated orchard,”
Agricultural and Forest Meteorology, vol. 67, pp. 239–252, 1994.
[33] T. E. Stoughton, D. R. Miller, X. Yang, and K. M. Ducharme, “A comparison of spray
drift predictions to lidar data,” Agricultural and Forest Meteorology, vol. 88, pp. 15–26,
1997.
[34] B. A. Holmén, W. E. Eichinger, and R. G. Flocchini, “Application of elastic lidar to
PM10 emissions from agricultural nonpoint sources,” Environmental Science Technology,
vol. 32, pp. 3068–3076, 1998.
[35] B. A. Holmén, T. A. James, L. L. Ashbaugh, and R. G. Flocchini, “Lidar-assisted
measurement of PM10 emissions from agricultural tilling in California’s San Joaquin
Valley, part I: Lidar,” Atmospheric Environment, vol. 35, pp. 3265–3277, 2001.
[36] B. A. Holmén, D. R. Miller, A. L. Hiscox, W. Yang, J. Wang, T. Sammis, and
R. Bottoms, “Near-source particulate emissions and plume dynamics from agricultural
field operations,” Journal of Atmospheric Chemistry, vol. 59, pp. 117–134, 2008.
[37] A. L. Hiscox, D. R. Miller, B. A. Holmén, W. Yang, and J. Wang, “Near-field dust
exposure from cotton field tilling and harvesting,” Journal of Environmental Quality,
vol. 37, pp. 551–556, 2008.
[38] W. E. Eichinger, H. E. Hoder, D. I. Cooper, L. E. Hipps, R. Knight, W. P. Kustas,
J. Nichols, and J. H. Prueger, “Lidar measurement of boundary layer evolution to
determine sensible heat fluxes,” Journal of Hydrometeorology, vol. 6, pp. 941–953, 2005.
[39] M. C. W. Sandford, “Laser scatter measurements in the mesosphere and above,” Journal
of Atmospheric and Terrestrial Physics, vol. 29, pp. 1657–1662, 1967.
[40] P. B. Russell, T. J. Swissler, and M. P. McCormick, “Methodology for error analysis
and simulation of lidar aerosol measurements,” Applied Optics, vol. 18, pp. 3783–3797,
1979.
[41] S. A. Young, “Analysis of lidar backscatter profiles in optically thin clouds,” Applied
Optics, vol. 34, pp. 7019–7031, 1995.
[42] W. Viezee, E. E. Uthe, and R. T. H. Collis, “Lidar observations of airfield approach
conditions: An exploratory study,” Journal of Applied Meteorology, vol. 8, pp. 274–283,
1969.
17
[43] P. A. Davis, “The analysis of lidar signatures of cirrus clouds,” Applied Optics, vol. 8,
pp. 2099–2102, 1969.
[44] J. D. Klett, “Stable analytical inversion solution for processing lidar returns,” Applied
Optics, vol. 20, pp. 211–220, 1981.
[45] F. G. Fernald, “Analysis of atmospheric lidar observations: some comments,” Applied
Optics, vol. 23, p. 652, 1984.
[46] J. D. Klett, “Extinction boundary value algorithms for lidar inversion,” Applied Optics
Letters, vol. 22, pp. 514–515, 1983.
[47] E. Yee, “Information-theoretic method for the inversion of the lidar equation,” Applied
Optics, vol. 28, pp. 1628–1637, 1989.
[48] F. Rocadenbosch, C. Soriano, A. Comeron, and J. Baldasano, “Adaptive filter solution
for processing lidar returns: optical parameter estimation,” Applied Optics, vol. 37, pp.
7019–7034, 1998.
[49] F. Rocadenbosch, C. Soriano, A. Comeron, and J. Baldasano, “Lidar inversion of
atmospheric backscatter and extinction-to-backscatter ratios by use of a Kalman filter,”
Applied Optics, vol. 38, pp. 3175–3189, 1999.
[50] J. M. B. Dias, J. M. N. Leitao, and E. S. R. Fonseca, “Reconstruction of backscatter
and extinction coefficients in lidar: a stochastic filtering approach,” IEEE Transactions
on Geoscience and Remote Sensing, vol. 42, pp. 443–456, 2004.
[51] R. E. Warren, R. G. Vanderbeek, A. Ben-David, and J. L. Ahl, “Simultaneous estimation
of aerosol cloud concentration and spectral backscatter from multiple-wavelength lidar
data,” Applied Optics, vol. 47, pp. 4309–4320, 2008.
[52] Y. Sasano, “Light scattering characteristics of various aerosol types derived from multiple
wavelength lidar observations,” Applied Optics, vol. 28, pp. 1670–1679, 1989.
[53] M. D. Guasta, M. Morandi, L. Stefanutti, B. Stein, and J. P. Wolf, “Derivation of Mount
Pinatubo stratospheric aerosol mean size distribution by means of a multiwavelength
lidar,” Applied Optics, vol. 33, pp. 5690–5697, 1994.
[54] K. Rajeev and K. Parameswaran, “Iterative method for the inversion of multiwavelengh
lidar signals to determine aerosol size distribution,” Applied Optics, vol. 37, pp. 4690–
4700, 1998.
[55] G. von Cossart, J. Fiedler, and U. von Zahn, “Size distributions of NLC particles
as determined from 3-color observations of NLC by ground-based lidar,” Geophysical
Resources Letters, vol. 26, pp. 1513–1516, 1999.
[56] J. Heintzenberg, H. Muller, H. Quenzel, and E. Thomalla, “Information content of
optical data with respect to aerosol properties: numerical studies with a randomized
minimization-search-technique inversion algorithm,” Applied Optics, vol. 20, pp. 1308–
1315, 1981.
18
[57] P. Qing, H. Nakane, Y. Sasano, and S. Kitamura, “Numerical simulation of the retrieval
of aerosol size distribution from multiwavelength laser radar measurements,” Applied
Optics, vol. 28, pp. 5259–5265, 1989.
[58] D. P. Donovan and A. I. Carswell, “Principal component analysis applied to multiwavelength lidar aerosol backscatter and extinction measurements,” Applied Optics, vol. 36,
pp. 9406–9424, 1997.
[59] C. Böckmann, “Hybrid regularization method for the ill-posed inversion of multiwavelength lidar data in the retrieval of aerosol size distributions,” Applied Optics, vol. 40,
pp. 1329–1342, 2001.
[60] W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, Numerical Recipes in C. New
York: Prentice Hall, 1971.
[61] T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal
Processing. Upper Saddle River: Prentice Hall, 2000.
19
Chapter 2
Aglite Description and Operation1
2.1
Application
Agricultural aerosol sources can contribute a significant component to air pollution
in many regions of the country. Characterization of the aerosol emissions of agricultural
operations is required to determine best management practices for controlling agricultural
aerosol emissions and to establish a scientific basis for crafting regulations concerning
agricultural aerosols. For regulatory purposes, aerosol mass concentration is measured in
terms of PM, specifically PM2.5 and PM10 , both of which are pollutants regulated by the
National Ambient Air Quality Standards [1, 2].
Examples of agricultural sources of PM include “fugitive dust” from soil tillage, aerosols
generated from crop processing, and chemical precipitate from vapors emitted by manure.
Agricultural aerosol sources differ from industrial aerosol sources in that the source is often
spread out over a significant area, resulting in a highly dispersed aerosol plume and a variable
rate of emission over time.
In the past, conventional methods for measuring and characterizing aerosol sources
have used point sensors and software models of atmospheric dispersion. Using these tools,
measurements are made by deploying a limited number of point sensor instruments around the
site of interest and using software models to interpolate and extrapolate aerosol concentration
values around the entire site. These methods offer low spatial resolution due to the sparse
number of instruments used. Additionally, these methods rely on assumptions of Gaussian
diffusion of aerosols and are therefore reliable only over relatively long periods of time.
1
C. C. Marchant, T. D. Wilkerson, G. E. Bingham, V. V. Zavyalov, J. M. Andersen, C. B. Wright, S.
S. Cornelsen, R. S. Martin, P. J. Silva, and J. L. Hatfield, Reproduced by permission of SPIE as: “Aglite
lidar: A portable elastic lidar system for investigating aerosol and wind motions at or around agricultural
production facilities,” J. Appl. Remote Sens., vol. 3, 033511, 2009.
20
Attempts have also been made to measure emissions from soil tillage operations by placing
instruments directly on a tractor implement [3–6].
The deficiency of these conventional techniques is that they are unable to make measurements over the volume of a dispersed aerosol plume. A sparse distribution of instruments
is unable to cover the entire area of the aerosol source, and the point sensors cannot take
measurements higher than the towers upon which they are mounted. As a result, these
techniques are insufficient for characterizing the emission rate of the whole plume.
This deficiency can be addressed using lidar technology, which provides a means
to measure the distribution of aerosols over large areas with high spatial and temporal
resolution. Previous applications of lidar technology to measuring agricultural aerosols
have demonstrated this capability by measuring the height and morphology of dust plumes
generated by field preparation [7, 8]. Mapping of aerosol concentration by lidar has been
demonstrated in agricultural settings [9, 10]. The interaction of lidar scan patterns and
moving aerosols provides valuable information on the wind structures responsible for aerosol
transport. In particular, certain types of scans permit the visualization of wind velocity,
variability of wind speed over large areas, and shapes of aerosol plumes. Buttler et al.
reviewed lidar wind velocity measurements, principally those based on cross-correlations
between successive positions of aerosol clouds, with the lidar direction oriented along the
wind or cross-wind [11]. They compared wind data collected with radiosondes, remote laser
Doppler profiles, and elastic backscatter lidar profiles. Good agreements were demonstrated
between these instruments, indicating that wind observations with elastic lidar afford valuable
data on the transport within the planetary boundary layer [11]. Kovalev and Eichinger
provide a comprehensive review of backscatter correlation methods and Doppler-based
lidar techniques in both the time and frequency domains, and point out the many types
of atmospheric observations in which wind lidar technology can now play an important
role [12]. Pioneering work on correlations was carried out by Derr and Little, Zuev, and
Eloranta [13–15]. Sasano et al. corrected the 2-D aerosol patterns for their motion during
the lidar scan, and found that the correlation-based wind velocities agreed with the wind
21
data from co-located meteorology towers [16].
3-D volume-imaging scans have been developed for wind profile measurements [17–19].
The lofting of aerosol plumes from a farm due to a combination of wind and convection has
been shown by Eichinger [20]. The development of holographic scanning lidars has led to
new methods of cross-beam wind analysis using aerosol and cloud tracking [21–25]. The
work reported here is based on simple, straightforward analyses of (1) 1-D plume motion as
a function of time, and (2) the visualization of 2-D plumes by time-dependent lidar scans
across the plume shape.
The Energy Dynamics Laboratory at Utah State University teamed with researchers
from the Department of Agriculture, Agriculture Research Service to build Aglite, an elastic
lidar system plus an array of point source instruments and software for measuring the
emission rate of agricultural aerosol sources. In addition to measuring the 3-D structure of
an agricultural aerosol plume over a large volume, the Aglite system is able to measure the
concentration of PM2.5 and PM10 . These data are used in conjunction with data on wind
speed and direction to characterize the emission rate of the source [26].
The design of the Aglite lidar is described here and its ability to map the aerosol
concentration and dynamics of aerosol plumes with high resolution is demonstrated. Aglite’s
ability to measure wind velocity by means of aerosol tracking is also demonstrated.
2.2
Aglite Lidar Description
The primary system requirements of the Aglite lidar were to (1) make the system
eye-safe at the operating range; (2) make the system sufficiently robust and portable to be
deployed at an agricultural site; and (3) make the scanning and data logging sufficiently fast
to capture the dynamics and structure of an entire plume.
The Aglite instrument is a portable lidar mounted in a small trailer and utilizing a
fixed vertical telescope with a steering mirror used as an azimuth-elevation beam director.
Figure 2.1 shows the Aglite lidar instrument in its trailer housing deployed at a farm. The
lidar has a measurement rate as fast as 1/10 of a second, with a minimum range resolution of
6 m. Its maximum practical range is roughly 3 km, depending on environmental conditions.
22
Aglite’s steering mirror and fast response time enable it to map aerosol concentration in
units of PM2.5 and PM10 over a large volume of air in a relatively short amount of time,
with a single 2-D scan taking less than a minute. This speed also enables Aglite to capture
plume dynamics with good temporal resolution.
2.2.1
Hardware Design
The requirement to make Aglite eye-safe drove the decision to make Aglite a micropulse
lidar. This mitigates eye-safety issues because of the lower pulse energy. Additionally, the
high pulse rate potentially allows for an extremely high measurement rate. As a consequence
of the low pulse-energy, photon-counting detection is used. The lidar has a narrow field of
view (FOV) and uses narrow-band filters to limit background noise from solar radiation.
The design concepts for Aglite and its component layout are shown in fig. 2.2, and are based
in part on the design by McGill et al. for the Cloud Physics lidar [27].
The transmitter chosen for Aglite is a solid-state Nd:YAG laser from Photonics Industries
International, Inc. (Photonics Industries International, Inc, 390 Central Avenue, Bohemia,
NY 11716). (The instruments and manufacturers named herein were used in the research
experiments described; however, their use does not constitute an endorsement or preferential
treatment by the project researchers or sponsors.) The laser generates three wavelengths:
1064, 532, and 355 nm using doubling and tripling crystals. These three wavelengths define
the measurement channels of the lidar and are called the infrared (IR), visible (V), and
ultra-violet (UV) channels, respectively. The laser operates at 10 KHz and is diode-pumped.
The laser head is vertically mounted on the optical bench of the lidar and is coupled to
the diodes and the power supply, which are located in the electronics rack. The laser
is water-cooled and is coupled to a separate chiller. Immediately after leaving the laser
head, the beam passes first through a 7x beam expander, at which point the beam far-field
full divergence angles are 0.37, 0.30, and 0.27 mrad, respectively. Average output powers
employed are 4.35, 0.48, and 0.93 W, respectively, yielding pulse energies of 435, 50, and
93 µJ. The beam is directed through a neutral density (ND) filter station, which allows for
controlled attenuation of the beam (20 to 100% power) as desired over the lidar’s operating
23
Fig. 2.1: Aglite lidar instrument in deployment configuration.
Fig. 2.2: Aglite lidar conceptual arrangement and layout of optical bench components.
24
range on grounds of eye safety. Reflection from the ND filter is separated into its component
wavelengths using dichroic reflectors and fed into power sensors to monitor laser output
strength, as shown in fig. 2.2. After passing through the ND filter, the beam is directed onto
a mirror mounted on the central obstruction of the telescope, at which point the laser is
co-aligned with the receiver telescope.
Aglite uses a simple Newtonian telescope with a 28-cm diameter primary mirror (fig. 2.2).
The mirror has a focal length of 0.91 meters. A field stop confines the telescope’s full fieldof-view to 0.45 mrad. After the laser beam reflects off the telescope’s central obstruction, a
pointing mirror directs the beam and collects the returned signal. At this point, the outgoing
beam and returned signal are coaxially aligned. Movement of the pointing mirror controls
the direction the lidar looks, but does not change the relative alignment of the outgoing
beam and return signal. The pointing mirror actuators have a pointing knowledge resolution
of 1 mrad and pointing repeatability of 5 mrad.
As shown in fig. 2.2, the signal is collected by the telescope and separated into its
component wavelengths using dichroic mirrors. Each channel then passes through a narrowband interference filter made by Barr Associates, Inc. (Barr Associates, Inc, 2 Lyberty Way,
Westford, Massachusetts 01886.) The 532 nm channel then passes through an etalon made
by TecOptics (TecOptics, 1760 Grand Avenue, Merrick, NY 11566.) The 1064 nm channel is
focused onto an avalanche photo-diode (APD), while the remaining two channels pass into
photo-multiplier tubes (PMT). The APD is model SPCM-AQR-14 made by PerkinElmer
(PerkinElmer, 44370 Christy Street, Fremont, California 94538), having a quantum efficiency
of 2%. The PMTs are model 9954A made by Electron Tubes (Electron Tubes, 100 Forge
Way, Unit F, Rockaway, New Jersey 07866), with quantum efficiencies of 14% and 27% at
532 and 355 nm, respectively. The choice of detectors for Aglite was based on the generally
superior signal-to-noise ratio for PMTs in the visible and ultraviolet wavelengths, given
sufficient filtering of the green intensity maximum of sunlight, and the infrared sensitivity of
the APD. This conclusion has also been confirmed by Agishev et al. [28]. A commercial high
speed, multichannel photon counting interface (APCS, ASRC Aerospace, Greenbelt, MD)
25
was used to log and record the photon counts for each channel. The electronic system for
operating the entire Aglite system and recording the multichannel data has been described
in detail by Cornelsen [29].
For example, the Fabry-Perot etalon inside Aglite on the 532 nm channel is not temperature controlled, as is sometimes done in lidar instrumentation. The Aglite system
compensates for temperature induced instrument changes by periodic (every 20 min) recalibration of the lidar return signal magnitude against an OPC placed in the field. It is
important that the instrument not fluctuate during the course of a single 20-minute scan.
The etalon discussed here is an air spaced etalon constructed of Class 0 Zerodur with a
nominal mirror spacing of 4.011 mm. The coefficient of thermal expansion of Zerodur is
0.002 ppm/K. During a typical 10-hour day of field experiments, the temperature inside of
the lidar enclosure can change as much as 25 K, corresponding to an etalon spacing change
of 0.5 ppm, or 2 nm. The transmission change due to a 2-nm spacing increase is < 1%, and
the peak shift of the transmission fringe is < 0.25 pm. Therefore the transmission changes
over any given 20-minute scan are limited to temperature effects of a single Kelvin or less,
and so are on the order of 0.05% transmission changes. Either way, the transmission changes
due to temperature effects are well within the 37 pm wavelength bandwidth of the etalon
and also that of the laser. Therefore, systematic temperature induced transmission changes
do not contribute significantly to the observed noise threshold for the entire instrument.
The actual range resolution of this lidar lies in the range of 12-18 m for wavelengths
between 355 and 1064 nm, owing to the data system’s time bins of 40 nsec, the respective
laser pulse lengths, and the pulse discrimination times of the detectors. The repetition rate
of the laser sets the lidar’s maximum range at 15 km; in practice, the maximum useful range
can be as close as 1.5 km due to high solar background radiation or unfavorable atmospheric
conditions.
The laser head, transmission optics, and receiver optics are all mounted on a single
optical bench, vertically oriented in a 6-point vibration-isolated frame along with the
telescope. The optical path is housed under light-tight aluminum covers that also serve to
26
keep the optics clean. The entire frame is covered with removable panels. An important
component of the lidar is an external air-blower. This draws air from outside of the trailer
through a filter, conditions it, and blows it into the lidar housing to maintain positive
pressure in the optical system and minimize flying insect intrusion. This is essential to
mitigate the risks of contamination from dust and insects during a campaign because the
receiver telescope is exposed to the outside environment.
The beam director is computer controlled by a rack-mounted PC, with scan control
points established using the visible camera and a joystick controller. Azimuth and elevation
control were achieved using Newport RV series and BG series precision motorized stages. It
can direct the beam with an azimuth range of 270 degrees and an elevation range between
-10 degrees to +45 degrees, and is retracted hydraulically inside the trailer for travel. A
video camera looks at the director mirror to provide the operator a 5 degrees field of view
along the beam path for safety monitoring. Data from the lidar can be linked by WiFi
network to other sites and bring environmental data to the operator. Table 2.1 summarizes
the system parameters of the lidar.
2.2.2
Eye-Safety
For safe field operation the lidar beam should be eye-safe at the facility under investigation, and a buffer area around the lidar where the beam strength is not eye-safe should
be guaranteed off-limits to personnel. The most demanding eye-safety criterion for Aglite
is the green light at 532 nm near the peak sensitivity of the human eye. While certain
other lidar groups have preferred to operate at “eye safe” infrared wavelengths such as
1555 nm [30], the Nd:YAG-derived triplet (355, 532, 1064 nm) was chosen for Aglite on the
basis of good control over the field test environment and the three wavelength capability
for discriminating between aerosol types. For Aglite the requirement is that the operating
distance of the lidar from the facility must meet or exceed the eye-safety range for 532
nm, which is approximately 450 meters using 20% of the laser power. Calculations of the
value of the Nominal Optical Hazard Distance, RNOHD , for eye exposure are carried out in
accordance with ANSI Laser Safety Standard Z136.1 [31]. The ophthalmic basis for these
27
Table 2.1: Aglite lidar system parameters.
Laser Type
Solid-State Nd:YAG
Data Time Resolution
0.1 s
Laser Repetition Rate
10 kHz
Steering Resolution
5 µrad
Telescope Type
Newtonian
Telescope Diameter
28 cm
450 µrad
Etalon Free
(full angle)
Spectral Range
Telescope FOV
Etalon Reflectivity
Finesse
Laser
Wavelengths
Output
Energy
Etalon Peak
6.1
Transmission
37 pm
≥98.4%
Detector
Filter
Filter
Range
Efficiency
Efficiency
Bandwidth
Resolution
355 nm
93 µJ
27%
76%
150 pm
18 m
512 nm
50 µJ
14%
70%
120 pm
12 m
1064 nm
435 µJ
2%
70%
150 pm
18 m
standards has most recently been treated by Delori et al. [32].
The minimum distance for safe eye exposure to N laser pulses at a given wavelength is
given by (2.1).
RNOHD
1
=
Θ
r
4Φ
MPE (N )
π
(2.1)
The expression Θ is the full divergence angle of the transmitted laser pulse (mrad),
Φ is the pulse energy (J), and MPE (N )=MPE P ·N −1/4 , where MPE P is the maximum
permissible exposure per pulse (J/cm2 ) at the human cornea for each wavelength. Table 2.2
provides representative values for RNOHD for the range of laser output powers of Aglite, based
on a standard blink response criterion of 0.25 seconds. At an operating distance of 600 meters
and reduced optical power, Aglite is “eye-safe” at all wavelengths. The equivalent ranges for
other exposure durations are readily calculated using recognized standards [31, 33, 34]. Table
2.2 shows a conservative analysis of eye-safety distances based on 100% optical efficiency of
laser transmission, for a “blink” exposure of 1/4 second at laser pulse repetition frequency
of 10 kHz.
28
Table 2.2: Representative eye-safety distances for Aglite lidar.
2.3
RNOHD (m)
RNOHD (m)
20% output
100% output
100
6.7
15
5.0e-7
50
450
1000
5.0e-6
500
360
810
λ(nm)
MPEp (J/cm2 )
Φ(µJ)
355
5.6e-3
532
1064
Lidar Data and Calibration
The information content of the lidar return is summarized by the well-known lidar
equation (2.2), which describes the lidar return signal as a function of range z for wavelength
λ.

Pλ (z) = P0 L
βλ (z)
cτ
Aλ (z)
exp −2
2
z2
Zz

σλ (z 0 ) dz 0 
(2.2)
0
The term Pλ (z) is the measured reflected power for distance z and is measured in
photon counts. P0 is the output power of the lidar, L is the lidar coefficient, which represents
the combined efficiency of the transmitter optics, reciever optics, and detectors, c is the
speed of light, τ is the pulse width of the lidar, and Aλ (z) is the receiver efficiency at range
z. Aλ (z) is defined as GFF ·A, where A is the geometric area of the telescope and GFF
is the geometric form factor (GFF) or overlap function defined by the geometry of the
transmitter and telescope. βλ (z) is the atmospheric backscatter coefficient, and σλ (z) is
the atmospheric extinction coefficient.
The backscatter and extinction coefficients both depend upon molecular scattering, as
well as on the characteristics of the background and emission aerosols. The solution of (2.2)
requires knowledge of the optical parameters of both the background and source aerosols, as
well as the contribution of molecular scattering; these need to be measured at one or more
reference points in order to interpret lidar return intensity in terms of particle concentration,
and to extract aerosol information [35].
The geometric form factor is an effect caused by mismatch between the transmitter and
receiver cones of sight. Its magnitude and shape are is highly sensitive to the alignment of
29
the lidar optics. The expected value of the GFF is approximately 80% at the lidar range of
650 m (fig. 2.3), as modeled using the optical design parameters of the lidar [36].
Experimentally derived values for the GFF were found to be within 10% of the modeled
GFF for ranges greater than 650 m [37]. Because of the sensitivity of the GFF to misalignment
of the optics, the lidar system is aligned before measurements are taken. This is done using
a target at distances greater than 1 km; the lidar is directed at the target, and the optics
are adjusted to maximize the magnitude of the signal reflected by the target.
To calibrate the aerosol density detected by Aglite, the local particulate concentrations
in the lidar’s FOV was determined using both optical and aerodynamic mass fraction
sensors. These sensors include real time OPCs that utilize a laser to count and size
particles into eight user-specified size bins (OPC Model 9722, Met One Instruments, Inc.,
Grants Pass, OR). The aerodynamic particulate impactors are portable, self-contained, filterbased particulate samplers containing Teflon filters that are weighed pre- and post-exposure
(MiniVol, Airmetrics, Eugene, OR.) By co-locating AirMetric samplers and OPC instruments,
the relationship between mass concentration and aerosol backscatter was established. Aerosol
parameters are developed for lidar characterization and calibration by directing the lidar
beam past the AirMetric/OPC instrument pair [35].
It is expected that photon-counting measurements by the Aglite system are Poissondistributed random variables, and this is confirmed by inspection of Aglite data. Figure 2.4(a)
shows the average measured signal from a 10-minute data set from a measurement campaign
at an Almond orchard, where Aglite was used to observe harvesting operations. This
particular data set was taken with Aglite staring continuously in one direction horizontally
through the atmosphere. Figure 2.4(b) shows a comparison of the average power measured
at 600 m range with the ensemble variance values over a range of varying period lengths.
The variance of the signal is either comparable to or greater than the average measured
power. Figure 2.4(c) illustrates the relationship between the average measured power and
variance at each range of the lidar signal for the UV channel. Figure 2.4(d) shows a particle
normalized histogram of the measured lidar signal at 600 m, together with the probability
30
Fig. 2.3: Aglite geometric form factor as a function of range.
mass functions of the Poisson distributions that most closely approximate them. A major
source of noise in Aglite data is photon-counting noise. Background solar radiation also
contributes significantly to the signal, which increases the expected total number of photons
measured by the detectors and hence increases the expected variance of the signal.
Representative plots of the SNR of the retrieved backscatter for each of the three
wavelengths as a function of distance are shown in fig. 2.5. In this case, SNR is defined
as the ratio of the mean aerosol backscatter amplitude over the standard-deviation of the
aerosol backscatter. Both graphs show the SNR measured from data taken near a cotton
gin operation. Figure 2.5 was taken during daylight operation at approximately 7:30 in the
evening, when the PM10 level was approximately 7 µg/m3 . Figure 2.5(b) shows the SNR at
approximately 9:30 in the evening, when background levels of PM10 had risen slightly to 8
µg/m3 . The improvement in SNR in the right panel is largely due to the absence of solar
background radiation during night operation. The bumps and dips in the plots are due to
the environmental variability of aerosol concentration at the site. Smooth portions of the
plots correspond to homogeneous regions of atmosphere.
For these measurements, in-situ instruments were used to absolutely calibrate the lidar
signal and to determine the ratio of the aerosol backscatter contribution to the backscatter
31
Fig. 2.4: Example return power signal and signal variance from Aglite.
Fig. 2.5: Two examples of SNR values observed by Aglite under field conditions.
32
contribution due to molecular scattering. The SNR at a given range can be improved either
by not attenuating the laser, or by increasing the integration time of the measurement.
2.4
Example Data and Discussion
The Aglite system has been deployed at several agricultural facilities in the U.S. since
becoming operational in the summer of 2005. Examples from these campaigns are given
below to illustrate Aglite’s ability to capture the structure and dynamics of plumes from
agricultural sources.
2.4.1
Aerosol Detection Capability
The lidar control computer has the useful ability to display the raw lidar signal in real
time, allowing the individual wavelength return signals to be monitored so that the data
integration time can be adjusted and pulse dead-time corrections can be made as needed.
The control computer also stores the raw lidar signal for later processing. Additionally, the
lidar includes a secondary display unit which shows 2-D images of the lidar scan regions in
near real time. This display removes the baseline component of the background noise and
performs range-correction to the signal amplitude before displaying the data, as illustrated
by fig. 2.6. The conversion of raw lidar data into aerosol concentration is performed after
the conclusion of a campaign.
The Aglite software consists of three parts: the lidar control software, the visualization
software, and the lidar retrieval software. The lidar control software governs the data
acquisition and pointing control of the lidar hardware. This program is written in the
Labview programming environment and is installed on the PC in the Aglite hardware rack.
The main functions of this program are to control the pointing of the steering mirror and
store the values measured by the photon detectors. The software allows the user to control
the lidar either manually, by means of a game controller, or in an automatic mode, in which
the program directs the lidar scan in a repeating predetermined pattern. This program also
controls parameters such as the PMT voltage levels, the range gate, and the integration time
of the lidar. Additionally, the control software writes the lidar measurements and orientation
33
Fig. 2.6: Typical “stare” image of range-normalized IR channel intensity.
parameters to a data file for storage and also broadcasts them over the local computer
network using the user datagram protocol (UDP). The visualization software receives the
UDP broadcast data and graphs in near real-time the current raw signal measured by Aglite.
The visualization software is a program with a graphical user interface written in
the Matlab programming environment. The function of this program is to assist the
operator by providing real-time visualization of lidar data. The visualization software is
installed on a separate laptop that is networked to the rack-mounted PC. The visualization
software captures the data broadcast by the rack-mounted PC broadcasts and plots them
in range-normalized form. The user selects the range and channel of data to view. The
visualization software automatically determines the type of motion currently being performedstare, horizontal sweep, or vertical sweep-and plots it in an appropriate 2-D graph. The
visualization software is also able to plot data from the archived data files generated by the
lidar control software.
The lidar retrieval software consists of a set of scripts written for the Matlab programming
environment. The lidar retrieval software retrieves PM concentration from the lidar data
files, allowing the lidar scans to be converted into maps of aerosol concentration. An aerosol
may scatter the three wavelengths of Aglite with different efficiencies. This allows the
34
retrieval software to extract information about the aerosol particle size distribution as well
as the concentration of the aerosol. This software does not run concurrently while the lidar
is taking data, but is executed offline, after the measurements have been completed. This
software requires additional inputs from point sensors to calibrate the lidar signal and to
calculate the lidar-ratio and other parameters needed to perform the lidar retrieval.
2.4.2
Fixed Direction Lidar Measurement
Figure 2.6 shows data collected during a lidar “stare” period in which the FOV is fixed
horizontally so the lidar can record the range-dependent backscatter as a function of time
(here about 15 minutes). These measurements are from a field-campaign at a swine-finishing
facility. This stationary stare past a barn complex shows an emission plume from the
facility (∼650 m) and two transient road dust plumes. The co-location of the lidar path and
the tower-mounted particle monitors close to the barns provides the essential step for the
optical-particulate calibration of the lidar.
The choice to design Aglite with three channels adds significant cost and complexity
over a one-channel design. The additional channels, however, provide additional information
about the aerosol characteristics under investigation. The point sensors deployed with Aglite
directly measure the particle size distribution of both the background atmosphere aerosol
and the target aerosol under investigation. The accuracy of these point measurements can
be gauged by comparing them to Aglite measurements.
An example is given here of retrieving aerosol concentration and mode radius using
Aglite’s lidar retrieval algorithm. A stationary stare past the barn complex was performed
for 400 seconds and averaged. This measurement was pre-processed to remove the baseline
noise component and to correct for the GFF. The measurement was then converted to
the logarithmic range-normalized form, as shown in fig. 2.7(a). The lidar inversion was
performed using the Klett solution for two scatterers to estimate the backscatter coefficients
as a function of range [38]. This is illustrated in fig. 2.7(b).
Point sensor measurements were used to provide the backscatter values of the homogeneous background and the lidar ratios for the two scatterers. A least-squares solution
35
Fig. 2.7: Example retrieval showing measured signal, estimated backscatter, mode radius,
and PM10 concentration.
for aerosol concentration and mode radius was then calculated at every range, assuming a
log-normal distribution with σ = 2.0 and indices of refraction for a water-soluble aerosol [39].
The retrieved mode-radius as a function of range is given in fig. 2.7(c). The solution for
mode radius was restricted to a window between 0 and 2 µm. It reasonable to assume that
when the solution to the mode radius was at the limits of this window, there may have been
insufficient aerosol to accurately measure the mode radius. This is supported by the fact that
at ranges where a plume is visible in fig. 2.7(b), the retrieved mode radius is comfortably
within the allowable window of values. Figure 2.7(d) shows the PM10 concentration that
results from the solution, assuming unit density. A complete description of the algorithm is
given elsewhere [35], along with details of the algorithm’s performance and more examples
of its use.
2.4.3
Crosswind Scans for Aerosol Flux Measurements
The lidar scan patterns typically used to characterize the amount of aerosol emissions
36
include vertical profiles between barns at upwind and downwind facility boundaries and
horizontal profiles above the facility. Interleaved with these scans of the facility area are
stationary stares near the in situ instruments to probe the aerosol parameters.
Figure 2.8(a) illustrates the use of horizontal scans that serve to locate the sources of
aerosol emissions around an agricultural facility. The dark dots located roughly 700 m west
of the lidar correspond to measurement towers set up between livestock barns and the dark
vertical streaks between 1200 and 1400 west of the lidar are fugitive dust plumes from a
north-south dirt road at that location. These scans are particularly useful when placing the
aerosol point sampling equipment. While even a single wavelength system can provide these
types of data, the multiple wavelength capability of Aglite comes into play in determining
and tracking the distribution of particulate size classes and their flux from the facility.
Figure 2.8(b) introduces a type of scan called a “staple” scan, which Aglite uses
extensively to bracket the net emission from an agricultural facility [26]. The range dependent
lidar signal is displayed in three steps: an elevation scan upwards, an azimuthal scan to the
right, and an elevation scan downwards. Here the wind blows south-to-north (left-to-right).
On the right side, the lidar captures a plume emitted from a swine barn at a distance of
roughly 600 m that have risen to about 30 meters in height. This specific scan was completed
in 1.5 minutes.
2.5
Aerosol Traced Wind Motion
2.5.1
Wind Velocity from Lidar “Stares”
Figure 2.9 shows aerosol return data collected with the beam parallel to a graveled road
in a fixed, horizontal stare. Each strong signal return represents a dust cloud generated by
a road traffic event. Direction motion of aerosols is towards the lidar, with a dashed line
tracking the movement of individual return features over time. The figure shows distance
from the lidar on the horizontal axis and time difference on the vertical access. Tracking
such long plume trains is possible because the lidar can see through individual plumes that
have very low aerosol density and optical extinction. The scale represents the lidar signal
37
Fig. 2.8: Examples of typical horizontal and staple scans by Aglite.
intensity from white (weak) to black (strong). As in fig. 2.6(b), this display is a space-time
(x,t) graph of plume motion, with time increasing upward and lidar range increasing to the
right. This type of lidar data record and subsequent velocity analysis is analogous to “streak
photography,” which has found wide applications in experimental fluid dynamics [40–43].
Holmén et al. adopted this method for lidar and pointed out its significance for quantitatively
measuring plume velocity and transport based on straightforward physical calibrations of
distance and time [6, 7].
In fig. 2.9, the wind motion is from right to left and, based on the persistence of the x-t
streaks, lies directly within the lidar field of view. When the lidar is not aligned with the
wind direction, the streaks are much shorter, as the cloud moves across the beam. The slopes
of these aerosol traces (dashed lines) are direct measures of the wind speed (distance/time).
The speed observed in this figure is consistent with the data measured on a tower at the
600 m range. As expected, the wind also demonstrates velocity variations with distance
(shown in the white boxes). The lidar is so sensitive that clumps and clouds of aerosols can
be tracked in daytime and nighttime situations, even when they are too tenuous to be seen
38
by eye.
Most of our “staring” observations have been made at reasonably steady winds above
3 m/s. During one of our experiments, one noteworthy occasion of very slow and highly
variable horizontal air motion was observed at the 20 m height. This state of motion suggests
almost stagnant air dominated by convection with a fluctuating horizontal wind component.
This is shown in fig. 2.10 where the horizontal speeds varied between 1.4 m/s toward the lidar
and 1.5 m/sec away from the lidar, out to a range of 5 km. In addition to the well-defined
parts of the plumes, there are low-contrast, triangular patches in this x-t image indicating
that a 1-1.5 km-wide pattern of drifting motion of aerosols persisted for at least 5 minutes.
This is a remarkable cross-sectional snapshot of an atmospheric pattern that could not be
seen by any means other than lidar. The capability of lidar as a tool for documenting wide
area patterns of air motion dominated by convection is an important addition to the study
of aerosol transport from agricultural sources.
2.5.2
Vertical Scans for Cross-Sections of Moving Plumes
The staring and staple scans with the lidar show that the aerosol transport consists of
clumps of highly variable particle concentrations that originated from ground level. To see
the full structure and height of these plumes and how they evolve, one must use vertical
lidar scans θ(t) = θ0 ±Ct aimed upwind or downwind, with the scans made rapidly enough
to follow the plume propagation over a range of hundreds of meters. These scans can display
complete cross sections of plume structure as opposed to accidental intersections orthogonal
to the motion. For the data shown in fig. 2.11, the lidar alternately scanned up and down at
a rate of C = 0.1 deg/s. The resulting images are divided into interleaved sequences of “up”
frames in the left column and “down” frames in the right column. Each sequence shows
radial aerosol plume motion with the wind towards the lidar. Each panel was recorded over
a 30-second vertical scan. This record of plume motion shows a total of 4 minutes of aerosol
transport of road dust plumes.
Two distinct plume populations are evident in fig. 2.12: (1) low-altitude, low-velocity
(0.8 m/s) plumes originating at 600 m; and (2) higher plumes with higher speeds (2.3 m/s)
39
Fig. 2.9: Local wind patterns shown in a time series of lidar returns at different ranges.
Fig. 2.10: Returns from an 8-minute horizontal stare at 20 m height during light and variable
wind conditions.
40
Fig. 2.11: Series of 30-second vertical scans showing the movement of plumes toward the
lidar.
that originate from more distant sources. The visualization of these structures provides
important information regarding the scales of aerosol inhomogeneities involved in particle
transport.
There are obvious differences between up and down images due to the kinematic
distortion inherent in the record of a moving object (plume) obtained by the time-dependent
scan angle θ(t). These images show that appearance of the plume profile, especially the
direction of its tilt, depends both on the plume’s true shape and velocity. The shape of the
plume is assumed to be made up of many smaller ragged plumes, which can be approximated
as a series of fairly straight pillars that tilt forward from vertical by some angle in the
direction of wind movement [44–47]. Laboratory observations of such fluid flows have also
been shown [48]. Instability in the atmospheric surface layer produces intermittent gusts
in which the local vertical velocity can be comparable to the horizontal wind, creating a
plume of soil aerosols carried upward at an angle tilted nearly 45 degrees forward in the
wind direction.
To demonstrate unscrambling the simultaneous time dependence of lidar scans and
41
plume motions, fig. 2.12 shows an example of vertical scans of a fugitive road dust plume
moving towards the lidar. The left pane shows an initial scan, with the lidar scanning
from 1.4 degrees up to 8 degrees elevation, immediately followed by a scan from 8 degrees
back down to 1.4 degrees, which is shown in the right pane. The approximate shape and
orientation of the tilted plume is represented by a heavy black slanted line. A series of
parallel dotted lines represent its position as the plume blows from right to left. As the
moving plume is intercepted by the lidar scan, the intersection first rises (left panel) in an
arc from A to B, and then connects to a down-going arc from B to C (right panel). The
velocity and forward tilt calculated for this plume are unique solutions to the kinematic
equations underlying these arcs. The streaks in the left panel correspond to a plume tilted
forward 20 degrees from vertical with a speed of 10 m/s and the streaks in the right panel
correspond to a tilt angle of 50 degrees from vertical with a speed of 7 m/s.
The observational value and advantage of lidar, as illustrated here, is that it clearly
provides an extremely sensitive way to see tenuous aerosol features that are hard to see or
photograph. Conventional pictures of the above plumes could not be obtained because of
low optical contrast. However, a successful attempt to photograph such plumes was made
elsewhere under more extreme conditions. An environment was found where wind, surface
temperature, lighting, and loose dust could make it possible to obtain visible images of
plumes. Figure 2.13 is a photograph taken in the Black Rock desert in Nevada in September
2006. The image has been inverted and its contrast increased to improve the viewability of
the dust plumes, which are indicated by the gray arrow. Similar to the lidar observations,
these plumes retained their shape as they marched steadily to the east with the wind, and
were tilted substantially forward at near -45 degree angles that varied slightly during any
single gust event.
2.6
Conclusion
The Aglite lidar was developed to provide a tool for rapidly characterizing agricultural
and other anthropogenic aerosol sources. It has normally been deployed as a member of a
suite of classical aerosol characterization instruments that provide the additional aerosol
42
Fig. 2.12: Distortion of the aerosol image due to simultaneous motion of wind and lidar
scan.
Fig. 2.13: Wind-blown plumes of clay dust in Nevada.
43
optical response functions required to fully understand the sources and their emission rates.
The lidar beam can be scanned in the horizontal and vertical planes to allow emission tracking
and profiling. The lidar allows the mapping the relative backscatter of an aerosol plume
at 1064, 532, and 355 nm wavelengths, with a resolution of 12 meters (range) and 5 mrad
(angle). Sensitivity at up to 10 samples/second is sufficient to identify non-visible facility
plumes of PM10 and PM2.5 particulates, which are regulated by the US Environmental
Protection Agency (EPA). The lidar has been used to create near real-time profiles of plume
structures and low-elevation clouds and to map the location of aerosol sources over a range
of facility types, including confined agricultural feeding operations, agricultural product
processing, and mobile source operations, where variable wind and short-duration treatment
evaluations are desired. Processing the lidar returns also allow the wind speed and the
character of the temporal and spatial variation of aerosol plume structures to be determined.
When the lidar is combined with point instruments, which provide the additional aerosol
characteristics needed to determine optical parameters, the system can provide calibrated
aerosol size-fraction information and emission fluxes from the studied operations [35]. The
most important capabilities for Aglite’s monitoring of agricultural emissions are (1) the
mapping of aerosol concentration and motion around a facility, and (2) the measurement of
aerosol mass concentration emission rates from a facility.
References
[1] Code of Federal Regulations-Title 40: Protection of Environment, “40 CFR
50.6-National primary and secondary ambient air quality standards for PM10 ,”
[http://vlex.com/vid/19784798], 2005.
[2] Code of Federal Regulations-Title 40: Protection of Environment, “40 CFR
50.7-National primary and secondary ambient air quality standards for PM2.5 ,”
[http://vlex.com/vid/19784804], 2005.
[3] J. B. Baker, R. J. Southard, and J. P. Mitchell, “Agricultural dust production in
standard and conservation tillage systems in the San Joaquin Valley,” Journal of
Environmental Quality, vol. 34, pp. 1260–1269, 2005.
44
[4] H. Clausnitzer and M. J. Singer, “Respirable-dust production from agricultural operations in the Sacramento Valley, CA,” Journal of Environmental Quality, vol. 25, pp.
877–884, 1996.
[5] H. Clausnitzer and M. J. Singer, “Intensive land preparation emits respirable dust,”
Journal of California Agriculture, vol. 51, pp. 27–30, 1997.
[6] B. A. Holmen, W. E. Eichinger, and R. G. Flocchini, “Application of elastic lidar to
PM10 emissions from agricultural nonpoint sources,” Environmental Science Technology,
vol. 32, pp. 3068–3076, 1998.
[7] B. A. Holmen, T. A. James, L. L. Ashbaugh, and R. G. Flocchini, “Lidar-assisted
measurement of PM10 from agricultural tilling in California’s San Joaquin Valley-part
I: Emission factors,” Atmospheric Environment, vol. 35, pp. 3251–3264, 2001.
[8] B. A. Holmen, T. A. James, L. L. Ashbaugh, and R. G. Flocchini, “Lidar-assisted
measurement of PM10 from agricultural tilling in California’s San Joaquin Valley-part
II: Emission factors,” Atmospheric Environment, vol. 35, pp. 3265–3277, 2001.
[9] B. A. Holmen, D. R. Miller, A. L. Hiscox, and R. G. Flocchini, “Near-source particulate emissions and plume dynamics from agricultural field operations,” Journal of
Atmospheric Chemestry, vol. 59, pp. 117–134, 2008.
[10] A. L. Hiscox, D. R. Miller, B. A. Holmen, W. Yang, and J. Wang, “Near-field dust
exposure from cotton field tilling and harvesting,” Journal of Environmental Quality,
vol. 37, pp. 551–556, 2008.
[11] W. T. Buttler, C. Soriano, J. M. Baldasano, and G. H. Nickel, “Remote sensing of
three-dimensional winds with elastic lidar: explanation of maximum crosscorrelation
method,” Boundary-Layer Meteorology, vol. 101, pp. 305–328, 2001.
[12] V. A. Kovalev and W. E. Eichinger, Elastic Lidar: Theory, Practice, and Analysis
Methods. Hoboken: Wiley-Interscience, 2004.
[13] V. E. Derr and C. G. Little, “A comparison of remote sensing of the clear atmosphere
by optical, radio, and acoustic radar techniques,” Applied Optics, vol. 99, pp. 1982–1983,
1970.
[14] V. E. Zuev, “Lidar sounding of the atmosphere to estimate static and dynamic characteristics of aerosol inhomogeneities,” in Proc. 5th Conf. Laser Radar Studies of the
Atmosphere, 1973.
[15] E. W. Eloranta, J. M. King, and J. A. Weinman, “The determination of wind speeds in
the boundary layer by monostatic lidar,” Journal of Applied, vol. 14, pp. 1485–1489,
1975.
[16] Y. Sasano, H. Hirohara, T. Yamasaki, H. Shimizu, N. Takeuchi, and T. Kawamura,
“Horizontal wind vector determination from the displacement of aerosol distribution
patterns observed by a scanning lidar,” J. Appl. Meteor., vol. 21, pp. 1516–1523, 1982.
45
[17] W. P. Hooper and E. W. Eloranta, “Lidar measurements of wind in the planetary
boundary layer: the method, accuracy, and results from joint measurements with
radiosonde and kytoon,” Journal of Climate and Applied Meteorology, vol. 25, pp.
990–1001, 1986.
[18] D. I. Cooper and W. E. Eichinger, “Structure of the atmosphere in an urban planetary
boundary layer from lidar and radiosonde observations,” Journal of Geophysical Research,
vol. 99, pp. 22 937–22 948, 1994.
[19] P. W. Young and E. W. Eloranta, “Calculation of divergence and vertical motion from
volume imaging lidar data,” Journal of Geophysical Research, vol. 100, pp. 25 557–25 583,
1995.
[20] W. E. Eichinger, D. I. Cooper, J. L. Hatfield, L. E. Hipps, J. J. Nicholls, R. L. Pfeiffer,
and J. Preuger, “Use of elastic lidar to examine the dynamics of plume dispersion from
an agricultural facility,” in Workshop on Agricultural Air Quality: State of the Science,
Ecological Society of America, 2006.
[21] G. K. Schwemmer, “Conically scanned holographic lidar telescope,” U.S. Patent No.
5,255,065, Oct. 1993.
[22] D. V. Guerra, G. K. Schwemmer, A. D. W. Jr., S. S. Chaudhuri, and T. D. Wilkerson,
“Prototype holographic atmospheric scanner for environmental remote sensing,” Journal
of Geophysical Research, vol. 104, pp. 22 287–22 292, 1999.
[23] G. K. Schwemmer, T. D. Wilkerson, J. A. Sanders, D. V. Guerra, D. O. Miller, and
S. E. Moody, “Ground based operational testing of holographic scanning lidars,” in
Advances in Laser Remote Sensing, A. Dabas, C. Loth, and J. Pelon, Eds. Ecole
Polytechnique, 2001, pp. 69–72.
[24] T. D. Wilkerson, J. A. Sanders, and I. Q. Andrus, “Kinematic analysis of conically
scanned environmental properties,” U.S. Patent No. 6,535,158, 2003.
[25] T. D. Wilkerson, S. Cornelsen, C. Earl, D. Huish, J. Cutts, S. Call, M. Anderson, and
G. K. Schwemmer, “Monitoring of air motion using lidar and video observations,” in Proceedings of the American Meteorological Society. 2nd Symposium of Lidar Atmospheric
Applications, 2005.
[26] G. E. Bingham, C. C. Marchant, V. V. Zavyalov, D. Ahlstrom, K. D. Moore, D. Jones,
T. D. Wilkerson, L. Hipps, R. S. Martin, J. L. Hatfield, J. H. Prueger, and R. L.
Pfeiffer, “Lidar based emissions measurement at the whole facility scale: method and
error analysis,” Journal of Applied Remote Sensing, vol. 3, p. 033510, 2009.
[27] M. McGill, D. Hlavka, W. Hart, V. S. Scott, J. Spinhirne, and B. Schmid, “Cloud
physics lidar: instrument description and initial measurement results,” Applied Optics,
vol. 41, pp. 3725–3734, 2002.
[28] R. Agishev, B. Gross, F. Moshary, A. Gilerson, , and S. Ahmed, “Simple approach to
predict APD/PMT lidar detector performance under sky background using dimensionless
parameterization,” Optics and Lasers in Engineering, vol. 44, pp. 779–796, 2006.
46
[29] S. Cornelsen, “Electronics design of the AGLITE instrument,” Master’s thesis, Utah
State University, Logan, UT, 2005.
[30] W. Gong, T. H. Chyba, and D. A. Temple, “Eye-safe compact scanning LIDAR
technology,” Optics and Lasers in Engineering, vol. 45, pp. 898–906, 2007.
[31] Z136.1, Laser Institute of America, “American national standard for the safe use of
lasers,” ANSI, 2000.
[32] F. C. Delori, R. H. Webb, and D. H. Sliney, “Maximum permissible exposures for ocular
safety (ANSI 2000), with emphasis on ophthalmic devices,” Journal of the Optical
Society of America, vol. 24, pp. 1250–1265, 2007.
[33] R. Baxter, “Laser safety training manual,” [http://chemistry.uchicago.edu/safety/
LaserSafety.pdf], 2001.
[34] A. L. Augustoni, “Laser hazard analysis for airborne AURA (big sky variant) proteus
platform,” SAND2004-0413, Sandia National Laboratories, 2004.
[35] V. V. Zavyalov, C. C. Marchant, G. E. Bingham, T. D. Wilkerson, J. L. Hatfield,
R. S. Martin, P. J. Silva, K. D. Moore, J. Swasey, D. J. Ahlstrom, and T. L. Jones,
“Aglite lidar: Calibration and retrievals of well characterized aerosols from agricultural
operations using a three-wavelength elastic lidar,” Journal of Applied Remote Sensing,
vol. 3, p. 033522, 2009.
[36] R. M. Measures, Laser Remote Sensing: Fundamentals and Applications. New York:
John Wiley, Inc., 1984.
[37] S. W. Dho, Y. J. Park, and H. J. Kong, “Experimental determination of a geometrical
form factor in a lidar equation for an inhomogeneous atmosphere,” Applied Optics,
vol. 36, pp. 6009–6010, 1997.
[38] J. D. Klett, “Extinction boundary value algorithms for lidar inversion,” Applied Optics
Letters, vol. 22, pp. 514–515, 1983.
[39] A. S. Jursa, Handbook of Geophysics and the Space Environment, Air Force Geophysics
Laboratory, Hanscom Air Force Base, 1985.
[40] E. Mallard and H. L. L. Chatelier, “On the propagation velocity of burning in gaseous
explosive mixtures,” Comptes-rendus Hebdomadaires des Seances de l’Académie des
Sciences de Paris, vol. 93, p. 145, 1881.
[41] H. B. Dixon, “On the movement of the flame in the explosions of gases,” Philosophical
Transactions of the Royal Society of London, vol. A200, pp. 315–352, 1903.
[42] B. Lewis and G. von Elbe, Combustion, Flames and Explosions of Gases. New York London: Academic Press, 1951.
[43] O. Laporte and T. D. Wilkerson, “Hydrodynamic aspects of shock tube spectroscopy,”
Journal of the Optical Society of America, vol. 50, pp. 1293–1299, 1960.
47
[44] R. B. Stull, An Introduction to Boundary Layer Meteorology.
Academic Publishers, 1998.
Netherlands: Kluwer
[45] J. C. Kaimal and J. A. Businger, “Case studies of a convective plume and a dust devil,”
Journal of Applied Meteorology, vol. 9, pp. 612–620, 1970.
[46] J. M. Wilczak and J. E. Tillman, “The three-dimentional structure of convection in
the atmospheric surface layer,” Journal of Atmospheric Science, vol. 37, pp. 2424–2443,
1980.
[47] J. M. Wilczak and J. A. Businger, “Thermally indirect motions in the convective
atmospheric boundary layer,” Journal of Atmospheric Science, vol. 40, pp. 343–358,
1984.
[48] L. Prandtl, Führer durch die Strömungslehre.
Braunschweig: Vieweg, 1942.
48
Chapter 3
An Iterative Least Square Approach to Elastic-Lidar
Retrievals for Well-Characterized Aerosols1
3.1
Background
Analytical solutions to the lidar equation have been known for some time. These
methods have generally relied on a two-step process: first, convert lidar data into backscatter
and extinction values; second, to convert these values into the PSD of the aerosol, from
which mass fraction concentration can be calculated. Solutions for calculating backscatter
and extinction have included the slope method [1], the ratio constraint methods, including
those by Klett [2, 3] and Fernald [4], and EKF methods [5–7]. Many methods for calculating
PSDs from optical parameters have been demonstrated using error minimization techniques,
with various forms of regularization to stabilize the solution [8–10].
This chapter presents a method of retrieving aerosol concentration from elastic lidar
data, which operates by formulating the lidar equation into a novel vector form, linearizing
it around some solution value, and computing the least-squares solution. This process
is repeated until the solution converges, similar to the Newton-Raphson method. The
method is a type of nonlinear least-squares (NLS) solution to the lidar problem. The
method requires a priori knowledge of backscatter values at a boundary point as well as
defined relationships between backscatter, extinction, and mass fraction concentration for all
scattering components. An algorithm using this method was validated using both simulated
and experimental data.
The ratio constraint methods by Klett and Fernald are analytical solutions to the
differential equation form of the lidar equation and have been shown to be mathematically
1
C. C. Marchant, T. K. Moon, J. H. Gunther, “An iterative least square approach to elastic-lidar
c
retrievals for well-characterized aerosols” IEEE.
Reprinted, with permission, from IEEE Trans. Geosci.
Remote Sens., vol. 48, pp. 2430-2444, 2010.
49
equivalent [3]. These solutions contain a singularity located on the far side of the boundary
point. As a result, the retrieved solution is not stable for ranges significantly beyond the
boundary point and practical application of these methods require the boundary point to be
placed at a range beyond the aerosol target. These methods have widespread use in many
lidar systems today [11].
The requirement that the boundary point must be placed beyond the aerosol target is an
important characteristic of Klett’s method because it directly impacts two major drawbacks
of the method [12]. One drawback is that the quality of the lidar return is very dependent
on the SNR value at the boundary point. The lidar signal has significantly inferior SNR
values at ranges beyond the aerosol target compared to ranges before the target due to the
range-squared dependent falloff of the signal. Averaging could reduce the noise at a distant
boundary point; however the same averaging could be used to lower the noise at a boundary
point located before the aerosol target. As a result, the closer point would still have a better
SNR value and it would still be more desirable to place the boundary point before the target
aerosol.
Another drawback is that the quality of a lidar return generated by Klett’s method is
very dependent on the quality of the calibration extinction value chosen for the boundary
point. It is common practice with Klett’s method to calibrate the lidar in the molecular
atmospheric layer above the aerosol boundary layer [13]; however in many circumstances
this is not possible. For example, Federal Aviation Administration regulations limit the
power of lasers that may be pointed vertically, particularly in sensitive airspace near airports.
In this case, a horizontally operating lidar would be able to operate pointing parallel to
the ground, but would not be allowed to point vertically to calibrate using the molecular
atmospheric layer. Alternatively, a lidar with a relatively short effective operating range,
such as a low-power micro-pulse lidar, might have insufficient power to reach an altitude
where molecular scattering dominates with sufficient SNR. The Aglite lidar instrument [14]
has faced both of the situations described above in field campaigns.
If the boundary point can be placed at relatively close range to the instrument, an
50
alternative means can be used to calibrate the lidar. As illustrated in fig. 3.1, a point-sensor
instrument for measuring backscatter can be positioned at relatively close range where the
SNR is relatively high. The lidar beam can shine past this point and the boundary point
placed at this location.
In addition to the requirement of boundary point placement, Klett’s method does not
account for the noise levels of the different channels or the relationships between the channels
when calculating backscatter and extinction. Although the formulation does not explicitly
require it, practical application of Klett’s solution for two scatterers requires that a constant
lidar ratio for the non-homogeneous component be assumed for each individual wavelength.
As a result, the method retrieves backscatter (or alternatively extinction) as a function of
range for each wavelength.
Different versions of the EKF have also been applied to the lidar inversion problem. By
updating a state space vector with sequential observations, the EKF generates a weighted
least-squares estimate of the state using a priori information from the previous measurements.
Rocadenbosch’s formulation of the EKF returns a state-vector containing backscatter
and lidar ratio values as a function of range, at the expense of spatial resolution, for
individual channels [5]. Further computations must still be performed to convert these
optical parameters to aerosol concentration and other physical parameters.
Alternatively, Warren’s formulation of the EKF uses a two step process, where a
maximum-likelihood estimator first estimates the aerosol type, and then a Kalman filter is
used to estimate aerosol concentration. Warren’s formulation linearizes the filter by assuming
an optically thin aerosol and dropping the extinction expression, limiting the versatility of
this formulation [6].
Dias presents a solution to the lidar equation in the form of an extended Kalman fixedinterval smoother [7]. In this formulation, the state vector is composed of the extinction
coefficient of the current range and the optical thickness of the path up to and including the
current range. The updated state equation is a function of the state at the previous range in
the current measurement. This formulation offers the significant advantages of being both
51
Fig. 3.1: Illustration of Aglite measuring an atmosphere that consists of a baseline component
plus a mixture of varying components.
computationally efficient and giving stable performance under noisy conditions.
It is convenient to divide the atmosphere into a baseline and a varying component.
Similar to Klett’s solution for two scatterers, the iterative least-squares method formulates
the lidar equation as a baseline background scatterer plus a non-homogeneous mixture
composed of one or more varying aerosol components. The method assumes that the
extinction/backscatter ratios of these individual mixture components at all wavelengths
are constant over the range of the signal and that only the amplitudes of the individual
components vary. This assumption may seem unrealistic for many circumstances, as aerosols
often have spatially varying characteristics. However by approximating an aerosol as an
external mixture of aerosol components, the aggregate characteristics of the aerosol are free
to vary with range, even though the characteristics of individual components are constant.
Another similarity with Klett’s method is that the formulation given here also requires a
boundary point. This boundary point calibrates the signal and allows the retrieval to work
even in situations with relatively low SNR values.
Unlike other lidar retrieval methods that typically give results in the form of optical
52
parameter values, the method presented here returns amplitude values of the individual
mixture components. If the mass fraction values of the component aerosols are also known, it
is straightforward to calculate the total mass fraction values of the atmosphere by multiplying
the amplitudes of the individual components by their corresponding individual mass fraction
values and summing them. If the PSDs of the individual components are also known, the
PSD of the aggregate aerosol mixture can be easily calculated as a linear combination of the
PSDs of the individual components.
One advantage of this formulation is that it is straightforward to deal with multiwavelength data. It is simply necessary to describe the optical parameters for each mixture
component at every wavelength, and it is possible to retrieve mixture component amplitudes
as long as the number of components is less than or equal to the number of channels.
Because each component has backscatter and extinction coefficient values for each channel,
the method takes full advantage of knowledge of the relationships between the different
channels.
For a given wavelength, the lidar equation is under-determined, where the equation
for returned power depends on coefficients for both extinction and backscatter. If the
assumption is made that the atmosphere consists a well-characterized baseline component
plus a well-characterized varying component, the lidar equation can be formulated as a
one-to-one mapping, similar to Klett’s method. For a multi-channel lidar, the atmosphere
can be modeled using as many scattering components as channels. As a result, the method
can be used with an arbitrary number of channels, ensuring that the retrieved solution is
consistent across all channels.
The iterative least-squares method works by calculating the least-squares solution to
the lidar equation consistent with a given set of constraints. The lidar equation is iteratively
linearized using a first-order Taylor series expansion and minimized using the weighted
least-squares solution until the solution converges. In the presence of Gaussian noise, if the
correct weighting matrix is selected, this estimator approximates a maximum-likelihood
estimator. The method is presented simply in the context of a least-squares estimator of
53
the atmospheric state at a single moment in time, not using any information from prior
or future measurements. In addition to the current measured signal, an EKF also takes
into account all information from past measurements and returns a solution based on all
measurements. It would be a straightforward task to incorporate the method described in
this paper into an EKF by choosing state-space equations that use the same state vector
formulation described below and by substituting the linearized form of the lidar equation
described below for the observation equation of the EKF.
3.2
Well Characterized Local Aerosol
For an elastic lidar with Λ channels, the vector form of the lidar equation can be
represented by (3.1).


Zz
β (z)
p (z) = k · g (z) · 2 · exp −2 α(z 0 ) dz 0 
z
(3.1)
0
The bold expressions in (3.1) are all Λx1 vectors, whose elements correspond to the
lidar detector channels. The variable z represents range [m], the expression p (z) has units
of power [W], the function g (z) represents the attenuation (unitless) due to the GFF at
range z, and α (z) [m−1 ] and β (z) [m−1 ·sr−1 ] represent total backscatter and extinction
coefficients as a function of range, respectively [15]. The exponential expression represents
attenuation due to extinction. The operator · represents element-by-element multiplication
and the exponent function operates on an element-by-element basis. The vector k is defined
as by (3.2).
k = (cτ /2) Ap0 · x
(3.2)
The term k has units [W·m3 ·sr], c is the speed of light [m/s] vector, τ is the detection
window length [s], the vector p0 is the power per channel [W], A is the receiver area [m2 ],
the vector x (unitless) is the combined transmitter and receiver efficiencies.
The geometric form factor can be removed from (3.1) in pre-processing if the form of
54
g (z) is known, or if z is sufficiently large that g (z) can be approximated as unity. In this
case, the relationship between k and the backscatter at some boundary point m can be
expressed by (3.3).
k=
p z2
m zm
Rm
0
0
β m · exp −2 α(z ) dz
(3.3)
0
If a lidar is calibrated (i.e. k is known) and if the exponential term is close to unity,
then the boundary value β m is known. Alternatively, β m can be obtained from another
source such as in-situ instruments or an atmospheric model, and be used to calculate k. In
any case, the formulation of (3.3) assumes that k is constant, not dependent at all on the
magnitude of the received power. This requires an optical detector with a linear response.
For example, the Aglite lidar instrument [14] uses PMTs and an APD, all in photon counting
mode, and as a result the instrument has a highly linear detector response on each channel
for low to moderate numbers of received photons. For very large numbers of photons, the
nonlinear response of photon-counting detectors can be well modeled mathematically and
processing of the lidar signal can correct for the nonlinear response. For the case of detectors
with nonlinear response, such as PMTs in current mode, characterization of the detector
response is required and the lidar signals must be processed to remove the nonlinear effects
before retrievals may be performed.
It is assumed that the PSD of a well characterized (i.e. well mixed) scatterer can be
described as a so-called external mixture, or linear combination, of S scattering components
plus a baseline component (which can have zero amplitude) that is constant with range,
where S ≤ Λ in order to avoid an under-constrained system of equations. This is illustrated
in fig. 3.1, which illustrates a typical setup using the Aglite lidar instrument. For aerosol
scatterers, the PSD as a function of range can then be described by (3.4).
n (r, z) = n0 (r) +
S
X
ni (z) ñi (r)
(3.4)
i=1
The variable r represents the particle’s optical radius [µm], n0 (r) is the PSD [#·m−3 ·
55
µm−1 ] of the homogeneous component, ñi (r) is the ith PSD [m−3 · µm−1 ], and ni (z) [#]is
the amplitude of the ith component.
The choice of functions for the PSDs of the S non-baseline scattering components
in (3.4) has a significant impact on the solution to the lidar equation. These functions
are a basis of the vector space representing the PSD. These functions must be linearly
independent; however it is not necessary that they be either orthogonal or normal. In
addition, each function has a corresponding vector of backscatter coefficients, and these
backscatter vectors must be linearly independent. In fact, because a linearly dependent
set of PSD functions cannot generate a linearly independent set of backscatter vectors, it
is sufficient for the backscatter vectors to be linearly independent to guarantee that the
PSDs are independent [16]. Ultimately, the proper selection of these functions is situation
dependent, but in general they should be chosen in such a way that they are able to
approximate the expected shape of the true PSD of the atmosphere.
Throughout this paper the subscript 0 is used to denote the baseline component of
the atmosphere, including both molecular Rayleigh scattering plus residual scattering from
baseline background aerosols. Let the vector v (z) be the vector of the amplitudes of all of
the non-baseline components, as shown by (3.5).
T
v (z) =
n1 (z) n2 (z) . . .
nS (z)
(3.5)
For each component ñi (r), there is a corresponding extinction, backscatter, and mass
fraction vector. Backscatter and extinction values due to molecular scattering can be
calculated using Rayleigh theory with measurements of temperature, pressure, and relative
humidity [17]. Given an individual component PSD ñi (r) and complex index of refraction
mj for wavelength λj and assuming spherical particles, Mie theory can be used to calculate
backscatter and extinction coefficients [18]. Assuming that the aerodynamic radius equals
the optical radius and the aerosol density is already known (it may be convenient to assume
ρ = 1), the PSD of the scattering component can also be used to calculate mass fraction
values for the individual components. The expression here for mass fraction concentration,
56
also called particulate matter, is P MD , which is defined as the total mass of particles in a
cubic meter of air [µg·m−3 ] with an aerodynamic diameter less than D. Assuming spherical
particles, the equations for extinction αλ [m−1 ], backscatter βλ [m−1 ·sr−1 ], and mass fraction
concentration P MD [µg·m−3 ] of an individual component ñi (r) for a given wavelength λj
are given by (3.6).
αi (λj ) = π
R∞
1
4
βi (λj ) =
P Mi,D
0
r2 Qext (kj , r, mj ) ñi (r) dr
R∞
r2 Qpi (kj , r, mj ) ñi (r) dr
R D/2
= 34 πρi 0 r3 ñi (r) dr
0
(3.6)
The variable k is the size parameter kj = 2π/λj . The expressions Qext and Qπ are the
scattering efficiency functions for extinction and backscatter, respectively.
Since the atmosphere at any given range can be described as a homogeneous baseline
scattering component plus a linear combination of other varying scattering components, the
total extinction, backscatter, and mass fraction vectors can be written in matrix form as
shown in (3.7).
α (z) = α0 + Av (z)
β (z) = β 0 + Bv (z)
(3.7)
pm (z) = pm0 + MP M v (z)
The column vectors of the matrices A, B, and MP M are the extinction, backscatter,
and PM vectors for the individual scattering components. These column vectors can be
calculated using Mie theory as described by (3.6), or can be taken from some other source.
As mentioned previously, the PSDs of the non-baseline aerosol components must be chosen
so that the corresponding backscatter vectors are linearly independent. As a consequence,
B must be full column rank. The expression pm represents the mass fraction vector.
Government air-quality regulations typically regulate P M2.5 and P M10 , so a convenient
formulation for pm might be (3.8).
57
T
pm =
(3.8)
P M2.5 (z) P M10 (z)
The vectors α, α0 , β, and β 0 are Λx1 elements, the vector v is Sx1 elements, and the
matrices A and B are ΛxS. Following the form shown in (3.8), pm and pm0 are 2x1, and
MP M is 2xS. The values of these vectors and matrices might be determined from an aerosol
database, atmospheric model, or using independent point sensors. In this way, extinction,
backscatter, and mass fraction can be described as linear functions of the vector v (z), which
represents the amplitudes of each of the component scatterers at range z.
3.3
State Vector Formulation
The lidar equation can be rewritten in terms of β m by substituting (3.3) into (3.1).
p (z) = pm ·
2 β (z)
zm
z2 βm
Zz

· exp −2

α(z 0 ) dz 0 
(3.9)
zm
Substituting (3.7) into (3.9) results in (3.10).
p (z) = pm ·
2 (β
zm
0

+ Bv (z))
· exp −2
z2 βm
Zz

α0 + Av z
0
dz 0 
(3.10)
zm
At this point, the lidar equation for a given range has been reduced to set of Λ equations
with S unknowns, which are the amplitudes of ni (z), the PSDs of the individual components.
In practice, any lidar signal will have error. Possible sources of error in a lidar system
include photo-induced shot noise from the background radiance and the returned lidarsignal itself, shot dark current noise, and thermal noise. Additionally, non-uniform detector
response and laser source power fluctuations can be another source of error. If the error is
assumed to be a zero-mean random variable, then the measured signal can be expressed by
adding the error term ν z to (3.10), which is a zero-mean random vector for range z.
58
p (z) = pm ·
2 (β
zm
0
Zz

+ Bv (z))
z2β
· exp −2
m

α0 + Av z
0
dz 0  + ν z
(3.11)
zm
Let the matrix U be defined as a matrix whose ith column vector is v (zi ). For a lidar
with L range values, U is a SxL matrix.

n1 (z1 )
n1 (z2 ) · · ·


 n2 (z1 ) n2 (z2 ) · · ·

U =
..
..
..

.
.
.


nS (z1 ) nS (z2 ) · · ·
n1 (zL )
T


n2 (zL ) 


..

.


nS (zL )
(3.12)
Let P (U ) be defined as a function of U that returns an ΛxL matrix whose ith column
vector is p (zi ). This matrix is the estimate of what the returned lidar signal should be given
U . In this case, the least-squares solution to the lidar equation is the value for an estimate
of the matrix U that minimizes the squared difference between the measured power and its
estimate.
Û = arg minU |P (U ) − Pmeas |2
(3.13)
Both Pmeas and P (U ) are matrices, and it is convenient to vectorize them. Let
y = vec (U ), q = vec (Pmeas ), and q̂ (y) = vec (P (U )). The vector y is SLx1 and the vector
q is ΛLx1. In this case the vectorized form of (3.13) becomes (3.14).
ŷ = arg miny |q̂ (y) − q|2
(3.14)
The structure of y is given by (3.15).
T
ŷ =
n1,1 n2,1 · · ·
nS,1 n1,2 · · ·
nS,L
(3.15)
The norm is defined using a weighted Euclidean inner product |A|2 = AT W A. If W is
59
chosen to be the inverse of the measurement covariance matrix, the least-squares solution to
(3.14) will in fact be the maximum-likelihood solution to the lidar equation for the case of
Gaussian noise [16].
At this point, a state vector given by (3.15) describes the aerosol concentration of the
atmosphere and the lidar retrieval problem can be restated in terms of finding the weighted
least-squares solution to this state-vector.
3.4
Linearization
Since the form of (3.11) is nonlinear, q̂ (y) is also nonlinear. Nevertheless, a solution
to (3.14) can still be calculated by iteratively linearizing and solving, similar to Newton’s
method. The details of applying this method to the lidar equation are presented here. The
function q̂ (y) and all of its component matrices can be expressed by (3.16).
q̂ (y) = B 0 ŷ + b0 · exp A0 ŷ + a0
(3.16)
Full details on how to construct the matrices A0 and B 0 and vectors a0 and b0 are given
in Appendix A.
A linear approximation of (3.16) around some point ŷ0 can be made by expanding it
into a Taylor series and discarding all but the terms up to the first order. Examples of how
to expand exponential matrix expressions are given in Appendix A. In general, the form of
a first order Taylor series approximation of a vector function around the point x0 is shown
below.
f (x0 ) ≈f (x0 ) +
df (x0 )
(x − x0 )
dx
(3.17)
Using (3.17), it is possible to approximate (3.16) with a linear expression with the same
value and first derivative at the point y0 .
60
q̂ ≈ Gŷ + h
G = diag [exp (A0 ŷ0 + d)] [B 0 + diag (B 0 ŷ0 ) A0 + diag (b)]
(3.18)
h = diag [exp (A0 ŷ0 + d)] [diag (b) (1 − A0 ŷ0 ) − diag (B 0 (ŷ)0 ) A0 ŷ0 ]
The dimensions of G are ΛLxLS and h is ΛLx1. Given an initial estimate ŷ0 , the linear
approximation to the minimum weighted least-squares solution of (3.16) can now be written
as shown in (3.19).
−1 T
ŷ = GT W G
G W (q − h)
(3.19)
Assuming there is no correlation between channels or ranges, the appropriate choice
for the best linear unbiased estimator is for W to be a diagonal matrix of matrices that
weights each element of y by the inverse of its variance, or in other words W −1 is the noise
covariance matrix of measured power vector described by (3.11) [16].
W = diag W1 · · · WΛ
2
2
Wi = diag 1/σ1,i · · · 1/σL,i
(3.20)
2 represents the observation noise variance [W2 ] at range z for the
The expression σh,i
h
ith channel. There is no universal method of how to determine the variance for a given
range and channel as this depends on the characteristics of the instrument, however for
the case of the simulated lidar data and Aglite lidar data described later in this paper, the
return signal was Poisson distributed so the variance was estimated by time-averaging the
signal and fitting each channel with a curve described by Az −2 , and then assuming that the
variance for each channel was equal to Az −2 .
Computer simulations have demonstrated that this retrieval method behaves correctly as
the SNR falls below 0 dB. Examples of simulated one-second return-power signals (symbols)
are shown in figs. 3.2(a,c,e), compared with estimated return-power signals according to the
NLS estimator (lines), which they match very well. Inspection of figs. 3.2(b,d,f) shows that
61
estimated concentration remains roughly centered around the simulated values at farther
ranges, but with progressively increasing error. Although it is unrealistic to expect arbitrarily
accurate retrievals as the SNR approaches 0 dB, it is reasonable to expect the retrieval to
fail gracefully. This means one can expect the algorithm to continue to deliver useful results
with zero-mean distributed solution error as the SNR goes to 0 dB. Whereas the SNR of
the returned signal is inversely related to the range of the signal, the weighting coefficients
in (3.20) are not. In fact, for the case of shot noise, both the variance and expected
power of the signal decrease with increasing range. Therefore, according to (3.20), the
weighting coefficients increase as range increases. Consequently, although closer signals have
relatively more influence on the solution because of their higher amplitudes, the weighting
coefficients give farther range signals sufficient importance so that their information is
correctly incorporated into the solution. Although shot noise is Poisson distributed, due to
the central limit theorem it is reasonable to approximate it as Gaussian noise, where the
variance equals the mean.
As the approximate solution approaches the true solution, the linear approximation
more closely approximates the lidar equation and returns a progressively better solution. In
turn, the improved solution allows for a better linear approximation of the lidar equation.
In this way the retrieved solution converges to the true solution.
The computational cost of each iteration is not trivial, as the matrix G can be quite
large. The main computational expense lies in computing the matrix multiplications and
matrix inverse from (3.19). Consider the matrix G, which has dimensions ΛLxLS. A 500
bin long 3-channel lidar signal, retrieved for two aerosol components, would require G to be
1500x1000, meaning computation of (3.19) requires the inversion of a 1000x1000 matrix. This
computation is readily achieved using modern computing power, however since Gaussian
elimination has a computational complexity bound of O n3 , it may be prohibitive to use
this method with significantly longer lidar signals.
Computational costs can be mitigated by restricting the retrieval to the region of interest
in the signal. An additional strategy to reduce computational costs and also to constrain the
62
Fig. 3.2: Comparison of synthesized data with the estimated solution for measured power
(left column) and PM10 concentration (right column) of (a,b) average continental, (c,d)
polluted continental, and (e,f) urban type homogeneous atmospheres.
63
solution would be the use of a regularization matrix. Since a detailed investigation of lidar
signal regularization is beyond the scope of this paper, a couple of comments on the topic
should be made. Warren et al. have previously demonstrated regularization in retrieving
lidar signals, decomposing the state vector into a reduced set of sinusoidal basis functions [6],
while the use of regularization to constrain PSDs retrieved from multi-wavelength extinction
and backscatter measurements have been demonstrated [8–10]. Regularization could be
incorporated into the algorithm described here by substituting the state vector with a
reduced state vector.
ŷ = Rŷ0
(3.21)
The matrix R is the SLxT L regularization matrix and ŷ0 is the T Lx1 reduced state
vector to be used in place of ŷ. The regularization matrix decomposes the state into a
reduced set of T basis functions, where T < S. As a result, the size of the state vector is
reduced, along with the cost of matrix inversion, which reduces from inverting an LSxLS
matrix to a smaller LT xLT matrix.
In summary, a flowchart of the algorithm is illustrated in fig. 3.3, and the steps of the
lidar retrieval are described as follows:
1. Initialize ŷ0 = 0,
2. Linearize the lidar equation around ŷ0 using (3.18),
3. Calculate ŷi using (3.19),
Fig. 3.3: Flowchart illustrating the operation of the iterative least-squares method.
64
4. Update ŷ0 = ŷ0 + ŷi ,
5. Repeat steps 2 through 4 until ŷ0 converges.
3.5
Laser Pulse Shape and Geometric Form Factor
The laser pulse shape and range resolution can have an appreciable effect on the lidar
data. The form of (3.1) is derived for a laser with a delta-function pulse shape and continuous
range measurement. In practice, the laser pulse shape can have significant width and the
measured lidar signal is integrated over a certain detection window length τ . As a result,
the measured lidar signal is the convolution of the lidar function (3.1) with the laser pulse
shape and with an integrating window, a rectangle function with width cτ /2. Previous work
has shown how to deconvolve the laser pulse shape from lidar data using a Wiener filter [6].
A unique way of compensating for the convolution effect of the lidar pulse shape is
presented here, which works by incorporating the convolution phenomenon into the lidar
equation. Convolution is a linear function, so it can be represented as a matrix multiplication
and appended to (3.16). In this way, the deconvolution of the laser pulse with the lidar
signal is not explicitly computed, but rather the solution is computed so as to compensate for
the convolution. This is very similar to the operation of a zero-forcing equalizing filter [19].
Zero-forcing filters often amplify stop-band noise, so it may be necessary to filter the solution
with a low-pass filter to control this noise. It is beyond the scope of this paper to investigate
the optimality of this method or compare it to other methods such as the Wiener filter, but
it is presented here simply to demonstrate the ability of the iterative least-squares method
to integrate this technique.
As an example of a real lidar system, the laser pulse shapes and detection time window
for Aglite are illustrated in fig. 3.4(a). The detection time window is a rectangle function,
whose width equals the range resolution of the lidar. The actual measured lidar signal is
(3.1) convolved with both the laser pulse shape and the detection time window. The laser
pulse shape and detection time window can be combined and treated as a low-pass filter.
The frequency responses for each channel of Aglite are shown in fig. 3.4(b) with units of
65
power decibels. The 3 dB point with the lowest frequency of all three channels is at .034
m−1 , corresponding to an equivalent spatial resolution of 29.4 m for the IR channel (further
details of the Aglite lidar are given by Marchant et al. [14]).
It is straightforward to incorporate this filtering effect into the retrieval algorithm. The
action of convolution can be represented as multiplication by the matrix F .
F = diag F1 · · · FΛ
Fj = diag f1,j f2,j · · · fL,j
(3.22)
The vector fh,j is an Lx1 vector containing the time-reversed convolved pulse shape and
measurement window of the jth channel, as illustrated in fig. 3.4(a), which has been shifted
down by h-1 elements, where h is the column of Fj . The expression L is the length of the
laser pulse in bins. The filter matrix F can be attached to (3.16).
q̂ = F A0 ŷ + b · exp C 0 ŷ + d
(3.23)
The rest of the procedure described in Section 3.5 can then be followed to achieve a
retrieval which takes into account the channel-dependent filtering effects of the lidar pulse
shape and measurement window.
In addition to lidar pulse shape and windowing effects, the effect of the GFF can also
be easily incorporated into F . The GFF can be calculated [15]; however there can be large
discrepancies between the expected and actual shape of the GFF due to misalignment or
inaccurate knowledge of the lidar optics parameters, such as the laser divergence angle and
beam profile shape. As a result, the GFF may need to be experimentally measured [20].
Normally it is desirable to take measurements in the region of the lidar signal where the
GFF is constant, however in some aerosol lidar applications it may be unavoidable to use a
region where the GFF has a significant impact on the shape of the return signal [21]. For
the case of the experimental data described in Section 3.8, the Aglite instrument was placed
650 m away from the emission source. This location was imposed by the geography of the
facility; a more distant location for the lidar was logistically impractical. At 650 m, the GFF
66
Fig. 3.4: (a) Laser pulse shape convolved with the integration window, and (b) its frequency
response.
of Aglite is only approximately 80% of its far-field value, so it was necessary to compensate
for this effect in the retrieval [14].
Inserting F into the lidar equation requires multiplying a ΛLxΛL matrix with a ΛLxSL
matrix. If a series of lidar retrievals are made, where identical values for A and b will be
used for every retrieval and the laser pulse shape and GFF are constant in time, A and
b only need to be multiplied by F once. The convolution will be then be compensated
automatically at no additional cost as part of the retrieval for every measurement in the
time series.
3.6
Solution Error
There are two main assumptions in the retrieval process. The first is that the aerosol is
well-characterized, or in other words, extinction and backscatter coefficients must be known
for the baseline aerosol, at the boundary point, and for each non-homogeneous component
aerosol, as previously described by (3.7). These might come from a database or from in-situ
instrument measurements. Inaccurate values for any of these extinction and backscatter
coefficients will lead to systematic errors in the retrieved concentration values.
The second assumption is that the atmosphere under investigation consists of a baseline
component plus a number of components whose amplitude is spatially dependent, but whose
67
other characteristics are uniform in space. The retrieval process uses these components as a
set of basis functions to represent the aerosol being measured by the lidar. Error can result
if the components are only weakly independent.
In addition to errors related to inaccurate assumptions in the retrieval process, there is
also error from the measurement noise of the system. An expression for the covariance of
the solution as determined by the covariance of the lidar signal noise is derived here. One
intuitively expects that as the SNR of the signal decreases, the variance of the retrieved
solution will increase. It is possible to estimate the influence of signal noise on the variance
of the retrieved solution.
The solution estimate ŷ is treated as the sum of the true state vector y plus some
random variable s, modeling the solution error.
ŷ = y + s
(3.24)
Inserting (3.24) into the solution (3.19) yields the following expression for the covariance
of ŷ.
−1 T
−1
Cov (ŷ) = GT W G
G W Cov (q) W G GT W G
(3.25)
As demonstrated by (B.8), the total error in q is due to both the noise in the lidar
calibration, described by (B.7), as well as the observation noise in all range bins of the lidar
power signal, described by (B.4). One should not be concerned that the weighting matrix
W depends on the variance of the noise. Because W depends on an a priori estimate of the
noise measurement, not on the actual value of a given noise vector, it is permitted to come
outside of the covariance expression. Inspection of (3.25) shows that the covariance of ŷ is a
function of the total-noise covariance matrix. Since the lidar calibration coefficient noise
and the individual range bin noise are either independent or sum to zero, the total-noise
covariance matrix is the sum of the range bin covariance and the lidar coefficient covariance.
The range bin covariance was defined in (3.20) as W−1 . The covariance of the solution error
68
is shown by (3.26).
Cov (q) = diag (h) Cov (dk ) diag (h) + W −1
2
2
2
σm,Λ
σm,2
σm,1
Cov (dk ) = diag P 2 1 P 2 1 · · · P 2 1
m,1
m,2
(3.26)
m,Λ
The expression 1 is an LxL submatrix, whose elements are all equal to 1. The derivation
of (3.26) is given in Appendix B.
The covariance of the retrieved extinction, backscatter, and mass fraction vectors can
be calculated as well. Using (3.7), the covariance matrix of the estimated extinction vector
for a given range can be calculated.
Cov([ α1 · · ·
αL ]) = diag[ A · · ·
A ]Cov (ŷ) [ AT
AT
··· ]
(3.27)
Similar definitions exist for the covariance of the backscatter and mass fraction vector
estimates. The equations above show how to estimate the error of all the retrieval parameters
for each bin in the lidar retrieval if the variance of the lidar power signal is already known.
In practice, this covariance can only be estimated. Additionally as described in Appendix B,
this method assumes ŷ = 0 when estimating the contribution of boundary point noise. This
means that the (3.25) returns an estimate of the solution variance, not the actual solution
covariance. Nevertheless, since the calculations use estimates of the lidar signal noise and
retrieval parameters, the calculated values are as close as could be hoped for and should be
sufficiently close to the “true” error to be useful.
3.7
Simulations
This section presents the results of validating this method using synthetic data, rep-
resenting a variety of aerosol loading conditions, while Section 3.8 presents the results of
applying this method to experimental data. In all cases, the retrieved values were consistent
with the simulated values. The following section presents the results from validating this
method with experimental data. Synthetic lidar data were created to simulate a variety of
atmospheric conditions. The synthetic lidar data are based on the parameters of the Aglite
69
lidar instrument [14], which is a micro-pulse lidar that uses photon-counting detection, with
channels at 355, 532, and 1064 nm. These were used to test the performance of the retrieval
method described above.
The Rayleigh scattering component was calculated corresponding to conditions of
20◦ C, 50% relative humidity, and barometric pressure of 1013.25 mbar. In addition to
Rayleigh scattering, three types of Mie scattering components were simulated. These are
the continental average, continental polluted, and urban aerosol mixtures, as described by
Hess et al. Backscatter, extinction, PM2.5 , PM10 , and total suspended particulate (TSP)
were calculated for these scatterers at 50% relative humidity using the OPAC database [22].
These aerosols are summarized in Table 3.1.
Simulated lidar returns were generated for three wavelengths using (3.1). The baseline
background component consisted of either continental average or continental polluted aerosols
added to the Rayleigh component. Lidar returns were also generated for the same baseline
components, but with an added emission plume composed of continental average, continental
polluted, or urban aerosol. The emission plume has a Gaussian shape, for which the half-max
width of the plume is 131 m, placed at 800 or 1600 m. An integration time of 1 second
was used to generate 1000 returns for each scenario. The signal was simulated assuming
channel power values of 1.15, 0.85, and 4.10 W, and lidar channel efficiency values of 2.88e-4,
7.71e-5, and 1.03e-5 [unitless] for the 355, 532, and 1064 nm channels, respectively, matching
typical values for the Aglite instrument. A noise component was also added to the signal to
simulate the effects of solar background radiation with values of 100, 250, and 10 photons
per bin, again corresponding to typical values during Aglite operation. The combined noise
and signal are then modeled as a Poisson distributed random process.
The algorithm described above in Sections 3.3 and 3.4 was applied to these simulated
returns, and the resulting retrievals were evaluated. The range of the boundary point was
600 m in all cases, and baseline backscatter vector that was used to generate the signal was
used for the boundary point backscatter vector βm . The convolution matrix F described
by (3.22) was incorporated into the retrieval to compensate for the laser pulse width and
70
Table 3.1: Aerosol optical and mass characteristics.
355 nm
532 nm
1064 nm
Extinction
Average
9.01e-5
5.26e-5
2.17e-5
(m−1 )
Polluted
2.16e-4
1.24e-4
4.99e-5
Urban
4.58e-4
2.63e-4
1.10e-4
Backscatter
Average
1.56e-6
9.26e-7
4.70e-7
(m−1 str−1 )
Polluted
3.61e-6
2.09e-6
9.76e-7
Urban
7.23e-6
4.22e-6
2.00e-6
Average
57.8
56.8
46.2
Polluted
59.8
59.3
51.1
Urban
63.3
62.3
55
PM2.5
PM10
TSP
Lidar Ratio
Mass Fraction
Average
10.5
16.6
24.2
Concentration
Polluted
24.1
33.1
44.6
(µg/m3 )
Urban
47.8
70.4
99.1
the geometric form factor. A low pass Kaiser-window filter of order 14 with a pass-band
cutoff frequency of .034 m−1 and a stop-band cutoff frequency of .068 m−1 was applied to
the solution to compensate for high-frequency noise generated by F .
3.7.1
Background Retrieval
For both the background and plume data sets, the individual solutions were calculated
over 1000 individual measurements for four of the synthetic data sets. Figigure 3.2 shows
examples of individual retrievals performed on synthetic data simulating continental average,
continental polluted, and urban atmospheres. Figures 3.2(a,c,e) illustrate a single 1 second
return of original signal (minus the solar background component) along with its corresponding
minimum least-squares solution, and figs. 3.2(b,d,f) compare the PM10 concentration of the
solution with the original backgrounds.
For these retrievals, a single varying component was used, and the same extinction,
backscatter, and mass fraction values were used for the varying component as for the aerosol
component of the baseline scatterer. Because the synthetic signal was constructed using
71
only the baseline aerosol component, the true state vector y is the zero-vector.
Inspection of fig. 3.2 confirms that there is no obvious bias in the retrieved signal. Because
of the progressively worsening SNR at farther ranges, the estimated aerosol concentration
values become noisier, however the retrieved state vector values remains roughly centered
around zero. Nevertheless, it is clear that the solution remains stable at distances past
the boundary point and the form of the solution is generally consistent with the true state
vector. Table 3.2 lists the average error of the retrieved solution compared with the true
state vector, as well as the 99% confidence interval.
3.7.2
Plume Retrieval
Like the background retrievals, 1000 individual 1-second retrievals were performed, and
then averaged. These signals consist of a superposition of the same background aerosol
signals used in the background retrievals, with an emission plume added to them, consistent
with (3.4). The average of these solutions is computed using both the NLS method described
here and Klett’s solution for two scatterers [3], with the results compared with the true PM10
concentration that was used to generate the synthetic data. Klett’s solution is computed by
applying Klett’s method to recover backscatter values, and then a least-squares method is
applied to calculate PM10 concentration [23]. Figures 3.5(a,c,e) clearly illustrates that the
signal is the sum of a Gaussian curve shaped emission plume plus a baseline background
aerosol.
The mean PM10 concentration and 99% confidence interval were calculated at 800 and
1600 m ranges for the NLS method and are summarized in Table 3.2. Although the retrieval
method clearly introduces some bias, in all cases the average error was on the same order of
magnitude as the 99% confidence interval. This bias may be introduced by the low-pass
filter that is applied to the solution. No bias is apparent when the low-pass filter is omitted;
however the magnitude of the error values increases.
An important benefit of the NLS method is that it is a straightforward process to
compute a covariance matrix for the solution. The standard deviation values of the individual
PM10 values, taken as the square root of the diagonal values of the solution covariance matrix,
72
Fig. 3.5: Estimated PM10 mean concentration and standard deviation compared to actual
values for synthesized data representing (a,b) average continental, (c,d) polluted continental,
(e,f) and urban type plumes.
73
Table 3.2: Average retrieval error comparison.
PM10 (µg/m3 )
800 m
1600 m
800 m
1600 m
Background
Plume
Error
99% CI
Error
99% CI
Average
None
0.28
±0.23
0.67
±0.92
Average
0.10
±0.27
-0.61
±0.94
Polluted
0.02
±0.29
-1.73
±0.88
None
0.27
±0.28
0.39
±1.15
Polluted
-0.51
±0.36
-2.01
±1.24
Urban
-0.14
±0.46
-4.18
±1.41
None
0.13
±0.47
0.74
±2.21
Urban
-0.42
±0.62
-4.31
±2.49
Polluted
Urban
are plotted in figs. 3.5(b,d,f). They are also compared with the standard deviation values
as measured from the set of individual retrievals. The predicted standard deviation values
compare very favorably with the measured standard deviation of the individual retrievals.
3.8
Experimental Data
This NLS method was also performed on experimental data from the Aglite lidar
instrument. The data were gathered at a hog farm located near Ames, Iowa, at 3:00 pm
on September 5th 2005. The lidar was placed about 600 m east of the facility and the
measurement was made with the lidar beam staring directly west at an elevation angle of
0 degrees elevation, parallel to the ground. The peak of the emission plume signal occurs
at about 650 m, at which range the GFF is approximately 80% of its far-field value. The
lidar was set up near the edge of the property line and we did not have permission to place
the lidar on the neighboring property, thus preventing us from taking measurements from a
greater range. The lidar integration time was set to 1 second and a range resolution of 4.8
meters was used. The measurement was taken for 400 seconds.
Two MetOne OPCs were deployed at the facility; one was mounted on an instrumentation
trailer directly north of the facility, downwind of the emission plume, while another OPC was
placed in the field on the east side of the facility where it was not expected to be impacted
74
by the emission plume of the facility. In addition, a Davis weather station was set up which
measured temperature, pressure, and humidity. Complete details on the experiment setup
are given by Bingham et al. [21].
The OPC measures particle counts at 20 second intervals and sizes the particles in to
one of seven size ranges, resulting in high time-resolution in-situ measurement of the PSD of
the aerosol. This PSD is converted into extinction and backscatter using (3.6) and using
the index of refraction values for water-soluble aerosols given by the Air Force Handbook of
Geophysics and the Space Environment [17]. The mass fraction concentrations for PM2.5
and PM10 are also calculated using the PSD, assuming the aerosol particle density equals
one and the aerosol aerodynamic diameter equals the aerosol optical diameter.
It was assumed that the OPC in the field was unaffected by the facility and measured
only the regional background atmosphere. The backscatter, extinction, and mass fraction
concentration measurements of that OPC were averaged over the duration of the lidar
measurement to calculate the optical and mass fraction coefficient vectors of the background
aerosol. The temperature, pressure, and humidity parameters measured by the weather
station were used to calculate the Rayleigh scattering contributions to backscatter and
extinction. These were added to the coefficient vectors of the background aerosol to get
the backscatter, extinction, and mass fraction vectors of the baseline component. It was
assumed furthermore that the trailer-mounted OPC downwind of the facility measured the
regional background aerosol plus the emission plume. This OPC was used to calculate the
backscatter, extinction, and mass fraction vectors of the varying aerosol component. This
was done by averaging the coefficients of the trailer-mounted OPC over the duration of the
experiment and subtracting the baseline coefficients from the field OPC.
The reference point was selected to be at 900 meters. Although the background OPC
was not mounted along the line-of-sight of the lidar, it was assumed that the backscatter at
the reference point was equal to the background baseline backscatter values. The retrieval
was performed on the average of 400 individual 1-second measurements.
The retrieval was performed both using only a single varying component, S = 1, and
75
using two varying components, S = 2. In both cases, the baseline component was the
same and was defined using the backscatter, extinction, and mass fraction vectors from the
field OPC. For the single-varying-component case, the component was described by the
emission plume aerosol, whereas for the two-varying-component case, the first component
was described by the emission plume aerosol and the second component was described using
the same backscatter, extinction, and mass fraction vectors as the baseline aerosol. The
backscatter vectors of the emission plume and regional background aerosols are linearly
independent, meaning they form a basis and are acceptable selections for the retrieval. As
indicated by (3.4), the PSD of the retrieved solution must be a linear combination of the
regional background and emission plume PSDs. This is a good choice of components as
it is reasonable to expect the PSD of the emission aerosol to be a combination the PSDs
previously measured by the two OPCs.
As in the case of the synthetic data retrieval, a convolution matrix was used to
compensate for both the GFF and the lidar pulse shape, and the same low-pass filter used
to remove high-frequency noise in the solution.
The results of the one-component and two-component retrievals on the individual
measurements are shown in fig. 3.6. Inspection of figs. 3.6(c,d) shows that the one-component
solution is smoother than the two-component solution. This is explained by the fact that
the solution is less constrained for the two-component case and is able to fit itself to more
of the noise than in the one-component case. This is also illustrated in figs. 3.6(a,b) by
the fact that the two-component solution provides a tighter fit to the observed data than
the one-component solution. If multiple components are necessary to get a good fit with
the measured aerosol, care must be taken in choosing them appropriately. The different
components should have orthogonal backscatter vectors and the shapes of their PSD functions
should be similar to the PSD of the target aerosol. Different choices of components may
allow for solutions which fit the data equally well but may give significantly different aerosol
concentration values.
76
Fig. 3.6: Estimated values for (a,b) returned power, (c,d) emission component amplitude,
and (e,f) PM concentration from swine facility measurement, assuming one component
aerosol (left column) and two component aerosols (right column).
77
The NLS method demonstrates good stability when used on experimental data. Comparisons of this method with Klett’s method for two-scatterers for experimental data at
ranges further than the boundary point are shown in figs. 3.7(a,b). The feature at 650
m is the emission from the swine facility, also illustrated in fig. 3.6, and the feature at
1600 m corresponds to an unpaved road running perpendicular to the lidar beam direction.
Figure 3.7(a) shows the results of applying both the NLS method and Klett’s method to
the experimental data previously illustrated in fig. 3.6. For fig. 3.7(a), both methods are
applied under two sets of conditions: first, the boundary point backscatter values are equal
to the baseline backscatter values, and second, the boundary point backscatter values are
equal to 1.25 times the baseline backscatter values. In the first case, both methods give
very similar results, and in the second case, both the NLS method and Klett’s method
give higher retrieved values for PM10 over all ranges. However, whereas the solution from
Klett’s method becomes unstable very quickly at ranges past the reference point (located
at 900 m), the solution generated by the NLS method remains stable at distant ranges.
Figure 3.7(b) also illustrates the results of both methods under two sets of conditions: first,
the baseline extinction coefficients are equal to the extinction values as calculated from
the point-sensor instruments; and second the baseline extinction coefficients are equal to
1.50 times the point-sensor instrument values. In this figure it is also shown that under
normal conditions both methods give similar results, however when the baseline extinction
coefficients are overestimated, the solution generated by Klett’s method becomes unstable.
Under the same 50% error conditions, the NLS solution also increases somewhat at ranges
beyond the boundary point, but to a much lower degree than the Klett solution.
3.9
Conclusion
This iterative least-squares lidar retrieval method has been shown to generate useful
results from elastic lidar data. This method is capable of handling an arbitrary number of
channels and generates stable solutions at ranges beyond the reference point and at low SNR
values. When processing synthetic data, the algorithm gives solutions which are consistent
with the original aerosol concentration values. When processing experimental data from
78
Fig. 3.7: Comparison of the stability of estimated PM10 concentration from the NLS and
Klett methods under the conditions of (a) 25% error in the boundary point backscatter
coefficients, and (b) 50% error in the baseline extinction values.
the field, the algorithm gave solutions that were consistent with both the measured lidar
returns and with the optical parameters calculated from point-sensor instruments. The
method requires certain a priori constraints, including that the aerosol under investigation
be modeled as an external mixture of component aerosols and that the terms in (3.7) be well
described. Care must be taken to correctly choose these component aerosols, as different sets
of components could potentially fit the data nearly equally well, while giving very different
PSDs and mass fraction concentration results. Although this method can be used to probe
the PSD of an aerosol, as described by (3.4), any PSD obtained using this method must
necessarily be a linear combination of the PSDs of the selected component aerosols. For this
reason, in order to encourage a physically realistic solution, components should be selected
whose PSD shape is similar to the expected PSD shape of the target aerosol.
The ability of this method to account for noise, to use all channels in the retrieval
simultaneously, and its stability at all ranges make this method especially useful for extracting
maximal information in the far tail-end of a lidar signal, where the SNR is low. Nevertheless,
it is possible for the estimater to give a solution that is physically unrealistic. A potential
strategy to deal with this is to add constraints using Lagrange multipliers to the solution.
Appropriate constraints can be selected depending on the expected characteristics of the
aerosol under investigation. It is beyond the scope of this dissertation to investigate the
79
consequences of adding physical constraints to the solution; however this may be an effective
way of improving the robustness of the algorithm.
References
[1] R. T. H. Collis, “Lidar: a new atmospheric probe,” Quarterly Journal of the Royal
Meteorological Society, vol. 92, pp. 220–230, 1966.
[2] J. D. Klett, “Stable analytical inversion solution for processing lidar returns,” Applied
Optics, vol. 20, pp. 211–220, 1981.
[3] J. D. Klett, “Extinction boundary value algorithms for lidar inversion,” Applied Optics
Letters, vol. 22, pp. 514–515, 1983.
[4] F. G. Fernald, “Analysis of atmospheric lidar observations: some comments,” Applied
Optics, vol. 23, p. 652, 1984.
[5] F. Rocadenbosch, C. Soriano, A. Comeron, and J. Baldasano, “Adaptive filter solution
for processing lidar returns: optical parameter estimation,” Applied Optics, vol. 37, pp.
7019–7034, 1998.
[6] R. E. Warren, R. G. Vanderbeek, A. Ben-David, and J. L. Ahl, “Simultaneous estimation
of aerosol cloud concentration and spectral backscatter from multiple-wavelength lidar
data,” Applied Optics, vol. 47, pp. 4309–4320, 2008.
[7] J. M. B. Dias, J. M. N. Leitao, and E. S. R. Fonseca, “Reconstruction of backscatter
and extinction coefficients in lidar: a stochastic filtering approach,” IEEE Transactions
on Geoscience and Remote Sensing, vol. 42, pp. 443–456, 2004.
[8] D. Müller, U. Wandinger, D. Althausen, I. Mattis, and A. Ansmann, “Retrieval of
physical particle properties from lidar observatison of extinction and backscatter at
multiple wavelengths,” Applied Optics, vol. 37, pp. 2260–2263, 1998.
[9] I. Veselovskii, A. Kolgotin, V. Griaznov, D. Müller, U. Wandinger, and D. N. Whiteman,
“Inversion with regularization for the retrieval of trophospheric aerosol parameters from
multiwavelength lidar sounding,” Applied Optics, vol. 41, pp. 3685–3699, 2002.
[10] C. Böckmann, “Hybrid regularization method for the ill-posed inversion of multiwavelength lidar data in the retrieval of aerosol size distributions,” Applied Optics, vol. 40,
pp. 1329–1342, 2001.
[11] M. Sicard, F. Molero, J. L. Guerrero-Rascado, R. Pedros, F. J. Exposito, C. CordobaJabonero, J. M. Bolarin, A. Comeron, F. Rocadenbosch, M. Pujadas, L. AladosArboledas, J. A. Martinez-Lozano, J. P. Diaz, M. Gil, A. Requena, F. Navas-Guzman,
and J. M. Moreno, “Aerosol lidar intercomparison in the framework of SPALINET - The
Spanish lidar network: methodology and results,” IEEE Transactions on Geoscience
and Remote Sensing, vol. 47, pp. 3547–3559, 2009.
80
[12] A. Comeron, F. Rocadenbosch, M. A. Lopez, A. Rodriguez, C. Munoz, D. GarciaVizcaino, and M. Sicard, “Effects of noise on lidar data inversion with the backward
algorithm,” Applied Optics, vol. 43, pp. 2572–2577, 2004.
[13] J. A. Reagan, X. Wang, and M. T. Osborn, “Spaceborne lidar calibration from cirrus
and molecular backscatter returns,” IEEE Transactions on Geoscience and Remote
Sensing, vol. 40, pp. 2285–2290, 2002.
[14] C. C. Marchant, T. D. Wilkerson, G. E. Bingham, V. V. Zavyalov, J. M. Andersen,
C. B. Wright, S. S. Cornelsen, R. S. Martin, P. J. Silva, and J. L. Hatfield, “Aglite
lidar: a portable elastic lidar system for investigating aerosol and wind motions at or
around agricultural production facilities,” Journal of Applied Remote Sensing, vol. 3, p.
033511, 2009.
[15] R. M. Measures, Laser Remote Sensing: Fundamentals and Applications. New York:
John Wiley, Inc., 1984.
[16] T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal
Processing. Upper Saddle River: Prentice Hall, 2000.
[17] A. S. Jursa, Handbook of Geophysics and the Space Environment, Air Force Geophysics
Laboratory, Hanscom Air Force Base, 1985.
[18] C. F. Bohren and D. R. Huffman, Absorption and Scattering of Light by Small Particles.
New York: Wiley, 1983.
[19] J. G. Proakis and M. Salehi, Digital Communications. New York: McGraw-Hill, 2008.
[20] S. W. Dho, Y. J. Park, and H. J. Kong, “Experimental determination of a geometrical
form factor in a lidar equation for an inhomogeneous atmosphere,” Applied Optics,
vol. 36, pp. 6009–6010, 1997.
[21] G. E. Bingham, C. C. Marchant, V. V. Zavyalov, D. Ahlstrom, K. D. Moore, D. Jones,
T. D. Wilkerson, L. Hipps, R. S. Martin, J. L. Hatfield, J. H. Prueger, and R. L.
Pfeiffer, “Lidar based emissions measurement at the whole facility scale: method and
error analysis,” Journal of Applied Remote Sensing, vol. 3, p. 033510, 2009.
[22] M. Hess, P. Koepke, and I. Schult, “Optical properties of aerosols and clouds: The
software package OPAC,” Bulletin of the American Meteorological Society, vol. 79, pp.
831–844, 1998.
[23] C. C. Marchant, “Algorithm development of the Aglite-lidar instrument,” Master’s
thesis, Utah State University, Logan, UT, 2008.
81
Chapter 4
Estimation of Aerosol Effective Radius by Multi-Wavelength
Elastic Lidar
4.1
Background
A major subject of research using elastic-lidar is the investigation of atmospheric aerosols,
including estimation of their concentration and PSD functions. These distributions can be
very complex, with concentration, density, and index of refraction varying with particle
size. In practical circumstances, when using a lidar with a limited number of channels,
some sort of simplifying approximation of the aerosol PSD must be made. Simple aerosols
are sometimes approximated using analytical functions such as the log-normal distribution,
power-law distribution, or a modified gamma distribution, while more complex aerosols are
often approximated as an “external mixture,” or in other words, a linear combination of
simpler component aerosols [1].
Significant effort has been spent in developing methods for estimating the PSD of an
aerosol using multi-wavelength lidar. Some of these methods assume that the aerosol fits a
particular distribution type and operate by estimating the distribution’s parameters [2, 3],
while other methods assume that the aerosol can be described as an external mixture and
operate by estimating the amplitudes of the basis-aerosols [4–6].
Conventional algorithms have relied on a two-step process for estimating the PSD of an
aerosol; first, estimating the scattering coefficients from the measured lidar signal, followed
by estimating the PSD from the scattering coefficients. The first step is often performed
using ratio-constraint techniques, such as the popular Klett method [7], and for the second
step, several algorithms have been demonstrated that were developed from an optimization
perspective [8, 9].
82
Several forms of the EKF have been demonstrated for estimating backscatter and
extinction coefficients from lidar signals. By updating a state space vector with sequential
observations, the EKF generates a weighted least-squares estimate of the state using a
priori information from the previous measurements. Rocadenbosch’s formulation of the
EKF returns a state-vector containing backscatter and lidar ratio values as a function of
range, at the expense of spatial resolution, for individual channels [10].
Alternatively, Warren’s formulation of the EKF uses a two-step process, where a
maximum-likelihood estimator first estimates the aerosol type, and then a Kalman filter
is used to estimate aerosol concentration [11]. Warren’s formulation linearizes the filter
by assuming an optically thin aerosol and dropping the extinction expression, limiting the
versatility of this formulation.
This chapter presents a solution to the lidar equation in the form of an adaptive
extended Kalman filter [12]. In this formulation, the state vector is composed of the
extinction coefficient of the current range and the optical thickness of the path up to and
including the current range. The updated state equation is a function of the state at the
previous range in the current measurement. This formulation offers the significant advantages
of being both computationally efficient and giving stable performance under noisy conditions.
Nevertheless, it is reasonable to expect improved solution quality using an algorithm
that estimates the aerosol PSD directly from measured lidar power, without any intermediate
estimation steps. An NLS algorithm has been previously demonstrated that directly estimates
basis-aerosol amplitudes from measured lidar power, using an iterative least-squares technique
[13]. However, a novel form of the EKF, as applied to lidar, is presented here that also
allows the direct estimation of basis-aerosol amplitudes from measured lidar power, with
higher relative computational efficiency than the NLS technique.
4.2
EKF Formulation
4.2.1
Forward Model
A more in-depth description of the vector formulation of the lidar equation and its
83
application to the Aglite lidar instrument has been given previously [14], but is briefly
outlined here for convenience. For an elastic lidar with c channels, the vector form of the
lidar equation can be represented as (4.1).

p (z) = k · g (z) ·
β (z)
· exp −2
z2
Zz

α(z 0 ) dz 0  + ν z
(4.1)
0
This equation represents the measured power of the lidar signal plus noise, where p(z)
has units of power [W], the variable z represents range [m], the function g(z) represents
the attenuation (unitless) due to the GFF at range z, and the vectors α(z) [m−1 ] and β(z)
[m−1 ·sr−1 ] represent total backscatter and extinction coefficients as a function of range,
respectively. The exponential expression represents attenuation due to extinction. The
operator · represents element-by-element multiplication, while the exponent function operates
on an element-by-element basis. The vector k is composed of the calibration constants for
each channel, and has units [W·m3 ·sr]. The vector ν z is a zero-mean random vector for range
z, representing the observation noise in the signal. All of these vectors have c elements.
The geometric form factor can be removed from (4.1) in pre-processing if the form of
g(z) is known, or if z is sufficiently large that g(z) can be approximated as unity. In this
case, the relationship between k and the backscatter can be determined for some boundary
point m.
k=
p z2
m zm
Rm
β m · exp −2 α(z 0 ) dz 0
(4.2)
0
If the value for β m is known, perhaps obtained from another source such as an in-situ
instrument, it can used to calculate k [13].
The lidar equation can be rewritten in terms of β m by substituting (4.2) into (4.1).


Zz
2
β (z) zm
p (z) = pm ·
· exp −2 α(z 0 ) dz 0  + ν z
βm z2
(4.3)
0
Assuming that the atmosphere at any given range can be described as a homogeneous
84
baseline scattering component plus a linear combination of other spatially varying aerosol
components, the total extinction and backscatter vectors can be written in matrix form.
α (z) = α0 + Av (z) , β (z) = β 0 + Bv (z)
(4.4)
The symbol v is a vertical vector with s elements, where s is the number of basis
aerosols needed to describe the spatially-varying part of the atmosphere. The vectors α0
and β 0 are sx1, and the matrices A and B are cxs. The correct selection of α0 , β 0 , A, and
B is essential for proper operation of the algorithm. In the case of the Aglite instrument,
this is carried out using OPC point sensors and is described in a later section.
Substitution of (4.4) into (4.3) results in an equation representing the measured lidar
power as a function of basis-aerosol amplitudes.


Zz
2
[β 0 + Bv (z)] zm
· exp −2 α0 + Av z 0 dz 0  + ν z
p (z) = pm ·
βm z2
(4.5)
0
4.2.2
State-Space Signal Model
For a given range, the measured power can be represented as a function of the current
basis-aerosol amplitudes and the summation of all previous basis-aerosol amplitudes. In
order to form a system of equations that represent the lidar measurement process, the lidar
equation needs to be represented in discrete form.
"
#
i
2
X
[β 0 + Bv (z)] zm
· exp −2 (zi − zm ) α0 − 2∆z A
vj + ν z
pi = pm ·
βm z2
m
(4.6)
The expression ∆z is the length of each range bin in meters. At first glance, the measured
power appears not to be a Markov process, or in other words pi does not exclusively depend
on vi , but also on all prior values of v, due to the presence of the summation term in the
exponent. Nevertheless, by making a substitution similar to the one by Dias et al. [12], the
lidar equation can be expressed in Markov form.
85
pi = pm ·
2
P
[β 0 + Bv (z)] zm
· exp [−2 (zi − zm ) α0 − 2∆z A (vi + γ i )] + ν z γ i = i−1
2
m vj
βm z
(4.7)
With this formulation, it is clear that the current observation pi depends strictly on
the current values of vi and γ i and an appropriate state vector, state update equation, and
observation equation can be constructed.

T
xi =
viT
γ Ti

 θI 0 
, xi+1 = 
 xi + wi , pi = f (xi ) + ν i
I I
(4.8)
The expression I is an identity matrix, f () represents the lidar equation, and θ is
a gain coefficient, which can reasonably have any value between 0 and 1. Setting θ = 0
will encourage the estimator to choose values for vi+1 that are close to zero, while setting
θ = 0 will encourage the estimator to choose values for vi+1 that are close to vi . The term
wi is a zero-mean random vector that describes the change in the state vector and ν i is
the zero-mean random vector that describes the measurement noise at range zi . For an
atmosphere described using s number of basis aerosols, the state vector is a vertical vector
with 2s elements. By combining coefficients and making a change of variables, (4.7) can be
represented in a simplified form.
pi = bi + Bi0 xi · exp ai + A0i xi + ν z
(4.9)
A more general state-space formulation could also contain a zero-order term in the
state-vector update equation shown in (4.8). However, it is normally not necessary because
any a priori known baseline aerosol value can be incorporated into the bi term in (4.9).
A linear approximation of the lidar equation can be made using a first-order Taylor series
around the vector x0 .
86
pi ≈ Ci xi + di + ν z
C = diag [exp (A0 x0 + a)] [B 0 + diag (B 0 x0 ) A0 + diag (b) A0 ]
(4.10)
d = diag [exp (A0 x0 + a)] [diag (b) (1 − A0 x0 ) − diag (B 0 x0 ) A0 x0 ]
Using the state-space signal model, it is straightforward to implement an EKF estimator.
This requires knowledge of the covariance matrices of ν i and wi .
(4.11)
cov (wi ) = Qi , cov (ν i ) = Ri
The values of these covariances might be provided by in-situ instruments or by a
computer model. It is assumed that these noise vectors are uncorrelated with respect to
range. If the individual channels are uncorrelated with each other, then Ri is a diagonal cxc
matrix of the individual channel noise variances, whereas if the gain coefficient θ equals zero,
Qi is a 2sx2s matrix containing the covariance matrix of the basis-aerosols as a submatrix.

 ν1
cov
(v
)
0

i


Qi = 
 , Ri = 
ν2


0
0



..
.





(4.12)
If θ equals one, Qi is composed of the covariance of the derivative of vi , and if θ is
some intermediate value, Qi is composed proportionally of both the covariance of vi and
the covariance of its derivative with respect to range.
At this point it is straight-forward to implement an EKF estimator [15]. The steps in
this process are as follows:
1. Select state vector values at every range bin, around which the observation equation
will be linearized;
2. Linearize the observation equation at every range bin;
3. Initialize the state vector and state covariance matrix, beginning at the reference range;
87
4. Run the Kalman filter forward with respect to range, beginning at the reference range
and ending at the far range of the signal;
5. Initialize the state vector and state covariance matrix, beginning at the reference range;
6. Run the Kalman filter backward with respect to range, beginning at the reference
range and ending at the near range of the signal;
7. Use the output of the filter, the estimated state vector values at each range, to linearize
the observation equation at each range;
8. Repeat steps 3-7 until the output of the filter converges.
In this way, an estimate of the amplitudes of the basis-aerosols is obtained over the
whole signal. It should be noted that it is convenient to choose a reference point where little
variable aerosol is expected. This way, the state vector can reasonably be initialized as a
zero vector.
4.2.3
State-Vector Augmentation
The form of (4.1) assumes that the laser pulse shape of the instrument is a delta
function. In some cases the laser pulse shape has significant temporal width and stretches
over multiple measurement bins. An example of this is the Aglite lidar system [14]. As a
result, the measured signal is a convolution of (4.1) with the lidar pulse shape. This can be
corrected by augmenting the state vector to include basis-aerosol amplitudes over several
bins.
If the laser pulse shape extends over f bins, the state vector can be expressed as an
sf x1 vector.
xi =
viT
T
vi−1
···
T
vi−f
+1
γ Ti−f
(4.13)
The influence of the laser pulse shape can be described by the matrix F with dimensions
cxf appended to the observation equation.
88
pi = F
bi + Bi0 xi · exp ai + A0i xi + ν z
(4.14)
The forms of vectors a, b, and the matrices A0i , Bi0 , Bi00 , and F are shown in (4.15).
T
ai = −2
zm ) αT0
zm ) αT0
2
zm
T
pT
m ·β 0
zi2 β T
m
T
pT
m ·β 0
2 βT
zi−1
m
T
,
, bi =
···
(zi −
(zi−1 −
···




A A ··· A A
B 00 /zi2
0
0








00
2




A
·
·
·
A
A
B
/z
i−1



0 =
A0i = −2∆z 
,
B



i
.. .. 
..
..



. . . 
.







00
2
0
A A
0
B /zi−f +1 0


0 f2,1
0
ff,1
0 
 f1,1
2


..
..
00 = diag zm pm B

F =
,
B
.
.
···
βm




0
f1,c 0
ff −1,c 0
ff,c
(4.15)
The expression fi,j is the relative magnitude of the laser pulse at the ith range bin and
jth channel. Linearizing (4.14) using (4.10) yields an appropriate form for the observation
equation of the EKF.
pi = F (Ci xi + di ) + ν i
(4.16)
The state-update equation for this augmented-state vector and state-noise covariance
matrix are described by (4.17).

xi+1
θ
0


 I

=
..

.


0
I I




 cov (vi )



 xi + wi , Qi = cov (wi ) = 
0






0





(4.17)
89
By constructing the EKF using the augmented-state vector together with (4.16) and
(4.17), the estimator becomes an instance of a fixed-lag Kalman smoother [15].
Each update step of the EKF involves the computation of a number of matrix additions,
multiplications, and a matrix inversion. The state vector is of length (f + 1) s, so the
necessary matrix computations of each update step are of complexity order (f + 1)3 s3 .
Therefore, the computational complexity for an entire lidar measurement consisting of y
range bins is (f + 1)3 s3 y, proportional to the number of bins in the measurement.
Some lidar retrieval algorithms use state vectors containing atmospheric values from all
range values at the same time [8, 9]. These methods do not apply the Kalman filter over
range, but over time, and in both cases the length of the state vector is proportional to
y. The iterative least-squares algorithm previously described by Marchant et al. also uses
a state vector proportional to y [13]. These types of algorithms require multiplying large
matrices, and as a result, the order of the computational complexity for retrieving one single
lidar pulse over all its range values is proportional to y 3 . On the other hand, forms of the
EKF that are applied over range, including the one described here, have computational
complexity simply proportional to y. Given the fact that many lidars like Aglite have
thousands of range bins per measurement, this can be a significant issue.
4.3
Simulation
The performance of the augmented-state EKF was demonstrated by applying it to a
set of synthetic test data. This test data was generated so as to match the parameters of
the Aglite instrument, a micro-pulse three-channel lidar with UV, V, and IR channels, at
wavelengths of 355, 532, and 1064 nm and a total output power of 5 W [14].
The synthetic data was generating by assuming that the atmosphere consisted of a
homogeneous baseline scattering component plus a variable aerosol component. The Rayleigh
scattering portion of the baseline component was modeled assuming a temperature of 293
K (68 F), relative humidity of 50%, and atmospheric pressure of 101.325 kPa. The Mie
scattering portion was modeled as a Continental Polluted type aerosol [1].
The variable scattering component was modeled as an aerosol with a log-normal
90
distribution, whose logarithmic standard deviation equals 2.24. The magnitude and moderadius of the distribution varied as functions of range, described by Gaussian functions
centered at 1000 m range with standard-deviations of 100 m.
The scattering coefficients of the atmosphere were modeled using Rayleigh theory and
the lidar signal was modeled based on the physical parameters of Aglite. Lidar channel
efficiency values of 5.8e-4, 3.0e-4, and 4.3e-5 were used for the UV, V, and IR channels,
respectively. The attenuation effects caused by the geometric form factor were applied to
the signal, as well as the convolution effects due to the laser pulse shape. The effects of
solar background radiation were modeled by adding a baseline noise component of 100, 250,
and 10 photons to the UV, V, and IR channels, respectively. Finally, in order to model the
effects of photon counting noise, the signal was modeled as a Poisson distributed random
vector. This was repeated to generate 1000 individual measurement signals for testing.
In order to perform the retrieval on the synthetic data, the spatially varying aerosol
must be approximated as a linear combination of basis-aerosols in order to satisfy (4.4). For
experimental data measurements, these basis-aerosols can be selected using point-sensor
instrument measurements. However in the case of synthetically generated data, this basis was
calculated from the solution itself. This may seem like a paradox, since the properties of the
aerosol must be known before those same properties can be retrieved from the measurement
data, but the use of point-sensor measurements can resolve this when processing real-world
experimental data.
The appropriate basis aerosols for the retrieval were calculated from the same backscatter
coefficient functions that were used to generate the synthetic data. Principal component
analysis was used to decompose these backscatter functions, resulting in three orthonormal
backscatter basis vectors with their corresponding amplitude functions [15]. The state-vector
covariance was assumed to be stationary and was formed using (4.17). The backscatter
coefficients as functions of range and the basis vectors of the two most significant components
are illustrated in fig. 4.1(a,b).
The volume distribution of the varying aerosol was calculated as a function of range,
91
Fig. 4.1: Backscatter coefficients of a (a) single-component, and (b) two-component aerosol
as a function of range. (c) The particle volume distributions of the two components.
and the PSD functions of the individual components were calculated so that their combined
volume distribution matched the varying aerosol as closely as possible. The volume distribution is defined by (4.18), where r is the particle radius [µm] and n (r) is the particle size
distribution of the aerosol [#·cm−3 · µm−1 ].
dV
4
= πr3 n (r)
dr
3
(4.18)
The particle volume distributions for the two components are illustrated in fig. 4.1(c).
Using the calculated PSD functions for the individual components, their extinction vectors
were also calculated. In addition, the baseline scattering coefficient vectors were chosen
so as to match those used to generate the synthetic data; the backscatter and extinction
vectors of a Rayleigh component plus a Continental Polluted component. The reference
point backscatter vector was chosen to be equal to the baseline backscatter vector.
The augmented-state EKF estimator was constructed following the steps outlined
92
previously and applied to the synthetic data. The gain coefficient θ was set to 0.75 and a
reference point range of 600 m was used. The estimator was applied to each of the individual
return power measurements, resulting in values for the component amplitude functions
and estimated returned power for each of the 1000 measurements. Using the estimated
component amplitudes, the aerosol PSD was calculated for every range.
An example of both the measured and estimated returned power for a single measurement
is shown in fig. 4.2(a). Even with the relatively low SNR of an individual measurement
(fig. 4.2(b)), the retrieved power estimate is stable and closely matches the measured power.
The SNR of the measured signal, expressed in power decibels, is also shown in the figure.
The SNR describes the portion of the returned power signal that is attributable to the
varying aerosol component, divided by the standard-deviation of the entire returned power
signal.
Additionally, fig. 4.2(c) is a plot showing the average of all 1000 estimates of the
component amplitudes, together with their standard deviation. Figure 4.2(d) shows the
average error between the estimated component amplitude values and the “true values” used
to generate the data, fig. 4.2(e) is a plot of the average aerosol volume-fraction concentration,
and fig. 4.2(f) is a plot of the average aerosol effective radius, as calculated from the aerosol
PSD as retrieved by the estimator.
A common measure of aerosol concentration is the PM10 mass-fraction concentration,
which is defined as the total mass in a volume of air of all the particles whose aerodynamic
diameter is less than some diameter D.
4π
P MD = ρ
3
Z
D/2
r3 n (r) dr
(4.19)
0
For this analysis, the simulant density ρ was unknown, so it was always assumed to
equal unity. As a result, instead of mass-fraction concentration, the parameter of interest was
volume-fraction concentration. Additionally, since both OPC and lidar instruments detect
aerosol particles optically, it was necessary to assume that the optical and aerodynamic
diameters were the same, even though in they might not be in a real-world scenario.
93
Fig. 4.2: Illustration of the performance of an EKF filter on a synthetic data set, including (a)
the estimated returned power, (b) SNR, (c) component amplitude, (d) retrieved component
bias, (e) volume concentration, and (f) effective radius.
94
The effective radius of an aerosol is defined as ratio of the third moment to the second
moment of the aerosol’s PSD.
Z
ref f =
∞
3
Z
r n (r) dr
0
∞
r2 n (r) dr
(4.20)
0
Since the particle size distributions of the two components are known a priori, it is
straightforward to calculate the volume-fraction concentration and effective radius at every
range once the component amplitudes have been estimated.
Inspection of fig. 4.2(f) shows that, although the simulated values for the effective radius
do not exactly match the estimated values, their average values fall within at least one
standard deviation. Furthermore, it is clear that while the estimated effective radius values
are stable between approximately 920 and 1060 meters, outside of this range the values of
the estimated effective radius become extremely unstable.
The volume-fraction concentration is a linear combination of the two components. This
means that it is related to the sum of the components, and as their magnitude approach
zero it remains stable. On the other hand, the effective radius is somewhat related to the
ratio of the components, meaning that as the component amplitudes approach zero, the
estimated value becomes unstable. This implies that reliable estimation of effective radius
requires higher SNR values than reliable retrieval of volume-fraction concentration.
4.4
Experimental Measurements
A set of experiments were conducted at Dugway Proving Grounds (DPG) involving
biological aerosol simulants from May 31 through June 18, 2009. The test facility was the
Joint Ambient Breeze Tunnel, an open-ended rectangular tunnel, 162 m long, 18 m high,
and 13 m wide. The Aglite lidar instrument was positioned 1220 m west of the structure
and aimed so that the transmitted beam travels along the long axis through the center of
the structure.
A system of sprayer equipment was mounted to the walls of the structure, which was
used to disperse various wet aerosols into the atmosphere contained within the structure.
95
The ends of the structure were open, allowing the free flow of air through it. A suite of
point-sensor instruments were placed on tables within the structure, including three MetOne
OPCs. The OPC measures particle counts in the ambient air at 2 s intervals and sizes the
particles into one of seven size ranges, resulting in high time-resolution in situ measurement
of the particle-size distribution of the aerosol.
For these experiments, the particle-size distribution was converted into extinction
and backscatter using Mie theory and the index of refraction values given by the Air
Force Handbook of Geophysics and the Space Environment [16, 17]. The volume-fraction
concentrations for PM2.5 and PM10 were also calculated using the PSD, assuming that the
aerosol particle density was equal to one, and the aerosol aerodynamic diameter was equal
to the aerosol optical diameter.
During the course of a night, several sets of biological aerosol simulants were released
into the tunnel. Each set consisted of three simulants. Each of these was continuously
released over a period of 3 minutes, with a 3 minute period in between releases. Each
simulant was released near the east opening and was wafted by the wind through the tunnel
and out the west opening. A rest period of 15 minutes followed the end of each third release
before the beginning of the next set of simulant releases.
A specific set of simulant releases is described here that was released in the very early
morning of June 1. The first simulant was a bacterial insecticide Bacillus thuringiensis
subspecies kurstaki (Btk), which was released from 12:32 am until 12:35 am using a Micronair
disperser. This was followed by a release of Arizona Road Dust (ARD) from 12:38 am to
12:41 am using a Skil-Blower disperser. Finally, the simulant Male-specific bacteriophage
(MS2) was released from 12:43 am to 12:46 am. The effective radius and assumed aerosol
type for each simulant are shown in Table 4.1. The average temperature, pressure, relative
humidity, wind speed, and direction are also shown in Table 4.1.
The Aglite instrument was operated from 12:29 am until 12:48 am, with 0.5 s temporal
resolution and measured the releases of these simulants. A reference range of 1150 m was
selected, and the gain coefficient θ was set to 0.75. The augmented-state EKF was applied to
96
Table 4.1: Simulant properties and conditions.
Eff.
Simulant
Type
Radius
(µm)
Btk
ARD
MS2
water
soluble
dust
like
water
soluble
Temp.
Press.
(◦ C)
(kPas)
Rel.
Wind
Hum.
Speed
(%)
(m/s)
Wind
Dir.
2.1
17.0
86.4
62.0
2.0
SE
2.2
17.1
86.4
61.5
1.7
SE
2.4
16.9
86.4
62.2
2.5
SSE
the measurements, resulting in estimates both for the returned power and for the amplitudes
of the basis aerosols, which were converted into PSD as a function of range. In turn, the
PSD functions used to calculate the P M 10 volume-fraction concentration and the effective
radius, which are plotted as a function of time and range in fig. 4.3 for the MS2 simulant.
Baseline measurements of the atmosphere were conducted during the rest periods.
The PSD measurements of the OPC were averaged over the duration of the rest period,
and the baseline backscatter and extinction coefficient values attributable to aerosol were
calculated using Mie theory, assuming that the baseline aerosol had index of refraction
values of a water-soluble type aerosol [17]. The portion of the baseline coefficients that were
attributable to Rayleigh scattering was calculated using meteorological measurements of
temperature, pressure, and humidity. The Mie and Rayleigh scattering coefficients were
added together, resulting in the total baseline extinction and backscatter coefficients, as
described by (4.4). The values of the baseline backscatter vector were also used for the
reference point backscatter vector.
For the retrieval, each simulant was classified as being a water-soluble or dust-like
aerosol for purposes of selecting appropriate index of refraction values. The basis aerosol
components were calculated for each simulant using the OPC measurements taken during
the actual time of release. First, the measurements of the three OPC instruments were
concatenated together and converted into backscatter coefficients. Next, principle component
97
Fig. 4.3: Images of the estimated (a) PM10 volume-fraction concentration, and (b) effective
radius for the MS2 simulant.
analysis was applied to these coefficients to identify the two main backscatter components
of the aerosol. Lastly, the PSD functions of these components were identified and used to
construct the matrices A and B in (4.4). In this way, the backscatter and extinction vectors
of the basis-aerosols were identified.
The results given by the augmented-state EKF for each of the Btk, ARD, and MS2 type
simulants are illustrated in fig. 4.4. Each measured return power signal was averaged over a
100 s interval during the peak release time for each simulant. The augmented-state EKF
was applied to each of these measured power signals, and the effective radius was estimated
as a function of range. Figure 4.4 shows comparisons of the average measured return power
with the estimated return power given by the EKF, as well as the estimated effective radius
as a function of range.
Inspection of the figure shows that the estimated return power matches the measured
return power reasonably well for the Btk and ARD type simulants, however they match
poorly for the MS2 type simulant. This may be due to incorrect assumed values for the
indices of refraction of the simulant, or it may be due to poor measurement of the simulant
particle size distribution by the OPC instruments.
Both the Btk and MS2 type simulants showed little variation in the estimated effective
radius value, meaning that each simulant’s backscatter values on the different channels
98
Fig. 4.4: Measured (symbols) and estimated (lines) average return power and estimated
effective radius for (a,b) Btk, (c,d) ARD, and (e,f) MS2 type simulants.
99
changed little relative to one another with respect to range. Although some structure is
visible in fig. 4.4, the simulant appears to be relatively homogeneous. The variation of the
effective radius value of the ARD type simulant might be explained by its much lower signal
magnitude.
References
[1] M. Hess, P. Koepke, and I. Schult, “Optical properties of aerosols and clouds: the
software package OPAC,” Bulletin of the American Meteorological Society, vol. 79, pp.
831–844, 1998.
[2] M. D. Guasta, M. Morandi, L. Stefanutti, B. Stein, and J. P. Wolf, “Derivation of Mount
Pinatubo stratospheric aerosol mean size distribution by means of a multiwavelength
lidar,” Applied Optics, vol. 33, pp. 5690–5697, 1994.
[3] G. von Cossart, J. Fiedler, and U. von Zahn, “Size distributions of the NLC particles
as determined from 3-color observations of NLC by ground-based lidar,” Geophysical
Research Letters, vol. 33, pp. 5690–5697, 1994.
[4] M. D. King, “Sensitivity of constrained linear inversions to the selection of the lagrange
multiplier,” Journal of Atmospheric Science, vol. 39, pp. 1356–1369, 1982.
[5] D. P. Donovan and A. I. Carswell, “Principal component analysis applied to multiwavelength lidar aerosol backscatter and extinction measurements,” Applied Optics, vol. 36,
pp. 9406–9424, 1997.
[6] D. Müller, U. Wandinger, and A. Ansmann, “Microphysical particle parameters from
extinction and backscatter lidar data by inversion with regularization: theory,” Applied
Optics, vol. 38, pp. 2346–2357, 1999.
[7] J. D. Klett, “Lidar inversion with variable backscatter/extinction ratios,” Applied
Optics, vol. 24, pp. 1638–1643, 1985.
[8] C. Böckmann, “Hybrid regularization method for the ill-posed inversion of multiwavelength lidar data in the retrieval of aerosol size distributions,” Applied Optics, vol. 40,
pp. 1329–1342, 2001.
[9] I. Veselovskii, A. Kolgotin, V. Griaznov, D. Müller, U. Wandinger, and D. N. Whiteman,
“Inversion with regularization for the retrieval of trophospheric aerosol parameters from
multiwavelength lidar sounding,” Applied Optics, vol. 41, pp. 3685–3699, 2002.
[10] F. Rocadenbosch, C. Soriano, A. Comeron, and J. Baldasano, “Adaptive filter solution
for processing lidar returns: optical parameter estimation,” Applied Optics, vol. 37, pp.
7019–7034, 1998.
100
[11] R. E. Warren, R. G. Vanderbeek, A. Ben-David, and J. L. Ahl, “Simultaneous estimation
of aerosol cloud concentration and spectral backscatter from multiple-wavelength lidar
data,” Applied Optics, vol. 47, pp. 4309–4320, 2008.
[12] J. M. B. Dias, J. M. N. Leitao, and E. S. R. Fonseca, “Reconstruction of backscatter
and extinction coefficients in lidar: a stochastic filtering approach,” IEEE Transactions
on Geoscience and Remote Sensing, vol. 42, pp. 443–456, 2004.
[13] C. C. Marchant, T. K. Moon, and J. H. Gunther, “An iterative least square approach to
elastic-lidar retrievals for well-characterized aerosols,” IEEE Transactions on Geoscience
and Remote Sensing, vol. 48, pp. 2430–2444, 2010.
[14] C. C. Marchant, T. D. Wilkerson, G. E. Bingham, V. V. Zavyalov, J. M. Andersen,
C. B. Wright, S. S. Cornelsen, R. S. Martin, P. J. Silva, and J. L. Hatfield, “Aglite
lidar: a portable elastic lidar system for investigating aerosol and wind motions at or
around agricultural production facilities,” Journal of Applied Remote Sensing, vol. 3, p.
033511, 2009.
[15] T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal
Processing. Upper Saddle River: Prentice Hall, 2000.
[16] A. S. Jursa, Handbook of Geophysics and the Space Environment, Air Force Geophysics
Laboratory, Hanscom Air Force Base, 1985.
[17] C. F. Bohren and D. R. Huffman, Absorption and Scattering of Light by Small Particles.
New York: Wiley, 1983.
101
Chapter 5
Estimation of Dairy PM Emission Rates by Lidar and
Inverse Modeling
5.1
Application
Agricultural production facilities are being increasingly investigated for emissions of
pollutants into the atmosphere in order to understand their contributions to and effects on
local and regional air quality. Under the Clean Air Act, the EPA has set threshold levels
for widespread criteria pollutants considered harmful to public health and the environment,
referred to as the National Ambient Air Quality Standards (NAAQS) [1]. The NAAQS
include standards for aerosol as PM2.5 and PM10 , which are defined as the sum of particles
with aerodynamic equivalent diameters ≤ 2.5 and ≤ 10 µm, respectively. Furthermore,
several state air quality regulatory agencies, such as the State of California Air Resources
Board, have begun to require air pollution permits for agricultural operations that exceed
certain sizes. The San Joaquin Valley Air Pollution Control District (SJVAPC) has required
agricultural operations of nearly all sizes to select and implement approved Conservation
Management Practices (CMP) since 2005 in order to meet PM10 emissions reductions
targeted under the EPA-accepted plan to bring the San Joaquin Valley airshed in California
into compliance with ambient PM10 levels. The EPA redesignated the airshed as being
in compliance with ambient PM10 levels in November 2008, but sources must continue to
implement measures that helped meet PM10 NAAQS, including CMPs, as part of their
federally approved maintenance plan.
The accurate quantification of agricultural aerosol emission rates is an important part of
the regulation process. To date, PM emission rate and emission factor values for dairies in the
United States are scarce in published literature. Sources of particulate matter from dairies
102
include: animal activity, handling of feed and manure, combustion, vehicle activities on
unpaved roads and areas, windblown soil, manure, and feed. It is expected that PM emission
rates vary with multiple factors, including diet, bedding, type of pen or housing, feed storage
and distribution practices, waste cleaning and storage practices, animal age, moisture level
of soil or animal bedding, and meteorological conditions. A report by the US Department of
Agriculture (USDA) estimated the PM10 emission rate for a dairy to be 1.8 g/day/animal [2].
This was made by extrapolating the previously reported emission rate from a feedlot, and
assuming that a dairy has 20% less emission. Schmidt et al. measured PM10 concentrations
in a naturally ventilated dairy barn during winter and summer, and calculated emission
rates by coupling measured concentrations with ventilation rates estimated based on carbon
dioxide exchange rates [3]. Winter average PM10 emissions were 1.7 g/day/animal and
summer average emissions were 0.3 g/day/animal. Goodrich et al. made measurements of
TSP concentration using filter-based samplers at a free-stall and open lot dairy over two
summers and then measured the PSD of the dust to determine the PM10 fraction [4]. An
inverse modeling technique was applied to the calculated PM10 fractions to estimate PM10
emission rates of 5.0 g/day/animal from the free-stall areas and 15.0 g/animal/day from the
open lot areas. Using a similar approach, Martin et al. measured the dust concentration
at a dairy during late fall using filter-based samplers and performed inverse modeling to
estimate emission rates of 2.3 g/day/animal and 9.2 g/day/animal for PM2.5 and PM10 ,
respectively [5].
These previous estimates of PM emission rates relied on indirect methods, either
extrapolating values from indirect measurements or by using computer modeling. Scanning
aerosol lidar allows direct measurement of aerosol concentration, so the Aglite lidar was
used to investigate particulate matter emissions from a free-stall and open-lot dairy in the
San Joaquin Valley employing CMPs over an eight day period during June 2008.
5.2
Methodology
The PM emission rates from the selected dairy were estimated using two different
techniques: a flux measurement technique using elastic lidar [6] and an inverse modeling
103
technique with the filter-based samples [7]. The PM emission rates were not estimated for
the individual potential PM sources within the dairy, but rather an overall PM emission
rate was estimated for the entire facility, normalized by the number of animal units in the
facility.
5.2.1
Site Description
The dairy was located near Hanford, California and was surrounded by agricultural
land, including two other dairies located approximately 500 m downwind. The dairy facility
is roughly square shaped, covering 22.6 hectares, including all associated storage areas and
access roads, which are mostly unpaved. It is bordered on its east side by a paved road and
on the three other sides by cropland. The total number of animals on the dairy was 1,885,
with: 950 milking cows, 100 dry milking cows, 30 bulls, 5 steers, and 800 heifers uniformly
distributed between birth and two years old. One animal unit (AU) is defined herein as one
heifer, steer, or bull cattle and 0.7 milking or dry cattle, according to the EPA definition [1],
totaling 2,335 AU on the dairy. The youngest calves were housed in individual small pens,
each with a shelter. Bulls, steers, dry cows, and heifers older than about four weeks old
were housed in open lot pens, most of which were equipped with an open shelter. Milking
cows were housed in a combination of open lot and covered free stall pens. Pens had a total
area of 13.7 hectares, about 65% of the total dairy footprint. Feed lanes were concrete and
sloped for drainage; all other surfaces were unpaved.
The CMP plan accepted by the San Joaquin Valley Air Pollution Control District
for this dairy operation addressed the following: feed cultivation, harvest, and storage;
unpaved roads and unpaved vehicle/equipment areas for crops; unpaved roads and unpaved
vehicle/equipment areas for animal feeding operations; and dairy. For the feed cultivation,
harvest, and storage categories, all implemented CMPs pertained solely to crop land and
have no impact on PM emissions from the dairy area; this was also the case with the unpaved
roads and unpaved vehicle/equipment areas for crops category. The unpaved roads and
unpaved vehicle/equipment areas for animal feeding operations category listed the following
CMPs: 1) water shall be applied to 1.4 km of private roads; 2) 0.2 km of private roads shall
104
be paved; 3) an 8 km per hour speed limit shall be placed on 1.4 km of private roads; 4)
water shall be applied to 0.2 hectares of private vehicle/equipment traffic areas; and 5) an 8
km per hour speed limit shall be placed on 0.2 hectares of private vehicle/equipment traffic
areas. The listed CMPs for the dairy category were the following, with the first three dealing
with corral/manure handling and the last two dealing with overall management/feeding:
1) manure from open corrals shall be frequently scraped or removed; 2) pull-type manure
harvesting equipment shall be used; 3) shaded areas shall be provided for cattle in open
corrals; 4) wet material shall be placed in the feed wagon prior to mixing; and 5) feed shall
be wetted during mixing. Each implemented CMP is targeted to reduce PM10 emissions
from the dairy.
The cows were milked twice a day, with a milking schedule from 8 am to 5 pm and from
8 pm to 5 am local time. A feed truck delivered feed to all pens from 5:30 am to 12 pm and
from 3 pm to 5 pm local time. Milk cow lanes were flushed with water several times daily,
while heifer, dry cow, bull and steer lanes were scraped approximately weekly. Corrals were
scraped as needed, with gathered material stored as a mound in each pen for later removal.
No corral scraping occurred during the measurement campaign, although a scraping was
performed during equipment setup.
5.2.2
Setup
Historical measurements of wind velocity for the previous three years were obtained from
station #15 of the California Irrigation Management and Information System near Stratford,
CA, 24 km southwest of Hanford. These records showed that wind conditions during the
months of May and June were very consistent, with winds coming dominantly from the
northwest. Based on these data, instruments were deployed so as to measure background
concentrations northwest of the facility and to measure emission plumes south and southwest
of the dairy. On-site measurements of the wind direction during the experiment confirmed
the dominant direction to be from the northwest.
Two 15.3 m towers dedicated to holding meteorological instruments were erected at
the site. One was located 400 m west of the dairy and the other was located just inside
105
the southern boundary of the dairy. Each tower was equipped with five cup anemometers
mounted at heights of 2.5, 3.9, 6.2, 9.7, and 15.3 m. Five temperature and relative humidity
sensors were also mounted at heights of 1.5, 2.5, 3.9, 6.2, and 9.7 m. Wind vanes were
mounted on top of both towers at 15.3 m and a sonic anemometer was mounted on the
tower inside the dairy boundary at 11.3 m. Campbell Scientific data-loggers were used to
record and store the data from the towers and sonic anemometers.
Twenty four MiniVol PM filter-based samplers (Airmetrics) were deployed in groups
of either two or three instruments at multiple locations around the dairy in order to
allow characterization of the particle mass distributions (PM2.5 , PM10 , and TSP) of both
background and emitted aerosols. The MiniVol is a portable, programmable, filter-based
sampler that yields mass concentration averaged over the sample time, with an impactor
plate assembly employed for a single-sized particle fractionation. A pair of samplers consisted
of one PM10 sampler and one PM2.5 sampler, whereas a group of three samplers consisted
of one TSP sampler, one PM10 sampler, and one PM2.5 sampler. Nine Met One Instruments
Aerosol Profilers, a type of OPC, were co-located with several of the sampler groups. The
OPCs measured the optical particle size distribution with a period of 20 s using eight discrete
size bins, counting the number of particles with diameters between two sequential cutoff
values. The lower bin values were 0.3, 0.5, 0.6, 1.0, 2.0, 2.5, 5.0, and 10.0 m, with the eighth
bin measuring all particles whose diameter was greater than 10 µm. Flow calibration of the
MiniVols was carried out just prior to the study; OPC flow measurements were conducted
prior to and after the study, as well as counting calibrations/comparisons between all OPCs.
Upwind/background point sampler groups were located to the northwest (A) and north
(B) of the dairy at 2 and 9 m elevations, respectively. Downwind measurements were made
along the southern edge of the dairy at locations C (9 m), D (2 m), E (2 m), F (2 m),
and AQ (2, 5, and 9 m), with sampler heights above ground level shown in parentheses.
An air quality instrumentation trailer at location AQ was used for the following: sample
preparation, collection, and storage; instrument handling, storage, and servicing; and data
storage. The lidar instrument was housed within a trailer that was placed approximately
106
800 m west of the southwest corner of the dairy.
The PM sampling layout during the field campaign is shown in fig. 5.1. The locations
of instrumentation towers are illustrated using solid black symbols, including the location
of Aglite in the bottom-left corner of the figure. The dotted lines extending from Aglite
represent the beam paths of the vertical profile scans. The dairy footprint is shown by a
gray rectangle, and within it the pen, free-stall, and manure storage areas of the dairy are
represented by white polygons. The expected dominant wind direction is shown by an arrow
in the top-left corner.
Samples of the soil on unpaved roads and in dry-lot pens were collected on June 16 and
analyzed for percent moisture level. The analysis showed that the average percent moisture
± one standard deviation was 0.56 ± 0.50 (n = 3) and 5.3 ± 5.1 (n = 7) for the unpaved
roads and pens, respectively.
5.2.3
Inverse Modeling
Sources of PM at the dairy were modeled using the American Meteorological Society
Regulatory Model (AERMOD) software. The interface used to run this model was the
commercially available AERMOD View package by Lakes Environmental, Inc. The model
assumes steady-state conditions, continuous emissions, and conservation of mass. It requires
the source type, size, location, and emission rate be specified, as well as sampler/receptor
locations. It also requires hourly averaged meteorological data including wind speed, wind
direction, temperature, and cloud cover. Wind and temperature data were supplied by
the meteorological towers and cloud cover was set to be zero for the entire period, as
there were clear skies throughout the measurement campaign. The surrounding land was
categorized as agricultural in the summer season for the purpose of estimating values for
the Bowen Ratio, midday albedo, and surface roughness, which were 0.30, 0.14, and 0.03 m,
respectively. Required upper air meteorological parameters were estimated by AERMET,
the meteorological pre-processor for AERMOD, from the measured surface conditions. Based
on these inputs, AERMOD calculated the period average concentrations at each sample
location.
107
Fig. 5.1: Map of instrumentation locations.
The models require a priori values for the emission rate, which we seek to derive, in
order to predict the sampler PM concentration values, which we have already measured.
Inverse modeling is the process of adjusting the assumed emission rate to determine the
actual emission rate by comparison of the already-measured concentration values to the
model-predicted concentrations; it consists of determining that model-input emission rate
that corresponds to the predicted concentration values that best fit measured values. AERMOD only predicts concentrations resulting from specified sources, in this case the dairy
pens, so background/upwind PM levels must be subtracted from actual concentrations
measured downwind of the dairy for comparison in inverse modeling. Facility-produced
PM concentrations, or concentrations resulting from the dairy activities, were calculated
on a location-by-location basis by subtracting the average upwind concentration from the
measured downwind concentration. This difference was determined to be significant if greater
than the 67% confidence interval about the upwind measurements, corresponding to one
standard deviation. Only facility-produced concentrations deemed significant were used in
inverse modeling.
108
Given a set of predicted PM concentration values, a cost metric is used to describe how
well they match the measured values. In this case, the measured concentration values were
assembled into a vector and the predicted concentration values were assembled into another
vector, and the cost metric was chosen to be the sum of the squares of the difference. The
optimal emission rate estimate was selected using a steepest descent algorithm; an initial or
“seed” emission rate was chosen, the predicted concentration values were calculated, and the
cost metric was calculated. The emission rate was then perturbed and a new cost metric
value was calculated, thus allowing the computation of the gradient of the cost metric with
respect to the emission rate. The emission rate was adjusted by a predetermined step size
so as to lower the cost metric value and the procedure was repeated until the cost metric
reached a minimum, at which point it was expected that the emission rate had converged to
its optimal estimate.
Modeled sources of PM emission at the dairy included the pens of the heifers, dry
and lactating cows, bulls and steers (13.3 hectares), as well as calf hutches (0.4 hectares).
Fugitive dust emissions were seen during the campaign due to vehicle traffic on the unpaved
service roads; however, these emissions were intermittent and not modeled, but were instead
attributed to pen emissions as part of the operational activities of the dairy. Also, it was
assumed that PM emissions from the wastewater lagoon and solids separator basin were
negligible compared to other sources.
5.2.4
Elastic Lidar
Elastic lidar is a remote sensing technology with an application in imaging aerosols in
the atmosphere [8]. Aglite is a lidar instrument that was used to map dust emissions at
this dairy. Lidar (also known as laser-radar) functions by emitting a pulse of laser light and
measuring the intensity and timing of the light as it is scattered by molecules and aerosols
in the atmosphere. By recording the direction of the laser beam, the timing and intensity of
the scattered light, it is possible to construct an image of the dust in the atmosphere.
Lidar technology can estimate the total PM emission rate from a source using a mass
balancing technique. PM vertical concentration profiles are measured both upwind and
109
downwind of the emission source. The difference between the profiles is multiplied with a
vertical wind velocity profile and the result is integrated over distance and height, resulting
in the total aerosol emission flux passing through the profile area. In this way, the net
emission rate of a facility can be directly observed with high temporal resolution and without
requiring a mathematical model of the emission process [6]. In addition, elastic lidar enables
measurement of dust concentration at thousands of points at relatively high temporal and
spatial resolution, in contrast to a handful of filter samplers, which only measure dust
concentration at a single point averaged over a time period typically measured in hours to
days.
Elastic lidar allowed direct measurement of the dust emission rate of this dairy. Aglite
is a three-wavelength micro-pulse scanning lidar, capable of imaging a 400 m high aerosol
profile of the atmosphere in 25 seconds, with 8 m vertical resolution and 6 m horizontal
resolution [9]. It requires data from both the OPCs and filter samplers for calibration
and conversion of optical data into mass concentrations. Aglite was regularly calibrated
throughout each day by co-locating its scanning beam next to a cluster of point sensors,
which was mounted on a tower upwind of the dairy. The OPC in the cluster provided
high temporal resolution measurements of dust concentration and the optical particle size
distribution of the background atmosphere. Additionally, OPCs were located downwind
of the dairy and measured the optical PSD of the impacted atmosphere. By taking the
difference between upwind and downwind OPC data, the PSD of the emission plume was
calculated. Using Mie scattering theory, the light-scattering properties of both the emission
plume and the background aerosol were calculated. By aiming Aglite past the upwind OPC,
calibration coefficients were calculated that established the relationship between the optical
properties of the atmosphere and the photons detected by Aglite [9].
Aerosol volume concentration values are estimated from the return power measurements
of Aglite using a form of the extended Kalman filter [10, 11]. The filter uses the optical
properties of the emission aerosol and calibration coefficients of Aglite, as measured by the
OPCs, to relate the measured return power to aerosol volume concentration.
110
Since both Aglite and the OPCs detect dust optically, they can only estimate the
effective volume concentration of an aerosol by assuming detected particles are spherical and
the same index of refraction as the calibration aerosol. This effective volume concentration
is not necessarily equivalent to the true total volume of the aerosol particles, as the particles
may be non-spherical and have a different index of refraction. Therefore, a conversion factor
is required to convert the lidar aerosol volume measurement to units of mass; this is called
the mass conversion factor (MCF) by the authors [12]. It is derived using co-located OPCs
and MiniVol samplers. The OPC volume concentration, defined as the total volume of
all particles in a given volume of air (VTSP ) or the total volume of those particles whose
optical diameter is less than or equal to either 2.5 (V2.5 ) or 10 µm (V10 ), is averaged over
the MiniVol sample time. The ratio of the MiniVol size fractionated concentration over
the same size fraction of OPC volume concentration is the MCF. Because it measures the
PSD, a single OPC can measure the separate effective volume concentrations corresponding
to PM2.5 , PM10 , and TSP. The MCF incorporates into a single coefficient the differences
between mass measurement and optical measurement techniques as well as the effects of
many aerosol characteristics that are otherwise difficult to accurately measure in ambient
air (i.e. particle shape, index of refraction, porosity, and density).
The configuration of the lidar sampling is illustrated in fig. 5.1. It began with a
continuous stare for calibration purposes at 0 degrees elevation and 41 degrees azimuth,
clockwise from true north, lasting 40 seconds and pointing past the upwind sample location
A. This was followed by two vertical scan pairs upwind of the facility, also at 41 degrees
azimuth. The dashed line extending from the Aglite trailer to location A represents the
direction of the upwind profile scan and calibration stare. The beam was then horizontally
scanned about 10 m over the dairy from the upwind to downwind sampling locations, which
was followed by five vertical downwind scan pairs. The vertical scan pairs consisted of one
up scan and one down scan, where the beam began pointing parallel to the ground, was
raised to 25 degrees elevation over 25 seconds, and then lowered back to ground level over
25 seconds. Three vertical scan pairs were made on the downwind border of the dairy, at
111
90 degrees azimuth, with two more at different distances downwind of the dairy, at 94 and
98 degrees azimuth. Unfortunately, due to a logistical error, the vertical scans made at
90 degrees azimuth, parallel to the downwind border of the dairy, were impaired by the
backscatter of laser light off of the support guy-wires of a downwind instrument tower. For
this reason, only vertical profiles taken at 94 and 98 degrees azimuth were used for estimating
aerosol emissions. The two dashed lines extending from the Aglite trailer past the south
of the dairy in fig. 5.1 represent these two scan angles for profile scans that did not suffer
interference from sample towers. The use of the two separate downwind profile scans was
motivated by an attempt to monitor changes in plume morphology at multiple ranges from
the emission source, which is not discussed here.
5.3
Measurements
The measurement campaign began at noon Pacific Standard Time (PST) on June 13th,
2008, and lasted through midnight on the 20th. All stated times are in PST. For logistical
reasons, the OPC and MiniVol instruments were not available to make measurements on the
afternoon of the 18th and the 19th . The lidar was operated continuously until it was stopped
at 19:30 on the 19th due to a malfunction of its laser. One more period of measurements was
conducted with the OPC and MiniVol instruments on the 20th . Meteorological conditions
throughout the field study were hot and dry, with diurnally consistent winds. The mean
temperature of the seven measurement periods at 10 m height was 28.8 degrees C; cloud
cover was absent or extremely light and at high altitudes throughout with no recorded
precipitation events. Calm or very low wind conditions existed each morning before sunrise
with unstable direction. During daylight and through most of the night the average wind
direction was from the northwest, with an average speed of 2.5 m/s at 10 m height.
5.3.1
Point Samplers
The filter samplers were run over seven separate periods from June 13 until June 20.
On the 13th they ran from noon until 23:00 PST. On the 14th , 15th , 16th , and 17th they ran
from 00:30 until 23:00. On the 18th , the filters ran from 00:30 until noon and on the 20th
112
the samplers ran from 11:00 until 23:00. There was a logistical break of one-and-a-half hours
between sample periods to allow for instrument inspection, data recording, and placement
of fresh filters in the MiniVol samplers.
Measured PM2.5 concentrations downwind of the dairy ranged from 15.4 µg/m3 to 56.0
µg/m3 , with upwind levels ranging from 13.6 µg/m3 to 31.4 µg/m3 . Measured downwind
PM10 concentrations ranged from 59.2 µg/m3 to 138.6 µg/m3 , and upwind PM10 levels
ranged from 42.3 µg/m3 to 104.5 µg/m3 . Measured TSP concentrations downwind of the
dairy ranged from 129.9 µg/m3 to 246.4 µg/m3 , with upwind levels ranging from 69.8
µg/m3 to 188.4 µg/m3 . In general, the highest downwind concentrations of all mass size
fractions were measured at 2 m above ground level, with elevated measurements at 5 m
and 9 m reporting slightly lower values. This same decreasing concentration with increasing
measurement height trend was observed in OPC measurements. Filter samples with noted
problems (dropped filter, sampler malfunction, insect on filter, etc.) were removed from
further calculations.
MCF values were estimated for each of the MiniVol sample periods using data from
sampler clusters that had both an OPC and MiniVols. Because the MCF can only be
calculated during time periods when both the OPCs and MiniVols are operating, emission
rates can only be determined from the lidar data during the filter sampler operational
periods. An MCF for each sample period was calculated as the sum of the measured mass
concentration values divided by the sum of the measured volume concentration values and
is shown in Table 5.1, along with the average of all seven periods ±95% confidence interval.
5.3.2
Aglite Measurements
The Aglite lidar was placed approximately 800 m due west of the southwest corner of
the dairy. It was run continuously from noon on June 13th until 19:30 on June 19th , at
which time the laser malfunctioned and prevented further measurements. The instrument
ran in a continuous repeating scan pattern as described above. These measurements were
converted to aerosol concentration profiles using an extended Kalman filter method [11]
and the calculated MCFs for each period. As verification of the quality of the lidar data,
113
Table 5.1: Average calculated mass conversion
period, June 2008.
MCF
13th 14th 15th 16th
PM2.5 /V2.5 5.56 2.71 2.67 2.03
PM10 /V10
1.43 1.28 1.27 1.24
TSP/VTSP 0.90 0.90 0.94 0.85
factors (±95% CI) for each measurement
17th
1.89
1.13
0.87
18th
1.96
1.17
0.89
20th
4.83
1.58
1.29
Average
3.09 ± 1.10
1.30 ± 0.12
0.95 ± 0.11
the PM concentrations measured by Aglite at 10 m above ground level were found to
be consistent with those measured by point-sensor instruments during all measurement
periods. Figure 5.2(a) is the average downwind vertical profile of the PM10 concentration
as measured by Aglite on the 13th , from 12:00 to 23:00. Figure 5.2(b) is the corresponding
horizontal concentration map of period-average PM10 concentrations as predicted using
inverse modeling with AERMOD. The figure also shows the downwind profile lidar beampaths used to construct fig. 5.2(a) and the footprint of the dairy. The dotted lines in
fig. 5.2(b) show the position of downwind vertical lidar scans used to construct fig. 5.2(a).
The range of maximum average concentration in fig. 5.2(a) matches somewhat with the
location of maximum modeled aerosol concentration in fig. 5.2(b). Some of the discrepancy
may be explained by the fact that during a portion of the measurement period the wind
shifted to blowing due east, preventing the lidar from seeing the plume. Additionally, the
lidar was unable to scan past a range of 1400 m due to obstruction by power lines.
5.3.3
Calculated Emission Rates
The size fractionated PM emission rates of the dairy were estimated for each measurement period using both inverse modeling coupled with the MiniVol sampler measurements
and by application of a mass balance approach to the mass-calibrated lidar measurements.
For inverse modeling, model predicted concentrations at each sample location were adjusted
to best match measured facility-produced concentrations by minimizing the sum of the
squares of the differences between the measured and modeled concentrations across all locations for each sample period as discussed previously. Facility-produced PM concentrations
were calculated by taking the difference between each downwind sample and the average of
114
Fig. 5.2: Average PM10 concentration (µg/m3 ) of (a) a vertical scan measured by lidar at
94 and 98 degrees azimuth, and (b) a horizontal map estimated by AERMOD, overlaid on
the dairy footprint.
the upwind measurements. The emission rate input into the model that corresponded to the
best fit between predicted and measured concentrations was the derived emission rate.
The net PM emissions of the dairy facility were also calculated from the mass-calibrated
lidar data by taking the difference between downwind and upwind vertical scans. Using wind
velocity measurements, these differences were then converted into individual measurements
of the aerosol emission rate. Lidar scans were visually inspected for potential problems,
such as aerosol-source activity in the upwind area or returns from solid objects; an observed
example of a solid object returning the laser signal is a sampling tower or its guy wires. All
scans with identified problems were removed from emission rate calculations, with impacted
upwind scans also removing corresponding downwind scans.
The calculated emission values per sample period from inverse modeling and the average
lidar measured emission rate ±95% confidence interval are tabulated in Table 5.2. The
overall averages, standard deviation, and 95% confidence intervals for each size fraction are
shown at the bottom of the table. The determined emission rates from both techniques were
of similar magnitude. There was significant variation in the estimated emission rates for the
different measurement periods throughout the study.
While the overall mean values for each size fraction from the two emission rate calculation
methods are very similar, day-to-day differences between the two were observed. These
115
Table 5.2: Average temperature, wind velocity, estimated emission rates, and ±95% confidence interval (g/day/AU) for each period, estimated by inverse modeling and lidar.
Date (June 2008)
13th 14th 15th 16th 17th 18th 20th
Temp. (◦ C)
33.0 27.2 27.1 26.7 25.4 21.9 34.8
Wind Speed @ 10 m (m/s)
2.9
2.4
2.3
2.6
3.1
2.1
1.4
Wind Dir. (degrees)
307 301 321 320 313 332 306
Sample
Inverse
Inverse
Inverse
Lidar
Lidar
Lidar
period
model
model
model
June 13
9.3
2.7 ± 0.9
25.5
19.0 ± 6.3
47.7
58.9 ± 19.6
June 14
2.7
1.6 ± 0.5
17.3
19.0 ± 5.4
71.9
54.1 ± 15.4
June 15
1.3
0.3 ± 0.2
9.8
4.0 ± 3.4
42.1
12.4 ± 10.5
June 16
1.1
0.8 ± 0.5
10.4
14.9 ± 8.7
54.0
45.0 ± 26.3
June 17
0.7
1.6 ± 0.6
8.7
27.2 ± 10.1
32.6
88.7 ± 32.9
June 18
1.0
0.6 ± 0.3
5.5
6.3 ± 3.7
18.0
19.1 ± 11.1
June 20
3.5
44.6
110.0
Mean
2.8
1.3
17.4
15.1
53.8
46.4
Std Dev
3.0
0.8
13.7
7.9
30.0
25.5
95% CI
2.3
0.2
10.2
2.2
22.2
7.0
differences are likely due to different measurement and emission rate calculation techniques
and their associated method uncertainties. This might also be attributed to variation in
weather conditions or day-to-day variation in the facility operation. As previously stated,
it is expected that the PM emission rate of a dairy may vary based on factors other than
the number of animal units, such as soil type, management practices, and local climate.
Furthermore, it is possible that there may be significant seasonal impacts that cause the
emission rate of the facility to vary throughout the year.
5.4
Conclusion
Particulate matter emissions from a free-stall and open lot dairy in the San Joaquin
Valley of California were investigated over eight days in June 2008. The dairy milked 950
cows daily, with a total of 1885 animals in 13.7 hectares of pens. As a source of PM10 in the
jurisdiction of the San Joaquin Valley Air Pollution Control District, the dairy was required
to implement CMPs to reduce PM10 emissions and help the airshed reach attainment status.
The selected CMPs were to reduce PM emissions from unpaved roads, unpaved storage areas,
116
manure handling, and feed mixing. Aerosol measurements were made using filter-based
samplers, optical particle counters, and a scanning lidar system calibrated to provide mass
concentration information. On-site meteorological measurements were combined with these
PM measurements to calculate PM2.5 , PM10 , and TSP emission rates for the dairy using
inverse modeling (with AERMOD) and a mass balance technique using lidar data.
The overall mean lidar- and inverse modeling-derived emission rates ± the 95% confidence
intervals are shown in units of g/d/AU in Table 5.3, and are also shown in units of g/d/animal
in Table 5.3. For comparison, also shown are the estimated emission values from previous
studies [2–5].
Meteorology, soil type, soil moisture, housing type, bedding type, feed, manure handling
and storage, associated vehicular traffic, and animal age and activity may all be significant
factors affecting PM emissions from a dairy. Additionally, quantified PM emissions may vary
depending on instrumentation and emission rate calculation techniques, as shown by the
similar but not identical emission rates found by the two methods employed in this study.
It is likely that the differences between all reported emission rates may be attributable to
varying combinations of these factors. The study herein reported differs from Schmidt [3]
and Martin [5] in climatic conditions or housing type. Goodrich reported similar emissions
for open lot pens during the summer in the Texas panhandle, and lower values from free
stall areas [3]. While Goodrich et al. also used an inverse modeling technique, the model
employed was the Industrial Source Complex Short Term Model, Ver. 3 (ISCST3), whereas
AERMOD was utilized in this study. Differences of maximum predicted concentrations from
ISCST3 and AERMOD of up to a factor of two were reported by Faulkner et al. from a
ground level area source with identical source, receptor, and meteorological inputs [13]. This
difference would be carried into emission rate calculations. AERMOD replaced ISCST3
as the EPA recommended air dispersion model for regulatory purposes in December 2006.
Based on the multiple potential factors affecting the quantification of emissions from dairies,
the variation seen in the literature and herein reported are not unexpected.
It should be noted that emissions reported herein are not representative of the emissions
117
Table 5.3: Emission rates estimated by inverse modeling, lidar measurements, and previous
studies.
Emission Rate
PM2.5
PM10
TSP
(g/d/animal)
Inverse Modeling 3.5 ± 2.8
21.6 ± 12.6
66.6 ± 27.5
Lidar
1.6 ± 0.2
18.7 ± 2.7
57.5 ± 8.7
USDA [2]
1.8
Schmidt [3]
1.7 (winter) / 0.3 (summer)
Goodrich [4]
5 (free stall) / 15 (open lot)
Martin [5]
2.3
9.2
throughout the year, as measurements were taken under summer conditions (hot and dry)
with relatively dry soil, and emissions during other seasons are expected to be lower due to
emission suppression from precipitation events, among other factors. In order to determine
suitable values for regulatory purposes, surveys of particulate emission rates should made at
a variety of different dairies and pen types during different times of the year.
References
[1] EPA, “National pollutant discharge elimination system permit regulation and effluent
limitation guidelines and standards for concentration animal feeding operations,” Federal
Register 66(9), pp. 2960–3138, 2001.
[2] USDA, “Quality research & technology transfer programs for concentrated animal
feeding operations,” Washington, DC, 2001.
[3] D. R. Schmidt, L. D. Jacobsen, and K. A. Janni, “Continuous monitoring of ammonia,
hydrogen sulfide, and dust emissions from swind, dairy, and poultry barns,” in ASAE
Annual International Meeting / CIGR XVth World Congress, p. 024060, 2002.
[4] L. B. Goodrich, C. B. Parnell, S. Mukhtar, and S. C. Capereda, “A PM10 emission
factor for free stall dairies,” in Proceedings of the Workshop on Agricultural Air Quality:
State of the Science, 2006.
[5] R. S. Martin, K. D. Moore, and V. S. Doshi, Logan, UT, 2006, Unpublished data.
[6] G. E. Bingham, C. C. Marchant, V. V. Zavyalov, D. Ahlstrom, K. D. Moore, D. Jones,
T. D. Wilkerson, L. Hipps, R. S. Martin, J. L. Hatfield, J. H. Prueger, and R. L.
Pfeiffer, “Lidar based emissions measurement at the whole facility scale: method and
error analysis,” Journal of Applied Remote Sensing, vol. 3, p. 033510, 2009.
118
[7] C. Cowherd, Aerosol Measurement: Principles, Techniques and Applications, 2nd
Edition, ch. 28, pp. 845–858. Hoboken: John Wiley and Sons, 2005.
[8] R. M. Measures, Laser Remote Sensing: Fundamentals and Applications. New York:
John Wiley, Inc., 1984.
[9] C. C. Marchant, T. D. Wilkerson, G. E. Bingham, V. V. Zavyalov, J. M. Andersen,
C. B. Wright, S. S. Cornelsen, R. S. Martin, P. J. Silva, and J. L. Hatfield, “Aglite
lidar: a portable elastic lidar system for investigating aerosol and wind motions at or
around agricultural production facilities,” Journal of Applied Remote Sensing, vol. 3, p.
033511, 2009.
[10] J. M. B. Dias, J. M. N. Leitao, and E. S. R. Fonseca, “Reconstruction of backscatter
and extinction coefficients in lidar: a stochastic filtering approach,” IEEE Transactions
on Geoscience and Remote Sensing, vol. 42, pp. 443–456, 2004.
[11] C. C. Marchant, M. D. Wojcik, and W. J. Bradford, “Estimation of aerosol effective
radius by multi-wavelength elastic lidar,” IEEE Transactions on Geoscience and Remote
Sensing, p. submitted for publication, 2010.
[12] V. V. Zavyalov, C. C. Marchant, G. E. Bingham, T. D. Wilkerson, J. L. Hatfield,
R. S. Martin, P. J. Silva, K. D. Moore, J. Swasey, D. J. Ahlstrom, and T. L. Jones,
“Aglite lidar: calibration and retrievals of well characterized aerosols from agricultural
operations using a three-wavelength elastic lidar,” Journal of Applied Remote Sensing,
vol. 3, p. 033522, 2009.
[13] W. B. Faulkner, B. W. Shaw, and T. Grosch, “Sensitivity of two dispersion models
(AERMOD and ISCST3) to input parameters for a rural ground-level area source,”
Journal of the Air & Waste Management Association, vol. 58, pp. 1288–1296, 2008.
119
Chapter 6
Concluding Remarks
6.1
Elastic Lidar Applications
Air quality issues have motivated the development of novel methods and technologies for
investigating aerosols from agricultural and industrial sources. Elastic lidar is a technology
that performs accurate measurement of aerosol concentration over large areas with high
temporal and spatial resolution, enabling techniques for accurately characterizing and
estimating aerosol emissions. Characterization of the properties, emission rates, and behavior
of aerosols in the atmosphere are necessary for effective government regulation of air quality,
determination of industrial and agricultural best practices, and homeland security issues.
Aglite’s multiwavelength and scanning abilities have made it an appropriate platform
for demonstrating lidar innovations in these types of applications. This dissertation has
described the use of Aglite to image plume morphology, estimate wind speed, aerosol emission
rate, mass concentration, and effective aerosol radius. Effective and practical algorithms
have been described and demonstrated for estimating aerosol properties from lidar signals.
The potential applications of a lidar instrument in conjunction with an effective algorithm
are potentially innumerable.
Aglite’s initial operation at a swine operation demonstrated the ability of elastic lidar
to map aerosol concentration. By scanning the atmosphere around the facility, the lidar can
image both emission plume from the facility and fugitive dust from bordering dirt roads
and discriminate them by location. Additionally, Aglite proved the capability of a scanning
multi-wavelength elastic lidar to estimate aerosol effective radius and also wind speed using
characteristic plume morphology.
120
6.2
Lidar Design Tradeoffs
Lidar usefulness may be limited by hardware performance. In general, it is desirable
for a lidar to have a high range resolution, many channels covering different wavelengths,
polarization sensitivity, and have a high signal-to-noise ratio. It is also desirable for a lidar
to be low-cost, durable, rugged, be eye-safe, have a fast scanning speed, and long range.
Several of these characteristics are often at odds and good engineering practices require the
selection of appropriate trade-offs between them.
There are certain design trade-offs that are particularly important in designing a lidar
for aerosol detection. In particular, multiple channels with wavelengths well spaced in a
range around the expected diameter of the target aerosol can enable estimation of particle
effective radius or discrimination between aerosol types with different indices of refraction.
Infrared wavelengths are often preferable to visible and ultra-violet wavelengths due to their
insensitivity to Rayleigh scattering and the ability to select eye-safe channels. Outdoor
use of lidar systems using typical power levels in the United States normally requires the
obtainment of a special permit from the Federal Aviation Administration. The use of
non-visible (>700 nm) eye-safe channels (i.e. 1550 nm) can greatly ease the obtainment
of such permits. The use of a linearly polarized transmitter, together with polarization
sensitive channels, makes a lidar sensitive to aerosol asphericity. The addition of Raman
scattering channels can improve the quality and stability of lidar aerosol estimates. However,
Raman channels typically have significantly poorer SNR characteristics when compared to
elastic channels and often can only be used during night operation. Differential absorption
lidar systems are typically designed to detect gases, but are also sensitive to aerosols and
such a lidar could potentially be used in conjunction with an elastic lidar system.
A high pulse-rate lidar can enable quick measurement of the atmosphere with high
resolution, but have the disadvantage of increased solar background noise and having a
shorter potential maximum range than a lower pulse-rate lidar. A high pulse-rate lidar
can also allow the use of photon-counting detectors, which have lower dynamic range than
current mode detectors, but have more convenient noise characteristics and are easier to
121
linearize. If the lidar scan speed is not significantly faster than the wind speed, this will
induce morphological features in the lidar images. The use of a scanning mirror to steer
the lidar beam may cause an azimuth dependency efficiency factor in the transmitter due
to polarization effects. The receiver optics parameters determine the shape of the GFF.
A narrow receiver field-of-view will reject solar background radiation, improving signal
SNR, but will also increase near-range attenuation by the GFF. In practice, increased lidar
complexity also leads to higher development and maintainance costs and decreased durability.
In general, lidar is effective at measuring aerosols’ optical characteristics, however often
it is the other characteristics that are of interest, such as mass concentration and chemical
composition. It is a difficult problem to estimate these with lidar alone, however the usefulness
of lidar can be greatly increased when used in conjunction with other significantly lessexpensive instruments. For example, meteorological instruments could measure temperature,
pressure, and humidity, to allow the estimation of Rayleigh scattering and its influence on the
lidar signal, or aerosol point sensors could be used to measure aerosol chemical composition
and particle size distribution, which can be used as constraints in the retrieval algorithm,
improving its performance.
Often, absolute calibration of a lidar is a difficult task. It is desirable to make the lidar
hardware isolated against vibration, temperature controlled, and to select robust hardware
components, so that the lidar performance is stable over time. Additionally for field-use
lidars, care should be taken to seal the instrument from intruding dust and insects. Even
with careful hardware design, often the calibration coefficients of a lidar change over time.
One strategy for compensating for this is to develop a calibration procedure that can be
performed and repeated in the field. This may involve calibrating using a hard calibration
target with known optical characteristics, calibrating using background atmospheric aerosols
and aerosol point sensors, or calibrating using Rayleigh scattering from air molecules at high
altitude.
6.3
Wavelength and Component Aerosol Selection
Much of the challenge in probing aerosols using lidar lies in the difficulty in tracking
122
spatial variability of aerosol properties. Two different lidar algorithms are described in this
dissertation, both of which handle variation in aerosol characteristics by treating target
aerosols as linear combinations of known basis aerosols. The choice of basis aerosols affects
performance of a retrieval algorithm, so it is an important question how to appropriately
select them. The backscatter vectors of the basis aerosols should be linearly independent.
In addition, they should be selected so that the composite basis PSD functions accurately
represent the true aerosol. Ultimately, whether a given basis is any good depends on whether
using that basis results in good quality retrievals. There is a need for developing a method
for correctly choosing a set of basis aerosols based on a lidar’s wavelengths and the expected
characteristics of the target aerosol.
On the flip-side of choosing basis aerosols, care should be taken when choosing lidar
channel wavelengths. There can be significant engineering constraints on this choice, as
affordable commodity lasers, detectors, and optical components are only available for certain
wavelengths. The values in the backscatter vector of an aerosol depend on its PSD and
index of refraction. If a lidar is purposed to measure variation in aerosol effective radius,
the direction of the backscatter vector should be strongly related to changes in effective
radius. A capability of important interest, especially in relation to homeland security, is the
ability to differentiate two different aerosols. The channel wavelengths should be selected so
that the backscatter vectors of the target aerosols are as close to orthogonal as possible. In
general, lidars are designed as general multi-purpose instruments, meant to look at many
different aerosols or an aerosol with unknown properties. The more wavelengths a lidar
has, the more likely it is that the backscatter vectors of different aerosols will be close to
orthogonal.
6.4
Information Retrieval from Lidar Returns
Two methods have been presented in this dissertation for estimating aerosol concentra-
tion from measured lidar returns. Both of these methods were derived from an optimization
perspective and approximate the target aerosol as a linear combination of basis aerosols.
One of the these is an NLS estimator, which functions by representing the entire atmosphere
123
over the entire length of the measurement as a single state vector and then estimating the
state vector by alternately linearizing and solving the lidar equation. The other algorithm
is a form of the EKF, estimating the value of a much smaller state vector as a function of
range. The EKF type is significantly more efficient than the NLS estimator.
Both of these operate along a single lidar measurement, however lidar data is often
captured in the form of an image, with one axis corresponding to range and the other
to measurement time. By applying either of these algorithms to a lidar image, image
reconstruction and restoration are performed along the range axis of the image. Image
quality could be further improved by performing image restoration along the time axis.
Another Kalman filter could be applied to the time axis, using for the output of the first
estimator as its input.
Depending on the intended application of a lidar instrument, it may be useful instead to
develop efficient algorithms that treat the target aerosol as having a particle size distribution
of known form but unknown parameters. Furthermore, an elastic lidar may be augmented
with the addition of a Raman channel or a fluorescence channel. In this case, an algorithm
should be formulated to take full advantage of the information contained in the inelastic
channel.
6.5
Research Context
The goal of this research has been to expand understanding of aerosol emission processes
from agricultural sources. In general, there is a lack of baseline data across the country on
aerosol emission rates from agricultural operations. To this end, the Aglite lidar instrument
was constructed and a suite of conventional point sensors was assembled especially for
the purpose of gathering this data. Although elastic lidars have been previously applied
to investigating agricultural aerosol emissions, the Aglite instrument system is a unique
and useful addition to the very small field of research instruments, increasing the available
capability of the scientific community to investigate aerosols using lidar. This research
has described the construction, characteristics, and performance of this particular lidar
instrument, with the hope that future aerosol lidars will improve upon the design decisions
124
made for Aglite.
Initial investigations with the Aglite lidar showed that conventional techniques for
estimating aerosol concentration were inadequate, giving results that were unstable or had
large error values. As a result, two new algorithms were developed for estimating aerosol
concentration from elastic lidar data and demonstrated on measurements from real-world
agricultural sources. These algorithms expand the choice of algorithms available to the
aerosol lidar investigator and have useful advantages not available from other retrieval
algorithms.
Furthermore, this research includes the pioneering work of the Aglite system to measure
aerosol emission rates from agricultural facilities. The specific example of a dairy measurement campaign is the first time aerosol emission rates from a dairy were measured using a
lidar. In addition to demonstrating the practicality of Aglite and its retrieval algorithm, this
contributes additional data to the very small amount of available baseline data regarding
dairy aerosol emissions. As illustrated by Table 5.3, there have been only four previous
attempts to estimate the aerosol emission of dairies.
6.6
Future Work
Previous measurement campaigns by Aglite have all been “one-off” activities, a mea-
surement event at a single location of duration never more than two weeks. In order to
develop effective air-quality regulation or generalize scientific conclusions concerning any of
these types of facilities, aerosol emission rates should be estimated for several different times
of the year and over several different locations, and elastic lidar systems like Aglite are well
suited to this task. Measurement sites should be selected that are broadly representative of
the facility type and measurement campaigns should be conducted during different seasons,
climate, and weather conditions.
For example, in order to determine the average aerosol emission rate of a generic
dairy throughout the year, measurement campaigns should be conducted during all four
seasons, under a variety of weather and temperature conditions. Dairies of different sizes
and locations should be investigated in order to determine how factors such as facility size
125
and local climate affect the emission rate. The efficacy of conservation practices to mitigate
aerosol emissions should be measured by comparing measurements taken at dairies that
use different conservation practices. Effective government regulation of agricultural aerosol
emissions will require knowledge of the average expected emission rate not only from dairies,
but many other types of operations as well, of which a few examples are cattle feed-lots, soil
tillage, crop harvest and processing.
To this end, there is a clear need to develop a simple, robust, and inexpensive methodology for estimating aerosol emissions and Aglite has proven that elastic lidar is a feasible
technology for this task. Experience with Aglite has shown that a simpler and more durable
lidar instrument would be needed for regularly monitoring facility compliance with aerosol
emission regulations. Nevertheless, if a low-cost and durable aerosol elastic-lidar system
were designed, then a number of capable and affordable lidar systems could be constructed
for monitoring the effectiveness of conservation practices. Hopefully future investigators
and lidars will follow in the footsteps of Aglite, improving on this research to cheaply and
effectively measure aerosol emissions.
126
Appendices
127
Appendix A
Construction of Lidar Equation Matrices
The function q̂ (y) is defined by (3.16) and its component matrices can be expressed by
(A.1) and (A.2).
A0
T
= A01 A02 · · · A0Λ
N T
0
Ai = −2 Qdiag
ai
z1 z 2 · · · z L − zm
A = a1 a2 · · · aΛ


··· 0 
 1 1 ··· 1 0 0

.. 


1 0 0
. 
 0 1



 ..
..
.
.
.
.

 .
.
.
.






1 0 0






Q=

0
0
0






0
0
1




.
.
.
.


.
.
.
.
.
.

.
. 



 .
.
 .
0 0 1
1 0 




0 ···
0 0 1 ··· 1 1
T
a0 = a0,1 a0,2 · · · a0,Λ
a0,i = −2α0,i
z1 z2 · · · z L − zm
(A.1)
128
B0
T
B10 B20 · · · BΛ0
0
2
T
2
T
2
T
2
Bi = zm Pm,i /β m,i diag bi /z1 bi /z2 · · · bi /zL
B = b1 b2 · · · bΛ
T
b0 = b0,1 b0,2 · · · b0,Λ
T
2 P
−2
−2
−2
b0,i = zm
β
/β
m,i 0,i
z1
z2
· · · zL
m,i
T
q̂ = P1,1 P2,1 · · · PL,1 P1,2 · · · PL,Λ
T
ŷ = n1,1 n1,2 · · · n1,S n2,1 · · · nL,S
=
(A.2)
Matrices A0 and B 0 are ΛLxLS, vectors a0 and b0 are ΛLx1, matrices A0i and Bi0 are
N
LxLS, and the vectors a0,i and b0,i are Lx1. The operator
is the Kronecker product.
The vector a0,i is a function of the expression a0,i , the baseline extinction coefficient of the
atmosphere at wavelength i. The vector b0,i is a function of β0,i , βm,i , and Pm,i , which are
the baseline backscatter coefficient, boundary point backscatter coefficient, and boundary
point returned power respectively at wavelength i. The matrices A and B have been used
previously in (3.7); their column vectors are defined as the extinction and backscatter vectors
for each non-baseline aerosol component and can be calculated using Mie theory as described
by (3.6).
A linear approximation of (3.16) around some point y0 can be made by expanding it into
a Taylor series and discarding all but the zero and first order terms. Consider the following
Taylor series approximations of the expressions a · exp(Bx) and Ax·exp(Bx) around the
point x0 .
Ax · exp (Bx) ≈ diag [exp (Bx0 )] (A + diag (Ax0 ) B) x
−diag [exp (Bx0 ) · Ax0 ] Bx0
a · exp (Bx) ≈ diag [a] diag [exp (Bx0 )] Bx
+diag [a] diag [exp (Bx0 )] (1 − Bx0 )
(A.3)
129
One can rearrange (3.16) into a more convenient form and substitute the approximations
given by (A.3) for the expressions b · exp (A0 y) and B 0 y · exp (A0 y).
q̂ = diag [exp (d)] B 0 ŷ · exp (A0 ŷ) + diag [exp (d)] b · exp (A0 ŷ)
q̂ ≈ diag [exp (d)] (diag [exp (Aŷ0 )] (B + diag (Bŷ0 ) A) ŷ
(A.4)
−diag [exp (Aŷ0 ) · Bŷ0 ] Aŷ0 ) + diag [exp (d)] (diag [b · exp (A0 ŷ0 )] A0 ŷ
+diag [b · exp (A0 ŷ0 )] (1 − A0 ŷ0 ))
Combining terms in (A.4) and using the definitions given in (3.18) yields a linear
approximation of the lidar equation.
130
Appendix B
Derivation of Covariance Matrices
If the observation noise is independent and zero-mean normally distributed, but the
variance is range dependent, then the lidar equation can be written including a noise term.
p (z)meas
Rz
β (z)
0
0
= k · g (z) · 2 · exp −2 α (z ) , dz + ν z ν z ∼ N (0, Σz )
z
0
(B.1)
The expression ν z is a zero-mean Gaussian distributed random noise vector [J] with
covariance Σz . It is assumed that the elements of ν z are uncorrelated, or in other words Σz
is a diagonal matrix. In this case, noise at the reference point leads to error in estimating
the lidar coefficient k.
k=
2
(pm + ν z ) zm
R zm
gm · β m · exp −2 0 α (z 0 ) dz 0
(B.2)
Disregarding theg (z) and GFF, and applying the substitution of (B.2) into (B.1) yields
a form of the lidar equation without the vector of calibration coefficients.
p (z)meas = (p + ν m ) ·
2 β (z)
zm
z2 βm
Zz

· exp −2

0
α z , dz 0  + ν z
(B.3)
zm
From this it is apparent that in addition to the noise term that is independent with
range, noise in the lidar coefficient estimation introduces a bias into the entire measurement.
This bias can be minimized by choosing the reference point where the signal-to-noise ratio or
Pm /σm is high. Care must be taken not to place the reference point in a part of the signal
where the GFF is poorly known.
131
The form of the lidar equation found in (3.16) can be modified to show these noise
terms as shown here.
dm
q ≈ (diag (dm ) + I) (Gy + h) + νB
T
νm,Λ T
νm,1 T
νm,2 T
= P 1
· · · Pm,Λ 1
Pm,2 1
m,1
T
νB = ν1 ν2 · · · νΛ
ν i = ν1,i ν2,i · · · νL,i
(B.4)
The matrix I is the identity matrix and the hth bin of the ith channel has observed
2 . From here, it follows that the signal can be linearly approximated
signal variance [J2 ] of σh,i
using (3.18).
q ≈ (diag (dm ) + I) (Gy + h) + νB
(B.5)
In fact, if the lidar calibration coefficient is calculated using a given signal, the effect of
the reference range noise is canceled by the lidar coefficient and the lidar solution at the
reference range does not depend on either the bin noise or the lidar coefficient noise. If the
lidar calibration coefficient is calculated separately from the current measurement, then this
cancellation does not occur. The approximation (B.4) can be written as a linear equation
plus two error terms, represented by the ν symbols covered by a brace.
z }| {
q ≈ Gy + h + ν m + νyB
(B.6)
ν m = diag (dm ) (Gy + h)
If an estimate of y exists, the entire term diag (dm ) (Gy + h) can be approximated by
the noise vector ν m , which represents the error contribution at every range caused by noise
in the lidar calibration coefficient. In order to compute the covariance of s, and with a view
to simplify the mathematics involved, let us approximate the term ν m .
ν m ≈ diag (dm ) h
(B.7)
132
By practical experiment, (B.7) is a reasonable approximation, taking into account that
the expected value of y is 0. Adding the two noise terms yields a zero-mean total-noise
vector for each individual range.
s = ν m + ν B ≈ diag (dm ) h + νB
The covariance of s is given by (3.25).
(B.8)
133
Appendix C
Publication Release Forms
134
135
136
137
138
139
140
141
142
143
144
145
146
Vita
Christian C. Marchant
Education
BS in Electrical Engineering, Brigham Young University, Provo, Utah. (5/04) GPA: 3.6
(4.0=A) Recipient of Engineering Academic Scholarship (Micron Scholarship) and Office
of Research and Creative Activities Scholarship. MS in Electrical Engineering, Utah State
University, Logan, Utah. (4/08) GPA: 3.4. PhD in Electrical Engineering, Utah State
University, Logan, Utah. (12/10) GPA: 3.9.
Experience
GRADUATE RESEARCH ASSISTANT, Energy Dynamics Laboratory, Logan, Utah
(9/04-Present).
SPACE SCHOLAR INTERN, USAF Research Lab, Space Vehicles Directorate, Kirtland
Air Force Base, New Mexico (05/09-08/09).
Published Journal Articles
• Aglite Lidar: A Portable Elastic Lidar System for Investigating Aerosol and Wind
Motions at or around Agricultural Production Facilities, C. C. Marchant et al., Journal
of Applied Remote Sensing, vol. 3, 033511, 2009.
• An Iterative Least Square Approach to Elastic-Lidar Retrievals for Well-Characterized
Aerosols, C. C. Marchant et al., IEEE Trans. Geosci. Remote Sens., vol. 48, pp.
2430-2444, 2010.
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertisement