Using crowd-sourced low-cost sensors in a land use regression of ambient fine particulate matter in 6 US cities

Abstract

Assessing exposure to ambient fine particulate matter (PM_2.5) is important for improving human health. With rapidly expanding low-cost sensor networks globally, it is possible for monitoring networks to be located by a variety of users (i.e., crowd sourcing) to increase measurement density and coverage for use in exposure assessment, e.g., national land use regression (LUR) models. Few studies have integrated low-cost sensors into LUR models across multiple cities, limiting the ability of modelers to fully utilize growing low-cost sensor networks worldwide. We developed five LUR models to predict annual average PM_2.5 concentrations using combinations of regulatory (six cities: n = 68; national: n = 757) and low-cost monitors (n = 149) from six US cities. We found that developing Hybrid LURs that include the low-cost (i.e., PurpleAir) network may better capture within-city variation. LURs with the PurpleAir data only (tenfold CV R² = 0.66, MAE = 2.01 µg/m³) performed slightly worse than a conventional LUR based on regulatory data only (tenfold CV R² = 0.67, MAE = 0.99 µg/m³). Hybrid models that included both low-cost and regulatory data performed similarly to existing national models that rely on regulatory data (hybrid models: tenfold CV R² = 0.85, MAE = 1.02 µg/m³; regulatory monitor models: R² = 0.83, MAE = 0.72 µg/m³). Integrating crowd-sourced low-cost sensor networks in LUR models has promising applications to help identify intra-city exposure patterns especially for regions with limited regulatory networks internationally.

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Published January 23, 2022 in Air Quality, Atmosphere & Health (2022)

Authors: 

1. Tianjun Lu, Department of Earth Science & Geography, California State University, Dominguez Hills, 1000 E. Victoria St, Carson, CA, 90747, USA

2. Matthew J. Bechle, Department of Civil & Environmental Engineering, University of Washington, 201 More Hall, Seattle, WA, 98195, USA

3. Yanyu Wan & Albert A. Presto, Department of Mechanical Engineering, Carnegie Mellon University, 2115 Doherty Hall, Pittsburgh, PA, 15213, USA

4. Steve Hankey, School of Public and International Affairs, Virginia Tech, 140 Otey Street, Blacksburg, VA, 24061, USA