Assessment of Tap Water Quality and Health Risks in Mymensingh City, Bangladesh
Tanzia, F. K. S.; Sojib, M. R. H.; Jahan, E.; Hossain, M. M.; Khan, M. A.-A.; Hasan, M.; Islam, M. T.; Bashar, S.
Show abstract
This study assesses the domestic water supply status, physicochemical quality, and associated health risks in a region reliant on groundwater-derived tap water. A cross-sectional survey revealed that 89% of households depend on tap water, yet 78% express significant concerns about its safety, prompting inconsistent treatment practices (45% treat water "seldom"). Physicochemical analysis of tap water samples identified critical contamination issues: 33.3% of samples exceeded Bangladesh (BD ECR 2023) iron (Fe) limits (0.3-2.05 mg/L), while 100% surpassed WHO guidelines (0.3 mg/L). Manganese (Mn) exceeded permissible levels (0.1 mg/L) in 33.3% of samples. Total coliform contamination (up to 12 CFU/100ml) was widespread, though fecal coliforms were absent. Principal Component Analysis (PCA) attributed 82.22% of water quality variance to dissolved solids (electrical conductivity, salinity) and metal contamination (Fe, Mn), linked to geogenic/anthropogenic sources. Health risk assessments revealed non-carcinogenic hazards, particularly for children, with Hazard Index (HI) values reaching 2.87-far exceeding the safety threshold (HI > 1). Iron posed the greatest risk (THQ up to 2.44 for children), underscoring vulnerabilities due to physiological sensitivity. Despite 67% of respondents reporting satisfaction with water quality, stark disparities exist between perception and analytical results, driven by aesthetic/metallic concerns. Urgent interventions are needed, including infrastructure upgrades to curb pipe corrosion, advanced metal removal filtration, and community water treatment education. Policymakers must prioritize stricter industrial regulations and routine monitoring. This study highlights the imperative to align public trust with scientific evidence to safeguard health in groundwater-dependent communities.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Bioaccumulation of heavy metals in commercially important fish species from the tropical river estuary suggests higher potential child health risk than adults 98%
- The impact of rainfall on drinking water quality in Antananarivo, Madagascar 98%
- Characterisation and chemometric evaluation of 17 elements in ten seaweed species from Greenland 96%
Similar papers in this journal
- Wastewater based surveillance system to detect SARS-CoV-2 genetic material for countries with on-site sanitation facilities: an experience from Bangladesh 97%
- Response of Wastewater-Based Epidemiology Predictor for the Second Wave of COVID-19 in Ahmedabad, India: A Long-term Data Perspective 96%
- Water quality assessments and metagenomic analysis of the polluted river Apatlaco, Mexico 96%
Similar papers in this journal
Similar papers in this journal
- Effect of earthworms in removal and fate of Antibiotic resistant bacteria (ARB) and antibiotic resistant genes (ARG) during clinical laboratory wastewater treatment by vermifiltration 97%
- Implementation and Integration of Microbial Source Tracking in a River Watershed Monitoring Plan 97%
- First wastewater surveillance-based city zonation for effective COVID-19 pandemic preparedness powered by early warning: A study of Ahmedabad, India 96%
Similar papers in this journal
- Assessing the knowledge, attitudes and practices of healthcare staff and students regarding disposal of unwanted medications: A systematic review 89%
- Study Design and Rationale for the PAASIM Project, a Matched Cohort Study on Urban Water Supply Improvements and Infant Enteric Pathogen Infection, Gut Microbiome Development, and Health in Mozambique 89%
- Protocol for the PATHOME Study: A Cohort Study on Urban Societal Development and the Ecology of Enteric Disease Transmission among Infants, Domestic Animals, and the Environment 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.