ABSTRACT
Presence of heavy metals in surface metals is the mounting concern in the world due rapid industrialization and modernization of society, and surface water is being utilized both as source and sink for water consumption and wastewater discharge, respectively. The chemometric method were applied on river for pre and post monsoon seasons to determine the potential heavy metals and development of heavy metal pollution index (HPI). The cluster and factor analysis were applied on thirteen heavy metals monitored over eleven locations for characterizing the highly correlating and potential, respectively heavy metals. The proliferation of health risks of heavy metals was determined through cluster analysis and GIS. The Fe and Mn found exceeding the permissible limits in river and drain samples for both the pre-monsoon and post-monsoon season, whereas other heavy metals were found within the permissible limits throughout the study period. The study suggested that classification of pollutants and assessment of associated risk to human health could provide a valuable insight for the development of remediation measures to minimize the degradation of public health and appropriate treatment technology for minimizing the contamination of water sources.
HIGHLIGHTS
River water quality is assessed using multivariate data analytics, GIS and HPI techniques.
Factor and cluster analysis derived the correlation and potential heavy metal affecting the water quality.
Health risk of heavy metals are assessed on human health.
Iron and manganese are found as potential and deteriorating heavy metals.
INTRODUCTION
Water pollution is a growing cause of premature deaths and contributes annually to 1.7–2.2 million deaths around the world (Fuller et al. 2022). The primary concern for deaths is contaminated source of water followed by unavailability of sanitation facilities, where women and children are more prone to health risk exposure to polluted water compared to men (GBD 2019). The economic losses due to household water and sanitation services has decreased from 3.2 to 1% of India's gross domestic product (GDP), however, economic losses from the modern pollution areas such as industrial discharge of heavy metals, agricultural runoff, depleting groundwater etc. has increased from 0.75 to 1.1% of India's GDP from 2000 to 2019 (GBD 2019; World Bank 2022). The expansion of urban areas, industrial activities and socio-economic development of cities are the major source of modern water pollution (Herath et al. 2018; Kumar et al. 2022). The bioaccumulation of heavy metals in water, soil, crops, vegetables; long residence time in environment, non-biodegradability and toxicity make them more hazardous for terrestrial and aquatic life forms. Surface and groundwater are more susceptible to heavy metal pollution, especially in developing regions where untreated or partially treated wastewater from industrial areas and agricultural runoffs is discharged into open drains. These contaminants eventually flow into rivers, infiltrate the soil, and leach into groundwater, exacerbating pollution levels (Pandey & Kumari 2023).
Arid and semi-arid regions majorly rely on groundwater for domestic and commercial water supplies, and rural populations consume the water from the available sources without any treatment (Gupta et al. 2024). Therefore, it is essential to determine whether the water source is adequate for drinking, industrial, irrigation or other activities and the associated risk to the human health. Rivers in India face significant pollution, primarily from urban settlements, with cities located along their banks, contributing substantial volumes of wastewater. Singh et al. (2017) conducted a study to determine the heavy metal in soil quality from different areas using a geo-accumulation index and pollution index and found that agrochemical, vehicular emission and dumping of waste are the primary causes of heavy metal concentration. Kumar et al. (2020) determined the distribution trend of heavy metals in three rivers (Indus, Beas and Sutlej rivers). The study revealed Satluj River contained high concentrations of Pb (lead), Cu (copper) and Cr (chromium) compared to the Indus and Beas rivers, exceeding the permissible limits set by BIS drinking water quality standards. Herath et al. (2018) performed a study to determine the source of heavy metal contamination of Colombo Metropolitan Region and studied the soil samples, canal sediments, water samples and crops. The study found high concentrations of zinc, cobalt and lead in all the regions, dominantly caused by the anthropogenic sources. Adimalla (2020) assessed the concentration of heavy metals in 15 states of India and compared the results with the different geochemical guidelines and evaluated the hazard index of heavy metals. Kumar et al. (2019a, b) carried out a study of tubewell water to determine the trace metals and the source of metals was determined using multivariate and geospatial techniques. High concentrations of trace metals were reported near the industrial area. Akhtar et al. (2021) conducted a review on the impact of anthropogenic activities on the water resources. The anthropogenic sources were classified based on the land use land change (LULC) to determine the effluent discharge characteristics from each LULC type. The impact of anthropogenic activities was studied on the natural physiognomies such as soil, groundwater, surface water, aquatic environment and emerging inorganic pollutants. The study reveals the challenges faced by water resources and food production due to extensive urban development, and lack of sufficient infrastructure for handling wastewater load. Goyal et al. (2022) collected samples from pond sludge and groundwater for the quantification of the presence of nine trace metals (Pb, Cu, Ni, Zn, Cr, Cd, Fe, As, and Mn) and reported the ecological risk associated with the high concentration of trace metals where Pb, Cu, Ni, Fe and Mn were found to exceed the groundwater standards.
Most of the studies conducted in India were performed on Ganga river basin and southern plateaus which receive significant rainfall during monsoon, which helps wash away a significant portion of pollutants annually through precipitation. Some of the studies were carried out to identify the incidence of heavy metals in soil and sludge to determine their percolation in the food chain. However, no relevant study was found in the arid and semi-arid regions on the contamination of heavy metals in surface water source and associated health impacts. A comprehensive analysis of water bodies to identify the sources of heavy metals and assess their impact on human health from consuming contaminated water has not been conducted.
The spatial study of heavy metals by producing a heavy metal pollution index (HPI) is helpful in identifying and quantifying trends in water quality (Reza & Singh 2010; Prasad et al. 2020). It can also provide comprehensive information and assessments in a format that helps resource managers and regulatory agencies evaluate alternatives and make informed decisions. Moreover, cluster analysis (CA) and factor analysis (FA) were also performed to summarize information of large data sets as they offer better interpretation and understanding of water quality (Kannel et al. 2007; Arora & Keshari 2021). In particular, these FA and hierarchical cluster analysis (HCA) are increasingly in use for environmental studies, including measurement and monitoring of heavy metals in environmental media (Loska & Wiechuła 2003; Filgueiras et al. 2004; Ting et al. 2012; Zhao et al. 2017; Mishra et al. 2018).
This study was carried out on Mandakini River, Madhya Pradesh, India to assess the heavy metal contamination using HPI, GIS and statistical analysis techniques. The multivariate techniques and GIS were applied to meet the following objectives: 1) assessment of spatial distribution of heavy metals in Mandakini River, 2) determination of critical pollutants in the river and subsequent cause of pollution, 3) assessment of degree of contamination with heavy metals and 4) associated impact of heavy metals on human health. The study aims to provide new insights into regularly monitoring of water quality for heavy metals and their associated health impacts.
MATERIAL AND METHODOLOGY
Study area
Data sampling
The water samples collected from 11 locations were divided into two categories to derive the variation in heavy metal concentration in the pre- and post-monsoon periods. The water sampling was performed for three years (2018, 2019 and 2021) in the months of April, May, September and October. The water samples collected during April and May are classified as pre-monsoon data and samples collected during September and October are taken as post-monsoon data. The location of sampling points along the drains and river are shown in Figure 2. The grab samples were collected at each point of the river from the centre of the stream or drain at about 0.6 m depth. Five hundred mL capacity polyethylene plastic bottles with a plastic cap were used for collecting samples for heavy metals parameters. Each sample bottle was washed three times before collecting water samples for trace metals. Firstly, every bottle was washed with 50% HNO3 acid and twice with nano pure distilled water. Samples were collected free from bottom sediment and away from stream banks by wading into the centre main current.
All the water samples were tested for heavy metals including arsenic (As) (μg/L), cadmium (Cd) (μg/L), chromium (Cr) (μg/L), copper (Cu) (μg/L), cobalt (Co) (μg/L), iron (Fe) (μg/L), manganese (Mn) (μg/L), nickel (Ni) (μg/L), lead (Pb) (μg/L), selenium (Se) (μg/L), vanadium (V) (μg/L), antimony (Sb) (μg/L) and zinc (Zn) (μg/L) using trace metal detection analytical techniques. Inductively coupled plasma mass spectrometry (ICP-MS) was used for analysis following the standard procedure prescribed by the American Public Health Association (APHA 2005). The concentration of heavy metals was compared with the BIS drinking standard 10500:2012 (BIS 2012), WHO Drinking Water Standard 2011 (WHO 2011) and USEPA Surface Water Standard. The most stringent permissible limit from these three standards was used for water quality analysis, as shown in Table 1. The referred permissible limit of heavy metals for assessment of water quality is highlighted with bold text in Table 1. For heavy metals where any agency does not prescribe any value, a zero concentration (considered acceptable in drinking water) is used to develop the Heavy Metal Pollution Index. All the parameters were tested from each sample, and testing was performed twice for the absent parameters to ensure no errors in the experimentation process. The experimentation results are categorized into pre- and post-monsoon scenarios.
Trace metal standard acceptable limit for drinking and surface water along with method and instrumentation technique used in analysis
S. no . | Parameters . | Unit . | BIS Drinking water, IS 10500: 2012 . | WHO Drinking Water Standard 2011 . | USEPA Surface Water Standard . | Method . | Instrumentation technique . |
---|---|---|---|---|---|---|---|
1 | As | μg/L | 10 | 10 | 10 | APHA-3125-B | ICP-MS |
2 | Cd | μg/L | 3 | 3 | 5 | APHA-3125-B | ICP-MS |
3 | Cr | μg/L | 50 | 50 | 100 | APHA-3125-B | ICP-MS |
4 | Cu | μg/L | 50 | 2,000 | 1,300 | APHA-3125-B | ICP-MS |
5 | Fe | μg/L | 300 | – | 300 | APHA-3125-B | ICP-MS |
6 | Mn | μg/L | 100 | – | 300 | APHA-3125-B | ICP-MS |
7 | Ni | μg/L | 20 | 70 | 100 | APHA-3125-B | ICP-MS |
8 | Pb | μg/L | 10 | 10 | 15 | APHA-3125-B | ICP-MS |
9 | Zn | μg/L | 5,000 | – | 5,000 | APHA-3125-B | ICP-MS |
10 | Co | μg/L | – | – | – | APHA-3125-B | ICP-MS |
11 | Se | μg/L | 10 | 40 | 50 | APHA-3125-B | ICP-MS |
12 | V | μg/L | – | – | – | APHA-3125-B | ICP-MS |
13 | Sb | μg/L | – | 20 | 6 | APHA-3125-B | ICP-MS |
S. no . | Parameters . | Unit . | BIS Drinking water, IS 10500: 2012 . | WHO Drinking Water Standard 2011 . | USEPA Surface Water Standard . | Method . | Instrumentation technique . |
---|---|---|---|---|---|---|---|
1 | As | μg/L | 10 | 10 | 10 | APHA-3125-B | ICP-MS |
2 | Cd | μg/L | 3 | 3 | 5 | APHA-3125-B | ICP-MS |
3 | Cr | μg/L | 50 | 50 | 100 | APHA-3125-B | ICP-MS |
4 | Cu | μg/L | 50 | 2,000 | 1,300 | APHA-3125-B | ICP-MS |
5 | Fe | μg/L | 300 | – | 300 | APHA-3125-B | ICP-MS |
6 | Mn | μg/L | 100 | – | 300 | APHA-3125-B | ICP-MS |
7 | Ni | μg/L | 20 | 70 | 100 | APHA-3125-B | ICP-MS |
8 | Pb | μg/L | 10 | 10 | 15 | APHA-3125-B | ICP-MS |
9 | Zn | μg/L | 5,000 | – | 5,000 | APHA-3125-B | ICP-MS |
10 | Co | μg/L | – | – | – | APHA-3125-B | ICP-MS |
11 | Se | μg/L | 10 | 40 | 50 | APHA-3125-B | ICP-MS |
12 | V | μg/L | – | – | – | APHA-3125-B | ICP-MS |
13 | Sb | μg/L | – | 20 | 6 | APHA-3125-B | ICP-MS |
Methodology
HPI



The HPI is sub-indexed into five categories and colour coded for easier evaluation of water quality, as shown in Table 2.
HPI categories of water quality
HPI score . | Category . | Colour coding . |
---|---|---|
0–20 | Excellent | Green |
21–40 | Good | Yellow |
41–60 | Moderate | Orange |
61–80 | Bad | Red |
81–100 | Severe | Maroon |
HPI score . | Category . | Colour coding . |
---|---|---|
0–20 | Excellent | Green |
21–40 | Good | Yellow |
41–60 | Moderate | Orange |
61–80 | Bad | Red |
81–100 | Severe | Maroon |
Hierarchically aligned cluster analysis









Factor analysis



The FA has the advantage of reducing dimension by observing the variance of components, eliminating less significant data and proving effective in handling large dimensional datasets. FA also works with non-parametric data and data pre-processing is not required for the identification of unique output.
RESULTS AND DISCUSSION
Exploratory analysis
Iron (Fe) was found to exceed the permissible limit at all sampling locations, while manganese (Mn) surpassed the permissible limit at a few locations, including Vaidehi Vatika Drain and Paisuni Drain, for both monitoring periods. As and Co had the shortest interquartile range during pre-monsoon and V during the post-monsoon period, showing no major variation in concentration of heavy metals spatially. The longest interquartile range with mediocre kernel density suggests significant variation in Zn concentration for pre and post-monsoon periods but remains within the permissible range. Similarly, multiple kernels were formed for Fe and Mn in both the seasons indicating the variation in concentration of variables at river and drain sampling locations. The lower kernel with greater width indicates the concentration of heavy metals at river sampling points, while the upper, narrower kernel represents the concentration at drain locations, which exceeds the permissible limits by multiple folds. The width of kernel density indicates that most of the river sampling sites maintain similar concentrations throughout the season. The upper kernel falls outside the violin plot for Mn, indicating it can be considered an outlier. The higher concentration of Fe and Mn illustrate the anthropogenic contamination of Paisuni drain might originate from discharge of effluents from corroded pipes, industrial effluent from metallurgical industries, etc.
HPI
The exploratory analysis suggests the considerable difference in the observed and acceptable concentration of heavy metals. Table 1 provides the permissible limits and Figures 4 and 5 provide observed concentrations during pre- and post-monsoon periods, respectively. The HPI was applied to evaluate the heavy metal contamination on a standardised scale, considering the difference in permissible limits of each heavy metal. The index ranges from 0 to 100, where 0 indicates no water source pollution, and 100 represents a highly polluted water source. Most of the sampling locations had excellent water quality, as HPI was found below 20, and few locations were found with HPI in the range of 21–23, as shown in Table 3. The water quality of Vaidehi Vatika Drain was categorized as ‘Good’ and does not significantly affect the water quality of the river. In contrast, the Paisuni Drain exhibited the most deteriorated water quality, classified under the ‘Severe’ and ‘Bad’ categories for pre- and post-monsoon periods, respectively. This indicates the substantial wastewater load carried by the Paisuni Drain from domestic and commercial areas. The water quality of the river downstream of Paisuni Drain falls into the ‘Good’ category, indicating that the impact of the drain is short-lived. This may be due to the significant flow in the river compared to the quantum of wastewater contributed by the Paisuni Drain. Tapping of drains from commercial activities could reform the water quality of the river to the ‘Excellent’ category.
HPI of sampling site for pre- and post-monsoon period
S.No. . | Sampling location . | HPI-Pre . | HPI-Post . |
---|---|---|---|
1 | Sati Anusuiya Temple | 17.76 | 19.71 |
2 | Sphtik Shila | 23.05 | 20.47 |
3 | Arogyadham Ghat | 18.61 | 17.06 |
4 | Vaidehi Vatika Drain | 28.27 | 25.55 |
5 | Jankikund | 20.42 | 18.93 |
6 | Pramodvan | 19.16 | 19.89 |
7 | Bharat Ghat | 16.81 | 16.07 |
8 | Paisuni Drain | 82.24 | 75.54 |
9 | Ramghat | 21.02 | 21.60 |
10 | Karwi Bridge upstream | 11.35 | 11.59 |
11 | Karwi Bridge downstream | 12.28 | 13.77 |
S.No. . | Sampling location . | HPI-Pre . | HPI-Post . |
---|---|---|---|
1 | Sati Anusuiya Temple | 17.76 | 19.71 |
2 | Sphtik Shila | 23.05 | 20.47 |
3 | Arogyadham Ghat | 18.61 | 17.06 |
4 | Vaidehi Vatika Drain | 28.27 | 25.55 |
5 | Jankikund | 20.42 | 18.93 |
6 | Pramodvan | 19.16 | 19.89 |
7 | Bharat Ghat | 16.81 | 16.07 |
8 | Paisuni Drain | 82.24 | 75.54 |
9 | Ramghat | 21.02 | 21.60 |
10 | Karwi Bridge upstream | 11.35 | 11.59 |
11 | Karwi Bridge downstream | 12.28 | 13.77 |
Spatial and temporal variability
The geographical distribution of heavy metals in the study area.
Hierarchical aligned cluster analysis (HACA)
During the post-monsoon season, Mn and V formed a cluster at the shortest distance, followed by Fe. Ni and Cr formed another cluster, which later joined the cluster consisting of Mn, V, and Fe. Additionally, Zn and Pb formed a separate cluster. The mean distance was observed to be 3.17, resulting in four clusters. The largest cluster contained three variables, two clusters contained two variables each, and one cluster consisted of a single variable (Cu).
The shorter mean distance and larger clusters were formed during the pre-monsoon period compared to post-monsoon period suggesting that during pre-monsoon period, variables have greater similarity. The Ni and Cr form a single cluster in both the seasons but at a marginally greater distance than Mn, V and Fe. These five variables do not show considerable pattern variation in both the seasons, where As and Co are not found in post-monsoon season. The Zn forms a cluster with Cu in pre-monsoon whereas Zn forms a cluster with Pb in post-monsoon season, at roughly a similar distance from the centre in both the seasons. The comparison of dendrograms suggested that with the change in flow of river/drain, the pattern of flux in heavy metals do not change significantly. Both the seasons form clusters with similar heavy metals, however the mean distance increases with the increase in flow. The dilution and distribution of heavy metals with flow during post-monsoon produces some change in interaction of heavy metals.
Factor analysis
Factor loading for pre- and post-monsoon seasons.
The factor loading suggests different and weak correlation of variables during post-monsoon season compared to pre-monsoon season. At pre-monsoon season, the first two factors contribute towards 84.85% of total variance in water quality, whereas the first two factors lead to 79.25% of total variance, while factor 3 contributes the remaining 20.74%, as shown in Figure 9. Mn, Fe, and V exhibited a strong positive correlation with Factor 1, while Ni showed a moderate positive correlation. Pb and Zn displayed a strong and moderate positive correlation, respectively, with Factor 2. Cu contributed to Factor 3 with a moderate positive correlation, whereas Cr and Ni demonstrated a moderate negative correlation with Factor 2. Consistent with the cluster analysis results, the variables showed variance across different factors. During the pre-monsoon season, Fe, Mn, As, V, and Co formed a single cluster at the mean distance, contributing to Factor 1. In the post-monsoon season, Fe, Mn, and V formed a single cluster at the mean distance and strongly correlated with Factor 1.
DISCUSSION
Assessment of critical parameters
The concurrence in the variables appearing in clusters and factor loading distribution pattern suggest that similar parameters are influencing the water quality significantly and have similar patterns of variation among both seasons. The Mn, Fe, As, V and Co were found as principal variables during pre-monsoon, and Mn, Fe and V were the principal variables at post-monsoon season, where As and Co were found below detectable limits. The results indicate that there are common sets of heavy metals affecting the water of river dominantly originating from Paisuni drain receiving the wastewater load from anthropogenic activities (Zhou et al. 2020; Mishra et al. 2021). The heavy metals found in lower concentrations throughout the study area are likely generated from natural sources, as these lower concentrations are observed in uninhabited areas. The temporal distribution of heavy metals suggests that Fe and Mn remain above the permissible limits throughout the year as per IS standards for drinking water. The monsoon has not significantly reduced the concentration of heavy metals, as anthropogenic activities contribute pollutants to the drain throughout the year. The spatial distribution maps suggest that the highest concentrations of Fe and Mn were found near the centre of the city, where Paisuni drain joins the river, and confirms the role of the drain in deteriorating the water quality of the river (Mishra et al. 2021). The factor analysis and clustering provide significant information about the presence and interaction of heavy metals that can be applied by biologists to determine the relevant heavy metals required to be estimated at different sampling sites and policy makers may utilize the findings to identify appropriate treatment techniques for the control and prevention of water quality deterioration.
Assessment of health risk
The presence of heavy metals in water sources beyond permissible limits may affect the mental and physical growth of humans and cause detrimental damage to the human body including the nervous system, cancer and even death due to prolonged consumption. The consumption of heavy metals such as As, Cd, Co, Ni, Cr, Pb and Zn have high toxicity levels and could cause skin manifestations, kidney disease, vascular disease (Rehman et al. 2018), liver damage, neurological signs, damage the fetal brain and diseases of the kidneys, circulatory system, and nervous system (Fallahzadeh et al. 2017). The quantified concentration of all the heavy metals was found to be below the permissible limits except for Fe and Mn. Fe was found above 1,000 μg/L against the permissible limit of 300 μg/L, whereas Mn was found up to 400 μg/L against the acceptable limits of 100 μg/L as per IS standards. The consumption of water with high concentrations of iron and manganese pose several health risks and practical issues. The consumption of water with high concentrations of Fe can cause nausea, stomach pain, vomiting, and constipation, however prolonged consumption may affect the internal organs, particularly the liver and heart (Fallahzadeh et al. 2017). Genetic disorders also allow the accumulation of iron in the body and lead to joint pain and fatigue (Abbaspour et al. 2014). Chronic exposure of manganese is more detrimental and leads to neurological disorders including Parkinson disease, muscle spasm, difficulty in walking etc. Infants and children under the age of five are at high risk of Mn toxicity, which can impair cognitive function and lower IQ development (Gražulevičienė & Balčius 2009). Studies have also shown excessive Mn intake can negatively affect reproductive growth (Studer et al. 2022). The consumption of water contaminated with Fe leads to liver cirrhosis if consumed above 1 mg/L and also induces chronic diseases such as heart disease and diabetes. While iron primarily affects gastrointestinal health and can contribute to microbial growth in water systems, manganese exposure is linked to neurological and developmental effects, particularly in children. Additionally, both metals cause significant aesthetic and maintenance problems in water supplies. It is crucial to monitor and manage the levels of these metals in drinking water to protect public health and maintain water quality.
CONCLUSIONS
Multivariate statistical techniques and GIS were applied to determine spatial and temporal variation, respectively. The critical heavy metals affecting the water quality of Mandakini River, Madhya Pradesh, India are being determined using factor analysis and overall water quality is evaluated using HPI. Violin plots have been used for the first time for the assessment of temporal variation in heavy metal concentration over different seasons. GIS plots were constructed to monitor the pre- and post-monsoon spatial variation in water quality. Both the pre-and post-monsoon seasons showed that iron and manganese remain above the permissible limits, whereas other parameters remain within the acceptable limit and were found in low concentrations throughout the year. The monsoon reduces the concentration of Fe and Mn slightly, but not enough to make it acceptable for any use. The Paisuni Drain, which receives wastewater from anthropogenic activities, contributes a significant concentration of Fe and Mn. Cluster analysis indicated that Fe, Mn, and V, followed by Ni and Cr, exhibit similar flux patterns. The factor analysis also found similar variables affecting the water quality considerably. The principal factors comprising Fe, Mn, V, As, and Co contribute strongly to affecting water quality. Similar variables in both analyses suggest that the variables with the most identical variations are also critical in affecting water quality, as they exhibit the highest concentrations. Integrating innovative approaches combining statistical, geographical methods, and indices provide a holistic methodology for effectively assessing water quality. The control and preventive measures on Fe and Mn could resolve water quality challenges. Policymakers can adopt a similar methodology to identify and monitor critical variables that need to be managed to improve water quality effectively.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.