Analyzing Land Cover Correlation and Population Exposure to Air Pollutants
Geographic Information Systems Lab Project
Academic Year 2024/25 - Politecnico di Milano
This project harnesses free and open geospatial data from the Copernicus programme to study historical trends of air pollutants in relation to environmental variables and evaluate exceedance above recommended health guidelines. Using CAMS (Copernicus Atmosphere Monitoring Service) reanalysis data from 2013-2022, we analyze the spatial distribution and temporal trends of major air pollutants.
| Pollutant | EU Annual Limit | WHO AQG |
|---|---|---|
| NO₂ | 40 μg/m³ | 10 μg/m³ |
| PM2.5 | 25 μg/m³ | 5 μg/m³ |
| PM10 | 40 μg/m³ | 15 μg/m³ |
Primary air pollutant mainly from vehicle emissions and industrial activities. NO₂ contributes to respiratory problems and is a precursor to ground-level ozone formation. Our analysis covers concentration patterns, land cover correlations, and population exposure across the study area.
Particles with diameter ≤ 2.5 micrometers that can penetrate deep into lungs and bloodstream. PM2.5 is linked to cardiovascular diseases, respiratory issues, and premature mortality. We analyze its distribution patterns and relationship with urban development.
Particles with diameter ≤ 10 micrometers from various sources including dust, pollen, and combustion. PM10 affects the respiratory system and visibility. Our study examines seasonal variations and correlations with different land cover types.
Our analysis employs standard GIS techniques for data preprocessing, processing, and advanced visualization. The workflow includes temporal aggregation, spatial analysis, and bivariate mapping to reveal relationships between air quality, land cover, and population exposure.
Downloaded CAMS NetCDF files, ESA CCI land cover maps, and WorldPop population data for the study period 2013-2022.
Processed hourly data to monthly and annual averages using QGIS mesh calculator and raster processing tools.
Conducted zonal statistics, land cover reclassification, and bivariate mapping to analyze pollutant-environment relationships.
Assessed population exposure to different pollutant concentration levels using health guideline classifications.
Analyzed 2013-2022 time series data to identify long-term trends and annual average differences from 5-year means.
Created bivariate maps, concentration classifications, and interactive charts for effective communication of results.
Explore our interactive web mapping application built with GeoServer and OpenLayers. The WebGIS provides dynamic visualization of pollutant concentrations, bivariate maps, and temporal analysis tools.
This project was developed as part of the Geographic Information Systems course at Politecnico di Milano, Academic Year 2024/25.
