Assessing active and passive samplers for water quality monitoring in Dutch surface waters

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6 september 2019

Monitoring of

pesticides in

surface water and

sediment

An assessment of an active and a

passive sampler

Romee Prijden

10761314

University of Amsterdam

Daily supervisor/Examiner: dhr. dr. S.T.J. Droge

Assessor: dhr. dr. M.H.S. Kraak

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1. Abstract

Protecting water bodies from pesticide contamination is a major task of water quality assessment. The presence of pesticides may have a negative impact on the environment, causing effects such as acute and chronic toxicity to aquatic organisms, pollution of drinking water sources, and accumulation of toxic compounds in the human food chain. In this study, two relatively new sampling methods for monitoring pesticides in surface water and sediment will be assessed. These are (1) active Continuous Low-Level Aquatic Monitoring (CLAM) and (2) passive silicone rubbers (SR). This will be done by investigating the accuracy and overall performance of both samplers in a controlled laboratory setting and in six agricultural ditches in Flevoland, the Netherlands. Water samples and extractions will be analyzed with an existing LC-MS methodology, provided by the University of Amsterdam. Subsequently, a toxicity test with Daphnia magna will be performed to determine how well both samplers can estimate toxicity in water and sediment. The aim of this research is to eventually create an efficient methodology for Dutch waterboards to monitor pesticides in water and sediment and to enhance their water quality assessments.

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2. Introduction

Protecting water bodies from contamination with toxic substances is a major task of water quality assessment (Brack et al., 2019). The Water Directive Framework (WFD) aims to protect the chemical status of both surface- and groundwater bodies across Europe. The chemical status of a water body is based on the absence or presence of the 45 Priority Substances and various sets of River Basin-Specific Pollutants (Brack et al., 2019). However, toxic events can often not be connected to these substances (de Baat et al., 2018). Aquatic monitoring of organic contaminants can be used to determine which contaminants are responsible for deteriorated water quality (Ensminger et al., 2019).

Agricultural run-off of pesticides is a significant contributor to deteriorating surface water quality in the European Union (Morrison, Luttberg & Belden, 2016). Present pesticides may have a negative effect on the environment, causing effects such as acute and chronic toxicity to aquatic organisms, pollution of drinking water sources, and accumulation of toxic compounds in the human food chain (European Environment Agency, 2018; Xie et al., 2018). Pesticides can be present at low concentrations and include organic compounds with a wide range of properties, which complicates sampling and analysis methods to monitor water quality (Martin et al., 2016). Pesticide monitoring is often done by taking grab samples, a method which has been criticized in multiple studies (Ensminger et al., 2019; Oost et al., 2017; Smedes, Bakker & de Weert, 2010).

According to a report from the RIVM, the Netherlands has national quality standards for approximately 700 active pesticides including several degradation products (Rijksinstituut voor Volksgezondheid en Milieu, 2014). However, there are two problems with the current monitoring program. Firstly, current monitoring of surface waters in the Netherlands is based on grab sampling (Oost et al., 2017). The majority of targeted substances are not detected during monitoring. In most cases, this is caused by the fact that those pesticides were not used during or prior to the sampling period. However, in some cases, pesticides were not detected because they were present below detection limits (RIVM, 2014). A second problem is that Dutch water monitoring is mainly focused on monitoring water quality. The reason is that the WFD only deals with water quality and does not target the sediment quality specifically, although it is considered to be an important link in overall water quality (Brils, 2008). This means that there is limited knowledge on the chemical status of the sediment, which might contribute to ecological degradation of present ecosystems.

Recent development in alternative sampling methods allowed (i) for lower detection limits, (ii) to better assess the diversity of pesticides in the environment and (iii) to yield the freely dissolved concentration, which is more comparable to the in situ bioavailability of a pesticide in water and in sediment (Kim Tiam et al., 2016). In this study, two relatively new sampling methods, active Continuous Low-Level Aquatic Monitoring and passive silicone rubber sampling, for monitoring pesticides in surface water and sediment will be assessed. This will be done by investigating the accuracy of both samplers in a controlled laboratory setting

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and in the field. Subsequently, a toxicity test will be done to determine how well both samplers can estimate toxicity.

2.1 Aim of the research

The general aim of this study is to assess alternative sampling methods for monitoring pesticides in freshwater aquatic systems. The specific aims for this study are: (1) to assess the efficiency of Continuous Low-Level Aquatic Monitoring as an active sampler of pesticides in surface water, (2) to assess the efficiency of Silicone Rubber as a passive sampler of pesticides in pore water, and (3) to estimate the toxicity of the water and pore water at six locations with expected emissions of pesticides.

3. Theoretical framework

3.1 Pesticides

The term pesticides is a collective term that includes all chemicals that can be used to control or kill pests. This includes insecticides, herbicides, fungicides, rodenticides and nematicides (Kim, Kabir & Jahan, 2017). Pesticides have different effects on the target species. Examples of effects are death of the organism, carcinogenesis, disruption of the endocrine system, inhibition of reproduction, and damage to DNA and cells (Gakuba et al., 2018). The effects can extend to other trophic levels in the ecosystem through bioconcentration, biomagnification, and through changes in the food chain (Ongley, 1996). Over the past decades, many advances have been made in the detection and analysis of potentially harmful pesticides. However, there are still many compounds present in aquatic systems that need to be identified and quantified in various environmental components such as water, sediment and ecological receptors (Gavrilescu et al., 2015). These compounds can be pesticides that were already used but could not be recognized until new detection methods were developed (Gavrilescu et al., 2015; Kim Tiam et al., 2016). Another source of these compounds is the synthesis of new pesticides or changes in use of existing ones (Geissen et al., 2015). The eco-toxicological significance of many pesticides is often underestimated, because the measured concentrations are relatively low or even below detection limit. Besides, many monitored pesticides have low persistency while their degradation products can be present in larger concentrations than the actual pesticide (Houtman, 2010). This study aims to determine the accuracy of an active and a passive sampling method to detect and quantify present pesticides in surface water and in sediments.

3.1.1 Pesticides in water-sediment systems

Pesticides may leach into surface water via field runoff, drainage or spray-drift (Szöcs et al., 2017). The environmental fate of a pesticide depends on the natural affinity of the chemical for one of the environmental matrices: solid matter (particulate organic carbon and mineral matter), liquid (solubility in soil, ground and surface water), gaseous form (volatilization), and the biota (Ongley, 1996). The environmental fate of a pesticide can be predicted with the use of parameters: the soil sorption coefficient (Koc), the solubility or Henry’s Constant (H), and the

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Sediment is derived from weathering and erosion of minerals, organic material and soils in upstream areas. As water flow velocity declines, the transported material is accumulated by sedimentation (Brils, 2008). Many persistent pesticides are hydrophobic and therefore mostly sequestered by organic matter in sediments (Supowit et al., 2016). Depending on the characteristics of the contaminant, sediments can act as both sink and as source of contaminants in aquatic systems (Lydy et al., 2014). The fate and transport of these contaminants is dependent

on physical,

chemical and

biological processes.

Various processes

control the distribution

of pesticides

between water and

sediment, shown in

figure 1.

3.2 Grab sampling versus active/passive sampling

Current monitoring of surface waters in the Netherlands is based on grab sampling, which might provide non-representative data (Oost et al., 2017). There are three main limitations about monitoring the chemical composition with a grab sampling method. One limitation is that grab sampling provides a snapshot of the chemical composition at the moment of sampling (Booij et al., 2016). Because the sample does not reflect the dynamic changes in the water body, episodic events can be missed (Monteyne, Roose & Janssen, 2013). This may lead to an under- or overestimation of the concentration of present chemicals. A second limitation is that the small sample sizes of grab samples do not allow for low level detection of chemicals (Ensminger et al., 2017; Monteyne et al., 2013). And thirdly, grab samples only yield the total dissolved and particulate concentration of a chemical in a sample, rather than the freely dissolved concentration (Booij, et al., 2016). Active and passive samplers were developed as an alternative to grab sampling. Both samplers are environmental monitoring techniques that use a collecting medium to accumulate contaminants in water over time. Passive sampling yields the

Figure 1: Transport, distribution and transformation processes of pesticides in a water-sediment system. AqB = aquatic biota; SDO = sediment dwelling organisms; DOM = dissolved organic matter; SS = suspended solids. (1) = hydrolysis, redox reactions, photolysis, biodegradation; (2) = hydrolysis, redox reactions, biodegradation; (3) = adsorption, desorption, diffusion; (4) = solubilization, catalysis, complex formation; (5) = adsorption, desorption, catalysis. Adapted from Katagi (2006).

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concentration of (freely) dissolved compounds, while active sampling can measure the time-average total and dissolved concentrations of a compound (Booij et al., 2016; Supowit et al., 2016). The freely dissolved concentration that can be obtained with passive sampling is considered to be a good proxy of sediment toxicity and bioaccumulation in benthic organisms (Booij et al, 2016; Lydy et al., 2014). These methods are less time consuming and more efficient because less field trips are required to collect representative samples. Consequently, the extracts of passive/active samplers are considered to be promising to apprehend the toxicity of pesticide mixtures (Kim Tiam et al., 2016). In this study, an active and a passive sampling method will be used to assess the pesticide composition of water bodies in Flevoland. These are: Continuous Low-Level Aquatic Monitoring as an active sampler and silicone rubber as a passive sampler.

3.2.1 Continuous Low-Level Aquatic Monitoring (CLAM)

Continuous Low-level Aquatic Monitoring (or the CLAM) actively filters water at low rate while capturing total and dissolved organic compounds (Aqualytical, nd). According to Ensminger et al. (2017), the CLAM sampler can be used to monitor a wide spectrum of organic contaminants in surface water bodies. Because of the automated sampling, it eliminates the use of large water samples (Supowit et al., 2016). The CLAM analysis can be split into several subsets, such as standard laboratory methods and low detection levels (Aqualytical, n.d.). According to Supowit et al. (2016), the CLAM is able to achieve detection limits in the parts-per-quadrillion range for several (non-)polar organic compounds in water, such as pesticides. The CLAM sampler is able to continually extract and concentrate organic contaminants from water onto a solid phase extraction (SPE) disk (Aqualytical, nd). Although different SPE disks could be used, the HLB Media Disk with a hydrophobic/lipophilic balance (HLB) sorbent was found to be the most effective at extracting a wide range of pesticides (Hladik, Smalling & Kuivila, 2008). A glass fiber pre-filtration disk can be added on top of the HLB media disk to limit clogging of the HLB filter and to ensure that only the freely dissolved concentration of pesticides reaches the HLB sorbent (Aqualytical, n.d.).

A limitation of this method might be the maximum biding capacity (MBC) of the amount of sorbent used. When the MBC is exceeded, breakthrough of substances can occur. A breakthrough study by using a second HLB Media Disk can be done to assess the MCB capacity of the disk. Another limitation could be that not all substances have the same affinity for the HLB sorbent. The affinity of substances can be assessed in a residue study in which water concentrations and residue concentrations are compared (personal communication Droge, 2019).

3.2.2 Silicone rubbers (RS)

Silicone rubbers (SRs) are used as a passive sampling tool for hydrophobic contaminants. A SR sampler is a piece of a silicon-based organic polymer polydimethylsiloxane (PDMS) (Smedes & Booij, 2010). Despite the hydrophobic characteristics, PDMS can absorb organic compounds with a wide range of polarities. Among these compounds are pesticides that pose a threat to both ecosystem and human health (Martin, et al, 2016). Equilibrium can be achieved for compounds with a logKow > 2-3, when exposure remains constant for long enough (Smedes & Booij, 2010).

Equilibrium times are generally faster at higher water flow rates and higher area/volume ratio of the sampler (Booij et al, 2016). Equilibrium can be reached for SR in sediments and lipid-rich

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phases, rather than for sampling in the water column, where equilibrium for hydrophobic compounds is often not observed (Booij et al, 2016).

The following equation can be used to calculate the silicone rubber-water partition coefficient/L kg-1 (KSRw), of organic contaminants in water:

KSRw= C_SR

Cw (1)

CSR is the concentration in the silicone rubber/μg kg−1 SR and Cw is the concentration of a

pesticide in water. This suggests that the Cw can be calculated when KSRw and CSR are known

(Yates et al, 2013). Previous studies found that for organic contaminants, the KSRw of PDMS

correlates with the Kow when measured after equilibrium (Ripszam, 2015).

4. Research questions

The aim of this research is to assess the effectiveness of two relatively new sampling methods for measuring pesticide concentration in surface water and sediment. Therefore, the following research question will be answered: “How effective are the Continuous Low-level Aquatic Monitoring

sampler and the passive silicone rubber sampler to respectively measure pesticide concentrations in surface water and in sediment?”. It is expected that with the use of the samplers, it will be possible

to accurately determine the concentration of pesticides in surface water and sediment. To assess the effectiveness, the following sub-questions will be answered:

What is the detection accuracy of the CLAM sampler in an aquarium spiked with 60 pesticides?

 What is the pumping rate of the CLAM sampler in clean water?  What is the pumping rate of the CLAM sampler in a ditch?

 To what extent does the outer glass fiber pre-filtration disk retain pesticides?  Is the HLB filter able to retain all 60 pesticides?

What is the detection accuracy of a SR sampler in an aquarium spiked with 60 pesticides?

 How long does it take for the pesticides to reach equilibrium between the SR sampler and water?

 Is there a correlation between the logKow of the pesticides and logKSRw determined during

the experiment in the lab?

 Is equilibrium reached for all 60 pesticides after 4 weeks?

Are the CLAM sampler and SR sampler useful for field application in small agricultural water bodies in Flevoland, the Netherlands?

 What pesticides are detected in the water column with the CLAM and what are the measured concentrations?

 How much water is sampled by the CLAM in the field after 24 hours?

 What pesticides are detected in the sediment with the SR and what are the measured concentrations?

 Is there a difference between the toxicity of the water column and the toxicity of the sediment based on a bioassay with Daphnia magna exposed to the sampler’s extracts?

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4.1 Hypothesis

The following assumptions have been made for the CLAM experiments:

Depending on the binding capacity, it is expected that some of the pesticides will not be retained by the first filter but will be retained by the second filter. This applies to acids that are present in the system as anions and for polar substances with a low logKow (logKow<1). Also, it is

expected that some of the pesticides with relatively high logKow (logKow>5) will partially be

retained by the outer glass filter. Overall, it is expected that the CLAM is able to detect all the 60 pesticides in the aquarium when spiked at 10 ug L-1, but back-calculated measured concentrations may deviate from actually present concentrations in the aquarium because of the factors described above. According to the manufacturer, >40 L of water will be sampled by the CLAM within one day.

The following assumptions have been made for the SR experiments:

It is expected that after four weeks all pesticides have reached equilibrium between sampler and water, because diffusion in the silicon material is relatively fast compared to water. Pesticides with higher Kow are expected to take longer to equilibrate, because a larger

concentration difference between the water and the silicon material needs to be achieved. Anionic pesticides are not expected to accumulate significantly in SR when spiked at 10 ug L-1_.

Therefore, it is expected that the KSRw can only be calculated for the neutral chemicals within the

set of 60 pesticides. It is also expected that there is a correlation between Kow and KSRw. This

correlation could enable the calculation of Cw of the measured pesticides at the field locations

that were not included in the set of 60 pesticides.

The following assumptions have been made for the field experiments:

It is expected that the CLAM sampler will operate without trouble in the field. The pumping speed is expected to be similar to the observed speed in the lab within a 24-hour timeframe. It is expected that the filter extract will provide an overestimation of the toxicity compared to the grab sample, therefore a dilution must be made based on the amount of water pumped through the CLAM.

5. Method

Several experiments will be done to assess the accuracy of both samplers. To do this, both samplers will be deployed in a controlled laboratory setting and in the field at six potentially polluted agricultural ditches in Flevoland, the Netherlands. In addition, a bioassay will be performed to determine the toxicity of the water at the field locations. The research will consist of 4 phases, including writing the final report. During the first phase, a series of experiments is done with both samplers in a laboratory setting. During the second phase, both samplers will be

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used in a field study. During the third phase, a bioassay will be executed, and data analysis will be carried out. Ultimately, the report will be written,

and results will be presented.

5.1 Material description

5.1.1 CLAM

The CLAM is purchased and will be provided by the University of Amsterdam. The sampler can be seen in

figure 2. The sampler consists of a polycarbonate housing, a small display, a micro pump, a counter and self-contained SPE extraction manifold (Aqualytical, nd). The rechargeable battery goes up to 36 hours and can record up to an extracted volume of 100 liters. In this research, the CLAM will be used to estimate the dissolved

concentration of pesticides in the water column.

5.1.2 SR

Silicone rubber sheet will be provided by the University of Amsterdam. For this study, Altec AlteSilTM_industrial

Silicone Sheet with a thickness of 0.5 mm will be used

(figure 3). Rectangular strips of SR will be used to estimate the freely dissolved concentration of pesticides in pore water.

5.1.3 Bioassay

By using the samplers in a laboratory setting, the accuracy of the detection of chemicals is assessed. By performing a validation study in the field, the suitability of these methods in monitoring programmes is assessed. In addition to these studies, a bioassay will be performed to assess the toxicity of the present pesticide mixture based on the sampler extracts at each study location. Bioassays are effect-based tools that enables the detection, identification and quantification of interactions between chemicals and environment (Brack et al., 2019). An acute toxicity test with Daphnia magna is a widely used method to determine the short-term toxicity of the water samples (Persoone et al., 2009). Moreover, bioassays with Daphnia are considered to be an effective and sensitive method to evaluate the toxicity of pesticides in pore water and sediment (Wernersson et al., 2015) These tests are typically conducted over a duration of 24-48 hours over different concentrations of contaminants. Brack et al. (2019) recommends using a 48-hour Daphnia immobilization test for detecting the effects of mixtures of pesticides in an ecosystem.

5.1.4 LC-MS

Liquid Chromatography – Mass Spectrometry (LC-MS) can be used for quantitative monitoring of contaminants in water. LC-MS allows for the detection of individual components in a mixture with high molecular specificity and high detection sensitivity. This method will be used because it is the most sensitive method for detection of 500 pre-selected pesticides in water samples

Figure 2: C.L.A.M sampler (Aqualytical, nd).

Figure 3: Altec AlteSil industrial Silicone Sheet (Altec, nd).

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(Alder et al., 2006). In this study, it will be used to determine the concentrations of pesticides in the sampler extracts of the CLAM and SR and in water samples. LC-MS data will be gathered with the Analyst software used to control the LC-MS. Pesticide concentrations can be determined from this data by using external calibration samples. Subsequently, the measured concentrations in the samples will be analyzed in excel.

5.2 Experimental design

5.2.1 Phase 1: Aquarium experiments

Several experiments will be done in a laboratory setting at UvA, Science Park Amsterdam, with both samplers. Both the CLAM sampler and the SRs will be deployed in an 18 L aquarium, spiked with 60 pesticides, to determine how accurately the two samplers can detect and measure the concentration of the added pesticides. The CLAM sampler will be equipped with two HLB filters and one glass fiber disk during the laboratory experiments. The second HLB filter will be used to assess breakthrough of chemicals from the first HLB filter. The SR sampler will consist of one gram of AlteSil Silicone Sheet of approximately 20 cm2_{, kept in a stirred water volume.}

Before use, each CLAM filter will be cleaned by pre-extraction with about 20 mL of methanol, 20 mL of dichloromethane (DCM) and 20 mL of demineralized water. First, the pumping rate of the CLAM sampler is to be determined. This will be done by running the CLAM for an hour and measuring the volume of the filtered effluent while writing down the counter value at 5, 15, 30 and 60 minutes. This allows for the calculation of the amount of water pumped per counting step. After, the CLAM will be deployed in a ditch at Science Park Amsterdam to determine the pumping capacity in a natural setting. Then, the CLAM sampler will be used in the aquarium for three different time periods to test whether (i) a larger sample of water leads to a proportional increase in adsorbed concentration on the HLB, or (ii) if low affinity chemicals break through the HLB filter, or (iii) whether displacement will occur once saturation levels have been reached. The time periods are 1, 3 and 8 hours. The extraction will be made by pressing 100 mL of ACN through each filter with a syringe. After each time period, a water sample of 5 mL will be taken to measure actual Cw. Water passing through the CLAM is

routed to a waste collection and disposed of carefully.

The SR sampler will be deployed in a similar set-up for 4 weeks. Each SR sampler will be cleaned by pre-extraction overnight with 100 mL acetonitrile (ACN), 100 mL methanol and 300 mL ultra-pure water while continuously moving on a rolling belt. To investigate the uptake profiles of the different pesticides and to determine the KSRw for the pesticides, one piece of SR of

about 1 g (~20 cm2_{) will be deployed in the aquarium for four weeks. A constant movement will}

be created in the aquarium by two magnetic PTFE stirring bars to increase uptake rates, which was recommended in a study by Ripszam (2015). About 0.1 g of SR will be cut off and split into three pieces of ~0.035 g after 1, 8 and 24 hours and after 2 and 4 weeks to measure CSR. Each

piece will be weighed, and an extraction will be made by adding 1 mL of ACN to each piece. Simultaneously, water samples of 5 mL will be taken from the aquarium to analyze Cw. Equation

1 will be used to calculate the KSRw for each measured pesticide. Finally, it will be tested whether

there is a correlation between the obtained KSRw values from the laboratory experiment and the

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As mentioned in paragraph 4.1.4, LC-MS will be used to analyse the samples and subsequently determine the concentration of pesticides in each sampler and in water samples. Water samples will be diluted four times with ACN, and ultra-pure water and all extracts will be diluted three times with ultra-pure water.

5.2.2 Phase 2: Field tests

Both samplers will be used to assess the presence of pesticides at six study locations. These locations will be agricultural ditches in Flevoland, the Netherlands. The chosen sample locations can be found in paragraph 4.2.2.1. The selection of locations was based on pesticide presence between 2011 and 2017. Data of the Zuiderzeeland waterboard and the Bestrijdingsmiddelenatlas was thoroughly read to select locations were at least one of the 60 pesticides exceeded the norm in this period (Bestrijdingsmiddelenatlas, 2017; Waterschap Zuiderzeeland, 2017). Two experiments will be done on for each field location. For the first experiment, the CLAM sampler will be deployed at each location for 24 hours to test whether it detects similar chemicals as were found during previous monitoring events with grab sampling. The CLAM sampler will be equipped with an HLB filter and a glass fiber disk during this experiment. For the second experiment, involving the SRs, a 1 kg sediment sample will be taken with a sediment grabber after the CLAM is has been retrieved to prevent mixing of the water. 1 L of demi water is added to the sediment sample. Three strips of SR will be exposed to this sample for one week in the laboratory while continuously moving on a rolling belt. This ex situ passive sampling of sediment allows for compounds to reach equilibrium within a reasonable time scale (Booij et al, 2016; personal communication Droge, 2019). This method is therefore preferred above in situ sampling because static in situ equilibration may take longer, and because Cw can be directly calculated from the detected CSR and the equilibrated KSRw,

determined in the aquarium study. An additional grab sample of 100 mL of surface water will be taken at each location and stored in the fridge. The samplers’ extracts will be analyzed for 100 pesticides that were found in the Netherlands in other studies, and for which an LC-MS methodology is available at UvA, including the 60 pesticides that were used in the laboratory setting. The ability of the CLAM to retain the remaining 40 pesticides in a controlled setting was already analyzed by UvA students during a 2019 MSc course and will therefore not tested in this research. The KSRw values of the remaining pesticides is unknown but will be derived from the

regression equation between Kow and KSRw.

As mentioned in paragraph 4.1.4, LC-MS will be used to analyse the samples and subsequently determine the concentration of pesticides in each sample.

5.2.2.1 Study locations

The potential presence of pesticides makes agricultural ditches suitable for assessing the efficiency of active and passive sampling techniques. Figure 4 shows the locations that were selected for this research. Appendix A provides an overview of the pesticides that were present above the norm between 2016 and 2017.

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5.2.3 Phase 3: Daphnia toxicity test

To determine the toxicity of the surface water at each location, a bioassay will be performed. Daphnia magna will be exposed to a re-diluted series of extracts of both samplers, and to a grab sample of filtered field water. By doing this for the CLAM, for which the volume of sampler water was accurately recorded, it can be determined whether the extracts of the samplers are representative for the toxicity of the water body itself. The sediment extract will mainly demonstrate the hazard of chemicals extracted from the sediment. If the SR is deployed directly in the test water, the water equilibrated with SR can be expected to accurately reflect pore water concentrations in the sampled sediments, and directly compare to toxicity observed in overlying water samples. The Daphnia will be acquired from a cultured stock at the laboratory of the University of Amsterdam. For each location, daphnia will be exposed to both samplers’ extracts,

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to a grab sample and to a control medium. After 48 hours of exposure, the immobile individuals will be counted and the 50% effect concentration (EC50) will be determined. A dilution series will be used if enough extract is available.

5.2.4 Phase 4: Data analysis and writing

During this phase, all obtained data and results will be analyzed and the research report will be written.

6. Timetable

The research will take around 6 months, depending on the availability of the lab. An overview of the planning can be found in figure 4. The first phase of the research will be done in the months May, June and July. The second phase will be executed in September. The third phase will take place in September and October. The final phase will take place in October and November if necessary.

7. Budget

The University of Amsterdam provides most of the equipment and analysis methods. The following expenses are therefore not taken into account:

-Glassware -Pipets

-CLAM sampler

-Glass fiber disks

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-Extraction materials -Silicone rubber sheets

-Extraction materials -Daphnia magna

-LC-MS analysis

The remaining expenses included in the research costs are the SPE disks needed for the lab (around 12) and field study (around 6). Also, a budget for gas during fieldwork is included. A specification of the research budget can be seen in table 1.

Table 1: Specification of the research budget.

7. Additional information

7.1 Scientific embedding

This research is carried out with the IBED-FAME research group at the University of Amsterdam. The manufacturer of the C.L.A.M (Aqualytical) will be contacted for a possible collaboration. Also, the waterboard of the Flevoland province will be contacted for additional data on water quality.

7.2 Data management and distribution

All data management will follow “FAIR” scientific data management principles. The field and lab data obtained during the research will be stored in excel spreadsheets on my personal computer and via google spreadsheets and will be available for interested parties to use. To make the data understandable, metadata will be added. Scripts for data analysis will be stored on my personal computer and as google documents. Both data and scripts will be added to the research report as appendices. All data, metadata and scripts will be in English. Metadata can be translated to Dutch on request. All data, metadata and scripts will be stored on the server of IBED, at the University of Amsterdam.

Month 1 (May) Month 2 (June) Month 3 (July) Month 4 (Sept) Month 5 (Oct)

Research costs (in k€)

Bench fee - - - -

-Equipment 1 - - -

-Consumables - - - -

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I. Appendix A

Table 2: An overview of the pesticides that were present above the norm between 2011 and 2017. Bold names are indicator substances that are included in the laboratory studies.

Loc 1 Loc 2 Loc 3 Loc 4 Loc 5 Loc 6

Name Kottertocht Gruttotocht 1 Gruttotocht 2 Wiertocht Rassenbeek-tocht

Onderduikers-tocht Coordinates (X, Y) 146526,491757 149929,488756 151680,484819 168780,50391 4 153184,479227 173415,527190 Identification NL37_26AZ-062-01 NL37_26AZ-049-01 NL37_26DN-044-01 NL37_20GZ-031-01 NL37_26DZ-001-01 NL37_15HZ-055-01 Code ₂₃₅₀ ₃₁₉₆ ₃₁₉₄ ₂₁₀ ₂₀₅ ₂₁₂ Substance above norm 2011-2016, (Waterschap Zuiderzeeland , 2017) Deltamethrin Fenpropathrin Imadacloprid Methiocarb Pymetrozine Thiamethozam Cumafos Florasulam Imidacloprid Mesotrion Metribuzin Thiacloprid Florasulam Imidacloprid Mesotrion Metribuzin Thiacloprid Imidacloprid Pyridaben Pyriproxyfen Imidacloprid Azoxystrobin Deltamethrin Dicofol Imidacloprid Linuron Mevinfos Pendimethalin Permethrin Pyraclostrobin Substance above norm (Bestrijdingsm iddelenatlas, 2017).

Imidacloprid Deltamethrin

Chloorpyrifos-methyl Deltamethrin Dimethenamid Pendimethalin - Deltametrhin Dimethanamide Cyfluthrin, Fluoxastrobin (, trans-) Imidacloprid Pirimifos-methyl Pendimethalin Pyraclostrobin