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. 2025 Jul 29;15(1):27704.
doi: 10.1038/s41598-025-12120-5.

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

M Seeththa Sankar Narayanan  1 , V Stephen Pitchaimani  2 , M Sivakumar  3 , T Dinesh Kumar  3 , S Richard Abishek  4 , Shankar Karuppannan  5   6
Affiliations

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

M Seeththa Sankar Narayanan et al. Sci Rep. .

Abstract

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.

Keywords: HPI; Health risk; Heavy metal; Kadaladi; Metal Index.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethics approval: The manuscript is conducted in the ethical manner advised by the targeted journal. Consent for publication: The research is scientifically consented to be published. Human face consent to publish the declaration: We have carefully reviewed all the images in our manuscript and confirm that no human faces are present.

Figures

Fig. 1
Fig. 1
Location map of the Study Area. This figure was prepared using ArcGIS Desktop 10.1 (https://www.esri.com/en -us/arcgis/products/arcgis-desktop/overview). The shapefile used in the figure were obtained from the Survey of India (https://surveyofindia.gov.in/).
Fig. 2
Fig. 2
Spatial distribution map of Copper (mg/L) in (a) Pre-monsoon and (b) post-monsoon of 2023. This figure was prepared using ArcGIS Desktop 10.1 (https://www.esri.com/en -us/arcgis/products/arcgis-desktop/overview). The shapefile used in the figure were obtained from the Survey of India (https://surveyofindia.gov.in/).
Fig. 3
Fig. 3
Spatial distribution map of Zinc (mg/L) in (a) Pre-monsoon and (b) post-monsoon of 2023. This figure was prepared using ArcGIS Desktop 10.1 (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview). The shapefile used in the figure were obtained from the Survey of India (https://surveyofindia.gov.in/).
Fig. 4
Fig. 4
Spatial distribution map of Manganese (mg/L) in (a) Pre-monsoon and (b) Post-monsoon of 2023. This figure was prepared using ArcGIS Desktop 10.1 (https://www.esri.com/en -us/arcgis/products/arcgis-desktop/overview). The shapefile used in the figure were obtained from the Survey of India (https://surveyofindia.gov.in/).
Fig. 5
Fig. 5
Spatial distribution map of Iron (mg/L) in (a) Pre-monsoon and (b) Post-monsoon of 2023. This figure was prepared using ArcGIS Desktop 10.1 (https://www.esri.com/en -us/arcgis/products/arcgis-desktop/overview). The shapefile used in the figure were obtained from Survey of India (https://surveyofindia.gov.in/).
Fig. 6
Fig. 6
Spatial distribution map of HPI (mg/L) for (a) Pre-monsoon and (b) post-monsoon of 2023. This figure was prepared using ArcGIS Desktop 10.1 (https://www.esri.com/en -us/arcgis/products/arcgis-desktop/overview). The shapefile used in the figure were obtained from the Survey of India (https://surveyofindia.gov.in/).
Fig. 7
Fig. 7
shows the graphical representation of THQI for both (a) pre-monsoon and (b) post-monsoon.
Fig. 8
Fig. 8
shows the Seasonal correlation of metal concentration in groundwater for both pre-monsoon and post-monsoon (a) Copper, (b) Manganese, (c) Zinc, (d) Iron.
See this image and copyright information in PMC

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