Use of Low-cost Sensors to Develop Land Use Regression Models for PM2.5 of 6 Urban Areas in the US

Abstract

Background/Aim: Existing national Land Use Regression (LUR) models are mainly developed based on ground-level regulatory monitors to predict and assess ambient exposure of air pollution at unmonitored locations. Emerging Low-cost sensors have improved network density and coverage that may refine the LUR model development.Methods: We retrieved and calibrated annual average PM2.5 concentrations from the low-cost sensors (i.e., PurpleAir sensors) of 6 urban areas in the US. Our independent variables included 11 categories (n = 339) of geographic features (e.g., traffic, population, land use, and satellite air pollution measurements). We developed PurpleAir LUR models (using only the PurpleAir sensors) and hybrid LUR models (using both the regulatory and low-cost monitors) for predicting annual average PM2.5 concentrations; we applied a partial least squares-universal kriging approach. We compared the exposure assessment of different LUR-derived population-weighted predictions.Results: LUR models using only the PurpleAir sensors showed reasonable performance: 10-fold CV R2 = 0.66, mean absolute error [MAE] = 2.01 µg/m3. However, the external evaluation using the EPA monitors suggested that the PurpleAir-only LUR models may consistently over-predict PM2.5 concentrations. We observed that the hybrid LUR models (R2: 0.85, MAE: 1.02 µg/m3) performed better as compared to the PurpleAir-only LUR indicating that regulatory monitors and low-cost sensors could be integrated to refine LUR models. We also noticed that the PurpleAir and hybrid LUR were spatially correlated with the regulatory-based LUR (e.g., Los Angeles: R2: 0.82, MAE: 0.77 µg/m3). The LUR-derived population-weighted predictions suggested that integrating low-cost sensors into LUR may help catch hot spots.Conclusions: LUR model development using low-cost sensor network is feasible to capture spatial variability that maybe missed by the regulatory monitors and obtain promising performance where regulatory monitors are unavailable. Future low-cost sensor-based LUR can be expanded nationally to track exposures more accurately and inform health policies.