Stockton Air Quality Dashboard

Problem: Neighborhood‐scale heterogeneity in air quality—driven by shading, surface albedo, moisture, and land use—is masked by city-wide monitors, and community perceptions of pollution hotspots are underreported. Background: Capturing spatial and social heterogeneity is critical to designing equitable adaptation measures for urban heat and air quality . Methods: We integrated: EPA daily PM₂.₅ & O₃ data (Jan 2022–Nov 2024), Three Arduino/PurpleAir PM₂.₅ sensors (Aug 2024–present), Three “egg” O₃ devices measuring O₃, temperature, humidity (Aug 2024–present), Visual Crossing temperature feeds and GIS layers (canopy, albedo). Data flows into a Power BI dashboard with spatial maps, time-series views, and “what-if” mitigation modeling. A street-intercept survey (N = 60) captures hotspot perceptions. Status/Key Insights: Dashboard and data model are fully implemented. Early spatial mapping reveals elevated PM₂.₅ zones near industrial corridors; community surveys show strong alignment between perceived and measured hotspots. Implications: This hyperlocal dashboard empowers stakeholders to prioritize adaptation strategies where they’ll yield the greatest public-health benefit.