Igor G. Dubus

Sources of uncertainty in pesticide fate modelling

There is worldwide interest in the application of probabilistic approaches to pesticide fate models to account for uncertainty in exposure assessments. The first steps in conducting a probabilistic analysis of any system are: (i) to identify where the uncertainties come from; and (ii) to pinpoint those uncertainties that are likely to affect most of the predictions made. This article aims at addressing those two points within the context of exposure assessment for pesticides through a review of the different sources of uncertainty in pesticide fate modelling. The extensive listing of sources of uncertainty clearly demonstrates that pesticide fate modelling is laced with uncertainty. More importantly, the review suggests that the probabilistic approaches, which are typically being deployed to account for uncertainty in the pesticide fate modelling, such as Monte Carlo modelling, ignore a number of key sources of uncertainty, which are likely to have a significant effect on the prediction of environmental concentrations for pesticides (e.g. model error, modeller subjectivity). Future research should concentrate on quantifying the impact these uncertainties have on exposure assessments and on developing procedures that enable their integration within probabilistic assessments.

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.

Identification of key climatic factors regulating the transport of pesticides in leaching and to tile drains

BACKGROUND Key climatic factors influencing the transport of pesticides to drains and to depth were identified. Climatic characteristics such as the timing of rainfall in relation to pesticide application may be more critical than average annual temperature and rainfall. The fate of three pesticides was simulated in nine contrasting soil types for two seasons, five application dates and six synthetic weather data series using the MACRO model, and predicted cumulative pesticide loads were analysed using statistical methods. RESULTS Classification trees and Pearson correlations indicated that simulated losses in excess of 75th percentile values (0.046 mg m(-2) for leaching, 0.042 mg m(-2) for drainage) generally occurred with large rainfall events following autumn application on clay soils, for both leaching and drainage scenarios. The amount and timing of winter rainfall were important factors, whatever the application period, and these interacted strongly with soil texture and pesticide mobility and persistence. Winter rainfall primarily influenced losses of less mobile and more persistent compounds, while short-term rainfall and temperature controlled leaching of the more mobile pesticides. CONCLUSIONS Numerous climatic characteristics influenced pesticide loss, including the amount of precipitation as well as the timing of rainfall and extreme events in relation to application date. Information regarding the relative influence of the climatic characteristics evaluated here can support the development of a climatic zonation for European-scale risk assessment for pesticide fate.

Development of agro-environmental scenarios to support pesticide risk assessment in Europe

This paper describes work carried out within the EU-funded FOOTPRINT project to characterize the diversity of European agricultural and environmental conditions with respect to parameters which most influence the environmental fate of pesticides. Pan-European datasets for soils, climate, land cover and cropping were intersected, using GIS, to identify the full range of unique combinations of climate, soil and crop types which characterize European agriculture. The resulting FOOTPRINT European agro-environmental dataset constitutes a large number of polygons (approximately 1,700,000) with attribute data files for i) area fractions of annual crops related to each arable-type polygon (as an indicator of its probability of occurrence); and, ii) area fractions of each soil type in each polygon (as an indicator of its probability of occurrence). A total of 25,044 unique combinations of climate zones, agricultural land cover classes, administrative units and soil map units were identified. The same soil/crop combinations occur in many polygons which have the same climate while the fractions of the soils and arable crops are different. The number of unique combinations of climate, soil and agricultural land cover class is therefore only 7961. 26-year daily meteorological data, soil profile characteristics and crop management features were associated with each unique combination. The agro-environmental scenarios developed can be used to underpin the parameterization of environmental fate models for pesticides and should also have relevance for other agricultural pollutants. The implications for the improvement and further development of risk assessment procedures for pesticides are discussed.

Comparison of methods for the detection and extrapolation of trends in groundwater quality.

Land use changes and the intensification of agriculture since the 1950s have resulted in a deterioration of groundwater quality in many European countries. For the protection of groundwater quality, it is necessary to (1) assess the current groundwater quality status, (2) detect changes or trends in groundwater quality, (3) assess the threat of deterioration and (4) predict future changes in groundwater quality. A variety of approaches and tools can be used to detect and extrapolate trends in groundwater quality, ranging from simple linear statistics to distributed 3D groundwater contaminant transport models. In this paper we report on a comparison of four methods for the detection and extrapolation of trends in groundwater quality: (1) statistical methods, (2) groundwater dating, (3) transfer functions, and (4) deterministic modeling. Our work shows that the selection of the method should firstly be made on the basis of the specific goals of the study (only trend detection or also extrapolation), the system under study, and the available resources. For trend detection in groundwater quality in relation to diffuse agricultural contamination, a very important aspect is whether the nature of the monitoring network and groundwater body allows the collection of samples with a distinct age or produces samples with a mixture of young and old groundwater. We conclude that there is no single optimal method to detect trends in groundwater quality across widely differing catchments.

Issues of replicability in Monte Carlo modeling: a case study with a pesticide leaching model.

Sensitivity and uncertainty analyses based on Monte Carlo sampling were undertaken for various numbers of runs of the pesticide leaching model (PELMO). Analyses were repeated 10 times with different seed numbers. The ranking of PELMO input parameters according to their influence on predictions for leaching was stable for the most influential parameters. For less influential parameters, the sensitivity ranking was severely influenced by the seed number used. For uncertainty analyses, probabilities of exceeding a particular concentration were significantly influenced by the seed number used in the random sampling of values for the two parameters considered, even for those cases in which 5,000 model runs were undertaken (coefficient of variation of 10 replicated analyses, 5%). A decrease in the variability of exceedance probabilities could be achieved by further increasing the number of model runs. However, this may prove to be impractical when complex deterministic models with a relatively long running time are used. Attention should be paid to replicability aspects by modelers when devising their approach to assessing the uncertainty associated with the modeling and by decision makers when examining the results of probabilistic approaches.

User subjectivity in Monte Carlo modeling of pesticide exposure

Monte Carlo techniques are increasingly used in pesticide exposure modeling to evaluate the uncertainty in predictions arising from uncertainty in input parameters and to estimate the confidence that should be assigned to the modeling results. The approach typically involves running a deterministic model repeatedly for a large number of input values sampled from statistical distributions. In the present study, six modelers made choices regarding the type and parameterization of distributions assigned to degradation and sorption data for an example pesticide, the correlation between the parameters, the tool and method used for sampling, and the number of samples generated. A leaching assessment was carried out using a single model and scenario and all data for sorption and degradation generated by the six modelers. The distributions of sampled parameters differed between the modelers, and the agreement with the measured data was variable. Large differences were found between the upper percentiles of simulated concentrations in leachate. The probability of exceeding 0.1 microg/L ranged from 0 to 35.7%. The present study demonstrated that subjective choices made in Monte Carlo modeling introduce variability into probabilistic modeling and that the results need to be interpreted with care.

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.

A refined lack‐of‐fit statistic to calibrate pesticide fate models for responsive systems

BACKGROUND Calibration by inverse modelling was performed with the MACRO transport and fate model using long-term (>10 years) drainflow and isoproturon (IPU) data from western France. Two lack-of-fit (LOF) indices were used to control the inverse modelling: sum of squares (SS) and an alternative statistic called the vertical-horizontal distance integrator (VHDI), which is designed to account for offsets in observed and predicted arrival times of peak IPU concentration. With these data, SS was artificially inflated because it is limited to comparison of predicted and observed IPU concentrations that are concurrent in time. The LOFs were used along with the index of agreement (d) and the correlation coefficient (r) to ascertain the fit of the calibrated models. RESULTS Predicted arrival times of peak IPU concentration differed somewhat from observed times. All four indices indicated better model fit for the second of two validation periods when inverse modelling was controlled by VHDI rather than SS (SS = 26.4, d = 0.660, r = 0.606 and VHDI = 1.25). The VHDI statistic was markedly lower compared with the uncalibrated model (38.0) and SS calibration results (24.5). The final maximum predicted IPU concentration (44.5 microg L(-1)) for the calibration period was very similar to the observed value (44 microg L(-1)). CONCLUSION VHDI is seen as an effective alternative to SS for calibration and validation of pesticide fate models applied to responsive systems. VHDI provided a more realistic assessment of model performance for the transient flows and short-lived concentrations observed here, and also effectively substituted for the objective function in inverse modelling.