Universitat de Girona, Institut d'Ecologia Aquàtica, E-17071 Girona, Spain.
Sci Total Environ. 2010 May 1;408(11):2319-26. doi: 10.1016/j.scitotenv.2010.02.002. Epub 2010 Mar 6.
Ecological risk assessment was conducted to determine the risk posed by pesticide mixtures to the Adour-Garonne river basin (south-western France). The objectives of this study were to assess the general state of this basin with regard to pesticide contamination using a risk assessment procedure and to detect patterns in toxic mixture assemblages through a self-organizing map (SOM) methodology in order to identify the locations at risk. Exposure assessment, risk assessment with species sensitivity distribution, and mixture toxicity rules were used to compute six relative risk predictors for different toxic modes of action: the multi-substance potentially affected fraction of species depending on the toxic mode of action of compounds found in the mixture (msPAF CA(TMoA) values). Those predictors computed for the 131 sampling sites assessed in this study were then patterned through the SOM learning process. Four clusters of sampling sites exhibiting similar toxic assemblages were identified. In the first cluster, which comprised 83% of the sampling sites, the risk caused by pesticide mixture toward aquatic species was weak (mean msPAF value for those sites<0.0036%), while in another cluster the risk was significant (mean msPAF<1.09%). GIS mapping allowed an interesting spatial pattern of the distribution of sampling sites for each cluster to be highlighted with a significant and highly localized risk in the French department called "Lot et Garonne". The combined use of the SOM methodology, mixture toxicity modelling and a clear geo-referenced representation of results not only revealed the general state of the Adour-Garonne basin with regard to contamination by pesticides but also enabled to analyze the spatial pattern of toxic mixture assemblage in order to prioritize the locations at risk and to detect the group of compounds causing the greatest risk at the basin scale.
对农药混合物对阿杜尔-加龙河盆地(法国西南部)造成的风险进行了生态风险评估。本研究的目的是使用风险评估程序评估该流域的农药污染总体状况,并通过自组织映射 (SOM) 方法检测有毒混合物组合的模式,以识别有风险的地点。采用暴露评估、基于物种敏感性分布的风险评估和混合物毒性规则,计算了用于不同毒性作用模式的六个相对风险预测因子:取决于混合物中化合物的毒性作用模式的物种的多物质受影响部分(取决于混合物中化合物的毒性作用模式的物种的多物质受影响部分(msPAF CA(TMoA) 值)。为评估本研究中 131 个采样点而计算的这些预测因子随后通过 SOM 学习过程进行了模式化处理。确定了四个具有相似毒性组合的采样点聚类。在第一个聚类中,包含 83%的采样点,农药混合物对水生物种造成的风险较弱(这些地点的平均 msPAF 值<0.0036%),而在另一个聚类中,风险显著(平均 msPAF<1.09%)。GIS 制图允许突出显示每个聚类的采样点分布的有趣空间模式,在法国的“Lot et Garonne”省发现了一个显著且高度本地化的风险。SOM 方法、混合物毒性建模和清晰的地理参考结果的组合使用不仅揭示了阿杜尔-加龙河盆地的农药污染总体状况,而且还能够分析有毒混合物组合的空间模式,以便优先考虑有风险的地点,并检测在流域范围内造成最大风险的化合物组。