Pesticide Risks in Small Streams â How to Get as ... - ACS Publications

Subscriber access provided by UNIV OF DURHAM

Characterization of Natural and Affected Environments

Pesticide Risks in Small Streams – How to Get as Close as Possible to the Stress Imposed on Aquatic Organisms Simon Spycher, Simon Mangold, Tobias Doppler, Marion Junghans, Irene Wittmer, Christian Stamm, and Heinz Singer Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b00077 • Publication Date (Web): 27 Mar 2018 Downloaded from http://pubs.acs.org on March 28, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 35

Environmental Science & Technology

1

Pesticide Risks in Small Streams – How to Get as

2

Close as Possible to the Stress Imposed on Aquatic

3

Organisms

4 5

Simon Spycher1*, Simon Mangold1, Tobias Doppler2, Marion Junghans3, Irene Wittmer2,

6

Christian Stamm1, Heinz Singer1*

7

1

8

Switzerland

9

2

Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf,

VSA, Swiss Water Association, Center of Competence for Surface Water Quality, 8600

10

Dübendorf, Switzerland

11

3

Swiss Center for Applied Ecotoxicology Eawag/EPFL, 8600 Dübendorf, Switzerland

12 13

Plant Protection Products, High Resolution Mass Spectrometry, Surface Water Quality,

14

Sampling Method, Sampling Frequency, Micropollutants, Insecticides, Fungicides, Herbicides,

15

Mixture Toxicity, Agriculture

16

ACS Paragon Plus Environment

1


Page 2 of 35

17 SPEZ screening

# Substances

Historic coverage 250

00 0 200

213 agricultural pesticides

150 100

0.5-day composite samples

0 50

March - August 2 4

14

10

6

2

6 8

18 19

Time period covered [M]

10

Sampling interval [d]

12

TOC/Abstract graphic.

20


2

Page 3 of 35


21

ABSTRACT

22

The risks associated with pesticides in small streams remain poorly characterized. The

23

challenges reside in understanding the complexities of (1) the highly dynamic concentration

24

profiles of (2) several hundred active substances with (3) differing seasonality. The present study

25

addressed these three challenges simultaneously. Five small streams in catchments under

26

intensive agricultural land use were sampled using half-day composite samples from March to

27

August 2015. Of 213 active substances quantified using liquid chromatography−high resolution

28

mass spectrometry, a total of 128 was detected at least at one of the sites. Ecotoxicological acute

29

and/or chronic quality criteria were exceeded for a total of 32 different active substances. The

30

evaluation of risks over time revealed the necessity to evaluate the sequences of different active

31

substances that are imposed on aquatic organisms. In contrast, a substance-specific perspective

32

provides only a very limited assessment. Scenarios for reduction of either temporal resolution,

33

number of substances or seasonal coverage were defined. It could be shown that risks can be

34

underestimated by more than a factor of ten in vulnerable catchments, and that an increased

35

temporal resolution is essential in order to cover acute risks, but that a focused selection of

36

substances is a possibility to reduce expenditures.

37 38 39 40 41


3


Page 4 of 35

42

INTRODUCTION

43

Driven by rapid improvements in analytical methods, the understanding of the type and

44

dynamics of chemical pollution of surface waters is continually improving. However, there

45

remain major knowledge gaps for small streams. Although they make up the majority of river

46

network length (e.g. an estimated 80% in Europe1) a recent evaluation of scientific literature on

47

pesticides in freshwater bodies showed that only 8% of the 2589 retrieved studies examined

48

small streams.2 An evaluation of Swiss routine monitoring data from 2005 to 2012 showed that

49

only 20% of samples were taken from small streams3 (i.e., stream order 1-2 after Strahler4) and

50

an evaluation of German routine monitoring data5, 6 showed that only 12% of sampling sites were

51

in catchments of less than 10 km2.

52

The evaluation of Swiss routine monitoring data revealed further limitations3 that hold also true

53

for other international studies : (1) limited temporal resolution (mostly grab samples with 8 or

54

less samples per year on 75% of sites), (2) limited number of target compounds (less than 44

55

compounds for 75% of samples), and (3) limited seasonal coverage of the sampling schemes3. It

56

is precisely these three factors that define which ecological risks are covered by a monitoring

57

study in a given catchment. For each of them individually, the limits have been pushed

58

considerably over the last years, i.e., more studies with either high temporal resolution,

59

comprehensive compound selection or extended seasonal coverage. The first factor (i.e.,

60

temporal resolution) is known to be a dominant source of uncertainty7 for exposure assessment in

61

general, but is most pronounced for agricultural pesticides in small streams. Field studies with

62

high temporal resolution have shown that during rain events concentrations can increase by a

63

factor of 10 to 100 or more within hours.8-13 In regions with humid climate one can expect rain

64

events to induce the predominant fraction of total substance losses from agricultural surfaces.14


4

Page 5 of 35


65

Hence grab samples at fixed time intervals (e.g. monthly or weekly intervals) are likely to miss

66

peak concentrations. Several authors therefore suggest sampling more frequently during storm

67

events 2, 10, 11, 15 with one recent study reporting sampling intervals as low as two minutes.13

68

While such high frequency sampling studies are highly valuable for detecting peak

69

concentrations (and potentially also yield additional information, such as insight into discharge

70

hysteresis or relevant entry paths), they are often lacking in numbers of compounds monitored:

71

less than 10 compounds were analyzed in the evaluated studies.8-13

72

For the second factor (i.e., the number of compounds) the reported figures have also increased,

73

with recently published studies reaching between 400 and 700 different pesticides,

74

respectively.16-18 However, the time coverage of the cited studies is limited (one, four and 6-8

75

grab samples, respectively).

76

The third factor (i.e., the seasonal coverage) is often limited to the main application period. The

77

Swiss routine monitoring data from 2005 to 2012 show that April, May and June were the

78

months during which sampling intensity was highest.3

79

Good examples for comprehensive studies accounting for all three factors are the long-standing

80

monitoring programs of Sweden19, Norway20 and the USGS,21, 22 which monitor more than 100

81

pesticides. Sweden and Norway’s programs continuously span eight months (April to October)

82

using continuous weekly or bi-weekly composite samples. The USGS program even spans the

83

full year, but uses grab samples at fixed intervals (complemented with additional samples during

84

storm events). All of these programs have a study design suited to its objectives such as detecting

85

long-term trends with feasible expenditures. The present study can help to put the chosen

86

temporal resolution in a context.


5


Page 6 of 35

87

The strength of the present study arises from its design maximizing the space covered by the

88

three factors (i.e., temporal resolution, substance selection and seasonal coverage) thereby

89

providing an assessment of the stress that organisms are exposed to over time in a hitherto

90

unknown comprehensiveness. For this purpose five catchments with intensive agricultural usage

91

were monitored using half-day composite samples over an entire growing season from March-

92

August 2015. The focus of the substance selection was on pesticides used for crop protection as

93

none of the catchments was connected to a waste water treatment plant (WWTP) and land use

94

was dominated by agriculture. A process-based sampling strategy was used to reduce the number

95

of samples to be measured while maintaining the full coverage of the six month investigation

96

period. The spatial resolution of the sampling sites as a fourth and highly important factor is

97

highly dependent on the spatial patterns of land use and was out of the scope of the present

98

study. Furthermore, the observed exposure profiles and the resulting ecotoxicological risks were

99

compared to different scenarios with either reduced temporal resolution, number of compounds

100

or seasonal coverage. These evaluations helps to put the current knowledge from routine

101

monitoring programs into an overall perspective.

102 103

MATERIALS AND METHODS

104

Characterization of sampling sites and year. Five small streams located in different parts of

105

Switzerland with catchment areas between 1.6 and 9 km2 were selected based on land use data.

106

The catchments represent a wide variety of crops (orchards, vineyards, arable land and

107

vegetables) and different topographic and climatic regions of Switzerland (Figure 1). The

108

influence of urban pesticide input was minimized by selecting streams without WWTPs or


6

Page 7 of 35


109

combined sewer overflow in the catchments, and by selecting catchments containing as little

110

urban area as possible.

111

112 113

Figure 1. Location and proportion of land use types in the five investigated catchments

114

catchments23.

115

Two of the catchments comprise flat plains (Mooskanal and Canale Piano di Magadino), two

116

have moderate topography in their catchments with median slopes of 3.5 and 5.0%, respectively

117

(Eschelisbach and Weierbach), while one has a very steep catchment with a median slope of

118

29.5% (Tsatonire). Average yearly precipitation in nearby Meteo Swiss weather stations ranges

119

from 603 mm (Tsatonire) to 1832 mm (Canale Piano di Magadino). The precipitation sum over

120

the whole study duration was close to the long-term average (detailed comparison of monthly

121

sums in Fig. SI 1).


7


Page 8 of 35

122

Monitoring strategy. Half-day time-proportional composite samples were taken at all sampling

123

sites. The composite samples consisted of 16 subsamples of 50 ml each, taken every 45 minutes

124

with an automatic sampling device (Isco 7612). The sampling campaign lasted from the

125

beginning of March to the end of August 2015 with a full coverage of the Eschelisbach and a

126

limited number of interruptions on the other four streams, yielding a total of 313-360 samples per

127

site (Details in SI 3.2). Precipitation (taken from nearby Meteo Swiss weather stations) and water

128

level data (measured in 10 min intervals with STS DL/N 70 probes, Sensor Technik Sirnach AG,

129

Sirnach, CH) were used to differentiate between discharge events and low-flow periods. As it

130

was too laborious to analyze each half-day sample individually a discharge-dependent flexible

131

temporal resolution was used. As one can expect the highest concentration peak to occur

132

discharge events8, 9, 12 the half-day composite samples of such events were analyzed individually.

133

During low flow periods between discharge events half-day samples were pooled to samples

134

corresponding to the length of the low flow period (five days on average) and then analysed.

135

This procedure resulted in a complete concentration profile over the entire monitoring period,

136

covered by 34 - 60 samples per site. Additional information on sample processing and blanks is

137

provided in SI 2.1.1.

138

Substance selection. In 2015, 257 different synthetic-organic compounds were approved as

139

active substances in Switzerland.24 179 of these (70 %) could be analyzed with the applied

140

analytical method (see below). The remaining 78 substances were either not measurable by

141

liquid chromatography (e.g., pyrethroids), required special analytics (e.g., glyphosate), or

142

degrade very quickly (e.g., folpet). In addition to the 179 current use pesticides 34 so-called

143

legacy pesticides, i.e., active substances banned before 2015 were analyzed.


8

Page 9 of 35


144

Analytics. 213 active substances were analyzed by online solid phase extraction (SPE) liquid

145

chromatography (LC) electrospray-ionization (ESI) mass spectrometry as described by Huntscha

146

et al.25 Briefly, 20 mL of the filtered sample were passed over a self-prepared multilayered

147

cartridge containing Oasis HLB (9 mg) and a mixture of Strata XAW, Strata XCW, and Isolute

148

ENV+ (9 mg total), enabling the enrichment of a broad spectrum of substances. The cartridge

149

was eluted with methanol containing 0.1% formic acid. The chromatographic separation was

150

carried out with an Atlantis T3 column (length 150 mm, ID 3 mm, particle size 5 µm) using

151

nanopure water and methanol both acidified with 0.1% formic acid as eluents (gradient see SI

152

2.1.1). HRMS and MS/MS data were generated on a QExactive Plus (Thermo Fisher Scientific

153

Corporation). Full scans with a mass resolution (R) of 140 000 (at m/z 200) and data-dependent

154

MS/MS (R = 17 500, Top 5) with separate runs for positive and negative electrospray ionization

155

were acquired (for detailed MS settings see SI 2.1.2).

156

Quantification was done using reference standards for each compound and 128 isotope-labeled

157

internal standards (ISTD). 55 substances had their own, structure-identical isotope-labeled

158

standard, while 113 were quantified with a structure non-identical ISTD which were corrected by

159

relative recovery (details in SI Figure 2.1.4). For 45 substances (all non-detected) only a 400

160

ng/L one-point calibration was available (see different quantification quality levels in SI 2.2).

161

The calibration ranged from 0.5 to 1000 ng/L based on 12 levels. Samples with concentrations

162

above calibration ranges were measured a second time via direct injection (without SPE

163

enrichment) using the same LC-MS conditions. Chronic quality criteria (definition in next

164

section) were above the limit of quantification (LOQ) for 94% of the measured substances. Ten

165

of the 13 compounds with LOQ above the chronic quality criterion had limited or no current

166

usage, but three compounds have a substantial usage in Switzerland (chlorpyrifos, chlorpyrifos-


9


Page 10 of 35

167

methyl and methiocarb) with sales known to be above 1 ton and therefore improved methods

168

with lower LOQs are needed. The ranges of the LOQs and measured concentrations are given in

169

SI 2.2 and 3.3, respectively.

170

Exposure. Two types of concentrations were used for the evaluations: First, the concentrations

171

measured for the original samples, second, time-weighted average concentrations, Ctwa over three

172

and 14 days (see next section). Ctwa were calculated with fixed time steps starting at the

173

beginning of the sampling campaign. In case the time period covered by Ctwa fell completely

174

within the duration covered by a pooled sample, e.g., during a prolonged dry period, Ctwa

175

corresponded to the concentration determined for the pooled sample. In order to check for the

176

variability due to differing starting times, Ctwa were additionally calculated by using a moving

177

window approach (i.e., by averaging the concentrations of an equal time period before and after

178

each half-day).

179

Non-detects were substituted with zero. This will result in lower bound estimates of RQmix,

180

but substation with LOQ or LOD (or a fraction thereof) would result in substantial

181

overestimation. However, a recent study with a similarly large number of compounds and also in

182

dynamic streams resulted in calculated risks that were very close to risks calculated using the

183

nonparametric Kaplan-Meier method.19

184

All data were analyzed using the statistical software R, version 3.2.226 and the full data set is

185

available at the public repository accompanying this publication (cf. SI 3.1).

186

Risk Assessment. For each of the 128 detected pesticides acute and chronic quality criteria

187

(AQC and CQC, respectively) were derived according to the study with a similarly large the EU

188

Water Framework Directive.27 Values prepared in view of implementation under the Swiss water


10

Page 11 of 35


189

protection ordinance were used when available,28 otherwise quality criteria were either searched

190

for in the open literature or ad hoc values were derived based on registration data (see SI Table

191

2.3).

192

Risk quotients (RQ) for single substances were calculated by dividing the measured

193

environmental concentration (MEC) with the quality criterion, i.e., RQ = MEC/QC. For

194

assessing the acute risk, by default the measured concentrations of the half-day composite

195

sample were compared with the AQC. For assessing the chronic risk it is necessary to use a MEC

196

weighted over a suitable reference period. Following Haber’s rule,29 an approach also used under

197

the authorization of active substances in the EU,30 the reference period was based on the average

198

duration (geometric mean) of the chronic ecotoxicity assays on which the CQC (like the AA-

199

EQS under the EU WFD) are usually based (algae: 3 d, duckweed: 7-10 d, water fleas: 21 d,

200

chironomids: 28 days and fish: at least 28 d). Hence, the Ctwa calculated over two weeks (14d-

201

Ctwa) is compared with the CQC. In an upcoming paper the adequacy of 14d-Ctwa for assessing

202

chronic risks will be discussed based on TKTD modelling based on the concentration profiles

203

determined in the present paper. As a second approach to assess acute risk, Ctwa was calculated

204

over three days (3d-Ctwa, i.e. the Haber’s rule duration for the AQC) study with a similarly

205

large.31

206

Risk quotients for mixtures were calculated separately for each of the three taxonomic groups

207

plants (P), invertebrates (I) and vertebrates (V) according to RQEQS_taxa approach32 which is a

208

refinement of the concentration addition based assessment of ref. 33, a tiered approach based on

209

the summation of risk quotients. Quality criteria are always based on the most sensitive taxon,

210

which differs from substance to substance. Hence, a simple summation of all risk quotients will

211

largely overestimate the mixture risk. In the refinement used in the present study AQC and CQC


11


Page 12 of 35

212

were labeled to identify which of the three taxonomic groups are sensitive for the respective

213

substance32 (criteria for labeling are given in SI Table 2.5). For taxonomic group j the overall

214

mixture risk of all detected substances i can then be expressed as

215

mix,j = ∑ ∈

216

meaning that only compounds having a label for taxonomic group j are summed (e.g., for plants

217

either "P", "PIV", "PI" or "PV"). Values of RQmix,j above 1 indicate a risk level at which effects

218

on the respective taxonomic group cannot be ruled out.

(1)

219 220

Scenario Calculations. To assess the added value of the comprehensive monitoring strategy

221

followed in this study (referred to as ORIG for original data), we compared it with approaches

222

commonly used in routine monitoring. Alternative scenarios were tested for each of the three

223

factors: (1) The temporal resolution of the original measurements with half-day sampling

224

intervals during rain events was compared to 14d-Ctwa and 3d-Ctwa. These scenarios were

225

referred to as TR (for reduced Temporal Resolution). (2) The substance selection of the present

226

comprehensive set of 213 agricultural pesticides was compared to a "historic" scenario derived in

227

a previous study34 which limited the number of compounds to the 28 most frequently measured

228

substances in the years 2005 - 2012, and a substance list of 38 compounds recommended as

229

priority compounds for monitoring in Switzerland was also evaluated (pesticides not used for

230

plant protection excluded in both lists).31 These scenarios were referred to as SS (for reduced

231

Substance Selection). (3) The full six-month coverage was compared to an approach lasting only

232

four months from March-June (referred to as SC for reduced Seasonal Coverage).


12

Page 13 of 35

233


Finally two possible combination of factors were evaluated, i.e. combinations of TR+SS+SC:

234

The first combination represents a historic approach with (1) 14d-Ctwa, (2) the 28 historically

235

most frequent compounds and (3) the four months period from March-June. The second

236

combination evaluates an improved monitoring strategy with (1) 3d-Ctwa and (2) 38 priority

237

substances (but with the full seasonal coverage).

238

For each scenario the profile, i.e., the time course, of the acute RQmix (ARQmix) was calculated

239

and the percentage of the study duration with ARQmix above 1 and the maximal ARQmix were

240

calculated.

241 242

RESULTS AND DISCUSSION

243

Exposure profiles. The number of detected active substances in the five catchments ranged from

244

69 to 98 and over all catchments a total of 128 different compounds was detected (Table 1). The

245

diversity of crops grown appeared to be much more relevant than the size of the catchment, as

246

the stream with the smallest catchment (Weierbach with 1.6 km2) had the highest number of

247

detected compounds (98 compounds). Over all sites a median of 27 compounds per sample was

248

detected. Herbicides, fungicides and insecticides constituted 45, 43 and 12% of detections,

249

respectively. However, in three catchments (Eschelisbach, Canale Piano di Magadino and

250

Tsatonire) fungicides were most frequent (Table 1) which contrasts other comprehensive

251

monitoring studies where herbicides were clearly the most frequent type of active substances

252

detected (e.g. percentages of herbicides of 58% for Switzerland34, 77% for Norway20 and 81%

253

for Sweden19). The higher share of crops with frequent fungicide applications contributed to this

254

result particularly vineyards and orchards which are the crops with the highest fungicide


13


Page 14 of 35

255

treatment frequencies.35 For example, a Swedish study on 8 catchments with predominant

256

horticultural usage also had a substantially lower percentage of herbicide detections (49%

257

herbicides, 38% fungicides and 13% insecticides).36

258

Table 1. Chemical and ecotoxicological key figures of pesticides measured for the five

259

catchments and average for all measurements Mooskanal Weierbach

Eschelisbach

Canale Piano Tsatonire di Magadino

All

Nb. of detected cpds

74

98

89

72

64

128

Median Nb. of detected cpds/sample

22

41

35

18

22

27

% H,F,I a

57, 38, 5

54, 37, 10

39, 41, 20

39, 56, 6

27, 64, 9

45.43,12

3135

2150

414

1219

1460

(65, 32, 3)

(81, 16, 4)

(21, 68, 11)

(50, 47, 3)

(82, 16, 2)

(61, 34, 6)

1

14

8

0

5

23

Nb. of days > AQC 9.5 (6%) (%)c

43 (24%)

41 (22 %)

0(0 %)

68 (41%)

19%

Nb. of cpds > CQC

19

12

1

6

29

168 (92%)

168 (92%)

14 (10%)

140 (86%) 66%

Mean sum concentrations b

of 380

(% H, F, I) [ng/L] Nb. of cpds > AQC

5

Nb. of days > CQC 70 (43%) (%)c 260 261 262 263 264

cpds: Compounds, ashare of herbicide (H), fungicide (F) and insecticide (I) detections on total number of detections, btime-weighted average over full monitoring period, cnumber of days (or percent of investigation period) with one or more substances above their acute quality criterion(AQC) dnumber of days (or percent of investigation period) with one or more substances above their chronic quality criterion(CQC)

265


14

Page 15 of 35


266

The mean sum of pesticide concentrations ranged from 380 ng/L (Mooskanal) to 3135 ng/L

267

(Weierbach), indicating large variability between the five sites. Beneath the aggregated figures of

268

Table 1 are complex and highly dynamic concentrations profiles (Figure 2). This chemical

269

fingerprint was grouped into four substance groups according to detection frequency (DF) and

270

maximum concentration (Cmax): (1) high DF and high Cmax (e.g., fluopyram), (2) low DF and

271

high Cmax (e.g. mesotrione), (3) high DF and low Cmax (e.g., atrazine) and (4) low DF and low

272

Cmax (e.g. fenoxycarb).

273 [ng/L] 100'000

Methomyl (1) Fluopyram (1) Napropamide (1) Azoxystrobin (1) Dimethoate (1) Thiamethoxam (1) Metolachlor (1) Dicamba (1) Metamitron (1) Terbuthylazine (1) Diuron (1) Bupirimate (1) Cyprodinil (1) Bentazon (1) Diazinon (1) Fenhexamid (1) Fludioxonil (1) MCPA (1) MCPP (Mecoprop) (1) Fluroxypyr (1) Thiacloprid (1) Pirimicarb (1) Metribuzin (1) Nicosulfuron (1) Carbendazim (1) Metalaxyl−M (1) Haloxyfop (1) 2,4−D (1) Chloridazon (1) Boscalid (1) Tribenuron−methyl (1) Difenoconazole (1) Flonicamid (1) Tebufenozide (1) Myclobutanil (1) Mesotrione (2) Propyzamide (2) Flufenacet (2) Prochloraz (2) Chlorpyrifos−methyl (2) Oryzalin (2) Ethofumesate (2) Linuron (2) Imazamox (2) Pyrimethanil (2) Tembotrione (2) Trifloxystrobin (2) Dimethenamid (2) Imidacloprid (3) Carbofuran (3) Methoxyfenozide (3) Clothianidin (3) Epoxiconazole (3) Penconazole (3) Tebuconazole (3) Dimethomorph (3) Lenacil (3) Atrazine (3) Flusilazole (3) Kresoxim−methyl (4) Foramsulfuron (4) Propamocarb (4) Fenoxycarb (4) Fluazinam (4) Methiocarb (4) Metsulfuron−methyl (4) Chlorpyrifos (4) Thifensulfuron−methyl (4) Ioxynil (4) Sulcotrione (4) Mesosulfuron−methyl (4) Bixafen (4) Pencycuron (4) Picoxystrobin (4) Fenamidone (4) Cyproconazole (4) Bromoxynil (4) Spirotetramat (4) Prosulfocarb (4) Iprovalicarb (4) Terbacil (4) Monuron (4) Monolinuron (4) Propiconazole (4) Triflusulfuron−methyl (4) Tepraloxydim (4) Clomazone (4) Fipronil (4) Simeton (4)

274

10'000

1000

100

10

1

10 to disappear (TR in Figure 3). Hence, the apparent risk is reduced.

369

On the other hand, the opposite holds true for the duration of ARQmix > 1, which for the case of


20

Page 21 of 35


370

invertebrates apparently increases from 36% to 61% (data in SI Table 4.4.1). Apparent increases

371

of the duration of ARQmix > 1 were observed for Weierbach and Tsatonire. A special case is the

372

Mooskanal, where ARQmix calculated with 14d-Ctwa are below 1 at all times.

373

ORIG (Original data) TR (Temporal Resolution :14 d Ctwa) SS (Substance Selection: 28 most frequent) SC (Seasonal coverage: Mar−Jun) TR + SS + SC combined

acute RQmix

10

20

30

2.0 1.5 1.0

0

0.0

0.5

acute RQmix

Weierbach

40

Mooskanal

Plants

Invertebrates

Vertebrates

Plants

Vertebrates

Tsatonire

5

10

acute RQmix

50 40 30

0

0

10

20

acute RQmix

60

15

70

Eschelisbach

Invertebrates

Plants

Invertebrates

Vertebrates

Plants

Invertebrates

Vertebrates

374 375 376 377 378 379 380

Figure 4. Highest ARQmix of the whole season at the four sites with exceedances of acute quality criteria. The original data are grey, the three scenarios with variation of a single factor are singlecolored and the combination of all three factors is three-colored. The dashed red line indicates an ARQmix of 1. The Canale Piano di Magadino is not shown due to generally low ARQmix (but values are given in SI Table 4.4.2)

381 382

The effect of TR on the maximal ARQmix of each stream was evaluated in more depth (Figure

383

4, data in SI Table 4.4.2). The strongest reduction was observed for invertebrates in the

384

Eschelisbach, namely by a factor of 16 (maximal ARQmix,invertebrates of 72 for the original data and


21


Page 22 of 35

385

maximal ARQmix,invertebrates of 4.4 for 14d-Ctwa). The smallest reduction of the maximal ARQmix

386

was determined for the Mooskanal (less than a factor of 2 for both invertebrates and vertebrates).

387

Increasing the sampling interval from a half-day to 14 days reduces the risk by less than the

388

theoretically possible factor of 28. Three causal factors can be identified: (1) tailing

389

(concentrations not dropping to zero after a peak), (2) increased diversification (ARQmix

390

containing a higher number of contributing compounds, as 14d-Ctwa includes concentration peaks

391

over two weeks), (3) pooling (compounds with the highest concentrations measured in samples

392

with a duration of more than a half-day). The observed results are similar to a study on a

393

Swedish catchment, where both event-based and one-week composite samples were measured

394

and the maximum risk for invertebrates and vertebrates determined by the one-week composite

395

samples were 6- and 7-times lower, respectively.41 The present study shows a strong variability

396

between catchments with underestimation factors ranging from 2-16.

397

For the Haber’s rule based 3d-Ctwa the effects are much less pronounced, and the reduction

398

ranged from a factor of 4.5 for invertebrates in the Eschelisbach to no reduction for invertebrates

399

and vertebrates in the Mooskanal, as the highest ARQmix were due to pooled samples consisting

400

of more than 6 six half-days (SI Table 4.4 and Figure 4.6). Note that the estimated reduction due

401

to 3d-Ctwa (and to a lesser extent also 14d-Ctwa) is affected by the pooling of samples during dry

402

periods and would be more pronounced if all half-days would have been measured separately.

403

The effect of reducing temporal resolution was also evaluated on single substance risk quotients.

404

For each compound at each site the highest ARQ based on the originally measured

405

concentrations was divided by the ARQ based on the 14d-Ctwa at the corresponding time (SI

406

Figure 4.5). The strongest underestimation was observed in the Weierbach (isoproturon and

407

aclonifen by a factor of 25) and the Eschelisbach (diuron by factor of 24). For the few (n = 23)


22

Page 23 of 35


408

compound-site combinations with an original maximal ARQ > 1, the underestimation had a

409

median of 9.5, and the 10th and 90th percentile were 3.4 and 22.5, respectively. Thus, reducing

410

temporal resolution has a stronger effect on ARQs of single compounds than on ARQmix. This

411

must be borne in mind when interpreting single substance routine monitoring data.

412

Scenario with reduced number of compounds (SS). The scenario consisted of evaluating

413

either only the 28 agricultural pesticides most frequently measured in the past or 38 compounds

414

from a list of priority compounds for monitoring in Switzerland. In the Eschelisbach the

415

"historic" scenario would cause almost all periods with elevated risk from mid-April onwards to

416

be unobserved, including three events with ARQmix > 10 (SS in Figure 3). For the two streams

417

Mooskanal and Tsatonire the historic scenario would result in little-to-no difference for all

418

taxonomic groups (SI Figure 4.1), thus the substances driving the risks in these two catchments

419

are among the 28 historically relevant compounds. No ARQmix.invertebrates > 1 at any time would be

420

observed in the Weierbach, which is a drastic change compared to the original data with

421

ARQmix, invertebrates > 1 during 24% of the investigation period and a maximal ARQmix, invertebrates

422

above 10 (SI 4.1.2).

423

The assessment based on the 38 priority compounds31 never underestimated the maximal ARQmix

424

by more than a factor of 2.5 (SI Table 4.4.2 and SI Figure 4.6), a very encouraging result.

425

However, further evaluations on other streams are required since five streams represent a low

426

spatial resolution even in small country. Furthermore usage evolves over time which might

427

reduce the coverage of such a focused compound selection.

428


23


Page 24 of 35

429

Scenario with reduced seasonal coverage (SC). Limiting the seasonal coverage from March to

430

end of June would cause 40% of relevant periods of elevated risks for invertebrates in the

431

Eschelisbach to go unnoticed (Figure 3). Furthermore the elevations in ARQmix were due to

432

substances that played little (azoxystrobin, fluopyram, thiacloprid) or no role (diuron,

433

thiamethoxam) before July. While highest acute ARQmix, invertebrates in the Eschelisbach occurred

434

in June and would not have been missed, both in Weierbach and Tsatonire the season’s highest

435

ARQmix, invertebrates would have been missed if the campaign had concluded by the end of June

436

(Figure 4). For plants the highest ARQmix occurred before the end of June at all sites.

437

Implications for quality assessment of small streams. The evaluation of the cases with

438

maximal ARQmix > 1 shown in Figure 4 reveal that in six of ten cases temporal resolution was

439

the most relevant factor (data in SI Table 4.4.2). In four of ten cases, substance selection lowered

440

maximum ARQmix most strongly. If all factors are combined maximum ARQmix was

441

underestimated by more than an order of magnitude for all taxonomic groups of Eschelisbach

442

and Weierbach and between a factor of 1.5 and 9.8 in Mooskanal and Tsatonire (vertebrates not

443

considered in Mooskanal and Tsatonire as they had ARQmix < 1). It is important to note that even

444

the full set of measured substances has some relevant gaps as there are several compounds with

445

LOQs above the CQC (cf. Materials and Methods). Furthermore the target list still does not

446

cover all relevant compounds with pyrethroids as the ecotoxicologically most relevant gap (more

447

details in ref. 19). Therefore there are still are relevant gaps in the risk assessment of insecticides.

448

In order to fully assess the acute risk it is therefore necessary to sufficiently balance all three

449

factors. At the same time options to reduce the required effort are desirable. One option to reduce

450

the number of samples would be to take event-based grab samples (i.e., high temporal resolution

451

during periods with high discharge and a low resolution during low flow). As the present and


24

Page 25 of 35


452

also earlier studies showed, concentration peaks can also occur during dry periods and therefore

453

inputs which are not rain-driven can be highly relevant.8, 42-44 Thus, a continuous coverage with

454

composite samples has the advantage of covering such events. The 3d-Ctwa approach showed

455

distinctly better results with the underestimation not exceeding a factor of 5 (SI Table 4.4.2 and

456

Figure 4.6). It is furthermore possible that new insights from toxicokinetik-toxicodynamic

457

modelling will show that 3d-Ctwa already is a sufficient time resolution to cover acute effects.40 A

458

further reduction of the number of samples does not seem feasible if conclusions on acute risks

459

are pursued.

460

A targeted substance selection seems feasible as the number of compounds exceeding QC is

461

limited. As a matter of fact an assessment based only on the 38 priority compounds seems to

462

cover a very large fraction of the ARQmix.

463

The results of this study make it clear that approaches common for older monitoring programs

464

fell short of capturing the full extent of the exposure and risks imposed by pesticides in small

465

streams. This explains why the awareness of the influence of pesticides on surface water

466

ecosystems has increased over the last decade. Currently many countries are developing and

467

implementing policies with the goal of reducing pesticide risks.45 Appropriate monitoring

468

strategies can align assessments of chemical and biological status and thus provide a clearer

469

picture of the chemical stress imposed on aquatic communities. Furthermore, they also deliver

470

the means to quantify the progress made towards improving surface water quality.

471


25


Page 26 of 35

472

Supporting Information. (1) Additional study site information, (2) additional substance

473

information with extra Table for derived ad-hoc quality criteria, (3) measured concentrations, (4)

474

risk assessment and scenario analysis. This material is available free of charge via the Internet at

475

http://pubs.acs.org.

476

AUTHOR INFORMATION

477

Corresponding Author

478

* Corresponding authors:

479

[email protected], phone: +41 58 765 5395 [email protected], phone: +41 58 765 5577

480 481

Author Contributions

482

The manuscript was written through contributions of all authors. All authors have given approval

483

to the final version of the manuscript.

484

Funding Sources

485

Swiss Federal Office for the Environment (FOEN)

486

ACKNOWLEDGMENT

487

This study was funded by the Swiss Federal Office for the Environment (FOEN). The sampling

488

by the cantonal authorities of the Canton Bern, Basel-Land, Thurgau, Ticino, and Valais is

489

gratefully acknowledged. We thank Rahel Comte (Eawag) for the help in the laboratory and in

490

the field, Hannah Wey and Samuel Schafer for valuable contributions during their master theses

491

and Bridget Ulrich (both Eawag) and Christoph Moschet (Interkantonales Labor Schaffhausen)


26

Page 27 of 35


492

for improving the manuscript. Nina Roth (Seminar for Statistics, ETH Zurich) is gratefully

493

acknowledged for her feedback on survival models.

494 495

REFERENCES

496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531

1. Kristensen, P.; Globevnik, L., European Small Water Bodies. Biology and EnvironmentProc. R. Ir. Acad. 2014, 114B, (3), 281-287. 2. Lorenz, S.; Rasmussen, J. J.; Süß, A.; Kalettka, T.; Golla, B.; Horney, P.; Stähler, M.; Hommel, B.; Schäfer, R. B., Specifics and challenges of assessing exposure and effects of pesticides in small water bodies. Hydrobiologia 2017, 793, (1), 213-224. 3. Munz, N.; Leu, C.; Wittmer, I., Pestizidmessungen in Fliessgewässern - Schweizweite Auswertung. Aqua & Gas 2012, 11, 10. 4. Strahler, A. N., Hypsometric (Area-Altitude) Analysis of Erosional Topography. Bull. Geol. Soc. Am. 1952, 63, (11), 1117-1142. 5. Brinke, M.; Szöcs, E.; Foit, K.; Bänsch-Baltruschat, B.; Liess, M.; Schäfer, R. B.; Keller, M. Implementation of the National Action Plan on sustainable use of pesticides – survey on the state of data on the pollution of small water bodies in the agricultural landscape; Federal Institute of Hydrology (BfG): 2015; p 144. 6. Szöcs, E.; Brinke, M.; Karaoglan, B.; Schäfer, R. B., Large Scale Risks from Agricultural Pesticides in Small Streams. Environ. Sci. Technol. 2017, 51, (13), 7378-7385. 7. Ort, C.; Lawrence, M. G.; Rieckermann, J.; Joss, A., Sampling for Pharmaceuticals and Personal Care Products (PPCPs) and Illicit Drugs in Wastewater Systems: Are Your Conclusions Valid? A Critical Review. Environ. Sci. Technol. 2010, 44, (16), 6024-6035. 8. Leu, C.; Singer, H.; Stamm, C.; Müller, S. R.; Schwarzenbach, R. P., Simultaneous assessment of sources, processes, and factors influencing herbicide losses to surface waters in a small agricultural catchment. Environ. Sci. Technol. 2004, 38, (14), 3827-3834. 9. Doppler, T.; Camenzuli, L.; Hirzel, G.; Krauss, M.; Lück, A.; Stamm, C., Spatial variability of herbicide mobilistaion and transport at catchment scale: insights from a field experiment. Hydrol. Earth Syst. Sci. 2012, 16, 1947-1967. 10. Petersen, J.; Grant, R.; Larsen, S. E.; Blicher-Mathiesen, G., Sampling of herbicides in streams during flood events. J. Environ. Monit. 2012, 14, (12), 3284-3294. 11. Xing, Z. S.; Chow, L.; Rees, H.; Meng, F. R.; Li, S.; Ernst, B.; Benoy, G.; Zha, T. S.; Hewitt, L. M., Influences of Sampling Methodologies on Pesticide-Residue Detection in Stream Water. Arch. Environ. Contam. Toxicol. 2013, 64, (2), 208-218. 12. Rabiet, M.; Coquery, M.; Carluer, N.; Gahou, J.; Gouy, V., Transfer of metal(loid)s in a small vineyard catchment: contribution of dissolved and particulate fractions in river for contrasted hydrological conditions. Environ. Sci. Pollut. Res. 2015, 22, (23), 19224-19239. 13. Lefrancq, M.; Jadas-Hecart, A.; La Jeunesse, I.; Landry, D.; Payraudeau, S., High frequency monitoring of pesticides in runoff water to improve understanding of their transport and environmental impacts. Sci. Total Environ. 2017, 587, 75-86.


27


532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575

Page 28 of 35

14. Leu, C.; Schneider, M. K.; Stamm, C., Estimating Catchment Vulnerability to Diffuse Herbicide Losses from Hydrograph Statistics. J. Environ. Qual. 2010, 39, (4), 1441-1450. 15. Stehle, S.; Knabel, A.; Schulz, R., Probabilistic risk assessment of insecticide concentrations in agricultural surface waters: a critical appraisal. Environ. Monit. Assess. 2013, 185, (8), 6295-6310. 16. Pitarch, E.; Cervera, M. I.; Portoles, T.; Ibanez, M.; Barreda, M.; Renau-Prunonosa, A.; Morell, I.; Lopez, F.; Albarran, F.; Hernandez, F., Comprehensive monitoring of organic micropollutants in surface and groundwater in the surrounding of a solid-waste treatment plant of Castellon, Spain. Sci. Total Environ. 2016, 548, 211-220. 17. Bradley, P. M.; Journey, C. A.; Romanok, K. M.; Barber, L. B.; Buxton, H. T.; Foreman, W. T.; Furlong, E. T.; Glassmeyer, S. T.; Hladik, M. L.; Iwanowicz, L. R.; Jones, D. K.; Kolpin, D. W.; Kuivila, K. M.; Loftin, K. A.; Mills, M. A.; Meyer, M. T.; Orlando, J. L.; Reilly, T. J.; Smalling, K. L.; Villeneuve, D. L., Expanded Target-Chemical Analysis Reveals Extensive Mixed Organic-Contaminant Exposure in US Streams. Environ. Sci. Technol. 2017, 51, (9), 4792-4802. 18. Baas, J.; Vijver, M.; Rambohul, J.; Dunbar, M.; van 't Zelfde, M.; Svendsen, C.; Spurgeon, D., Comparison and Evaluation of Pesticide Monitoring Programs using a ProcessBased Mixture Model. Environ. Toxicol. Chem. 2016, 35, (12), 3113-3123. 19. Gustavsson, M.; Kreuger, J.; Bundschuh, M.; Backhaus, T., Pesticide mixtures in the Swedish streams: Environmental risks, contributions of individual compounds and consequences of single-substance oriented risk mitigation. Sci. Total Environ. 2017, 598, 973-983. 20. Stenrod, M., Long-term trends of pesticides in Norwegian agricultural streams and potential future challenges in northern climate. Acta Agric. Scand. B 2015, 65, 199-216. 21. Gilliom, R. J.; Barbash, J. E.; Crawford, C. G.; Hamilton, P. A.; Martin, J. D.; Nakagaki, N.; Nowell, L. H.; Scott, J. C.; Stackelberg, P. E.; Thelin, G. P.; Wolock, D. M. The Quality of Our Nation’s Waters—Pesticides in the Nation’s Streams and Ground Water, 1992–2001: U.S. Geological Survey Circular 1291; 2006; p 172. 22. Stone, W. W.; Gilliom, R. J.; Ryberg, K. R., Pesticides in U.S. Streams and Rivers: Occurrence and Trends during 1992–2011. Environ. Sci. Technol. 2014, 48, (19), 11025-11030. 23. BAFU Gewässerabschnittsbasierte Einzugsgebietsgliederung der Schweiz (GAB-EZGGCH); 2013. 24. Swiss Confederation, Ordinance on Plant Protection Products (SR-916.161, Status: January 1st 2015). 2010. 25. Huntscha, S.; Singer, H. P.; McArdell, C. S.; Frank, C. E.; Hollender, J., Multiresidue analysis of 88 polar organic micropollutants in ground, surface and wastewater using online mixed-bed multilayer solid-phase extraction coupled to high performance liquid chromatography-tandem mass spectrometry. J. Chromatogr. A 2012, 1268, 74-83. 26. R Core Team R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2015. 27. European Commission Common Implementation Strategy for the Water Framework Directive. Technical Guidance for Deriving Environmantal Quality Standards; 2011. 28. Swiss Confederation Waters Protection Ordinance (SR-814.201); 1998. 29. Ashauer, R.; Escher, B. I., Advantages of toxicokinetic and toxicodynamic modelling in aquatic ecotoxicology and risk assessment. J. Environ. Monit. 2010, 12, (11), 2056-2061.


28

Page 29 of 35

576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620


30. EFSA PPR Panel (EFSA Panel on Plant Protection Products and their Residues), Guidance on tiered risk assessment for plant protection products for aquatic organisms in edgeof-field surface waters. EFSA Journal 2013, 11, (7), 3290. 31. Wittmer, I.; Junghans, M.; Stamm, C.; Singer, H. Mikroverunreinigungen – Beurteilungskonzept für organische Spurenstoffe aus diffusen Einträgen; Eawag: Dübendorf, 2014. 32. Junghans, M.; Kunz, P.; Werner, I., Toxizität von Mischungen - Aktuelle, praxisorenitierte Ansätze für die Beurteilung von Gewässerproben. Aqua & Gas 2013, 5, 54-61. 33. Price, P.; Dhein, E.; Hamer, M.; Han, X.; Heneweer, M.; Junghans, M.; Kunz, P.; Magyar, C.; Penning, H.; Rodriguez, C., A decision tree for assessing effects from exposures to multiple substances. Environ. Sci. Eur. 2012, 24, 26. 34. Moschet, C.; Wittmer, I.; Simovic, J.; Junghans, M.; Piazzoli, A.; Singer, H.; Stamm, C.; Leu, C.; Hollender, J., How A Complete Pesticide Screening Changes the Assessment of Surface Water Quality. Environ. Sci. Technol. 2014, 48, (10), 5423-5432. 35. de Baan, L.; Spycher, S.; Daniel, O., Use of Plant Protection Products in Switzerland from 2009 to 2012. Agrarforschung Schweiz 2015, 6, (2), 48-54. 36. Kreuger, J.; Graaf, S.; Patring, J.; Adielsson, S., Pesticides in surface water in areas with open ground and greenhouse horticultural crops in Sweden 2008. Ekohydrologi 2010, 117, 1-47. 37. Kase, R.; Eggen, R. I. L.; Junghans, M.; Götz, C.; Hollender, J., Assessment of Micropollutants from Municipal Wastewater-Combination of Exposure and Ecotoxicological Effect Data for Switzerland. In Waste Water - Evaluation and Management, Garcia Einschlag, F. S., Ed. 2011; pp 31-54. 38. Cedergreen, N.; Christensen, A. M.; Kamper, A.; Kudsk, P.; Mathiassen, S. K.; Streibig, J. C.; Sorensen, H., A review of independent action compared to concentration addition as reference models for mixtures of compounds with different molecular target sites. Environ. Toxicol. Chem. 2008, 27, (7), 1621-1632. 39. Junghans, M.; Backhaus, T.; Faust, M.; Scholze, M.; Grimme, L. H., Application and validation of approaches for the predictive hazard assessment of realistic pesticide mixtures. Aquat. Toxicol. 2006, 76, (2), 93-110. 40. Ashauer, R.; O'Connor, I.; Escher, B. I., Toxic Mixtures in Time-The Sequence Makes the Poison. Environ. Sci. Technol. 2017, 51, (5), 3084-3092. 41. Bundschuh, M.; Goedkoop, W.; Kreuger, J., Evaluation of pesticide monitoring strategies in agricultural streams based on the toxic-unit concept - Experiences from long-term measurements. Sci. Total Environ. 2014, 484, 84-91. 42. Holvoet, K.; van Griensven, A.; Gevaert, V.; Seuntjens, P.; Vanrolleghem, P. A., Modifications to the SWAT code for modelling direct pesticide losses. Environmental Modelling & Software 2008, 23, (1), 72-81. 43. Kreuger, J., Pesticides in stream water within an agricultural catchment in southern Sweden, 1990-1996. Sci. Total Environ. 1998, 216, (3), 227-251. 44. Müller, K.; Bach, M.; Hartmann, H.; Spiteller, M.; Frede, H. G., Point- and nonpointsource pesticide contamination in the Zwester Ohm catchment, Germany. J. Environ. Qual. 2002, 31, (1), 309-318. 45. Barzman, M. S.; Bertschinger, L.; Dachbrodt-Saaydeh, S.; Graf, B.; Jensen, J. E.; Nistrup Joergensen, L.; Kudsk, P.; Messéan, A.; Moonen, A.-C.; Ratnadass, A.; Sarah, J.-L.; Sattin, M., Integrated Pest Management policy, research and implementation: European initiatives. In


29


621 622

Page 30 of 35

Integrated Pest Management - Experiences with Implementation, Global Overview, Peshin, R.; Pimentel, D., Eds. Springer: 2014; Vol. 4, pp 415-428.

623


30

Page 31 of 35


TOC-Abstract graphic 45x26mm (300 x 300 DPI)



139x74mm (300 x 300 DPI)


Page 32 of 35

Page 33 of 35


639x403mm (600 x 600 DPI)



Single substance and mixture risk profiles for acute risks 742x577mm (600 x 600 DPI)


Page 34 of 35

Page 35 of 35


Reduction of observed risk quotients for scenarios with reduced temporal resolution or reduced substance selection or seasonal coverage or all combined. 304x202mm (300 x 300 DPI)


Pesticide Risks in Small Streams â How to Get as ... - ACS Publications

Recommend Documents

Pesticide Risks in Small Streams â How to Get as ... - ACS Publications