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Related Experiment Video

Updated: Jun 20, 2026

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
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Published on: December 12, 2025

Assessment and spatial characterization of ground-level ozone exposure using a low-cost sensor network.

Dong Gao1, Jiarong Qi2, Sarita Hudson3

  • 1Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.

Environmental Pollution (Barking, Essex : 1987)
|June 18, 2026
PubMed
Summary

This study used low-cost sensors to map ground-level ozone (O3) with machine learning in Springfield, MA. Findings reveal how local and regional factors influence ozone levels, improving air quality data accessibility.

Keywords:
Air quality monitoringData-driven methodEnvironmental justiceMachine learningRandom forestSensor calibration

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Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer
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Area of Science:

  • Environmental Science
  • Air Quality Monitoring
  • Public Health

Background:

  • Ground-level ozone (O3) is a significant air pollutant impacting public health, especially in areas with complex emissions and limited monitoring.
  • Understanding O3's spatial variability is crucial for targeted mitigation and health protection.

Purpose of the Study:

  • To investigate the hyperlocal spatial variability of ambient O3 in the Greater Springfield area, Massachusetts.
  • To develop and validate a machine learning model for O3 estimation using low-cost sensors.
  • To differentiate contributions of regional and local sources to O3 concentrations.

Main Methods:

  • Deployment of a calibrated network of 13 metal oxide semiconductor-based low-cost sensors (LCS).
  • Development of a random forest regression model for O3 calibration using a one-year dataset.
  • Analysis of sensor-derived O3 data to attribute variance to photochemical processes and local emission sources.

Main Results:

  • The machine learning model demonstrated robust O3 predictive performance (R2 = 0.82) using only sensor data.
  • Regional photochemical processes accounted for 37.7% of O3 variance, while local sources (industry, traffic) contributed 22.3%.
  • Spatial O3 patterns aligned with known emission sources and land use features, indicating model effectiveness.

Conclusions:

  • Low-cost sensor networks, when calibrated with machine learning, can effectively capture spatially resolved O3 dynamics.
  • This approach enhances air quality data accessibility, particularly in resource-limited communities.
  • Findings support targeted interventions by distinguishing between regional and local O3 influences.