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Article:

Ramos, A., Whelan, M.J., Guymer, I. et al. (2 more authors) (2019) On the potential of on-line free-surface constructed wetlands for attenuating pesticide losses from agricultural land to surface waters. Environmental Chemistry, 16 (8). pp. 563-576. ISSN 1448-2517

https://doi.org/10.1071/EN19026

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On the potential of on-line free-surface constructed 3

wetlands for attenuating pesticide losses from agricultural 4

land to surface waters 5

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A. Ramos1, M.J. Whelan2*, I. Guymer3, R. Villa1 and B. Jefferson1 13

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1 Water Sciences Institute, Cranfield University, UK 24

2 Centre for Landscape and Climate Research, University of Leicester, UK 25

3 Department of Civil Engineering, University of Sheffield, UK 26

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*Author for Correspondence: mjw72@le.ac.uk 28

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Abstract 34

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Pesticides make important contributions to agriculture but losses from land to water can 36

present problems for environmental management, particularly in catchments where surface 37

waters are abstracted for drinking water. “On-line” constructed wetlands have been 38

proposed as a potential means of reducing pesticide fluxes in drainage ditches and 39

headwater streams. Here, we evaluate the potential of two free-surface constructed wetland 40

systems to reduce pesticide concentrations in surface waters using a combination of field 41

monitoring and dynamic fugacity modelling. We specifically focus on metaldehyde, a 42

commonly-used molluscicide which is moderately mobile and has been regularly detected at 43

high concentrations in drinking water supply catchments in the UK over the past few years. 44

We also present data for the herbicide metazachlor. Metaldehyde losses from the upstream 45

catchment were significant with peak concentrations occurring in the first storm events in 46

early autumn, soon after application. Concentrations and loads appeared to be minimally 47

affected by transit through the monitored wetlands over a range of flow conditions. This was 48

probably due to short solute residence times (quantified via several tracing experiments 49

employing rhodamine WT – a fluorescent dye) exacerbated by solute exclusion phenomena 50

resulting from patchy vegetation. Model analyses of different scenarios suggested that, 51

even for pesticides with a relatively short aquatic half-life, wetland systems would need to 52

exhibit much longer residence times (RTs) than those studied here in order to deliver any 53

appreciable attenuation. If the ratio of wetland surface area to the area of the contributing 54

catchment is assumed to be a surrogate for RT (i.e. not accounting for solute exclusion) then 55

model predictions suggest that this needs to be greater than 1% to yield load reductions of 3 56

and 7% for metaldehyde and metazachlor, respectively. 57

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1. Introduction 59

60

Pesticides make important contributions to maintaining crop yield and quality in modern 61

agriculture. However, pesticide losses from land to surface waters have the potential to 62

cause ecological damage if their concentrations exceed effect thresholds for key taxa (Brock 63

et al., 2006). Furthermore, they can represent major problems for water companies 64

abstracting for domestic supply. The European Drinking Water Directive (80/778/EEC, 65

amended 98/83/EC) sets limits for drinking water supplies of 0.1 µg L-1 for any individual 66

pesticide and 0.5 µg L-1 for all pesticides at the point of supply (effectively in water leaving 67

treatment works for distribution). A significant fraction of most pesticides in raw waters can 68

be removed by the treatment processes commonly employed by water supply companies. 69

However, some active ingredients are more difficult and/or expensive to remove. Recent 70

examples include clopyralid, metaldehyde, propyzamide and carbetamide which are 71

commonly applied to arable crops. Furthermore, even where significant removal is possible, 72

high concentrations in raw waters may still occasionally present compliance challenges (e.g. 73

Tediosi et al., 2012). Metaldehyde (a commonly-used molluscicide) has been regularly 74

detected at high concentrations in drinking water supply catchments in the UK over the past 75

few years (Kay and Grayson, 2014; Lu et al., 2017) and is particularly expensive to remove 76

by standard water treatment processes (Rolph et al., 2018). The UK water industry is coming 77

under increasing pressure from regulators (e.g. the Drinking Water Inspectorate and the 78

Environment Agency) to find alternative solutions to supplement improvements in water 79

treatment technologies and the focus has started to shift to source control options 80

(Mohamad Ibrahim et al., 2019). Indeed, this is a requirement of Article 7 of the EU Water 81

Framework Directive (EC 2000) for catchments used for water supply (Dolan et al., 2014). 82

Possible mitigation strategies include employing buffer zones to reduce overland flow and 83

associated pesticide transport, taking more care during sprayer fill-up and washdown 84

operations, treating farm yard runoff (e.g. using biobeds: Cooper et al., 2016), changing the 85

mix of active ingredients used, where possible (including product substitution: Mohamad 86

Ibrahim et al., 2019) and changing crop rotations to reduce overall catchment-scale usage. 87

Many of these options will involve increased costs to farming and should, therefore, be 88

justified by a sound understanding of the processes contributing to pesticide loss from the 89

farm, as well as demonstrating efficacy. For example, establishing edge-of-field buffer 90

zones will be of little use if, as we have shown in the Upper Cherwell catchment (Tediosi et 91

al., 2012), the main pathway for pesticide transfer from land to water is via field drains – 92

which can effectively short-circuit riparian zones. 93

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In cases where it is difficult or undesirable to effect land use change or to reduce application 94

rates, it may still be possible to reduce concentrations in the receiving surface water network 95

by constructing attenuation features in fields or in ditches (Newman et al., 2015). Natural 96

and man-made wetland habitats are well known to provide efficient removal of organic 97

materials, nutrients and metals (e.g. CEH, 2001; Newman et al., 2015) via a combination of 98

sorption and biodegradation before they are released into the natural drainage network or 99

reach water supply reservoirs. Ponds and free-surface wetlands are open areas of shallow 100

water which can be used for temporary storage of excess water during storm events. If the 101

storage period is long enough, relative to the removal rates operating within them (e.g. 102

degradation by microbes, volatilisation, sorption to sediment or uptake by vegetation), such 103

features can be effective for removing pollution from surface water runoff (Gregoire et al. 104

2009). A number of mesocosm studies have indicated that constructed wetland systems do 105

have the potential to dissipate a range of pesticides (e.g. Moore et al., 2000), and there is 106

some evidence that such systems may also perform effectively at the field scale (Schulz and 107

Peall, 2001; Moore et al., 2002; Moore et al., 2009; Passeport et al., 2013), although this 108

evidence is rather limited. There is, therefore, a need to investigate the potential for 109

reducing pesticide transfers from land to surface waters using field- and ditch-scale 110

mitigation features (typically vegetated free-surface constructed wetlands). In this paper, we 111

assess the utility of such features for attenuating pesticide transport from land to surface 112

waters with a specific focus on metaldehyde using a combination of high frequency 113

monitoring and numerical modelling. 114

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2. Methods 117

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2.1 Monitoring 119

The behaviour of metaldehyde and metazachlor in two existing on-line (in-stream) free-120

surface constructed wetland systems was investigated at Hope Farm in Knapwell, 121

Cambridgeshire, UK (Figure 1). The farm occupies 181 ha on calcareous clay loam 122

belonging to the Hanslope soil series (Cranfield University, 2017). Metaldehyde is applied 123

via a bespoke hopper mounted on the back of a quadbike – most frequently in the August 124

post-harvest period. Metazachlor, a herbicide used to control grass and broad-leaved 125

weeds in arable crops, is applied via spraying in late August. The two monitored systems 126

(located to the north and south of the farm) have a free water surface (FWS) with emergent 127

vegetation. Vegetation cover is seasonal and is dominated by reeds (Phragmites australis) 128

and bulrushes (Typha latifolia) which start growing in spring (March onwards) and die back 129

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in late autumn (October-November). The winter period is characterised by progressive stem 130

decay which may result in reduced flow resistance. At the south site, two wetlands with 131

bank-full surface areas of 242 and 322 m2 (see Table 1) have been placed in series (about 132

114 m apart) on a small stream (catchment area: 3.9 km2) which drains land which is not 133

under the control of Hope Farm: South Wetland 1 and South Wetland 2 (Figure 1; Figure 2), 134

located upstream and downstream respectively. At the north site, a single constructed 135

wetland with a bank-full surface area of 262 m2 (North Wetland, Figure 2) has been 136

excavated on a drainage ditch (see Table 1 for details). This feature drains a catchment of 137

approximately 0.66 km2 containing four fields. At the downstream end, a bund has been 138

constructed which outflows via a pipe (37 cm internal diameter). This means that the flow 139

regime on this system is intermittent (i.e. the wetland only flows when water levels exceed 140

the level of the outflow pipe). 141

142

FIGURE 1 HERE 143

144

TABLE 1 HERE 145

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Monitoring was conducted in a number of storm events and under base flow conditions over 147

one autumn-winter period (August-December 2014). At each feature, gauging structures (a 148

combination of flat plate 60° V-notch weirs and Washington State College Design Venturi 149

flumes) were established on the inlet and outlet to measure discharge. Water levels (stage) 150

were measured using a combination of pressure transducers (Mini-Divers: Van Essen 151

Instruments, Netherlands) and ultrasonic devices (AV2150 submerged probe: Teledyne-152

ISCO, Lincoln, NE, USA). A tipping bucket rain gauge was installed locally (603 m from the 153

South Wetland 2 outlet and 1059 m from the North Wetland outlet) for hydrological modelling 154

and data interpretation. Water samples were collected using a combination of manual grab 155

sampling and ISCO 6712 automatic samplers (Teledyne-ISCO, Lincoln, NE, USA). The 156

automatic samplers were used to collect samples every 8 hours. Where possible, samples 157

were typically analysed within a week of sample collection. In this case, samples were kept 158

in the dark at 4°C before analysis. Instrument issues meant that some samples needed to be 159

stored for several weeks. In this case, samples were either refrigerated (maximum storage 160

time 3 weeks) or frozen. Possible losses of pesticides by sorption or degradation during 161

sample storage were evaluated via a stability study. Briefly, pesticide standards were added 162

to wetland water in polyethylene plastic bottles at nominal concentrations of 0.2 to 10 µg L-1, 163

incubated at either 4°C or -20 °C and analyzed over a period of 112 days. No significant 164

sorption was observed in the filter membrane and losses of all pesticides were below 3% 165

6

(refrigerated samples) and 10% (frozen samples), suggesting that stored samples were 166

stable (see Ramos et al., 2017 for full details). Samples were analysed for five pesticide 167

active ingredients (metaldehyde, propyzamide, metazachlor, carbetamide and quinmerac) 168

using a novel LC/MS-MS method. Only data for metaldehyde and metazachlor are 169

presented here, as examples, but similar outcomes were observed for the others. The paper 170

by Ramos et al. (2017) details the method and the quality control measures which were 171

implemented. Briefly, the method employed a Waters Alliance 2695 liquid-chromatography 172

system coupled to a Quattro premier XE tandem quadrupole. A Kinetex C18 column (5 µm 173

150 2.1 mm, Phenomenex, UK) thermostated at 60 °C was used for chromatographic 174

separation with a flow rate of 0.3 mL min-1 and an injection volume of 50 µL. The mobile 175

phase consisted of ultra-pure water with 0.1% acetic acid and methanol with 0.1% acetic 176

acid. Electrospray ionization was performed in positive mode. The mass spectrometer was 177

operated under multiple reaction monitoring focussed, for metaldehyde, on a reaction with 178

precursor and fragment ions of m/z 198.9 and m/z 66.9, respectively, as recommended by 179

the Environment Agency (2009). Basic environmentally-relevant properties of metaldehyde 180

are shown in Table 2. Data for the herbicide metazachlor are also presented in this paper 181

and this compound was analysed simultaneously using the same method. Method Limit of 182

Detection (LoD) and Limit of Quantification (LoQ) for both metaldehyde and metazachlor 183

were 0.09 and 0.3 g L-1, respectively. These are high compared with the 0.1 µg L-1 EU limit 184

for drinking water (80/778/EEC, amended 98/83/EC) but are adequate for monitoring 185

concentrations in headwater catchments where concentrations during runoff events are often 186

considerably higher. 187

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FIGURE 2 HERE 189

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TABLE 2 HERE 191

192 193

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2.2 Dye tracing 195

A number of dye tracing exercises were conducted on each feature under different 196

conditions (see Fox et al., 2000 and Whelan et al., 2007 for examples). In each exercise, 197

Rhodamine WT was injected as a pulse upstream of the inlet of each feature to ensure that 198

the tracer was cross-sectionally well-mixed and, hence able to fully describe solute transport. 199

The breakthrough curve of the dye was monitored by measuring fluorescence with 200

instruments installed in-situ at the inlet and outlet of the feature. We employed two 201

approaches to modelling solute behaviour in the studied wetlands: (1) the Aggregated Dead 202

7

Zone (ADZ) model of Beer and Young (1983) and (2) maximum entropy deconvolution 203

(Sonnenwald et al., 2015) to determine solute Residence Time Distributions (RTDs) for each 204

system under different conditions. The “non-parametric” (empirical) RTD is the theoretical 205

distribution of solute in a system in response to an instantaneous input (Levenspiel, 1972) 206

and makes no a priori assumptions about how the system operates (Stovin et al. 2010). 207

Both the ADZ and RTD reveal insights into the mixing characteristics of the studied systems 208

such as the identification of “short-circuiting” phenomena. Incomplete mixing can reduce the 209

effectiveness of wetland features for pesticide mitigation by reducing contact with degrading 210

micro-organisms or sorption sites. 211

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2.3 Numerical Modelling 214

A mathematical model was constructed to describe the fate and transport of pesticides in 215

free-surface wetland systems. This provides an explanatory framework for interpreting the 216

findings from the monitoring work and laboratory studies. It also allows the understanding 217

gained from the project to be extrapolated to implementation scenarios (e.g. to predict the 218

effectiveness of different ratios of catchment area to treatment size [surface area and 219

volume]) for different assumptions about pesticide-specific properties. 220

221

The model was a dynamic adaptation of the steady-state QWASI model (Mackay et al., 222

1983) and has recently been applied to investigate the behaviour of volatile methyl siloxanes 223

in an Arctic lake (Krogseth et al., 2017). The processes represented by the model are 224

shown in Figure 3. The mass balance equations for pesticide in water and sediment can be 225

written: 226

227

(1) 228

229

(2) 230

231

where Mw (mol) is the pesticide mass in the water column, Ms (mol) is the pesticide mass in 232

the sediment, E (mol h-1) is direct emission to water, fw is the pesticide fugacity in the water 233

column, fa is the pesticide fugacity in air, fIN is the pesticide fugacity in inflowing water and fs 234

is the pesticide fugacity in sediment. The D values represent the magnitudes of chemical 235

).().(.. resSWsdepWSSSQvrwaaININ

w DDfDDDDDDfDfDfEdt

dM

).().( resbrsSWsdepWSw

s DDDDfDDfdt

dM

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transfer or transformation with subscripts as follows: IN is inflowing water, a is air to water 236

transfer, r is reaction in water, v is volatilisation, Q is advection in outflowing water, SS is 237

suspended solids advection in the outflow, WS is diffusive exchange between the water 238

column and the sediment, dep is the deposition of contaminated sediment, SW is diffusive 239

exchange between the sediment and the water column, rs is reaction in sediment, b is burial 240

and res is sediment resuspention. Fugacity (f) is the medium-equivalent to partial pressure 241

in the gas phase (which is the reference fugacity) and is related to concentration via the 242

general relationship: 243

244

ZfC . (3) 245

246

where Z is the fugacity capacity (mol m-3 Pa-1). For air, Zair is simply calculated from 247

temperature (T) as 248

249

TRZair

.

1 (4) 250

251

where R is the gas constant. For the other compartments Z is expressed in terms of 252

partition coefficients at equilibrium. This means that by writing the mass balance equations 253

in fugacity form, rather than in concentration form, thermodynamic gradients (i.e. the 254

direction and magnitude of phase disequilibrium) are incorporated automatically via 255

differences in fugacity (phases are in equilibrium if they have equal fugacities). All fugacity 256

terms have units of Pa and all D values have units of mol Pa-1 h-1. 257

258

FIGURE 3 HERE 259

260

Model equations were solved numerically using Euler’s method with a time step of 0.1 hour. 261

Scenarios for free-surface wetland deployment were developed and treatment effectiveness 262

assessed. 263

264

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3. Results and Discussion 266

267

3.1 Monitoring: South Wetland 268

Examples of monitored discharge and metaldehyde concentrations in the inlet of SW1 and 269

the outlet of SW2 are shown in Figure 4 for the Autumn and early Winter (August-November) 270

2014. Conditions over this period were slightly wetter than the seasonal average for this part 271

of the UK (total rainfall 1st of August to 31st of December was 335 mm compared to a long-272

term average of 262 mm for this period reported in nearby Cambridge: https://en.climate-273

data.org). The maximum daily rainfall in the monitoring period was 34 mm d-1 which is 274

relatively high. Two early storm events (14 and 26 mm d-1 with maximum intensities of 10 275

and 5.4 mm h-1) on the 14th and 25th of August 2014 generated peak discharges of 46 and 276

78 L s-1, respectively and resulted in elevated concentrations of metaldehyde. Application 277

dates for metaldehyde in this catchment are unknown because most land is not part of 278

Hope Farm. However, we know that Hope Farm applied metaldehyde in accordance with 279

agronomic recommendations on the 19th of August, so we can assume that most 280

application in the wider South Wetland catchment will have occurred around this time. In 281

the second of these events inlet metaldehyde concentrations peaked at about 9 g L-1. It is 282

worth reiterating that metaldehyde is applied as solid (wheat-based) pellets which act as bait 283

for slugs. This means that metaldehyde may be leached from the recently-applied pellets by 284

these early rainfall events, as pellets do not typically disintegrate rapidly. Thereafter, 285

concentrations responded to subsequent events in October and November but not to the 286

same levels. This is because the later events occurred several weeks after the main 287

application period so a significant fraction of metaldehyde would have broken down in the 288

soil. Similar “exhaustion” responses of concentrations to a series of storm events have been 289

observed for propyzamide and carbetamide in the Cherwell catchment (Tediosi et al., 2012; 290

2013). Although the increase in concentration starts early on each storm event (i.e. 291

concentrations increase with increasing discharge), concentrations continue to increase after 292

peak discharge (i.e. during hydrograph recession). This suggests that pathways for 293

metaldehyde transport to the stream are slower than those for bulk water transport. This is 294

consistent with the fact that metaldehyde is applied in pellet form and, thus needs to move 295

from the pellets to the soil prior to displacement from the soil. It is also consistent with the 296

fact that only a small mass of pesticide is required (both in relative and absolute terms) to 297

elevate concentrations significantly. Simultaneously, it is supported by the general insight 298

from tracer studies which have been conducted elsewhere that, in most catchments, the 299

biggest contribution to stream flow is from the displacement of “old” (pre-event) water which 300

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has been resident in the catchment from some time (e.g. Klaus et al., 2013). In most cases, 301

this old water should have low residual pesticide concentrations. 302

303

Metaldehyde concentrations in paired inlet and outlet samples (i.e. where the outlet sample 304

was collected within approximately one hour of the inlet sample time) in the South Wetland 305

system are shown in Figure 5. The red dashed line shows the 1:1 relationship and the solid 306

black line shows the best-fit linear regression. The relationship between the inlet and outlet 307

concentrations was strong (R2 > 0.9). Moreover, most of the points fall close to the 1:1 line 308

suggesting that the outlet concentrations were similar to those in the inlet and the slope of 309

the best-fit line was (0.9) close to unity. These concentrations were also compared formally 310

using a paired t-test to assess whether differences were statistically significant. This 311

confirmed that there was no significant difference between them (p >0.01). 312

313

FIGURE 4 HERE 314

315

FIGURE 5 HERE 316

317

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3.2 Monitoring: North Wetland 319

Figure 6 shows daily rainfall, inflow discharge (V-notch weir) and the concentrations of 320

metaldehyde and metazachlor (for comparison) in the North Wetland inflow and outflow 321

between late August and early October 2014. Both the inlet ditch and the wetland were 322

relatively dry in early August. However, the rainfall event on the 25th of August (26 mm day-323

1) triggered a short (24 hour) hydrograph with a peak discharge of 20 L s-1 on the 26th. The 324

flashy nature of this hydrograph suggests that rapid hydrological pathways such as 325

overland flow played a significant role in this event. Soil moisture content prior to the event 326

was low and 26 mm of rainfall is unlikely to have reduced soil moisture deficits to anywhere 327

near field capacity. In fact, we calculate that less than 2% of the rainfall was translated into 328

runoff, so it is unsurprising that the shallower recession curves often associated with soil 329

drainage (evident in Figure 4 and to a lesser extent in Figure 7) are not apparent. By the 330

end of the 27th of August (and through to the 13th of October) flow in both the inlet and outlet 331

of the wetland were negligible, although subsequent small precipitation events resulted in 332

some minor inflows. During this period, the North Wetland essentially behaved as a static 333

system with no significant inlet or outlet flows. 334

335

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Concentrations of both metaldehyde and metazachlor in the inlet increased and then 336

decreased sharply during this storm event. However, inlet concentrations beyond the 28th 337

of August probably represent those in the pond behind the inlet weir and, hence, do not 338

represent real inflows. No inlet samples were collected beyond the 6th of September. In 339

the wetland itself (sampling point close to the outlet), concentrations of both pesticides also 340

increased during the storm event of the 25th-26th of August (to about 18 and 150 µg L-1 for 341

metaldehyde and metazachlor, respectively). However, subsequent decreases were more 342

gradual and may have resulted from a combination of dilution caused by minor rainfall 343

events in this period, some water turnover (high-concentration advective outflow replaced 344

with lower concentration inflow), net sorption to sediment and in-situ losses (e.g. by 345

degradation and or volatilisation). For both compounds, the rate of decrease in 346

concentration to the 3rd of September was steeper than in the period thereafter. This may 347

reflect some dilution (inflow concentrations < outflow concentrations) but may also be a 348

result of some net sorptive losses from the water column to the sediment. Net sorption will 349

be negligible as thermodynamic equilibrium is reached. Unfortunately, no metaldehyde data 350

are available between the minor storm event on the 25/09/2014 and mid-October which 351

makes full interpretation of metaldehyde behavior challenging for this system – at least in 352

this period of static water. However, concentrations did decrease approximately 353

monotonically from 9.4 µg L-1 on the 4th of September to about 7 µg L-1 on the 25th. The 354

behaviour of metazachlor during this period is also worthy of comment; concentrations 355

decreased quasi-exponentially from 54 µg L-1 on the 4th of September to 2.6 µg L-1 in early 356

October, with a DT50 of 9 days. The mechanisms for this removal are still not completely 357

understood but the exponential shape of the concentration decay suggests that some 358

biodegradation of metazachlor may have occurred within the wetland over this period. For 359

reference, the laboratory-derived DT50 of metazachlor reported for a water-sediment system 360

(PPDB, 2016) is 20 days. Advective solute losses via water infiltration into the bed and 361

banks of the wetland would not have changed concentration in the water remaining. This is 362

explored in more detail in Section 3.4 on modelling. 363

364

FIGURE 6 HERE 365

366

Figure 7 shows the time series of metaldehyde concentrations in the inlet and outlet of the 367

North Wetland, along with rainfall and discharge in the outflow, between mid-October and 368

early December for periods when the outflow was active. As in Figure 4, concentrations in 369

the inlet and outlet of this system were very similar (slope of regression between inlet and 370

outlet concentrations = 1.03, R2 = 0.96) and there was no significant difference between 371

paired concentrations (paired t test p < 0.01). Again, similar observations were made for the 372

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other pesticides including metazachlor (slope = 1.18, R2 = 0.86: data not shown). Thus, as 373

for the South Wetland system, there is little or no pesticide removal in the North Wetland 374

system for flow-active periods, even though the catchment area for this system is much 375

smaller than that for the South Wetland. 376

377

FIGURE 7 HERE 378

379

3.3 Dye Tracing 380

Example fluorescence data from the inlet and outlet of South Wetland 1 is given in Figure 8. 381

These data were collected under approximately steady state flow conditions with a discharge 382

of 13.5 L s-1. The change in peak fluorescence between the inlet and outlet is principally 383

caused by hydrodynamic dispersion and dilution (mixing) with the existing water in the 384

wetland. 385

386

FIGURE 8 HERE 387

388

For steady state, the theoretical (or nominal) residence time can be estimated as the 389

quotient of the water volume and the flow rate (i.e. assuming idealised “plug flow”). For the 390

Hope Farm wetlands, which typically have an approximate volume of 105 m3, this yields a 391

nominal residence time of 130 minutes for a discharge of 13.5 L s-1 (Figure 8). For a 392

discharge of 5 L s-1 the nominal residence time would be around 350 minutes, whilst for 40 L 393

s-1 it is approximately 44 minutes. However, the peak-to-peak delay shown in Figure 8 is 394

only 25 minutes; far shorter than the nominal residence time. This discrepancy is not 395

uncommon and reflects “short circuiting”, where a significant fraction of a tracer is retained 396

for periods which are much shorter than the nominal residence time. 397

398

Fitting the Aggregated Dead Zone (ADZ) model of Beer and Young (1983) to the tracer data 399

obtained in South Wetland 1 at Hope Farm suggested that solute fully mixes in only about 400

50% of the wetland volume, with the remainder exhibiting pure advection. Cumulative RTDs 401

(CRTD) obtained using maximum entropy deconvolution for several dye traces obtained at 402

Hope Farm are shown in Figure 9. This analysis shows that the maximum solute residence 403

time in both the South Wetland systems was less than about 90 minutes for all flow 404

conditions examined. The t50 (the time for half the introduced solute to pass through the 405

wetland) was between 10 and 50 minutes for South Wetland 1 and between 10 and 20 406

minutes for South Wetland 2. The t50 of 25 minutes for South Wetland 1 at 13.5 L s-1 is 407

comparable with the peak-to-peak delay of 25 minutes observed for this discharge in Figure 408

13

8. These times are significantly shorter than the nominal residence times (44 - 350 minutes 409

for flow rates of 40 and 5 L s-1, respectively). The North Wetland has a smaller catchment 410

area so discharges tend to be lower. In addition, the outflow is controlled by the bund at the 411

downstream end which is drained via a pipe. This creates longer residence times in this 412

system – particularly when the outflow is limited. Values of t50 of 60 and 360 minutes were 413

observed for discharge conditions of 17 and 0.5 L s-1 respectively, although these values 414

were also significantly shorter than the nominal residence times for this system. Again, this 415

implies that the mixing volume is smaller than the total volume of the wetland and suggests 416

that more direct solute transport pathways exist, i.e. some solute is “excluded” from some 417

zones - reflecting the complexity of processes associated with real heterogeneous 418

vegetation (Sonnenwald et al., 2017). 419

420

FIGURE 9 HERE 421

422

3.4 Modelling 423

424

3.4.1 South Wetland System 425

Predictions of metaldehyde behaviour in the South Wetland system generated using the 426

dynamic fugacity model based on QWASI (Mackay et al., 1983; Mackay, 2001; Krogseth et 427

al., 2017) are shown in Figure 10 for the first two storm events in autumn 2014, under 428

different assumptions of water depth and wetland water surface area. In all cases, the DT50 429

in water was assumed to be 12.2 days (i.e. the reported value for a water-sediment test in 430

PPDB, 2016) and that in sediment was (arbitrarily) assumed to be 122 days. 431

432

The red lines show the model predictions of concentration in the outflow, the open symbols 433

show the measured concentrations in the outflow and the green line shows the measured 434

concentrations in the inflow (linearly interpolated to give a time-continuous curve). In all 435

cases, the mass influx of chemical was calculated as the product of the measured influent 436

concentration and the influent discharge. Figure 10a shows the expectation based on the 437

best-estimate of wetland conditions at the time of monitoring (water depth, z = 0.5 m, water 438

surface area, A = 210 m2). There is a good match between the model predictions and the 439

measured data – primarily because there is such a low hydraulic residence time in the South 440

Wetland, particularly during storm events. Increasing the water depth to 1 m had little effect, 441

although there was a slight reduction in concentrations due to increased dilution and 442

residence time (Figure 10b). Increasing the water surface area by a factor 2, with other 443

factors remaining constant (Figure 10c) resulted in some (again, relatively minor) additional 444

14

attenuation. More significant attenuation was only really achieved (i.e. concentrations were 445

reduced by 36%) with a factor 10 increase in surface area (Figure 10d), although most of the 446

concentration change was due to dilution and the total mass removal of metaldehyde was 447

still predicted to be <1%. This is largely due to the fact that even for the largest wetland 448

volume, the nominal residence time is still very low compared with the assumed DT50 of 449

metaldehyde (12.2 days). At peak discharge (69 L s-1), the nominal solute residence times 450

for these scenarios range from just 0.42 h (25 minutes) (Figure 10a) to 8.45 h (507 minutes) 451

(Figure 10c). 452

453

FIGURE 10 HERE 454

455

456

3.4.2 North Wetland System 457

Model results for the North Wetland static period are illustrated in Figure 11 for metazochlor 458

(a and b) and metaldehyde (c and d). In all cases, an arbitrary system temperature of 15 °C 459

was assumed, together with an Arrhenius activation energy of 60 kJ mol-1 which is typical of 460

many organic pollutants in environmental systems [Rocha and Walker 1995; Fenner et al., 461

2007] but which is slightly lower than the value of 65.4 kJ mol-1 recommended by EFSA for 462

regulatory evaluations (https://www.efsa.europa.eu/en/efsajournal/pub/622). As for metaldehyde 463

in the South Wetland, the initial concentration of both compounds in the wetland were 464

reasonably well predicted by the model suggesting that the input loads (the product of 465

discharge and measured concentrations) were accurate. In Figure 11a, standard PPDB 466

(2016) chemical-specific parameters for metazachlor were assumed. This clearly 467

underestimated the loss rate under static conditions in this system. In Figure 11b, the DT50 468

in water for metazachlor was reduced to 5.6 days (135 h). Although this is shorter than the 469

apparent half-life derived from directly fitting an exponential decay curve to the data (ca 9 470

days), this is adjusted in the model for partitioning between the freely dissolved phase and 471

the suspended solids and for temperature (see Whelan et al., 2015). Figure 11c shows the 472

predicted concentrations of metaldehyde for default system dimensions and chemical 473

properties (PPDB, 2016). Again, model performance was reasonably good in terms of the 474

peak concentrations triggered by the storm event. However, the measured concentration 475

data do not display first order kinetics (the initial decline in concentrations was more rapid 476

than in the period beyond 250 h) and the model performance is, therefore, relatively weak 477

overall and particularly in the latter part of the record. This implies that the DT50 adopted for 478

metaldehyde (12.2 days) may be too low, even considering temperature adjustment, 479

although some model assumptions (e.g. the assumption of a time-constant water volume in 480

each scenario may explain some of the discrepancy). The effect of hypothetically changing 481

15

pond surface area (by factors of 2, 5 and 10, with all other factors kept constant) on 482

predicted concentrations in the North Wetland for the static period are also shown in Figure 483

11d. The increase in system volume reduces the predicted peak concentration mainly due 484

to dilution. 485

486

FIGURE 11 HERE 487

488

The model was also applied to a period in November when the North Wetland was flowing 489

continuously (cf Figure 7) in order to evaluate the effect of increasing wetland area on 490

metaldehyde concentrations. The predictions are shown in Figure 12 for the default case 491

and for increases in surface area by factors of 2, 5, 10 and 20 (i.e. A = 600, 1500, 3000 and 492

6000 m2, respectively). The assumption of A = 6000 m2 represents a wetland area to 493

catchment area ratio of approximately 1%. This is a feasible area for a constructed wetland 494

and is consistent with farm wetland siting for nutrient removal in Demark (e.g. Hashemi et 495

al., 2018) and with recommendations (1–2%) by Passeport et al. (2013) for “on-line” 496

features. Passeport et al. (2013) reported load reductions between 45 and 96% for 11 497

herbicides and 5 fungicides (mainly due to partly-reversible sorption) in a 1280 m2 free 498

surface constructed wetland serving a tile-drained catchment of 46 ha (average hydraulic 499

retention time of about 8 hours) but this system was off-line (i.e. water was diverted from the 500

ditch to the wetland during the post application period). As in the case of the South Wetland 501

simulations, the modelled influx of metaldehyde was assumed to be the product of discharge 502

and interpolated inflow concentrations. For the default assumptions, the predicted outflow 503

concentrations match the measured data reasonably well, reflecting the similarity of the 504

measured inflow and outflow concentrations. As the assumed wetland area increases (with 505

all other factors remaining constant), the wetland volume and nominal residence times 506

increase and predicted peak concentrations decrease by approximately 4, 12, 22 and 32%, 507

respectively. However, the attenuation in metaldehyde flux is predicted to be only 0.06, 508

0.14, 0.41, 0.99 and 2.4%, respectively, for the default, x2, x5, x10 and x20 cases. In other 509

words, predicted concentration changes are principally the result of dilution rather than loss 510

processes. This is illustrated by the fact that predicted concentrations between 100 and 300 511

hours increase with increasing wetland surface area (i.e. the peak concentrations for 512

increasing areas are lower but the residence times are longer so the pesticide mass remains 513

in the system for longer). When the model was run for metazachlor, with an assumed DT50 of 514

135 h - as inferred from the static period data, these predicted reductions were 0.29, 0.60, 515

1.52, 3.24 and 6.84% (data not shown). For metadehyde in the x20 scenario 83% of the in-516

system losses were predicted to be due to degradation (Dr) and 16% due to volatilisation 517

(Dv). For metazachlor 97% of in-system losses in the x20 scenario were predicted to be due 518

16

to degradation (Dr). Net sorption to sediment is not predicted to represent a significant sink 519

in most storm events for either pesticide. Of course, incomplete mixing would reduce 520

residence times and, hence, reduce likely removal rates in reality. 521

522

It is widely recognised that biodegradation is often more rapid in shallow water bodies than 523

in ones. For example, high first order rate constants (k) of 0.3 and 0.75 h-1 were reported for 524

linear alkylbenzene sulphonate (LAS) in shallow streams in Yorkshire (Fox et al., 2003) and 525

the Philippines (McAvoy et al., 2003). In contrast, lower values of k for LAS of 0.05 and 526

0.096 h-1, respectively, have been reported in deeper rivers in Italy (Whelan et al., 1999) and 527

Laos (Whelan et al., 2007). Similarly, in-stream denitrification rates have been inversely 528

related to depth (Seitzinger et al., 2002). This is probably because the size of the competent 529

microbial biomass in the water column is relatively low compared with the microbial 530

community in biofilms attached to the wetted perimeter of the channel, which means that 531

degradation will depend on contact time with the fixed biofilm. In shallow systems, dissolved 532

pollutants are more likely to come into contact with the bed than in deep ones – especially if 533

turbulence is also higher. For similar reasons, biodegradation is believed to proceed more 534

rapidly in natural river channels compared with channels with relatively smooth (e.g. 535

concrete lined) beds. This is because natural beds typically have a higher surface 536

roughness and, hence, a higher surface area on bed materials for biofilm development 537

(Boeije et al., 2000). Wetland shape can also influence performance. Carty et al. (2008) 538

suggest that wide farm wetlands with low aspect ratio (length to width less than 2.2) tend to 539

perform better than long and thin ones, all other factors being equal and assuming full 540

mixing, although residence time still needs to be sufficient for the relevant treatment 541

processes to have an effect. The wetlands investigated here have low aspect ratios 542

(between 1.8 and 2.4) but without long residence times they were relatively ineffective. 543

544

FIGURE 12 HERE 545

546

4. Conclusions 547

548

Some pesticides present significant challenges for European Drinking Water Directive 549

compliance in catchments used for domestic water supply. This is often the case, 550

periodically, in catchments with a high fraction of intensive agriculture. When artificial field 551

drainage is prevalent, drainflow usually represents a major transport pathway and this 552

makes in-field mitigation of pesticide transfers difficult. 553

17

554

Free-surface wetland features are relatively cheap to install using diggers and loaders 555

commonly available on farms and offer a potential means of attenuating pesticide transfers 556

from agricultural land to surface waters (Passeport et al., 2013). We monitored the pesticide 557

attenuation performance of three small free-surface wetlands with different catchment areas 558

in Cambridgeshire, UK. Observed removal rates were relatively low – particularly during 559

storm events, when pesticide loads were highest. A dynamic fugacity-based model of 560

chemical behaviour in free-surface wetlands was constructed in order to provide a 561

framework for interpreting the observed data and for evaluating different combinations of 562

controlling factors in different scenarios. Model predictions broadly agree with 563

measurements for the wetlands investigated (although some adjustment of chemical-specific 564

properties was required to get a good match for the North Wetland system) and suggest that 565

poor attenuation performance can be largely explained by low solute residence times. Dye-566

tracing exercises and subsequent analysis suggest that incomplete mixing (short circuiting) 567

often occurs in the monitored wetlands, which will reduce residence times and attenuation 568

potential. This can be exacerbated by patchy vegetation, which can promote exclusion 569

zones for solute. 570

571

There is some potential for reducing peak concentrations and loads for relatively larger 572

wetlands – particularly for low-discharge, high-concentration events, provided that long water 573

residence times can be achieved. Pesticide removal for the North Wetland system was 574

estimated to be approximately 12% for metaldehyde and 20% for metazachlor under static 575

conditions. However, total mass removal (flux reduction) was negligible during storm events 576

(<1%). Although predicted peak concentrations of both compounds decreased when 577

assumed wetland surface area to catchment area ratio (RA) was increased (cet. par.), 578

appreciable load reduction was predicted only when RA was greater than about 1%. Even 579

then, load reduction was still <3% for metaldehyde and <7% for metazachlor, given a 29-fold 580

increase in wetland area. 581

582

Overall, our data suggest that small on-line FWS wetland features may be relatively 583

ineffective at reducing pesticide concentrations and loads during storm events and, hence, 584

are probably not a viable approach to deliver effective catchment management of pesticide 585

fluxes into receiving water bodies used for drinking water production. Wetland performance 586

will improve if the catchment size is small relative to the wetland dimensions (i.e. where 587

residence times are high). This means that a large number of such features would need to 588

be constructed in the landscape to make an appreciable difference at the catchment scale. 589

Wetland performance may also be enhanced by ensuring that contact between solutes and 590

18

wetland surfaces are maximised – e.g. by installing baffles to enhance mixing, by designing 591

wetlands with low water depth, segmenting wetlands into discrete basins (Scholz et al., 592

2007) and reducing the aspect ratio (provided full mixing can be ensured). The introduction 593

of sorptive surface materials into the wetland may also be of value and further research is 594

required to explore the efficacy of different materials for this purpose. 595

596

597

598

5. Acknowledgements 599

600

This work was jointly funded by the Chemicals Regulation Division (CRD) of the UK Health 601

and Safety Executive (Project PS2248) and Lonza. Interpretations of the data are those of 602

the authors and are not necessararily endorsed by the sponsors. We are grateful to the 603

RSPB for allowing us to use Hope Farm as a study site and, in particular, the farm manager 604

Ian Dillon. We also acknowledge Ian Bayliss and Vassia Ioannidou for help with weir and 605

instrument installation, sample collection and dye tracing. None of the authors declare any 606

conflict of interest. 607

608 609

19

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611

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741

24

Table 1. Details of the wetland features assessed in this study. All systems were unlined 742

and had a natural bed consisting of approximately 15cm of organic rich fine sediment. All 743

systems were excavated around 2000 and planted with reeds (Phragmites australis) and 744

bulrushes (Typha latifolia) but the plants are unmanaged and the original vegetation 745

distributions have altered over time. Water depth in each system during the winter is typically 746

around 0.5 m. 747

748

Wetland Surface

Area (m2)

Length

(m)

Width

(m)

Length:

Width

Ratio

South Wetland 1 242 30 16.8 1.79

South Wetland 2 322 36 18.6 1.93

North Wetland 262 28 11.5 2.43

749

750

751

Table 2. Environmentally-relevant properties for (a) metaldehyde (PPDB, 2016) and (b) 752

metazachlor. Kd is the soil solid to water partition coefficient, KOC is the organic carbon to 753

water partition coefficient, KfOC is the Freundlich isotherm organic carbon to water partition 754

coefficient range derived from EFSA (2010), Sol is aqueous solubility, KAW is the air to water 755

partition coefficient (the dimensionless Henry’s Law constant) and DT50 is the dissipation half 756

life. W-S is water and sediment, * refers to the range of water DT50 in water-sediment tests 757

derived from EFSA (2010). 758

759 760

Kd

(L kg-1)

KOC

(L kg-1)

KfOC

(L kg-1)

Sol

(mg L-1)

KAW DT50 (days)

Soil W-S W-S (water)*

(a) 0.23 240 38-149 188 1.43 x 10-3 5.1 12.2 10 - >1000

(b) 0.78 54 54-220 450 1.80 x 10-7 8.6 20.6 49 - 384

761

762

763

25

764

765

766

767

Figure 1. (a) General location of Hope Farm, Cambridgeshire; (b) The location of the 768

monitored wetlands on the stream and drainage network (NW is North Wetland; SW is South 769

Wetland; automatic samplers and gauge structures were installed at the inlet and outlet of 770

each feature); (c) Catchment area and elevation map for the South Wetland system derived 771

from UK Ordnance Survey Terrain 5 Data (5 m gridded digital elevation model). 772

773

m asl

3.9 km2 catchment

NW

SW1

SW2

200 m

Wetland Feature

Field Boundary

Drainage Ditch

Stream

(a)

(b)

Cambridge

(c)

Location of SW1

26

774

775

Figure 2. Detailed plans of the monitored wetlands showing the location of automatic 776

samplers and gauging structures. The wetland perimeter represents a bank-full water level. 777

The 1 m contours are drawn relative to a datum on the perimeter. 778

779

27

780

781

782

Figure 3. Schematic illustration of the processes represented by the QWASI lake model 783

(after Mackay et al., 1983). 784

785

786

DB

BURIED SEDIMENT

SURFACE SEDIMENT

WATER COLUMN

SEDIMENT

TRANSFORMATION

WATER

TRANSFORMATION

AIR

DR DT

DTDD

SEDIMENT

RESUSPENSION

SEDIMENT

DEPOSITION

SEDIMENT/WATER

DIFFUSION

VOLATILIZATION

WATER INFLOW

DI

PARTICLE INFLOW

DX

WATER OUTFLOW

DJ

PARTICLE OUTFLOW

DY

ABSORPTION

AIR

PARTICLE

DEPOSITION

RAIN DISSOLUTION

DRY

WET

DC DQ DVDVDM

DW

DS

fW

fS

fI

EMISSION

E

DB

BURIED SEDIMENT

SURFACE SEDIMENT

WATER COLUMN

SEDIMENT

TRANSFORMATION

WATER

TRANSFORMATION

AIR

DR DT

DTDD

SEDIMENT

RESUSPENSION

SEDIMENT

DEPOSITION

SEDIMENT/WATER

DIFFUSION

VOLATILIZATION

WATER INFLOW

DI

PARTICLE INFLOW

DX

WATER OUTFLOW

DJ

PARTICLE OUTFLOW

DY

ABSORPTION

AIR

PARTICLE

DEPOSITION

RAIN DISSOLUTION

DRY

WET

DC DQ DVDVDM

DW

DS

fW

fS

fI

EMISSION

E

fA

DB

BURIED SEDIMENT

SURFACE SEDIMENT

WATER COLUMN

SEDIMENT

TRANSFORMATION

WATER

TRANSFORMATION

AIR

DR DT

DTDD

SEDIMENT

RESUSPENSION

SEDIMENT

DEPOSITION

SEDIMENT/WATER

DIFFUSION

VOLATILIZATION

WATER INFLOW

DI

PARTICLE INFLOW

DX

WATER OUTFLOW

DJ

PARTICLE OUTFLOW

DY

ABSORPTION

AIR

PARTICLE

DEPOSITION

RAIN DISSOLUTION

DRY

WET

DC DQ DVDVDM

DW

DS

fW

fS

fI

EMISSION

E

DB

BURIED SEDIMENT

SURFACE SEDIMENT

WATER COLUMN

SEDIMENT

TRANSFORMATION

WATER

TRANSFORMATION

AIR

DR DT

DTDD

SEDIMENT

RESUSPENSION

SEDIMENT

DEPOSITION

SEDIMENT/WATER

DIFFUSION

VOLATILIZATION

WATER INFLOW

DI

PARTICLE INFLOW

DX

WATER OUTFLOW

DJ

PARTICLE OUTFLOW

DY

ABSORPTION

AIR

PARTICLE

DEPOSITION

RAIN DISSOLUTION

DRY

WET

DC DQ DVDVDM

DW

DS

fW

fS

fI

EMISSION

E

fA

28

787

788

789

790

Figure 4. Stream discharge at the V-notch weir (Q, right axis) and the concentration of 791

metaldehyde (left axis) in samples collected from the inlet (SW1) and outlet (SW2) of the 792

South Wetland system between mid-August and early-December 2014. 793

794

0

5

10

15

20

25

30

35

Ra

infa

ll (m

m d

-1)

0

50

100

150

200

250

0

1

2

3

4

5

6

7

8

9

10

Q (

L s-1

)

Me

tald

eh

yd

e c

on

c. (g

L-1

)

Metaldehyde (ug/L) Inlet

Metaldehyde (ug/L) Outlet

Q V-notch (l/s) IN

29

795

796

797

Figure 5. Comparison of equivalent inlet and outlet concentrations in the South Wetland. 798

Dashed lines show the 1:1 relationship. Solid black lines show the best-fit linear regression 799

(equation and R2 values also shown). 800

801

802

803

y = 0.9048x + 0.0692

R² = 0.9043

0

2

4

6

8

10

0 2 4 6 8 10

Co

nc.

Ou

tle

t S

W 2

(g

L-1

)

Conc. Inlet SW1 (g L-1)

30

804

805

806

807

Figure 6. Inflow discharge and the concentrations of metaldehyde and metazachlor in the 808

inlet and outlet of the North Wetland between late August and early October 2014. 809

810

811

0

1

2

3

4

5

0

20

40

60

80

100

120

140

160

Q (

L s-1

)

Co

nce

ntr

ati

on

(g

L-1

)

METAZACHLOR OUT

METALDEHYDE OUT

METAZACHLOR IN

METALDEHYDE IN

Inlet Q

0

10

20

30

40Ra

infa

ll (

mm

d-1

)

31

812

813

Figure 7. Stream discharge at the outflow (Q, right axis) and the concentration of 814

metaldehyde (left axis) in samples collected from the inlet and outlet of the North Wetland 815

system between mid-October and early-December 2014. 816

817

0

50

100

150

200

250

300

350

400

0

1

1

2

2

3

3

4

4

5

08/10/2014 22/10/2014 05/11/2014 19/11/2014 03/12/2014

Q (

L s-1

)

Me

tald

eh

yde

co

nc.

(g

L-1

)

Inlet Outlet Q

0

5

10

15

20

25

30

35

Ra

infa

ll (m

m/d

)

32

818

Figure 8. Example fluorescence data for South Wetland 1 for a discharge of 13.5 L s-1.

819

820

821

822

33

823

a) South Wetland 1 b) South Wetland 2

c) North Wetland

Figure 9. Cumulative Residence Time Distributions for Hope Farm Wetlands (a) South

Wetland 1; (b) South Wetland 2; (c) North Wetland for tracing exercises conducted under

a range of different discharges in 2014.

824

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 10 20 30 40 50 60

CR

DT

(-)

Time, t (mins)

18/11/2014 @ Q = 65 l/s

04/12/2014 @ Q = 24 l/s

27/01/2014 @ Q = 10 l/s

13/11/2014 @ Q = 10 l/s

34

825

826

Figure 10. Predicted changes in aqueous-phase metaldehyde concentrations over time in 827

South Wetland 1 in response to two storm events. The time scale is from 14:30 on the 14th of 828

August. The DT50 in water was assumed to be 12.2 days (i.e. the reported value for a water-829

sediment test in PPDB, 2016) and that in sediment was (arbitrarily) assumed to be 122 days. 830

In all cases, the dashed line is stream discharge at the outlet, the open square symbols 831

show the measured concentrations at the outlet, the green line shows the predicted 832

concentration at the inlet and the red line shows the predicted concentration at the outlet for 833

(a) z=0.5 m, A=210 m2; (b) z=1 m, A=210 m2; (c) z=1 m, A=420 m2; (d) z=1m, A=2100 m2 834

where z is the water depth and A is the water surface area. 835

836

837

838

839

0

10

20

30

40

50

60

70

80

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 100 200 300 400 500 600 700 800

Dis

cha

rge

(L/

s)

Co

nc

(ng

/L )

Time (hours)

C pred

C meas

C input

Q meas

0

10

20

30

40

50

60

70

80

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 100 200 300 400 500 600 700 800

Dis

cha

rge

(L/

s)

Co

nc

(ng

/L )

Time (hours)

C meas

C input

Cpred

Q meas

0

10

20

30

40

50

60

70

80

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 100 200 300 400 500 600 700 800

Dis

cha

rge

(L/

s)

Co

nc

(ng

/L )

Time (hours)

C meas

C input

Cpred

Q meas

0

10

20

30

40

50

60

70

80

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 100 200 300 400 500 600 700 800

Dis

cha

rge

(L/

s)

Co

nc

(ng

/L )

Time (hours)

C pred

C meas

C input

Q meas

(a) (b)

(c) (d)

35

840

841

842

Figure 11. Predicted changes in aqueous-phase metazachlor and metaldehyde 843

concentrations over time in the North Wetland in August and September 2014. The time 844

scale is from 0:00 on the 24th of August. (a) metazachlor with a DT50 in water of 494 h (20.6 845

days) (i.e. reported from a water sediment test in the PPDB Pesticide Database); (b) 846

metazachlor with a DT50 in water of 135 h (i.e. equivalent to an effective temperature-847

adjusted DT50 of ca 207 h [9 d]); (c) metaldehyde with a DT50 in water of 292 h (12.2 days). 848

In all cases z=0.5 m and the catchment area was assumed to be 0.66 km2; (d) metaldehyde 849

with changes in A (x1=300 m2; x2=600 m2; x5=1500 m2; x10=3000 m2). 850

851

852

0

5

10

15

20

25

0

5,000

10,000

15,000

20,000

25,000

30,000

0 200 400 600 800 1000 1200

Dis

cha

rge

(L/

s)

Co

nc

(ng

/L )

Time (hours)

C input

Cpred

C meas

Q meas (IN)

0

5

10

15

20

25

0

5,000

10,000

15,000

20,000

25,000

30,000

0 200 400 600 800 1000 1200

Dis

cha

rge

(L/

s)

Co

nc

(ng

/L )

Time (hours)

C input

C meas

x1

x2

x10

x5

Q meas (IN)

0

5

10

15

20

25

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

0 200 400 600 800 1000 1200

L/

Co

nc

(ng

/L )

Time (hours)

0

5

10

15

20

25

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

0 200 400 600 800 1000 1200

Dis

cha

rge

(L/

s)

Co

nc

(ng

/L )

Time (hours)

C meas

C input

C pred

Q meas (IN)

(a) (b)

(c) (d)

x1

x2

x5

x10

36

853

854

855

Figure 12. Measured and predicted concentrations of metaldehyde and outflow discharge 856

(Q) in the North Wetland for November 2014. The time scale is from 0:00 on the 13th of 857

November. (a) Default case (A=300 m2); (b) Predictions for A = x2, x5, x10 and x20 (600, 858

1500, 3000 and 6000 m2). 859

0

50

100

150

200

250

300

350

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

0 50 100 150 200 250 300 350 400 450

Dis

cha

rge

(L/

s)

Co

nc

(ng

/L )

Time (hours)

C meas

Default

x2

x5

x10

x20

Q

0

50

100

150

200

250

300

350

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

0 50 100 150 200 250 300 350 400 450

Dis

cha

rge

(L/

s)

Co

nc

(ng

/L )

Time (hours)

C meas

Default

Q

(a)

(b)