Max Anjos ^* Fred Meier

• Technical University of Berlin, Berlin, Germany

Artificial Intelligence (AI) tools based on Machine learning (ML) have demonstrated their potential in modeling climate-related phenomena. However, their application to quantifying greenhouse gas emissions in cities remains under-researched. Here, we introduce a ML-based bottom-up framework to predict hourly CO 2 emissions from vehicular traffic at fine spatial resolution (30x30m). Using data-driven algorithms, traffic counts, spatio-temporal features, and meteorological data, our model predicted hourly traffic flow, average speed, and CO 2 emissions for passenger cars (PC) and heavy-duty trucks (HDT) at the street scale in Berlin. Even with limited traffic information, the model effectively generalized to new road segments. For PC, the Relative Mean Difference (RMD) was +16% on average. For HDT, RMD was 19% for traffic flow and 2.6% for average speed. We modeled seven years of hourly CO 2 emissions (2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022) and identified major highways as hotspots for PC emissions, with peak values reaching 1.639 kgCO 2 m -2 d -1 . We also analyzed the impact of COVID-19 lockdown and individual policy stringency on traffic CO 2 emissions. During the lockdown period (March 15 to June 1, 2020), weekend emissions dropped substantially by 25% (-18.3 tCO 2 day -1 ), with stay-at-home requirements, workplace closures, and school closures contributing significantly to this reduction. The continuation of these measures resulted in sustained reductions in traffic flow and CO 2 emissions throughout 2020 and 2022.These results highlight the effectiveness of ML models in quantifying vehicle traffic CO 2 emissions at a high spatial resolution. Our ML-based bottom-up approach offers a useful tool for urban climate research, especially in areas lacking detailed CO 2 emissions data.