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Effectiveness of TSS, TN, and TP as Indicators of Stormwater Runoff Pollutant Concentration and Partitioning

机译:TSS,TN和TP作为雨水径流污染物浓缩和分配指标的有效性

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The literature has shown that for a single study site and, in some cases, for single watersheds, models can be developed based on Pearson correlations and principal component analysis (PCA). These models can predict the concentrations of desired pollutants based on the concentrations in runoff of more conventional and easy-to-measure parameters. This project aimed to use the methods suggested in the literature to investigate the potential for models to be constructed for pollutants such as metals and organics based on their correlations with the conventional parameters of total suspended solids (TSS), total nitrogen (TN) and total phosphorus (TP). The dataset used in this analysis was the National Stormwater Quality Database Version 3.0 and the Pearson paired correlations that have been finished to date. In general, few paired correlations were found with the traditional surrogates of TSS, TN and TP. This indicates that while site studies or single land use studies may find correlations with these conventional surrogates, transferring these correlations broadly across land uses and geographical regions is more problematic. It is anticipated that models built based on PCA may be able to predict the concentrations of less conventional parameters based on the measured concentrations of several conventional ones.
机译:文献已经表明,对于单一的研究现场,在某些情况下,对于单个流域,可以基于Pearson相关性和主成分分析(PCA)来开发模型。这些模型可以基于更常规且易于测量的参数的径向浓度来预测所需污染物的浓度。该项目旨在使用文献中提出的方法来研究用于根据与总悬浮固体(TSS),总氮(TN)和总量的常规参数的相关性的污染物构建污染物(如金属和有机物)的模型磷(TP)。该分析中使用的数据集是国家暴风水质量数据库3.0版和已完成约会的Pearson成对相关性。通常,使用TSS,TN和TP的传统代理人发现了很少有成对的相关性。这表明,虽然现场研究或单一土地使用研究可能与这些传统替代品的相关性,但是在陆地使用和地理区域广泛地转移这些相关性更为问题。预计基于PCA构建的模型可能能够基于几种常规浓度来预测较少常规参数的浓度。

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