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Urbanization and land use change are associated with rising urban CO\n                    <jats:sub>2<\/jats:sub>\n                    emissions, highlighting the need to study spatiotemporal trends in intraurban CO\n                    <jats:sub>2<\/jats:sub>\n                    to inform sustainable city planning. This study investigates the use of land use regression (LUR) to predict intraurban CO\n                    <jats:sub>2<\/jats:sub>\n                    concentrations, using data from the BEACO\n                    <jats:sub>2<\/jats:sub>\n                    N monitoring network in the San Francisco Bay Area. Additionally, LUR is compared to machine learning (ML) algorithms capable of capturing non-linear relationships, representing a two-fold novel contribution. Model performance is evaluated using reserved data from training sensors as well as unseen sensor locations. For training sensors, extreme gradient boosting (XGBoost) and a convolutional neural network (CNN) achieved the highest predictive accuracy (R\u00b2=0.58), outperforming traditional LUR (R\u00b2=0.34). XGBoost and CNN also outperformed traditional LUR for unseen sensor locations, accounting for up to 42% of the variability in observed CO\n                    <jats:sub>2<\/jats:sub>\n                    concentrations. These models offer insight into urban land use and carbon dynamics, supporting more informed approaches to urban planning and decarbonization.\n                  <\/jats:p>","DOI":"10.1007\/s12665-025-12582-w","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T11:45:23Z","timestamp":1758887123000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Machine learning-based land-use regression models for predicting carbon dioxide concentrations in San Francisco Bay area"],"prefix":"10.1007","volume":"84","author":[{"given":"Anna C.","family":"Smith","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linfeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiansheng","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangxin","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"12582_CR1","doi-asserted-by":"publisher","first-page":"1564","DOI":"10.1016\/j.atmosenv.2010.12.043","volume":"45","author":"O Bergeron","year":"2011","unstructured":"Bergeron O, Strachan IB (2011) CO2 sources and sinks in urban and suburban areas of a Northern mid-latitude city. 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