Associations between climate variables and water quality in low- and middle-income countries: A scoping review

Comprehensive analysis and human health risk assessment of tap water quality in Dhaka City, Bangladesh: Integrating source identification, index-based evaluation, and heavy metal assessment

Despite potential contamination, tap water remains the primary source of drinking in megacities. However, the sources of heavy metal(oid)s contamination and associated health hazards have not been thoroughly addressed in many developing cities, including Dhaka. Therefore, we made the first attempt to assess tap water quality in Dhaka City using indices, identify pollution sources with state-of-the-art techniques, and quantify associated health risks. Tap water samples from 35 locations were collected and analyzed for physicochemical properties and heavy metal(loid)s concentrations. While most parameters were within acceptable ranges, Hg (1.18 ± 0.15 µg/L) exceeded safety thresholds with concerns for Mn (51.08 ± 2.3 µg/L) and Fe (177.34 ± 5.6 µg/L). The calculated indices indicated that Dhaka City's tap water ranged from unfit (Heavy Metal Evaluation Index, HEI: 2.61), very poor (Heavy Metal Pollution Index, HPI: 95.67, Water Quality Index, WQI: 37.76), moderately affected (Metal Index, MI: 2.61, Synthetic Pollution Index, SPI: 0.96) to slightly polluted (Single-factor Index, Pij: 2.61, Nemerow Pollution Index, NPI: 1.41), healthy but not tasty (Taste Index, TI: 1.8, Health Index, HtI: 10.41), with Matuail, followed by Jurain, being the most contaminated. Non-carcinogenic health risk (NCR) values revealed that children were twice as susceptible to health risks from Pb, Co, and As. Alongside, Carcinogenic health hazards risk (CR) expressed potential cancer risks from Cr Collapse

Key Words

• Contamination
• DWASA
• Illegal industrial discharge
• Threshold
• Toxicity

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MESH Headings

• Bangladesh
• Metals, Heavy/analysis
• Metals, Heavy/toxicity
• Risk Assessment
• Humans
• Water Pollutants, Chemical/analysis
• Water Pollutants, Chemical/toxicity
• Drinking Water/analysis
• Water Quality
• Environmental Monitoring
• Cities
• Water Supply

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Affiliation(s)

Tasrif Nur Ariyan Department of Environmental Science, Bangladesh University of Professionals, Mirpur Cantonment, Dhaka 1216, Bangladesh
Shamshad Begum Quraishi Planning and Development Division, Bangladesh Atomic Energy Commission (BAEC), Dhaka 1207, Bangladesh
Md Nur E Alam Atomic Energy Centre (AEC), Shahbagh, Dhaka 1000, Bangladesh
Muhammad Shahidur Rahman Khan Atomic Energy Centre (AEC), Shahbagh, Dhaka 1000, Bangladesh
Farzana Ferdous Faria Department of Environmental Science, Bangladesh University of Professionals, Mirpur Cantonment, Dhaka 1216, Bangladesh
Alamgir Kabir Department of Environmental Science, Bangladesh University of Professionals, Mirpur Cantonment, Dhaka 1216, Bangladesh.

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Associations between weather extremes and faecal contamination along pathogen transmission pathways in rural Bangladeshi households: a prospective observational study

BACKGROUND

Weather extremes are predicted to influence pathogen exposure but their effects on specific faecal-oral transmission pathways are not well investigated. We evaluated associations between extreme rain and temperature during different antecedent periods (0-14 days) and Escherichia coli along eight faecal-oral pathways in rural Bangladeshi households.

METHODS

We used data from the WASH Benefits Bangladesh cluster-randomised controlled trial (NCT01590095). E coli was enumerated in hand rinses from children younger than 5 years and their mothers, food, stored drinking water, tubewells, captured flies, ponds, and courtyard soil using IDEXX Quanti-Tray/2000 in nine rounds over 3·5 years and spatiotemporally matched to daily weather data. We used generalised linear models with robust standard errors to estimate E coli count ratios (ECRs) associated with extreme rain and temperature, defined as greater than the 90th percentile of daily values during the study period.

FINDINGS

A total of 26 659 samples were collected during the study period. Controlling for temperature, extreme rain on the sampling day was associated with increased E coli in food (ECR=3·13 [95% CI 1·63-5·99], p=0·0010), stored drinking water (ECR=1·98 [1·36-2·88], p=0·0004), and ponds (ECR=3·46 [2·34-5·11], p<0·0001), and reduced E coli in soil (ECR=0·36 [0·24-0·53], p<0·0001). Extreme rain the day before sampling was associated with reduced E coli in tubewells (ECR=0·10 [0·02-0·62], p=0·014). Associations were similar for rainfall 1-7 days before sampling and slightly attenuated for rainfall 14 days before sampling. Controlling for rainfall, extreme temperature on the sampling day was associated with increased E coli in stored drinking water (ECR=1·49 [1·05-2·12], p=0·025) and food (ECR=3·01 [1·51-6·01], p=0·0020). Associations with temperature were similar for all antecedent periods and particularly pronounced for food. Neither rainfall nor temperature were consistently associated with E coli on hands and flies.

INTERPRETATION

In rural Bangladesh, measures to control enteric infections following weather extremes should focus on water treatment and safe storage to reduce contamination of drinking water and food stored at home and on reducing exposure to surface waters.

FUNDING

Bill & Melinda Gates Foundation, National Institutes of Health, World Bank.

Assessment of water quality and emerging pollutants in two fish species from the mallorquin swamp in the Colombian Caribbean

The Mallorquín Swamp, an important ecosystem in Atlántico, Colombian Caribbean, underwent environmental monitoring at eight points during rainy, transition, and dry seasons. This was to assess water quality, seasonal variation, and the bioaccumulation of metals, emerging pollutants, and organic compounds in the fish Ariopsis canteri and Mugil incilis. Water parameters were analyzed using descriptive statistics and multifactorial ANOVA with the Tukey HSD test for seasonal differences. Normality and variance of the fish results were verified, and differences between groups were evaluated using ANOVA or Kruskal-Walli's method when data transformation failed. Spearman correlation was used to relate the results. Water sampling revealed variations in temperature, dissolved oxygen, salinity, and nutrient levels. Significant differences in alkalinity and hardness were observed across seasons and sample points. The most probable number (MPN) levels of Total coliform and E. coli peaked near areas with domestic wastewater inputs, reaching 5.4x106 and 4.0x106 MPN, respectively, indicating potential microbiological contamination of water. Fish samples revealed high concentrations of persistent substances such as methylmercury, polycyclic aromatic hydrocarbons (PAHs), and emerging pollutants. Heavy metal analysis showed elevated iron levels (5.28 ± 0.657 mg/L), while emerging pollutants, including ibuprofen (218 μg/L) and naproxen (343.89 μg/L), exhibited high concentrations near human settlements. Ariopsis canteri showed higher bioconcentration tendencies for methylmercury (238.5 ± 100 μg/kg), and acenaphthene (7782 ± 4123.8 μg/kg), possibly influenced by its feeding habits and habitat preferences. In contrast, Mugil incilis exhibited higher bioaccumulation trends of PAH (2376.23 ± 599.63 μg/kg acenaphthene) and emerging pollutants like galaxolide (139.49 ± 34.98 μg/kg), possibly due to its mobility and exposure to various contaminants in their environment. These findings emphasize the need to monitor and manage aquatic ecosystems' health to mitigate anthropogenic influences on water quality and biodiversity. This research serves as a reference for global conservation efforts, emphasizing the need for comprehensive monitoring and regulatory frameworks to protect aquatic environments and ensure their sustainability for future generations.

Wastewater surveillance for viral pathogens: A tool for public health

A focus on water quality has intensified globally, considering its critical role in sustaining life and ecosystems. Wastewater, reflecting societal development, profoundly impacts public health. Wastewater-based epidemiology (WBE) has emerged as a surveillance tool for detecting outbreaks early, monitoring infectious disease trends, and providing real-time insights, particularly in vulnerable communities. WBE aids in tracking pathogens, including viruses, in sewage, offering a comprehensive understanding of community health and lifestyle habits. With the rise in global COVID-19 cases, WBE has gained prominence, aiding in monitoring SARS-CoV-2 levels worldwide. Despite advancements in water treatment, poorly treated wastewater discharge remains a threat, amplifying the spread of water-, sanitation-, and hygiene (WaSH)-related diseases. WBE, serving as complementary surveillance, is pivotal for monitoring community-level viral infections. However, there is untapped potential for WBE to expand its role in public health surveillance. This review emphasizes the importance of WBE in understanding the link between viral surveillance in wastewater and public health, highlighting the need for its further integration into public health management.

Saltwater intrusion and human health risks for coastal populations under 2050 climate scenarios

Populations consuming saline drinking water are at greater risk of high blood pressure and potentially other adverse health outcomes. We modelled data and used available datasets to identify countries of higher vulnerability to future saltwater intrusion associated with climate change in 2050 under Representative Concentration Pathways (RCP)4.5 and RCP8.5. We developed three vulnerability criteria to capture geographies with: (1) any coastal areas with projected inland saltwater intrusion of ≥ 1 km inland, (2) > 50% of the population in coastal secondary administrative areas with reliance on groundwater for drinking water, and 3) high national average sodium urinary excretion (i.e., > 3 g/day). We identified 41 nations across all continents (except Antarctica) with ≥ 1 km of inland saltwater intrusion by 2050. Seven low- and middle-income countries of higher vulnerability were all concentrated in South/Southeast Asia. Based on these initial findings, future research should study geological nuances at the local level in higher-risk areas and co-produce with local communities contextually appropriate solutions to secure equitable access to clean drinking water.

Influence of Temperature and Precipitation on the Effectiveness of Water, Sanitation, and Handwashing Interventions against Childhood Diarrheal Disease in Rural Bangladesh: A Reanalysis of the WASH Benefits Bangladesh Trial

BACKGROUND

Diarrheal disease is a leading cause of childhood morbidity and mortality globally. Household water, sanitation, and handwashing (WASH) interventions can reduce exposure to diarrhea-causing pathogens, but meteorological factors may impact their effectiveness. Information about effect heterogeneity under different weather conditions is critical to refining these targeted interventions.

OBJECTIVES

We aimed to determine whether temperature and precipitation modified the effect of low-cost, point-of-use WASH interventions on child diarrhea.

METHODS

We analyzed data from a trial in rural Bangladesh that compared child diarrhea prevalence between clusters (N = 720 ) that were randomized to different WASH interventions between 2012 and 2016 (NCT01590095). We matched temperature and precipitation measurements to diarrhea outcomes (N = 12,440 measurements, 6,921 children) by geographic coordinates and date. We estimated prevalence ratios (PRs) using generative additive models and targeted maximum likelihood estimation to assess the effectiveness of each WASH intervention under different weather conditions.

RESULTS

Generally, WASH interventions most effectively prevented diarrhea during monsoon season, particularly following weeks with heavy rain or high temperatures. The PR for diarrhea in the WASH interventions group compared with the control group was 0.49 (95% CI: 0.35, 0.68) after 1 d of heavy rainfall, with a less-protective effect [PR = 0.87 (95% CI: 0.60, 1.25)] when there were no days with heavy rainfall. Similarly, the PR for diarrhea in the WASH intervention group compared with the control group was 0.60 (95% CI: 0.48, 0.75) following above-median temperatures vs. 0.91 (95% CI: 0.61, 1.35) following below-median temperatures. The influence of precipitation and temperature varied by intervention type; for precipitation, the largest differences in effectiveness were for the sanitation and combined WASH interventions.

DISCUSSION

WASH intervention effectiveness was strongly influenced by precipitation and temperature, and nearly all protective effects were observed during the rainy season. Future implementation of these interventions should consider local environmental conditions to maximize effectiveness, including targeted efforts to maintain latrines and promote community adoption ahead of monsoon seasons. https://doi.org/10.1289/EHP13807.

Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty

Two fundamental problems have inhibited progress in the simulation of river water quality under climate (and other) uncertainty: 1) insufficient data, and 2) the inability of existing models to account for the complexity of factors (e.g., hydro-climatic, basin characteristics, land use features) affecting river water quality. To address these concerns this study presents a technique for augmenting limited ground-based observations of water quality variables with remote-sensed surface reflectance data by leveraging a machine learning model capable of accommodating the multidimensionality of water quality influences. Total Suspended Solids (TSS) can serve as a surrogate for chemical and biological pollutants of concern in surface water bodies. Historically, TSS data collection in the United States has been limited to the location of water treatment plants where state or federal agencies conduct regularly-scheduled water sampling. Mathematical models relating riverine TSS concentration to the explanatory factors have therefore been limited and the relationships between climate extremes and water contamination events have not been effectively diagnosed. This paper presents a method to identify these issues by utilizing a Long Short-Term Memory Network (LSTM) model trained on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite reflectance data, which is calibrated to TSS data collected by the Ohio River Valley Water Sanitation Commission (ORSANCO). The methodology developed enables a thorough empirical analysis and data-driven algorithms able to account for spatial variability within the watershed and provide effective water quality prediction under uncertainty.

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009-2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.

Exploration of the factors that influence total phosphorus in surface water and an evaluation of surface water vulnerability based on an advanced algorithm and traditional index method

Due to the continuous influence of human activities, phosphorus pollution in surface water has become a persistent problem that needs to be addressed since phosphorous entails certain risks and degrees of damage to ecosystems and humans. The presence and accumulation of total phosphorus (TP) concentrations in surface waters is the result of a combined effect of many natural and anthropogenic factors, and it is often difficult to intuitively identify the individual importance of each factor in regard to the pollution of the aquatic environment. Considering these issues, this study provides a new methodology to better understand the vulnerability of surface water to TP pollution and the factors that influence TP pollution through the application of two modeling approaches. This includes the boosted regression tree (BRT), an advanced machine learning method, and the traditional comprehensive index method (CIM). Different factors, such as natural variables (including slope, soil texture, normalized difference vegetation index (NDVI), precipitation, and drainage density) and point and nonpoint source anthropogenic factors were included to model the vulnerability of surface water to TP pollution. Two methods were used to produce a vulnerability map of surface water to TP pollution. Pearson correlation analysis was used to validate the two vulnerability assessment methods. The results showed that BRT was more strongly correlated than CIM. In addition, the importance ranking results showed that slope, precipitation, NDVI, decentralized livestock farming and soil texture had a greater influence on TP pollution. Industrial activities, scale livestock farming and population density, which are all contributing sources of pollution, were all relatively less important. The introduced methodology can be used to quickly identify the area most vulnerable to TP pollution and to develop problem specific adaptive policies and measures to reduce the damage from TP pollution.

Modeling, challenges, and strategies for understanding impacts of climate extremes (droughts and floods) on water quality in Asia: A review

The increasing frequency and intensity of extreme climate events are among the most expected and recognized consequences of climate change. Prediction of water quality parameters becomes more challenging with these extremes since water quality is strongly related to hydro-meteorological conditions and is particularly sensitive to climate change. The evidence linking the influence of hydro-meteorological factors on water quality provides insights into future climatic extremes. Despite recent breakthroughs in water quality modeling and evaluations of climate change's impact on water quality, climate extreme informed water quality modeling methodologies remain restricted. This review aims to summarize the causal mechanisms across climate extremes considering water quality parameters and Asian water quality modeling methods associated with climate extremes, such as floods and droughts. In this review, we (1) identify current scientific approaches to water quality modeling and prediction in the context of flood and drought assessment, (2) discuss the challenges and impediments, and (3) propose potential solutions to these challenges to improve understanding of the impact of climate extremes on water quality and mitigate their negative impacts. This study emphasizes that one crucial step toward enhancing our aquatic ecosystems is by comprehending the connections between climate extreme events and water quality through collective efforts. The connections between the climate indices and water quality indicators were demonstrated to better understand the link between climate extremes and water quality for a selected watershed basin.

Sustainable Management of the African Great Lake Coastal Areas: Motivations and Perspectives of Community Citizen Scientists

The long-term sustainability of the African Great Lakes is strongly connected to the management and monitoring of their coastal areas. Yet, the communities that live in these areas are rarely involved in monitoring and have limited influence on key management issues. Furthermore, regulatory activities and knowledge sharing in these transnational ecosystems are strongly limited by funding and infrastructure limitations. Citizen science has great potential to advance both scientific and public understanding of the state of the environment. However, there remains a limited understanding of participants' motivations and expectations, especially in developing countries, where citizen science has great potential to complement regulatory monitoring. The present study explores the motivations of citizen scientists in villages along Lake Tanganyika's northern coast and their potential to take a more active role in lake management. Motivations were examined through qualitative interviews, focus groups, and quantitative surveys with 110 citizen scientists and 110 non-citizen scientists from participating villages. Key motivational factors identified were the desire to contribute to scientific research and local knowledge, as well as aspects of financial compensation. The results confirm that participation in citizen science provides many benefits to participants beyond their role as data aggregators and final knowledge users. However, the incentives to participation varied to those typically considered in citizen science programs conducted in developed countries. To create sustainable long-term community based environmental monitoring, these motivations should be incorporated in the program design and participant recruitment.

Evaluation of water safety plan implementation at provincial water utilities in Vietnam

This study evaluated the experience of implementing water safety plans (WSPs) in Vietnam. WSPs were introduced in Vietnam by the World Health Organization (WHO) in collaboration with the Ministry of Construction in 2006 and have been a mandatory requirement for municipal water supplies since 2012. Using a mixed-methods approach, we collected data on the perceived benefits and challenges of WSP implementation from 23 provincial water companies between August and November 2021. Potential public health benefits of improved water quality were a key motivation; 87% of the water utilities were also motivated by the risk of climate change and prepared response plans to climate-related extreme events as part of WSPs. A decrease in E. coli and an improvement in disinfectant residual in treated water were reported by 61 and 83% of the water supplies, respectively. Sixty-five percent of the water supplies also reported improved revenue and cost recovery. Key barriers to WSP implementation were a lack of WSP guidance suitable for the local context (87%) and insufficient funds for WSP implementation (43%). Our study highlights the need for improved support and capacity building along with locally suited guidance on WSP implementation and audit.

Evaluation of water quality at national scale from 2011 to 2021: Advances and challenges

More environmental policies and larger investments in protecting the aquatic environment in China have been made in the last decade than previously. It is important to assess how this will affect river water quality. Here, changes in water quality in China between 2011 and 2021 are assessed. Water bodies meeting class III or better defined in the Chinese Environmental Quality Standards for Surface Water (GB3838-2002) were labeled WQI, water bodies meeting class V or better but below class III were labeled WQII, and water bodies below class V were labeled WQIII. The percentage of WQI water bodies increased from 66.1 % in 2011 to 81.0 % in 2021, and the percentages of WQII and WQIII water bodies decreased between 2011 and 2021. The percentage of WQI water bodies increased more quickly and the percentage WQIII water bodies decreased more quickly after 2017 than between 2011 and 2016. The percentages of WQI water bodies in the Northwest River Basin (RB), Pearl RB, Southeast RB, Southwest RB, and Yangtze RB were >80 %, and were higher than the percentages of WQI water bodies in the other five RBs. The percentages of WQI and WQII water bodies increased but the percentage of WQIII water bodies decreased in the Hai RB. The percentage of WQI water bodies increased but the percentages of WQII and WQIII water bodies decreased in the Huai RB, Liao RB, Yangtze RB, and Yellow RB. The river monitoring capacity increased and pollution sources, particularly point sources, became more controlled, and this improved river water quality. River management in China has passed the first stage of controlling pollution sources after 10 years of centralized management. The next stage should be focused on strengthening control of non-point sources of pollution and rehabilitating ecological systems to improve river health.

Assessing the Climate Resilience of Community-Managed Water Supplies in Ethiopia and Nepal

Understanding the resilience of water supplies to climate change is becoming an urgent priority to ensure health targets are met. Addressing systemic issues and building the resilience of community-managed supplies, which serve millions of people in rural LMIC settings, will be critical to improve access to safe drinking water. The How Tough is WASH (HTIW) framework to assess resilience was applied to community-managed water supplies in Ethiopia and Nepal to assess the effectiveness of this framework in field conditions. The resilience of these water supplies was measured along six domains—the environment, infrastructure, management, institutional support, community governance and supply chains—that can affect how they respond to climate change effects. We found that the HTIW framework provided an objective measure of resilience and could be used to rank water supplies in order of priority for action. We also found that systemic issues could be identified. The tools and methods used in the framework were easy to deploy by field research teams. The water supplies studied in Ethiopia and Nepal had low to moderate resilience to climate change. Service management and institutional support were weak in both countries. The data from Ethiopia and Nepal suggests that many water supplies in rural and small-town communities are unlikely to be resilient to future climate change without increased investment and support. The use of simple frameworks such as HTIW will be important in supporting decisions around such investments by identifying priority communities and actions.