Evaluating Fine-Scale Air-Quality Heterogeneity Using A Low-Cost Multipollutant Sensor Network In Twin Cities, Minnesota

Author

• Varuni Abhayaratne
• Weixing Hao
• Chen Ye
• Anjana Yatawara
• Philip K. Hopke
• Jiayu Li
• Yang Wang, Missouri University of Science and Technology

Abstract

There is growing interest and effort in deploying low-cost sensors (LCSs) to understand fine-scale spatiotemporal heterogeneity of ambient air pollutants in urban areas. The Assessing Urban Air Quality project conducted by the Minnesota Pollution Control Agency deployed 45 low-cost air-quality monitors (AQMesh, Environmental Instruments Ltd.) in the Minneapolis/St. Paul metropolitan area network. Hourly averaged pollutant concentrations or mixing ratios for carbon monoxide (CO), ozone (O₃), nitrogen dioxide (NO₂), nitric oxide (NO), and sulfur dioxide (SO2), as well as particulate matter (PM), including PM1, PM2.5, and PM10, were obtained. The network data from June 2019 to June 2021 were analyzed for temporal air-quality variations related to traffic patterns and atmospheric chemistry. Compared to the PM concentrations, gaseous pollutant (CO, NO₂, O₃, and SO₂) mixing ratios among the different sites showed higher correlations (higher pairwise Pearson r² values). However, pairwise coefficients of divergence (COD) show higher spatial heterogeneity for O₃ and NO₂. The conditional bivariate probability function coupling the sensor and meteorological data can partly explain the fine-scale heterogeneity. There was little to no correlation between the pollutant concentrations/mixing ratios and census-based income or site groups in different redlined areas. This study demonstrates the unitality of a multiyear, dense network of LCS to provide high-resolution spatiotemporal data with desirable performance.

Recommended Citation

V. Abhayaratne et al., "Evaluating Fine-Scale Air-Quality Heterogeneity Using A Low-Cost Multipollutant Sensor Network In Twin Cities, Minnesota," ACS Es T Air, vol. 3, no. 4, pp. 1057 - 1068, American Chemical Society, Apr 2026.

The definitive version is available at https://doi.org/10.1021/acsestair.5c00439

Department(s)

Civil, Architectural and Environmental Engineering

Publication Status

Open Access

Keywords and Phrases

AQMesh; conditional bivariate probability function; environmental justice; low-cost sensor network; spatial heterogeneity; urban air quality

International Standard Serial Number (ISSN)

2837-1402

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2026 American Chemical Society, All rights reserved.

Creative Commons Licensing

This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

10 Apr 2026