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Integrative model-based assessment of heavy metal contamination and source apportionment in groundwater of West Bengal, India

International Journal on Smart Sensing and Intelligent Systems
Volume 18 (2025): Issue 1 (January 2025)
By:
Open Access
|Oct 2025

Figures & Tables

Figure 1:

Sample location map of two sites (Baruipur and Bhagar) of West Bengal, India.
Sample location map of two sites (Baruipur and Bhagar) of West Bengal, India.

Figure 2:

Physicochemical attributors (pH and EC) of groundwater samples between two sites. EC, electrical conductivity.
Physicochemical attributors (pH and EC) of groundwater samples between two sites. EC, electrical conductivity.

Figure 3:

HM concentration of groundwater samples between two sites. HM, heavy metal.
HM concentration of groundwater samples between two sites. HM, heavy metal.

Figure 4:

Source allocation of HMs in the studied area: (A) factor contribution percentage of each HMs, (B) HM factor profile by PMF model, and (C) Pearson correlation coupled with PMF factors to evaluate the relationship among HMs. HMs, heavy metals; PMF, positive matrix factorization.
Source allocation of HMs in the studied area: (A) factor contribution percentage of each HMs, (B) HM factor profile by PMF model, and (C) Pearson correlation coupled with PMF factors to evaluate the relationship among HMs. HMs, heavy metals; PMF, positive matrix factorization.

Figure 5:

(A) Spatial distribution pattern of HMs (As, Cr, and Mn) of study area through IDW interpolation. Here, site 1: brown to blue shade and site 2: green to light shade.
(A) Spatial distribution pattern of HMs (As, Cr, and Mn) of study area through IDW interpolation. Here, site 1: brown to blue shade and site 2: green to light shade.

Figure 5:

(B) Spatial distribution pattern of HMs (Cu, Ni, Pb, and Mn) of study area through IDW interpolation. Here, site 1: brown to blue shade and site 2: green to light shade. HMs, heavy metals; IDW, inverse distance weighting.
(B) Spatial distribution pattern of HMs (Cu, Ni, Pb, and Mn) of study area through IDW interpolation. Here, site 1: brown to blue shade and site 2: green to light shade. HMs, heavy metals; IDW, inverse distance weighting.

Figure 6:

Distribution pattern of each HMs and water quality parameters of study area through SOM, site wise HM concentration distribution maps, and clustering of U-matrix depicts the two sampling sites. EC, electrical conductivity; HMs, heavy metals; SOM, self-organizing map.
Distribution pattern of each HMs and water quality parameters of study area through SOM, site wise HM concentration distribution maps, and clustering of U-matrix depicts the two sampling sites. EC, electrical conductivity; HMs, heavy metals; SOM, self-organizing map.
DOI: https://doi.org/10.2478/ijssis-2025-0052 | Journal eISSN: 1178-5608
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Language: English
Submitted on: Apr 9, 2025
Published on: Oct 4, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Heavy metals,
Groundwater,
Geospatial distribution,
Self-Organizing Map,
Positive Matrix Factorization
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2025 Soumi Banerjee, Saibal Ghosh, Arijit Dasgupta, Sonali Banerjee, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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