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Updated: Sep 15, 2025

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Incorporating Community Knowledge Into Analysis of Air Quality Monitoring Network Data.

R Gardner-Frolick1, S Jain1, N Martinussen2

  • 1Department of Mechanical Engineering University of British Columbia Vancouver BC Canada.

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|July 18, 2025
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Summary
This summary is machine-generated.

Community knowledge significantly improved air quality models by identifying pollution sources missed by traditional data. This pilot study integrated resident input for better spatial and temporal air pollution assessments.

Keywords:
air pollutioncitizen sciencecommunity knowledgeland use regressionlow‐cost sensors

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Area of Science:

  • Environmental Science
  • Public Health
  • Community-Based Participatory Research

Background:

  • Accurate air quality assessment is crucial for public health.
  • Traditional methods often miss localized, transient pollution sources.
  • Integrating community knowledge can enhance spatial and temporal air quality pattern analysis.

Purpose of the Study:

  • To explore methods for incorporating qualitative community knowledge into quantitative air quality assessments.
  • To compare traditional land use regression (LUR) models with community-informed LUR models.
  • To identify the value of community-reported data in understanding air pollution patterns.

Main Methods:

  • Deployment of a low-cost sensor network measuring nitrogen oxides (NO, NO 2) and fine particulate matter (PM2.5).
  • Utilization of community knowledge from surveys, odor reports, and mapping events.
  • Development and comparison of traditional and community-informed land use regression (LUR) models.

Main Results:

  • Community-informed LUR models showed improved fit for NO 2 and NOx compared to traditional models.
  • Community knowledge identified key pollution sources like vehicle idling, construction, and wood stoves.
  • Discrepancies between reported events and monitor data suggest correlation with unmeasured pollutants.

Conclusions:

  • Qualitative community knowledge offers valuable insights into uncaptured air pollution sources.
  • Community-based approaches can enhance the accuracy of air quality modeling.
  • Future research should focus on accessible methods for collecting community pollution data.