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H. Organization, \u201cAmbient air pollution: A global assessment of\nexposure and burden of disease,\u201d https:\/\/www.who.int\/publications\/i\/item\/9789241511353, 2018."},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"B. Alfano et al., \u201cA review of low-cost particulate matter sensors from\nthe developers\u2019 perspectives,\u201d Sensors, vol. 20, no. 23, p. 6819, 2020.","DOI":"10.3390\/s20236819"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"A. Moln\u00e1r et al., \u201cAerosol hygroscopicity: Hygroscopic growth proxy\nbased on visibility for low-cost pm monitoring,\u201d Atmospheric Research,\nvol. 236, 2020.","DOI":"10.1016\/j.atmosres.2019.104815"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"M. Casari, L. Po, and L. Zini, \u201cAirmlp: A multilayer perceptron neural\nnetwork for temporal correction of pm2.5 values in turin,\u201d Sensors,\nvol. 23, no. 23, p. 9446, 2023.","DOI":"10.3390\/s23239446"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"M. Casari and L. Po, \u201cMith: A framework for mitigating hygroscopicity\nin low-cost pm sensors,\u201d Environmental Modelling & Software, vol. 173,\np. 105955, 2024.","DOI":"10.1016\/j.envsoft.2024.105955"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"S. Mahajan et al., \u201cCar: The clean air routing algorithm for path\nnavigation with minimal pm2.5 exposure on the move,\u201d IEEE Access,\nvol. 7, pp. 147 373\u2013147 382, 2019.","DOI":"10.1109\/ACCESS.2019.2946419"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"T. Desai et al., \u201cComparative analysis of machine learning algorithms\nfor air quality index prediction,\u201d in Machine Learning for Computational\nScience and Engineering, 2025.","DOI":"10.1007\/s44379-025-00014-2"},{"key":"ref8","doi-asserted-by":"publisher","unstructured":"P. Zhivkov, \u201cOptimization and evaluation of calibration for low-cost air\nquality sensors: Supervised and unsupervised machine learning models,\u201d\nAnnals of Computer Science and Information Systems, vol. 25, pp.\n255\u2013258, 2021. [Online]. Available: https:\/\/doi.org\/10.15439\/2021F95","DOI":"10.15439\/2021F95"},{"key":"ref9","doi-asserted-by":"publisher","unstructured":"F. Rollo, B. Sudharsan, L. Po, and J. G. Breslin, \u201cAir quality\nsensor network data acquisition, cleaning, visualization, and analytics:\nA real-world iot use case,\u201d in UbiComp\/ISWC \u201921: 2021 ACM\nInternational Joint Conference on Pervasive and Ubiquitous Computing\nand 2021 ACM International Symposium on Wearable Computers,\nVirtual Event, September 21-25, 2021, A. Doryab, Q. Lv, and\nM. Beigl, Eds. ACM, 2021, pp. 67\u201368. [Online]. Available:\nhttps:\/\/doi.org\/10.1145\/3460418.3479277","DOI":"10.1145\/3460418.3479277"},{"key":"ref10","doi-asserted-by":"publisher","unstructured":"M. Arsov, E. Zdravevski, P. Lameski, R. Corizzo, N. Koteli,\nK. Mitreski, and V. Trajkovik, \u201cShort-term air pollution forecasting\nbased on environmental factors and deep learning models,\u201d in\nProceedings of the 2020 Federated Conference on Computer Science\nand Information Systems, FedCSIS 2020, Sofia, Bulgaria, September\n6-9, 2020, ser. Annals of Computer Science and Information Systems,\nM. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., vol. 21, 2020,\npp. 15\u201322. [Online]. Available: https:\/\/doi.org\/10.15439\/2020F211","DOI":"10.15439\/2020F211"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"B. Cengiz et al., \u201cA survey on data fusion approaches in IoT-based smart\ncities: Smart applications, taxonomies, challenges, and future research\ndirections,\u201d Information Fusion, vol. 121, 2025.","DOI":"10.1016\/j.inffus.2025.103102"},{"key":"ref12","doi-asserted-by":"publisher","unstructured":"R. Sinnott and S. Zhong, \u201cReal-time route planning to reduce pedestrian\npollution exposure in urban settings,\u201d in Proceedings of the IEEE\/ACM\n10th International Conference on Big Data Computing, 2023, pp. 1\u201310.\n[Online]. Available: https:\/\/doi.org\/10.1145\/3632366.3632381","DOI":"10.1145\/3632366.3632381"},{"key":"ref13","unstructured":"F. Bistaffa and P. C. Oliveira, \u201cGreen routes in barcelona: A pedestrian routing prototype to reduce air pollution exposure,\u201d https:\/\/filippobistaffa.github.io\/papers\/2025greenroutes.pdf, 2025, accessed on\nMay 27, 2025."},{"key":"ref14","doi-asserted-by":"publisher","unstructured":"C. Bachechi, F. Desimoni, L. Po, and D. M. Casas, \u201cVisual analytics for\nspatio-temporal air quality data,\u201d in 24th International Conference on\nInformation Visualisation, IV 2020, Melbourne, Australia, September\n7-11, 2020, E. B. et. al., Ed. IEEE, 2020, pp. 460\u2013466. [Online].\nAvailable: https:\/\/doi.org\/10.1109\/IV51561.2020.00080","DOI":"10.1109\/IV51561.2020.00080"},{"key":"ref15","doi-asserted-by":"publisher","unstructured":"M. Casari and E. Montorsi, \u201cMartinacasari\/airqualitydatasetsrepository:\nAqdr - v1.0.0 (v1.0.0),\u201d Zenodo https:\/\/doi.org\/10.5281\/zenodo.13982208, 2024.","DOI":"10.5281\/zenodo.13982208"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"M. Casari, P. A. Kowalski, and L. Po, \u201cOptimisation of the adaptive\nneuro-fuzzy inference system for adjusting low-cost sensors pm\nconcentrations,\u201d Ecological Informatics, vol. 83, p. 102781, 2024.\n[Online]. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1574954124003236","DOI":"10.1016\/j.ecoinf.2024.102781"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"M. Casari, L. Po, and L. Zini, \u201cAirmlp: A multilayer perceptron neural\nnetwork for temporal correction of pm2.5 values in turin,\u201d Sensors,\nvol. 23, no. 23, 2023. [Online]. Available: https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9446","DOI":"10.3390\/s23239446"},{"key":"ref18","unstructured":"A. Das, W. Kong, R. Sen, and Y. Zhou, \u201cA decoder-only\nfoundation model for time-series forecasting,\u201d 2024. [Online]. Available:\nhttps:\/\/arxiv.org\/abs\/2310.10688"},{"key":"ref19","unstructured":"V. Ekambaram, A. Jati, P. Dayama, S. Mukherjee, N. H. Nguyen,\nW. M. Gifford, C. Reddy, and J. Kalagnanam, \u201cTiny time\nmixers (ttms): Fast pre-trained models for enhanced zero\/few-shot forecasting of multivariate time series,\u201d in Advances in\nNeural Information Processing Systems, A. Globerson, L. Mackey,\nD. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang,\nEds., vol. 37. Curran Associates, Inc., 2024, pp. 74 147\u201374 181.\n[Online]. 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