ArticleEnvironmental science and pollution research international2020
Water quality assessment and pollution source apportionment using multi-statistic and APCS-MLR modeling techniques in Min River Basin, China.
Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
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- Evaluation of spatio-temporal variation of water quality and source identification of conducive parameters in Damodar River, India.Environmental monitoring and assessment · 2022Article
- Predicting flocculant dosage in the drinking water treatment process using Elman neural network.Environmental science and pollution research international · 2022Article
- A geostatistical approach to estimating source apportionment in urban and peri-urban soils using the Czech Republic as an example.Scientific reports · 2021Article
- Assessment of performance indicators for different sizes of water supply systems in Minas Gerais, Brazil.Environmental monitoring and assessment · 2021Article
- Temporal trends and source apportionment of water pollution in Honghu Lake, China.Environmental science and pollution research international · 2021Article
- Prediction of water quality parameters using machine learning models: a case study of the Karun River, Iran.Environmental science and pollution research international · 2021Article
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Abstract
Anthropogenic activities pose challenges on security of water quality. Identifying potential sources of pollution and quantifying their corresponding contributions are essential for water management and pollution control. In our study, 2-year (2017-2018) water quality dataset of 15 parameters from eight sampling sites in tributaries and mainstream of the Min River was analyzed with multivariate statistical analysis methods and absolute principal component score-multiple linear regression (APCS-MLR) receptor modeling technique to reveal potential sources of pollution and apportion their contributions. Temporal and spatial cluster analysis (CA) classified 12 months into three periods exactly consistent with dry, wet, and normal seasons, and eight monitoring sites into two regions, lightly polluted (LP) and highly polluted (HP) regions, based on different levels of pollution caused by physicochemical properties and anthropogenic activities. The principal component analysis (PCA) identified five latent factors accounting for 75.84% and 73.46% of the total variance in the LP and HP regions, respectively. The main pollution sources in the two regions included agricultural activities, domestic sewage, and industrial wastewater discharge. APCS-MLR results showed that in the LP region, contribution of five potential pollution sources was ranked as agricultural non-point source pollution (22.13%) > seasonal effect and phytoplankton growth (19.86%) > leakage of septic tanks (15.73%) > physicochemical effect (12.86%) > industrial effluents and domestic sewage (11.59%), while in the HP region ranked as point source pollution from domestic and industrial discharges (20.81%) > municipal sewage (16.66%) > agricultural non-point source pollution (15.23%) > phytoplankton growth (14.82%) > natural and seasonal effects (12.67%). Based on the quantitative assessment of main pollution sources, the study can help policymakers to formulate strategies to improve water quality in different regions.
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