首页> 外文期刊>Journal of the American Water Resources Association >AN OPTIMIZATION METHOD FOR ESTIMATING CONSTITUENT MEAN CONCENTRATIONS IN BASE FLOW-DOMINATED FLOW
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AN OPTIMIZATION METHOD FOR ESTIMATING CONSTITUENT MEAN CONCENTRATIONS IN BASE FLOW-DOMINATED FLOW

机译:基流控制流中均质浓度估算的优化方法

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摘要

Pollutant coefficients have been widely used to assess runoff nonpoint source pollution from individual land uses (e.g., agricultural, residential) of a watershed. Pollutant coefficients, known as event mean concentrations (EMCs), were developed by the U.S. Environmental Protection Agency's Nationwide Urban Runoff Program (NURP) to serve as a national measure for characterizing pollutant loading in a receiving water body. The term "baseflow pollutant coefficient (BPC)" is used in this study as a surrogate for EMC to describe mean concentration of pollutants in base flow-dominated flow. A method for characterizing base flow quantity and quality for different land uses was explored using inverse modeling with two optimization techniques (a least square method and a genetic algorithm [GA] optimization), land use information, and streamflow quantity and quality data. The inverse model was formulated as a constrained minimization problem and demonstrated with data for 15 watersheds in Indiana. Results showed that estimated pollutant coefficients are comparable to the published literature. This indicates that the proposed method has the potential to effectively estimate constituent mean concentrations for pollutant load determination in gauged and ungauged watersheds, albeit more analysis with larger and more robust datasets is desirable to further refine and validate the accuracy of the approach.
机译:污染物系数已被广泛用于评估流域单个土地用途(如农业,住宅)的径流面源污染。污染物系数,称为事件平均浓度(EMC),是由美国环境保护署的全国城市径流计划(NURP)制定的,可作为表征接收水体污染物负荷的国家标准。在本研究中,术语“基流污染物系数(BPC)”用作EMC的替代物,用于描述以基流为主的流量中污染物的平均浓度。探索了一种使用两种优化技术(最小二乘法和遗传算法[GA]优化),土地利用信息以及河流流量和质量数据的逆建模方法来表征不同土地利用的基本流量和质量的方法。逆模型被公式化为约束最小化问题,并用印第安纳州15个流域的数据进行了证明。结果表明,估计的污染物系数与已发表的文献相当。这表明所提出的方法有可能有效地估算出确定的和未加水的流域中污染物负荷的平均浓度平均值,尽管需要使用更大,更可靠的数据集进行更多分析以进一步完善和验证该方法的准确性。

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