{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T04:39:28Z","timestamp":1775018368139,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T00:00:00Z","timestamp":1689811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of California Davis Open Access Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Black carbon (BC) is a significant source of air pollution since it impacts public health and climate change. Understanding its distribution in the complex urban environment is challenging. We integrated a land use model with four machine learning models to estimate traffic-related BC concentrations in Oakland, CA. Random Forest was the best-performing model, with regression coefficient (R2) values of 0.701 on the train set and 0.695 on the validation set with a root mean square error (RMSE) of 0.210 mg\/m3. Vehicle speed and local road systems were the most sensitive variables in estimating BC concentrations. However, this approach was inefficient at identifying hyperlocal hotspots, especially in a complex urban environment where highways and truck routes are significant emission sources. Using the land use method to estimate BC concentrations may lead to underestimating some localized hotspots. This work can improve air quality exposure assessment for vulnerable populations and help emphasize potential environmental justice issues.<\/jats:p>","DOI":"10.3390\/ijgi12070290","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T01:58:38Z","timestamp":1689904718000},"page":"290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Black Carbon Concentration Estimation with Mobile-Based Measurements in a Complex Urban Environment"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2848-9712","authenticated-orcid":false,"given":"Minmeng","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Land, Air, and Water Resources, University of California Davis, Davis, CA 95616, USA"},{"name":"Department of Civil and Environmental Engineering, Cornell University, New York, NY 14850, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0886-4201","authenticated-orcid":false,"given":"Tri Dev","family":"Acharya","sequence":"additional","affiliation":[{"name":"Institute of Transportation Studies, University of California Davis, Davis, CA 95616, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deb A.","family":"Niemeier","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Maryland, 1173 Glenn Martin Hall, College Park, MD 20742, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.envres.2017.08.037","article-title":"Associations between Fine Particulate Matter and Mortality in the 2001 Canadian Census Health and Environment Cohort","volume":"159","author":"Pinault","year":"2017","journal-title":"Environ. 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