STOTEN-20349; No of Pages 11
Science of the Total Environment xxx (2016) xxx–xxx
Contents lists available at ScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Evaluation of spot and passive sampling for monitoring, flux estimation
and risk assessment of pesticides within the constraints of a typical
regulatory monitoring scheme
Zulin Zhang a,⁎, Mads Troldborg a, Kyari Yates c, Mark Osprey a, Christine Kerr a, Paul D. Hallett b,
Nikki Baggaley a, Stewart M. Rhind a,1, Julian J.C. Dawson a,1, Rupert L. Hough a
a
The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH, UK
University of Aberdeen, St Machar Drive, Aberdeen AB24 3UU, UK
c
The Robert Gordon University, Riverside East, Garthdee, Aberdeen AB10 7JG, UK
b
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
• Passive and spot sampling was evaluated for pesticide regulatory monitoring.
• Pesticide fluxes were estimated by
using different sampling strategies.
• Potential exposure risk of aquatic organism to pesticides was assessed.
• Temporal and spatial changes of pesticides in the river water were assessed.
• Contamination levels of pesticides in a
Scottish priority catchment were
assessed.
Figure of passive and spot sampling for pesticide monitoring.
a r t i c l e
i n f o
Article history:
Received 5 May 2016
Received in revised form 21 June 2016
Accepted 27 June 2016
Available online xxxx
Editor: Simon Pollard
Keywords:
Pesticides
Passive sampling
Monitoring
Fluxes
a b s t r a c t
In many agricultural catchments of Europe and North America, pesticides occur at generally low concentrations
with significant temporal variation. This poses several challenges for both monitoring and understanding ecological risks/impacts of these chemicals. This study aimed to compare the performance of passive and spot sampling
strategies given the constraints of typical regulatory monitoring. Nine pesticides were investigated in a river currently undergoing regulatory monitoring (River Ugie, Scotland). Within this regulatory framework, spot and passive sampling were undertaken to understand spatiotemporal occurrence, mass loads and ecological risks. All the
target pesticides were detected in water by both sampling strategies. Chlorotoluron was observed to be the
dominant pesticide by both spot (maximum: 111.8 ng/l, mean: 9.35 ng/l) and passive sampling (maximum:
39.24 ng/l, mean: 4.76 ng/l). The annual pesticide loads were estimated to be 2735 g and 1837 g based on the
spot and passive sampling data, respectively. The spatiotemporal trend suggested that agricultural activities
were the primary source of the compounds with variability in loads explained in large by timing of pesticide applications and rainfall. The risk assessment showed chlorotoluron and chlorpyrifos posed the highest ecological
⁎ Corresponding author.
E-mail address: zulin.zhang@hutton.ac.uk (Z.L. Zhang).
1
Deceased.
http://dx.doi.org/10.1016/j.scitotenv.2016.06.219
0048-9697/© 2016 Elsevier B.V. All rights reserved.
Please cite this article as: Zhang, Z., et al., Evaluation of spot and passive sampling for monitoring, flux estimation and risk assessment of pesticides
within the constraints o..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.06.219
2
Z. Zhang et al. / Science of the Total Environment xxx (2016) xxx–xxx
Risk assessment
Water
risks with 23% of the chlorotoluron spot samples and 36% of the chlorpyrifos passive samples resulting in a Risk Quotient greater than 0.1. This suggests that mitigation measures might need to be taken to reduce the input of pesticides into the river. The overall comparison of the two sampling strategies supported the hypothesis that passive
sampling tends to integrate the contaminants over a period of exposure and allows quantification of contamination
at low concentration. The results suggested that within a regulatory monitoring context passive sampling was more
suitable for flux estimation and risk assessment of trace contaminants which cannot be diagnosed by spot sampling
and for determining if long-term average concentrations comply with specified standards.
© 2016 Elsevier B.V. All rights reserved.
1. Introduction
The Water Framework Directive (WFD) 2000/60/EC implemented in
2000 by the European Union (EU) established a common framework for
monitoring water quality and obliged member states to reach good ecological and chemical status for all water bodies by 2015 (EU, 2000;
Poulier et al., 2014), with extended deadlines set for 2021 and 2027.
In this context, 45 molecules including 20 pesticides were listed as priority compounds and Environmental Quality Standards (EQS) were determined (EU, 2013; Poulier et al., 2014).
Pesticide pollution is a major concern worldwide (Stehle and Schulz,
2015; McKnight et al., 2015; Sabatier et al., 2014). Agricultural pesticides are a significant contributor of diffuse pollution to surface waters
through run-off and leaching from agricultural fields as well as dry deposition and spray drift (Dalton et al., 2014). Based on monitoring
data of 223 organic pollutants measured at 4000 different monitoring
sites across Europe, a recent study suggested that the health of almost
half of all European freshwaters is at risk from organic chemical pollution and pesticides were one of the major ecological risk factors
(Malaj et al., 2014). Assessment of the potential risks of pesticides to
non-target organisms (i.e. non-pest species including many aquatic organisms) and human health requires improved understanding of their
occurrence, transport and fate in the environment (Fernandez et al.,
2014; Stehle and Schulz, 2015).
In order to assess the ecological and chemical status of a water body,
the WFD requires that water quality is monitored at least once a month
(12 sampling a year) and compared with the EQS. The EU Directive on
priority substances (EU, 2013) specifies both maximum allowable and
annual average concentrations for surface waters, which must be met
in order to meet good ecological status. Currently, the only authorized
sampling strategy is spot (grab) sampling, which has several limitations
including inadequate sampling frequency and its high limit of quantification (Poulier et al., 2014). Passive samplers (including integrative or
kinetic and equilibrium samplers) are relatively new emerging tools
for sampling micro-pollutants in the aquatic environment, which
could potentially overcome some of the problems related to grab sampling. Since the appearance of the first passive sampler for surface waters, these tools have quickly become widespread for micro-pollutant
monitoring and water quality assessment (Sodergren, 1987; Morin et
al., 2012). Different samplers have been developed for the monitoring
of a large range of molecules. Among them, the Polar Organic Chemical
Integrative Sampler (POCIS) is now commonly used to monitor microorganic pollutants in water all over the world. Its ability to capture a
range of compounds gives this tool a wide scope (Lissalde et al., 2014;
Poulier et al., 2014). Passive sampling theory and modelling are well described elsewhere (Huckins et al., 1993; Vrana et al., 2005;
Stuer-Lauridsen, 2005; Alvarez et al., 2004; Poulier et al., 2015). Briefly,
the strategy consists of an integrating device composed of a receiving
phase (liquid or solid) exposed in the water body for a defined period
(several weeks to months) that continuously accumulates contaminants. After retrieval and analysis of the receiving phase, a TimeWeighted Average Concentration (TWAC) can be calculated based on
the period of exposure and sampler characteristics. Passive sampling offers significant benefits compared to traditional spot (or grab) sampling.
First, the in-situ accumulation of contaminants in the device allows
quantification at lower limits of detection without an additional sample
pre-concentration step (Lissalde et al., 2011; Poulier et al., 2015). Secondly, and as a consequence of sampling duration, potential contaminant peaks occurring after flood events are integrated (Allan et al.,
2007), and the representativeness of the data collected is enhanced, especially for the assessment of contaminant fluxes at trace concentration
levels and/or (sub-) chronic exposures. Several studies have compared
spot and passive sampling for monitoring concentrations of pesticides
and other organic contaminants (Terzopoulou and Voutsa, 2016;
Lissalde et al., 2014; Morin et al., 2012). However, to our knowledge,
there is only very limited information available on using passive sampling strategy for estimation of pesticide fluxes. Poulier et al. (2015)
compare concentrations and estimated fluxes for a range of pesticides
based on bi-weekly monitoring using both POCIS and spot sampling in
a French catchment. However, they only present and compare the results of the calculated fluxes over a two month period (May–June
2012), and provide only paired estimates of fluxes (POCIS and spot sampling) for one pesticide (acetochlor) for two pairs of these data points.
Also, the study of Poulier et al. (2015) was not designed around an
existing regulatory monitoring scheme. If passive samplers are to be
adopted in a regulatory context it is therefore imperative to demonstrate their added value in comparison to spot sampling within the operational constraints that regulators face.
The River Ugie catchment (with a catchment area of 335 km2) in the
North East of Scotland is utilised by Scottish Water, a public body responsible for providing water and sewerage services across Scotland,
as a drinking water source for the town of Peterhead and its surrounding area, supplying a population of approximately 40,000 people. On occasions, concentrations of pesticides in the river have exceeded drinking
water standards (0.1 μg/l for individual pesticide and 0.5 μg/l for the
total pesticides) as set by the EU Drinking Water Directive (EU, 1998).
Due to the failure of meeting “good ecological status” in terms of
water quality standards set by the WFD, the River Ugie has been selected by the Scottish Environment Protection Agency (SEPA) as one of 14
priority catchments in Scotland for restoration and protection (SEPA,
2010; Bloodworth et al., 2014).
The main objective of this study was to investigate if passive sampling, compared to spot sampling, can be used to improve the monitoring of pesticides given the operational constraints of regulatory
monitoring. To achieve this objective, this study used both spot and passive sampling to monitor contamination levels and assess spatiotemporal fluctuations of different types of pesticides (including insecticides,
herbicides, fungicides and molluscicides, Table 1) with a wide range of
physico-chemical properties (e.g. log Kow of 0.12–6.1, Table 2) in the
Ugie catchment (NE Scotland), and to estimate pesticide fluxes and
the associated risks to the aquatic environment.
2. Materials and methods
2.1. Reagents
All the solvents used including methanol, ethyl acetate, acetone, dichloromethane, iso-hexane, and diethyl ether, purchased from
Rathburn (Walkerburn, Scotland), were of HPLC grade. All pesticides
(metaldehyde, isoproturon, simazine, chlorotoluron, atrazine,
Please cite this article as: Zhang, Z., et al., Evaluation of spot and passive sampling for monitoring, flux estimation and risk assessment of pesticides
within the constraints o..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.06.219
Z. Zhang et al. / Science of the Total Environment xxx (2016) xxx–xxx
3
Table 1
Range, mean and median concentrations and detection frequencies (D.F) of pesticides in water (ng/l) by spot sampling from the River Ugie of Scotland (July 2013–July 2014).
Compound
LOD
Pesticide type
Water (ng/l)
ng/l
Metaldehyde
Simazine
Atrazine
Isoproturon
Chlorotoluron
Chlorpyrifos
Epoxiconazole
Permethrin
Cypermethrin
2.01
0.02
0.03
0.01
0.01
0.02
0.14
0.06
1.35
Molluscicide
Herbicide
Herbicide
Herbicide
Herbicide
Insecticide
Fungicide
Insecticide
Insecticide
epoxiconazole, chlorpyrifos, cypermethrin and permethrin) and internal standards (dimethylphenol, atrazine-d5 and triphenyl phosphate)
were purchased from Sigma-Aldrich (Dorset, UK). Separate stock solutions of individual compounds were made up at 500–1000 mg/l by dissolving an appropriate amount of each substance in ethyl acetate, which
were diluted further for use. All standard solutions were stored at 4 °C
prior to use. The Solid Phase Extraction (SPE) cartridges used in this
study were Strata-X (polymer-based sorbent, 200 mg/6 ml;
Phenomenex, Cheshire, UK). All glassware used throughout the experiments was machine washed and then baked at 450 °C for 12 h to eliminate the presence of any organic contamination. The ultrapure water
used during the experiment was supplied by a Maxima Unit from USF
Elga, UK.
2.2. Sample collection
Monthly water samples were collected at 10 different sites (in triplicates at sites 1, 8 and 10) from the River Ugie (Fig. 1) from July 2013
to July 2014. Sodium azide (0.01 M, Sigma-Aldrich, Dorset, UK) was
added to each sample as a general biocide to eliminate bacteria and prevent sample degradation during storage and processing. The samples
were stored in a refrigerator below 4 °C until filtration. Along with the
water samples, a series of measurements were taken for the water quality including pH, conductivity, dissolved oxygen, temperature and
water depth. The samples (500 ml) were filtered under vacuum
through pre-ashed glass-fibre filters (GF/F, 0.7 μm, Whatman, Camlab,
Cambridge, UK). The filtrates were spiked with 20 ng of internal standards and ready for SPE extraction.
Passive samplers (POCIS) were deployed at 3 of the 10 water sampling sites (site 1 — South Ugie, site 8 — North Ugie, site 10 — Inverugie:
mouth of the Ugie). At each of these sites, three POCIS samplers were
contained in a cage at a water depth of 20–50 cm by attaching to a scaffold pole. The passive samplers were deployed on a monthly (4 weeks)
basis at these three sites from July 2013 to July 2014, accordingly.
Range
Mean
Median
D.F.
b2.01–26.35
b0.02–3.20
b0.03–5.78
b0.01–11.25
b0.01–111.8
b0.02–14.24
b0.14–13.26
b0.06–0.39
b1.35–7.85
3.76
0.69
1.40
0.95
9.35
0.70
0.84
0.01
0.12
4.10
0.71
1.48
0.47
3.79
2.06
0.93
0.35
2.02
65%
83%
92%
99%
98%
25%
65%
2.3%
3.8%
2.3. Sample extraction and instrumental analysis
For the water samples, the target compounds were extracted following a previously established method using the SPE cartridge (Strata-X)
(Zhang et al., 2014). Briefly, all the cartridges were first conditioned
with 10 ml of dichloromethane, followed by 10 ml of methanol and ultrapure water (2 × 5 ml) passing through the cartridges at a rate of 1–
2 ml/min. Then, water samples were extracted at a flow rate of 5–
10 ml/min. After the extraction, the cartridges were dried under vacuum for 30 min, with the analytes eluted into 20 ml vials from the sorbents with 12 ml of ethyl acetate: dichloromethane:acetone
(45:10:45) followed by additional elution with 6 ml of dichloromethane
at a flow rate of 1 ml/min.
The POCIS device was similar to the one described in our previous
work except for the amount of sorbent used (Zhang et al., 2008). Briefly,
POCIS fitted with polyethersulfone (PES, 0.1 μm pore size; Pall Life Sciences, Portsmouth, UK) membrane and Oasis HLB sorbent (0.2 g, Waters, Herts, UK) as the receiving phase was deployed at each site. After
4 weeks of exposure, the POCIS was removed and dismantled. The sorbent in POCIS was spiked with 20 ng of internal standards and extracted
with 10 ml of ethyl acetate three times. The recoveries of the 9 target
pesticides for POCIS ranged from 76% (isoproturon) to 116% (simazine)
with RSD of 6–16%.
All the extracts (including SPE and POCIS) were reduced to near dryness under a gentle flow of nitrogen at less than 30 °C. The sample was
transferred to 0.1 ml ethyl acetate and analysed by GC–MS (Zhang et al.,
2014).
An Agilent 5975C MSD (mass spectrometer detector) linked to
7890A GC with an autosampler (7683B) was used for pesticide analysis
with selected ion mode (SIM) (Zhang et al., 2014). The capillary column
was ZB-SemiVolatiles (30 m × 0.25 mm i.d. × 0.25 μm film thickness,
Phenomenex, Macclesfield, UK). The operating temperature for pesticides was programmed from 40 °C (1 min) to 110 °C at 10 °C/min,
then ramped to 200 °C at 20 °C/min, 200 °C to 310 °C at 5 °C/min and
held for 12 min. Helium was used as carrier gas at a constant flow of
Table 2
Range, mean and detection frequencies (D.F.) of pesticides accumulated in POCIS (ng/g-POCIS) deployed at three selected sites (sites 1, 8 and 10) in the River Ugie, Scotland (July 2013–July
2014).
Compound
Metaldehyde
Isoproturon
Simazine
Chlorotoluron
Atrazine
Epoxiconazole
Chlorpyrifos
Cypermethrin
Permethrin
LogKow
0.12
1.59
2.3
2.5
2.7
3.3
4.7
5.3
6.1
LOD
Site 1
ng/g
Range
Mean
D.F.
Site 8
Range
Mean
D.F.
Site 10
Range
Mean
D.F.
2.28
0.02
0.05
0.02
0.07
0.32
0.05
1.53
0.14
18.18–329.7
1.15–49.89
b0.05–20.67
21.22–662.7
0.97–15.79
3.51–138.2
b0.05–19.61
b1.53–27.7
b0.14–0.62
67.24
7.83
8.03
253.9
6.53
28.77
4.07
2.26
0.05
100
100
83
100
100
100
75
8
8
11.32–302.4
2.42–230.3
b0.05–27.79
17.61–106.9
2.35–74.07
4.03–81.19
b0.05–49.97
b1.53–24.92
b0.14–2.94
68.57
12.08
11.30
58.06
26.70
14.33
14.08
2.08
0.50
100
100
92
100
100
100
83
8
42
18.74–222.3
8.74–343.7
b0.05–46.04
31.99–1703
10.89–77.57
5.22–68.10
b0.05–21.87
b1.53–33.21
b0.14–2.64
96.37
58.13
15.24
307.3
32.61
21.95
9.66
2.77
0.47
100
100
92
100
100
100
83
25
8
Please cite this article as: Zhang, Z., et al., Evaluation of spot and passive sampling for monitoring, flux estimation and risk assessment of pesticides
within the constraints o..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.06.219
4
Z. Zhang et al. / Science of the Total Environment xxx (2016) xxx–xxx
Fig. 1. Map of sampling locations in the River Ugie, Scotland.
1 ml/min. Samples were injected in splitless mode. The injector and
mass spectrometer (ion source) were held at 250 °C and 200 °C, respectively. The electron impact energy was set at 70 eV for the mass spectrometer. Before sample analysis, relevant standards were analyzed to
check instrumental performance, peak height and resolution. With
each set of samples to be analysed, reference standard mixtures, quality
control samples, and procedural blanks were run in sequence to check
for contamination, instrumental performance, peak identification and
quantification. Compounds were identified mainly by selected ion and
by their retention times. The results for water samples were reported
on the water sample volume basis, while it is on sampler dry weight
basis for the passive samplers.
2.4. Calculation of the Time-Weighted Average Concentration (TWAC)
When the receiving phase of a passive sampler is acting as an infinite
sink, and assuming the accumulation of analytes is linear with time, the
TWA concentrations CTWA (ng/l) of the contaminants can be estimated
as follows (Zhang et al., 2008; Alvarez et al., 2004; Huckins et al., 1993):
CTWA ¼ Ms :=ðRs tÞ
ð1Þ
where Ms is the mass (ng) of the analyte accumulated in the POCIS sorbent material, t is the exposure time (days) and Rs is the sampling rate
constant (l/day). Alvarez et al. (2004) used a POCIS device with the
same amount and type of HLB sorbent as in this study and found that
the uptake of pesticides remained linear for an exposure time of up to
56 days. We therefore assume that the linear accumulation will be
valid for this study, where an exposure time of 4 weeks was used for
the POCIS devices. In this study we furthermore use published sampling
rate constants Rs from previous studies (Poulier et al., 2015; Alvarez et
al., 2007; Ahrens et al., 2015, Table 3). It should be noted that sampling
rate constants will depend on the environmental conditions, particularly temperature, water flow rate and biofouling (Poulier et al., 2015;
Harman et al., 2008; Li et al., 2010; Togola and Budzinski, 2007;
Mazzella et al., 2008; Alvarez et al., 2004) and that the Rs values used
in our study therefore are associated with uncertainty. However, previous studies have shown that the variation in Rs constants with environmental conditions are generally less than twofold (Harman et al., 2012;
Morin et al., 2012), which is quite moderate and which we deem acceptable for our application of studying detection frequencies or occurrence.
2.5. Flux calculation
Fluxes were calculated in two different ways, depending on the sampling strategy used. For POCIS, the fluxes of pesticides for exposure period i (FPOCIS,i) (e.g. in g per exposure period) were calculated for each
analyte using Eq. (2) (Poulier et al., 2015):
FPOCIS;i ¼ CTWA;i Q m;i ti
ð2Þ
where CTWA,i (ng/l) is the TWAC measured with the POCIS for exposure
period i, Qm,i is the average water flow (e.g. l/day) during the same exposure period, and ti is the exposure time (days) (Table 4). Fluxes were
set to be null when the TWAC was below the limit of detection.
For comparison purposes, the fluxes of pesticides were also calculated based on the spot sample data using Eq. (3) (Richard, 1999; Poulier et
al., 2015; Liu et al., 2015):
Fw;i ¼ Cw;i Q m;i ti
ð3Þ
where Fw,i is the fluxes of analyte for the corresponding POCIS exposure
period (e.g. g per exposure period), and Cw,i (ng/l) is the concentration
measured by spot sampling taken during this exposure period. Values
below the LOD were again set to be null for the calculation of Fw.
The annual fluxes of pesticides were then calculated by summing the
fluxes for the successive exposure periods.
2.6. Ecological risk assessment
The potential ecological risks of the pesticides detected in the surface
water of River Ugie were assessed based on the risk quotient (RQ) approach following the Technical Guidance Document on Risk Assessment
from the European Commission (Van Leeuwen, 2003; Liu et al., 2015).
The RQ values of pesticides were calculated by dividing the measured
environmental concentration (MEC) by the predicted no-effect concentration (PNEC) for each chemical using Eq. (4):
RQ ¼ MEC=PNEC:
ð4Þ
Please cite this article as: Zhang, Z., et al., Evaluation of spot and passive sampling for monitoring, flux estimation and risk assessment of pesticides
within the constraints o..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.06.219
Z. Zhang et al. / Science of the Total Environment xxx (2016) xxx–xxx
5
Table 3
Comparison of target pesticides measured in water samples by spot sampling (Cw: ng/l; n = 12) and derived from POCIS samplers (CTWA: ng/l; n = 12) at three selected sites (sites 1, 8 and
10) from the River Ugie, Scotland.
Compound
Isoproturon
Simazine
Chlorotoluron
Atrazine
Epoxiconazole
Chlorpyrifos
Rs
Site 1
L/d
Cw (ng/l)
CTWA (ng/l)
Cw (ng/l)
Site 8
CTWA (ng/l)
Cw (ng/l)
CTWA (ng/l)
0.316
0.281
0.341
0.240
0.17
0.053
0.29 ± 0.14
0.94 ± 0.57
21.82 ± 30.81
0.81 ± 0.67
2.80 ± 3.81
1.20 ± 1.40
0.19 ± 0.33
0.22 ± 0.18
5.85 ± 5.69
0.21 ± 0.14
1.33 ± 1.83
0.60 ± 0.82
1.51 ± 2.07
1.17 ± 0.40
5.23 ± 6.00
1.83 ± 0.71
0.71 ± 0.36
0.95 ± 0.64
1.00 ± 1.71
0.32 ± 0.22
1.34 ± 0.69
0.87 ± 0.78
0.66 ± 0.99
2.09 ± 2.35
1.78 ± 2.23
0.87 ± 0.31
13.21 ± 15.53
1.59 ± 0.60
1.08 ± 0.57
0.50 ± 0.71
1.45 ± 2.32
0.43 ± 0.34
7.08 ± 10.66
1.07 ± 0.67
1.01 ± 0.77
1.43 ± 1.10
For the purpose of interpreting the risk calculations, the RQ values
were classified into the following four levels: minimal risk
(RQ b 0.01), low risk (0.01 ≤ RQ b 0.1), medium risk (0.1 ≤ RQ b 1)
and high risk (RQ ≥ 1) (Hernando et al., 2006; Liu et al., 2015). The
PNEC values of the detected pesticides were derived using an assessment factor approach based on ecotoxicity data from the PPDB database
(PPDB, Pesticide Properties Database, University of Hertfordshire,
2016). Briefly, the PNEC value was calculated by dividing the reported
EC50 or LC50 value by an assessment factor of 1000 when only short
term or acute toxicity data were available. When long term or chronic
toxicity data were available, the PNEC value was calculated from the
lowest no observed effect concentration (NOEC) divided by an appropriate assessment factor (Supplemental Table 1, Van Leeuwen, 2003).
For data acquisition, the PPDB database was used due to its comprehensive data on fish, algae and particularly invertebrate (main component
of the ecosystem) toxicity. Fortunately, all the aquatic toxicity data of
the target pesticides considered in this study were available in the
PPDB and the PNEC values were therefore derived from NOEC data
and used for the risk assessment (Table 5). Where possible, three trophic levels (algae, aquatic invertebrates and fish) were taken into consideration for the determination of the most sensitive species. For the
risk assessment of surface water, both the measured pesticide concentrations by spot and passive sampling were used as the MEC values.
3. Results and discussion
3.1. Spatiotemporal occurrence of pesticides based on spot sampling
All nine target pesticides were detected at least once in the monthly
water samples from the River Ugie (spot sampling from all the ten
sites). The concentration levels (range, mean, median and detection frequency) in waters for the detected pesticides are summarized in Table
1. Overall, the total concentration of the nine pesticides in water from
each sampling point ranged from 0.97 to 128.0 ng/l with an average of
17.81 ng/l. All of the pesticides, except for chlorpyrifos, permethrin
and cypermethrin, were frequently detected in the spot samples from
the ten sites (N 65% detection frequency) with mean concentrations
(i.e. average over the duration of the sampling campaign from July
2013 to July 2014) ranging from 0.69 ng/l (simazine) to 9.35 ng/l
(chlorotoluron). The herbicide chlorotoluron (CTU) exhibited a relatively high concentration (maximum concentration: 111.8 ng/l; mean:
9.35 ng/l) in comparison with other pesticides. This observation is consistent with the findings reported in the River Ythan catchment, Scotland (Emelogu et al., 2013) located just south of the River Ugie, and is
likely to reflect the increasingly common usage of chlorotoluron as a
soil-acting herbicide in lieu of isoproturon (maximum: 11.25 ng/l;
mean: 0.95 ng/l), which has been banned from use within the EU
since June 2009. Both the maximum and mean concentration of
isoproturon in this study was 10 times lower than those of
chlorotoluron, which supports this hypothesis. The molluscicide metaldehyde (MTD) was generally found in the second highest concentrations in the water samples (maximum: 26.35 ng/l; mean: 3.76 ng/l),
while the mean concentration of the remaining pesticides were all less
than 1.5 ng/l. The concentration of CTU and MTD contributed 73.6% to
Site 10
Rs Reference
Poulier et al. (2015)
Poulier et al. (2015)
Poulier et al. (2015)
Alvarez et al. (2007)
Ahrens et al. (2015)
Alvarez et al. (2014)
the total concentration of the nine target compounds. Therefore, when
analysing the temporal and spatial trends of pesticides in the catchment,
we have in the following mainly focussed on total pesticide, CTU and
MTD.
Fig. 2. shows the seasonal variation in the concentration of the target
pesticides at the ten monitoring sites together with the average monthly rainfall in Ugie. CTU, which had the highest observed concentrations,
was mainly found to be present from late autumn to early spring (Fig.
2b); a similar pattern was also observed for total pesticide concentrations (Fig. 2a). The temporal patterns in pesticide concentrations align
with the expected timing of pesticide application in the catchment.
For example, CTU is largely applied to cereal crops such as barley, a
crop grown widely in the catchment. Applications are made pre- and
post-emergence in the late autumn and early spring. High concentrations and loads can also be linked to catchment and climate conditions.
This might for example be the case for MTD (Fig. 2c), where peaks in
concentrations are observed in the autumn, which was a particularly
wet and mild period, conditions known to be linked with high slug populations (Choi et al., 2004; Bloodworth et al., 2014) and pollutant mobilization. The results (Fig. 2) also show that the majority of peaks in
pesticide concentration appear to coincide with recorded periods of
high rainfall (MetOffice, 2016). For example, there are two peaks in
the total pesticides and CTU concentrations in November 2013 and
March 2014, which are coinciding with the rainfall. Others have also reported that rainfall events following pesticide application are important
in determining pesticide transport and subsequent loss (e.g., Tediosi et
al., 2012; Bloodworth et al., 2014).
The spatial variability in the pesticide distribution can also largely reflect patterns of land use. The highest concentrations of pesticides (with
total concentration over 0.2 μg/l) were generally observed at sampling
sites 1, 2 and 3 (South Ugie) and site 9 (North Ugie), which are all located in the areas with the high proportion of arable land cover. Relatively
high concentrations are also observed at sampling site 10 (Inverugie),
which is located at the outlet of the catchment and hence can be considered to be an integrating discharge point for the entire catchment. The
lowest concentrations were generally found at sites 4 and 6, which are
located in the up-stream sections of the catchment with less degree of
arable land cover.
Amongst the pesticides measured in the rivers during this study,
some are banned or have severely restricted uses within Europe as set
down by various European Commission Regulations and Directives
(EU, 2009; EU, 2013; EU, 2015). Five of the studied pesticides, namely
isoproturon, chlorpyrifos, cypermethrin, simazine and atrazine, are included in the list of the 45 priority substances in the field of water policy
defined in Annex X of the Directive 2013/39/EU (EU, 2013). The sources
of their environmental occurrence in the catchment cannot be
established with certainty from this study, but their presence might reflect a legacy from previous legitimate agricultural use and/or more recent, post restriction, use. The fate of pesticides in the environment is
complex and cannot be exclusively predicted from expected sorption
behaviour based on e.g. log Kow values. Pesticides that are adequately
resistant to degradation and relatively soluble in water can easily be
transported to surface waters and detected months after application.
The relatively long persistence of some of these pesticides including
Please cite this article as: Zhang, Z., et al., Evaluation of spot and passive sampling for monitoring, flux estimation and risk assessment of pesticides
within the constraints o..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.06.219
0.40 ± 0.05
ND
ND
ND
ND
ND
ND
ND
ND
5.88 ± 1.97
ND
ND
6±2
12.59 ± 0.60
0.84 ± 0.27
8.54 ± 1.08
14.90 ± 3.15
5.52 ± 0.36
43.11 ± 26.45
26.06 ± 2.64
9.53 ± 2.73
4.28 ± 0.73
2.76 ± 0.59
5.78 ± 1.36
3.51 ± 0.40
137 ± 40
Passive
Spot
10.16 ± 0.55
2.95 ± 0.47
14.42 ± 0.76
22.29 ± 2.69
6.82 ± 0.63
14.02 ± 9.91
10.11 ± 0.80
7.17 ± 2.38
9.77 ± 1.05
5.88 ± 0.53
5.02 ± 0.63
3.11 ± 0.34
112 ± 21
0.00
2.81 ± 0.27
19.37 ± 1.83
14.85 ± 5.85
19.47 ± 7.92
28.14 ± 5.39
32.95 ± 1.66
20.42 ± 2.12
9.33 ± 0.37
13.67 ± 0.28
12.69 ± 0.67
8.08 ± 0.35
182 ± 27
08/2013
09/2013
10/2013
11/2013
12/2013
01/2014
02/2014
03/2014
04/2014
05/2014
06/2014
07/2014
Yearly
1.49
1.35
2.66
5.52
4.35
10.10
11.73
5.16
2.83
2.43
2.24
1.50
33.55 ± 3.17
4.77 ± 0.32
18.83 ± 1.28
38.20 ± 3.61
19.60 ± 4.54
29.34 ± 1.19
18.30 ± 3.43
10.09 ± 0.41
1.87 ± 0.18
2.63 ± 0.49
5.43 ± 0.58
2.37 ± 0.06
185 ± 19
34.17 ± 2.47
4.39 ± 0.19
16.01 ± 1.35
23.59 ± 2.77
6.80 ± 0.38
24.52 ± 14.05
10.18 ± 1.15
3.01 ± 0.83
2.62 ± 0.58
2.03 ± 0.39
4.01 ± 0.39
0.91 ± 0.29
132 ± 25
2.17 ± 0.49
ND
ND
22.98 ± 5.24
11.64 ± 0.28
23.43 ± 1.99
29.01 ± 3.11
14.07 ± 0.86
4.34 ± 0.70
4.65 ± 1.29
4.33 ± 0.71
2.50 ± 0.17
119 ± 15
2.33 ± 0.14
ND
5.87 ± 0.24
18.42 ± 2.83
3.60 ± 0.58
8.99 ± 7.00
10.18 ± 0.42
5.09 ± 1.91
1.93 ± 0.57
1.98 ± 0.07
0.92 ± 0.06
1.32 ± 0.25
61 ± 14
7.60 ± 0.66
8.57 ± 1.72
197.0 ± 39.47
794.2 ± 69.25
236.0 ± 43.95
309.9 ± 33.07
381.0 ± 6.16
83.06 ± 2.50
11.50 ± 0.51
27.45 ± 1.97
23.98 ± 0.56
40.47 ± 1.55
2121 ± 201
13.34 ± 0.82
4.07 ± 0.26
58.43 ± 24.87
561.6 ± 26.47
111.0 ± 11.59
277.2 ± 31.45
110.9 ± 25.66
33.99 ± 8.41
21.66 ± 3.55
9.49 ± 0.41
10.12 ± 0.93
2.97 ± 0.70
1215 ± 217
1.48 ± 0.20
0.82 ± 0.62
5.57 ± 4.22
24.36 ± 3.28
ND
ND
50.43 ± 5.74
20.92 ± 3.73
5.90 ± 0.59
ND
6.77 ± 2.10
5.64 ± 0.39
122 ± 21
Passive
Spot
Chlorpyrifos
Passive
Epoxiconazole
Spot
Passive
Spot
Atrazine
Passive
Spot
Chlorotoluron
Passive
Spot
Simazine
Passive
Spot
m3/s
Monthly Ave. flow Isoproturon
Table 4
Pesticide monthly/annual fluxes (g) calculated by spot and passive sampling in the River Ugie, Scotland.
12.97 ± 1.00
45 ± 5
86 ± 6
ND
17 ± 3
12 ± 1
13.61 ± 3.14
241 ± 47
117 ± 31
20.90 ± 4.58
895 ± 87
662 ± 42
35.92 ± 11.55
287 ± 57
170 ± 25
51.35 ± 36.51
391 ± 42
419 ± 207
4.15 ± 0.38
512 ± 20
172 ± 31
17.29 ± 2.10
149 ± 10
76 ± 18
7.86 ± 1.83
33 ± 2
48 ± 8
ND
54 ± 6
22 ± 2
12.85 ± 3.86
53 ± 5
39 ± 7
3.67 ± 0.63
59 ± 3
15 ± 3
181 ± 66
2735 ± 285 1837 ± 383
Z. Zhang et al. / Science of the Total Environment xxx (2016) xxx–xxx
∑6 pesticides
6
isoproturon, atrazine, simazine, chlorpyrifos and cypermethrin in soil
and water systems after the last application has been demonstrated
both under controlled conditions and in some field studies (Buhler et
al., 1993; Johnson et al., 1996; Kreuger, 1998; Muendo et al., 2012;
Emelogu et al., 2013). Nevertheless, the sources of their environmental
occurrence and potential ecological impact warrant further
investigation.
During the monitoring period (July 2013–July 2014), the results
showed (Table 1) that the concentration of chlorotoluron at sampling
site 1 on one occasion (111.8 ng/l measured in November 2013)
exceeded the EU permitted concentration value (PCV) of 0.1 μg/l
(100 ng/l). The other pesticides were all well below the PCV level at
all times. The sum of the nine pesticide concentrations was also less
than EU regulation of 0.5 μg/l (500 ng/l) for total pesticide concentration
at all times. However, Bloodworth et al. (2014) presented data from the
existing regulatory monitoring activities based entirely on spot sampling between June 2011 and June 2013, which indicated that at least
one pesticide was detected above the PCV limit at each of the ten monitoring locations. Also the concentrations levels in that study were on
some occasions much higher than those found in our study (e.g. the
maximum concentration of chlorotoluron was 1.31 μg/l, which is 10
times higher than in this study). Compared to Bloodworth et al.
(2014), the pesticide contamination of the River Ugie could seem to
be decreasing, both in terms of detection frequencies above PCV and
contamination levels, which could be the result of a number of management measures that have recently been implemented in the catchment
by the stakeholders (SW, 2013). However, the statistical power of the
existing regulatory monitoring design is limited and caution is needed
when making inference about long-term trends in pesticide
concentrations.
3.2. Comparison of the POCIS and spot sampling strategies for pesticide
monitoring
The amounts of the nine target pesticides accumulated by POCIS at
the three selected sites (sites 1, 8 and 10) between July 2013 and July
2014 are summarized in Table 2. All of the nine pesticides were detected
at least once in the monthly POCIS samples at all three sites. The detection frequencies of all the pesticides were higher for POCIS than for spot
sampling. Except for permethrin and cypermethrin, the detection frequency was over 75% for all pesticides with mean concentrations in
the POCIS (ng/g-POCIS) ranging from 0.05 ng/g for permethrin to
307.3 ng/g for chlorotoluron. As also observed for the spot sampling,
permethrin and cypermethrin had lower detection frequencies (8% 42%) than the remaining pesticides and were both also found to accumulate in smaller amounts in POCIS (Table 2). The lower accumulated
amounts might reflect a general lower usage of these two pesticides in
the catchment, but are also likely to be due to their relatively higher
log Kow values (5.3 and 6.1 for cypermethrin and permethrin, respectively; Table 2) suggesting that they will adsorb strongly to soil and sediments and are less likely to be found in soluble form in water samples.
However, it should also be noted that Ahrens et al. (2015) found that
POCIS (with Oasis HLB sorbent) is generally more suitable for compounds with log Kow values below 5.3.
In order to calculate the time-weighted average concentration
(TWAC) of the pesticides from the POCIS sampling, it is necessary to
know the sampling rate constants (Rs) of the pesticides. To our knowledge, published Rs values are only available for six of the nine target pesticides considered in this study (isoproturon, simazine, chlorotoluron,
atrazine, epoxiconazole and chlorpyrifos) (Alvarez et al., 2007; Alvarez
et al., 2014; Ahrens et al., 2015; Poulier et al., 2015). Consequently,
TWAC were only calculated for these six analytes using Eq. (1). As
shown in Table 3 and Fig. 3, the overall comparison of the measured
concentrations from the two sampling methods is within the same
magnitude for most of the analytes. The concentrations from spot sampling were in most cases found to be slightly higher than the TWAC
Please cite this article as: Zhang, Z., et al., Evaluation of spot and passive sampling for monitoring, flux estimation and risk assessment of pesticides
within the constraints o..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.06.219
Z. Zhang et al. / Science of the Total Environment xxx (2016) xxx–xxx
7
Table 5
Environmental risk of pesticides detected in the River Ugie as risk quotient (RQ = PEC/PNEC, PNEC = NOEC/AF) based on spot monitoring data.
Compound
Metaldehyde
Isoproturon
Simazine
Chlorotoluron
Atrazine
Epoxiconazole
Chlorpyrifos
Cypermethrin
Permethrin
NOEC μg/l
Critical conc.
Fish
Aquatic invertebrates
Algae
μg/l
37,500
1000
700
400
2000
10
0.14
0.03
0.12
90,000
120
2500
16,700
250
630
4.6
0.04
–
–
52
600
1
100
7.8
43
1300
0.9
37,500
52
600
1.0
100
7.8
0.14
0.03
0.12
calculated by passive sampling, which might be due to the dependence
of the Rs values on the environmental conditions under which they
were derived. The Rs values used in this study were estimated from
other laboratory calibration or field applications and these might be
higher than the actual Rs values in the Ugie catchment, in which case
the “true” TWAC in the catchment would be underestimated (Morin
et al., 2012). There are very few exceptions, where the concentrations
from spot sampling were much higher than the TWAC calculated by
passive sampling, e.g. the chlorotoluron concentration from November
2013 at site 1 was 111.8 ng/l compared to a CTWA of 9.85 ng/l. On the
other hand, some analytes were found to be below limit of detection
by spot sampling (e.g. atrazine at sites 8 and 10: b LOD in August
2013), while the contaminants were detected by passive sampling
(e.g. atrazine CTWA: 0.27 and 2.25 ng/l of sites 8 and 10, respectively).
These results illustrate the potential to miss spikes of contamination
with spot sampling due to the temporal variability in the pesticide concentrations in the river. Conversely, passive samplers such as POCIS tend
to smooth the contamination curve by integrating over the temporal
variability in the concentrations (Poulier et al., 2015). Particularly for
chlorpyrifos, most of the spot samples were below the limit of detection,
while they were captured by the POCIS (Fig. 3). This finding supports
the hypothesis that the in situ accumulation of contaminants in passive
sampling devices (such as POCIS) allows quantification of contaminants
at lower limits of detection (Lissalde et al., 2011; Poulier et al., 2015). It
also demonstrates that in a typical regulatory monitoring context, passive sampling is likely to provide better and more representative values
of longer term average concentration compared to spot sampling, especially for contaminants exhibiting large temporal variations, and suggests that passive samplers may be particularly useful for determining
if annual average concentrations as specified in the EQS under the
WFD are met.
3.3. Comparison of the POCIS and spot sampling strategies for pesticide flux
estimation
When a water body fails to achieve “good status”, remediation actions or mitigation measures may have to be implemented. The reduction of pesticide use is encouraged, and landscape design can be
modified to reduce transfer (e.g. grass buffer strips bordering streams
or hedgerow construction). The best way to evaluate the efficiency of
such mitigation strategies required by the WFD and/or to evaluate impacts on any downstream receptors is usually by monitoring and
assessing the pesticide fluxes or loads (Poulier et al., 2015). As apparent
from Eqs. (2) and (3), pesticide fluxes depend on both the concentration
and the water flow in the river. If water flows are high, pesticide fluxes
can still be high even at very low concentrations of pesticides, because
of dilution effects. If concentrations are below the limit of detection,
fluxes cannot be calculated despite potentially still being high. Flux estimates can benefit regulatory investigative and operational monitoring
and result in better understanding of the contamination of a watershed
and in the implementation of more reliable remediation strategies. Considering the advantages of POCIS (i.e. allowing the quantification of
AF
50
10
10
10
10
10
10
10
50
PENC
Minimal risk
Low risk
Medium risk
High risk
μg/l
≤0.01 (%)
0.01–0.1 (%)
0.1–1 (%)
≥1 (%)
750
5.2
60
0.1
10
0.78
0.014
0.003
0.0024
100
100
100
16.1
100
98.5
76.9
96.1
97.7
–
–
–
60.8
–
1.5
7.7
–
0.8
–
–
–
22.3
–
–
–
–
0.8
–
13.9
3.1
1.5
1.5
0.8
–
contamination at low concentration levels and the integration of contaminants peaks over longer periods of time), the monitoring of pesticide concentrations in water and the calculation of pesticide fluxes
could potentially benefit from using a passive sampling strategy, especially for pesticides present in trace concentration levels.
In this study, daily river flow data from the National River Flow Archive (NFRA) (http://nrfa.ceh.ac.uk/ accessed May 2016) were available
at sampling site 10 located at the mouth of the river, and site 10 was
therefore chosen for the calculation of pesticide fluxes from the catchment to the estuary and North Sea. Monthly fluxes of the six pesticides
for which concentrations from both POCIS and spot sampling were
available were calculated using Eqs. (2) or (3). The results are shown
in Table 4, which also summarises the annual individual and total
loads of the six pesticides. The total annual pesticide loads was estimated to 2735 g and 1837 g by spot and passive sampling, respectively. In
terms of the individual pesticides, the estimated annual pesticide
loads ranged from 6 g of chlorpyrifos to 2121 g of chlorotoluron for
spot sampling, while it ranged from 61 g (simazine) to 1215 g
(chlorotoluron) for the passive sampling. The existing regulatory monitoring scheme (Bloodworth et al., 2014) estimates pesticide fluxes
based on spot sampling only. They estimated the annual loads of
chlorotoluron to be 3007 g and 1196 g for the 2011–2012 and the
2012–2013 periods, respectively, and our estimate of 2121 g (based
on spot sampling) is therefore comparable to the existing regulatory activity. Also the sum of the annual fluxes of the six pesticides considered
in this study were within the range of the total annual fluxes of the six
pesticides reported by the existing regulatory monitoring scheme (i.e.
chlorotoluron, CMPP, metaldehyde, MCPA, metazachlor and 2.4-D)
who estimated a total annual fluxes for the Ugie Catchment of 4499 g
in 2011–2012 and 2617 g in 2012–2013, although the target chemicals
are not completely same. All previous (2011–2012, 2012–2013;
Bloodworth et al., 2014) and current monitoring efforts (2013–2014;
this work) showed that the annual fluxes of chlorotoluron were the
largest of all the target pesticides, which again indicates that
chlorotoluron is the dominant pesticide in this catchment. Poulier et
al. (2015) also presented pesticide flux estimates based on spot and passive sampling in the Auvezere River, France with a catchment area of
90 km2. They estimated the mass loads for DEA and Diuron by passive
sampling over a 9 month period to be 28 g and 96 g, respectively.
These values are comparable with the estimates in our study (61 g (simazine)–1215 g (chlorotoluron), Table 4), although it should be noted
that Poulier et al. (2015) considered a different catchment, period of
time and target pesticides.
Generally, the annual fluxes by POCIS were close to or slightly less
than the values estimated by spot sampling particularly for the pesticides isoproturon, simazine, chlorotoluron, atrazine and epoxiconazole.
The difference between passive and spot sampling was less than a factor
2 (Table 4). The exception is chlorpyrifos for which the annual fluxes
were estimated as 181 g by passive sampling and only 6 g for spot sampling. This difference is due to the measured concentrations of chlorpyrifos in many of the spot samples being below the detection limit for spot
sampling, while they were captured by POCIS. These characteristics of
Please cite this article as: Zhang, Z., et al., Evaluation of spot and passive sampling for monitoring, flux estimation and risk assessment of pesticides
within the constraints o..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.06.219
8
Z. Zhang et al. / Science of the Total Environment xxx (2016) xxx–xxx
Fig. 2. Temporal changes of total pesticides (a), CTU (b) and MTD (c) in the Ugie catchment, Scotland.
POCIS for integrating the contaminants over a period of time and
allowing quantification at low concentrations is what potentially make
passive samplers more suitable and reliable for flux estimation
(especially for contaminants at trace level concentrations) than those
calculated from grab samples taken once a month as is the case in the
majority of regulatory monitoring programmes. However, the
Please cite this article as: Zhang, Z., et al., Evaluation of spot and passive sampling for monitoring, flux estimation and risk assessment of pesticides
within the constraints o..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.06.219
9
Fig. 3. Passive and spot sampling for pesticides monitoring in the River Ugie, Scotland.
Z. Zhang et al. / Science of the Total Environment xxx (2016) xxx–xxx
Please cite this article as: Zhang, Z., et al., Evaluation of spot and passive sampling for monitoring, flux estimation and risk assessment of pesticides
within the constraints o..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.06.219
10
Z. Zhang et al. / Science of the Total Environment xxx (2016) xxx–xxx
Table 6
Comparison of spot and passive sampling for risk assessment of chlorotoluron and chlorpyrifos.
Pesticide
Chlorotoluron
Chlorpyrifos
Spot sampling (%)
POCIS (%)
Minimal risk
Low risk
Medium risk
High risk
Minimal risk
Low risk
Medium risk
High risk
16.1
76.9
60.8
7.7
22.3
13.9
0.8
1.5
16.7
22.2
69.4
41.7
13.9
36.1
0
0
estimation of in-stream contaminant fluxes based on river flows and
concentration data from either spot sampling or passive sampling is
generally associated with considerable uncertainty, typically because
the sampling frequency (especially of concentrations) is not high
enough to capture the sometimes complex relationship between flow
and concentration. The concentration–discharge relationship will depend on the type of catchment and also strongly on the type of pollutant. While some pollutants experience a dilution effect during storm
events, the fluxes of other pollutants, such as particulate contaminants
from diffuse sources, are often found to vary dramatically over time,
with fluxes being particularly high during e.g. storm events compared
to those during low flow periods. According to Richard (1999), it is
not uncommon for 80–90% or more of the annual pollutant load to be
delivered during the 10% of the time with the highest fluxes. In such
cases, the timing of the sampling is obviously critical in order to determine accurate fluxes.
According to Harman et al. (2012) and Morin et al. (2012), the variation of Rs constants with environmental conditions is generally less
than twofold. Therefore, a confidence interval taking into account the
maximum variation of the Rs could be calculated by, respectively, dividing and multiplying the POCIS TWAC by a factor of 2 when calculating
fluxes based on POCIS data. The minimum and maximum fluxes generated this way would give an estimate of the range of the flux value
(Poulier et al., 2015).
3.4. Environmental risk assessment
Pesticides present in the aquatic environment may cause adverse effects on aquatic organisms. The stepwise surface water risk assessment
for pesticide registration in the European Union is based on toxicity
studies (fish, aquatic invertebrates, algae) and predicted environmental
concentrations (PEC), which are calculated by computer simulation
models. Although validated mathematical models have been utilised
for estimating PECs, the use of monitoring data instead of modelled
data for the risk assessment gives remarkable advantages since it provides an actual measurement of pesticide residue concentration, hydrologic response and incorporates the inherent heterogeneity of the basin
environment (ECOFRAM, 1999; Vryzas et al., 2011).
In the present study, we firstly used measured environmental concentrations by spot sampling and RQ values were calculated using Eq.
(4). The RQ values calculated for metaldehyde, isoproturon, simazine
and atrazine (Table 5) were all very low (i.e. minimal risk) mainly due
to their relatively low toxicity to fish, algae or aquatic invertebrates
(e.g. PNEC of metaldehyde: 750 μg/l). The risk of chlorotoluron and
chlorpyrifos were generally low, however, 22.3% and 13.9% of the total
monitoring data of chlorotoluron and chlorpyrifos, respectively, exhibited medium risk. Although cypermethrin generally was only observed in
very low concentrations and in most cases below the detection limit, the
risk of cypermethrin has been estimated to be medium and high for, respectively, 3.1% and 0.8% of the total monitoring data, because the PNEC
for cypermethrin is very low (Table 5). The maximum RQ value of
cypermethrin was 2.62 (site 9), while the maximum RQ values of
chlorotoluron and chlorpyrifos were 1.12 (site 1) and 1.02 (site 2), respectively. Therefore, mitigation measures might be necessary to reduce
the input of pesticides into the river systems.
When comparing the risk assessments based on POCIS and spot
sampling data for the six pesticides, there were no differences in the estimated risks for isoproturon, simazine, atrazine, epoxiconazole with
most of the data inferring minimal risk. However, when considering
chlorpyrifos, large differences in the risk estimation were observed
using the two different sampling strategies (Table 6). For example,
chlorpyrifos spot sampling showed 8.3% low risk and 5.6% medium
risk, while POCIS showed 41.7% and 36.1% for low and medium risk, respectively. This result suggests that the performance of POCIS (allowing
quantification at low concentration) may be very useful for identifying
the potential risk of trace contaminants which cannot be diagnosed by
spot sampling (Zhang et al., 2008, 2014; Lissalde et al., 2011; Poulier
et al., 2015).
4. Conclusions
The results of this study showed the detection of 9 pesticides by spot
and passive sampling in the Ugie catchment of Scotland with all sampling being undertaken within the operational constraints of a typical
regulatory monitoring scheme. Overall, the measured concentrations
from the two sampling strategies were comparable in magnitude for
most of the considered pesticides. The main exception was chlorpyrifos
which was much more frequently detected with the passive sampling
compared to the spot sampling. The total annual fluxes of six pesticides
transported to the Ugie Estuary and North Sea were estimated to be
2735 g and 1837 g based on the spot and passive sampling data, respectively. Both the measured concentrations and the estimated fluxes suggested that the dominant pesticide was chlorotoluron in the Ugie
catchment. The temporal and spatial trends suggested that agricultural
activities might be responsible for these chemicals in the catchment,
however, the complex sources of their environmental occurrence warrant further investigation. Environmental risk assessment found medium risk or higher to aquatic organisms posed by four of the
considered pesticides (cypermethrin, chlorotoluron, chlorpyrifos and
permethrin) with chlorotoluron and chlorpyrifos being particularly of
concern. The results from this work support the hypothesis that the
ability of passive samplers (POCIS) to integrate the contaminant concentrations over a period of exposure allows for a better quantification
of contamination at low (trace) concentration levels (particularly evident for chlorpyrifos in this study) as well as for determining whether
long-term average concentrations, such as annual average EQS specified
under the WFD, are compliant. The latter holds particularly true within
the operational constraints posed by typical regulatory monitoring
schemes and this is the first study to provide regulators with comprehensive evidence of the added value that passive samplers, alongside
spot sampling, could bring to such schemes.
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.scitotenv.2016.06.219.
Acknowledgements
This work was funded by the Scottish Governments' Rural and Environment Research and Analysis Directorate.
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Please cite this article as: Zhang, Z., et al., Evaluation of spot and passive sampling for monitoring, flux estimation and risk assessment of pesticides
within the constraints o..., Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.06.219