Air Pollution of Alabama and Correlation with Population using GIS Interpolation

This project explored the spatial relationship between PM2.5 air pollution levels and population density across counties in Alabama using ArcGIS Pro. Key GIS techniques included data integration, spatial interpolation using the Inverse Distance Weighting (IDW) method, and zonal statistics to analyze air quality data from the USEPA alongside population data from the US Census. The analysis produced several maps, including the spatial distribution of PM2.5 monitoring stations, interpolated pollution surfaces for 2018 and 2020, and a population density map categorized using the equal interval classification method. Results showed that while Montgomery had the highest PM2.5 levels (11.12 µg/m³), this did not consistently correlate with population density across other counties, indicating that additional factors like industrial activity and geography also influence air quality. A temporal comparison between 2018 and 2020 revealed minimal change in pollution levels during the COVID-19 pandemic, challenging assumptions about decreased human activity leading to cleaner air. The project highlights how GIS enables effective spatial analysis and visualization for environmental assessment, supporting urban planning and public health decision-making.