{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T21:15:31Z","timestamp":1793135731709,"version":"4.5.0"},"reference-count":54,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T00:00:00Z","timestamp":1753056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP19679885"],"award-info":[{"award-number":["AP19679885"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The air quality within urban public transport is a critical determinant of passenger health. In the crowded and poorly ventilated cabins of Almaty\u2019s metro, buses, and trolleybuses, concentrations of CO2 and PM2.5 often accumulate, elevating the risk of respiratory and cardiovascular diseases. This study investigates the air quality along three of the city\u2019s busiest transport corridors, analyzing how the concentrations of CO2, PM2.5, and PM10, as well as the temperature and relative humidity, fluctuate with the passenger density and time of day. Continuous measurements were collected using the Tynys mobile IoT device, which was bench-calibrated against a commercial reference sensor. Several machine learning models (logistic regression, decision tree, XGBoost, and random forest) were trained on synchronized environmental and occupancy data, with the XGBoost model achieving the highest predictive accuracy at 91.25%. Our analysis confirms that passenger occupancy is the primary driver of in-cabin pollution and that these machine learning models effectively capture the nonlinear relationships among environmental variables. Since the surveyed routes serve Almaty\u2019s most densely populated districts, improving the ventilation on these lines is of immediate importance to public health. Furthermore, the high-temporal-resolution data revealed short-term pollution spikes that correspond with peak ridership, advancing the current understanding of exposure risks in transit. These findings highlight the urgent need to combine real-time monitoring with ventilation upgrades. They also demonstrate the practical value of using low-cost IoT technologies and data-driven analytics to safeguard public health in urban mobility systems.<\/jats:p>","DOI":"10.3390\/s25144521","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T11:44:05Z","timestamp":1753098245000},"page":"4521","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Low-Cost IoT Sensor and Preliminary Machine-Learning Feasibility Study for Monitoring In-Cabin Air Quality: A Pilot Case from Almaty"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3039-6715","authenticated-orcid":false,"given":"Nurdaulet","family":"Tasmurzayev","sequence":"first","affiliation":[{"name":"Institute of Combustion Problems, Almaty 050012, Kazakhstan"},{"name":"LLP \u201cDigitAlem\u201d, Almaty 050042, Kazakhstan"},{"name":"Faculty of Information Technologies, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4089-6337","authenticated-orcid":false,"given":"Bibars","family":"Amangeldy","sequence":"additional","affiliation":[{"name":"Institute of Combustion Problems, Almaty 050012, Kazakhstan"},{"name":"LLP \u201cDigitAlem\u201d, Almaty 050042, Kazakhstan"},{"name":"Faculty of Information Technologies, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2943-5222","authenticated-orcid":false,"given":"Gaukhar","family":"Smagulova","sequence":"additional","affiliation":[{"name":"Institute of Combustion Problems, Almaty 050012, Kazakhstan"},{"name":"Department of General Physics, Intistute of Energy and Mechanical Engineering Named after A. Burkitbayev, Satbayev University, Almaty 050013, Kazakhstan"},{"name":"Department of Chemistry, Abai Kazakh National Pedagogical University, Almaty 050010, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1919-3570","authenticated-orcid":false,"given":"Zhanel","family":"Baigarayeva","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"},{"name":"LLP \u201cKazakhstan R&D Solutions\u201d, Almaty 050056, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3792-6512","authenticated-orcid":false,"given":"Aigerim","family":"Imash","sequence":"additional","affiliation":[{"name":"Institute of Combustion Problems, Almaty 050012, Kazakhstan"},{"name":"Faculty of Information Technologies, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"},{"name":"Department of General Physics, Intistute of Energy and Mechanical Engineering Named after A. Burkitbayev, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ogundiran, J.O., Nyembwe, J.-P.K.B., Ribeiro, A.S.N., and Gameiro da Silva, M. (2025). A Systematic Review of Indoor Environmental Quality in Passenger Transport Vehicles of Tropical and Subtropical Regions. Atmosphere, 16.","DOI":"10.3390\/atmos16020140"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Papp, E., Angyal, A., Furu, E., Szoboszlai, Z., T\u00f6r\u00f6k, Z., and Kert\u00e9sz, Z. (2022). Case Studies of Aerosol Pollution in Different Public Transport Vehicles in Hungarian Cities. Atmosphere, 13.","DOI":"10.3390\/atmos13050692"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Guo, Q., He, Z., and Wang, Z. (2023). 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