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

Abstract

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 PM2.5 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 PM2.5 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, PM2.5 measurements show that living near roadways significantly increases the vulnerability to PM2.5. Fulbarigate attain a maximum PM2.5 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 PM2.5 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 (rs = 0.79), no statistically significant relation between these variables since (ƿ > 0.05), shows pollution sources are not similar, point sampled PM2.5 data are not suitable for defining overall PM2.5 zonal pollution level.