ARPEGES: A Bayesian Belief Network to Assess the Risk of Pesticide Contamination for the River Network of France.

Pesticides are priority concerns in aquatic risk assessment due to their widespread use, ongoing development of new molecules, and potential effects from short- and long-term exposures to aquatic life. Water quality assessments are also challenged by contrasting pesticide behaviors (e.g., mobility, half-life time, solubility) in different environmental contexts. Furthermore, monitoring networks are not well adapted to the pesticide media transfer dynamics and therefore fail at providing a reliable assessment of pesticides. We present here a Bayesian belief network that was developed in a cooperative process between researchers specializing in Bayesian modeling, soil sciences, agronomy, and diffuse pollutants to provide a tool for stakeholders to assess surface water contamination by pesticides. It integrates knowledge on dominant transfer pathways according to basin physical context and climate for different pesticides properties, such as half-life duration and affinity to organic C, to develop an assessment of risks of contamination for every watershed in France. The resulting model, ARPEGES (Analyse de Risque PEsticide pour la Gestion des Eaux de Surface; trans. Risk analysis of contamination by pesticides for surface water management), was developed in R. A user-friendly R interface was built to enable stakeholders to not only obtain ARPEGES' results, but also freely use it to test management scenarios. Though it is applicable to any chemical, its results are illustrated for S-Metolachlor, a pesticide that was widely used on cereals crops worldwide. In addition to providing contamination potential, ARPEGES also provides a way to diagnose its main explaining factors, enabling stakeholders to focus efforts in the most potentially affected basins, but also on the most probable cause of contamination. In this context, the Bayesian belief network allowed us to use information at different scales (i.e., regional contexts for climate, pedology at the basin scale, pesticide use at the municipality scale) to provide an expert assessment of the processes driving pesticide contamination of streams and the associated uncertainties. Integr Environ Assess Manag 2021;17:188-201. © 2020 SETAC.

Responses of chronically contaminated biofilms to short pulses of diuron. An experimental study simulating flooding events in a small river.

An experimental study was undertaken to highlight the potential ecotoxicological impact of the herbicide diuron on biofilms during flooding events in a small river (Morcille) in the Beaujolais vineyard area (France). We investigated the responses of chronically contaminated biofilms exposed to short-term pulses (3 h) of diuron. Biofilms were grown in indoor microcosms that were either non-contaminated or exposed to low-level chronic contamination, and not exposed, or exposed to single or double pulses of two environmental concentrations (7 and 14 microg L(-1)) of diuron. Exposure to pollution and its impact on biofilms were assessed by measuring pesticide concentrations in biofilms, biomass parameters (chl a, AFDW), community structure (using 18S and 16S rDNA gene analysis by DGGE, and HPLC pigment analysis to target eukaryotes, bacteria and photoautotrophs, respectively) and by performing a physiological test. Control biofilms displayed very low diuron concentrations, whereas the herbicide was found in the contaminated biofilms. Nevertheless, diuron concentrations were not higher in the pulsed biofilms than in the non-pulsed ones. AFDW and chl ain vivo fluorescence increased in both microcosms during the experiment and biomass was higher in chronically exposed biofilms than in control ones. The impact on biomass was higher for the control double-pulsed biofilms than for the non-pulsed ones. Carbon incorporation by the chronically exposed biofilms was greater during the first 28 days of growth than during the first 28 days of growth in the control biofilms. Both single and double pulses inhibited carbon incorporation of all biofilm communities, especially of the control ones. Short-term inhibition of photosynthesis was never significantly different in exposed and non-exposed biofilms. Few differences in the pigment structure were found between chronically exposed and control biofilms, but pulses impacted on the pigment structure of all biofilm communities. Bacterial structural differences were observed between single-pulsed and non-pulsed biofilms, but not between double-pulsed and non-pulsed biofilms. The different pulses affected the eukaryotic community structures of the control biofilms, but not of the chronically exposed ones. Unlike the bacterial communities, the control eukaryotic communities were structurally different from the chronically exposed ones. This preliminary experimental study indicates that exposure to environmental concentrations of diuron and other agricultural contaminants and further exposure to diuron can have measurable effects on small river biofilm communities. The effects of a pulsed acute exposure to diuron on biofilms depended on whether the biofilms had previously been exposed to the same stressors or not.

The cumulative impacts of small reservoirs on hydrology: A review.

The number of small reservoirs has increased due to their reduced cost, the availability of many favourable locations, and their easy access due to proximity. The cumulative impacts of such small reservoirs are not easy to estimate, even when solely considering hydrology, which is partially due to the difficulty in collecting data on the functioning of such reservoirs. However, there is evidence indicating that the cumulative impacts of such reservoirs are significant. The aim of this article is to present a review of the studies that address the cumulative impacts of small reservoirs on hydrology, focusing on the methodology and on the way in which these impacts are assessed. Most of the studies addressing the hydrological cumulative impacts focused on the annual stream discharge, with decreases ranging from 0.2% to 36% with a mean value of 13.4% ± 8% over approximately 30 references. However, it is shown that similar densities of small reservoirs can lead to different impacts on stream discharge in different regions. This result is probably due to the hydro-climatic conditions and makes defining simple indicators to provide a first guess of the cumulative impacts difficult. The impacts also vary in time, with a more intense reduction in the river discharge during the dry years than during the wet years. This finding is certainly an important point to take into consideration in the context of climate change. Two methods are mostly used to estimate cumulative impacts: i) exclusively data-based methods and ii) models. The assumptions, interests and shortcomings of these methods are presented. Scientific tracks are proposed to address the four main shortcomings, namely the estimation of the associated uncertainties, the lack of knowledge on reservoir characteristics and water abstraction and the accuracy of the impact indicators.

Predictive quality of 26 pesticide risk indicators and one flow model: A multisite assessment for water contamination.

Stakeholders need operational tools to assess crop protection strategies in regard to environmental impact. The need to assess and report on the impacts of pesticide use on the environment has led to the development of numerous indicators. However, only a few studies have addressed the predictive quality of these indicators. This is mainly due to the limited number of datasets adapted to the comparison of indicator outputs with pesticide measurement. To our knowledge, evaluation of the predictive quality of pesticide indicators in comparison to the quality of water as presented in this article is unprecedented in terms of the number of tested indicators (26 indicators and the MACRO model) and in terms of the size of datasets used (data collected for 4 transfer pathways, 20 active ingredients (a.i.) for a total of 1040 comparison points). Results obtained on a.i. measurements were compared to the indicator outputs, measured by: (i) correlation tests to identify linear relationship, (ii) probability tests comparing measurements with indicator outputs, both classified in 5 classes, and assessing the probability i.e. the percentage of correct estimation and overestimation (iii) by ROC tests estimating the predictive ability against a given threshold. Results showed that the correlation between indicator outputs and the observed transfers are low (r<0.58). Overall, more complex indicators taking into account the soil, the climatic and the environmental aspects yielded comparatively better results. The numerical simulation model MACRO showed much better results than those for indicators. These results will be used to help stakeholders to appropriately select their indicators, and will provide them with advice for possible use and limits in the interpretation of indicator outputs.