Predicting pesticide fate in small cultivated mountain watersheds using the DynAPlus model: Toward improved assessment of peak exposure.
Predicting pesticide fate in small cultivated mountain watersheds using the DynAPlus model: Toward improved assessment of peak exposure.
复制标题
使用 DynAPlus 模型预测小型耕作山地流域的农药归宿：改进峰值暴露评估。
DOI：
10.1016/j.scitotenv.2017.09.287
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
A. Di Guardo
中科院分区：
文献类型：
--
作者：
M. Morselli;C. M. Vitale;A. Ippolito;S. Villa;R. Giacchini;M. Vighi;A. Di Guardo
在农业地区使用植物保护产品意味着地表沃茨的潜在化学负荷，这可能对水生生态系统和人类健康构成风险。由于公私伙伴关系应用的时空变异性以及调节其向地表沃茨迁移的过程的时空变异性，水生生物通常会受到污染物脉冲的影响。在小山区流域，由于坡度陡峭，径流量更快，特别可能出现这种暴露峰值。在这项工作中，一个空间上明确的，动态模型预测农药暴露在地表沃茨的耕地山区盆地（DynAPlus）已开发。该模型已被应用到一个小的山区流域（133平方公里）位于意大利东部阿尔卑斯山，其特点是集约农业（苹果园）周围的主要河流及其支流。使用2011年和2012年生产季节收集的样品，通过实验监测评估了DynAPlus的毒死蜱性能。预测和测量之间的比较产生了良好的一致性（R2= 0.49，效率因子0.60），尽管输入场景中更准确的空间信息（例如，实地具体应用、降雨量、土壤特性）将大大提高模型的性能。针对三个购买力平价进行的一组说明性模拟强调了DynAPlus在改善生态风险评估和农药管理实践的暴露预测方面的潜在作用（例如，用于活性成分和施用率的选择），以及用于规划有效的监测活动和/或解释监测数据。然而，一些模型改进（例如，固体侵蚀和输运）和更彻底的模型验证，以扩大适用范围。
The use of plant protection products (PPPs) in agricultural areas implies potential chemical loadings to surface waters, which can pose a risk to aquatic ecosystems and human health. Due to the spatio-temporal variability of PPP applications and of the processes regulating their transport to surface waters, aquatic organisms are typically exposed to pulses of contaminants. In small mountain watersheds, where runoff fluxes are more rapid due to the steep slopes, such exposure peaks are particularly likely to occur. In this work, a spatially explicit, dynamic model for predicting pesticide exposure in surface waters of cultivated mountain basins (DynAPlus) has been developed. The model has been applied to a small mountain watershed (133 km2) located in the Italian Eastern Alps and characterized by intensive agriculture (apple orchards) around the main river and its tributaries. DynAPlus performance was evaluated for chlorpyrifos through experimental monitoring, using samples collected during the 2011 and 2012 productive seasons. The comparison between predictions and measurements resulted in a good agreement (R2= 0.49, efficiency factor 0.60), although a more accurate spatial information in the input scenario (e.g., field-specific applications, rainfall amount, soil properties) would dramatically improve model performance. A set of illustrative simulations performed for three PPPs highlighted the potential role of DynAPlus in improving exposure predictions for ecological risk assessment and pesticide management practices (e.g., for active ingredient and application rate selection), as well as for planning efficient monitoring campaigns and/or interpreting monitoring data. However, some model improvements (e.g., solid erosion and transport) and a more thorough model validation are desirable to enlarge the applicability domain.
影响因子：
11.4
作者：
Knaebel, Anja;Stehle, Sebastian;Schulz, Ralf
通讯作者：
Schulz, Ralf