Assessments of hydrogeochemical characteristics, water quality, and health risk of groundwater of Chapi-Nawabganj District of Bangladesh

Globally, groundwater is considered one of the most essential natural resources for human health (Taiwo et al. 2012; Woke & Umesi 2018; Islam & Mostafa 2024a). In Bangladesh, it is the primary drinking water source for both rural and urban areas. It is also a substantial water source for both agricultural and industrial sectors (Duggal et al. 2017; Islam & Mostafa 2024b). To meet its 98% national water requirement, Bangladesh uses about 4% of global water resources (Hanasaki et al. 2018; Shamsudduha et al. 2018; Kabir et al. 2021a, 2021b). In Bangladesh, protecting groundwater quality from a variety of geogenic (naturally occurring fluoride, salinity, iron, manganese, lead, and arsenic) and anthropogenic inputs such as industry, agriculture, or human settlements is a major concern (Rahman et al. 2017; Islam et al. 2021; Kabir et al.2021a, 2021b). According to a previous study, over 97% of Bangladesh's population uses groundwater for drinking, while for irrigation purposes, approximately 70–86% of water is used (BGS-DPHE 2001; Kabir et al. 2021a, 2021b; Islam & Mostafa 2022a).

Water resources and water quality are crucial for ecological balance, human survival, economic prosperity, and sustainable regional development (Xiao et al. 2019; Hasan et al. 2020; Long & Luo 2020; Ma et al. 2020). Metal contamination in aquatic environments has recently received worldwide attention due to its environmental toxicity, abundance, and persistence. Groundwater is degraded by anthropogenic inputs including industrial waste, urban waste, and agricultural activities, as well as natural processes such as atmospheric deposition, erosion, and mineral weathering, which limit its use for drinking, agricultural, industrial, and other reasons (Krishna et al. 2009; Li & Zhang 2010; Iqbal et al. 2013; Islam et al. 2015; Hasan et al. 2018; Shakil et al. 2020).

Groundwater quality varies from location to location, depending on the underlying geological formations. The distribution and quantity of trace metals in groundwater vary from one area to another based on the pH, redox potential, geological locations, hydrogeometry of aquifers, etc. Water quality and polluting source monitoring, along with controlling change, make it feasible to plan for the efficient use of water (Burgan et al. 2013). The BECR, BDWS, WHO, and USEPA have all set regulations for controlling the maximum permissible limits for metals in water to protect human health. Human health and the sustainability of socioeconomic systems directly depend on water quality. Groundwater contamination has made life difficult for humans (Habib et al. 2024). Heavy metal toxicity can damage or inhibit brain and central nervous system functioning, as well as lower energy levels, and the chemical composition of blood, lung, kidney, liver, and other vital organs (Donaldson et al. 2010). As human activity intensifies, contaminants, especially trace elements, infiltrate the aquatic system, posing risks to human health. Trace metals like chromium (Cr), iron (Fe), manganese (Mn), nickel (Ni), cadmium (Cd), and lead (Pb), are major pollutants in contaminated water and present serious toxicity concerns, according to several studies conducted by various researchers (Katsoyiannis & Katsoyiannis 2006; Yasuhiro et al. 2007; Uddin et al. 2011; Islam & Mostafa 2021e, 2024c; Monira & Mostafa 2023).

Through a literature review of the past two decades, a few studies have assessed the vertical distribution of major and trace metals in groundwater in Chapi Nawabganj district, northwestern Bangladesh (Saha & Zaman 2011). Their study on groundwater focused solely on arsenic, such as occurrence, distribution in core sediment, vertical geochemical variation, vertical distribution, enrichment, mobility, and impact of organic matter on arsenic mobilization, health hazards and health risk assessment, and groundwater level fluctuations (Hasan et al. 2013; Reza et al. 2013; Hossain et al. 2014; Islam et al. 2017; Kabir et al. 2018). Thus, the study is necessary to understand the hydrogeochemical characteristics, water quality, pollution level, and health risks of the groundwater in Chapi Nawabganj district. The study combines the evaluation of groundwater pollution using several water quality indices: WQI, CCMWQI, HMEI, HMPI, and CD. The parameters used include pH, TH, EC, DO, TDS, Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, ⁠, ⁠, ⁠, ⁠, Cr, Mn, Fe, Ni, Cu, Zn, Cd, and Pb. This technique provided insights into water quality and potential health hazards related to heavy metal contamination. Additionally, the study utilized hydrogeochemical analysis to understand the controlling mechanisms of hydrochemistry, such as silicate weathering, carbonate dissolution, reductive dissolution processes, and calcite and dolomite dissolution. The objectives of this study are to assess: (a) hydrogeochemical characteristics; (b) contamination level of trace metals (Cr, Mn, Fe, Ni, Cu, Zn, Cd, and Pb); (c) water quality and pollution levels; and (d) health risk in certain groundwater in the study area. The water quality index (WQI), the Canadian water quality index (CWQI), degree of contamination (C_D), Heavy metal evaluation index (HEI), Heavy metal pollution index (HMPI), and human health risk assessment (HRA) indices, hazard quotient (HQ), and hazardous index (HI) indices were used to assess the water quality and health risks (Tong et al. 2021). The results of this study provide information to decision-makers about water quality, potential human health risks, and risk-reduction strategies for the development of sustainable groundwater resource management in this area.

A total of 60 groundwater samples (from shallow tubewells with depth ranges of 30–50 m) were collected from the study area (Chapi Nawabganj Sador and Shibganj upazila of Chapi Nawabganj district) in pre-monsoon (PRM), monsoon (MON), and post-monsoon (POM) for analyzing physicochemical parameters, major ions, and trace metals. Before sampling, each tubewell was pumped for five minutes. The physicochemical parameters such as temperature, pH, turbidity, total dissolved solids (TDS), electrical conductivity (EC), and dissolved oxygen (DO) were measured using the microprocessor-based portable bench meter (Model: HI 9813–6, Hanna Instrument, Portugal; a xylem brand YSI Pro1030 pH and Conductivity Meter, USA) during sample collection. Groundwater samples were collected for anions and cations in two 500-ml plastic bottles, then corked instantly and placed in an ice box to prevent oxidation. For trace metals and major cations analyses, the groundwater samples were filtered, and acidified with conc. HNO₃ (Fluka Analytical, Sigma-Aldrich, Germany) in a cation bottle, and transported to the laboratory within 8 h. 2 ml of conc. HNO₃ and 3 ml of conc. HCl were added to 100 ml of water samples in the digestion case for heavy metal analysis. Major cations, trace elements, and anions were analyzed by atomic absorption spectrometry (AAS) (AAS220FS) and a UV-VIS spectrophotometer at the Rajshahi University Central Laboratory. In this study, statistical analysis was performed using SPSS version 20 software. The simple kriging interpolation model was implemented using Arc GIS version 10. Basic statistical analysis, such as the determination of minimum, maximum, mean value, standard deviation, and calculation of various indices, including WQI, CCMWQI, C_D, HI, HMI, HQ, and HI was conducted using MS Excel.

The study used the WQI, the Canadian Council of Ministers of the Environment (CCMEWQI), degree of contamination (C_D), HEI, HMPI, and HRA indices to determine the groundwater quality and contamination levels of groundwater in areas. The appropriateness of water for human consumption can be evaluated based on physical, chemical, and biological parameters. Both organic and inorganic contaminants in water harm human health and ecology (Al-Farraj et al. 2013). Groundwater that is used for drinking can be affected by variety of factors, including landfills, significant groundwater mineralization, excessive use of agrochemicals and petroleum-based products, and municipal solid and liquid waste (Islam & Mostafa 2021a, 2021c).

Here, C_i = concentrations of investigated trace metals, and S_i = permissible limit of trace metals (WHO standard). W_i = relative weight, where, and the number 100 is constant. The target element's weight (w_i) is determined by how important it is to potability and how it proportionately impacts human health (Meng et al. 2016; Sener et al. 2017). The particularly detrimental effects on water quality of trace metals such as toxicity assigned the highest weights of 5 for Cd, Cr, Mn, and Pb; 4 for Fe; 2 for Cu; and 1 for Co, Ni, and Zn (Gao et al. 2019).

The Canadian Council of Ministers of the Environment (CCME 2001) recognized the CWQI based on the water quality index equation, that was developed by the British Columbia Ministry of Environment. The CWQI does not specify particular water quality parameters or timeframes because these variables vary for specific locations and environments. To calculate this index, a minimum of four parameters and a minimum of four measurements of these variables are required (CCME 2001; UNEP-GEME/Water 2007; Lumb et al. 2011). The CWQI contains three factors, each of which has a scale from 0 to 100. The three variances, i.e., scope, frequency, and amplitude, are combined to form a vector in a hypothetical ‘objective exceedance’ space. The length of this vector is set to be between 0 and 100, and the result is an index value that is subtracted from 100. The values are close to 100 for ‘excellent’ quality and near 0 for ‘very poor’ water quality across all purposes. The CCMEWQI comprises three factors (CCME 2001, 2006; UNEP-GEME/Water 2007).

• Step 1:F₁ is factor 1, which stands for scope. It provides the percentage of variables that exceed the objective or standard value in the recognized guidelines relative to the total number of variables.

Since the three separate index factors can range in value as high as 100, the value of 1.732 increases. This indicates that the vector length's maximum value is ⁠. The maximum vector length is reduced to 100 as a maximum value by dividing 1.732.

Here, C_fi is the contamination factor, where, ⁠, C_mi is the monitored value, C_ni is the highest permissible limit for the ith parameter, and denotes the normative value.

Here, H_c and H_MAC are the investigated values and the maximum admissible concentration (MAC) of ith metal parameters, respectively.

Here, S_i = standard values (the highest permissible limit). The S_i values for Cr, Mn, Fe, Ni, Cu, Zn, Cd, and Pb are 0.05, 0.3, 1, 0.1, 2, 3, 0.003, and 0.01 mg/L, respectively; I_i = ideal values (the lowest acceptable limit). The l_i values are 0, 0.1, 0.3, 0.07, 0.5, 0.5, 0, and 0 mg/L for Cr, Mn, Fe, Ni, Cu, Zn, Cd, and Pb, respectively.

Here, HQ = Hazard Quotient (ingestion or dermal absorption) (unitless), RfD_oral= Oral Reference Dosage for a particular metal, and GIABS = Gastro-intestinal Absorption Factor, which is dimensionless, and the reference values are taken from the USA risk-based concentration table (US EPA 2019). Values of RfD_oral for analyzed metals Cr, Mn, Fe, Ni, Cu, Zn, and Pb are 1.5, 0.046, 0.7, 0.02^, 0.04, 0.3, 0.005, and 0.004; GIABS for analyzed metals are 1 for Fe, Cu, Zn, and Pb, 0.01 for Cr and Cd, and 0.04 for Mn and Ni (US-DOE 2011). The HI (unit less) describes the potential non-carcinogenic health risk associated with all heavy metals. According to the US EPA (2004), there are no detrimental effects on human health when HQ or HI < 1. On the other hand, when HQ or HI > 1, there may be a possibility of adverse effects on human health. The potential non-carcinogenic risk associated with an HI (unit less) value is categorized as follows: HI < 0.1: indicates a negligible risk, 0.1 ≤ HI < 1: indicates a low risk, 1 ≤ HI < 4: indicates a moderate risk, and HI ≥ 4: indicates a high risk (Alver 2019; Rahim et al. 2019).

Analysis of major ions such as Na⁺, K⁺, Mg²⁺, Ca²⁺, ⁠, ⁠, and Cl⁻ is the basis for understanding the hydrogeochemistry of groundwater (Zhou et al. 2016). The investigated result showed that the concentration order of major cations and anions: Ca²⁺ > Na⁺ > Mg²⁺ > K⁺ and in the PRM, MON, and POM, respectively, where Ca²⁺ and are the dominant cation and anion, respectively. Dolomite and calcite dissolution are responsible for the abundance of Ca²⁺ and ions (Abdalla & Al-Abri 2014). The concentration ranges of dominant cations, Ca²⁺ are 39.60–110, 44.0–86, and 78.0–162 mg/L, and are 319–514, 375–525, and 350–750 mg/L, with averages of 110.0, 65.6, and 104.90 mg/L in the PRM, MON, and POM, respectively. The concentration of the dominant cation, Ca²⁺, exceeded the WHO standard limit of 15, 30, and 100% of samples in the PRM, MON, and POM, respectively, whereas the concentrations of the major anion, ⁠, were below the standard limit. The concentration order of trace metals is as follows: Fe > Mn > Ni > Pb > Cr > Cd; Fe > Mn > Ni > Cr > Pb > Cd; and Fe > Mn > Cr > Pb > Ni > Cd in the PRM, MON, and POM, respectively. The concentration of Fe and Mn in 100% of samples exceeded all permissible limits in three seasons, but the concentration of Pb in 100% of samples exceeded all permissible limits in PRM and POM, whereas 65% of samples exceeded all permissible limits in MON. The concentrations of Cd, Cr, and Ni are 70, 75, and 65%; 50, 0, and 100%; 10, 55, and 0% of samples, respectively, exceeded all respective permissible limits. The analysis of skewness and coefficient of variation (CV) provide valuable insights into the distribution and variability of water quality parameters across the different season. The skewness of the dataset in three season showed that most of the data points are clustered at the lower end of the range, and the mean is typically greater than the median. This suggests a right-skewed distribution, where a few higher values pull the mean above the median. The CV represents the ratio of a dataset's standard deviation to its mean, and is employed to evaluate the relative variability. A higher CV indicates greater relative variability, whereas lower CV denotes reduced variability. The results of CV of the investigated parameters revealed that moderate (10% < CV < 30%) and high (CV >30%) variability (Table 1) accepts pH and EC (CV < 10%) in three seasons. The parameters such as pH and EC have low variability; TH, DO, Cl⁻, ⁠, K, Ca, Cr, and Ni have moderate variability; TDS, ⁠, ⁠, Na, Mg, Mn, and Cd have high variability across the three seasons.

Water quality and pollution indices are used to assess the overall quality of water based on multiple parameters for drinking purposes. Several recognized indices, such as WQI, CWQI, CD, HI, and HMPI, have been introduced by various researchers. Assessing the quality and pollution level, groundwater is categorized based on the value of the respective indices for drinking purposes as presented in Table 3.

The results showed that the water quality of the study area is poor to undrinkable. None of the studied samples were of excellent or good quality in three seasons except two samples (groundwater sample nos. 2 & 11) which are good quality in monsoon (Figure 3(a)) on the basis of water classification according to WQI values (Table 3). The study observed WQI values vary with seasonal variations. Compared to PRM and POM, water quality is relatively better in MON because groundwater is more dilated during MON, resulting in lower concentrations of major ions and trace metals. Based on the WQI values, the water category of studied samples is in PRM 55% poor, 30% very poor and 10% undrinkable; in MON 10% good, 55% poor, 25% very poor, and 10% undrinkable; in POM 10% poor, 50% very poor, and 40% undrinkable (Figure 3(a)). The calculated CCMEWQI values varied from 56.25 to 70.34, with an average of 62.55. The results showed that fair groundwater quality is found at five locations (20% samples, groundwater sample nos 2, 10, 18, 19) and marginal groundwater quality is found at 15 locations (80% samples, groundwater sample nos 1, 3, 5, 17, and 20) in the study area (Figure 3(b)).

The HMEI model gives an insight into the overall quality of the groundwater with its respective heavy metals (Bodrud-Doza et al. 2016), and it was calculated by Equation (13). Figure 4(b) illustrates the HMEI value of the studied groundwater samples. The result showed that all values were greater than six in three seasons. So, all the samples are seriously affected. HMPI is regarded as a useful technique for characterizing the combined effect of metals on the overall water quality (Sheykhi & Moore 2012). Equations (15) and (16) calculate the HMPI value for groundwater in the studied samples, as presented in Tables 3 and Figure 4(c). The result showed 100% of the samples are undrinkable in three seasons except groundwater sample No. 2 in MON. This indicates that the groundwater samples pose health risks to humans if used for drinking. The high concentration of Pb in groundwater samples is responsible for this. The result showed that the order of the HMPI value was POM > PRM > MON. The WQI, CCMEWQI, C_D, HEI, and HMPI indices were used to assess the quality and pollution levels of the groundwater. The results strongly suggest that human exposure to this groundwater may have potential health impacts.

To assess the potential non-carcinogenic risks of the study, calculate HQ_ingestion, HQ_dermal, and trace elements HI for children and adults. Calculating HQ_ingestion, HQ_dermal, and HI, Equations (15)–(18) were used. The results showed no adverse effects on human health and potential non-carcinogenic risk as the average value of HQ_ingestion, HQ_dermal, and trace elements HI was below 1 for both children and adults except the child HI value for Cd in three seasons (Table 4). The HI value for children of C_D is 113, 1.29, and 1.04 in the PRM, MON, and POM, respectively. Additionally, children's HQ_ingestion, HQ_dermal, and HI values were higher than those of adults, suggesting that children were more vulnerable than adults to the adverse effects of trace elements when exposed to the same water medium (Gao et al. 2019; Xiao et al. 2019). The calculated values of HQ_ingestion, HQ_dermal, and HI for children and adults through consumption of trace metal-contaminated groundwater for ingestion of oral and dermal adsorption pathways in the study area are shown in Table 4.

To determine the sources and regulate the factors of the primary hydrochemical components of the groundwater samples, the major ions were used. The graph shown in Figure 7(a)—7(i) is useful to understand the weathering processes of rocks that regulate groundwater chemistry. The major mechanisms for controlling groundwater chemical constituent concentrations are primarily regulated by the weathering of parent rocks (Marghade et al. 2012).

The bivariate plots of Ca²⁺/Na⁺ vs. (Figure 7(d)) and Ca²⁺/Na⁺ vs. Mg²⁺/Na⁺ (Figure 7(e)) denote the carbonate, silicate, and evaporation weathering processes on groundwater ion concentration (Mukherjee & Fryar 2008; Brindha et al. 2020). The plot Ca²⁺ /Na⁺ vs. (Figure 7(d)) suggests characteristics of the dissolving processes that influenced silicate weathering and evaporation dissolution. Figure 7(d) and 7(e) illustrate that most of the samples sit near silicate weathering and in the middle of evaporation and silicate weathering zones. Figure 7(d) also indicates that the carbonate minerals are completely dissolved. The dissolution processes of rock–water interaction are influenced by silicate weathering (Kumar 2014) and evaporation dissolution. Silicate weathering and carbonate dissolution control the hydrochemistry in this region. The plot of Ca²⁺ vs. Mg²⁺ (Figure 7(f)) also confirms this process. The scattered plot Ca²⁺ vs. Mg²⁺ (Ca²⁺ = 2Mg²⁺) (1:2 Line) (Figure 7(f)) illustrates that some samples lie below the 1:1 line, some are between the 1:1 and 1:2 lines, and a few samples are above the 1:2 line, which indicates the silicate weathering process (Rajmohan & Elango 2004; Ghesquière et al. 2015). The Ca²⁺/Mg²⁺ ratio of the studied groundwater samples showed that dolomite and calcite dissolution predominated over silicate weathering. Figure 7(g) and 7(h) showed that the total cations were plotted against Ca²⁺ + Mg²⁺ and Na⁺ + K⁺, respectively, to illustrate the silicate weathering. The groundwater samples are aligned close to the 1:1 equiline for the Ca²⁺ + Mg²⁺ vs. TZ⁺ (total cation) plot and deviate more from the 1:1 equiline in the case of Na⁺ + K⁺ vs. TZ⁺ (total cation) plot. This indicates that when TDS increases, Ca²⁺ and Mg²⁺ contribute more. Additionally, some data points fall below the 1:1 equiline, indicating probable origins from silicate weathering. The contribution of Na + K to the total cations (Figure 7(h)) sited the 1:1 line additionally confirms that silicate weathering is responsible for Na⁺ and K⁺ in groundwater (Senthilkumar & Elango 2013; Kanagaraj & Elango 2019).

The analysis results illustrated that the concentration of Fe and Mn in all samples exceeded all permissible limits in three seasons, but for the Pb, all samples in the PRM and POM exceeded all permissible limits, but 65% of samples in MON. The concentrations of Cd, Cr, and Ni are of 70% in the PRM, around 50% in the MON, 30% of samples in the POM, exceeded permissible limits. The concentration order of major ions and trace metals are as follows: Ca²⁺ > Na⁺ > Mg²⁺ > K⁺; ⁠, and Fe > Mn > Pb > Cd in three seasons. The groundwater type of the areas was identified as type and Ca²⁺ and are the dominant cation and anion spices. The study suggested the major contributing sources of aquifer water contamination are mostly geogenic and slightly influenced by anthropogenic factors. Water–rock interactions, calcite and dolomite dissolution, and over silicate weathering are the dominant natural mechanisms of hydrochemistry in this region. The water quality of studied samples ranged from poor to undrinkable. Compared to PRM and POM, water quality is relatively better in MON. According to WQI, the water category of studied samples are in PRM 55% poor, 30% very poor and 10% undrinkable; in MON 10% good, 55% poor, 25% very poor, and 10% undrinkable; in POM 10% poor, 50% very poor, and 40% undrinkable. The CCMWQI results revealed that 20 and 80% of samples are fair and marginal quality, respectively. The result of C_D, 90 and 85% of samples are considerable in PRM and POM, respectively, but 60 and 35% of samples are moderate and low in MON, respectively, about pollution level. The context of HEI and HMPI revealed that 100% of samples are seriously affected and undrinkable in all seasons. A non-carcinogenic health effect not found in either adult or child over three seasons. The study provides information and understanding of hydrogeochemistry and water quality for researchers and policymakers to take necessary steps in further research and sustainable water resource management. For future research, it is recommended to collect more samples in six seasons along with aquatic soil samples for further analysis.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.