Combining Satellite‐Derived PM_2.5 Data and a Reduced‐Form Air Quality Model to Support Air Quality Analysis in US Cities

Abstract

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 PM 2.5 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 PM 2.5 components it simulates ( p SO 4 : −48%, p NO 3 : 8%, p NH 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 ( p SO 4 : 53%, p NO 3 : 52%, p NH 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%).

Environmental Justice and Systems Analysis for Air Quality Planning in the Port of Oakland in California

“…We elected to use InMAP/the ISRM, which models the physical transport and chemical formation of pollutants using 2005 WRF-Chem based meteorological data, as our mechanistic air quality model because of its efficiency , and applications to exposure domains of similar scale. ,− InMAP has also been used outside of the United States, − making the methodological steps presented in this research adaptable for other locations. Despite some limitations in the InMAP/ISRM (e.g., based on 2005 meteorological data, use of annually averaged input data, parametrization of some chemical relationships, underestimation of particulate sulfate and overestimation of particulate ammonium formation), it has been shown to have similar performance to other reduced-complexity air quality models and is within published air quality model performance criteria used to quantify monetized health damages. ,− …”

“…Despite some limitations in the InMAP/ISRM (e.g., based on 2005 meteorological data, use of annually averaged input data, parametrization of some chemical relationships, underestimation of particulate sulfate and overestimation of particulate ammonium formation), it has been shown to have similar performance to other reduced-complexity air quality models and is within published air quality model performance criteria used to quantify monetized health damages. 33 , 52 − 54 …”