Stockton Air Quality Dashboard

Poster Number

75

Lead Author Affiliation

Business Analytics

Lead Author Status

Masters Student

Faculty Mentor Name

mcamarillo@pacific.edu

Research or Creativity Area

Business

Abstract

  • 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.

Location

University of the Pacific, DeRosa University Center

Start Date

26-4-2025 10:00 AM

End Date

26-4-2025 1:00 PM

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Apr 26th, 10:00 AM Apr 26th, 1:00 PM

Stockton Air Quality Dashboard

University of the Pacific, DeRosa University Center

  • 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.