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Past, Present, and Future of Groundwater Remediation Research: A Scientometric Analysis

1 College of Environmental Science and Engineering, Taiyuan University of Technology, Jinzhong 030600, China

Guilian Fan

2 Faculty of Business Administration, Shanxi University of Finance and Economics, Taiyuan 030006, China

3 Shanxi Province C&M Operation Center for Xishan Yellow River-lifting Irrigation Project, Taiyuan 030002, China

Jianguo Cui

Associated data.

In this study, we characterize the body of knowledge of groundwater remediation from 1950 to 2018 by employing scientometric techniques and CiteSpace software, based on the Science Citation Index Expanded (SCI-E) databases. The results indicate that the United States and China contributed 56.4% of the total publications and were the major powers in groundwater remediation research. In addition, the United States, Canada, and China have considerable capabilities and expertise in groundwater remediation research. Groundwater remediation research is a multidisciplinary field, covering water resources, environmental sciences and ecology, environmental sciences, and engineering, among other fields. Journals such as Environmental Science and Technology, Journal of Contaminant Hydrology , and Water Research were the major sources of cited works. The research fronts of groundwater remediation were transitioning from the pump-and-treat method to permeable reactive barriers and nanoscale zero‑valent iron particles. The combination of new persulfate ion‑activation technology and nanotechnology is receiving much attention. Based on the visualized networks, the intelligence base was verified using a variety of metrics. Through landscape portrayal and developmental trajectory identification of groundwater remediation research, this study provides insight into the characteristics of, and global trends in, groundwater remediation, which will facilitate the identification of future research directions.

1. Introduction

Groundwater is an important natural resource that supports socioeconomic development and maintains ecological balance in modern societies [ 1 ]. It provides 36% of drinking water, 42% of water for agriculture, and 24% of water for industry [ 2 , 3 ]. The quality of groundwater resources globally is threatened by the natural geochemical background and anthropogenic pollution [ 4 , 5 ]. To clean polluted groundwater and ensure the sustainability of groundwater resources, a variety of remediation technologies (e.g., pump-and-treat, biodegradation, chemical oxidation, and reduction to adsorption) have been developed and applied [ 6 , 7 , 8 , 9 ]. Each treatment option has associated merits and demerits, depending on remedial goals and site conditions. There are several challenges for selecting sustainable remediation technologies and designing remediation strategies today, including evolving groundwater treatment goals, complex geophysical–chemical characterization, current understanding of available technologies, contaminant mixtures, and economic considerations [ 8 , 10 , 11 , 12 ].

Groundwater remediation research has been reviewed from a variety of perspectives, and the extant review articles focus on technologies for the remediation of contaminated groundwater and their applications. Examples include natural attenuation processes [ 13 ], permeable reactive barriers [ 14 ], sustainability appraisal tools [ 15 ], nanoscale zero‑valent iron particles [ 16 ], and iron sulphide particles for groundwater remediation [ 17 ]. Nevertheless, little attention has been devoted to quantitative analyses of the evolution of groundwater remediation research. In short, they did not provide an overall landscape of the groundwater remediation literature. In a recent article, Zhang, Mao, Crittenden, Liu and Du [ 8 ] used social network analysis and bibliometric technology to evaluate publications related to groundwater remediation from 1995 to 2015, and the results provided valuable insight into groundwater remediation. However, they did not identify and evaluate hotspots and there is, to date, no knowledge base for groundwater remediation.

This study has used scientometric approaches to describe the development trajectory and landscape of groundwater remediation research quantitatively and systematically, and the research frontiers and emerging trends of the groundwater remediation literature were detected and identified using the visualization tool CiteSpace. The results will provide a useful reference for academics, researchers, and policy decision makers.

2. Data Acquisition and Methods

2.1. data acquisition.

It is essential for researchers to quickly and accurately locate publications, using the search strategy and screen methods. The article retrieval source for analysis was the SCI-E databases, which are frequently used in scientific research [ 18 ]. Several topic terms, including “groundwater restoration”, “groundwater reclamation”, and “groundwater remediation”, were used to retrieve publications that contained these words in publications’ titles, keyword lists, and abstracts. These terms helped to locate the majority of groundwater-remediation-related publications. Though there may be other groundwater-remediation-related terminology, they account for a small percentage of publications and may have marginal relation to groundwater remediation research [ 19 ]. The search results have been refined or filtered by web of science categories, research areas, and document types. To do this, several categories needed to be excluded, such as (i) unrelated categories—physiology, pharmacology pharmacy, genetics heredity—(ii) document types—book chapter, data paper, proceedings abstract—and (iii) research areas—imaging science photographic technology, business economics. The search resulted in 2867 publications dated from January 1950 to September 2018. The entire records, including the titles, abstracts, keywords, and references, were then exported for subsequent analyses. Based on the frequency of groundwater remediation research over the past seven decades, we reviewed numerous studies published between 1950 and 2018 (see the Supplementary Materials ).

2.2. Scientometric Analytical Methods

Scientometrics, created by Tibor Braun [ 20 ], has been defined as the “quantitative study of science and technology” [ 21 ]. As a branch of informatics, scientometrics is used to analyze patterns in scientific literature quantitatively, to understand the knowledge structure of emerging trends in a research field [ 22 ]. As a scientometric approach, CiteSpace is used to clarify multidisciplinary relationships, assess research status, map knowledge domains, reveal research frontiers, and predict emerging trends, by analyzing the bibliographic records of associated publications [ 23 ]. In the net knowledge maps generated by CiteSpace, a node represents one item (e.g., country, author, subject, term, journal, or reference), and links describe co-citations or co-occurrences between these nodes [ 24 ]. Furthermore, each node is described as a series of tree rings of different colours, with blue representing the oldest and orange the most recent [ 25 ]. A purple ring around an item indicates good centrality.

To date, CiteSpace has been employed in studies of, for example, nonpoint source pollution [ 26 ], information sciences [ 27 ], psychological science [ 28 ], and global green innovation [ 29 ]. In this study, we produced and analyzed co-occurrence networks of subject categories and countries, and co-citation networks of journals, authors, and references using CiteSpace.

3. Results and Discussion

3.1. characteristics of publication output.

To give an overview of research in groundwater remediation, the annual number of articles published from 1950 to 2018 (total, 2867) is shown in Figure 1 . In 1950, only one article, titled “Ground-Water Pollution in Michigan”, was published [ 30 ]. Subsequently, the annual number of publications was fewer than 10 until 1990. The number of articles increased significantly after this period, from 23 in 1991 to 207 in 2016.

Publication output performance during the period 1950–2018.

3.2. Co-Operations of Countries/Territories and Institutions

3.2.1. co-operation among countries/territories.

Running CiteSpace, we obtained a countries/territories distribution with 78 nodes and 296 links ( Figure 2 ); this map can help researchers find their colleagues elsewhere in the world and establish collaborations. Each node represents a different country or territory, and the size of the node represents the number of publications. Similarly, the lines connecting countries/territories indicate their co-operation, while the thickness of the line represents the degree of co-operation [ 8 ]. The United States (U.S.) was the hub of the co-operation network, and the leader in groundwater remediation research, in collaboration with other productive countries/territories. The discovery of hazardous waste at Love Canal in Niagara, New York, and many other places in the United States, heralded a new era in hazardous waste problems by the end of the 1970s. Subsequently, civil and environmental engineers, hydrologists, hydrogeologists, and other scientists became involved in the identification, evaluation, and remediation of groundwater-contaminated hazardous waste sites [ 31 ]. Groundwater remediation research was distributed among 78 countries/territories, and the top 10 countries/territories published 2607 articles, accounting for 90.9% of the total ( Table 1 ). The U.S. and China published 1152 and 464 articles, respectively, ranking first and second, and accounting for 56.4% of total articles. Thus, the U.S. and China were two major research powers in groundwater remediation and far ahead of other countries/territories.

Distribution of co-operation among countries/territories.

Distribution of 10 co-operative countries/territories and institutions.

3.2.2. Co-Operation among Institutions

Institutional co-operation was also analyzed using CiteSpace ( Figure 3 ). The top 10 productive institutions are shown in Table 1 . These 10 productive institutions worked closely with organizations in geographical proximity, e.g., the University of Waterloo and University of Regina in Canada, the University of Illinois and the University of Arizona in the U.S., and China University of Geosciences and Jilin University in China. Therefore, it is necessary to strengthen international co-operation in the future.

Distribution of co-operation among research institutions.

The top 10 research institutions issued 455 articles, accounting for 15.9% of the total. According to Table 1 and Figure 3 , the first major research echelon was led by the University of Waterloo, where hydrologists first used zero-valent iron (Fe 0 ) to treat contaminated groundwater in situ approximately three decades ago [ 32 , 33 ]. Of these top 10 institutions, three were in the U.S. and three in China, confirming that the U.S., Canada, and China have considerable capabilities in groundwater remediation research, and strong expertise in research and development.

3.3. Co-Occurrence of Subject Categories

Based on co-occurrence analyses of subject category, the disciplines involved in groundwater remediation can be detected. In this study, we selected the top 30 subject categories with the largest number of reoccurrences each year for category characteristic analysis. The information on subject categories was extracted from the SCI-E databases using CiteSpace and analyzed. Figure 4 shows the co-occurrence network from 1950 to 2018, where one node represents a subject category, and the edge connecting two nodes represents the co-occurrence of two subject categories. The top three popular research categories were environmental sciences and ecology, environmental sciences, and engineering. Of the top 10 subject categories, engineering had the central position and played an important role in groundwater remediation. Material science was second, followed by water resources, and chemistry, physical. Therefore, groundwater remediation is a multidisciplinary research field, involving an extensive range of interests.

Disciplines involved in the field of groundwater remediation, shown as a network of subject categories.

3.4. Journal Citation Analyses

“Core journals” usually refer to top-ranking journals with high citation frequencies. We produced a groundwater remediation journal co-citation map with 199 nodes and 1149 links, using CiteSpace software ( Figure 5 ). The top 10 most productive journals, with >500 citations, are listed in Table 2 . The total of 12,090 citations from the 10 journals accounts for 34.74% of the total citation count. Thus, the citation distribution was concentrated. In addition, these 10 journals were defined as the “core journals” in the field of groundwater remediation.

Journal co-citation knowledge map.

Distribution of 10 “core journals” and IF in 2017.

Environmental Science and Technology and Water Research were the core journals in groundwater remediation research, with 1850 and 1204 citations, respectively ( Table 2 ). Water Research and Environmental Science and Technology also had the highest IFs, at 7.051 and 6.653, respectively.

3.5. Author Citation Analyses

White and McCain [ 34 ] first proposed the author co-citation concept in the U.S. Author co-citation maps, which reflect the closeness of the research directions and importance of the authors, and have been widely used to assess scientific research ability and relevance. Herein, one node represents a cited author, and an author co-citation knowledge map was created using CiteSpace ( Figure 6 ).

Author co-citation map/the co-operation network of productive authors.

The largest node corresponded to Blowes DW, whose articles were cited 251 times; this was followed by Gillham RW (233), Wilkin RT (204), Matheson LJ (190), Scherer MM (177), Phillips DH (165), Su CM (157), and Noubactep C (145). Thus, these authors’ works had a marked impact on groundwater remediation research and development, and they represent the core research strength in the field.

As mentioned above, the first major research echelon, led by the University of Waterloo, was composed of Gillham RW, Blowes DW, and other authors, suggesting that this group had the greatest impact on groundwater remediation research.

3.6. Documents Co-Citation Analyses

The co-citation network was divided into many clusters of co-cited references in CiteSpace, so that references are closely connected within the same cluster but loosely connected among different clusters ( Figure 7 ). The 10 major clusters are listed in Table 3 by size, which represents the number of members in each cluster. The silhouette score of a cluster reflects its quality, i.e., homogeneity or consistency. If the silhouette value of a cluster is close to 1.0, then it was homogenous [ 22 ]. All the clusters in Table 3 were highly homogeneous, as indicated by their high silhouette scores. Noun phrases from the terms (e.g., titles or abstracts) used in articles in the cluster were used to label each cluster. Labels selected by the log-likelihood ratio (LLR) test were used in subsequent discussions [ 35 ].

The synthetic network of co-cited references.

Major clusters of co-cited references.

MI = mutual information, LLR = log-likelihood ratio, Ave = average.

We can identify the average year of the publications in a cluster by their recentness, i.e., Cluster #6 on aquifer remediation had an average year of publication of 1985. The recently formed clusters, Clusters #2 and #3 (nano-zero‑valent iron and metallic iron, respectively), had an average year of publication of 2009 and 2008, respectively.

3.6.1. Analyses of Research Fronts

Price [ 36 ] proposed the concept of the research front, and postulated that a research front can characterize the momentary nature of a research field. Garfield [ 37 ] defined a research front as “a cluster of co-cited articles and all articles that cite the cluster”. Chen [ 38 ] defined a research front as “an emergent and transient grouping of concepts and underlying research issues”. CiteSpace shapes the network knowledge map of research fronts, with mutant terms that can be extracted from the index terms, abstracts, titles, and record indicators of the references. Specific methods include selecting a cited reference as the net node, the g-index ( k = 10) as threshold willing, and the key pathfinder algorithm. We obtained 17 clusters by selecting “Find clusters” and abstracted the names of the clusters by selecting “Label clusters with indexing terms”. Figure 7 shows the net knowledge map generated.

There were 558 nodes, 874 links, and 17 clusters. Clusters #2, #3, and #4 had a high concentration of nodes with citation bursts, which echoed the fact that these were the most recently formed clusters.

If a cluster has a larger area, it has more bibliographic entries, and large clusters generally indicate main research directions, i.e., each cluster corresponds to a research front. The research fronts and major trends in groundwater extraction, in situ groundwater remediation, permeable reactive barriers, metallic iron, and nanoscale zero‑valent iron particles are shown in Figure 7 and Table 3 .

3.6.2. Timeline of Research Fronts

Figure 8 shows timelines of the 17 distinct co-citation clusters and their interrelationships. All timelines run from left to right [ 39 ], show the times at which research fronts appear and disappear, and display structural information about the research front clusters [ 40 ]. Analysts can visually identify various characteristics of a cluster, such as its citation classics, historical length, citation bursts, and connection to other clusters.

Timelines of co-citation clusters. Major clusters are labelled on the right.

The following paragraphs provide an interpretation of the research fronts’ timelines. Groundwater remediation research has a long history ( Figure 8 ). The earliest research front, “aquifer remediation”, provided basic information for subsequent research. Next, technologies to deal with contaminated groundwater were developed. The containment and/or control of contaminated groundwater can generally be accomplished using one, or a combination, of several available techniques, which can be broken down into aquifer rehabilitation, physical containment measures, and withdrawal, treatment, and use [ 41 ].

The second research front, “groundwater extraction”, began around 1979, and pump‑and‑treat as a groundwater extraction technology began at selected sites in 1982 [ 42 ], in response to groundwater pollution control and contamination remediation. Earlier pump‑and‑treat systems, which did not consider the presence of geologic heterogeneity, poor definition of initial condition in source zones, did not clean aquifers to the required level. Many of the original systems worked adequately for a period of time, but, after they were switched off, the contaminant levels at many sites reached values higher than those before remediation [ 31 ]. Subsequent to the pivotal 1989 article by Mackay [ 43 ], the research front became inactive. A number of new technologies for groundwater remediation are under development, and these may accelerate contaminant removal from the subsurface (e.g., injection of steam, surfactants) or destroy the contaminant in situ [ 43 ]. Hence, research fronts are discontinuous, and start and end abruptly when scientists move from one puzzle to the next [ 40 ].

In the 1990s, scientists and engineers had to prepare to deal with recent puzzles, which included residual oils, source zones with non-aqueous phase liquids (NAPLs), and vapours in the unsaturated zone [ 31 ]. During this period, four research fronts, “anionic surfactant remediation (1995)”, “decision analyses (1997)”, “laboratory column test (1999)”, and “in situ groundwater remediation (1999)”, were created in response to growing concern over efficient and cost-effective clean‑up solutions. Among these research fronts, “in situ groundwater remediation” was worthy of note. This research front experienced a period of stability and extends to the present. A growing number of researchers focused on the development of in situ remediation technologies, e.g., in situ chemical oxidation [ISCO]. ISCO, a type of advanced oxidation process technology, has proven useful for in situ remediation technology for the most prevalent organic contaminants in groundwater. The development of in situ remediation technologies led to the formation of three research fronts in the new century: “permeable reaction barriers (PRBs) (2002)”, “metallic iron (2008)”, and “nano zero‑valent iron (nZVI) (2009)”.

The research front, “PRB”, which dates to 1989, had a median publication date of 2002. Over the last two decades, PRBs have been emerging as an effective alternative passive in situ remediation technology. In the 1990s, research on PRBs increased considerably, which led to many new approaches for suitable reactive materials, target contaminants, and PRB design.

“nZVI” is the latest research frontier, showing rapid growth and a professional pattern. Gillham and O’Hannesin [ 44 ] discovered that halogenated aliphatic compounds in groundwater can be reduced using bulk ZVI. This characteristic of iron led to the advanced Fe-PRB, in which vertical trenches were filled with granular ZVI, placed in the flow path of the underground contaminant plumes [ 45 , 46 ].

A report [ 47 ] by the Chinese Academy of Sciences indicated that the third top research front in ecology and environmental sciences, entitled “Activation of persulfate for degradation of aqueous pollutants by transition metal and nanotechnology”, is receiving much global attention. The combination of persulfate ion activation technology and nanotechnology will improve the efficiency of polluted water treatment, reduce energy consumption, and promote recycling.

3.6.3. Analyses of the Intelligence Base

Chen [ 38 ] defined “the intellectual base of a research front as its citation and co-citation footprint in the scientific literature, an evolving network of scientific publications cited by research-front concepts”.

(1) Most‑Cited Articles

The most‑cited articles are generally considered landmarks, owing to their ground‑breaking contributions [ 22 ]. Cluster #7 had three of the top 10 landmark articles, and Clusters #3 and #10 each had two ( Table 4 ). The most-cited articles in the databases were by Blowes (2000), with 154 citations, followed by Gillham (1994), with 144 citations and Matheson (1994), with 135 citations, and the most recent was a review article by Fu (2014). Interestingly, the titles of the most‑cited articles contained the terms “permeable reactive barriers”, “zero-valent iron”, “nanoscale iron particles” ( Table 4 ), which were in accordance with the research fronts noted above.

The top 10 most-cited references.

Gillham and O’Hannesin [ 44 ] investigated the potential of Fe 0 in the dehalogenation of ethanes, ethenes, and 14 chlorinated methanes. The results demonstrated biotic reductive dechlorination, in which iron serves as the source of electrons. In response to the rapid degradation rates, an application for in situ remediation of contaminated groundwater was proposed.

Blowes, et al. [ 48 ] was cited the most frequently. This paper reviewed the recent research progress in PRBs for the remediation of inorganic contamination of groundwater.

(2) Betweenness Centrality

The betweenness centrality measure that Freeman [ 49 ] proposed is used to give prominence to potential pivotal points in the synthesized network shifts over time. The betweenness centrality of nodes in a network is indicative of the importance of the location of the nodes. We are especially interested in the nodes located between different node groups, because they probably offer insight into emerging trends [ 22 ]. Table 5 shows 10 structurally crucial references in the network, and three of these nodes were in Cluster #3, and five in Cluster #7. These references can be identified as landmark works in the field of groundwater remediation.

Cited citations with the highest between centrality.

(3) Citation Bursts

A reference citation burst may indicate an emergent research front, and the citation-burst-detection algorithm of Kleinberg [ 50 ] is adapted for identifying emergent research front concepts. Table 6 lists the references that had the strongest metric of citation bursts across the entire database during the period 1950–2018. Among the articles with strong citation bursts ( Table 6 and Figure 9 ), Mackay and Cherry [ 43 ] is worthy of note. Their article explored the reasons for the difficulty of groundwater clean-up, noted some implications, and suggested that achieving stringent health-based clean-up standards is unlikely, and the ultimate cost of clean-up is high in many cases. Thus, they suggested that site characterization and remediation have much room for improvement, by both the development of new tools and ongoing training of staff [ 43 ]. Subsequently, the development of permeable reactive barrier technology using zero-valence iron filings has proceeded from recognition, evaluation, technology conceptualization, and proof of concept, to commercialization.

The top 20 references with the strongest citation bursts.

The top five references with the strongest metric of citation bursts.

The structural centrality and citation burstness of cited references can be measured by the Sigma metric measure, i.e., the Sigma value of a reference that is strong in both measures will be higher than that of a reference that is strong in only one of the two measures [ 22 ] ( Table 7 ). The pioneering article by Fu, et al. [ 51 ] had the highest Sigma of 101,578.09, indicating it to be structurally indispensable in the field, due to its strong citation burst. This article reviewed the recent advances of ZVI and the progress made in groundwater remediation using ZVI technology.

Structurally and temporally significant references.

4. Conclusions

4.1. summary.

This study offers a comprehensive scientometric review of groundwater remediation research. There were 2867 journal articles related to this field published from 1950 to 2018, and the increasing annual number of publications suggests a continued research interest and a globally urgent need to remediate contaminated groundwater, since 1991. The U.S. and China contributed 56.4% of the publications and were the major powers in groundwater remediation research. Groundwater remediation research is a multidisciplinary research field and covers an extensive range of interests, from environmental sciences and ecology to environmental sciences, engineering, and water resources. Furthermore, journals such as Environmental Science and Technology , Water Research , and Journal of Contaminant Hydrology were the main sources of cited works in groundwater remediation research. The research fronts of groundwater remediation were transitioning from the pump-and-treat method to PRBs and nanoscale zero‑valent iron particles. The combination of persulfate ion activation technology and nanotechnology shows promise. Meanwhile, based on the visualized networks, the intelligence base was verified using a variety of metrics. Our study provides a valuable reference for researchers in the field of groundwater remediation, and others with interests in this area.

4.2. Future Outlook

(1) Development of treatment trains. Great advances have been made in the field of groundwater remediation research over recent decades. As the “One Size Fits All” remedy technology does not work effectively at most contaminated sites, the groundwater remediation technologies used are generally parts of a “treatment train”. Hence, tailored approaches and remediation techniques on a site-by-site basis are needed. In addition, research into technologies for pollution remediation of fractured bedrock aquifers, low permeability formations, and green remediation technology, is needed. Hence, these topics will remain areas of active research for many years.

(2) Optimization of groundwater remediation design under uncertainty. The technical and environmental challenges in designing optimal groundwater remediation systems are the spatial variability of natural aquifers, uncertain aquifer parameters, and complex site characteristics, which affect both the cost and efficiency of remediation. Investment in data collection and accurate site characterization may minimize uncertainty. Simultaneously, effort has been made to include uncertainty analyses in optimal groundwater remediation designs, using optimization methods and a coupled simulation–optimization approach. Owing to the complexity and inherent uncertainty of groundwater remediation technologies, the success of their field application is limited. This suggests the need to incorporate new methods and means of quantitatively analyzing uncertainty into the design of optimal groundwater remediation technologies.

(3) Development of green and sustainable remediation. Green and sustainable remediation (GSR) is a new movement in the land and groundwater remediation field that has drawn much attention globally in recent years, and it requires consideration of the environmental, economic, and social dimensions of sustainability. GSR technologies for contaminated groundwater, including biochar materials, green synthesis of engineered nanoparticles [ 52 ], sustainable PRB [ 53 ] and sustainably released long-term green remediation materials, have made rapid progress. However, case studies indicate that public participation must be improved to promote social sustainability, and region-specific factors should be considered when implementing GSR.

Acknowledgments

The authors would like to thank Prof. Guishen Fan for his thoughtful review of a later draft.

Supplementary Materials

Publications list for scientometric analysis (topic searched results in SCI-E database) includes title of the article, journal name, authors, year of publication, volume, page range. The following is available online at https://www.mdpi.com/1660-4601/16/20/3975/s1 , Spreadsheet: Publications list.

Author Contributions

Methodology, G.F.; Q.C.; literature search and data analysis, Q.C.; W.N.; writing—Original draft, Q.C.; J.L.; writing—Review and editing, Q.C.; H.L.; supervision: J.C.

This study was funded by Shanxi Provincial Water Conservancy Science and Technology Research and Promotion Project of China, the Shanxi Provincial Natural Science Foundation of China (Grant No. 201601D102037).

Conflicts of Interest

The authors declare that they have no conflict of interest.

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• Published: 20 February 2024

Source identification and potential health risks from elevated groundwater nitrate contamination in Sundarbans coastal aquifers, India

• Subodh Chandra Pal   ORCID: orcid.org/0000-0003-0805-8007 1 ,
• Tanmoy Biswas 1 ,
• Asit Kumar Jaydhar 1 ,
• Dipankar Ruidas 1 ,
• Asish Saha 1 ,
• Indrajit Chowdhuri 1 ,
• Sudipto Mandal 2 ,
• Aznarul Islam 3 ,
• Abu Reza Md. Towfiqul Islam 4 , 5 ,
• Chaitanya B. Pande 6 , 7 ,
• Edris Alam 8 , 9 &
• Md Kamrul Islam 10  

Scientific Reports volume  14 , Article number:  4153 ( 2024 ) Cite this article

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• Environmental sciences

In recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl − , the value of which is 0.71. Other factors like—As, F − , EC and Mg 2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies.

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Introduction

Groundwater is an essential resource for all living beings in the world and plays a key role in reducing the water crisis and increasing agricultural productivity and industrial activity 1 . Different research articles showed that about 780 million people face water scarcity globally 2 . In various activities like agriculture, industry, drinking water and other domestic purposes, groundwater plays a key role in fulfilling such criteria 1 . Yet, coastal aquifers are at risk at various activities like the intrusion of salt water,over-exploitation of groundwater, contamination of nitrate and different trace metals due to agricultural activities 3 . Cattle barns and sewage effluent are the other major source of nitrate concentration in coastal areas (Bernhard et al. 4 ). In the coastal area of West Bengal, the major source of nitrate contamination is the decomposition of organic matter in the soil, agricultural fertilizer used in the field, and industrial effluents. In this coastal area, groundwater is the only potable source 3 . The matter of nitrate concentration and related issues is recently a great threat all over the world such as in Asia 5 , 6 , 7 , America 8 , Australia 9 . In densely polluted coastal areas countries like India, groundwater quality is contaminated due to nitrate concentration (Pal et al. 28 ). In agricultural fields, excess use of livestock residue is another vital cause of nitrate concentration in the lower or shallow aquifer 9 , 10 . In a coastal area, the groundwater level is shallower and the only drinking water source so nitrate contamination increases daily 11 , 12 . The most common contaminant in groundwater has been nitrates since 1970; nitrate is a natural component found in groundwater, but contamination occurs when it exceeds 3 mg/l. The United States Environmental Protection Agency (USEPA) sets the level of nitrate in groundwater for blue baby syndrome at > 10 mg/l. It ensures that below 10 mg/l is considered safe for everyone for drinking purposes 13 . An excess nitrate increase in groundwater (more than 50 mg/l) causes diseases like blue baby syndrome (WHO 14 ). Human life quality can be improved by improving drinking water quality 15 ; recently, industrialization and fertilizers used in agricultural fields have increased the nitrate concentration and related health issues in different parts of India as well as different countries in the world (Asadi et al. 16 ). In the Indian agricultural field for farm production, huge amount of nitrogen is used as a result, nitrogen increases in groundwater, river water, pond etc. (Pal et al. 28 ; Rehman et al. 17 ). In groundwater, concentration of nitrate rapidly increases due do its more solubility and mobility. Various side effects are reported due to its ill effects like, blue baby syndrome (infants), gland problems and colon cancer 18 . Different factors and processes are responsible for assessing nitrate concentration in groundwater (Pal et al. 28 ).

Different researchers used different strategies for determining the pollution status in various parts of the world, like the index method and interpolation method 19 . According to Narany et al. 20 , a sampling point which is very depth is needed for the interpolation method. Expert knowledge is needed to make an accurate position in a statistical model 21 . According to Islam et al. 3 , Groundwater nitrate concentration data is very rare till now and collection of data from the field is very cost-effective so it is critical to evaluate nitrate concentration and its effects on different aspects. To eradicate this problem, we used different modelling approaches and techniques and noticed the measures of groundwater nitrate concentration. In our study, we used the Mean Decrease Accuracy Method (MDA), the Logistic Regression model (LR) in the RS-GIS environment, different software including ArcGIS 10.8, IBM SPSS 20, and various statistical methods. Fourteen causative factors have been considered for determining the concentration of nitrate, which is pH, chloride, arsenic, fluoride, electrical conductivity, magnesium, nitrate, potassium, temperature, sulphate, phosphate, sodium, salinity, depth, and bicarbonate.

According to Pitchaikani et al. 22 , the coastal aquifer in West Bengal is facing nitrate problems, which leads to heath-related issues in the populations of that region. In this coastal region, few researchers gave attention to determining the nitrate concentration and its effect on human health. This research determines the NO 3 − concentration through the LR method and health-related issues. Therefore, the main objective of this work is to determine the hydro-chemical properties of groundwater and probable health risks in coastal aquifers of the Sundarbans region. In our research, we prepared a NO 3 − susceptibility map to show the health-related issues and determine the reasons for elevated nitrate concentration and health hazards. The novelty and objectives of this research work are that people can easily find the pattern of nitrate concentration in this study area and find the nitrate hazard distribution map, which some other researchers have not created till now. With the help of this research work, the government can take required action and strategy for reducing the ill-effects of nitrate concentration.

There are two coastal districts in West Bengal: East Medinipur and South 24 Parganas. Study revealed, South 24 Parganas is the largest district in of area (9960 sq. km) and second largest as per population concern. On 1st March 1986, this district was separated into two parts, North and South 24 Parganas. The locational extent of this district is 22° 12′ 13″ and 22° 46′ 55″ North latitude and 87° 58′ 45″ and 88° 22′ 10″ East latitude which is shown in (Fig.  1 ). Kolkata district is in the north, and Howrah and East Medinipur are in the West. It has 2042 villages. This district shares a long international border (Bangladesh) to the east and southern parts of the Bay of Bengal. The largest mangrove ecosystem is in the south, south–west, south–east and eastern parts of this district. This district has five subdivisions, including Kakdwip, Baruipur, Alipore Sadar, Diamond Harbour and Canning with seven municipalities and 29 community blocks and 111 census towns (Census 2011). The total population of this district is 8,161,961, almost equal to the country as Honduras or the Virginia state of USA. The population density of inhabitants is 819 per sq. km. The population growth rate over 2001–2011 is 18.2%. According to the census 2011, the sex ratio is 956/1000 males, and a 77.51% is literacy rate. Baruipur northern Plain and Kulpi-Diamond Harbour Plain is found in the north part of this district ie, almost 5–6 m above sea level.

Location map of the study area (this map was generated using ArcGIS, version: 10.3.1, www.esri.com/arcgis ).

In this portion, the land creation process is also going on. Hot and humid climatic condition is found all over the district throughout the year, and rainfall occurs by the southwest monsoon wind. The highest temperature occurred in Diamond Harbour (37 °C) and the lowest temperature occurred at 9 °C (Census 2011). This region is famous for its natural environment, like the Sundarbans, which is well known as the habitat of the Royal Bengal Tiger. Sundarbans is the world's largest mangrove forest area. It is revealed that this district is a deposit of various natural resources like groundwater, oil, and natural gas conducting different tests. Due to the presence of other rivers and bills, khals and the Bay of Bengal; this region’s soil is divided into two types: saline and non-saline. Deposit soil of Ganga is saline free, which is rich in nutrients which is favourable for the cultivation of different crops like rice, wheat, barley, maize, etc.

Hydrogeological setting

Total South 24 Parganas district is situated under the Gangetic delta; the southern portion of this district a large are is covered by the Sundarban Biosphere Reserve (SBR), and rivers flowing over this area like Matla, Thakuran, Raidighi, Bidya, Raimangal and Saptamukhi etc. Islands are situated in this district i.e., Sagar Island, Fraserganj, Lothian Island, Bulcherry, Halliday Island, Dalhousie Island, and Bangaduni Island at the mouth of the river Gosaba of these few Islands are submerged under seawater. The study area also includes the primary intertidal deltaic mass and the coast sand associated with estuaries and tidal streams; alluvial and marine silt of the Quaternary era make up the majority of the South 24 Parganas district's geological features in the Bengal basin 23 . Das et al. 24 state, that although delta formation is still ongoing, the northern portion of the South 24 Parganas is a component of the active delta zone; the restricted aquifer serves as the primary supply of drinking water in this area, and deeper aquifers have also been observed there. According to Datta and Kaul 25 , depending on their vertical position, aquifers can range in depth from 160 to 335 m, which are notable sources of drinking and irrigation water; tertiary silt and alluvium from the Pleistocene to the present comprise the majority of the aquifer strata in this region. This region significantly falls under the lower ganga basin area of the Holocene Sediments predominantly collected in lacustrine, marine, and fluvial settings 26 . The porous alluvial and coastal sediments in the area allowed undesirable pollutants to seep and infiltrate into the groundwater aquifer 27 ; at a depth of 160 to 400 m below the surface, the aquifer is composed primarily of freshwater layers, whereas the shallow aquifer, about 60 m below the surface, is dominated by salty water. In the study region, parent rock played a noteworthy role in salinity intrusion, hydro-geological interaction and cation exchange which significantly impact water quality 28 .

Methodology and data sources

Big data is required for conducting the research, a total of 58 samples have been collected throughout this district. By using Google Earth-pro software, we determined the tube well samples in this region. GPS was used for documentation and recording the data. Before gathering the water, disconnecting the standing water 10 to 15 min, groundwater was pumped. -density washed bottles were used to collect water (Jaydhar et al. 26 );After that, the samples were immediately transferred to the Burdwan University laboratory and stored at below 5 °C for laboratory analysis of the hydro-chemical properties of groundwater. Cations and anions were determined by ion chromatography using Dionex ICS-90. The inductive coupled plasma mass spectrometry method is used for analysing As (Islam et al. 3 . Quality control tools and critical procedures of the lab were used for quality assurance of groundwater. To conduct this research, Logistic Regression (LR) method is used and ArcGIS 10.2.4 is used for the thematic layer of different parameters like depth of water, the temperature of water, salinity, EC, pH, K + , Mg 2+ , Na + , As, F − , Cl − , HCO 3 −, PO 4 2− , SO 4 2− , and NO 3 −. The susceptibility map of NO 3 − and human health hazard map was prepared by ArcGIS 10.2.4 software. Piper diagram and USSL diagram is crafted for describe the water quality. The flow chart of the methodology is shown in Fig.  2 .

Methodological flow chart.

Logistic regression

One important and commonly used modelss is Logistic Regression (LR); in several applications, various researchers cite the LR model on their research topic (Pradhan and Lee 29 ). In real situations, it is challenging to use; the severe assumption was defined by the LR model, which is measured the difficulty of the approaches in this study. Several statistical approaches based on the LR model can overwhelm this difficulty and formulate a straightforward approach which uses different analyses like bivariate such as frequency ratio 30 . Still LR method is much suitable than other methods, several drawbacks are present in this method. To solve this problem, multiple studies apply bivariate analysis of LR; despite some drawbacks, one advantage of the LR model is that it can calculate the discrete and continuous data separately or together. LR model was done by using the “Statistical Package for Social Science (SPSS) V 15 programme”. By using the following equation, we calculate LR

where, P represents the subsequent equation can calculate particular observational possibility possibilities and z –

where \(\mathrm{\beta o}\) represents algorithm intercept, n and X1 represent conditioning factors, \(\upbeta 1\) represents independent variable contribution.

Health risk estimation (HRE)

The health risk of the people was estimated by adopting the subsequent equations introduced by (US EPA 31 ):

where ‘CDIi’ represents ingestion of chronic regular dose of specific trace element (μg/kg/day); ‘Cw’ implies concentration of heavy metal in potable water (μg/l); ‘IR’ suggests the consumption rate of drinking water (0.70 for children and 21.00 for adults); ‘EF’ reveals rate of exposure; ‘ED’ denotes duration of exposure (6 years for children and 30 years for adults); ‘BW’ suggests the body weight of person (15 kg for children and 70 kg for adults) whereas ‘AT’ represents the average time of exposure (2,190 days for children and 10,950 days for adults).

Absorption of CDI dermal calculated through the following expression (Eq.  4 ) (US EPA 31 ):

where, ‘CDId’ indicates dermal of day-to-day dosage of chronic trace elements (μg/kg/day); ‘SA’ signifies exposure of skin area; ‘Kp’ represents permeability coefficient; ‘ET’ suggests time of contaminants exposure rate (h/day) and ‘CF’ means factors responsible for units of conversion (L/cm 3 ).

Hazard quotient (HQ) of every trace element was measured by applying the successive equation (Eq.  5 ):

RfD of every contaminant was obtained from regulations of (US EPA 31 ).

Probable health risk of the people was estimated through the subsequent equation (Eq.  6 ):

where, HI is Health Risk Index.

Statistical analysis of causative factors

Physical properties of groundwater in coastal aquifers.

Each of the conditioning elements that have been chosen has unique physical and chemical characteristics that play a significant role in regulating the water quality of a given location. This is especially true in the complex coastal zone, where the quality of aquifers is equally influenced by both land and seawater. Generally speaking, the distributional pattern of several the conditioning factors chosen for this study varies during the investigation rather than remaining constant. The descriptive statistics state the distributional pattern of all adopted conditioning factors mentioned in Table 1 . The conditioning factors, including EC, temperature, and pH varies 340.84–4773.8 (Fig.  3 a), 23.19 °C–28 °C (Fig.  3 b), and 7.55–8.81(Fig.  3 c); accordingly, the highest concentration of EC was observed in Diamond Harbour I and II block along with this north–western and north–eastern part were experienced with higher temperature; salinity and groundwater depth ranges from 0.20–1.61 mg/l (Fig.  3 e) to 0.06–33.39 m (Fig.  3 n). Another critical component like F − , average value is 0.79 and ranges from 3.76 to 0.002 mg/l (Fig.  3 d), primarily found in southern part of Namkhana and Kulpi region; average values of Mg 2+ , Na + and K + are 36.34 mg/l, 182.38 mg/l and 8.276 mg/l (Table 1 ) which ranges from 96.85 to 1.09 mg/l (Fig.  3 f), 737.71 to 15.38 mg/l (Fig.  3 g) and 40.95 to 1.03 mg/l (Fig.  3 h) respectively. As, PO 4 2− , and SO 4 2− are very distinctive hydro-chemical properties of groundwater, average values are 0.204 mg/l, 2.29 mg/l and 31.87 mg/l (Table 1 ); values range from 0.37 to 0.11 mg/l (Fig.  3 m), 4.60 to 0.62 mg/l (Fig.  3 l) and 184.76 to 0.002 mg/l (Fig.  3 k) accordingly. In addition to this, Fig.  3 i and j represent spatial distribution of CI and HCO 3. The distributional pattern is very uneven throughout the entire study region; the highest proportion of salinity was observed in the middle part and northern part of this study area, whereas the concentration of Mg 2+ is high in the western part of this district, which also another important causative factor; Na + is high near Diamond Harbour II, and K+ mostly found in north and north and north–eastern part of this study region.

Causative factors for nitrate susceptibility; ( a ) EC, ( b ) Temperature, ( c ) pH, ( d ) F − , ( e ) Salinity, ( f ) Mg 2+ , ( g ) Na + , ( h ) K + , ( i ) Cl − , ( j ) HCO 3 −, ( k ) SO 4 2− , ( l ) PO 4 2− , ( m ) As, ( n ) Depth (all this map was generated using ArcGIS, version: 10.3.1, www.esri.com/arcgis ).

Correlation among hydro-chemical parameters

All groundwater samples were characterised with distinctive hydro-chemical compositions. Using Pearson's correlation matrix analysis in SPSS software, these physicochemical characteristics were mentioned in Fig.  4 . The validity of the results is demonstrated by the statistical analysis, which also included descriptive statistics and Pearson's correlation, which logically supported the decision to use of parameters. After analysing all groundwater samples, several conditioning factors are considered, including As, PO 4 2− , SO 4 2− , HCO 3 −, Cl − , K + , Na + , Mg 2+ , F − , pH, EC, depth, temperature, and salinity. Our research shows that some causative factors have a highly positive and negative correlation to each other. Figure  4 states NO 3 − and K + have significant interdependence (0.702) to each other; Cl − strongly correlated with Na + (0.821), EC (0.947) and Mg 2+ (0.664), whereas Na + have distinctive interdependence with HCO 3 − (0.982) and EC (0.833). Apart from these, all parameters have interdependence with each other but are very negligible. This result helps us to understand the interdependence among all adopted conditioning factors; it works very beneficial in determining the appropriate causative factors in current research work.

Correlation among all variables.

Multi-collinearity assessment of variables

We used multi co linear analysiWe used multi co linear analysis to study the linear relationship among variables to check the linear relationship among variables. We used fourteen hydro-chemical properties for analysis. The variance Inflation Factor (VIF) and Tolerance of the sample are shown in the Table 2 . VIF and tolerance are highly negatively correlated with each other. If the VIF value increases, then the Tolerance value also decreases. In case of EC, Cl − and As, the Tolerance values are 0.056, 0.041 and 0.021, which is below the threshold value. In the case of Na + the highest VIF value is 8.75. In our study, the VIF value extends within 10, so we can say that there is no multi-collinearity problem among all variables.

Population pressure related stress on water quality

In many countries, coastal tourism is increasing rapidly, so it negatively impacts coastal region's water, air and othernegatively affects coastal regions water, air and other environments 32 . In our study, we assessed the effect of population pressure on water quality. The population density of this district varies from one block to another. The average population density of this district is 819 sq/km., which is 214% more than the Indian population density. We classified five zones of stress on water quality like- very high, high, moderate, low, and very low. The North–western and northern part of this district is very high population pressure; southern islands of this district like Sagar Island, southern part of Namkhana etc., are less stress; the South–eastern and some north–eastern parts represent moderate stress, which is shown on (Fig.  5 ). Due to the density of this region, People suffer by pure drinking water scarcity. They depend only on shallow and deep tube wells for their daily potable water, and pond water is used for other activities like baths, toilet, etc. which is comparatively arsenic and fluoride contaminated. Due to this, contaminated water is the main source of drinking, so residents of this region suffer from several diseases like diarrhoea, kidney damage, and several diseases.

Population pressure (this map was generated using ArcGIS, version: 10.3.1, www.esri.com/arcgis ).

Groundwater vulnerability and health risk analysis

In South 24 Parganas district, various patterns of health risk were observed. Some blocks represent high health risk, a few blocks representsrepresent high health risk, a few blocks represent high health risk, a few represent a high health risk, a few represent high health risk, and a few characterise low health risk. In this study area, five classes have been carried out like, very high, high, moderate, low and very low risk zones based on local conditions; because every location has distinctive locational settings, shown in Fig.  6 . The derived result about groundwater vulnerability and corresponding health risk is fully controlled by regional geohydrological conditions as well as several environmental factors, including closeness to the ocean, geological settings, and aquifer depth, which significantly control this region's groundwater status. Maheshtola, Diamond Harbour II, Falta, Budge Budge, Western Bhangar very high health risks, and the north part of Kulpi represent very high health risks (Fig.  6 ); the southern part of Kulpi, some part of Patharpratima, Jaynagar I, II and north–eastern Canning II represents high human health hazard. A moderate human health hazard is observed in Baruipur, Magarhat II, major part of Gosaba and few part of Kakdwip. Major parts of this district like Sagar Island, the southern part of Namkhana, some parts of Basanti, and the southern portion of this study area fall under low human health hazard (Fig.  6 ). Result of Hazard quotient (HQ) for adult and children among four selected parameters is presented in Supplementary Table 1 .

NO 3 susceptibility (this map was generated using ArcGIS, version: 10.3.1, www.esri.com/arcgis ).

Hydro-chemical properties

The Piper diagram can easily interpret the Chemistry of the water sample; sources of groundwater contamination can easily be predicted using the Piper diagram. The Piper diagram (Fig.  7 ) shows that the maximum samples fall under the alkaline type (Na++K+), which contains pH 8.5. Its characteristics are poor soil structure and low infiltration capacity. Sodium chloride and mixed types of samples are found in this study area. From the diagram (Fig.  7 ) we can predict that most wells have strong acids surpassing weak ones. Agriculture surface runoff is the main HCO 3 source 33 ; high exposure of Na + increased in groundwater due to cation exchange capacity in clay. In groundwater, the highest concentration of alkaline organisms make water unfit for consumption.

Piper diagram.

Model evaluation

Appropriate validation procedures are essential to any scientific investigation; without them, the results obtained have no practical value. In this current research, six notable statistical validation methods have been employed, including specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), F score and receiver operating characteristics curve (ROC)- area under curve (AUC) in validating the derived prediction measures with ground level; samples are used in two such as training and validating section. In these validation techniques, four distinctive parameters are applied, including true positive (TP), true negative (TN), false negative (FN), and false positive (FP) to estimate the validity of the result. These values from the validation procedure determine how accurate the adopted model are; greater values indicate better results from the model, and vice versa 34 . The validation results are shown in Table 3 ; among all the validating techniques AUC-ROC gives higher values 0.928 and 0.892 in training and validation section followed by specificity (training- 0.911, validation- 0.882), sensitivity (training- 0.915, validation- 0.885), PPV (training- 0.912, validation- 0.874), NPV (training- 0.91, validation- 0.875) and F score (training- 0.92, validation- 0.89). Therefore, the results state about the model accuracy; the adopted LR model is very much acceptable in this region according to geographical conditions; Fig.  8 shows the graphical representation of the performance of all adopted validating techniques.

validating stage model evaluation through graphical presentation.

Relative importance of causative factors

Mean Decrease Accuracy Method (MDA) is applied in this research work, and it is beneficial for ranking and choosing the factors of fourteen parameters related to nitrate concentration in groundwater. The very important factor is shown in Fig.  3 . According to importance, these fourteen factors are ranked. Among all variables, pH is critical and ranked one, the value is 0.77, which highly controls the nitrate concentration in the coastal aquifer in South 24 Parganas; Cl occupies the second position- (value is 0.71) and other factors like—As, F − , EC and Mg 2+ ranked third, fourth and fifth position with their value is 0.69, 0.69, 0.67 and 0.55, respectively. Other factors like—depth, temperature, and HCO 3 − are less influential factors for nitrate concentration in groundwater in this study, and their values are 0.32, 0.25 and 0.23, respectively. Moderately important factors are K + , SO 4 2− , and PO 4 2− are moderate importance factors and their values are 0.53, 0.48 and 0.44, respectively. The overall study stated that all the selected causative factors are essential for nitrate concentration in the coastal groundwater aquifers of South 24 Parganas.

Chemical analysis of coastal groundwater

The USSL diagram is a plot between salinity hazard on the X axis and sodium hazard (SAR) on the Y axis which is proposed by “United State Salinity Laboratory (USSL)” for the classification of water which used for irrigation. This diagram (Fig.  9 ) classified water into 16 classes. For determine the salinity and sodium hazard 42 samples are selected. C3S1 represents medium salinity and low alkalinity which occupied 34.2%. C2S1 represents 17.02% total area, indicating moderate salinity and low alkalinity. C3S2 classes indicate high salinity and moderate alkalinity, representing 32% of tube wells32% of tube wells, and 32% of tube wells. Other important classes are C3S4 which indicates very high alkalinity and high salinity, which covered 12.76% total tube well. Only 2.12% tube well samples were covered by C4S4 represents very high alkalinity and salinity.

USSL diagram.

Wilcox diagram is an essential diagram for analysis the quality of groundwater. This diagram is categorized into five classes :- i. excellent to good ii. Good to permissible iii. Permissible to doubtful iv. Doubtful to unsuitable category and v. unsuitable category. FallThe highest percentage of data falls: The highest percentage falls under the acceptable to doubtful category (59.23%), then the doubtful to unsuitable category (27.27) and good to permissible category holds 9.09%; very few percentages occupied by excellent and unsuitable category (2.27%). It can be concluded that the highest number of samples are doubtful condition, so agriculture practices are threatened.

Identifying the hydro-chemical properties, eIdentifying the hydro-chemical properties, especially nitrate contamination, and its mitigation strategy in the coastal district in South 24 Pargana is an important work. In our research study, we identified the nitrate susceptibility map among all districts, and it depictss where the high, medium and low nitrate susceptibility occurred using the LR model. Different anthropogenic ies like industrial activity, agricultural activity, sewage etc. are highly correlated with groundwater nitrate concentration. Several researchers have shown that nitrate concentration is directly associated with different land-use patterns 35 , 36 . According to Kumazawa 37 in agricultural activities use of nitrogen fertilizer create a great negative impact. Groundwater pollution and nitrate concentration are highly correlated with each other 38 .

Various studies still describe the hydro-chemical properties of groundwater and nitrate concentration susceptibility in the coastal district using other methodsstill describe the hydro-chemical properties of groundwater and nitrate concentration susceptibility in the coastal district using different methods and models like, LR. In our study a large proportion of area falls under the very high nitrate susceptibility zone. The total area is divided into five susceptibility zone including very high, high, moderate, low, and very low 39 use RF and Genetic Algorithm (GA) for assessment of groundwater vulnerability. (Pal et al. 28 ) used the RF and MDA method for determining the concentration of nitrate susceptibility prediction approach in coastal district. In our research study, we used fourteen nitrate conditioning factors. By using multi-collinearity analysis, we ranked them using MDA method. Among all variables, pH was essential and ranked one, value is 0.77 which is highly controlled the nitrate concentration in the coastal aquifer in South 24 Parganas followed by Cl − , value is 0.71. Other factors like—As, F − , EC and Mg 2+ ranked third, fourth and fifth position and their value is 0.69, 0.69, 0.67 and 0.55, respectively. Other factors like depth, temperature, HCO 3 − are fewer effective factors for nitrate concentration in groundwater in this study, and their values are 0.32, 0.25 and 0.23 respectively. In our study the values of specificity, sensitivity, AUC and F score of training stage is greater (0.911, 0.915, 0.92 and 0.928) than validation stage. While, validation stages the values of sensitivity, specificity, F score and AUC are 0.885, 0.882, 0.89 and 0.892 , which shows that the model is significantly applicable.

The nitrate concentration in South 24 Parganas district is very high, so different diseases like blue baby syndrome, fluorosis, diarrhoea and skin cancer are common in this area 40 . Many researchers have done research work about the coastal regions groundwater quality by using different methods like machine learning and GIS-based method 3 , 41 , 42 . To determine the health risk due to nitrate contamination we used acceptable field-based methods and techniques. (Pal et al. 28 ) uses the same technique for assessing the nitrate susceptibility prediction approach in Indian coastal aquifers.

Conclusions

Different parameters are used for determining the concentration of nitrate in coastal multi aquifers like—pH, Cl − , As, F − , EC, Mg 2+ , NO 3 −, K + , Temp., SO 4 2− , PO 4 2− , Na + , Salinity, Depth and HCO 3 −. Fifty-eight samples were used in this work; the highest relative important factor is pH (0.77) then Cl − (0.71) and other variables like depth, temperature and HCO 3 − are less important than other factors. Concentration of nitrate in groundwater comes from several sources like, anthropogenic activities, agricultural activity, and sewage water etc. and its effects in coastal aquifer. In this research work we used data mining techniques like SPSS, Diagramme software, ArcGIS etc. to determine the nitrate concentration in coastal district, South 24 Parganas. The LR model is used to determine the nitrate concentration of this study area. In our study the values of specificity, sensitivity, AUC and F score of the training stage is greater (0.911, 0.915, 0.92 and 0.928) than validation stage. While validation stages the sensitivity, specificity, F score and AUC values are 0.885, 0.882, 0.89 and 0.892 , which shows that the model is significantly applicable. In this region, some portions face nitrate concentration more than the rest of the portions. North–western, mid western and some part of northern portion is facing high nitrate concentrations. To determine the water quality and agricultural suitability for crop production, we used Piper’s diagram and USSL diagram. Different unscientific activities like, industry, agricultural practices and use of high chemical fertilizers also lead to high nitrate concentrations in this region. Another main problem in this region is saltwater intrusion in the agricultural field due to different naturally occurrings, cyclones, and floods. People of this region suffer by pure drinking water scarcity. They depend only on shallow and deep tube well for their daily potable water, which is comparatively arsenic and fluoride-contaminated. Due to this contaminated water is the primary source of drinking so residents of this region suffer by several diseases like diarrhoea, kidney damage, and several diseases. In this current research, we have several limitations. Firstly, we do not consider geology, soil type, land use, land cover pattern, and other hydrogeochemical parameters that may be responsible for nitrate concentration in an area. Still, here we have considered several nitrate conditioning factors that are incredibly accountable and mostly come from the abovementioned parameters. Secondly, only one model, LR, is used to determine the nitrate concentration of this coastal district. So, in the future, more advanced and scientific methods is applicable for predicting nitrate susceptibility. However, LR gives noteworthy ground truth prediction, which is quite similar to the actual condition of this region that also comes up in the result of all employed validating techniques. Therefore, this study is very similar to a ground scenario and accurately describes the existing alarming condition; thus, policymakers and stakeholder can take appropriate steps to reduce this lousy effect and create a healthy environment for the local people of this region.

Data availability

“The datasets used and/or analyzed during the current study are available by the corresponding author from the reasonable request”.

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Acknowledgements

This publication was supported by the Deanship of Scientific Research at the King Faisal University, Saudi Arabia (Grant: 5087).

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Subodh Chandra Pal, Tanmoy Biswas, Asit Kumar Jaydhar, Dipankar Ruidas, Asish Saha & Indrajit Chowdhuri

Ecology and Environmental Modelling Laboratory, Department of Environmental Science, The University of Burdwan, Purba Bardhaman, West Bengal, 713104, India

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S.C.P.: Conceptualization, Supervision, Investigation, Formal analysis, Visualization, Writing—Original Draft, Writing—review & editing. T.B.: Formal analysis, Data collection, Methodology, Validation, Writing-Original Draft, Writing—review & editing. A.K.J.: Formal analysis, Data collection, Methodology, Validation, Writing-Original Draft, Writing—review & editing. D.R.: Formal analysis, Data collection, Methodology, Validation, Writing-Original Draft, Writing—review & editing. A.S.: Formal analysis, Data collection, Methodology, Validation, Writing-Original Draft, Writing—review & editing. I.C.: Formal analysis, Data collection, Methodology, Validation, Writing-Original Draft, Writing—review & editing. S.M.: Formal analysis, Data collection, Methodology, Validation, Writing-Original Draft, Writing—review & editing. A.I.: Formal analysis, Data collection, Methodology, Validation, Writing-Original Draft, Writing—review & editing. Abu Reza Md. T.I.: Formal analysis, Data collection, Methodology, Validation, Writing-Original Draft, Writing—review & editing. C.B.P.: Formal analysis, Data collection, Methodology, Validation, Writing-Original Draft, Writing—review & editing. E.A.: Formal analysis, Data collection, Methodology, Validation, Writing-Original Draft, Writing—review & editing. M.K.I.: Formal analysis, Data collection, Methodology, Validation, Writing-Original Draft, Writing—review & editing.

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Pal, S.C., Biswas, T., Jaydhar, A.K. et al. Source identification and potential health risks from elevated groundwater nitrate contamination in Sundarbans coastal aquifers, India. Sci Rep 14 , 4153 (2024). https://doi.org/10.1038/s41598-024-54646-0

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Evaluation of groundwater quality and its impact on human health: a case study from Chotanagpur plateau fringe region in India

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• Baisakhi Chakraborty 1 ,
• Sambhunath Roy 1 ,
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• Sumana Bhattacharjee 4 ,
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Groundwater is a vital and purest form of natural resource. In the recent years, various anthropogenic causes threat its natural quality. Therefore, its suitability for drinking, irrigation and other purposes make doubtful conditions of human well-being, especially in developing countries. In this present study, groundwater quality was evaluated for drinking, irrigation and human health hazard purposes particularly in Chotanagpur plateau fringe of India. In total, 58 water samples were collected from different locations in pre-monsoon (February–March 2020) and post-monsoon (October–November 2020) seasons to delineate seasonal variation of groundwater quality according to as reported by WHO (WHO guidelines for drinking-water quality, World Health Organization, Geneva, 2011) guidelines. Groundwater Quality Index (GWQI) and Heavy metal Pollution Index (HPI) have been applied to assess the suitability of drinking purposes. Irrigation parameters (SAR, SSP, MAR, PI, KR) showed the significant deterioration of water quality in pre-monsoon than post-monsoon period. Major cations (such as sodium, calcium) and major anions (such as bicarbonate, nitrate and fluoride) exceeded their standard limit in both the seasons. Non-carcinogenic health risk is found due to heavy metal contamination through drinking water. The health risk index was higher for children in comparison with adults. This research finding can definitely help to planners and administrators for immediate decision making regarding public health (for groundwater quality improvement).

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Groundwater quality assessment using water quality index (WQI) under GIS framework

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Introduction

In the pre-industrial era, surface and subsurface water was considered as safe for drinking and human welfare. No such significant negative impression of water quality and related environmental hazard was found during that period. After industrial revolution in eighteenth century, uncontrolled development of anthropogenic activities leads water resources vulnerable through contamination of various pollutants (Al-Sudani 2019 ). Population explosion, over-exploitation and inappropriate usages of chemicals in different sectors continuously threat the precious natural resource. Once groundwater (GW) was purest form of water resource, but today, the quality is being deteriorated day by day and exposed to human health risk due to improper knowledge and management. Generally, quality of GW depends on physiochemical properties of aquifer and its composition. This state is controlled by the properties of soil and rocks of aquifer media (Acheampong and Hess 1998 ; Foster et al. 2000 ; Raji and Alagbe 1997 ). Different hydrogeochemical process determines different physicochemical characteristics of GW particularly in the zone of saturation (Islam et al. 2017a ; Ahmed et al. 2020 ; Bhuiyan et al. 2016 ). Chemical components of industrial, agricultural, urban sewage, mining extracts are the main causes of high pollutant contamination in GW (Amirabdollahian and Datta 2013 ; Carpenter et al. 1998 ; Simeonov et al. 2003 ). Many previous studies were conducted on assessment of GW pollution in different parts of the world and find significant deterioration in water quality for drinking and irrigation purposes in recent times (Al- Futaisi et al. 2007 ; Pritchard et al. 2008 ; Jalali 2006 ; Bhunia et al. 2018 ). Groundwater unsuitability for drinking and irrigation purposes in Volta River basin of Ghana was analyzed by Kaka et al. 2011 . Jennings et al. ( 1997 ) showed the increased level of nitrates in GW and its negative impact on human health. Al- Sudani ( 2003 ) assessed GW quality of Debagah basin in Iran and found higher contamination of anions, nitrates and salinity from agricultural source. Kong et al ( 2004 ) analyzed GW quality of agricultural field in Hebei and Shandong, and it indicated the deterioration of quality of groundwater due to high mixing of pesticides in GW. Rapid water quality deterioration and unsuitability for drinking or irrigation is a burning issue in most of the developing countries. Rapid population explosion, high yielding varieties of crop cultivation, excessive application of chemical fertilizers, pesticides, fungicides and herbicides, urban and industrial effluents and improper management are the principal cases of groundwater pollution particularly in developing countries (Nagaraju et al. 2018 ; Shaji et al. 2018 ). Several studies reported that the magnitude of GWP has been amplified by both natural and anthropogenic sources in Bangladesh (Hasan et al. 2019 ; Kabir et al. 2021 ; Islam et al. 2020 ). In India, GW plays a significant role particularly in domestic and irrigation sectors. It has been reported that GW uses nearly 80% for domestic use in rural areas and approximately 50% in urban areas (Kundu and Nag 2018 ). In India, around 50% of agricultural lands depend on groundwater irrigation (Central Water Commission 2006 ). But in the recent years, this most valuable resource is going to vulnerable for human consumption. Many studies were conducted in this approach. Water quality of coastal aquifers in Tamil Nadu state, India, was assessed for its hydrochemical condition and suitability of human consumption (Chandrasekar et al. 2014 ). Similarly, groundwater quality of an agricultural region of Uttar Pradesh, India, was assessed for drinking suitability (Ashwini and Abhay 2014 ). Degree of GWP in rural areas of Telangana state was showed that major cations and anions along with their permissible limit were exceeded due to high activity of geogenic condition and anthropogenic contamination (Subba Rao et al. 2018 ). Gautam et al. 2015 analyzed GWQ of Subarnarekha River basin of Chotonagpur region and explained large-scale release of ions through weathering and agricultural practices. Satish Kumar et al. ( 2016 ) assessed GWQ of hard rock aquifer of Pudunagaram, Palakkad district of Kerala, and indicated most of water samples were suitable for drinking and irrigation purposes, though some samples were higher than their standard limit due to anthropogenic contribution. Groundwater quality (GWQ) of Bankura I and Bankura II blocks of West Bengal indicated moderately suitable for drinking and irrigation purposes except some places (Nag and Das 2017 ). The most warning problem related to GW pollution is its adverse impact on human health in developing countries. Regular intake of toxic elements (heavy metals) accelerates chronic non-carcinogenic or carcinogenic diseases of humans (Tasneem et al. 2021 ; Ahmed et al. 2021 ; Mridul et al. 2020 ). In developing countries, poor drinking water and unhealthy sanitary system are responsible for 80% water borne diseases reported by UNESCO 2007 . Health risk assessment on an agricultural region of Nanganur, South India, showed higher health risk (non-carcinogenic) of children than adult by intake of nitrate in drinking water (Adimalla and Qian, 2019 ). Adimalla and Wu ( 2018 ) assessed non-carcinogenic health risk of central Telangana and showed that infant was most exposed to health risk by ingestion of nitrate and fluoride in drinking water. Therefore, it is highly required to assess the GWQ for drinking, irrigation and human health particularly in developing countries and it needs for implementation of sustainable development practice (Islam et al. 2020 ). As quality of groundwater depends on numerous physiochemical parameters, it is quite hard to deal with large dataset. In this case, it is revealed that Water Quality Index (WQI) is the most suitable technique to evaluate water quality standard. In this process, different weightage values are assigned for different parameters according to their relative importance on overall groundwater quality (Singh et al. 2018 ; Shahid et al. 2014 ). WQI is an appropriate technique to determine GWQ depending on physiochemical parameters for drinking purpose. Therefore, Heavy Metal Pollution Index (HPI) is necessary to assess drinking water quality on the basis of toxic metal contamination (Edet and Offiong 2002 ; Sultana et al. 2016 ; Rikta et al. 2016 ; Ojekunle et al. 2016 ; Karunanidhi et al. 2020 ). Many relevant studies on groundwater quality (GWQ) of irrigation suitability has been assessed by different indices such as sodium adsorption ratio (SAR), permeability index (PI), soluble sodium percentage (SSP), Kelly’s ratio (KR) and magnesium adsorption ratio (MAR) and along with using different ionic concentration in GW (Ashraf and Afzal 2011 ; Kundu and Nag 2018 ; Chakraborty et al. 2021a , b , c ). In the recent years, modern scientific techniques are applied with the combination of traditional methods particularly in applied research. Application of remote sensing and spatial zonation by GIS techniques easily helped to identify the pollution pockets of GW and associated health risk (HR) (Taheri et al. 2015 ; Singh et al. 2015 ; Thapa et al. 2017 ).

Our present study area is located in Santuri block of Purulia district of West Bengal. It is situated at fringe area of Chotanagpur plateau, and geomorphologically, it is an undulating terrain with Precambrian granite gneiss residual hillocks. Some medium-sized iron-based industries and secondary activities have been grown in the recent decades. High demand of groundwater for drinking and agricultural purposes emerges severe water crisis in this area day by day. Prior study on groundwater quality of its adjacent block Neturia showed that anthropogenic sources are responsible for groundwater contamination along with some natural factors and study also identified magnitude of health hazard through consuming high amount of iron and fluoride in drinking water (Chakraborty et al. 2021a , b , c ). But no such previous study was found on groundwater quality with an integrated approach in any part of the country, especially on eastern fringe of Chotanagpur region. Therefore, the principal objective of this study is to i) evaluate hydrochemical characteristics of groundwater and ii) assess the GWQ suitability for drinking, irrigation and non-carcinogenic health risk of humans using GIS techniques and research also highlights some sustainable remedial measures for regional planners and local people.

Materials and methods

Santuri Block is situated in the northeastern part of Purulia district within geographical extension 23°27′43″ N/ 86°45′50″N to 23°39′35″N/ 86°54′33″N with 179.69 km 2 area (Fig.  1 ). Damodar River is the northern boundary of Santuri Block as well as Purulia district. Geological or more specifically lithology of this block is composed by Gondowana crystalline rock formation, granite gneiss, mica schist. Meta basic rocks are also found in some places along with lineaments. Geomorphologically, it is an undulating plateau with plain, and generally, slope of the land is toward east. Annual temperature is varying between 4 and 45 °C. Annual rainfall is recorded within the range 1100–1400 mm. Topsoil is characterized by weathered laterites along with sandy residue. Agriculture is mostly being practiced by the river water (Damodar) and dam. Groundwater is the main source of drinking water in this block. Pit well, dug well, bore well, etc. are common structural modes of extraction from groundwater aquifer.

Location of the study area and sampling sites

Sample collection and procedures

Groundwater samples were collected from 29 bore wells of different location in pre-monsoon (February–March) and post-monsoon (October–November) of the year 2020 (Fig.  1 ). Sampling locations were recorded by portable GPS instrument (GARMIN GPS map 78 s). Each bottle of 500 ml was deionized by 1: 1 hydrochloric acid and thoroughly cleaned by sampling water of respective site. After sampling, all bottles were labeled accordingly and sealed with plastic coat for prevention of evaporation and carried to laboratory within 24 h. Similarly, pH and EC were measured by their portable meters in situ (Hanna). TDS was measured by multiplying EC with 0.64 (Hem 1991). All chemical parameters were analyzed by the recommended method of American Public Health Association (APHA 2012). TH and cations as Ca 2+ and Mg 2+ were measured by EDTA titration method (0.05 N and 0.01 N, respectively). Na 2+ and K + were measured by flame photometer method. Anions as Cl − and Hco 3 − were obtained by volumetric method. So 4 − , No 3 − and F − were estimated by spectrophotometric method (Chakraborty et al. 2021a , b , c ). All concentrations were expressed in mg/l except pH and EC. Heavy metals like iron, chromium, manganese, cadmium, zinc, copper, lead and nickel were estimated by anodic stripping voltammetry (VA 797, Switzerland) using three types of pulse analyzer. Voltametric pulse peak was recorded in a window computer (Chakraborty et al. 2020 ). All heavy metals are expressed in µg/l.

Ionic balance was conducted for each groundwater samples in quality assurance of chemical analysis (Huh et al. 1998 ). In this process, ionic concentration of all chemicals was converted into meq/l and estimated ionic balance by the following equation

Groundwater quality index (GWQI)

Groundwater quality index or GWQI can easily determine overall groundwater quality on the basis of physicochemical parameters of water sample for drinking. GWQI has been conducted using the following formula (Vasanthavigar et al. 2010 ; Islam et al. 2017b ).

Variables, their assigned weight and standard values of parameters for GWQI calculation are given in Table 1 (Vasanthavigar et al. 2010 ; Chakraborty et al. 2021a , b , c ).

• Heavy metal pollution index (HPI)

HPI has been used to evaluate water quality and its suitability for drinking (Rezaei et al. 2017 ). HPI has been calculated applying the formula.

where Wi indicates weightage of each metal, MAC indicates Maximum Allowable Concen tration of each metal as suggested by Bureau of Indian Standard (BIS 2012 ). ‘K’ is constant value, Qi shows quality rating of each metal, Mi denotes measured concentration of respective heavy metal, Ii is ideal value and Si is standard value of each metal as prescribed by BIS 2012. The critical value of heavy metal pollution index is 100. HPI > 1 indicates unsuitable for drinking purpose.

Irrigation water quality

Irrigation water quality of Santuri block was determined by various indices using certain chemical parameters which are as follows: (Richards 1954 ), (Todd 1980 ), (Doneen 1964 ), (Raghunath 1987 ), (Kelly 1976 )

All ionic concentrations were calculated by meq/l.

• Human health risk (HHR)

Human non-carcinogenic health risks (via oral consumption) of heavy metals in drinking water were evaluated for children and adult residents of Santuri block. Traditional USEPA ( 2011 ) method was applied using the following formulas.

where CDI oral is chronic daily dose intake via oral pathway and expressed in µg/kg/day. C w is respective concentration in sample water (µg/l). IR is ingestion rate of water (0.70 lit for children and 2 lit for adult), EF is exposure frequency (365 days) for children and adult, respectively). BW denotes body weight of children (15 kg) and adult (70 kg), AT is average time (2190 days for children and 10,950 days for adult), and RfD (µg/kg/day) represents reference dose of heavy metals in drinking water suggested by USEPA ( 2011 ). HQ indicates Hazard Quotient (unitless) and HI signifies Hazard Index (unitless). HI value < 1 suggests no immediate health hazard, and HI > 1 shows possibilities of health hazard in the assessed area (Yang et al. 2012 ).

Statistical and spatial techniques

Descriptive statistics and index analysis were applied applying SPSS 16 software. Spatial mapping of water quality in pre- and post-monsoon season was performed using inverse distance weighted (IDW) method in ArcGIS 10.4 software. IDW is such useful technique which is used to interpolate of cell values by linear weighted combination of a sample point (Adhikary et al. 2011 ; Bhunia et al. 2018 ). In this method, best prediction of neighboring samples value was obtained from observed data and estimated data (Yao et al. 2013 ).

Results and discussion

General hydrochemistry of the groundwater.

The statistical summary of groundwater samples in pre-monsoon season (Table 2 ) showed that the mean concentration of pH (7.66), EC (1100.00), TDS (704) stands acceptable limit according to WHO ( 2011 ) guidelines and indicated weak alkaline-type water. Total hardness (TH) exceeded its permissible limit in this season. During post-monsoon season, mean value of pH (7.46), EC (847.03) and TDS (542.10) also showed the permissible limit. In this period, TH was below permissible limit due to high availability of water supply in subsurface area. In both pre- and post-monsoon seasons, cation concentration of GW was in an order of Na 2+  > Ca 2+  > Mg 2+  > K + by their mean values. Anion was in their descending order of Hco 3 −  > No 3 −  > Cl −  > So 4 −  > F − . Na 2+ and Ca 2+ exceeded their permissible limit in groundwater. It can be due to higher ionic modification between these two cations and weathering of silicate minerals (Islam et al. 2017a ). On the other hand, Hco 3 − (508.48), No 3 − (104.83) and F − (2.63) crossed their upper permissible limit because of agricultural fertilizer and pesticides contamination in groundwater. Geologically, the region is composed by fluoride-bearing host rocks and minerals. Therefore, natural concentration and contamination of F − has been increased in GW. The presence of Cl − and SO 4 − in the aquifer system was due to weathering of silicate minerals and gypsum (Fisher and Mullican Iii 1997 ). Mean concentration of heavy metals in groundwater of pre- and post-monsoon season showed their descending order of accumulation as Fe > Mn > Zn > Pb > Ni > Cr > Cu > Cd. Natural weathering of iron-rich minerals and mixing of agricultural, industrial effluents with subsurface water are the main causes of higher availability of heavy metals in this area. In both the seasons, all heavy metals exceeded their acceptable limit according to BIS ( 2012 ).

Ionic balance of 29 sample water in pre-monsoon showed that no sample was fresh type (value between −10% and + 10%) in this season. In post-monsoon season, 31.03% sample indicated fresh or good type water with low TDS value by their ionic concentration (Table 3 ).

Groundwater quality index

Groundwater quality of Santuri block showed the range from 132.19 to 210.63 with 171.22 mean value. In this season, 76.66% water samples were ‘poor’ quality and 23.33% were ‘very poor’ quality of groundwater (Table 3 ). On the other hand, GWQI of post-monsoon season showed the ranges from 79.92 to 123.28 with 96.49 mean value. 66.66% groundwater samples appeared as ‘good’ quality and 33.33% water samples appeared as ‘poor’ quality for drinking purpose in post-monsoon. In pre-monsoon, lowering down of groundwater quality is mainly due to increase in ionic exchange activities in aquifer zone. But in post-monsoon season, higher availability of water recharge helped to ameliorate water quality for drinking purpose. Similar results have been found in previous researches on groundwater quality of Purulia district (Kundu and Nag 2018 ; Chakraborty et al. 2021a , b , c ). Spatial pattern of GWQI showed (Fig.  2 ) deterioration of water quality in industrial belt of northern part of Santuri block.

Spatial distribution of groundwater quality (GWQ) and Heavy metal Pollution Index (HPI)

Drinking water suitability on the basis of heavy metal contamination of Santuri block by HPI method showed the ranges from 82.56 to 197.11 with 142.28 mean value in pre-monsoon season. Around 93.34% and 6.66% sample water was observed as highly and no pollution particularly in monsoon season (Table 3 ). In post-monsoon season, HPI was ranging from 32.31 to 113.80 with 58.43 mean value. In this season, 6.66% water sample indicated ‘high pollution’ and 93.34% samples indicated ‘no pollution’ (HPI < 100) for drinking purpose. In pre-monsoon season, higher leaching of heavy metals by irrigated water increased metal contamination than post-monsoon season. Spatial distribution of HPI was classified into three categories as no pollution (HPI < 100), medium pollution (HPI = 100–150) and high pollution (HPI > 150) and it showed that the northern and eastern part of this block was highly polluted by heavy metal contamination in pre-monsoon season (Fig.  2 ).

Assessment of irrigation water quality

Irrigation water is mixed with excess chemical components originated by either geogenic or anthropogenic factors which can negatively affect to the soil fertility, water permeability and crop production efficiency (Jalali 2009 ). Excess supply of irrigation water in agricultural field is generally responsible for existence of alkalis and salt on the top layer of soil and it provides salt to the crop roots (Jalali 2011 ). In this context, irrigation water quality of Santuri block has been assessed through SAR, SSP, PI, MAR, KR to determine chemical effects on crop production in this block (Liu et al. 2021 ).

Sodium adsorption ratio (SAR)

Excess concentration of sodium in GW produces alkaline-type soil. It increases soil compactness and lowered down percolation capacity of rain water into soil. Alkaline soil is considered as poor quality for agricultural production without irrigation. Cultivation is difficult in this type of soil particularly in dry period. In Santuri block, irrigation water quality by SAR showed the range from 4.53 to 13.04 with 7.12 mean value in pre-monsoon season. In post-monsoon season, SAR ranged from 0.87 to 11.77 with 3.83 mean value. In both the seasons, 93.10% samples were under ‘excellent’ quality and 6.89% ‘good’ quality samples were observed (Table 3 ). The increase in ionic interactions, climatic influence, leaching of fertilizer, pesticides from topsoil by irrigated water promotes SAR in groundwater in pre-monsoon season. Overall groundwater of Santuri block suggested water suitability for crop production (by SAR method). Spatial pattern of SAR in pre- and post-monsoon season is presented in Fig.  3 .

Spatial distribution of irrigation water quality

Soluble sodium percentage (SSP)

Irrigation water is mixed with high sodium content which can be absorbed by clay minerals as a dispersing for calcium and magnesium ions. It makes soil harder, reduces the water infiltration rate and leads to low crop production capacity of soil (Singh et al. 2008 ). SSP analyzed the GW of pre-monsoon season that is ranged from 53.77 to 76.80 with 63.70 mean value. In this period, 20.68% samples showed ‘permissible’ water and 79.31% sample showed ‘doubtful’ for irrigation purpose (Table 3 ). In post-monsoon season, SSP is ranged from 19.02 to 74.58 with 42.82 mean value. In this period, 48.27% samples indicated ‘good’ for irrigation. 10.34% samples showed excellent for crop production while 41.37% samples presented ‘permissible’ for agricultural usages. Mean SSP of pre-monsoon was higher than post-monsoon season and it indicated lower suitability of groundwater for irrigation in pre-monsoon. Spatial distribution of SSP in pre- and post-monsoon season of Santuri block is presented in Fig.  3 .

Permeability index (PI)

Sodium, calcium, bicarbonate and magnesium ions are the main controlling factors of permeability of soil. Permeability Index showed the quality of GW to soil permeability in any region. PI of pre-monsoon season showed the range from 79.66 to 114.80 with 94.59 mean value. 3.44% sample water showed ‘good’ for soil permeability. 72.41% sample showed ‘moderate’ for soil permeability and 24.13% sample indicated ‘poor’ for soil permeability (Table 3 ). In post-monsoon season, PI ranged from 49.06 to 152.25 with 95.94 mean value. 31.03% samples indicated ‘good’ quality while 31.03% samples showed ‘moderate’ quality and 37.93% sample indicated ‘poor’ quality for permeability. Spatial distribution of PI in the study area is presented in Fig.  3 .

Magnesium adsorption ratio (MAR)

Suitable quantity of magnesium ion is very helpful for plant growth. But high availability of Mg 2+ affects the plant health and reduces soil infiltration rate. High level of magnesium can decrease the presence of K + in soil. Therefore, MAR is suggested for groundwater irrigation (Kacmaz and Nakoman 2009 ) in the study area. MAR of pre-monsoon is ranged from 17.39 to 37.43 with 26.92 mean value. In post-monsoon, MAR is ranged from 10.45 to 46.10 with 22.99 mean value. In both seasons, 100% sample water indicated suitability of GW in respect of magnesium content for irrigation purpose (Table 3 ). Spatial zonation of MAR in Santuri block is presented in Fig.  3 .

Kelley’s ratio (KR)

Kelley’s ratio is based on sodium availability of groundwater for irrigation. Sodium mainly is originated from natural weathering of feldspar minerals. KR > 1 suggests higher sodium content and unsuitability for irrigation. In pre-monsoon season, KR is ranged from 1.13 to 3.26 with 1.78 mean value. 100% sample of this season indicated KR > 1, hence unsuitability for irrigation (Table 3 ). In post-monsoon season, KR is ranged from 0.21 to 2.92 with 0.95 mean value. 68.96% water samples showed KR > 1, i.e., ‘suitable’ and 31.03% sample showed KR < 1, i.e., ‘unsuitable’ for agricultural purpose. Spatial pattern of KR is presented in Fig.  4 .

Spatial distribution of susceptible to human health risks

Assessment of human health risk (HHR)

Toxicity of groundwater on the basis of heavy metal consumption via oral ingestion of drinking water can give significant impact on human health (Rasool et al. 2017 ; Ahmed et al. 2019 ). Long-term ingestion of heavy metals can cause chronic non-carcinogenic or carcinogenic-type diseases (Islam et al. 2019 ). In the Santuri block, susceptibility of non-carcinogenic health risk by ingestion of groundwater was evaluated for both children and adult population. Heavy metal such as Fe, Cr, Cd, Zn, Mn, Cu and Pb were considered for health risk assessment. The total HI (Hazard Index) value of children in pre-monsoon and post-monsoon was ranged from 0.68 to 1.49 with 1.14 mean value and 0.28 to 1.21 with 0.50 mean value, respectively. Mean value of HI (children) of pre-monsoon was higher (HI > 1) than post-monsoon (HI < 1) which indicated more potentiality to health hazard during pre-monsoon season. In pre-monsoon, 27.58% samples indicated HI < 1 and suggested no immediate health risk in this region. 72.40% water sample showed HI > 1 and indicated health hazard of children during this period (Table 4 ). HI values of post-monsoon season indicated that 100% samples were safe for drinking and no possibilities of health risk of children in this season.

During pre-monsoon, HI values of adults ranged from 0.43 to 0.94 with 0.71 mean value. In post-monsoon, HI of adults ranged from 0.17 to 0.74 with 0.31 mean value. 100% samples of both seasons indicated safe for oral consume (HI < 1) for adult population. Children were found more susceptible to health hazard than adult in this block as indicated by their average values. This is because of children have lower body weight and low metabolism power than adults. Similar results have been found in previous studies also (Kabir et al. 2020 ; Ojekunle et al. 2016 ; Tiwari et al. 2015 ; Sharma et al. 2019; Chakraborty et al. 2021a , b , c ). Spatial interpolation mapping showed that high health risk was found in industrially developed northern part of Santuri block (Fig.  4 ).

Remedial techniques

Safe and healthy groundwater is always desirable for people of green globe. It ensures human health and hygiene and also protects the environment. Good quality of groundwater helps to promote crop growth and it maintains soil quality. Therefore, appropriate monitoring and innovative measurement techniques are effective weapon for sustainable natural resource management. There are mainly two types of remediation techniques in this field, i.e., in situ and ex situ. In situ process is involved for groundwater treatment by different thermal, biological or chemical techniques within the aquifer. Ex situ process involves the water treatment by extracting contaminated groundwater from aquifer. Generally, two methods are highly expensive. Therefore, some cost-effective and efficient remediation will be more helpful for common people and government of developing countries.

The present study on groundwater quality of Santuri block of Purulia district has been analyzed for suitability of drinking, irrigation and human health s in different seasons. The analytical result of groundwater hydrochemistry showed that the GW is mainly low alkaline type with acceptable concentration of EC and TDS in both seasons. High concentration of Na2 + , Ca2 + in groundwater was due to weathering of silicate minerals. Agricultural activities lead to an increase in the level of Hco3-, No3- and F- in groundwater. Heavy metals like iron and manganese are found higher amount than their standard limit because of weathering of parent rocks in this region. Various ions are released from the weathered hillocks and these are being mixed in the soil and water. Drinking water suitability by GWQI and HPI showed the significant deterioration of water quality in pre-monsoon in comparison with post-monsoon. Irrigation water quality (by SAR, SSP, PI, MAR, KI) gives overall suitability of GW for agricultural practices. However, groundwater of pre-monsoon has low suitability than post-monsoon due to amplification of ionic activities in summer season. Human health risk of non-carcinogenic diseases suggested that children were more vulnerable than adult in this block and obvious health risk was found for children (HI > 1) particularly in pre-monsoon season. This applied research will assist to policy makers and government will take necessary steps for the improvement of public health applying emergency mitigation measures. There is a huge scope for scholars and researchers to find out a new innovative ideas and models considering the natural hydrogeomorphic diversity and modern societal integrity.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors show their kind acknowledgment to the Department of Geography and Microbiology, Raja N. L. Khan Women’s College (Autonomous), Department of Geology & Geophysics, Indian Institute of Technology (IIT), Kharagpur, West Bengal, India, for their laboratory facilities and kind encouragement.

This research was supported by the Department of Geography, Raja N. L. Khan Women’s College (Autonomous), affiliated to Vidyasagar University, Midnapore, West Bengal, India. The author (P. K. Shit) grateful acknowledges West Bengal DSTBT for financial support through R&D Research Project Memo no. 104(Sanc.)/ST/P/S&T/ 10G-5/2018).

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Baisakhi Chakraborty, Sambhunath Roy & Pravat Kumar Shit

Department of Geography, Sidho Kanho Birsha University, Purulia, India

Biswajit Bera

ICAR Indian Institute Water Management, Bhubaneswar, Odisha, 751023, India

Partha Pratim Adhikary

Department of Geography, University of Calcutta, 35, Ballygunge Circular Road, Ballygunge, Kolkata, 700019, India

Sumana Bhattacharjee

Department of Geology and Geophysics, Indian Institute of Technology (IIT), Kharagpur, West Bengal, 721302, India

Debashish Sengupta

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P.K.Shit conceptualized and planned the study and reviewed and edited the manuscript. B. Chakraborty conducted the survey and water sampling, analyzed the data and interpreted the results. S.Roy conducted the survey, water sampling, prepared the maps. S. Bhattacharjee reviewed and edited the manuscript. P.P.Adhikary reviewed and edited the manuscript. B. Bera supervised the study and reviewed and edited the manuscript. D. Sengupta supervised the overall research, interpreted the results. All authors have read and approved the final manuscript.

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Chakraborty, B., Roy, S., Bera, B. et al. Evaluation of groundwater quality and its impact on human health: a case study from Chotanagpur plateau fringe region in India. Appl Water Sci 12 , 25 (2022). https://doi.org/10.1007/s13201-021-01539-6

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Received : 10 September 2021

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Published : 07 February 2022

DOI : https://doi.org/10.1007/s13201-021-01539-6

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