Methods comparison for detecting trends in herbicide monitoring time-series in streams.

An inadvertent consequence of pesticide use is aquatic pesticide pollution, which has prompted the implementation of mitigation measures in many countries. Water quality monitoring programs are an important tool to evaluate the efficacy of these mitigation measures. However, large interannual variability of pesticide losses makes it challenging to detect significant improvements in water quality and to attribute these improvements to the application of specific mitigation measures. Thus, there is a gap in the literature that informs researchers and authorities regarding the number of years of aquatic pesticide monitoring or the effect size (e.g., loss reduction) that is required to detect significant trends in water quality. Our research addresses this issue by combining two exceptional empirical data sets with modelling to explore the relationships between the achieved pesticide reduction levels due to mitigation measures and the length of the observation period for establishing statistically significant trends. Our study includes both a large (Rhine at Basel, ∼36,300 km2) and small catchment (Eschibach, 1.2 km2), which represent spatial scales at either end of the spectrum that would be realistic for monitoring programs designed to assess water quality. Our results highlight several requirements in a monitoring program to allow for trend detection. Firstly, sufficient baseline monitoring is required before implementing mitigation measures. Secondly, the availability of pesticide use data helps account for the interannual variability and temporal trends, but such data are usually lacking. Finally, the timing and magnitude of hydrological events relative to pesticide application can obscure the observable effects of mitigation measures (especially in small catchments). Our results indicate that a strong reduction (i.e., 70-90 %) is needed to detect a change within 10 years of monitoring data. The trade-off in applying a more sensitive method for change detection is that it may be more prone to false-positives. Our results suggest that it is important to consider the trade-off between the sensitivity of trend detection and the risk of false positives when selecting an appropriate method and that applying more than one method can provide more confidence in trend detection.

Challenges of spatially extrapolating aquatic pesticide pollution for policy evaluation.

Aquatic pesticide pollution is an important issue worldwide. Countries rely on monitoring programs to observe water bodies quality and on models to evaluate pesticide risks for entire stream networks. Measurements are typically sparse and discontinuous which lead to issues in quantifying pesticide transport at the catchment scale. Therefore, it is essential to assess the performance of extrapolation approaches and provide guidance on how to extend monitoring programs to improve predictions. Here we present a feasibility study to predict pesticide levels in a spatially explicit manner in the Swiss stream network based on the national monitoring program quantifying organic micropollutants at 33 sites and spatially distributed explanatory variables. Firstly, we focused on a limited set of herbicides used on corn crops. We observed a significant relationship between herbicide concentrations and the areal fraction of hydrologically connected cornfields. Neglecting connectivity revealed no influence of areal corn coverage on the herbicide levels. Considering chemical properties of the compounds slightly improved the correlation. Secondly, we analysed a set of 18 pesticides widely used on different crops and monitored across the country. In this case, the areal fractions of arable or crop lands showed significant correlations with average pesticide concentrations. Similar results were found with average annual discharge or precipitation if two outlier sites were neglected. The correlations found in this paper explained only about 30 % of the observed variance leaving most of the variability unexplained. Accordingly, extrapolating the results from the existing monitoring sites to the Swiss river network comes with substantial uncertainty. Our study highlights possible reasons for weak matches, such as missing pesticide application data, limited set of compounds in the monitoring program, or a limited understanding of factors differentiating the loss rates from different catchments. Improving the data on pesticide applications will be essential to progress in this regard.

A review of long-term pesticide monitoring studies to assess surface water quality trends.

Aquatic pesticide pollution from both agricultural and urban pest control is a concern in many parts of the world. Making an accurate assessment of pesticide exposure is the starting point to protecting aquatic ecosystems. This in turn requires the design of an effective monitoring program. Monitoring is also essential to evaluate the efficacy of mitigation measures aimed to curb pesticide pollution. However, empirical evidence for their efficacy can be confounded by additional influencing factors, most prominently variable weather conditions. This review summarizes the experiences gained from long-term (>5 years) pesticide monitoring studies for detecting trends and provides recommendations for their improvement. We reviewed articles published in the scientific literature, with a few complements from selected grey literature, for a total of 20 studies which fulfill our search criteria. Overall, temporal trends of pesticide use and hydrological conditions were the two most common factors influencing aquatic pesticide pollution. Eighteen studies demonstrated observable effects to surface water concentrations from changes in pesticide application rates (e.g., use restriction) and sixteen studies from interannual variability in hydrological conditions during the application period. Accounting for seasonal- and streamflow-related variability in trend analysis is important because the two factors can obscure trends caused by changes in pesticide use or management practices. Other mitigation measures (e.g., buffer strips) were only detectable in four studies where concentrations or loads were reduced by > 45%. Collecting additional agricultural (e.g., pesticide use, mitigation measures) and environmental (e.g., precipitation, stream flow) data, as well as establishing a baseline before the implementation of mitigation measures have been consistently reported as prerequisites to interpret water quality trends from long-term monitoring studies, but have rarely been implemented in the past.