{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T12:54:58Z","timestamp":1791550498279,"version":"4.3.4"},"reference-count":76,"publisher":"American Chemical Society (ACS)","issue":"10","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000066","name":"National Institute of Environmental Health Sciences","doi-asserted-by":"publisher","award":["1-P20ES036775"],"award-info":[{"award-number":["1-P20ES036775"]}],"id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Quantifying air pollution concentrations at fine spatial scales is important for resolving intra-urban exposure variability and identifying sources that disproportionately impact population groups across locations. We developed a neighborhood-scale resolution (1 \u00d7 1 km2) WRF\u2013SMOKE\u2013CMAQ modeling framework to characterize O3, NO2, and PM2.5 concentrations in Washington, D.C. in 2017. In evaluations against EPA Air Quality System observations and the National Climatic Data Center meteorological data, WRF reproduced the temporal variability of temperature and specific humidity with reasonable agreement (average r = 0.8 and 0.7, respectively), although biases varied across seasons. CMAQ reproduced seasonal and diurnal patterns of O3 and PM2.5 with r up to 0.7 for hourly O3, though NO2 was negatively biased (\u221214% to \u221229%), reflecting challenges in resolving near-road gradients. We identified pronounced intra-urban concentration variability of NO2, whose coefficient of variation was greater than that of O3 and more than of three times that of PM2.5. O3 concentrations were lower in all months by 4% to 11% in the urban core compared to outside the urban core due to NOx titration\/inhibition. Relative to 4 \u00d7 4 km2 resolution, 1 \u00d7 1 km2 simulations increased population-weighted exposure (PWE) for O3 (+0.5% to +1.5%) but decreased PWE for NO2 (\u22123% to \u22126%) and PM2.5 (\u22121.5% to \u22123%). VOC\/NOx ratios indicated predominantly NOx-saturated chemistry within the urban core. This work presents the value and the remaining challenges of high-resolution regional air quality modeling in heterogeneous urban settings.<\/jats:p>","DOI":"10.1021\/acsestair.6c00155","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T21:02:03Z","timestamp":1788555723000},"page":"2640-2653","source":"Crossref","is-referenced-by-count":0,"title":["Neighborhood-Scale\nO3, NO2,\nand PM2.5 in Washington, D.C. from a 1 \u00d7 1 km2 WRF\u2013SMOKE\u2013CMAQ Framework"],"prefix":"10.1021","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8528-1214","authenticated-orcid":true,"given":"Hoda","family":"Hallaji","sequence":"first","affiliation":[{"name":"George Mason University , , , ,","place":["Fairfax, Virginia, United States, 22030"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqi","family":"Ma","sequence":"additional","affiliation":[{"name":"George Mason University , , , ,","place":["Fairfax, Virginia, United States, 22030"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel Q.","family":"Tong","sequence":"additional","affiliation":[{"name":"George Mason University , , , ,","place":["Fairfax, Virginia, United States, 22030"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1957-8761","authenticated-orcid":true,"given":"Lucas R. F.","family":"Henneman","sequence":"additional","affiliation":[{"name":"George Mason University , , , ,","place":["Fairfax, Virginia, United States, 22030"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"key":"2026100908070523900_cit1","unstructured":"US EPA.\n          ,  Integrated Science Assessments (ISAs). https:\/\/www.epa.gov\/isa.Accessed:\nFeb,25, 2026."},{"issue":"4","key":"2026100908070523900_cit2","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1289\/ehp.1307049","article-title":"An Integrated\nRisk Function for Estimating the Global\nBurden of Disease Attributable to Ambient Fine Particulate Matter\nExposure","volume":"122","author":"Burnett","year":"2014","journal-title":"Environ. 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A."},{"issue":"48","key":"2026100908070523900_cit7","doi-asserted-by":"publisher","first-page":"19532","DOI":"10.1021\/acs.est.3c03999","article-title":"Ethnoracial Disparities\nin Nitrogen Dioxide Pollution in the United States: Comparing Data\nSets from Satellites, Models, and Monitors","volume":"57","author":"Kerr","year":"2023","journal-title":"Environ. Sci. 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Sci.\nAnthr."},{"issue":"9","key":"2026100908070523900_cit10","doi-asserted-by":"publisher","DOI":"10.1029\/2025GH001431","article-title":"Source-Specific\nAir Pollution Emissions Inequalities From 2011 to 2020 in Virginia","volume":"9","author":"Henneman","year":"2025","journal-title":"GeoHealth"},{"key":"2026100908070523900_cit11","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.rse.2015.05.016","article-title":"Estimating long-term\nPM2.5 concentrations in China using satellite-based aerosol optical\ndepth and a chemical transport model","volume":"166","author":"Geng","year":"2015","journal-title":"Remote\nSens. 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Meteorol."},{"key":"2026100908070523900_cit35","unstructured":"OpenStreetMap, .https:\/\/www.openstreetmap.org\/.Accessed: Mar,02, 2026."},{"key":"2026100908070523900_cit36","unstructured":"Eyth, A.; Vukovich, J.; Farkas, C.; Godfrey, J.\n          Technical Support Document\n(TSD): Preparation of Emissions Inventories for the 2017 North American\nEmissions Modeling Platform; U.S. Environmental\nProtection Agency2020."},{"issue":"1","key":"2026100908070523900_cit37","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1038\/s41597-022-01790-9","article-title":"Neighborhood Emission Mapping Operation (NEMO): A 1-km\nanthropogenic emission dataset in the United States","volume":"9","author":"Ma","year":"2022","journal-title":"Sci. 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