Abstract
Groundwater resources are prone to arsenic poisoning, and millions of people around the world are at risk as a result, particularly in densely populated areas in Nadia district (West Bengal, India). In this research, GIS was utilized to visualize the distribution of arsenic concentrations. The geospatial map correlates the arsenic events with the groundwater depth of the Karimpur block. Present study examines the spatial distributions of groundwater arsenic and compares and contrasts several deterministic and stochastic prediction methods to better understand the nature of arsenic geospatial distributions in aquifers. Geostatistical model (Radial Basis Function, areal interpolation) was used to identify the spatial distribution of arsenic within the study area. Spatial clustering analysis were performed to identify arsenic hot-spot villages. Most of the hotspot villages are observed in the south and north-east part of Karimpur-II block. The highest concentration of arsenic in groundwater was found at Mahisbathan in Karimpur-II block, at 1.18 mg/l. The spatial correlation study between arsenic contamination and groundwater shows, the highest arsenic-contaminated villages are concentrated in shallow groundwater depth (less than 3.6 m/bgl) region in the post-monsoon season. In the pre-monsoon season, the maximum concentration of arsenic-contaminated villages is distributed in 6.1 m/bgl groundwater depth area. According to the findings of the current study, these are critical initiatives for future generations’ groundwater protection and prevention.
Zusammenfassung
Grundwasserressourcen sind anfällig für Arsenvergiftungen, und Millionen von Menschen auf der ganzen Welt sind dadurch gefährdet, insbesondere in dicht besiedelten Gebieten im Distrikt Nadia (Westbengalen, Indien). In dieser Studie wurde GIS verwendet, um die Verteilung der Arsenkonzentrationen zu visualisieren. Die abgeleiten kartographischen Visualisierungen aus Geodaten korrelieren die Arsenereignisse mit der Grundwassertiefe des Karimpur-Blocks. Die vorliegende Studie untersucht die räumliche Verteilung von Arsen im Grundwasser und vergleicht und kontrastiert mehrere deterministische und stochastische Vorhersagemethoden, um die Natur der räumlichen Verteilung von Arsen in Grundwasserleitern besser zu verstehen. Ein geostatistisches Modell (radiale Basisfunktion, Flächeninterpolation) wurde verwendet, um die räumliche Verteilung von Arsen innerhalb des Untersuchungsgebiets zu identifizieren. Räumliche Clustering-Analysen wurden durchgeführt, um Arsen-Hotspot-Dörfer zu identifizieren. Die meisten Hotspot-Dörfer konnten im südlichen und nordöstlichen Teil des Karimpur-II-Blocks beobachtet werden. Die höchste Arsenkonzentration im Grundwasser wurde bei Mahisbathan im Karimpur-II-Block mit 1,18 mg/l gefunden. Die räumliche Korrelationsstudie zwischen Arsenkontamination und Grundwasser zeigt, dass die am stärksten mit Arsen kontaminierten Dörfer in der Nachmonsunzeit in einer Region mit geringer Grundwassertiefe (weniger als 3,6 m/bgl) konzentriert sind. In der Vormonsunzeit verteilt sich die maximale Konzentration von arsenverseuchten Dörfern auf 6,1 m/bgl Grundwassertiefe. Nach den Erkenntnissen der aktuellen Studie sind dies entscheidende Erkenntnisse für den Grundwasserschutz und die Grundwasservermeidung künftiger Generationen.
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Acknowledgements
The authors are highly thankful to Mr. Srikanta Das and Md. Mizanur Rahman, laboratory assistant of Binoy Badal Dinesh Club sub-district laboratory of Mahishbathan, Karimpur for their constant support through interview and village (mouza) wise arsenic distribution data supplying. BLRO of karimpur Block and as well as Souvik Dutta, BLRO staff of Karimpur Block has a lot of contribution for field survey and identification of village (Mouza) Karimpur Block. Also thankful to Dr. Apurbo pramanik, Assistant professor of the geography of Karimpur pannadevi college for the development of literature.
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Mazumder, A., Bhunia, G.S. Arsenic Contamination in Shallow Groundwater in Karimpur Block of Nadia District (West Bengal, India)—A Spatial and Geostatistical Approach. KN J. Cartogr. Geogr. Inf. 72, 173–182 (2022). https://doi.org/10.1007/s42489-022-00103-9
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DOI: https://doi.org/10.1007/s42489-022-00103-9