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Pesticide fate on catchment scale: conceptual modelling of stream CSIA data

机译:流域尺度上的农药命运:CSIA数据流的概念模型

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Compound-specific stable isotope analysis (CSIA) has proven beneficial in the characterization of contaminant degradation in groundwater, but it has never been used to assess pesticide transformation on catchment scale. This study presents concentration and carbon CSIA data of the herbicides S-metolachlor and acetochlor from three locations (plot, drain, and catchment outlets) in a?47?ha agricultural catchment (Bas-Rhin, France). Herbicide concentrations at the catchment outlet were highest (62?μg?Lsup?1/sup) in response to an intense rainfall event following herbicide application. Increasing iδ/isup13/supC?values of S-metolachlor and acetochlor by more than 2?‰ during the study period indicated herbicide degradation. To assist the interpretation of these data, discharge, concentrations, and iδ/isup13/supC?values of S-metolachlor were modelled with a?conceptual mathematical model using the transport formulation by travel-time distributions. Testing of different model setups supported the assumption that degradation half-lives (DT50) increase with increasing soil depth, which can be straightforwardly implemented in conceptual models using travel-time distributions. Moreover, model calibration yielded an estimate of a?field-integrated isotopic enrichment factor as opposed to laboratory-based assessments of enrichment factors in closed systems. Thirdly, the Rayleigh equation commonly applied in groundwater studies was tested by our model for its potential to quantify degradation on catchment scale. It provided conservative estimates on the extent of degradation as occurred in stream samples. However, largely exceeding the simulated degradation within the entire catchment, these estimates were not representative of overall degradation on catchment scale. The conceptual modelling approach thus enabled us to upscale sample-based CSIA information on degradation to the catchment scale. Overall, this study demonstrates the benefit of combining monitoring and conceptual modelling of concentration and CSIA data and advocates the use of travel-time distributions for assessing pesticide fate and transport on catchment scale.
机译:化合物特异性稳定同位素分析(CSIA)已被证明可用于表征地下水中污染物的降解,但从未用于评估集水区农药的转化情况。这项研究提供了面积为47公顷的农业流域(法国下莱茵)中三个位置(地,排水和流域出口)的除草剂S-甲草胺和乙草胺的浓度和碳CSIA数据。流域出口处的除草剂浓度最高(62?μg?L ?1 ),以应对除草剂施用后的强降雨事件。在研究期间,异丙甲草胺和乙草胺的δ 13 C>值增加2?‰以上表明除草剂降解。为了帮助解释这些数据,使用概念数学模型,通过旅行的运输公式,用概念数学模型对S-甲草胺的排放量,浓度和δ 13 CC值进行建模。时间分布。对不同模型设置的测试支持以下假设:降解半衰期(DT50)随着土壤深度的增加而增加,这可以在使用传播时间分布的概念模型中直接实现。此外,与封闭式系统中基于实验室的富集因子评估相比,模型校准得出了场积分同位素富集因子的估计值。第三,我们的模型测试了在地下水研究中普遍使用的瑞利方程,它有潜力量化流域尺度上的退化。它提供了对流样品中降解程度的保守估计。但是,这些估算值大大超过了整个流域内的模拟退化,不能代表流域规模上的总体退化。因此,概念建模方法使我们能够将基于样本的CSIA信息升级到流域规模。总的来说,这项研究证明了对浓度和CSIA数据进行监测和概念建模相结合的好处,并提倡利用旅行时间分布来评估流域规模的农药命运和运输。

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