Sensitivity and first-step uncertainty analyses for the preferential flow model MACRO

Sensitivity analyses for the preferential flow model MACRO were carried out using one-at-a-time and Monte Carlo sampling approaches. Four different scenarios were generated by simulating leaching to depth of two hypothetical pesticides in a sandy loam and a more structured clay loam soil. Sensitivity of the model was assessed using the predictions for accumulated water percolated at a 1-m depth and accumulated pesticide losses in percolation. Results for simulated percolation were similar for the two soils. Predictions of water volumes percolated were found to be only marginally affected by changes in input parameters and the most influential parameter was the water content defining the boundary between micropores and macropores in this dual-porosity model. In contrast, predictions of pesticide losses were found to be dependent on the scenarios considered and to be significantly affected by variations in input parameters. In most scenarios, predictions for pesticide losses by MACRO were most influenced by parameters related to sorption and degradation. Under specific circumstances, pesticide losses can be largely affected by changes in hydrological properties of the soil. Since parameters were varied within ranges that approximated their uncertainty, a first-step assessment of uncertainty for the predictions of pesticide losses was possible. Large uncertainties in the predictions were reported, although these are likely to have been overestimated by considering a large number of input parameters in the exercise. It appears desirable that a probabilistic framework accounting for uncertainty is integrated into the estimation of pesticide exposure for regulatory purposes.     

Using the expert model PERPEST to translate measured and predicted pesticide exposure data into ecological risks

An important topic in the registration of pesticides and the interpretation of monitoring data is the estimation of the consequences of a certain concentration of a pesticide for the ecology of aquatic ecosystems. Solving these problems requires predictions of the expected response of the ecosystem to chemical stress. Up until now, a dominant approach to come up with such a prediction is the use of simulation models or safety factors. The disadvantage of the use of safety factors is a crude method that does not provide any insight into the concentration–response relationships at the ecosystem level. On the other hand, simulation models also have serious drawbacks like that they are often very complex, lack transparency, their implementation is expensive and there may be a compilation of errors, due to uncertainties in parameters and processes. In this paper, we present the expert model prediction of the ecological risks of pesticides (PERPEST) that overcomes these problems. It predicts the effects of a given concentration of a pesticide based on the outcome of already performed experiments using experimental ecosystems. This has the great advantage that the outcome is more realistic. The paper especially discusses how this model can be used to translate measured and predicted concentrations of pesticides into ecological risks, by taking data on measured and predicted concentrations of atrazine as an example. It is argued that this model can be of great use to evaluate the outcome of chemical monitoring programmes (e.g. performed in the light of the Water Framework Directive) and can even be used to evaluate the effects of mixtures.     

Pesticides in rainfall in Europe

Papers and published reports investigating the presence of pesticides in rainfall in Europe were reviewed. Approximately half of the compounds that were analysed for were detected. For those detected, most concentrations were below about 100 ng/l, but larger concentrations, up to a few thousand nanograms per litre, were detected occasionally at most monitoring sites. The most frequently detected compounds were lindane (gamma-HCH) and its isomer (alpha-HCH), which were detected on 90-100% of sampling occasions at most of the sites where they were monitored. For compounds developed more recently, detection was usually limited to the spraying season. A classification of pesticides according to their deposition pattern is proposed.     

Sorption of weak organic acids in soils: clofencet, 2,4-D and salicylic acid.

The sorption behaviour of a new wheat hybridising agent (clofencet, 2-4-(chlorophenyl)-3-ethyl-2,5-dihydro-5-oxopyridazine-4-carboxylic acid) was investigated in batch equilibrium experiments and compared to that of two other organic acids (2,4-D and salicylic acid). Sorption coefficients Kd for the three compounds were determined in 18 Cambisols and Ferralsols. Kd values for clofencet were 0.3-9.4 l/kg for Cambisols and 2.1-68 l/kg for Ferralsols. Sorption of clofencet was strongly related statistically to that of salicylic acid. Sorption of clofencet and salicylic acid decreased exponentially with increasing solution pH in Cambisols whereas a bell-shaped curve was obtained for the sorption of salicylic acid in Ferralsols. Sorption of 2,4-D (2,4-dichlorophenoxyacetic acid) was not statistically related to the pH of the different soils. Positively charged oxide surfaces were shown to play a significant role in the sorption of clofencet and salicylic acid. The use of simple correlation and multiple linear regressions suggested that the main sorption mechanisms of clofencet in soils were likely to be ligand exchange on oxide surfaces and, to a lesser extent, cation bridging. Differences in the sorption behaviour of clofencet/salicylic acid and 2,4-D might be attributed to the possibility of the two former compounds forming bidentate complexes with metals.     

Stationary and non-stationary autoregressive processes with external inputs for predicting trends in water quality

An autoregressive approach for the prediction of water quality trends in systems subject to varying meteorological conditions and short observation periods is discussed. Under these conditions, the dynamics of the system can be reliably forecast, provided their internal processes are understood and characterized independently of the external inputs. A methodology based on stationary and non-stationary autoregressive processes with external inputs (ARX) is proposed to assess and predict trends in hydrosystems which are at risk of contamination by organic and inorganic pollutants, such as pesticides or nutrients. The procedures are exemplified for the transport of atrazine and its main metabolite deethylatrazine in a small agricultural catchment in France. The approach is expected to be of particular value to assess current and future trends in water quality as part of the European Water Framework Directive and Groundwater Directives.     

Developing climatic scenarios for pesticide fate modelling in Europe

A climatic classification for Europe suitable for pesticide fate modelling was constructed using a 3-stage process involving the identification of key climatic variables, the extraction of the dominant modes of spatial variability in those variables and the use of k-means clustering to identify regions with similar climates. The procedure identified 16 coherent zones that reflect the variability of climate across Europe whilst maintaining a manageable number of zones for subsequent modelling studies. An analysis of basic climatic parameters for each zone demonstrates the success of the scheme in identifying distinct climatic regions. Objective criteria were used to identify one representative 26-year daily meteorological series from a European dataset for each zone. The representativeness of each series was then verified against the zonal classifications. These new FOOTPRINT climate zones provide a state-of-the-art objective classification of European climate complete with representative daily data that are suitable for use in pesticide fate modelling.