Mapping drinking water contamination in Gazipur City restaurants using WQI and multivariate analysis.

Groundwater serves as a primary source of drinking water for domestic, industrial, and agricultural purposes in many countries, including Bangladesh, and is crucial to sustaining populations worldwide (UNESCO 2021). In urban areas, approximately 66% of households (with a range of 17% to 93% across individual countries) and 60% of rural households (with a range of 22–95%) rely on groundwater for drinking (Carrard et al. 2019). However, when the quality of drinking water does not meet established international and national guideline values, such as those set by the World Health Organization (WHO 2022) or the Environmental Conservation Rules (ECR 2023), it can lead to significant adverse effects on human health. Traditionally, the suitability of drinking water is determined by analyzing a range of physical, chemical, and microbiological parameters and comparing the results to regulatory standards (Ameen 2019). While this approach ensures legal compliance, interpreting test results across multiple parameters and assessing overall water quality can be a complex task. To streamline and simplify this process, water quality indices (WQIs) have been widely adopted. These indices condense extensive data on drinking water quality into a single, easily interpretable number, offering a comprehensive representation of water quality while maintaining scientific rigor (Menniti & Guida 2020; Uddin et al. 2021, 2022; Fatima et al. 2022).

In recent years, WQI has become a common tool for classifying and characterizing water resources for various purposes, including drinking, agricultural, surface water, and industrial uses (Yadav et al. 2010; Udeshani et al. 2020; Khan et al. 2023; Nsabimana & Li 2023). Numerous countries, including Bangladesh (Akter et al. 2016; Rahaman et al. 2019), India (Yadav et al. 2018; Khangembam & Kshetrimayum 2019; Mukate et al. 2019; Banerjee et al. 2024), Saudi Arabia (Al-Omran et al. 2015), Tunisia (Ketata et al. 2012), Ecuador (Roldán-Reascos et al. 2024), China (Wu et al. 2020), and Pakistan (Solangi et al. 2020; Saleem et al. 2024; Ullah et al. 2024) have applied WQI to assess water quality for drinking purposes. Various WQI have been developed worldwide, including the US National Sanitation Foundation Water Quality Index (NSFWQI) (Brown et al. 1970), the Canadian Council of Ministers of the Environment Water Quality Index (CCMEWQI) (Khan et al. 2003), the British Columbia Water Quality Index (BCWQI), and the Oregon Water Quality Index (OWQI) (Debels et al. 2005; Kannel et al. 2007).

In addition to WQI, multivariate statistical techniques such as principal component analysis (PCA) and factor analysis (FA) are frequently used to assess the quality of surface and groundwater (Deng & Liu 2020). These methods help identify specific contaminants and their sources, providing insight into the environmental processes affecting water quality (Liu et al. 2003; Shrestha & Kazama 2007; Bu et al. 2009; Varol & Davraz 2014; Howladar et al. 2017, 2021; Dubrovskaya 2023). For example, Bu et al. (2009) used hierarchical cluster analysis (HCA) to assess contamination levels in the Jinshui River, China, classifying 12 sampling spots into three clusters based on 22 variables. They also used FA, which revealed five components explaining 90.01% of the overall variance. Similarly, Shrestha & Kazama (2007) grouped 13 sampling spots into three clusters based on water quality, using FA and PCA to identify latent factors explaining 65.39, 73.18, and 77.61% of the overall variance in terms of pollution levels. These methods are invaluable for understanding hydro-chemical processes and contamination sources (Kumar et al. 2006).

Given the growing environmental and public health concerns, Pearson correlation analysis is often employed to establish relationships between physicochemical parameters and to trace the origins of contaminants. For instance, studies have used PCA and FA to assess groundwater pollution in areas prone to health issues such as black foot disease in Taiwan (Liu et al. 2003) and other contaminated regions like Dinajpur, Bangladesh (Howladar et al. 2017). These analyses help identify the most significant factors influencing water quality, such as industrial effluents, agricultural runoff, and natural geological processes.

Many studies have utilized WQI and multivariate analysis to assess water quality. A recent study by Howladar et al. (2021) demonstrated the utility of these methods in identifying contaminants like turbidity, dissolved oxygen (DO), total dissolved solids (TDS), hardness, chemical oxygen demand (COD), total suspended solids (TSS), and microbial contamination. Their findings highlighted the poor water quality in many regions, largely due to improper management of waste and drainage systems. Rahaman et al. (2019) used cluster analysis and WQI to assess water quality in Rajshahi City, Bangladesh, identifying different water types based on chemical compositions. Similarly, studies like those by Akter et al. (2016) have shown how groundwater contamination, particularly from heavy metals such as arsenic, manganese, and iron, contributes to the decline in water quality.

Bangladesh faces significant challenges in managing groundwater quality due to contamination from industrial effluents, agricultural runoff, and high population density in urban centers like Gazipur (Hossain & Rahman 2018). The UNESCO World Water Assessment Programme (2024) report underscores the continuing reliance on groundwater, exacerbating the risk of water contamination. To address this, Bangladesh has implemented various programs aimed at improving the health of vulnerable communities and promoting access to clean water. Previous studies have focused on tube well water and water-related health issues, but no comprehensive studies have been conducted on water quality in Gazipur City using WQI and multivariate analysis to identify contamination zones.

Gazipur, an industrial hub and one of the most densely populated areas in Bangladesh, is experiencing increasing demand for groundwater, which has led to significant depletion and contamination of water sources (Aziz & Sulaiman 2022; Rana & Moniruzzaman 2024). Since 2003, population and industrial activities in the city have rapidly increased, impacting groundwater levels (Parvin 2019). According to Banglapedia (2001), approximately 85.62% of the population relies on tube wells, with the remainder using other sources. Many people, particularly in restaurants and tea stalls, rely on groundwater or locally sourced jar water, which is prone to contamination. Studies such as that by Sarker et al. (2016) in Sylhet City have revealed high levels of fecal coliform and excessive iron in water used in restaurants, highlighting the public health risks associated with unsafe drinking water practices. The uncontrolled extraction of groundwater to meet demand poses serious long-term health risks, as customers often consume water unaware of its contamination (Chandnani et al. 2022).

To date, no comprehensive study has evaluated groundwater quality specifically in restaurant settings within Gazipur City using an integrated approach combining multiple WQI methods and advanced multivariate analyses. The novelty of this research lies in the simultaneous application of integrated WQI (IWQI), assigned weight WQI (AWWQI), and weighted arithmetic WQI (WAWQI), combined with hierarchical clustering, Pearson correlation, FA, and microbial assessments, to provide an in-depth understanding of groundwater contamination. Specifically, this study aims to: (1) evaluate physicochemical and microbial water quality parameters collected in 2019 from restaurants across Gazipur City; (2) identify contamination zones using hierarchical clustering; and (3) investigate pollution sources through correlation and factor analyses. The findings will support targeted water quality management and policy development to safeguard public health in rapidly urbanizing regions.

The Gazipur City Corporation research area comprises 1741.53 km² which is located on the northern side of Dhaka, the capital of Bangladesh. It has 9 wards and 31 mahallas (localized community) (Gazipur Sadar Upazila – Banglapedia 2001). The area of the town is 49.32 km² and the population of that area is 123,531. About 52.52% of males and 47.48% of females live in that area, which has a density of 2,505 per km² (Gazipur Sadar Upazila – Banglapedia 2001). In Figure 1, the 18 busiest locations sited around the highway are chosen to cover most of the regions of the research area.

… Bacterial contamination zones

The bacteriological assessment of 18 zones, divided into three clusters, is shown in Figure 8. The percentage of bacterial contamination is indicated for each zone out of the 173 samples collected from restaurants and tea stalls. Among the three clusters, Cluster III areas (Gazipura, Board Bazar, Kodda, Jajhar, Borobari, Dhirasrom, Bypass, and Signboard) have the highest level of bacterial contamination. The Boro Bari zone, in particular, shows the highest bacterial contamination in restaurants and tea stalls, with 90% total coliform, 70% fecal coliform, and 70% E. coli. On the other hand, the Konabari zone in Cluster I has the lowest bacterial contamination, with 40% total coliform, 10% fecal coliform, and 40% E. coli. Cluster II also shows bacterial contamination, with the Tongi Bazar zone having 60% total coliform, 30% fecal coliform, and 30% E. coli contamination. The presence of bacterial contamination has been illustrated in studies conducted by Karim et al. (2023), Mou et al. (2023), Charles et al. (2021) and Shaibur et al. (2021) in Bangladesh. These studies all mention that bacterial contamination occurs due to unhygienic management, poor sanitation, and storage practices in these restaurants and tea stalls. Furthermore, Charles et al. (2021) highlighted that 32% of piped water and 30.4% of tubewell water in Bangladesh are contaminated with E. coli, posing a significant health risk. Mou et al. (2023) demonstrated that almost 100% of the samples were found to be contaminated with both total and fecal coliform, with 70% of the samples testing positive for E. coli. Additionally, Shaibur et al. (2021) also indicated that a majority of the water samples were contaminated with pathogens. In a recent study conducted by Tareq et al. (2024) in Patuakhali, Bangladesh, it was reported that the drinking water quality in restaurants is contaminated by numerous bacterial species such as Enterobacter aerogenes, Staphylococcus epidermis, E. coli, Pseudomonas spp., Vibrio cholera, Klebsiella oxytoca, S. auerous, Bacillus spp., Aeromonas salmonicida, and Salmonella spp.

Figure 8