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Use of Multivariate Statistical Techniques to Study Spatial Variability and Sources Apportionment of Pollution in Rivers Flowing into the Laizhou Bay in Dongying District

by 1,†, 1,*,†, 2, 1 and 1
1
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
2
Shandong Zhiteng Environmental Testing Co. Ltd., Dongying 257000, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Bahram Gharabaghi
Water 2021, 13(6), 772; https://doi.org/10.3390/w13060772
Received: 26 January 2021 / Revised: 8 March 2021 / Accepted: 8 March 2021 / Published: 12 March 2021
(This article belongs to the Section Aquatic Systems—Quality and Contamination)
Spatial variability and source apportionment of river pollution flowing into the Bohai Sea are of great significance to the pollution liability and development of control strategies to reduce the terrestrial discharge of pollution in the ocean. In this study, ten water quality variables from 14 monitoring sites in rivers flowing into Laizhou Bay were obtained to investigate the spatial variation and pollution sources in Dongying District from 2018–2019. The survey area was divided into a low pollution (LP) zone and a high pollution (HP) zone by cluster analysis based on ten indicators. Principle component analysis/factor analysis with a geographic information system was performed to identify the four main pollution sources in the survey area. Compared with the positive matrix factorization model, the absolute principal component score-multiple linear regression (APCS-MLR) model was more appropriate for the source apportionment of pollution in the surface water of Dongying District. The point source pollution of domestic sewage (23.6%) was the most crucial pollution source of water in the LP zone, followed by non-point pollution from agricultural activity (16.4%). The contribution rate in the HP zone analyzed by the APCS-MLR model followed a decreasing order: point source pollution from domestic sewage (28.5%) > non-point pollution source of overland runoff (14.8%) > point source pollution of hybrid wastewater (12.4%) > point source pollution from industries sewage (10.6%). Therefore, the spatial distribution and sources of pollution in the investigated area should be considered while developing control measures to reduce the discharge of pollution to Laizhou Bay. View Full-Text
Keywords: multivariate statistical techniques; spatial variation; source apportionment; APCS-MLR; PMF; Laizhou Bay multivariate statistical techniques; spatial variation; source apportionment; APCS-MLR; PMF; Laizhou Bay
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MDPI and ACS Style

Su, J.; Qiu, Y.; Lu, Y.; Yang, X.; Li, S. Use of Multivariate Statistical Techniques to Study Spatial Variability and Sources Apportionment of Pollution in Rivers Flowing into the Laizhou Bay in Dongying District. Water 2021, 13, 772. https://doi.org/10.3390/w13060772

AMA Style

Su J, Qiu Y, Lu Y, Yang X, Li S. Use of Multivariate Statistical Techniques to Study Spatial Variability and Sources Apportionment of Pollution in Rivers Flowing into the Laizhou Bay in Dongying District. Water. 2021; 13(6):772. https://doi.org/10.3390/w13060772

Chicago/Turabian Style

Su, Jing; Qiu, Yanhua; Lu, Yuling; Yang, Xiaosong; Li, Songyan. 2021. "Use of Multivariate Statistical Techniques to Study Spatial Variability and Sources Apportionment of Pollution in Rivers Flowing into the Laizhou Bay in Dongying District" Water 13, no. 6: 772. https://doi.org/10.3390/w13060772

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