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September 10, 2025Open Access

Spatial assessment of heavy metal contamination in groundwater in the Kadaladi region, Tamil Nadu, India

Puntos clave

Mn and Fe concentrations exceeded WHO limits in 20% of samples, highlighting significant contamination.
Site 33 demonstrated the highest pollution levels with a Heavy Metal Pollution Index of 99.1, indicating severe risks.
Post-monsoon samples had higher contamination levels due to agricultural runoff and enhanced leaching effects.
Children were particularly vulnerable, with health risk quotients exceeding 2.0 in areas impacted by manganese.

Resumen

Groundwater quality in Kadaladi is a critical concern due to contamination from natural and human activities. This study assesses heavy metal concentrations across pre-monsoon and post-monsoon seasons at 44 sampling sites, using the Heavy Metal Pollution Index (HPI), Metal Index (MI), and non-carcinogenic risk (HQ) assessments. Key heavy metals analysed include Cu, Pb, Zn, Ni, Cd, Mn, and Fe, in Pre-monsoon and Post-monsoon with comparisons to WHO (2017) standards using atomic absorption spectrometry. The Heavy Metal Pollution Index (HPI) and Metal Index (MI) were computed to assess contamination levels, and health risks were evaluated through non-carcinogenic hazard quotient (HQ) models for adults and children. Results revealed that Mn and Fe concentrations exceeded WHO permissible limits in 20% of the samples, with Site 33 showing the highest pollution in HPI (99.1) and MI (17.89). Post-monsoon samples showed notably higher contamination, attributed to monsoonal leaching and runoff from agricultural and saltpan activities. GIS-based spatial analysis identified persistent hotspots at Sites 6, 24, and 33. Children were particularly vulnerable, with HQ values exceeding 2.0 in affected zones, especially due to Mn exposure. HQ values indicated that children face higher health risks, particularly from Mn, exceeding permissible limits in 4.55% of samples. Pearson correlation revealed strong Mn-Fe geogenic associations, while Cd-Mn correlations post-monsoon pointed to anthropogenic sources. The integration of spatial mapping using GIS and statistical methods provides insight into contamination hotspots, emphasizing the critical role of monsoonal hydrology in mobilizing pollutants. These findings underscore the need for region-specific mitigation strategies and regular water quality monitoring. The study fills a regional knowledge gap and contributes globally relevant insights on managing groundwater quality in vulnerable coastal systems.

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