Literature DB >> 32020405

Quantitative assessment of background pollutants using a modified method in data-poor regions.

Maoqing Duan1,2, Xia Du3,4, Wenqi Peng3,4, Cuiling Jiang5, Shijie Zhang3,4, Yang Ding3,4.   

Abstract

Heavy background pollutant loads pose a difficult problem for the assessment and management of regional water quality, especially in areas where surface water quality is less affected by anthropogenic pollution. Deducting background values from those derived from water quality monitoring is a new method for evaluating surface water environments in areas with heavy background loads. In this study, river source reserves in Heilongjiang province were evaluated with an export coefficient model (ECM) that considers the rainfall influence factor, has an improved timescale, and is based on synchronous rainfall monitoring data and concentrations. Moreover, the ECM was combined with a mechanism model. The chemical oxygen demand, ammonia nitrogen, and other water quality indices are affected by background environment, and therefore, suitable export coefficients for the study area were determined and a regression equation between the rainfall influence factor and precipitation was established. By combining the ECM and mechanism model, the concentrations entering the river during eight rainfall events in 2018 were predicted, and the background value was calculated to evaluate surface water quality. The predicted values were found to approximate the monitored values. Therefore, this study is of great significance for water quality assessment and management in areas with heavy background pollutant loads.

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Keywords:  Background loads; Export coefficient model; Mechanism model; Rainfall influence factor; Water quality

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Year:  2020        PMID: 32020405     DOI: 10.1007/s10661-020-8122-8

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  1 in total

1.  Response of surface water quality characteristics to socio-economic factors in Eastern-Central China.

Authors:  Maoqing Duan; Shilu Zhang; Mingxia Xu; Junyu He; Xinrui Li; Jun Zhang
Journal:  PLoS One       Date:  2022-04-12       Impact factor: 3.240

  1 in total

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