Groundwater pollution source identification and apportionment using PMF and PCA-APCA-MLR receptor models in a typical mixed land-use area in Southwestern China

Zhang, Han; Cheng, Siqian; Li, Hongfei; Fu, Kang; Xu, Yi

doi:10.1016/j.scitotenv.2020.140383

Cited by 186 publications

(70 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the use of PCA has been reported, for source identification probably due to the ease of application in many statistical software [21][22][23][24][25]27]. Thus, in this study the results obtained using PMF were corroborated by PCA.…”

Section: Resultssupporting

confidence: 69%

See 1 more Smart Citation

Chemical Characteristics and Identification of PM10 Sources in Two Lima Districts, Peru

Ilizarbe-Gonzáles

Rojas-Quincho

Torres

et al. 2020

DYNA

View full text Add to dashboard Cite

This study evaluates the concentration of PM10 and PM2.5 and identification of source in the districts of San Juan de Lurigancho and Puente Piedra (PPD) in Lima-Peru. The samples were collected from April to May 2017 by the National Meteorology and Hydrology Service of Peru (Senamhi). The concentration of PM10 and PM2.5, measured by gravimetric techniques, exceeded the international (WHO) and national standards; with maximum values for PM10 and PM2.5 of 160 and 121.56 µg/ m3 in PPD and 295.06 and 154.58 µg/ m3 in SJL. Identification of sources by the Positive Matrix Factorization Model (PMF 5.0) and Principal Component Analysis (ACP), showed similar sources for both districts. In SJL, the combination of vehicular traffic and resuspension of soil dust, marine aerosol and iron and steel industry was determined, while in PPD the resuspension of soil dust, vehicular source, industrial activity and marine aerosol.

show abstract

Section: Resultssupporting

confidence: 69%

“…The models available to solve this equation are EPA-CMB, EPA-Unmix and EPA-PMF [18]; the PMF model has become a frequently used tool in recent years, with more than 1,000 papers reporting this application [2,[19][20][21][22][23][24][25][26].…”

Section: Resultsmentioning

confidence: 99%

Chemical Characteristics and Identification of PM10 Sources in Two Lima Districts, Peru

Ilizarbe-Gonzáles

Rojas-Quincho

Torres

et al. 2020

DYNA

View full text Add to dashboard Cite

show abstract

“…Based on the correlation, PFA was carried out and factor load calculated. The contribution rate of water quality parameters in principal components can be determined through factor load calculation, to identify the primary pollution impact factors [38,[40][41][42].…”

Section: Methodsmentioning

confidence: 99%

Groundwater Quality Evaluation of the Dawu Water Source Area Based on Water Quality Index (WQI): Comparison between Delphi Method and Multivariate Statistical Analysis Method

Liu

Zhu

Cui

et al. 2021

Water

View full text Add to dashboard Cite

Water quality in the Dawu water source area is primarily studied through the traditional water quality assessment method that measures the chemical parameters of water against the existing standards, which is simple but cannot accurately describe the water quality. Although the water quality index (WQI) proposed by Horton is widely used for comprehensive water quality evaluation, parameter selection and weight determination are primarily based on the Delphi method, which is subjective and random. Moreover, in groundwater evaluation, the focus is primarily laid on general chemical parameters, such as Total Dissolved Solids, hydrogen ion concentration, Electrical Conductivity, and heavy metals, such as Hydrargyrum, Arsenic, and Chromium, with limited consideration for organic pollutants. In this study, WQI technology in combination with the entropy weight method was used to evaluate the groundwater environmental quality of the Dawu water source area, and the scientific results were analyzed by comparing the full index, Delphi, and multivariate statistical analysis methods. The results showed that the groundwater in the Dawu water source area generally had good quality and was potable and that the application of multivariate statistical analysis method was more suitable than the Delphi method in the index selection process.

show abstract

“…where u ij is the uncertainty of the jth variable in the ith sample. In addition, an additional uncertainty of 20%, as suggested, was considered in this study to represent the uncertainty associated with field sampling operations, such as sample collection, transport, and storage [25,26]. The number of factors is an important parameter for obtaining optimal results in the PMF model process.…”

Section: Estimating Contribution Of Pollution Sources Based On the Pmf Modelmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Groundwater pollution source identification and apportionment using PMF and PCA-APCA-MLR receptor models in a typical mixed land-use area in Southwestern China

Cited by 186 publications

References 52 publications

Chemical Characteristics and Identification of PM10 Sources in Two Lima Districts, Peru

Chemical Characteristics and Identification of PM10 Sources in Two Lima Districts, Peru

Groundwater Quality Evaluation of the Dawu Water Source Area Based on Water Quality Index (WQI): Comparison between Delphi Method and Multivariate Statistical Analysis Method

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

Contact Info

Product

Resources

About