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Heavy Metal Analysis and Health Risk Assessment of Groundwater and Soil in and Around Peenya Industrial Area, Bengaluru

N. Pavithra, C. R. Ramakrishnaiah

Ecological Engineering & Environmental Technology

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2024

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Vol. 25, iss. 7

63--79

EN

The present study in the Peenya Industrial Area in Bengaluru, India, carried out to assess the level of heavy metal contamination in the industrial area’s soil and groundwater. The study also discusses the potential health risks that inhabitants would suffer from consuming contaminated groundwater. In 116 bore well water samples collected before and throughout the monsoon season, heavy metals including cadmium, chromium, copper, zinc, arsenic, mercury, lead, nickel, and aluminium were examined. Heavy metals concentration (mg/kg) was analysed for 36 soil samples collected in the research area and for heavy metals like chromium, nickel, copper, zinc, arsenic and cadmium. In the current study, the heavy metal pollution index (HPI), hazard index (HQ(ing) ) and cancer risk factor (CR) were calculated to assess the potential health risk. The HPI value inside the Peenya industrial area exceeded the critical pollution index value of 100. The hazard index (HQ(ing)) via oral ingestion was found to be > 1.00 in Cr, Hg and As during both seasons, indicating maximum health impacts in the inhabitants of the study area. Cancer index values were > 10-4 in Cr, Ni, Cd, and Pb in the research area, posing cancer risk in people of all ages, from children to adults. Environmental and human health are both put at risk in a polluted region. To assess soil contamination, the following indices were utilized: geoaccumulation (I geo ), single contamination index (PI), and pollution load index (PLI).

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Evaluation of groundwater in aluminium slag disposal area

Putranto Thomas Triadi, Febriane Wenny

Journal of Water and Land Development

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2023

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no. 57

78--90

EN

Aluminium slag waste is a residue from aluminium recycling activities, classified as hazardous waste so its disposal into the environment without processing can cause environmental problems, including groundwater pollution. There are 90 illegal dumping areas for aluminium slag waste spread in the Sumobito District, Jombang Regency. This study aims to evaluate the quality of shallow groundwater surrounding aluminium slag disposal in the Sumobito District for drinking water. The methods applied an integrated water quality index (WQI) and heavy metal pollution index (HPI), multivariate analysis (principal component analysis (PCA) and hierarchical clustering analysis (HCA)), and geospatial analysis for assessing groundwater quality. The field campaign conducted 40 groundwater samples of the dug wells for measuring the groundwater level and 30 of them were analysed for the chemical contents. The results showed that some locations exceeded the quality standards for total dissolved solids (TDS), electrical conductivity (EC), and Al2+. The WQI shows that 7% of dug well samples are in poor drinking water condition, 73% are in good condition, and 20% are in excellent condition. The level of heavy metal contamination based on HPI is below the standard limit, but 13.3% of the water samples are classified as high contamination. The multivariate analysis shows that anthropogenic factors and natural sources/geogenic factors contributed to shallow groundwater quality in the study area. The geospatial map shows that the distribution of poor groundwater quality is in the northern area, following the direction of groundwater flow, and is a downstream area of aluminium slag waste contaminants.

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Evaluation of spatial and temporal variations of surface water quality in the Nile River Damietta branch

El-Tohamy W. S., Abdel-Baki S. N., Abdel-Aziz N. E., Khidr A. A.

Ecological Chemistry and Engineering. S

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2018

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Vol. 25, nr 4

569--580

EN

The objective of this study is to reveal the spatial and temporal variations of surface water quality in this part of the River Nile with respect to heavy metals pioneerution. Seventeen parameters in total were monitored at seven sites on a monthly basis from October 2013 to September 2014. The dataset was treated using the tools of univariate and multivariate statistical analyses. Cluster analysis showed three different groups of similarity between the sampling sites reflecting the variability in physicochemical characteristics and pollution levels of the study area. Six PCs factors were identified as responsible for the data structure explaining 91 % of the total variance. These were eutrophication factor (23.2 %), physicochemical factor (20.6 %), nutrients (16.3 %) and three additional factors, affected by alkalinity and heavy metals, recorded variance less than 15 % each. Also, the heavy metals pollution index (HPI) revealed that most of the calculated values were below the critical index limit of 100. However, two higher values (124.89 and 133.11) were calculated at sites V and VI during summer due to the temperature and increased run-off in the river system.