Estimating daily PM2.5 concentrations in New York City at the neighborhood-scale: Implications for integrating non-regulatory measurements.

Previous PM2.5 related epidemiological studies mainly relied on data from sparse regulatory monitors to assess exposure. The introduction of non-regulatory PM2.5 monitors presents both opportunities and challenges to researchers and air quality managers. In this study, we evaluated the advantages and limitations of integrating non-regulatory PM2.5 measurements into a satellite-based daily PM2.5 model at 100 m resolution in New York City in 2015. Two separate machine learning models were developed, one using only PM2.5 data from the US Environmental Protection Agency (EPA), and the other with measurements from both EPA and the New York City Community Air Survey (NYCCAS). The EPA-only model obtained a cross-validation (CV) R2 of 0.85 while the EPA + NYCCAS model obtained a CV R2 of 0.73. With the help of the NYCCAS measurements, the EPA + NYCCAS model predicted distinctly different PM2.5 spatial patterns and more pollution hotspots compared with the EPA model, and its predictions were >15% higher than the EPA model along major roads and in densely populated areas. Our results indicated that satellite AOD and non-regulatory PM2.5 measurements can be fused together to capture neighborhood-scale PM2.5 levels and previous studies may have underestimated the disease burden due to PM2.5 in densely populated areas.