Measuring and modeling PM2.5 zonal distributions, assembling geospatial and meteorological variables in the Khulna metropolitan area.

In this study, we used the built-in model builder tool with other spatial, meteorological parameters as inputs in the GIS system to identify spatial distribution and concentrations of PM 2.5 pollutants for the Khulna Metropolitan area. Then, a spatial Inter distance weighting (IDW) method was applied to the monitored data to further validate the model. Measurements and predictions of mean PM 2.5 at the Shonadanga area surpassed other Khulna regions due to the influence of low NDVI (around 0.06), elevated LST (26.05 °C), and (near 6 °C) slope variations. Besides, PM 2.5 measurements show that living near roadways significantly increases the vulnerability to PM 2.5. Fulbarigate attain a maximum PM 2.5 hourly peak of 148 μg/m³ in the 2021 late winter (when trains move and car breaking coincides), attributed to the re-suspended road dust and diesel rail emissions. Our model predicted PM 2.5 against monitored values in spatial interpolation showing significantly higher deviations in the Shonadanga and Fulbarigate zones, with approximately 77% to 92%. Although strong associations were observed between satellite-derived zonal measurements and point sampled data (r s = 0.79), no statistically significant relation between these variables since (ƿ > 0.05), shows pollution sources are not similar, point sampled PM 2.5 data are not suitable for defining overall PM 2.5 zonal pollution level. • A new hybrid geospatial model for PM 2.5 distributions has been studied. • Model predicted near 91% to 31% less zonal value than ground point measurements. • Meteorological parameters, LST, and NDVI changes are crucial in PM 2.5 levels. • Results showed that the highest prevalence of PM 2.5 in Khulna is in winter. [ABSTRACT FROM AUTHOR]