Source–Receptor Relationships Between Precursor Emissions and O3 and PM2.5 Air Pollution Impacts

Reduced complexity tools that provide a representation of both primarily emitted particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), secondarily formed PM2.5, and ozone (O3) allow for a quick assessment of many iterations of pollution control scenarios. Here, a new reduced complexity tool, Pattern Constructed Air Pollution Surfaces (PCAPS), that estimates annual average PM2.5 and seasonal average maximum daily average 8 h (MDA8) O3 for any source location in the United States is described and evaluated. Typically, reduced complexity tools are not evaluated for skill in predicting change in air pollution by comparison with more sophisticated modeling systems. Here, PCAPS was compared against multiple types of emission control scenarios predicted with state-of-the-science photochemical grid models to provide confidence that the model is realistically capturing the change in air pollution due to changing emissions. PCAPS was also applied with all anthropogenic emissions sources for multiple retrospective years to predict PM2.5 chemical components for comparison against routine surface measurements. PCAPS predicted similar magnitudes and regional variations in spatial gradients of measured chemical components of PM2.5. Model performance for capturing ambient measurements was consistent with other reduced complexity tools. PCAPS also did well at capturing the magnitude and spatial features of changes predicted by photochemical transport models for multiple emissions scenarios for both O3 and PM2.5. PCAPS is a flexible tool that provides source-receptor relationships using patterns of air quality gradients from a training data set of generic modeled sources to create interpolated air pollution gradients for new locations not part of the training database. The flexibility provided for both sources and receptors makes this tool ideal for integration into larger frameworks that provide emissions changes and need estimates of air quality to inform downstream analytics, which often includes an estimate of monetized health effects.