Like all infectious diseases, the infection rate of COVID-19 is dependent on many variables. In order to effectively prepare a localized plan for infectious disease management, it is important to find the relationship between COVID-19 infection rate and other key variables. This study aims to understand the spatial relationships between COVID-19 infection rate and key variables of air pollution, geo-meteorological, and social parameters in Dhaka, Bangladesh. The relationship was analyzed using Geographically Weighted Regression (GWR) model and Geographic Information System (GIS) by means of COVID-19 infection rate as a dependent variable and 17 independent variables. This study revealed that air pollution parameters like PM_2.5 (p < 0.02), AOT (p < 0.01), CO (p < 0.05), water vapor (p < 0.01), and O₃ (p < 0.01) were highly correlated with COVID-19 infection rate while geo-meteorological parameters like DEM (p < 0.01), wind pressure (p < 0.01), LST (p < 0.04), rainfall (p < 0.01), and wind speed (p < 0.03) were also similarly associated. Social parameters like population density (p < 0.01), brickfield density (p < 0.02), and poverty level (p < 0.01) showed high coefficients as the key independent variables to COVID-19 infection rate. Significant robust relationships between these factors were found in the middle and southern parts of the city where the reported COVID-19 infection case was also higher. Relevant agencies can utilize these findings to formulate new and smart strategies for reducing infectious diseases like COVID-19 in Dhaka and in similar urban cities around the world. Future studies will have more variables including ecological, meteorological, and economical to model and understand the spread of COVID-19.

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.