Water quality assessment and pollution source apportionment using multivariate statistical techniques: a case study of the Laixi River Basin, China

被引：21
作者：
Xiao, Jie [1 ]
Gao, Dongdong [1 ]
Zhang, Han [2 ]
Shi, Hongle [1 ]
Chen, Qiang [1 ]
Li, Hongfei [3 ]
Ren, Xingnian [2 ]
Chen, Qingsong [1 ]
机构：
[1] Sichuan Acad Ecol & Environm Sci, Chengdu 610041, Peoples R China
[2] Southwest Jiaotong Univ, Faulty Geosci & Environm Engn, Chengdu 610031, Peoples R China
[3] Adm Comm Sichuan Tianquan Econ Dev Zone, Yaan 625000, Peoples R China
来源：
ENVIRONMENTAL MONITORING AND ASSESSMENT | 2023年 / 195卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Surface water quality; Source apportionment; Multivariate statistical techniques; APCS-MLR; SOURCE IDENTIFICATION; COASTAL WATER; APCS-MLR; GROUNDWATER; INDEX;
D O I：
10.1007/s10661-022-10855-6
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Identifying potential sources of pollution in tributaries and determining their contribution rates are critical to the treatment of water pollution in main streams. In this paper, we conducted a multivariate statistical analysis on the water quality data of 12 parameters for 3 years (2018-2020) at six sampling sites in the Laixi River to qualitatively identify potential pollution sources and quantitatively calculate the contribution rates to reveal the tributaries' pollution status. Spatio-temporal cluster analysis (CA) divided 12 months into two parts, corresponding to the lightly polluted season (LPS) and highly polluted season (HPS), and six sampling sites were divided into two regions, corresponding to the lightly polluted region (LPR) and highly polluted region (HPR). Principal component analysis (PCA) was used to determine the potential sources of contamination, identifying four and three potential factors in the LPS and HPS, respectively. The absolute principal component score-multiple linear regression (APCS-MLR) receptor model quantitatively analyzed the contribution rates of identified pollution sources, and the importance of the different pollution sources in LPS can be ranked as domestic sewage and industrial wastewater and breeding pollution (33.80%) > soil weathering (29.02%) > agricultural activities (20.95%) > natural influence (13.03%). HPS can be classified as agricultural cultivation (41.23%), domestic sewage and industrial wastewater and animal waste (33.19%), and natural variations (21.43%). Four potential sources were identified in LPR ranked as rural domestic sewage (31.01%) > agricultural pollution (26.82%) > industrial effluents and free-range livestock and poultry pollution (25.13%) > natural influence (14.82%). Three identified latent pollution sources in HPR were municipal sewage and industrial effluents (37.96%) > agricultural nonpoint sources and livestock and poultry wastewater (33.55%) > natural sources (25.23%). Using multivariate statistical tools to identify and quantify potential pollution sources, managers may be able to enhance water quality in tributary watersheds and develop future management plans.
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页数：17
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共 53 条
[1]   Water quality assessment using multivariate statistical techniques in Rio Tercero Reservoir, Argentina [J].
Bonansea, Matias ;
Ledesma, Claudia ;
Rodriguez, Claudia ;
Pinotti, Lucio .
HYDROLOGY RESEARCH, 2015, 46 (03) :377-388
[2]   Groundwater pollution and risk assessment based on source apportionment in a typical cold agricultural region in Northeastern China [J].
Chen, Ruihui ;
Teng, Yanguo ;
Chen, Haiyang ;
Hu, Bin ;
Yue, Weifeng .
SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 696
[3]   Source apportionment of water pollutants in the upstream of Yangtze River using APCS-MLR [J].
Cheng, Guowei ;
Wang, Mingjing ;
Chen, Yan ;
Gao, Wei .
ENVIRONMENTAL GEOCHEMISTRY AND HEALTH, 2020, 42 (11) :3795-3810
[4]   Identification and Apportionment of Potential Pollution Sources Using Multivariate Statistical Techniques and APCS-MLR Model to Assess Surface Water Quality in Imjin River Watershed, South Korea [J].
Cho, Yong-Chul ;
Choi, Hyeonmi ;
Lee, Myung-Gu ;
Kim, Sang-Hun ;
Im, Jong-Kwon .
WATER, 2022, 14 (05)
[5]   Nitrogen and Phosphorus Removal Associated with Changes in Organic Loads from Biological Reactors Monitored by Multivariate Criteria [J].
de Oliveira, Jacineumo Falcao ;
Fia, Ronaldo ;
Nunes, Bianca Selvati Brandino ;
Siniscalchi, Luciene Alves Batista ;
de Matos, Mateus Pimentel ;
Fia, Fatima Resende Luiz .
WATER AIR AND SOIL POLLUTION, 2020, 231 (10)
[6]   Influences of the land use pattern on water quality in low-order streams of the Dongjiang River basin, China: A multi-scale analysis [J].
Ding, Jiao ;
Jiang, Yuan ;
Liu, Qi ;
Hou, Zhaojiang ;
Liao, Jianyu ;
Fu, Lan ;
Peng, Qiuzhi .
SCIENCE OF THE TOTAL ENVIRONMENT, 2016, 551 :205-216
[7]   Spatial variation and source apportionment of surface water pollution in the Tuo River, China, using multivariate statistical techniques [J].
Fu, Dong ;
Wu, Xuefei ;
Chen, Yongcan ;
Yi, Zhenyan .
ENVIRONMENTAL MONITORING AND ASSESSMENT, 2020, 192 (12)
[8]   Water quality assessment and apportionment of pollution sources using APCS-MLR and PMF receptor modeling techniques in three major rivers of South Florida [J].
Gholizadeh, Mohammad Haji ;
Melesse, AssefaM. ;
Reddi, Lakshmi .
SCIENCE OF THE TOTAL ENVIRONMENT, 2016, 566 :1552-1567
[9]   Anthropogenic influences on the water quality of the Baiyangdian Lake in North China over the last decade [J].
Han, Quan ;
Tong, Runze ;
Sun, Wenchao ;
Zhao, Yue ;
Yu, Jingshan ;
Wang, Guoqiang ;
Shrestha, Sangam ;
Jin, Yongliang .
SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 701
[10]   Quality characterization and pollution source identification of surface water using multivariate statistical techniques, Nalagarh Valley, Himachal Pradesh, India [J].
Herojeet R. ;
Rishi M.S. ;
Lata R. ;
Dolma K. .
Applied Water Science, 2017, 7 (05) :2137-2156
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