Evaluating Fine-Scale Air Quality Heterogeneity Using a Low-Cost Multi-pollutant Sensor Network in Twin Cities, Minnesota

Varuni Abhayaratne, Chen Ye, Anjana Yatawara, Philip K. Hopke, Jiayu Li, YANG WANG, University of Miami

     Abstract Number: 114
     Working Group: Urban Aerosols

Abstract
There is growing interest and effort in deploying low-cost air quality sensors 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 a Minneapolis-St. Paul metropolitan area network. Hourly averaged pollutant concentrations or mixing ratios for CO, O₃, NO₂, NO, and SO₂ and particulate matter (PM) (PM₁, PM_2.5, 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 (with pairwise Pearson r2 values above 0.6). However, pairwise coefficients of divergence (COD) show higher spatial heterogeneity for O₃ and NO₂, with COD values above 0.2. The conditional bivariate probability function coupling the sensor and wind data can partly explain the fine-scale heterogeneity. There was little to no correlation between pollutant concentration/mixing ratio and census-based income or site groups in different redlining areas. This study demonstrates the capability of a multi-year, dense network of low-cost sensors to provide high-resolution spatiotemporal data with desirable performance.