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动态预测有效径流泥沙粒径,提高植被过滤带减蚀效率评估。

Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips.

机构信息

knoell France SAS, 5 rue Gorge de Loup, 69009 Lyon, France.

Bayer AG, 40789 Monheim, Germany.

出版信息

Sci Total Environ. 2023 Jan 20;857(Pt 3):159572. doi: 10.1016/j.scitotenv.2022.159572. Epub 2022 Oct 20.

Abstract

The most widely implemented mitigation measure to reduce transfer of surface runoff pesticides and other pollutants to surface water bodies are vegetative filter strips (VFS). The most commonly used dynamic model for quantifying the reduction by VFS of surface runoff, eroded sediment, pesticides and other pollutants is VFSMOD, which simulates reduction of total inflow (∆Q) and of incoming eroded sediment load (∆E) mechanistically during the rainfall-runoff event. These variables are subsequently used to calculate the reduction of pesticide load by the VFS (∆P). Since errors in ∆Q and ∆E propagate into ∆P, for strongly-sorbing compounds an accurate prediction of ∆E is crucial for a reliable prediction of ∆P. The most important incoming sediment characteristic for ∆E is the median particle diameter (d50). Current d50 estimation methods are simplistic, yielding fixed d50 based on soil properties and ignoring specific event characteristics and dynamics. We derive an improved dynamic d50 parameterization equation for use in regulatory VFS scenarios based on an extensive dataset of 93 d50 values and 17 candidate explanatory variables compiled from heterogeneous data sources and methods. The dataset was analysed first using machine learning techniques (Random Forest, Gradient Boosting) and Global Sensitivity Analysis (GSA) as a dimension reduction technique and to identify potential interactions between explanatory variables. Using the knowledge gained, a parsimonious multiple regression equation with 6 predictors was developed and thoroughly tested. Since three of the predictors are event-specific (eroded sediment yield, rainfall intensity and peak runoff rate), predicted d50 vary dynamically across event magnitudes and intensities. Incorporation of the improved d50 parameterization equation in higher-tier pesticide assessment tools with VFSMOD provides more realistic quantitative mitigation in regulatory US-EPA and EU FOCUS pesticide risk assessment frameworks. The equation is also readily applicable to other erosion management problems.

摘要

减少地表径流农药和其他污染物向地表水体转移的最广泛实施的缓解措施是植被过滤带 (VFS)。用于量化 VFS 减少地表径流、侵蚀泥沙、农药和其他污染物的最常用动态模型是 VFSMOD,它在降雨-径流事件中对总流入量 (∆Q) 和输入侵蚀泥沙负荷 (∆E) 的减少进行了机械模拟。这些变量随后用于计算 VFS 减少的农药负荷 (∆P)。由于 ∆Q 和 ∆E 的误差会传播到 ∆P 中,对于强吸附化合物,准确预测 ∆E 对于可靠预测 ∆P 至关重要。对于 ∆E 而言,最重要的输入泥沙特征是中值粒径 (d50)。当前的 d50 估算方法过于简单,基于土壤特性产生固定的 d50,而忽略了具体事件的特征和动态。我们根据来自异质数据源和方法的 93 个 d50 值和 17 个候选解释变量的大量数据集,为监管 VFS 情景推导了一个改进的动态 d50 参数化方程。首先使用机器学习技术(随机森林、梯度提升)和全局敏感性分析 (GSA) 对数据集进行分析,作为降维技术并识别解释变量之间的潜在相互作用。利用所获得的知识,开发了一个具有 6 个预测因子的简约多元回归方程,并对其进行了彻底测试。由于三个预测因子是事件特定的(侵蚀泥沙产量、降雨强度和峰值径流量),因此预测的 d50 在事件规模和强度上呈现动态变化。在具有 VFSMOD 的更高层次的农药评估工具中采用改进的 d50 参数化方程,可以为美国环保署和欧盟 FOCUS 农药风险评估框架中的监管提供更现实的定量缓解。该方程也可方便地应用于其他侵蚀管理问题。

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