Comprehensive Cost-Benefit and Implementation Feasibility Analysis of Lower-Cost Particulate Matter Sensor Deployment Models based on Extended Field Observations

Riley Ortt, Gabriella Gonzalez, Abigail Hadden, PINGFAN MO, Junhyuk Jeon, Chong Qiu, University of New Haven

     Abstract Number: 324
     Working Group: Remote and Regional Atmospheric Aerosol

Abstract
Exposure to air pollutants like particulate matters (PMs) can pose significant adverse effects on human health and the environment. The ambient air quality is currently monitored by a network of air quality monitoring stations using federal Equivalent Methods (FEMs). Although highly accurate, FEMs are expensive to implement, leading to a current air monitoring network with a low density of FEM stations, often concentrated in metropolitan areas. Since PM concentrations could vary within a short distance, the current FEM network may not provide some communities with timely and relevant PM pollution information. While not as rigorous as FEM ones, lower-cost PM sensors (LCPMs) have become widely available. However, independent evaluations on their long-term field performance remain elusive. Complex pricing structures also create barriers for community stakeholders and policymakers to accurately determine the feasibility of establishing their own air monitoring stations with these sensors.

In this study, a commercial LCPM was deployed in New Haven, Connecticut for two years (June 2022 – June 2024) to independently evaluation its long-term performance. The LCPM performed well during the first 12 months and was able to capture elevation of PM levels during the Canadian Wildfires in 2023, but experienced premature sensor failures afterwards. Our results suggested that with a network of LCPMs may serve as a semi-quantitative air monitor network to provide early/cautionary warnings of local air quality to communities, but the cost consideration will require more complex models. Therefore, comprehensive cost-benefit and feasibility analysis models have been developed in single-point and network modes, considering initial and recurring costs, sensor network maintenance optimization, per capita financial burden, and other financial and societal factors. We applied our models in regions in the United States and South Korea based on the local market conditions to evaluate the applicability of our models with global perspectives. Our models may provide quantitative tools for the public to make informed decisions on how to best utilize lower-cost air monitors for their community air quality monitoring needs.