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Combining Satellite‐Derived PM(2.5) 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 sca...

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Autores principales: Gallagher, Ciaran L., Holloway, Tracey, Tessum, Christopher W., Jackson, Clara M., Heck, Colleen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169548/
https://www.ncbi.nlm.nih.gov/pubmed/37181009
http://dx.doi.org/10.1029/2023GH000788
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author Gallagher, Ciaran L.
Holloway, Tracey
Tessum, Christopher W.
Jackson, Clara M.
Heck, Colleen
author_facet Gallagher, Ciaran L.
Holloway, Tracey
Tessum, Christopher W.
Jackson, Clara M.
Heck, Colleen
author_sort Gallagher, Ciaran L.
collection PubMed
description 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 (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%).
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spelling pubmed-101695482023-05-11 Combining Satellite‐Derived PM(2.5) Data and a Reduced‐Form Air Quality Model to Support Air Quality Analysis in US Cities Gallagher, Ciaran L. Holloway, Tracey Tessum, Christopher W. Jackson, Clara M. Heck, Colleen Geohealth Research Article 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 (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%). John Wiley and Sons Inc. 2023-05-09 /pmc/articles/PMC10169548/ /pubmed/37181009 http://dx.doi.org/10.1029/2023GH000788 Text en © 2023 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Gallagher, Ciaran L.
Holloway, Tracey
Tessum, Christopher W.
Jackson, Clara M.
Heck, Colleen
Combining Satellite‐Derived PM(2.5) Data and a Reduced‐Form Air Quality Model to Support Air Quality Analysis in US Cities
title Combining Satellite‐Derived PM(2.5) Data and a Reduced‐Form Air Quality Model to Support Air Quality Analysis in US Cities
title_full Combining Satellite‐Derived PM(2.5) Data and a Reduced‐Form Air Quality Model to Support Air Quality Analysis in US Cities
title_fullStr Combining Satellite‐Derived PM(2.5) Data and a Reduced‐Form Air Quality Model to Support Air Quality Analysis in US Cities
title_full_unstemmed Combining Satellite‐Derived PM(2.5) Data and a Reduced‐Form Air Quality Model to Support Air Quality Analysis in US Cities
title_short Combining Satellite‐Derived PM(2.5) Data and a Reduced‐Form Air Quality Model to Support Air Quality Analysis in US Cities
title_sort combining satellite‐derived pm(2.5) data and a reduced‐form air quality model to support air quality analysis in us cities
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169548/
https://www.ncbi.nlm.nih.gov/pubmed/37181009
http://dx.doi.org/10.1029/2023GH000788
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