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Suitability ModelingRaster AnalysisPublic Health

Mapping Air Pollution & Public Health Risk in India

Role
Sole analyst & cartographer
Affiliation
GEOG 4xx — Advanced Spatial Analysis
Date
2025
Tools
ArcGIS ProPython (arcpy)Spatial Analyst
Research question

Where do elevated PM2.5 concentrations overlap with vulnerable populations to create the highest public-health risk?

Objective

Combine remotely-sensed pollution estimates with demographic exposure to produce a transparent, district-ranked risk index that planners can act on.

Data
Van Donkelaar et al. surface PM2.5 (annual, 0.01°)source ↗WorldPop population density, Indiasource ↗GADM administrative boundaries (district)source ↗
Methodology / Workflow
  1. 1

    Resampled and clipped the PM2.5 raster to the national boundary; reprojected all layers to a common equal-area CRS.

  2. 2

    Reclassified PM2.5 and population density into 1–5 suitability scales against WHO and national thresholds.

  3. 3

    Applied a weighted overlay (PM2.5 0.6, population 0.4) to generate a continuous risk surface.

  4. 4

    Aggregated zonal statistics to district polygons and ranked the top decile of exposure.

Maps & results

Click any map to enlarge

Fig. 1 — Annual mean PM2.5 surface, classified to WHO interim targets.
Fig. 2 — Weighted-overlay composite risk surface.
Fig. 3 — District ranking, top-decile exposure highlighted.
Live ArcGIS web map
Findings