Dara_STOTEN

Dara_STOTEN
Science of the Total Environment 541 (2016) 218–229
Contents lists available at ScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Nutrient, metal and microbial loss in surface runoff following treated
sludge and dairy cattle slurry application to an Irish grassland soil
D.P. Peyton a,b, M.G. Healy b, G.T.A. Fleming d, J. Grant c, D. Wall a, L. Morrison e, M. Cormican f, O. Fenton a,⁎
a
Teagasc, Environment Research Centre, Johnstown Castle, Co. Wexford, Ireland
Civil Engineering, National University of Ireland, Galway, Co. Galway, Ireland
Teagasc, Ashtown, Co. Dublin, Ireland
d
Microbiology, National University of Ireland, Galway, Co. Galway, Ireland
e
Earth and Ocean Sciences and Ryan Institute, National University of Ireland, Galway, Co. Galway, Ireland
f
School of Medicine, National University of Ireland, Galway, Co. Galway, Ireland
b
c
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• This study investigated surface runoff of
contaminants from biosolids in field
plots.
• Contaminants investigated were nutrients, metals, microbes and trace elements.
• Compared to slurry, biosolids do not
pose a greater risk of contaminant
losses.
• Fears concerning contaminant losses
from land applied biosolids may be unfounded.
a r t i c l e
i n f o
Article history:
Received 15 June 2015
Received in revised form 4 September 2015
Accepted 12 September 2015
Available online xxxx
Editor: Simon Pollard
Keywords:
Biosolids
Dairy cattle slurry, rainfall simulator
Surface runoff
Nutrients
Metals, faecal coliforms, total coliforms
⁎ Corresponding author.
E-mail address: [email protected] (O. Fenton).
http://dx.doi.org/10.1016/j.scitotenv.2015.09.053
0048-9697/© 2015 Elsevier B.V. All rights reserved.
a b s t r a c t
Treated municipal sewage sludge (“biosolids”) and dairy cattle slurry (DCS) may be applied to agricultural land as
an organic fertiliser. This study investigates losses of nutrients in runoff water (nitrogen (N) and phosphorus (P)),
metals (copper (Cu), nickel (Ni), lead (Pb), zinc (Zn), cadmium (Cd), chromium (Cr)), and microbial indicators of
pollution (total and faecal coliforms) arising from the land application of four types of treated biosolids and DCS
to field micro-plots at three time intervals (24, 48, 360 h) after application. Losses from biosolids-amended plots
or DCS-amended plots followed a general trend of highest losses occurring during the first rainfall event and
reduced losses in the subsequent events. However, with the exception of total and faecal coliforms and some
metals (Ni, Cu), the greatest losses were from the DCS-amended plots. For example, average losses over the
three rainfall events for dissolved reactive phosphorus and ammonium-nitrogen from DCS-amended plots
were 5 and 11.2 mg L−1, respectively, which were in excess of the losses from the biosolids plots. When compared with slurry treatments, for the parameters monitored biosolids generally do not pose a greater risk in
terms of losses along the runoff pathway. This finding has important policy implications, as it shows that concern
related to the reuse of biosolids as a soil fertiliser, mainly related to contaminant losses upon land application,
may be unfounded.
© 2015 Elsevier B.V. All rights reserved.
D.P. Peyton et al. / Science of the Total Environment 541 (2016) 218–229
1. Introduction
In the European Union (EU), implementation of directives and other
legislative measures in recent decades concerning the collection,
treatment and discharge of wastewater, as well as technological advances in the upgrading and development of wastewater treatment
plants (WWTPs) (Robinson et al., 2012), has resulted in a rise in the
number of households connected to sewers, increasing the loadings
on WWTPs (EC., European Commission, 2014). Production of untreated
sewage sludge across the EU has increased from 5.5 million tonnes of
dry matter (DM) in 1992 to an estimated 10 million tonnes in 2010
(EUROSTAT. EC, European Commission, 2014), with production further
expected to increase to 13 million tonnes in all EU member states by
2020 (EC., European Commission, 2010).
The treatment and disposal of sewage sludge presents a major
challenge in wastewater management and, consequently, there is a
need to find a cost-effective and innovative solution for its disposal
(Hall, 2000). In the EU, the drive to reuse sewage sludge has been
accelerated by legislation such as the Landfill Directive 1999/31/EC
(EC., European Commission, 1999), the Urban Wastewater Treatment
Directive 91/271/EEC (EEC., European Economic Community, 1991),
the Waste Framework Directive 2008/98/EC (EC., European Commission, 2008), and the Renewable Energy Directive 2009/28/EC (EC.,
European Commission, 2009). This has prompted those involved in
sewage sludge management to find alternative uses for it, such as in
the production of energy, bio-plastics, polymers and other potentially
useful materials (Healy et al., 2015). Recycling to land is currently
considered the most economical and beneficial way for sewage sludge
management (Haynes et al., 2009; Peters and Rowley, 2009; Healy
et al., 2015). However, before this can occur, it must be treated to
prevent harmful effects on soil, vegetation, animals and humans (EC.,
European Commission, 2014). Chemical, thermal or biological treatments, which may include composting (USEPA., US Environmental
Protection Agency, 2002), aerobic and anaerobic digestion (USEPA.,
US Environmental Protection Agency, 2006a), thermal drying (USEPA.,
US Environmental Protection Agency, 2006b), or lime stabilisation
(USEPA., US Environmental Protection Agency, 2000), produces
a stabilised organic material frequently referred to as ‘biosolids’.
The term biosolids was formally adopted in 1991 by the Name
Change Task Force of the Water Environment Federation (WEF., Water
Environment Federation, 2005) to differentiate raw, untreated sewage
sludge from treated and tested sewage sludge that can legally be as a
soil amendment and fertiliser.
The are many benefits of recycling biosolids to grassland: (1) their
use completes the urban–rural cycle (Fehily Timoney and Company,
1999) (2) they may be used as a soil conditioner, improving its physical,
chemical and biological properties, and reducing the possibility of soil
erosion (Lucid et al., 2014) and (3) they are a cheap organic alternative
to commercial fertiliser (Lu et al., 2012).
There are many potential problems associated with the land application of biosolids, and these have been reviewed by Lu et al. (2012) and
Singh and Agrawal (2008), amongst others. Nutrient losses in runoff
are affected not only by biosolid type, but also application rate. In the
EU, land application of biosolids is based on the pH, metal and nutrient
content of the soil and the nutrient and metal content of the biosolids
(Fehily Timoney and Company, 1999). Frequently, the phosphorus
(P) content of the biosolids becomes the limiting factor in determining
the land application rate (Lucid et al., 2013). In the USA, the
application of biosolids to land is governed by the standard for the
use or disposal of sewage sludge (USEPA., US Environmental
Protection Agency, 1993) and as a result, the rate of application of
biosolids to land are applied based on an estimate of crop nitrogen
(N) need and biosolids N availability (Lu et al., 2012), and is not
based on a soil test (McDonald and Wall, 2011). However, due to
concerns about the effects of repeated manure or biosolids applications on the soil and the risk of P loss to surface water, some states
219
(e.g. Maryland) have introduced regulations based on the P content
of the biosolids (Lu et al., 2012).
Losses of nutrients to surface or subsurface waters bodies originates
in two ways: as chronic (long-term, due to the build-up of nutrients in
soil), or as incidental (short-term losses within 48 h of application)
losses following episodic rainfall events soon after land application of
a fertiliser or amendment (Brennan et al., 2012). Such losses to a surface
waterbody occur via direct discharges, surface and near surface pathways, and/or groundwater discharge, where there is a hydrological
transfer continuum between a nutrient source (chronic or incidental)
and surface water receptor (Wall et al., 2011). Losses of P have been
reported by Lucid et al. (2013, 2014) following the application of
thermally dried (TD), lime stabilised (LS) and anaerobically digested
(AD) biosolids. Increased N losses have also been reported following
biosolids application to land. For example, Ojeda et al. (2006) reported
elevated concentrations of ammonium (NH4–N) and nitrate (NO3–N)
in surface waters following the application of TD and composted biosolids at rates of 10 t DM ha−1. Quilbe et al. (2005) measured elevated
runoff NH4–N concentrations following the spreading of AD biosolids
applied at 7.5 DM ha−1, whereas LS biosolids had no significant effect
on such concentrations in runoff when applied at the same rate.
Although many studies have not reported elevated metal concentrations in runoff following the application of various types of biosolids
(Joshua et al., 1998; Dowdy et al., 1991; Eldridge et al., 2009; Lucid
et al., 2013), there is a dearth of data comparing the impact of several
types of biosolids, applied during the same application, on surface runoff
of metals. In addition, concerns have been raised about the accumulation of heavy metals in both soil and crops after repeated applications
of biosolids (McBride, 2003; Bai et al., 2010) and the migration of metals
from the soil profile to surface and subsurface waters (Lu et al., 2012).
Other concerns associated with the land spreading of biosolids have
focused around human enteric pathogens found in biosolids, as inactivation of pathogens is difficult to achieve (Sidhu and Toze, 2009).
Typically, the densities of pathogens are reduced by two to three orders
of magnitude by the wastewater treatment and biosolids processing
(Apedaile, 2001). Whilst these reductions are significant, appreciable
numbers of pathogens survive, which may subsequently re-grow to
hazardous levels when exposed to favourable environmental conditions
(Zaleski et al., 2005), especially during storage (Iranpour and Cox,
2006). Pathogen survival is evidenced by the survival of faecal coliforms
(FC) as indicators for the possible presence of microbial pathogens. The
use of indicator organisms allows for the limitation of potential contaminating effects (Sidhu and Toze, 2009).
Studies have shown that elements of pathogen population may
exhibit enhanced survival due to advantageous physiological properties
or colonisation of more favourable sites (Brennan et al., 2012). However,
as the soil environment is very hostile to the survival of pathogens, their
survival time, following the land application, is 2 to 4 months (Brennan
et al., 2012). Consequently, pathogens are more likely to be transported
to watercourses in incidental rainfall events soon after land application.
Studies examining the transport of pathogens in runoff following the
application of biosolids have generally shown increased runoff of FC
compared to control plots (Dunigan and Dick, 1980; Nelson and Choi,
2005; Wallace et al., 2014).
Understanding the environmental persistence and fate of enteric
pathogens introduction following land application of biosolids and
organic amendments is necessary, as it provides a sound scientific
basis for management practices designed to mitigate the potential microbiological health risks associated with spreading on agricultural
land (Lang et al., 2007). The risk associated with biosolids-derived and
other organic amendment pathogens is largely determined by their
ability to survive and maintain viability in the soil environment after
land spreading. In general, enteric pathogens are poorly adapted to
survival in the soil environment, and pathogens that are land applied
from biosolids and dairy cattle slurry (DCS) are influenced by climatic
and agronomic variables (Lang et al., 2003).
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D.P. Peyton et al. / Science of the Total Environment 541 (2016) 218–229
As demands for food and energy are expected to increase from
a growing population (FAO., Food and Agriculture Organization,
2009), the demands for N, P, and potassium (K) are also expected to
increase at an average rate of 2.5% per year to 2020 (Heffer and
Prud'homme, 2013) and as a result, the price of chemical fertiliser is
also expected to rise (Heffer and Prud'homme, 2013). As biosolids are
often considered a waste product, they may be used as a cheap source
of fertiliser and may provide an excellent opportunity to improve crop
profit margins by means of reducing the input costs of chemical
fertilisers. However, any nutrient recovery from biosolids must be considered against possible adverse impacts associated with their use.
Therefore, there is a need for continued research into land spreading
practices to ensure that environmental losses and associated concerns
are minimised.
The objectives of this study were to (1) quantify runoff losses of
nutrients (N, P), metals (copper (Cu), nickel (Ni), lead (Pb), zinc (Zn),
cadmium (Cd), chromium (Cr)), and microbes (total coliforms (TC)
and FC), from experimental micro-plots at time intervals of 24, 48
and 360 h, following application of four types of biosolids at the
legal application rate based on current EU legislation (2) compare
the losses arising from the application of the biosolids to land to
losses on similar micro-plots following application of another
commonly spread organic fertiliser in Ireland, DCS. At the scale of
the present study, any losses represent worst case scenario losses,
as further attenuation is expected along the transfer continuum
before discharge to a waterbody.
2.2. Micro-plot installation and characterisation
Thirty grassland micro-plots, each 0.9 m in length and 0.4 m in width
(0.36 m2), were isolated using continuous 2.2 m-long, 100 mm-wide
rigid polythene plastic strips, which were pushed to a depth of 50 mm
into the soil to isolate three sides of the plot. All the edges were sealed
with clay to prevent infiltration along the strips into the ground. A
0.6-m polypropylene plastic runoff collection channel was fitted at the
end of each plot (Fig. 1). Micro-plots were orientated with the longest
dimension in the direction of the slope. Once installed, plots were left
uncovered to allow natural rainfall to wash away any soil that had
been disturbed during their construction.
For textural analysis, each micro-plot was tested at before start of
experiment (t0) for particle size distribution (% sand/silt/clay) using
the hydrometer method (ASTM, 2002). Results of analyses are presented in Table 2. Soil nutrient status of each micro-plot was taken at t0 and
analysed for soil pH, Mehlich 3-P, Pm, K, Mg, water extractable P (WEP),
organic matter (OM) and lime requirement (LR) (Table 2). In addition,
composited soil samples were oven dried and grinded to 2 mm before
being sent to ALS Environmental Global, Co. Dublin, Ireland at t0 for
metal content (Cu, Ni, Pb, Zn, Cd, Cr) by Inductively Couples Plasma
Optical Emission Spectrometry (ICP-OES) (MEWAM., Methods for the
Examination of Waters and Associated Materials, 1992), following
aqua-regia digestion (MEWAM,. Methods for the Examination of
Waters and Associated Materials, 1986) (Table 3). Soil nutrient and
metal status analysis was also repeated immediately at the end of the
experiment (t360) (Tables 2 and 3). Background checks were performed
on the soil microbial status (TC and FC) (Table 4) at t0 and t360 by taking
composite soil samples from the four corners outside the micro-plots
(top left, top right, bottom left, bottom right). Total coliforms were
tested in accordance with ISO 4832 (ISO., International Standards
Organisation, 2006a,b) at both t0 and FC were tested in accordance
with ISO 16649-2 (ISO., International Standards Organisation, 2001) at
t0 and ISO 4831 (ISO., International Standards Organisation, 2006a,b)
at t360.
2. Materials and methods
2.1. Field site characterisation
The study site was a 0.6-ha plot located at Teagasc, Johnstown
Castle Environment Research Centre, Co. Wexford, Ireland (latitude
52.293415, longitude − 6.518497) in the southeast of Ireland. The
area has a cool maritime climate, with an average temperature of
10 °C and mean annual precipitation of 1002 mm. The site has been
used as a grassland sward for over twenty years with nutrient inputs
(organic and inorganic) applied based on routine soil testing. The site
has undulating topography with average slopes of 6.7% along the
length of the site and 3.6% across the width. Overall, the site is moderately drained with a soil texture gradient of clay loam to sand silt
loam, as classified by Brennan et al. (2012). Soil nutrient analysis
for the field site was characterised by dividing the site into an
upper, middle and lower section, and by taking three composite
soil samples (n = 20) to characterise each section separately. The
soil nutrient status at these locations (Morgan's P (Pm ), K, and
magnesium (Mg)) was determined using Morgan's extractant
(Morgan, 1941), and is presented in Table 1. Mehlich-3 P extractant
was also used to determine P levels (Mehlich, 1984). Soil pH (n =
3) was determined using a pH probe (Mettler-Toledo Inlab Routine)
and a 2:1 ratio of deionised water to soil as determined previously in
Brennan et al. (2012).
2.3. Biosolids characterisation
Four types of biosolids were examined in this study: two types of AD
sludge, one sourced from a WWTP in Ireland (ADIRE) and another used
in an EU-funded FP7 project (END-O-SLUDG, 2014) (ADUK); TD and LS
biosolids (Fig. 1). With the exception of ADUK, all biosolids were
sourced from the same WWTP in Ireland. As the Irish WWTP only
employed two methods to treat sludge (anaerobic digestion and thermal drying), an untreated, dewatered sewage sludge cake was also
collected from the same WWTP, so that it could be manually lime treated. The treated sludge and the dewatered sludge cake were collected in
sealed, 50 L-capacity plastic storage boxes and transported to Teagasc,
Environment Research Centre, Johnstown Castle, Co Wexford, South
East Ireland, where they were labelled and stored at 4 °C. In accordance
with standard methods in Ireland (Fehily Timoney and Company,
1999), the AD treatment process must have a retention period of at
least 1 h at 70 °C or 2 h at 55 °C, the TD treatment process must result
Table 1
Soil characteristics from the upper, middle and lower section of the 0.6 ha field site.
Position
pH
Morgan P
mg L
UPPER
MIDDLE
LOWER
AVERAGE
STD. DEV
a
b
c
5.6
5.4
5.5
5.5
0.1
2.3
2.3
2.6
2.4
0.2
−1
Mehlich 3-P
mg L
36.1
35.3
25.9
32.6
4.6
−1
WEP
mg kg
6.8
5.6
9.0
7.1
1.4
P index
−1
Ka
mg L
1.0
1.0
1.0
1.0
0.0
Morgan's extractable potassium (K) and magnesium (Mg), lime requirement (LR).
Brennan et al. (2012).
USDA classification system.
Mga
−1
128.9
70.5
121.6
107.0
26.0
mg L
LRa
−1
133.0
108.8
137.0
126.3
12.5
Sandb
−1
Siltb
Clayb
t ha
%
%
%
4.0
5.5
5.0
4.8
0.6
44%
47%
52%
47.7
4
36%
36%
30%
34
3.5
21%
18%
18%
19
1.7
Textural classc
Clay loam
Sandy silt loam
Sandy loam
D.P. Peyton et al. / Science of the Total Environment 541 (2016) 218–229
221
Fig. 1. A) ADUK biosolids; B); ADIRE biosolids, C) LS biosolids; D) TD biosolids; E) Plot dimensions with application quadrant; F) Rainout shelter.
in a product of approximately 90% solids, and lime (calcium oxide (CaO)
of 98% purity sourced from Clogrennan Lime Ltd) must be added, if
necessary, to the raw dewatered sewage sludge to raise the pH to greater than 12 and to generate heat. The treated sludge samples (each at
n = 3) were tested by Brookside Laboratories Inc., Ohio, USA for: DM,
total Kjeldahl nitrogen (TKN), nitrite (NO2-N), NH4–N, organic-N, total
P (TP), P as phosphorus pentoxide (P2O5), K, K as potassium oxide
(K2O), pH, and metal content (Cu, Ni, Pb, Zn, Cd, Cr, Hg) (Blinc., 2000)
Table 2
Average topographical and soil characteristics for the 25 individual micro-plots pooled together as per treatment applied, on the day before experiment (t0) and immediately after the
experiment ended (t360).
Treatment
Slope
pH0/pH360
%
ADUK
TD
LS
ADIRE
SOIL
DCS
2.89
3.69
2.84
2.87
3.53
2.73
5.94/5.90
5.90/5.90
5.90/6.25
5.96/5.93
5.99/5.96
5.81/6.10
WEP0/WEP360
Morgan's
P0/P360
Mehlich 3P0/P360
K0/K360a
Mg0/Mg360a
LR0/LR360a
OM0/OM360a
Sandb
Siltb
Clayb
mg kg−1
mg L−1
mg L−1
mg L−1
mg L−1
t/ha
%
%
%
%
7.10/5.9
9.25/7.5
6.60/5.4
7.7/6.1
8.6/6.9
2.86/1.63
3.60/5.57
4.80/6.79
3.82/6.24
4.32/6.11
4.71/5.59
5.00/9.13
38.0/37.1
47.4/41.9
38.3/32.7
41.4/35.7
46.8/39.2
31.93/−
94.94/60.78
66.08/55.66
58.20/52.12
78.39/55.74
65.95/54.30
62.40/208.42
147.13/147.80
156.75/164.00
136.47/146.40
152.68/147.40
149.49/149.60
84.20/167.17
2.70/3.00
2.30/2.70
2.60/1.00
2.40/2.70
2.80/2.90
3.30/1.60
8.1/
9.0/
8.1/
8.1/
8.8/
45.70
47.41
48.74
48.17
45.52
50.00
39.49
37.63
36.58
36.55
39.43
29.20
14.82
14.97
14.69
15.28
15.05
20.80
8.3/
a
b
c
Morgan's extractable potassium (K) and magnesium (Mg), lime requirement (LR) and organic matter (OM).
ASTM (2002).
USDA classification system.
Textural
classc
Loam
Loam
Loam
Loam
Loam
-
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D.P. Peyton et al. / Science of the Total Environment 541 (2016) 218–229
Table 3
Average soil metals concentration of copper (Cu), nickel (Ni), lead (Pb), zinc (Zn), cadmium (Cd), chromium (Cr) before start of experiment (t0) and after the experiment (t360).
Cd0/Cd360
Treatment
mg kg – 1
ADUK
TD
LS
ADIRE
SOIL
b0.20/0.54
b0.2/0.56
b0.2/0.54
b0.2/0.54
b0.2/0.56
Cr0/Cr360
Cu0/Cu360
Pb0/Pb360
Ni0/360
Zn0/Zn360
11.8/13.8
11.5/14.4
11.6/13.8
12/14.4
11.8/14.4
8.12/6.74
9.54/7.8
7.8/7.4
8.42/7.34
8.62/7.16
15.5/27.2
16.12/25
15/22
16/21.8
17.22/24.4
7.14/9
6.86/9.42
7.2/8.96
7.66/9.4
7.28/9.34
35.2/29.8
33.2/31.2
34.6/27.6
36/30
35.2/31.2
(Table 5). Water extractable P was tested after Kleinman et al. (2007)
(Table 5). In addition, the biosolids samples (each at n = 3) were also
tested for TC and FC immediately after collecting using the same
methods as for soil (Table 4).
2.4. Slurry characterisation
Dairy cattle slurry was collected from the dairy farm unit at the
Teagasc, Environmental research centre, Johnstown Castle. Cattle slurry
was collected from a large underground slurry tank (25 long × 4.8
wide × 2.9 m deep), which had been filled with slurry in the previous
4 months. Prior to sampling, the tank was fully agitated (using a
mechanical tractor-mounted agitator) to mix and homogenise the slurry. Following this, the slurry sample was collected by dropping a bucket,
attached to a rope, into the tank and retrieving the sample. This slurry
was placed into a sealed 25 L container, which was kept refrigerated
(4 °C). Prior to application, the slurry in the container was thoroughly
mixed to suspend any solids that may have settled during the shortterm storage.
Slurry pH was determined using a pH probe and a 2:1 ratio of
deionised water to soil (Table 5). The DCS (each at n = 3) were tested
for (Southern Scientific Ireland, Co. Kerry, Ireland): DM, N (Kjeldahl,
1883), P and K and metal content (Cu, Ni, Pb, Zn, Cd and Cr) (Table 5).
In addition, the DCS samples (each at n = 3) were also tested for TC
and FC immediately after collection using the same methods as for soil
(Table 4).
2.5. Rainfall event simulation and application
One Amsterdam drip-type rainfall simulator, as described by
Bowyer-Bower and Burt (1989), was used to provide rainfall in this
study. It was designed to form droplets with a median diameter of
2.3 mm, spaced 30 mm apart in a 1000 mm × 500 mm × 8 mm Perspex
plate over a 0.5 m2 simulator area. The simulator was calibrated to
deliver a rainfall intensity of 11 mm hr.−1. Water samples, used in the
rainfall simulations, were collected over the duration of the three
rainfall events, and had average concentrations of: 0.07 ± 0.0 mg
NH4–N L−1, 3.81 ± 0.02 mg NO3–N L−1, 3.80 ± 0.02 mg total oxidised
nitrogen (TON) L− 1, 0.01 ± 0.00 mg dissolved reactive phosphorus
(DRP) L−1, 0.02 ± 0.0 mg TP L− 1, 0.30 ± 0.09 μg Cd L−1, 0.38 ±
0.07 μg Cr L−1, 10.10 ± 0.75 μg Cu L−1, 0.65 ± 0.46 μg Ni L−1, 0.93 ±
1.25 μg Pb L−1, 78.91 ± 6.67 μg Zn L− 1, 11.04 ± 1.05 μg aluminium
(Al) L−1, 0.00 ± 0.00 μg iron (Fe) L−1 and 9.95 ± 0.05 μg manganese
(Mn) L−1.
The six treatments (four biosolids, DCS and one soil-only study
control) used in this study were assigned to 30 μ-plots by dividing the
plots in five blocks (five ‘blocks’ each containing six micro-plots). As
metal content was not limiting in soil, DCS or biosolids application to
the micro-plots was governed by the P content of the biosolids, and
DCS and the P index of the soil. For comparable results, all micro-plots
were classified into Index 2 P soil, which meant that all biosolids and
DCS treatments were applied to all plots at a rate of 40 kg P ha− 1
(Coulter and Lalor, 2008). As a result of the P content and the DM of
each individual biosolid, application rates per individual plot was of
96.6 g of TD, 242.2 g of ADIRE, 1063.3 g of LS, 243.9 g of ADUK biosolids
were applied to each designated plot. The DCS was spread at 2880 g per
individual plot.
Prior to application, grass on all plots was cut to 50 mm, 48 h before
the first rainfall simulation (RS1). For better control of rainfall simulations and to prevent runoff losses caused by natural rainfall events,
individual micro-plots were covered from the time of grass cutting
to the end of the last rainfall event by ‘rainout’ shelters (Fig. 1f)
(Hoekstra et al., 2014). Biosolids were hand surface applied to each
micro-plot. To ensure even distribution, each micro-plot was divided
into four quadrants (each 0.09 m2 in area) and a proportionate amount
of biosolids was applied in each quadrant (Fig. 1e). The DCS was applied
in rows using a watering can to replicate normal trailing shoe application. The biosolids and DCS were then left 24 h with the soil before
RS1. The RS1 event occurred 24 h after biosolids and DCS application,
so as to demonstrate losses representative of a worst-case scenario.
The second rainfall event (RS2) was two days (48 h) after initial
biosolids/DCS application, which was representative of current legislation, and the third (RS3) 15 days (360 h) after initial application.
Volumetric water content of the soil in each plot (n = 3) was
measured immediately prior to each rainfall event using a time domain
reflectrometry device (Delta-T Devices Ltd., Cambridge, UK), which was
calibrated to measure resistivity in the upper 50 mm of the soil in each
plot. Prior to each rainfall event, collection channels from the microplots were also rinsed with boiling hot water to sterilise them.
2.6. Runoff sample collection
Surface runoff was judged to occur once 50 mL of water was collected from the runoff collection channel from the start of simulated rainfall
to runoff. The collection of the first 50 mL (t = 0) was used to indicate
Table 4
The average total and faecal coliforms (±std. dev.) for soil and biosolids on the day before experiment (t0) and after the experiment (t360).
MICROBE
ADUK
TD
LS
ADIRE
SLURRY
Presumptive Coliforms
(cfu g−1) (t0)
ß-Glucuronidase + E. coli
(cfu g−1) b100 (t0)
Total coliform
(Product) (t360)
Faecal Coliforms
(MPN) (t360)
b1.0 × 107
b1.0 × 107
b1.0 × 107
b1.0 × 107
5.43 × 104 (6.34 × 103) b1.0 × 107
6.5 × 103 (3.6 × 103) b1.0 × 102
b1.0 × 102
b1.0 × 102
1.10 × 103
7.4 × 102 (4.5 × 102)
6.3 × 101 (4.5 × 101)
1.3 × 101 (4.7 × 100)
1.7 × 101 (2.1 × 101)
1.9 × 100 (1.7 × 100) b3.0 × 10−1 (0)
SOIL
b1.0 × 102
5.0 × 101 (5.0 × 100) –
1.3 × 103 (6.9 × 102)
2.3 × 100 (0)
7.7 × 100 (4.9 × 100)
–
D.P. Peyton et al. / Science of the Total Environment 541 (2016) 218–229
223
Table 5
Average nutrient and metal characteristics of the biosolids (±standard deviation) before start of experiment (t0).
Treatment
ADUK
LS
TD
ADIRE
DCS
a
b
DM
Total N
%
mg kg–1
25
(0.1)
34
(0.2)
87
(0.1)
24
(0.2)
8
(0.2)
43,216
(1671)
17,621
(396)
51,446
(2897)
54,578
(1530)
2
(0.2)
Total P
Total K
23,512
(274)
3939
(396)
17,114
(187)
25,186
(609)
1
(0)
2146
(40)
2230
(44)
2055
(51)
2199
(78)
4
(0.4)
pH
8
(0.0)
13
(0.0)
7
(0)
8
(0)
8
(0)
WEP (dry)
OM
Cu
g kg−1
%
mg kg–1
28
(1)
80
(2)
72
(1)
287
(4)
111.7
(11)
505
(19)
756
(21)
3.9
0
16
(8)
9
(0.3)
493
(26)
302
(1)
93
(3)
Ni
140
(2)
12
(0.3)
19.6
(2)
26.3
(1)
0.44
(0.3)
Pb
Zn
Cd
Cr
Hg
NO3–N
NH4–N
Organic - N
115
(1)
11
(1)
63
(1)
91.6
(3)
b0.25
0
683
(3)
219
(20)
877
(6)
1110
(22)
14
(0.2)
2
(0)
0.4
(0)
1.0
(0)
2
0
b0.2
0
31
(1)
8.1
(0.3)
22
(0.1)
32
(2)
1
(1)
0.0
(0)
0
(0)
0.4
(1)
0
0
3979
(14)
2922
(13)
1148
(1)
4235
(38)
3847
(294)
449
(29)
573
(32)
3428
(240)
39,370
(1962)
17,171
(395)
50,873
(2876)
51,150
(1776)
P2O5a
K2Ob
53,876
(628)
9138
(790)
39,216
(428)
57,711
(1395)
2585
(48)
2686
(52)
2476
(61)
2649
(95)
P2O5 - Phosphorus pentoxide.
K2O - Potassium oxide.
time to runoff (TR), and was used for part of the microbial analysis.
Samples for nutrient and metal analysis were collected every 10 min
(t = 10, T = 20, T = 30) from TR to allow for the flow weighted
mean concentration (FWMC) to be calculated (Brennan et al., 2012).
After this time, another 50 mL of surface runoff water was collected
for microbial analysis, so that it could be bulked with the first 50 mL
of runoff to create a 100 mL sample for microbial analysis. The rainfall
simulator was then switched off and a final sample was collected to
determine the final runoff ratio. This sample was also analysed for nutrient and metal content. Immediately after collection, all samples were
stored in cool boxes with ice until they were returned to the laboratory
for analysis.
2.7. Nutrient and metal runoff analysis
Runoff water samples were filtered through 0.45 μm filters (Sarstedt
- Filtropur S 0.45) and a sub-sample was analysed calorimetrically for
DRP, NO3–N, NO2–N and NH4–N using a nutrient analyser (Aquachem
Labmedics Analytics, Thermo Clinical Labsystems, Finland). A second
filtered sub-sample was analysed for total dissolved phosphorus
(TDP) using acid persulphate. Unfiltered runoff water samples were
analysed for TP with an acid persulphate digestion and total reactive
phosphorus (TRP) using the Aquachem Analyser. Metal analysis was
tested on the filtered samples using inductively coupled plasma optical
emission spectroscopy (ICP-OES). Particulate phosphorus (PP) was calculated by subtracting TDP from TP. The DRP was subtracted from the
TDP to give the dissolved un-reactive phosphorus (DUP). All samples
were tested in accordance with the Standard Methods (APHA, 2005).
2.8. Total and faecal coliform analysis
Samples (2 × 50 mL aliquots) of runoff water were collected at the
start and towards the end of rainfall simulation experiments, and
were stored in cool boxes filled with ice until they were returned to
the laboratory for analysis. The time interval between the first collection
and analysis was always less than 9 h, with samples maintained at 4 °C.
Samples were appropriately diluted using sterile water from a Millipore
automatic sanitization module, and 100-mL aliquots were apportioned
for analysis in accordance with standard methods (APHA, 2005). Total
and faecal coliforms were enumerated using the IDEXX Coilisure Quanti
Tray/2000 method (IDEXX Laboratories, Westbrook, ME) after incubation at 37 ± 0.5 °C for 24 h. Results were expressed as the Most Probable
Number (MPN) of TC and FC per 100 mL.
2.9. Data analysis
The structure of the data set was a blocked one-way classification
(treatments) with repeated measures over time (rainfall events (RS1–
RS3)). The analysis was conducted using Proc Mixed in SAS software
(SAS., Statistical Analysis System, 2013) with the inclusion of a covariance model to estimate the correlation between rainfall events. A
large number of covariates were recorded, including measurements
on the simulators and for each analysis; this set of covariates was
screened for any effects that should be included in an analysis of covariance. The interpretation was conducted as a treatment by time factorial.
Comparisons between means were made with compensation for multiple testing effects using the Tukey adjustment to p-values. Significant
interactions were interpreted using simple effects before making
mean comparisons. For comparison of soil characteristics before and
after the experiment, the relationship between the paired measurements, adjusted for treatment, was tested and, given a significant
relationship, the difference between each pair of results was analysed
by treatment. In some cases an intercept-only model was fitted to determine if there had been an overall change across all treatments. Residual
checks were made in all cases to ensure that the assumptions of the
analyses were met.
3. Results
3.1. Nutrient losses in runoff
The average FWMC of TP, comprising DUP, PP and DRP, for all
treatments and rainfall events is shown in Fig. 2. The application of TD
and ADIRE biosolids and DCS increased the average FWMC of DRP in
RS1 and RS2 compared to the study control, but this highly mobile P
fraction was low for the other biosolids treatments. The highest median
FWMC of DRP in the biosolids treatments (0.86 mg L−1) was measured
during RS1 for TD-amended plots, and this decreased significantly (p =
0.02) over subsequent rainfall events to 0.14 mg L−1 for RS3. In comparison, the median FWMC of DRP from the ADIRE treatment was highest
for RS2 (0.78 mg L− 1), although results for the three events were
similar. However, losses for DRP from biosolids treatments were low
compared to the DCS. Dissolved reactive phosphorus loss for DCS during
RS1 was 7.0 mg L−1 and remained higher than any of the biosolids treatment losses during all simulation events.
Losses of PP were detected across all treatments, including the study
control. Particulate P comprised N 45% of TP losses for ADUK, ADIRE and
LS biosolids, and the study control. Particulate P losses comprised only
14% and 32% of TD biosolids and DCS, respectively, due to the high
proportion of DRP losses. However, when only considering the PP
losses, DCS plots for RS1 and RS2 had higher PP losses (p b 0.05) than
all other measurements, which were statistically indistinguishable.
The average FWMC of TN across all treatments is shown in Fig. 2.
There was a significant interaction between treatment and the rainfall
simulation for NH4–N. The application of all biosolids treatments and
DCS increased the average FWMC of NH4–N for RS1 compared to the
study control, and while there was a downward trend between RS1
and RS3 for all treatments except the control, the decrease was not
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Fig. 2. Flow weighted mean concentrations of phosphorus (top) and nitrogen (bottom) in the runoff over three successive rainfall events at 24 h (RS1), 48 h (RS2) and 360 h (RS3) after
application to grassland. (std dev error bars).
significant for LS. The ADUK-amended plots had the highest FWMC
of surface runoff of NH4–N for all biosolids treatments in RS1
(15.3 mg L− 1). Thermally dried and ADIRE treatments had the next
highest FWMCs of NH4–N, but these were not significantly different
from each other or from the LS runoff during RS1. While total losses
from DCS were greatest, they were significantly different only from LS
(p = .005) and the control (p b 0.001). The median FWMC of NH4–N
in RS1 for DCS was 17.4 mg L−1. The addition of biosolids and DCS
had no effect on FWMCs of NO3–N in runoff, except for LS biosolids,
which significantly reduced, relative to the control, the incidental losses
of NO3–N during RS1 and RS2 (p b 0.001), before it increased during
RS3. Nitrite losses were negligible in all treatments, with only exception
being the DCS.
For Cu, the LS-amended plots had significantly higher FWMCs than all
other treatments (p b 0.001), with the highest median concentration
of 202 μg L−1 measured during RS1. There was a decreasing trend in
Ni concentrations across all treatments from RS1 to RS3, except for
the study control, but there were no significant differences within
treatments. All Ni concentrations were elevated compared to control.
The highest median FWMC for Pb (1.5 μg L−1) was measured during
RS3 for the DCS and the second highest was 0.82 μg L−1 during RS1
for TD-amended plots. However, there was no significant difference
between the treatments and the study control. The highest median
FWMC of Zn (30.8 μg L− 1) was during RS1 for DCS-amended plots,
but there were no significant differences across treatments or events.
3.3. Microbial losses in runoff (Total and faecal coliform)
3.2. Metal losses in runoff
The average FWMC of metals (Cu, Ni, Pb, Zn, Cd, Cr) in runoff
is shown in Fig. 3. All runoff samples were below their respective drinking water standards intended for human consumption (Statutory
instrument, 2014). There was no difference in the FWMCs in surface
runoff of Cd and Cr of any treatment compared to the study control,
except for DCS. Cadmium losses for DCS during RS1 were significantly
lower than other treatments, but were significantly higher during RS3.
The average losses of TC and FC are shown in Fig. 4. The ADUKamended plots produced runoff with the lowest number of TC
(averaged over the three rainfall simulations), but produced the highest
average number of FC: 7.1 × 103 MPN per 100 mL during RS1 and RS2.
For TC losses there was an interaction between treatment and event
(p = 0.01), but only the highest and lowest event outcomes were significantly different. While median losses from the TD-amended plots
increased with successive rainfall events from 1.9 × 105 MPN per
D.P. Peyton et al. / Science of the Total Environment 541 (2016) 218–229
225
Fig. 3. Flow weighted mean concentrations of cadmium (A), chromium (B), copper (C), nickel (D), lead (E), zinc (F), in the runoff over three successive rainfall events at 24 h (RS1), 48 h
(RS2) and 360 h (RS3) after application to grassland. (std dev error bars).
100 mL during RS1 to 1.0 × 106 MPN per 100 mL during RS3, there
were no significant differences within treatments. There was no evidence of interaction between treatment and event for TC, so it is impossible make inference about the factors separately. There was no
change from RS1 to RS2, but there was a decrease from RS2 to RS3
(p b 0.0001) from a median of 7.6 × 101 MPN per 100 mL during RS1
to 5.4 × 101 MPN per 100 mL during RS3. Overall losses from DCS
(3.1 × 102 MPN) were greatest and significantly greater than LS,
ADIRE and the control. ADUK losses (1.7 × 102 MPN) were not statistically different from DCS, but were significantly greater than the control
(p = 0.009). The highest median count of TC and FC measured in LS
biosolids-amended plots was 5.6 × 105 and 1.5 × 101 MPN per
100 mL, respectively. The highest median loss of TC for DCS-amended
plots was 1.5 × 105 MPN per 100 mL.
3.4. Soil test P, Mehlich-3 P, K, LR, pH and metal
Morgan's P, Mehlich-3 P, WEP, Mg, K, pH, LR and metals results from
analysis of plots before (t0) and at the end of the experiment (t360) are
presented in Tables 2 and 3. Average Pm (3.6 to 4.8 mg L−1), Mehlich3 P (38.0 to 47.4 mg L−1), K (58.2 to 94.94 mg L− 1), LR (2.3 to
2.6 t ha−1) and pH (5.90 to 5.99) across all plots before application of
treatments were similar. At the end of the experiment, Pm increased
across all treatments (p b 0.0001), with no significant differences
between treatments. The Pm of the control plots also increased
by 18%. Mehlich-3 P decreased across all treatments (p = 0.0001),
with no significant differences between treatments. Potassium concentrations showed no significant decrease for LS and TD treatments, while
the greatest reduction was in the ADUK plots (35%) and the lowest in
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D.P. Peyton et al. / Science of the Total Environment 541 (2016) 218–229
Fig. 4. Total coliforms (top) and faecal coliforms (bottom) in the runoff per 100 mL over three successive rainfall events at 24 h (RS1), 48 h (RS2) and 360 h (RS3) after application to
grassland (std dev error bars).
the lime-amended plots (10%). Magnesium showed no significant
changes over the duration of the experiment. Lime requirement
increased in the ADUK, TD, control plots and ADIRE by 11%, 10% 8%
and 3.8%, respectively, but reduced by 56% in the lime-amended plots.
Average metal results across all treatments before the start of the
experiment were similar (Table 3). At the end of the experiment, Cd
and Cr (p b 0.0001) increased across all treatments, while Cu showed
a significant decrease only for TD. Lead (p = b 0.0001) and Ni (p b
0.0001) increased across all treatments, but there were no significant
differences between treatments. The average increase for Pb was
50.8% and was 27.6% for Ni. Zinc decreased (p b 0.0001) across all
treatments, but there was no difference between treatments.
4. Discussion
4.1. Incidental nutrient losses for all rainfall events
With the exception of LS biosolids, FWMCs of TP and DRP across all
treatments were significantly higher than the study control and, in
some cases, were in breach of maximum admissible concentrations
(MACs) for surface water. The volumetric water content of all study
micro-plots was approximately 40% and the runoff ratio (the volume
of runoff as a percentage of the volume of water applied to each
micro-plot) was broadly similar across treatments (data not shown).
Therefore, the nutrient load from each micro-plot was proportional to
the FWMCs.
The FWMCs of TP and TN generally decreased across successive
rainfall events. This trend is similar to several studies that have examined runoff of nutrients resulting from the land application of different
types of biosolids and DCS (Rostagno and Sosebee, 2001; Penn and
Sims, 2002; Ojeda et al., 2006; Eldridge et al., 2009; Lucid et al., 2014).
The DRP losses measured in the current study were proportional to
the WEP of the biosolids. Several studies have shown that WEP is an
effective quantitative indicator of dissolved P losses from surface applied biosolids (Kleinman et al., 2002; Elliott et al., 2005; Kleinman
et al., 2007). Thermally dried and ADIRE biosolids, which also had high
WEPs (Table 5), had the highest losses of dissolved P from their respective plots.
All biosolids treatments had elevated FWMCs of NH4–N in runoff
compared to the study control across all rainfall simulations, whereas
the study control and biosolids-amended plots had the same NO3–N
concentrations. Ammonium can be volatilised (or rapidly mobilised by
runoff and leaching) after organic matter spreading (Quilbe et al.,
2005). ADUK biosolids, which had the highest initial NH4–N concentration in the biosolids at the time of application (3846 mg kg− 1 DM;
Table 5), also had the highest FWMC of NH4–N in runoff compared to
biosolids treatments during RS1. Similar trends were noted for the
ADIRE and LS biosolids. However, the initial concentration of NH4–N
in TD biosolids before application (573 mg kg−1; Table 5) was lower
D.P. Peyton et al. / Science of the Total Environment 541 (2016) 218–229
than the ADIRE biosolids (3428 mg kg− 1; Table 5), but had similar
losses of NH4–N in surface runoff during RS1. These types of anomalies
may be due to the consistency of the biosolids, which means that different types of biosolids will have varying surface area exposure to rainfall.
Therefore, TD biosolids could possibly be easier diluted and transported
in the runoff compared to the ADIRE, ADUK and LS biosolids, due to their
finer particle granulated consistency. This is also the reason for the high
proportion of runoff measured for the DCS. Dairy cattle slurry had the
highest FWMC of NH4–N and DRP. A possible reason for this is that
DCS had a DM of 8%, and was highly mobile following an episodic
rainfall event. This study shows that biosolids, although having a higher
DM than DCS, are not as easily mobilised.
227
The concentrations of metals in runoff were below drinking water
standards intended for human consumption (Statutory instrument,
2014). Similar results have been reported for several runoff studies
using different types of biosolids at higher application rates than the
current study (Joshua et al., 1998; Dowdy et al., 1991; Eldridge et al.,
2009; Lucid et al., 2013). This shows that the codes of good practice
for the use of biosolids in agriculture (Fehily Timoney and Company,
1999) are appropriate in limiting metal application and, therefore,
losses to waterbodies. The metal content in the biosolids was not
the limiting factor in terms of runoff for the spreading rate, and the
soil metal content was also below maximum permissible guidelines
(Fehily Timoney and Company, 1999). The soil pH and clay content
were within the recommended guidelines set out in code of good practices (Fehily Timoney and Company, 1999).
While there was generally low FWMC of metals over all rainfall
simulations, the LS biosolids-amended plots released the highest quantity of Cu, Ni and Zn compared to other plots. One possible explanation
for this is that Cu, Ni and Zn are more soluble metals (Joshua et al.,
1998), and as LS biosolids consists of larger sized particles of a more
compact consistency, time to runoff increased (results not shown),
giving these metals more contact time to dissolve and subsequently
be released compared to the other biosolids treatments. The pH adjustment and temperature increase, resulting from the LS treatment,
reduced the biological activity within the biosolid material, affecting
both N mineralisation and nitrification. This reduced the NO3–N concentration initially after application (RS1 and RS2). Copper is more likely to
be complexed as pH increases; however, under these circumstances Cu
is likely to be complexed with soluble organic matter. Following rainfall
and transport of dissolved organic matter-Cu complexes, high concentrations of Cu were transported in surface runoff from the LS treatment
compared to others.
Metal concentration was low in DCS in comparison to the biosolids
(Table 5) before application. However, the FWMC of Cd and Cr in DCSamended plots were higher than any of the biosolids plots, with peak
concentrations of 1.68 μg L −1 during RS3 for Cd and 3.89 μg L −1 during
RS1 for Cr, respectively. However, even at these concentrations, they
were still well below drinking water standards.
have been regrowth of the FC in the ADIRE and LS biosolids between
RS1 and RS2. Similar FC regrowth in AD biosolids was also reported by
Zaleski et al. (2005). All TC and FC in biosolids decayed by RS3, which
was most likely due to desiccation of pathogens rather than the
influence of UV, as all plots were covered by the rainout shelter, which
prevented natural rainfall between RS2 and RS3.
ADUK biosolids had significantly higher concentrations of FC in
runoff during RS1 and RS2 compared to other treatments. At the
start of the experiment, the ADUK biosolids were above the recommended standards of N 1 × 103 MPN g− 1 (Fehily Timoney and
Company, 1999), and, as a result, were equivalent to Class B microbial
matter under the US EPA Part 503 regulations (USEPA., US Environmental Protection Agency, 1993), which allows detectible levels of FC up to
2 × 106 MPN g−1 DS. All the Irish biosolids were some 10-fold below the
Class A Irish standard (Table 4). Dairy cattle slurry had high FC losses
compared to the Irish biosolids, suggesting that pathogen losses to
surface water bodies following land application of untreated organic
fertiliser may be a concern in Ireland. This may be particularly important, given that incidence of shiga-toxigenic E. coli infection (STEC) in
Ireland is amongst the highest in Europe and that waterborne transmission from cattle (zoonotic source) to humans is considered to play an
important role in human infection in rural areas.
It is important to evaluate the risks arising from the application of
biosolids to land relative to other common agricultural practices such
as the land application of animal waste (Vinten et al., 2004), which is
commonly spread as an organic fertiliser. Hubbs (2002) reported that
land application of DCS as a fertiliser had FC concentrations in surface
runoff of up to 1.2 × 105 CFU per 100 mL, 2 days after application, and
after five rainfall events over 30 days, the mean FC concentrations in
runoff, although decreasing, remained at high levels compared to the
biosolids in the same study (4.0 × 103 CFU per 100 mL). This was
also observed in the current study, as the DCS had the second highest
FC during RS1 and RS2, but was the highest by RS3, showing that FC
survive for a longer period in DCS compared to biosolids, and
may result in losses of pathogen to waterbodies for a longer period following application. Moreover, Payment et al. (2001) found that the
pathogen concentration was lower in untreated sludge (3 × 102 to
6 × 102 cfu g− 1) compared to fresh and stored cattle slurries
(2.6 × 108 to 7.5 × 104 cfu g−1) (Hutchison et al., 2004). When considered within this context, the risk of infectious diseases arising from the
land application of biosolids appears to be low in magnitude. This study
also provided no buffering capacity to the runoff samples, and overland
flow was not sampled at delivery end of the transfer continuum, so the
bacterial results represent a worst case scenario.
While this study and many others focus on the TC group as an
indicator of the presence of pathogens, the drawback of relying on
them is that it they are a poor indicator for the presence of viruses and
parasitic protozoa, which may survive for much longer periods
(NHMRC, 2003). However, due to the lack of well-developed methods
for the detection and enumeration of these pathogens (Sidhu and
Toze, 2009), the use of indicator organisms allows for the limitation of
potential contaminating effects.
4.3. Incidental pathogen losses for all rainfall events
4.4. Soil characteristics before and after experiment
When biosolids and DCS are incorporated into the soil, pathogen
survival is affected by factors such as pH, OM, soil texture, temperature,
moisture content, and competition with other microorganisms (Lang
et al., 2007). These factors have been reviewed by Erickson et al.
(2014). However, when biosolids and DCS are surface applied, as in
the current study, desiccation and ultraviolet light are the key factors
in the decay of pathogens (Lu et al., 2012). Desiccation of pathogens is
influenced by the soil, biosolids and DCS moisture content. In the
current study, soil moisture remained consent at approximately 40%,
which was unlikely to affect pathogen survival or regrowth. However,
as the rainfall simulator provided moisture to the biosolids, there may
In the current study, differences in soil nutrient concentration
following amendments were observed. The application of all biosolids
increased the Pm in all amended plots from an Index 2 soil to an Index
3. Whilst the Pm of the control plots also increased from an Index 2
soil to an Index 3 soil, the increase was less than half the increase of
the nearest biosolids amendment (ADIRE). Lime stabilised biosolids
had the greatest increase in Pm, and this may have been a result of the
evaluated pH in the soil, as liming improves the availability of soil P.
This result also shows that although LS biosolids are low in nutrient content, they can be applied for their pH adjusting characteristics and, as a
result, may enhance nutrient availability to soil and plants.
4.2. Incidental metal losses for all rainfall events
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This study also investigated the accumulation of metals before and
after the experiment. Results showed that while there was an increase
for some metals, none exceed the recommended guideline limits for
soil set out in code of good practices (Fehily Timoney and Company,
1999). It should be noted, however, that the current study encompassed
a single application of biosolids, and that concerns have been raised
about the accumulation of metals in both soil and crops after repeated
applications of biosolids (McBride, 2003; Bai et al., 2010). However, in
Ireland, the application rate of biosolids to land is governed by legislation and whilst best practice is followed, problems in terms of metal
or nutrient build-up will be avoided.
5. Conclusions
The results of this plot-scale study showed that there were elevated
losses of nutrients (nitrogen and phosphorus), faecal coliforms and
some metals (Cu, Ni, Pb, Zn) from biosolids-amended plots compared
to unamended plots. However, surface runoff concentrations of nutrients, metals (with the exception of Cu and Ni), total coliforms (from
both types of anaerobically digested biosolids used in this study) and
faecal coliforms (from thermally dried, lime stabilised and biosolids
originating from a WWTP in Ireland) were lower than the concentrations in surface runoff from plots treated with dairy cattle slurry. This
means that in these respects, biosolids do not pose a greater risk than
dairy cattle slurry in terms of surface runoff losses following land
application. This study did not examine the surface runoff for the presence of emerging contaminants, such as pharmaceuticals, personal care
products, micro-plastics, or nanomaterials. While the findings of this
study suggest that surface runoff of nutrients, metals and microbial
matter for biosolids and dairy cattle slurry are comparable, the surface
runoff water from the biosolids-amended micro-plots of the current
study must be tested for these, and other, emerging contaminants.
Acknowledgments
The authors acknowledge funding from the EPA (Project reference
number 2012-EH-MS-13). They are also grateful to the End-o-sludge
project (http://www.end-o-sludg.eu/) for providing samples.
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