Evaluating groundwater pollution with emphasizing heavy metal hotspots in an urbanized alluvium watershed of Yamuna River, northern India

Ahmed, Salman; Akhtar, Naseem; Rahman, Abdur; Mondal, N. C.; Khurshid, Shadab; Sarah, Sarah; Khan, Mohammad Muqtada Ali; Kamboj, Vishal

doi:10.1016/j.enmm.2022.100744

Cited by 24 publications

(11 citation statements)

References 52 publications

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“…Researchers generally use PCA to determine the most essential factors and minimize data with minimal data loss [ 59 ]. Before the source identification, the Kaiser–Meyer–Olkin index was 0.759 and the Bartlett sphericity test result was below 0.001, indicating that the PCA model would be a suitable method for determining the principal components (PCs) of the sources of HM in fugitive dust in the study area [ 6 ].…”

Section: Resultsmentioning

confidence: 99%

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China

Chen

Wen

Wang

et al. 2022

IJERPH

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To develop accurate air pollution control policies, it is necessary to determine the sources of different types of fugitive dust in mining and metallurgy areas. A method integrating principal component analysis and a positive matrix factorization model was used to identify the potential sources of heavy metals (HMs) in five different types of fugitive dust. The results showed accumulation of Mn, Fe, and Cu can be caused by natural geological processes, which contributed 38.55% of HMs. The Ni and Co can be released from multiple transport pathways and accumulated through local deposition, which contributed 29.27%. Mining-related activities contributed 20.11% of the HMs and showed a relatively high accumulation of As, Sn, Zn, and Cr, while traffic-related emissions contributed the rest of the HMs and were responsible for the enrichment in Pb and Cd. The co-applied source-identification models improved the precision of the identification of sources, which revealed that the local geological background and mining-related activities were mainly responsible for the accumulation of HMs in the area. The findings can help the government develop targeted control strategies for HM dispersion efficiency.

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Section: Resultsmentioning

confidence: 99%

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China

Chen

Wen

Wang

et al. 2022

IJERPH

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“…where, Ti is the biological toxicity factor of i th heavy metal (Fe, Mn, Zn, Cr = 1; Co, Ni, Cu, Pb = 5; Cd = 30) (Ahmed et al 2022).…”

Section: Ecological Risk Index (Eri)mentioning

confidence: 99%

“…Despite of highest polluted water at WBBD and WBAD nitrate concentration was low, and the system was found to be nitrogen-limited from a stoichiometric perspective. Algal growth might have consumed nitrate (Ahmed et al 2022), making the system nitrogen-limited in this region. However, there is no pollution in the Delhi region due to Nitrate.…”

Section: Resistivitymentioning

confidence: 99%

Insights into Water Quality of River Yamuna, India: A Comprehensive Spatial-Temporal Analysis Using Advanced Indices and Multivariate Statistical Techniques.

Antil,

Maurya,

Dagar

et al. 2024

Preprint

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The escalating issue of heavy metal pollution is a global concern, primarily due to its toxic and persistent nature, further exacerbated by a continuous surge in concentrations above recommended thresholds. This study examines the spatial-temporal dynamics of water quality along the Yamuna River through physicochemical parameters, heavy metal analysis, advanced approaches such as the Heavy Metal Pollution Index (HPI) & Ecological Risk Index (ERI), and multivariate statistical techniques (Principal Component Analysis). It was identified that Wazirabad after drain (WBAD) exhibits poor water quality, with levels of Dissolved Oxygen (DO), Ammonia, Electrical Conductivity (EC), and five heavy metals (Fe, Mn, Pb, Cr, and Ni) exceeding Bureau of Indian Standards (BIS) permissible limits. During the pre-monsoon season, HPI values surpassed the critical threshold (100) in WBBD (146.69), WBAD (267.13), and PJ (204.80), while in the post-monsoon season, only PJ (115) exhibited elevated HPI values. Ecological risk assessment highlighted Cd, Pb, and Ni as major risk elements, posing significant environmental threats. Pearson Correlation analysis and PCA identified two distinct groups of heavy metals, suggesting distinct contamination sources for Co, Cd, and Cu compared to the Fe-Cr-Pb-Mn-Zn-Ni group. This thorough study not only emphasizes the critical condition of water quality in the Delhi region but also provides valuable insights into the sources and distribution of heavy metals, offering a valuable foundation for targeted intervention and mitigation strategies to address the pressing issue of heavy metal pollution.

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“…Soil acts as a sink and has the potential for contaminants’ transference to the food chain through crop production [ 3 ]. Soil quality, particularly its contamination level is closely linked to human well-being [ 4 , 5 ], as many food commodities can accumulate trace and toxic elements [ 6 – 8 ] which often induce cancer. Consequently, human health could be impacted by certain critical levels of trace elements through crop plant consumption [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Trace elements concentrations in soil contaminate corn in the vicinity of a cement-manufacturing plant: potential health implications

Abatemi-Usman,

Akindele,

Ayanlade

et al. 2023

J Expo Sci Environ Epidemiol

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Background Cultivated lands in the vicinity of industry are vulnerable due to trace element releases from industrial activities. One such situation concerns the surrounding of the largest cement-manufacturing plant in sub-Saharan Africa, located in Obajana, Nigeria. Objective This study aimed at examining the trace element concentrations in the soil as they contaminate corn crops in the vicinity of a cement manufacturing plant. A case study of the cement-manufacturing plant located in Obajana, Nigeria is presented. Methods We used inductively coupled plasma-mass spectrometer to analyse for total arsenic (As), cadmium (Cd), chromium(Cr), copper (Cu), lead (Pb), and nickel (Ni) concentrations and microwave-induced plasma-atomic emission spectrometer to measure total iron (Fe) and zinc (Zn) contents in 89 samples of corn and surface soil (0–15 cm) from five farmlands including reference farmland and evaluated health hazard of human exposure to the trace elements via the consumption of corn cultivated in the area. Results The results showed the average Cr concentrations in µg/g dry weight (±standard error of the mean) in corn ranged from 2.08 ± 0.17 to 3.56 ± 0.65 in all the farmlands including control, while the mean Pb levels in µg/g dry weight (± standard error of the mean) in corn extended from 0.23 ± 0.03 to 0.38 ± 0.02 in the farmlands downwind of the cement plant. The Cr values were several factors higher than the stable concentration range of 0.01 to 0.41 µg/g reported in cereal grains, while the Pb values exceeded the limit of 0.2 µg/g set by the Food and Agriculture Organization of the United Nations/World Health Organization in grains. Lead is a trace element of environmental concern and its average levels in the farmlands downwind of the plant were found to be several orders of magnitude higher than the values in µg/g dry weight (± standard error of the mean) (0.01 ± 0.00 to 0.02 ± 0.00) observed in the farmlands upwind of the plant and were statistically significant (p < 0.0001). Impact statement Our findings provide the first health hazard assessment from the consumption of corn cultivated in the vicinity of the largest cement-manufacturing plant in Nigeria as far as we know.

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Evaluating groundwater pollution with emphasizing heavy metal hotspots in an urbanized alluvium watershed of Yamuna River, northern India

Cited by 24 publications

References 52 publications

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China

Insights into Water Quality of River Yamuna, India: A Comprehensive Spatial-Temporal Analysis Using Advanced Indices and Multivariate Statistical Techniques.

Trace elements concentrations in soil contaminate corn in the vicinity of a cement-manufacturing plant: potential health implications

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