## 4 Conclusions
An additional analysis using back-trajectory footprints was used to illustrate the complex local dynamics over Marseille, which may require specific network designs. Notably, a greater area of the region contributes to measurements more often during the summertime period than during the wintertime or springtime periods. A case study looking at the wind conditions between 23 July 2016 at 16:00 UTC and 24 July 2016 at 16:00 UTC demonstrated the nuanced ability of the model to capture short-term sea-breeze conditions with a low mixing ratio versus land-breeze events with a high mixing ratio at the coastal SME station. In the case study, it was again found that simulations with and without the UHI module were sensitive to these conditions with respect to how the boundary layer is affected and, correspondingly, the CO\({}_{2}\). Future studies may want to use these back-trajectories to create influence functions for use in a Bayesian analysis of optimized emissions estimates. The establishment of this CO\({}_{2}\) modeling framework in the urban-coastal Aix-Marseille region can be useful for policy and practical applications. Our model can be used in conjunction with continuously monitoring CO\({}_{2}\) measurement sites to track pollution events to their most probable source sectors, assess the effectiveness of mitigation efforts over time, and inform decision-makers about the best areas to implement new mitigation strategies. The results from this and future studies could be used to better constrain emissions in other urban-coastal regions. With such a system in place, urban planners, environmental agencies, and decision-makers will be better positioned to implement plans with long-term rewards for improving air quality and combating climate change. Conceptualization, B.N., I.X.-R. and T.L. ; methodology, B.N., I.X.-R. and T.L. ; software, C.Y. and M.H. ; validation, B.N., I.X.-R. and T.L. ; formal analysis, B.N. and I.X.-R.; investigation, B.N. and I.X.-R.; resources, I.X.-R., C.Y. and J.P.; data curation, I.X.-R., D.P., A.A., T.O., M.M. and C.W.-R.; writing--original draft preparation, B.N., I.X.-R. and T.L. ; writing--review and editing, B.N., I.X.-R., T.L., C.W.-R., T.O. and M.H. ; visualization, B.N. and I.X.-R.; supervision, I.X.-R.; project administration, I.X.-R.; funding acquisition, I.X.-R. and J.P. All authors have read and agreed to the published version of the manuscript. This study is part of the AMC project (Aix-Marseille Carbon Pilot program) through the A*MIDEX project ANR-11-IDEX-0001-02, funded by the French National Research Agency (ANR). Not applicable. The data presented in this study are available on request from the corresponding author due to technical and security limitations. We acknowledge the ICOS-Fr National Observation Service for support in acquisition and validation of the OHP and ERSA datasets. We thank the ICOS-France team for their technical support, in particular [PERSON], [PERSON], [PERSON], and [PERSON], and for providing us with the ERSA data. CarbonTracker Europe results were provided by the Wageningen University and Research Center (Wageningen, NL) [[http://www.carbontracker.eu](http://www.carbontracker.eu)]([http://www.carbontracker.eu](http://www.carbontracker.eu)), accessed on 27 September 2024. This work was supported by the French research program Make Our Planet Great Again (Project CUDAD). We thank [PERSON] and [PERSON] for their contributions to the acquisition of the pCO\({}_{2}\) data gathered in the Bay of Marseille. The ODIAC data product is available from the Global Environmental Database hosted by the Center for Global Environmental Research at Japan's National Institute for Environmental Studies (NIES) ([[https://db.cger.nies.go.jp/dataset/ODIAC/](https://db.cger.nies.go.jp/dataset/ODIAC/)]([https://db.cger.nies.go.jp/dataset/ODIAC/](https://db.cger.nies.go.jp/dataset/ODIAC/)), accessed on 27 September 2024). The authors declare no conflicts of interest.