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Using multivariate statistical analyses to identify and evaluate the main sources of contamination in a polluted river near to the Liaodong Bay in Northeast China

Author:
Bu, Hongmei, Song, Xianfang, Zhang, Yuan
Source:
Environmental pollution 2019 v.245 pp. 1058-1070
ISSN:
0269-7491
Subject:
anthropogenic activities, chemical oxygen demand, ecosystems, factor analysis, human population, industrial effluents, nitrogen, nitrogen content, pesticides, phosphorus, pollution control, regression analysis, river water, rivers, sewage, wastewater treatment, water pollution, water quality, watersheds, China
Abstract:
Using multivariate statistical analysis, the study evaluated anthropogenic sources of river water contamination and their relationships with river water quality in the Haicheng River basin near to the Liaodong Bay in Northeast China. The results showed that nitrogen (N) and phosphorous (P) were identified as the main pollutants in the river water by factor analysis. Human population and elevational gradient were all significantly correlated with N, P, and other water quality variables in correlation analysis and explained chemical oxygen demand (COD), N, and P variables from 23.9% (TN) to 53.1% (NH3+-N) of the total variances in regression analysis, indicating that population and its distribution were all responsible for river contaminations, especially for COD, N, and P contaminations. The excessive applications of fertilizers and pesticides were all positively correlated with nitrogen variables and nitrogen pollution factor in correlation analysis, suggesting that agricultural activities were contributed to the river nitrogen pollution. Due to inadequate or lack wastewater treatment facilities, huge amounts of domestic sewage and industrial effluents were released into the river, becoming the predominant anthropogenic sources for the river water deterioration of COD, N, and P. Multivariate statistical analysis provided useful tools to correlate sources of contamination with water quality data. This approach will provide a better management for river pollution control in a human-driven river ecosystem.
Agid:
6251658