Similarities and differences between measured and predicted concentrations of pesticides in Dutch surface waters

Transcription

1 Comm. Appl. Biol. Sci, Ghent University, 76/2, Similarities and differences between measured and predicted concentrations of pesticides in Dutch surface waters M.G. Vijver 1, R. Kruijne 2, M. Van t Zelfde 1, A.M.A. van der Linden 3, W.L.M. Tamis 1, G.R. de Snoo 1 1 Institute of Environmental Sciences (CML), Leiden University, The Netherlands 2 Alterra, University of Wageningen, The Netherlands 3 National Institute for Public Health and the Environment (RIVM), The Netherlands To whom correspondence may be addressed: vijver@cml.leidenuniv.nl Summary In order to have a thorough evaluation of the progress and effectiveness of Dutch crop protection policy, both model predictions and measured pesticide concentrations in surface waters are considered. To this purpose, monitoring data obtained by various water boards and other monitoring institutes were processed. Data were aggregated over a two year time period and over space (at 1x1 km-grid). A geographic view is given in the Dutch Pesticides Atlas ( The model used for the predictions was the Dutch National Environmental Indicator NMI version 2 ( that has input data regarding spray drift data, crop interception, soil and climate and many more. Information on aggregation steps over time and space, grid sizes, information on crop areas was geared to one another for both instruments. Results on measured pesticide concentrations in surface waters and model predictions were compared to each other at the national scale. For this study, 10 different cases were selected covering a large range of pesticides characteristics and pesticides use. In 60% of the cases, the results were largely in agreement with each other when expressed as absolute numbers of measurements exceeding the environmental quality standard. This is very accurate and useful for policy purposes. Based on concentrations and on the order of magnitude, no significant agreement between measurements and model predictions was found. Differences were explained by various factors, and an overview of predominant systematic differences between the measurements and the model predictions was presented. Using both measurements and model predictions in supporting environmental policy evaluations is warranted, because of higher Weight-of-Evidence. Combining both can assist in optimizing the knowledge on pesticides behaviour, fate and ecological problems and therefore this is the preferred evaluation method. Key words: emissions, measurements, pesticides, surface water, MPC exceedance

2 2 1. Introduction Pesticides are widely used within agriculture and for activities such as controlling roadside weeds, trees and brushes (Kristoffersen et al., 2008). Use of pesticides can have unintended effects on the environment. Pesticides contaminate surface waters via many different pathways. Major pathways of pesticides are spray drift, drainage flow, run off from arable fields, leaching, point sources due to losses from farm yards and/or storage buildings, and discharge of waste water from greenhouses. In many countries, national and regional water authorities monitor pesticide concentrations in surface waters frequently. These monitoring programmes are a tool in policy making, either to keep track on the effectiveness of environmental -, water -, or pollution policies (e.g. EU- Water Framework Directive) or as surveillances indicating where actions should be taken such as clean up actions of locations, restriction of use of certain pesticides in specific areas or re-evaluation of the standards of active ingredients. In the Netherlands, the mid-term evaluation of the 2 nd National Action Plan on sustainable agriculture was aimed at the reduction of ecological risks for the aquatic environment (Van Eerdt et al, 2008). In order to have a thorough evaluation of the progress and effectiveness of Dutch Crop Protection policy, measured pesticides concentrations in surface waters and predicted emissions are both considered. Considering both predictions and measurements also enlarges the interpretation of the results on pesticides concentrations in surface waters. Predictions on emissions and risk of pesticides in the surface waters were obtained using the Dutch National Environmental Indicator NMI 2 ( that has input data regarding drift data, crop interception data, soil, climate data and many more (Van der Linden et al 2004, 2008). Measurements on pesticide concentrations were obtained from the Pesticides Atlas ( Vijver et al 2008, De Snoo et al 2006) in which surveillance data obtained by various water boards are gathered and processed. The aim of the current study is to identify systematic similarities and dissimilarities in measured pesticide concentrations and model predictions in surface waters. Measurements and predictions were compared based on 1) exceedances of the maximum permissible concentration (MPC) values, and 2) based on concentrations using either the order of magnitude (according concordance testing) and on the basis of actual concentrations (according linear regression). Using both predictions

3 Comm. Appl. Biol. Sci, Ghent University, 76/2, and measurements in environmental policy is warranted, because a stronger Weight-of-Evidence can be obtained. 2. Material and methods 2.1 Instrument for predicting pesticides concentrations Predictions on pesticides concentrations in the surface waters were obtained using the Dutch National Environmental Indicator NMI version 2 ( The model calculates the potential environmental impact of all pesticides applied in Dutch agriculture in the years 1998 and These national average applications are based on farm surveys conducted by Statistics Netherlands, and combined with application techniques and drift reduction measures implemented by farmers. Fate and ecotoxicological data are available for all pesticides registered at the Dutch market, including relevant metabolites. The calculations result in emissions to surface water resulting from spray drift, treatment of plant material and harvested products, and leaching from the root zone towards the saturated zone. Emissions resulting from spray drift and point source emissions are translated into an exposure concentration in a ditch next to the field with standard dimensions and standing water. Acute and chronic aquatic risks are expressed by means of the Exposure Toxicity Ratio. The acute exposure is based on the maximum peak concentration and the chronic exposure is based on the 21-daystime weighted average concentration, taking into account the dissipation by degradation and volatilization from the water in the field ditch (Van der Linden et al 2004, 2008, Deneer et al., 2003). These results are available at 1 sqkm resolution at each location with an area of the crop treated. The predicted exposure concentrations are multiplied with a weighting factor equal to the area treated (in ha per 1 x 1 sqkm). 2.2 Monitoring data of pesticides concentrations Pesticides monitoring data were derived from databases owned and administered by the 28 Water Boards in the Netherlands with prior checks being made on data quality and quantity (Van t Zelfde et al 2003). Raw monitoring data were processed, and aggregated using a stepwise procedure. Within each step monitoring data are aggregated in the form of the 90% percentile. Spatial aggregation is carried out at the level of either: 1 x 1 kilometre grid cells. Temporal aggregation is

4 4 carried out firstly over annual periods followed by 2-years periods (De Snoo et al 2006). The database of the Pesticides Atlas covers data from 1997 till recent years. Details on the measurements are visible at the internet The data set as taken for this study was from the period 1999 and The pesticides concentrations were evaluated against the Maximum Permissible Concentration (MPC) which is an ecotoxicological relevant surface water quality standard. These MPCs are pesticide-specific and vary, depending on the toxicity and the environmental, chemical and physical properties of the pesticide. For the calculation of exceedances on the MPC a minimum of 5 to 15 measurements is taken, depending on the kind of product, in order to exclude incidental measurements from the national overviews. Further details on the calculation procedures are given in Vijver et al. (2008). The results on pesticides concentrations measured were reported at the individual active ingredient level in µg/l. 2.3 Tuning of data input In order to compare the results on measured pesticides concentrations and model predictions in the surface waters, information in both the NMI 2 and the Pesticides Atlas data were geared to one other. Information on crop areas and the depicting of grid sizes were tuned. Pesticides identification (e.g. CAS numbers and names) and metabolites were checked. The results were aggregated at the scale of 1 x 1 km 2 and over a 2-year period. 2.4 Pesticides selected Ten different cases were selected to study the similarities and dissimilarities of measured pesticide concentrations and predicted emissions in the surface waters. The ten case studies included nine different pesticides covering a large range of pesticides characteristics, usage, and number of measurements. Some of the important pesticides characteristics are summarized in Table 1.

5 Comm. Appl. Biol. Sci, Ghent University, 76/2, Table 1: Pesticides identification, properties and Maximum Permissible Concentration = MPC (NMI 2, compound database CTBase ). Case study compound CAS no. Chemi-cal use Molar mass (g/mole -1 ) Saturated vapour pressure (mpa) Solubility in water (mg L- 1) DegT50 watersediment (d) MPC (µg L -1 ) 1 Bentazone H , 3 Carbendazim F Fenoxycarb I Glyphosate H AMPA Isoproturon H Metamitron H Monolinuron H Pirimiphos-methyl I Tolclophos-methyl F # aminomethylphosphonic acid, metabolite of parent compound glyphosate (with the median values of all compounds in the database taken for Pvap and DegT50ws) The concentrations on Glyphosate and its metabolite AMPA were taken as one single (added) concentration. Carbendazim was studied using two different ways of calculating the exposure concentrations; the maximum peak concentrations and the maximum time weighted average concentrations. Depending on authorisation, pesticides may be used only within certain crops, whereas others may have an area of use which consists of a wide range of crops in different agricultural sectors. The pesticides selected for our study were intended to cover a large range of different uses. Information on the sales volume and use of the pesticides in the type of crops is given in Table 2. The predominant crop in which the pesticides are used are mentioned together with the usage in percentage (according to NMI 2 based on survey data).

6 6 Table 2: The use of pesticides in the predominant crops (according to NMI 2 based on survey data 1998, Statistics Netherlands). Compound Sales volume class Predominant crop Other crops and crop groups (x 10 3 kg) Crop Usage Number Crop groups (%) Bentazone Maize Livestock breeding, Arable farming Carbendazim Flower bulbs Bulbs, Arable farming, Mushrooms, Fruit growing, Greenhouse crops, Vegetable growing Fenoxycarb 1-5 Apples 74 4 Fruit growing Glyphosate >500 Grassland Arable farming, Livestock breeding, Fruit growing, Pavements Isoproturon Cereals 97 8 Cereals Metamitron Sugarbeet Arable farming, bulbs Monolinuron Potatoes Potatoes Pirimiphos-methyl Flower bulbs Bulbs, Greenhouse crops Tolclophos-methyl Chrysanthemum Bulbs, Greenhouse crops 2.5 Statistical analyses Descriptive statistics such as average, standard deviation and minimum and maximum values per individual active ingredient were calculated as could be obtained from the measurements or the model results. Results from the pesticide measurements and model predictions were compared to each other. The comparisons were done based on: 1) The exceedance of environmental criteria expressed as the amount of observations that exceed the MPC and the amount of predictions that exceed the MPC. In order to analyse this, results were compared by taking the absolute amount and percentages of values above and below the MPC. Some measurements were below the limit of quantification, in those cases no evaluation compared to the MPC could be made. 2) The concentrations of pesticides measured in the surface water and the model predictions for weighted concentrations in the field ditch at national scale. In order to analyse this, results were log-transformed because of their nonlinear characteristics. After log-transformation the results often could be described by a non-parametric distribution. Measurements below limit of quantification were taken as half that limit value within the calculations. For statistical testing, nullhypothesis was that measured concentrations and predictions gave similar results.

7 Comm. Appl. Biol. Sci, Ghent University, 76/2, Each result of measurements and predictions was obtained for a certain xycoordinate, but using different approaches. Therefore, the results were tested paired-wise. Firstly, results of the predictions versus measurements were tested on their correspondence. Results were ranked and assigned in different tiles. The number of tiles was dependent on the amount of paired datasets and the range of concentrations based on the minimum and maximum values. Crosstabs outcomes of the Concordance test were compared to the slope = 1. The degree of accordance was defined as kappa. If kappa > 0.75 then there was accordance between the predictions and measurements, if kappa < 0.4 then there was no accordance (De Vocht 2005). Secondly, results of the predictions versus measurements were tested on their correlation. To do this, the Wilcoxon Matched Pairs test was used for signed-ranks results. The magnitude of correlation was defined as Z-value together with p-value for significance. The Z-value represents the difference between observed and expected sum of signed ranks given as the variance of a value above or below this expected sum. The value was considered significant when the p-value was < 0.05 (De Vocht, 2005). 3. Results One case of Bentazone is here described as an illustration of all other 9 cases. After the case study of Bentazone, an overview of all selected case studies is given with their main results. 3.1 Case study of Bentazone Bentazone is an herbicide used as a contact herbicide for the control of weeds in crops, such as maize, beans, and grassland. It is applied after emergence by spraying. Bentazone is stable to hydrolysis, but photodegrades in water with a half-life of < 24 h. Under aerobic conditions, Bentazone degrades with a half-life ranging from < 7 days to 14 weeks, depending on soil types and conditions (WHO, 1990). Bentazone is mobile in soil, and, therefore, has the potential to contaminate groundwater by leaching and surface water by the drainage pathway. Bentazone is slightly degradable in surface water. From 1998 to 2000 on average kg of Bentazone/year was sold in the Netherlands (see Table 2).

8 8 For Bentazone, 203 grid cells within the Netherlands had paired results on pesticides concentrations. The measured concentrations in the surface waters were ranging from 0 24 µg/l. Average Bentazone concentrations were µg/l, the 90 th percentile of all measurements was 0.3 µg/l. The temporal distribution of the measurements is adjusted to the application season for Bentazone, as can be seen in Figure 1. HERE FIGURE 1 The concentrations in surface water as predicted by the weighted exposure concentrations were ranging from µg/l. Average Bentazone concentrations were µg/l, the 90 th percentile of all calculated concentrations was 1 µg/l. 3.2 Exceedances The pesticides concentrations were evaluated against the MPC being either below or exceeding this value. All Bentazone concentrations measured in the surface waters in the 203 grid cells were below the MPC. Bentazone weighted concentrations as predicted by the model in all 203 grid cells were also below the MPC. The percentage of Bentazone concentrations below the MPC-value is for both measurements and predictions 100%, meaning that measurements and predictions gave similar results. 3.3 Concentrations: concordance test and correlation Based on concentrations of Bentazone as measurements and predictions, the significance of concordance was tested. Log-transformed concentrations of Bentazone as measured varied between log = -3.6, which is the measurement quantification limit, to log = 1.4 (see Figure 2). Log-transformed weighted emissions of Bentazone as predicted by the model varied between log = -4 to log = 0 (see Figure 2). HERE FIGURE 2

9 Comm. Appl. Biol. Sci, Ghent University, 76/2, In order to calculate the concordance between both predictions and measurements, data were categorized into 5 tiles. Each tile contained approximately 40 paired results. Details on the exact number of data pairs as categorized are given in Table 3. Table 3: Cross table with results for Bentazone categorized in tiles with the number of data pairs with log-transformed measurements and model predictions per order of magnitude. NTILES of BMAlogbentazon * NTILES of NMIlogbentazon Crosstabulation Count NTILES of BMAlogbentazon Total NTILES of NMIlogbentazon Total The kappa value was calculated to be 0.095, meaning that no concordance was found between the predictions and the measurements. The slope of the correlation between predicted emissions and measured concentrations was This was compared to the perfect match of a slope of 1. The degree of correlation between the results obtained using both approaches was defined as Z-value = and p-value = Meaning that there is no correlation between the Bentazone concentrations in the surface waters as predicted by the model and as measured by the water boards. The data pairs that have been on accordance are plotted in Figure 3 using a dark grey colour. Data pairs that either gave that measurements had higher concentrations than predicted or predictions that were higher than the measurements were also visualized in a geographic way. HERE FIGURE 3

10 10 As can be seen in Figure 3, most of the data pairs for Bentazone are composed of measured concentrations and model predictions with different orders of magnitude. Spatial explicit differences could be found, as in the northern part of the country, the middle and in south-western part measurement were higher that predictions. Predictions were found to be higher mostly clustered around the southeast part of the country. But also to the south-west and at the north-east nearby the border of Germany. A general explanation on the differences and similarities as mapped geographically was difficult to make, as many factors can play a role. 3.4 Summarized results of the selected pesticides. Considering the measured quality standard exceedance, 6 out of the 10 cases gave similar results: i.e. Bentazone, Carbendazim (with the model predictions based on the chronic exposure concentrations), Glyphosate and metabolite AMPA, Metamitron, Pirimiphos-methyl and Tolclophos-methyl. Table 4: Similarities and dissimilarities between results obtained by measurements versus predictions: Pesticide based on exceedance based on concentrations (concordance and correlations) Bentazone equal no agreement Carbendazim (peak) no agreement no agreement Carbendazim equal no agreement (time-weighted average) Fenoxycarb no agreement no agreement Glyphosate + AMPA equal no agreement Isoproturon no agreement no agreement Metamitron equal no agreement Monolinuron no agreement no agreement Pirimiphos-methyl equal no agreement Tolclophos-methyl equal no agreement In all cases no similar outcomes between all measurement and the model predictions was observed, as can be seen in Table 4.

11 Comm. Appl. Biol. Sci, Ghent University, 76/2, Discussion The results reported in Table 4 show that in 60% of all cases the predictions and measurements are in agreement with each other when expressed as exceedance of the MPC. From a regulatory perspective this is really a good association, very useful in policy making. No agreement was found based on correlations, which can be expected as both instruments, model and measurements, have different assumptions. Based on concordance, we expected some agreement; as it is simply elevated concentrations were expected to have an association with larger emission. However this was not found in this study. Systematic dissimilarities between the measured concentrations and model predictions could be explained by many factors. Within the scope of this pilot study the influence of each separate factor could not be further analysed. The major differences between the methodology behind both datasets and their possible influence on the similarities and differences between the measured concentrations and the model predictions are discussed. These methodological differences are summarized in Table 5. Table 5: Methodological differences between predictions and measurements. Measurements Based on measurements obtained from different monitoring institutes, set up according to own regional purpose Predictions Based on information at a national level, averaged over crops, obtained from interviews and pesticides use data Measurement locations usually not in field ditches Different methods of data collection (sampling and monitoring set up), and chemical analyses. Model predictions for the surface water in field ditch adjacent to the field treated. Assumption of 100% Good Agricultural Practise with application according to the authorisation. No detailed spatial information present. Aggregation of data losing details on type of waterbody, hydromorphological information, distance to crop field, etc. No detailed information on the spatial aggregation of concentrations over crops, e.g. differences between uniform and mixed crops, crop rotation. No detailed temporal information present; e.g. application time compared to time of monitoring. All emissions on pesticides + metabolites are gathered, including misuse, illegal use and concentrations originating from neighbouring countries. Use of time-weighted average at e.g. drift percentages of pesticides. Only pesticide use from agricultural practices

12 12 Both instruments have their own weaknesses, assumptions and strengths. This means that they can be used for different policy related questions, and are complementary to each other. For example, measurements can give the actual problems of water quality standard exceedance for pesticides and have advantages in being a warning. Furthermore they reflect daily practice of land use activities that influence water quality. For example, predictions can give an overview anticipating possible water quality problems. Moreover, they are strongly related to authorisation of pesticides and can make distinction between different emission routes towards the surface waters. Using both measurements and model predictions in supporting environmental policy evaluations is warranted, because of higher Weightof-Evidence. Combining both can assist in optimizing the knowledge on pesticides behaviour, fate and ecological problems and therefore this is the preferred evaluation method. Currently both instruments got an update of their technique and those versions will be used for the end evaluation of the current policy period (2010). The new model version NMI 3 does include the contributions from drainage flow and emissions from greenhouse crops to the aquatic risk. The Pesticides Atlas is now improved by using real sampling locations instead of 1 sqkm and has additional information on the type of waterbody in which the sampling was done. Acknowledgement We kindly acknowledge financial support by the Ministry of Economics, Agriculture and Innovation and the Ministry of Infrastructure and the Environment as provided within the Interim Evaluation of the 2 nd Dutch Crop Protection Policy Plan. 5. Literature Deneer J.W., Van der Linden A.M.A., Luttik R., Smidt RA. (2003). An environmental indicator used on national and regional scales for evaluating pesticide emissions in the Netherlands. Symposium Pesticide Chemistry De Snoo G.R., Tamis W.L.M., Vijver M.G., Musters C.J.M, Van t Zelfde M. (2006). Risk mapping of pesticides: the Dutch atlas of pesticide concentrations in surface waters; Comm. Appl. Biol. Sci. Ghent University. 71: De Vocht A. (2005). Basisboek SPSS12 voor windows. Bijleveld Press, Utrecht.

13 Comm. Appl. Biol. Sci, Ghent University, 76/2, Directive 2000/60/EC of the European parliament and of the council of 23 October 2000 establishing a framework for Community action in the field of water policy. Kristoffersen P, Rask A.M., Grundy A.E., Franzen I., Kempenaar C., Raisio J., Schroeder H., Spijker J., Verschwele A., Zarina I. (2008). A review of pesticide policies and regulations for urban amenity areas in seven European countries. Weed Research. 48: Van der Linden A.M.A., Deneer J.W., Luttik R., Smidt R.A. (2004). Dutch Environmental Indicator for Plant Protection Products. Description of input data and calculation methods. RIVM report Van der Linden A.M.A., Groenwold B., Kruijne R., Luttik R., Merkelbach R. (2008). Dutch Environmental Indicator for Plant Protection Products, version 2. RIVM report Van Eerdt M.M., Van der Linden A.M.A., De Lauwere C.C., Van Zeijts H. (2008). Interim evaluation of the Dutch Crop Protection Policy. In: Del Re AAM, E Capri, G Fragoulis, M Trevisan (eds). Environmental fate and ecological effects of pesticides. Proceedings XIII Symposium Pesticide Chemistry, Italy, Piacenza, September Van t Zelfde M., De Snoo G.R. (2003). Atlas of pesticide concentrations in Dutch surface waters: A pilot study. Comm. Appl. Biol. Sci. Ghent University. 68: Vijver M.G., Van t Zelfde M., Tamis W.L.M., Musters C.J.M., De Snoo G.R. (2008). Spatial and Temporal Analysis of Pesticides Concentrations in Surface Water: Pesticides Atlas. Journal of Environmental Science and Health Part B. 43: World Health Organization. (1990). IPCS International programme on chemical safety, Health and Safety Guide No. 48, Bentazone health and safety guide. Geneva.

14 FIGURES 14 Figure 1: The number of Bentazone measurements per month during the period (Pesticides Atlas) number of measurements month

15 Comm. Appl. Biol. Sci, Ghent University, 76/2, Figure 2 Correlation between log-transformed concentrations of Bentazone, predictions versus measurements. Bentazone predictions (log scale) measurements (log scale) y = x R² =

16 16 Figure 3: Similarity of measurements and predictions (dark grey dots denotes data pairs with the same order of magnitude, white dots denote measurements with a higher order of magnitude than predictions, and light grey dots denote measurements with a lower order of magnitude than predictions).