Using multivariate statistical analysis in assessment of surface water quality and identification of heavy metal pollution sources in Sarough watershed, NW of Iran

Piroozfar, Parisa; Alipour, Samad; Modabberi, Soroush; Cohen, David R.

doi:10.1007/s10661-021-09363-w

Cited by 11 publications

(2 citation statements)

References 42 publications

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“…The Heavy Metal Evaluation Index (HEI) is a numerical value that reflects the level of heavy metal contamination in a given area [ 48 , 49 , [53] , [54] ]. The HEI is calculated by measuring the concentration of one or more heavy metals in a sample (e.g., soil, water, air), and then comparing the results to established standards.…”

Section: Methodsmentioning

confidence: 99%

Nutrient and heavy metal dynamics in the coastal waters of St. Martin’s island in the Bay of Bengal

Alam,

Kamal,

Ahmed

et al. 2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

Nutrient and heavy metal dynamics in the coastal waters of St. Martin’s island in the Bay of Bengal

Alam,

Kamal,

Ahmed

et al. 2023

Heliyon

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“…It is used to achieve dimensionality reduction through factor analysis, which organizes complex data sets into a small number of uncorrelated variables (Li et al 2020). These new variables are linear combinations of the original variables, which can signi cantly simplify the information of the original data (Piroozfar et al 2021). The Kaiser-Meyer-Olkin (KMO) and Bartlett's spherical tests were used to determine the suitability of the data set for PCA (with KMO values of 0 to 1).…”

Section: Pca and Camentioning

confidence: 99%

Water quality assessment and pollution source apportionment using multivariate statistical and PMF receptor modeling techniques in a sub-watershed of the upper Yangtze River, Southwest China

Ren

Yang

Zhao

et al. 2023

Environ Geochem Health

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The rapid industrial and agricultural development as well as urbanization signi cantly affect the water environment, especially in subwatersheds where the contaminants/constituents present in the pollution sources are complex and the ow is unstable. Water quality assessment and quantitative identi cation of pollution sources are the primary prerequisites for the improvement of water management and quality. In this work, 168 water samples were collected from seven stations throughout 2018-2019 along the Laixi River, which is an important pollution control unit in the upper reaches of the Yangtze River. Multivariate statistics and positive matrix factorization (PMF) receptor modeling techniques were used to evaluate the characteristics of the river-water quality and reveal the pollution sources. Principal component analysis (PCA) was used to screen the crucial parameters and establish an optimized water quality assessment procedure to reduce the analysis cost and improve the assessment e ciency. Cluster analysis (CA) further illustrates the spatiotemporal distribution characteristics of river water quality. Results indicated that high-pollution areas are concentrated in the tributaries, and the high-pollution periods are the spring and winter, which veri es the reliability of the evaluation system. The PMF model identi ed ve and six potential pollution sources in the cold and warm seasons, respectively. Among them, pollution from agricultural activities and domestic wastewater shows the highest contributions (33.2% and 30.3%, respectively) during the cold and warm seasons, respectively. The results of this study can provide corresponding theoretical support for pollutant control and water quality improvement, and avoid the ecological and health risks caused by the deterioration of water quality. IntroductionRiver-water quality is a serious problem, especially for urban managers and researchers worldwide. Clean rivers provide a safeguard for human health and the ecosystem and prevent the environmental, economic, and social risks associated with pollution (Liu et al. 2019a, Sener et al. 2017). However, with the increase in population, urbanization, and industrialization, pollution from industrial and domestic wastewaters as well as agricultural and urban runoffs has increased the load on surface water. Thus, water resource management faces serious challenges to reduce pollutants (Casillas-Garcia et al. 2021, Zhang et al. 2020a). In the sub-watersheds that lack supervision, the water quality is especially unstable, and the pollution sources are complicated. (Cheng et al. 2020, Huang et al. 2010. Therefore, rapid water quality assessment and accurate identi cation of pollution sources can help researchers develop strategies for water management and pollutant control (Zhang et al. 2022).Recently, China has mainly been relying on sampling analysis and automated monitoring stations to obtain water quality data. Various complex monitoring procedures are typically used, and additional physical and chemical parameters for wa...

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Water Quality Index (WQI) Significance and Efficiency as a Measure of Water Quality

Mohammed,

Salih,

Thiruchelvam

2023

Water Resources Development and Management

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Using multivariate statistical analysis in assessment of surface water quality and identification of heavy metal pollution sources in Sarough watershed, NW of Iran

Cited by 11 publications

References 42 publications

Nutrient and heavy metal dynamics in the coastal waters of St. Martin’s island in the Bay of Bengal

Nutrient and heavy metal dynamics in the coastal waters of St. Martin’s island in the Bay of Bengal

Water quality assessment and pollution source apportionment using multivariate statistical and PMF receptor modeling techniques in a sub-watershed of the upper Yangtze River, Southwest China

Water Quality Index (WQI) Significance and Efficiency as a Measure of Water Quality

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