A major portion of Bangladesh is currently experiencing a scarcity of safe drinking water because of arsenic contamination, high salinity and human-induced pollution. The objectives of this study were to identify locations with a high scarcity of drinking water and suitability of harvesting rainwater. Kriging interpolation algorithms of Geographical Information System (GIS) was employed to identify the probable water scarce zones as well as suitable zones of harvesting rain water from the available data of secondary sources. Statistical methods were employed to cluster, correlate, and regress variables such as rainfall, salinity, and As. The results showed that groundwater quality in the southwestern parts of Bangladesh is saline with high concentration (>10000 μS/cm). On the other hand, the northeastern and southwestern parts of Bangladesh are also vulnerable to arsenic contamination (60 %–97 % of tubewells), compared to other regions. The rainfall zonation map, covering the years 1951–2022, indicated that the Sylhet division had the highest potential for rainfall (ranging from 2600 to 3900 mm). From this study it was demonstrated that Sylhet, Noakhali, Bhola, Barishall, Patuakhali, Bagerhat, and Khulna were identified as suitable places for sustainable rainwater harvesting (RWH). The findings of this study may play significant role towards achieving sustainable potable water supply in vulnerable zones, if they receive attention from policymakers.

The major industrial cities of Bangladesh are experiencing significant air-pollution-related problems due to the increased trend of particulate matter (PM_2.5) and other pollutants. This paper aimed to investigate and understand the relationship between PM_2.5 and land use and climatic variables to identify the riskiest areas and population groups using a geographic information system and regression analysis. The results show that about 41% of PM_2.5 concentration (μg/m³) increased within 19 years (2002–2021) in the study area, while the highest concentration of PM_2.5 was found from 2012 to 2021. The concentrations of PM_2.5 were higher over barren lands, forests, croplands, and urban areas. From 2002–2021, the concentration increased by about 64%, 62.7%, 57%, and 55% (μg/m³) annually over barren lands, forests, cropland, and urban regions. The highest concentration level of PM_2.5 (84 μg/m³) among other land use classes was found in urban areas in 2021. The regression analysis shows that air pressure (hPa) (r² = −0.26), evaporation (kg m⁻²) (r² = −0.01), humidity (kg m⁻²) (r² = −0.22), rainfall (mm/h) (r² = −0.20), and water vapor (kg m⁻²) (r² = −0.03) were negatively correlated with PM_2.5. On the other hand, air temperature (k) (r² = 0.24), ground heat (W m⁻²) (r² = 0.60), and wind speed (m s⁻¹) (r² = 0.34) were positively correlated with PM_2.5. More than 60 Upazilas were included in the most polluted areas, with a total population of 11,260,162 in the high-risk/hotspot zone (1,948,029 aged 0–5, 485,407 aged 50–69). Governmental departments along with policymakers, stainable development practitioners, academicians, and others may use the main results of the paper for integrated air pollution mitigation and management in Bangladesh as well as in other geographical settings worldwide.

A cross-sectional study of climate change vulnerability (CCV) on 205 ultra-poor households of Bauphal Upazila of southern coastline were conducted. Exposure, sensitivity and adaptation practices were evaluated for this study. About 70% families of treatment group provided positive feedback to adjust to CCV. The endline CCV had dropped by 18% compared to baseline score. Exposure and adaptable capacity had grown by 14% and 13%, respectively. Besides, endline sensitivity was dropped 11% than baseline score. The adaptation indices for social organizations, personal savings, training on climate change topics, and loan facilities from various bank types were all high (>0.90). Significant progress has been made in the community's volunteerism for local development projects, during disasters. Under the treatment condition, there was a significant improvement in livestock farming (0.20), planted trees (0.34), rainwater harvesting (0.23), GO-NGOs' support (0.37), and social event attendance (0.34). However, understanding of farm services (0.107) and the early warning system/messages (0.14) showed a discernible improvement. The sequence of adaptation measures to climate change are as follows: livelihood>livestock>social and financial security>health-sanitation>water security>knowledge on comprehension warning systems,> social inclusivity. The results of this study may assist policymakers in developing appropriate measures prior developing a disaster management framework for coastal areas.