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Water Quality Assessment and Apportionment of Pollution Sources of Xilin River Using Multivariate Statistics, APCS-MLR and PMF Receptor Model


Paper Type 
Contributed Paper
Title 
Water Quality Assessment and Apportionment of Pollution Sources of Xilin River Using Multivariate Statistics, APCS-MLR and PMF Receptor Model
Author 
Pengfei Yu and Minquan Feng
Email 
mqfeng@xaut.edu.cn
Abstract:

     This study aims to investigate water quality and pollution source identification in the Xilin River Basin using Environmental Science and Engineering terminology. The study employs Water Quality Index (WQI), Principal Component Analysis (PCA), Factor Analysis (FA), Absolute Principal Component Scores Multiple Linear Regression (APCS-MLR), and Positive Matrix Factorization (PMF). WQI results indicate values of 96.55, 138.36, and 115.47 during normal season (NS), wet season (WS), and dry season (DS) respectively, with NS showing better water quality compared to WS and DS. PCA and FA identified four key factors explaining 80.08% to 83.55% of the total variance. APCS-MLR modeling results indicate that agricultural and livestock farming pollution sources (ALS), as well as industrial wastewater and urban domestic pollution sources (IUS), are the primary contributors to river water pollution. PMF simulations reveal slight variations in pollution sources across each season. Comparing the R2 values of APCS-MLR and PMF simulations, with averages of 0.80 and 0.67 respectively, indicates that APCS-MLR demonstrates higher stability and better simulation results.

Graphical Abstract:
Article ID
e2025093
Received Date 
2024-11-12
Revised Date 
2025-09-28
Accepted Date 
2025-10-20
Keyword 
APCS-MLR, PMF, source apportionment, water quality, Xilin river
Volume 
Vol.52 No.6 In progress (November 2025). This issue is in progress but contains articles that are final and fully citable.
DOI 
https://doi.org/10.12982/CMJS.2025.093
Citation 

Yu P. and Feng M., Water quality assessment and apportionment of pollution sources of Xilin river using multivariate statistics, APCS-MLR and PMF receptor model. Chiang Mai Journal of Science, 2025; 52(6): e2025093. DOI 10.12982/CMJS.2025.093.

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