Source apportionment of water pollutants in the upstream of Yangtze River using APCS–MLR

Cheng, Guowei; Wang, Mingjing; Gao, Wei

doi:10.1007/s10653-020-00641-z

Cited by 58 publications

(20 citation statements)

References 39 publications

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“…Further multivariate statistical analyses of data were also done with Microsoft excel toolpak and IBMS SPSS version 22.0 software. Source apportionment using the APCS-MLR receptor model [94] , [95] , [5] , [96] on data was also conducted using the IBMS SPSS version 22.0. Details on the mathematical explanation of the APCS-MLR receptor model for source apportionment are provided in our earlier study [5] .…”

Section: Methodsmentioning

confidence: 99%

Occurrence, ecological risk assessment and source apportionment of pharmaceuticals, steroid hormones and xenoestrogens in the Ghanaian aquatic environments

et al. 2022

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Section: Methodsmentioning

confidence: 99%

Occurrence, ecological risk assessment and source apportionment of pharmaceuticals, steroid hormones and xenoestrogens in the Ghanaian aquatic environments

et al. 2022

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“…Absolute factor loadings larger than 0.75, in the range of 0.50-0.75, and in the range of 0.30-0.50 are considered to be strong, moderate, and weak loadings, respectively (Huang et al, 2010;Liu et al, 2015). Additionally, CA of factor scores and land use was incorporated to improve the precision of assessing possible sources (Cheng et al, 2020).…”

Section: Multivariate Statistical Methodsmentioning

confidence: 99%

“…According to Liu et al (2021), one-to-one relations of R-squared (R 2 ) between landscape metrics and hydrochemistry variables vary from 0.32 to 0.74. However, few studies considered adding land-use parameters to the improvement of source identification accuracy in PCA (Cheng et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Source apportionment of water pollutants in Poyang Lake Basin in China using absolute principal component score–multiple linear regression model combined with land-use parameters

Hou

Gao

Zhang

et al. 2022

Front. Environ. Sci.

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Source apportionment of surface water is essential for effective pollution control and sustainable water management. Physical mechanism models usually need so much data and parameters for calibration that their application for complex hydrologic condition watershed becomes difficult. However, reverse source tracing methods only based on water quality parameters present a certain subjectivity and uncertainty. In this research, additional land-use parameters were applied as an auxiliary in principal component analysis (PCA) for accurate identification of pollution sources. Thirteen water quality parameters and two meteorology parameters were used in the PCA and absolute principal component score–multiple linear regression (APCS–MLR) model to quantitatively identify potential pollution sources and their contributions to surface water pollution of the Poyang Lake Basin, in which frequent flow and sediment flux exchange with Yangtze River make the river–lake relationship complex. The results showed that urban wastewater with 34% contribution and agricultural non-point sources with 16% contribution, were the major sources of pollution in water quality. TP and NH3–N, the most serious pollutants, causing agricultural non-point source pollutions with 40% contributions and urban wastewater with 21% contributions were the major sources in the Poyang Lake Basin. Urban wastewater with 60% contributions was the major source of organic contamination. It can be concluded that with associated land-use parameters, the GIS approach with the APCS–MLR model can improve the accuracy and certainty of source apportionment, providing aid decision information for managers on protection of surface water quality.

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“…The untreated sewage from these plants exhibited high COD. Furthermore, it is known that a large amount of NH 3 -N can arise from the decomposition of organic pollutants [10,17,36]. Thus, VF4 represents the effect of point source pollution from this industrial sewage.…”

Section: Number Of Pollution Sources Identified Based On the Pca Analysismentioning

confidence: 99%

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

Qiu

Lu³

et al. 2021

Water

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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.

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Source apportionment of water pollutants in the upstream of Yangtze River using APCS–MLR

Cited by 58 publications

References 39 publications

Occurrence, ecological risk assessment and source apportionment of pharmaceuticals, steroid hormones and xenoestrogens in the Ghanaian aquatic environments

Occurrence, ecological risk assessment and source apportionment of pharmaceuticals, steroid hormones and xenoestrogens in the Ghanaian aquatic environments

Source apportionment of water pollutants in Poyang Lake Basin in China using absolute principal component score–multiple linear regression model combined with land-use parameters

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

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