Combining Satellite-Derived PM2.5 Data and a Reduced-Form Air Quality Model to Support Air Quality Analysis in US Cities

Air quality models can support pollution mitigation design by simulating policy scenarios and conducting source contribution analyses. The Intervention Model for Air Pollution (InMAP) is a powerful tool for equitable policy design as its variable resolution grid enables intra-urban analysis, the scale of which most environmental justice inquiries are levied. However, InMAP underestimates particulate sulfate and overestimates particulate ammonium formation, errors that limit the model's relevance to city-scale decision-making. To reduce InMAP's biases and increase its relevancy for urban-scale analysis, we calculate and apply scaling factors (SFs) based on observational data and advanced models. We consider both satellite-derived speciated PM2.5 from Washington University and ground-level monitor measurements from the U.S. Environmental Protection Agency, applied with different scaling methodologies. Relative to ground-monitor data, the unscaled InMAP model fails to meet a normalized mean bias performance goal of <+/- 10% for most of the PM2.5 components it simulates (pSO(4): -48%, pNO(3): 8%, pNH(4): 69%), but with city-specific SFs it achieves the goal benchmarks for every particulate species. Similarly, the normalized mean error performance goal of <35% is not met with the unscaled InMAP model (pSO(4): 53%, pNO(3): 52%, pNH(4): 80%) but is met with the city-scaling approach (15%-27%). The city-specific scaling method also improves the R-2 value from 0.11 to 0.59 (ranging across particulate species) to the range of 0.36-0.76. Scaling increases the percent pollution contribution of electric generating units (EGUs) (nationwide 4%) and non-EGU point sources (nationwide 6%) and decreases the agriculture sector's contribution (nationwide -6%).Plain Language Summary Air quality models can support the design of pollution reduction strategies by assessing sources of pollution and simulating policy scenarios. The Intervention Model for Air Pollution (InMAP) is an air quality model that can evaluate fine particulate matter (PM2.5) differences within cities, which makes it valuable as tool to assess equity of PM2.5 exposure. However, InMAP's simplified atmospheric chemistry equations results in errors that limit the model's relevance to city-scale decision-making. To reduce the model's biases and errors, we calculate and apply SFs based on observational data and advanced models, specifically ground-level monitor measurements from the U.S. Environmental Protection Agency and a satellite-derived data product. We find that applying SFs derived from satellite observations over cities or individual grid-cells improves model performance. Scaling InMAP affects the source contribution analysis nationwide and for individual cities, specifically by increasing the contribution of power plants and industry and decreasing the contribution of the agriculture sector.