Combining Satellite-Derived PM 2.5 Data and a Reduced-Form Air Quality Model to Support Air Quality Analysis in US Cities.

Autor: Gallagher CL; Nelson Institute Center for Sustainability and the Global Environment University of Wisconsin-Madison Madison WI USA., Holloway T; Nelson Institute Center for Sustainability and the Global Environment University of Wisconsin-Madison Madison WI USA.; Department of Atmospheric and Oceanic Sciences University of Wisconsin-Madison Madison WI USA., Tessum CW; Department of Civil and Environmental Engineering University of Illinois-Urbana-Champaign Urbana IL USA., Jackson CM; Nelson Institute Center for Sustainability and the Global Environment University of Wisconsin-Madison Madison WI USA., Heck C; Nelson Institute Center for Sustainability and the Global Environment University of Wisconsin-Madison Madison WI USA.
Jazyk: angličtina
Zdroj: GeoHealth [Geohealth] 2023 May 09; Vol. 7 (5), pp. e2023GH000788. Date of Electronic Publication: 2023 May 09 (Print Publication: 2023).
DOI: 10.1029/2023GH000788
Abstrakt: 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%).
Competing Interests: The authors declare no conflicts of interest relevant to this study.
(© 2023 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)
Databáze: MEDLINE
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