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

Previous PM related epidemiological studies mainly relied on data from sparse regulatory monitors to assess exposure. The introduction of non-regulatory PM 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 PM measurements into a satellite-based daily PM model at 100 m resolution in New York City in 2015. Two separate machine learning models were developed, one using only PM 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) R of 0.85 while the EPA + NYCCAS model obtained a CV R of 0.73. With the help of the NYCCAS measurements, the EPA + NYCCAS model predicted distinctly different PM 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 PM measurements can be fused together to capture neighborhood-scale PM levels and previous studies may have underestimated the disease burden due to PM in densely populated areas.