Within-city variation in ambient carbon monoxide concentrations: Leveraging low-cost monitors in a spatiotemporal modeling framework

Abstract

Exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes based on human and animal experimental evidence, but current epidemiologic research is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize potential fine-scale variation of CO. This study aimed to develop a high-resolution ambient CO prediction model at the daily scale based on both regulatory agency monitoring data and measurements from calibrated low-cost gas monitors in Baltimore, Maryland. We also evaluated the contribution of three novel parameters to model performance, including high-resolution meteorological data, satellite remote sensing data, and co-pollutant concentrations (PM2.5, NO2, and NOx). The CO model had spatial cross-validation (CV) R² (RMSE) of 0.70 (0.02 parts per million, ppm) and temporal CV R² (RMSE) of 0.61 (0.04 ppm). The predictions revealed spatially resolved CO hotspots associated with population, traffic, and other non-road emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads. The three novel parameters did not substantially improve model performance, suggesting that our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiologic research.