The characteristics of leachate and its probable risks on surface and groundwater pollution were investigated following the analysis of some chemical parameters of the leachate generated in the Matuail landfill site, Dhaka, Bangladesh. The laboratory test results on untreated grab leachate samples show high concentration of TDS (734 ppm), COD (1631 ppm), NH4 +-N (1253 ppm), HCO3 - (27962 ppm) and certain heavy metals such as Ni (1.05 ppm) and Cr (0.74 ppm) and have very high potential for contaminating ground and surface water. Biological treatment through aeration and sedimentation improves the quality of leachate significantly. However, parameters such as COD (1437 ppm) are found to be still high even after treatment. The surface water samples around the landfill site appear to be contaminated, most probably, through the overflow of leachate. Groundwater parameters, however, satisfy drinking water quality standard except, the concentration of NH4 +-N (maximum 74.2 ppm) and K (maximum 25 ppm). Key words: Leachate; Pollution; Solid waste; Landfill DOI: http://dx.doi.org/10.3329/jbas.v35i2.9418 JBAS 2011; 35(2): 153-160

Although existing studies mainly focused on the air quality status in Bangladesh, quantifying the natural and manmade effects, the frequency of high pollution levels, and the associated health risks remained beyond detailed investigation. Air quality and meteorological data from the Department of Environment for 2012–2019 were analyzed, attempting to answer those questions. Cluster analysis of PM_2.5, PM₁₀, and gaseous pollutants implied that Dhaka and neighboring cities, Narayangonj and Gazipur, are from similar sources compared to the other major cities in the country. Apart from the transboundary sources, land use types and climate parameters unevenly affected local pollution loadings across city domains. The particulate concentrations persistently remained above the national standard for almost half the year, with the peaks during the dry months. Even though nitrogen oxides remained high in all three cities, other gaseous pollutants, such as CO and O₃, except SO₂, showed elevated concentrations solely in Dhaka city. Concentrations of gaseous pollutants in Dhaka vary spatially, but no statistical differences could be discerned between the working days and holidays. Frequency analysis results and hazard quotients revealed the likelihood of adverse health outcomes in Narayangonj ensuing from particulate exposures surpasses the other cities for different age, gender, and occupation groups. Nonetheless, school-aged children and construction workers were most at risk from chronic exposure to gaseous pollutants mostly in Dhaka. One limitation of this study was that the routine air quality monitoring happens just from five sites, making the evidence-based study concerning health outcomes quite challenging.

Water chemistry data (1965–2015) from the Chicot and Evangeline aquifers in Nueces and Kleberg counties of Texas were analyzed to assess the hydrogeochemical processes based on variations in ion concentrations affecting groundwater salinity levels in those coastal aquifers. A further aim of this study was to identify a proxy using major-ion concentrations to predict groundwater salinity. Correlation analysis suggests that the hydrogeochemical processes operating in these aquifers differ significantly. In principal component analyses, the first three principal components explain 91% and 94.8% of the total variabilities of the variation of groundwater chemistry in the Evangeline and Chicot aquifers, respectively. The rotated factors (factors 1, 2, and 3) using the varimax rotation method implied that depth control and salinity variations predominantly cause the highest variabilities in water chemistry in the Chicot and Evangeline aquifers in the study area. Results showed depth control on water quality parameters was more pronounced in the Evangeline aquifer compared to the Chicot aquifer. Stepwise elimination of the least significant predictors to identify the proxy for groundwater salinity revealed chloride (Cl) could be the most significant predictor to estimate groundwater salinity variations in both aquifers. However, regression models generated from 75% of the training datasets predicted total dissolved solid (TDS) variations with 78% and 43% accuracies in Chicot and Evangeline aquifers, indicating that Cl can be considered the proxy for the Chicot aquifer only but not suitable for the Evangeline aquifer in the study area.

This study examined how socio-economic driving forces influence households’ choice of water, ranging from a piped water supply provided by Veolia to untreated sources contaminated with high levels of arsenic and pathogens. Households fall into three cluster groups based on variations in socio-economic status and physical, infrastructure, and institutional elements. About 64% of the variations are related to differences in awareness, willingness, and ability to pay for safe water sources. Families with higher monthly income showed interest in accepting Veolia’s house connection options, resulting in the shutdown of six community tap points and ultimately affecting the low-income households’ accessibility to Veolia water. A causal loop diagram showed five feedback loops influencing the choice of drinking contaminated water. Bayesian Network models were sensitive to the ability, accessibility, and willingness to pay for safe water, deep tube well distributions, installation and maintenance costs, ownership of tube wells, household income level, and the level of awareness. Results suggest that the risks of drinking contaminated water can be minimized by raising awareness; accepting arsenic removal techniques; sharing expenses; training for deep tube well installations and maintenance; increasing Veolia pipeline coverage; and redesigning the tap point distributions. These results help identify the relative importance of such interventions to improve water security in safe water-poor areas.

Existing literature reported high concentrations of ambient particulates, specifically during the dry months, severely affecting street and construction site workers, elderly, and school-aged children in Dhaka city, Bangladesh. The available air quality data from only three continuous air monitoring stations is inadequate for any evidence-based study regarding public health outcomes. Significant knowledge gaps exist due to air monitoring networks being limited to low-cost optical sensors, key suburban areas, slum areas, and industrial areas remaining uncovered and little information about sources contributing to air pollution. Therefore, this ongoing air monitoring study aimed to conduct a city-wide measurement campaign of fine particulate matter (PM2.5) and black carbon (BC), investigate source contributors to air pollution using source apportionment analysis, and produce spatiotemporal land use regression (LUR) models to estimate PM2.5 and BC concentrations across the city. The study team has recently conducted two seasonal (two months each) air monitoring campaigns from systematically designed eight fixed sites and sixty-one rotating sites considering the major land use classes across the city domain. Sites covered different land use types such as commercial, residential, industrial, suburban, major roads, green space, and brick kilns. The significant challenges during the air monitoring campaign included high road traffic from religious congress, political protests, and waterlogging from sudden intense rainfall. Weather conditions such as high heat and heavy rain affected the functioning and performance of equipment. Besides, exposure of the field team to dengue outbreaks, particularly during the wet season, had to be dealt with. Preliminary results showed that the unadjusted concentration of PM2.5 from the Zefan sensors was substantially higher (often exceeded the WHO 24-hour standard) in the dry season compared to the wet season across the different monitoring sites. Concentrations were also higher during nighttime compared to daytime in both seasons, and this difference was much more pronounced in the dry season. The fixed site in a significant industrial area, Shyampur, showed the highest concentrations compared to the other sites during both seasons, with a dry season average of approximately 290 ug/m3 and a wet season average of about 175 ug/m3. The real-time PM2.5 data will be further validated with filter-based gravimetric measurements for quality assurance. Filters are currently being analyzed for mass, black carbon, and chemical composition in a geochemistry laboratory. Incorporating source apportionment analysis and land use-based regression models of the datasets will support source identification. This will help to improve air pollution mitigation policies and implementation plans for reducing pollution while targeting its sources. The ultimate findings of this research will be conducive to assessing public health outcomes by incorporating socio-economic, demographic, and health data with the air quality data from this study, which is much needed in formulating an improved public health policy.