Water quality assessment and pollution source apportionment using multi-statistic and APCS-MLR modeling techniques in Mi
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RESEARCH ARTICLE
Water quality assessment and pollution source apportionment using multi-statistic and APCS-MLR modeling techniques in Min River Basin, China Han Zhang 1
&
Hongfei Li 1 & Haoran Yu 1 & Siqian Cheng 1
Received: 18 May 2020 / Accepted: 20 July 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Surface water quality . Source apportionment . Cluster analysis . Principle component analysis . APCS-MLR
Introduction Clean and safe surface water is an essential requirement for human health and economic development. Water quality in a region was regarded as a function of natural and anthropogenic factors (Carpenter et al. 1998; Zhang et al. 2010). However, anthropogenic factors, such as wastewater discharge, fertilizer applications, and livestock breading, have led to the deterioration of surface water quality around the world (Zeinalzadeh and Responsible Editor: Xianliang Yi * Han Zhang [email protected] 1
Faulty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Ch
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