{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T19:26:59Z","timestamp":1787686019661,"version":"build-2784847793"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Environ Monit Assess"],"DOI":"10.1007\/s10661-026-15384-0","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T01:53:28Z","timestamp":1778637208000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Operational 24-h PM2.5 forecasting using ensemble methods and open-access satellite data: application to Dakar, Senegal"],"prefix":"10.1007","volume":"198","author":[{"given":"Moustapha","family":"Kebe","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alassane","family":"Traore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malick","family":"Sow","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Modou","family":"Gueye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ababacar","family":"Gueye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abou","family":"Dia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmadou","family":"Wague","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"15384_CR1","doi-asserted-by":"publisher","DOI":"10.31248\/GJEES2024.191","author":"KA Abudulawal","year":"2025","unstructured":"Abudulawal, K. A., & Bello, I. O. (2025). Spatial estimation of particulate matter (PM2.5) in selected African cities using machine learning-based models for improved air quality assessment. Global Journal of Earth and Environmental Science. https:\/\/doi.org\/10.31248\/GJEES2024.191","journal-title":"Global Journal of Earth and Environmental Science"},{"issue":"Part A","key":"15384_CR2","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1016\/j.envpol.2016.09.042","volume":"220","author":"AK Amegah","year":"2017","unstructured":"Amegah, A. K., & Agyei-Mensah, S. (2017). Urban air pollution in Sub-Saharan Africa: Time for action. Environmental Pollution, 220(Part A), 738\u2013743. https:\/\/doi.org\/10.1016\/j.envpol.2016.09.042","journal-title":"Environmental Pollution"},{"issue":"9","key":"15384_CR3","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1039\/D4EA00057A","volume":"4","author":"AG Amooli","year":"2024","unstructured":"Amooli, A. G., Koffi, A., Coulibaly, A., Kouadio, K. A., & Kouassi, M. K. (2024). Fine particulate air pollution estimation in Ouagadougou using satellite aerosol optical depth and meteorological parameters. Environmental Science: Atmospheres, 4(9), 1012\u20131025. https:\/\/doi.org\/10.1039\/D4EA00057A","journal-title":"Environmental Science: Atmospheres"},{"key":"15384_CR4","volume-title":"Cinqui\u00e8me Recensement G\u00e9n\u00e9ral de la Population et de l\u2019Habitat (RGPH-5) \u2013 Rapport pr\u00e9liminaire","author":"ANSD","year":"2023","unstructured":"Agence Nationale de la Statistique et de la D\u00e9mographie (ANSD). (2023). Cinqui\u00e8me Recensement G\u00e9n\u00e9ral de laPopulation et de l\u2019Habitat (RGPH-5) : Rapport pr\u00e9liminaire. https:\/\/www.ansd.sn\/sites\/default\/files\/2023-10\/RAPPORT-PRELIMINAIRE-RGPH-5_2023-.pdf"},{"issue":"13","key":"15384_CR5","doi-asserted-by":"publisher","first-page":"8057","DOI":"10.1021\/acs.est.5b01236","volume":"49","author":"JS Apte","year":"2015","unstructured":"Apte, J. S., Marshall, J. D., Cohen, A. J., & Brauer, M. (2015). Addressing global mortality from ambient PM2.5. Environmental Science & Technology, 49(13), 8057\u20138066. https:\/\/doi.org\/10.1021\/acs.est.5b01236","journal-title":"Environmental Science & Technology"},{"key":"15384_CR6","unstructured":"AQMC. (2019). Air Quality Management Centre, Senegal. Available online: http:\/\/www.denv.gouv.sn\/index.php\/air-et-climat\/centre-de-gestion-de-la-qualite-de-l-air-cgqa\/pollution (accessed on 7 August 2019)."},{"issue":"6","key":"15384_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s11270-014-2063-1","volume":"225","author":"A Azid","year":"2014","unstructured":"Azid, A., Juahir, H., Toriman, M. E., Kamarudin, M. K. A., Saudi, A. S. M., Hasnam, C. N. C., Aziz, N. A. A., Azaman, F., Latif, M. T., Zainuddin, S. F. M., Osman, M. R., & Yamin, M. (2014). Prediction of the level of air pollution using principal component analysis and artificial neural network techniques: A case study in Malaysia. Water, Air, & Soil Pollution, 225(6), Article 2063. https:\/\/doi.org\/10.1007\/s11270-014-2063-1","journal-title":"Water, Air, & Soil Pollution"},{"key":"15384_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/0471725153","author":"DA Belsley","year":"1980","unstructured":"Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics: Identifying influential data and sources of collinearity. John Wiley & Sons. https:\/\/doi.org\/10.1002\/0471725153","journal-title":"John Wiley & Sons"},{"key":"15384_CR9","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.ins.2011.12.028","volume":"191","author":"C Bergmeir","year":"2012","unstructured":"Bergmeir, C., & Ben\u00edtez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192\u2013213. https:\/\/doi.org\/10.1016\/j.ins.2011.12.028","journal-title":"Information Sciences"},{"issue":"Feb","key":"15384_CR10","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(Feb), 281\u2013305.","journal-title":"Journal of Machine Learning Research"},{"key":"15384_CR11","unstructured":"Bloomberg report on the dust storm: Air quality in Dakar plummeted due to Saharan dust, with PM concentrations exceeding 1,000 \u00b5g\/m3 on December 19, 2024. https:\/\/www.bloomberg.com\/news\/articles\/2024-12-19\/dakar-s-air-quality-plummets-as-saharan-dust-descends-on-senegal"},{"issue":"1","key":"15384_CR12","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Machine Learning"},{"key":"15384_CR13","doi-asserted-by":"publisher","unstructured":"Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785\u2013794). ACM. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"issue":"1\u20132","key":"15384_CR14","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s00704-016-1914-7","volume":"130","author":"H Citakoglu","year":"2017","unstructured":"Citakoglu, H. (2017). Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey. Theoretical and Applied Climatology, 130(1\u20132), 545\u2013556. https:\/\/doi.org\/10.1007\/s00704-016-1914-7","journal-title":"Theoretical and Applied Climatology"},{"key":"15384_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s12517-021-08484-3","volume":"14","author":"H Citakoglu","year":"2021","unstructured":"Citakoglu, H. (2021). Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey. Arabian Journal of Geosciences, 14, Article Article 2131. https:\/\/doi.org\/10.1007\/s12517-021-08484-3","journal-title":"Arabian Journal of Geosciences"},{"issue":"10082","key":"15384_CR16","doi-asserted-by":"publisher","first-page":"1907","DOI":"10.1016\/S0140-6736(17)30505-6","volume":"389","author":"AJ Cohen","year":"2017","unstructured":"Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., \u2026 Murray, C. J. L. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), 1907\u20131918. https:\/\/doi.org\/10.1016\/S0140-6736(17)30505-6","journal-title":"The Lancet"},{"key":"15384_CR17","unstructured":"Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. arXiv preprint arXiv:1706.09516 (extended version in Proceedings of Machine Learning Research). https:\/\/arxiv.org\/abs\/1706.09516"},{"key":"15384_CR18","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.psep.2020.10.048","volume":"149","author":"AH Elsheikh","year":"2021","unstructured":"Elsheikh, A. H., Saba, A. I., Elaziz, M. A., Lu, S., Shanmugan, S., Muthuramalingam, T., Kumar, R., Mosleh, A. O., Essa, F. A., & Shehabeldeen, T. A. (2021). Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia. Process Safety and Environmental Protection, 149, 223\u2013233. https:\/\/doi.org\/10.1016\/j.psep.2020.10.048","journal-title":"Process Safety and Environmental Protection"},{"key":"15384_CR19","unstructured":"Eskes, H., van Geff en, J., Boersma, F., Eichmann, K.-U., Apituley, A., Pedergnana, M., Sneep, M., Veefkind, J. P., &Loyola, D. (2022). Sentinel-5P TROPOMI Level-2 Product User Manual: Nitrogen Dioxide (Report S5P-KNMI-L2-0021-MA, version 4.1.0, 11 July 2022). Royal Netherlands Meteorological Institute (KNMI). https:\/\/sentinels.copernicus.eu\/documents\/247904\/2474726\/Sentinel-5P-Level-2-Product-User-Manual-Nitrogen-Dioxide.pdf"},{"issue":"5","key":"15384_CR20","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1007\/s00376-022-2161-8","volume":"40","author":"IE Gbode","year":"2023","unstructured":"Gbode, I. E., Babalola, T. E., Diro, G. T., & Intsiful, J. D. (2023). Assessment of ERA5 and ERA-Interim in reproducing mean and extreme climates over West Africa. Advances in Atmospheric Sciences, 40(5), 912\u2013932. https:\/\/doi.org\/10.1007\/s00376-022-2161-8","journal-title":"Advances in Atmospheric Sciences"},{"key":"15384_CR21","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.rse.2015.05.016","volume":"166","author":"G Geng","year":"2015","unstructured":"Geng, G., Zhang, Q., Martin, R. V., van Donkelaar, A., Huo, H., Che, H., Lin, J., & He, K. (2015). Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model. Remote Sensing of Environment, 166, 262\u2013270. https:\/\/doi.org\/10.1016\/j.rse.2015.05.016","journal-title":"Remote Sensing of Environment"},{"issue":"1","key":"15384_CR22","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomised trees. Machine Learning, 63(1), 3\u201342. https:\/\/doi.org\/10.1007\/s10994-006-6226-1","journal-title":"Machine Learning"},{"issue":"477","key":"15384_CR23","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1198\/016214506000001437","volume":"102","author":"T Gneiting","year":"2007","unstructured":"Gneiting, T., & Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477), 359\u2013378. https:\/\/doi.org\/10.1198\/016214506000001437","journal-title":"Journal of the American Statistical Association"},{"key":"15384_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemosphere.2023.139518","volume":"338","author":"R Gokulan","year":"2023","unstructured":"Gokulan, R., Hayder, G., Kanagarathinam, K., Alagumalai, A., & Sonne, C. (2023). Air quality prediction by machine learning models: A predictive study on the Indian coastal city of Visakhapatnam. Chemosphere, 338, Article 139518. https:\/\/doi.org\/10.1016\/j.chemosphere.2023.139518","journal-title":"Chemosphere"},{"key":"15384_CR25","doi-asserted-by":"publisher","unstructured":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https:\/\/doi.org\/10.1007\/978-0-387-84858-7","DOI":"10.1007\/978-0-387-84858-7"},{"issue":"730","key":"15384_CR26","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1002\/qj.3803","volume":"146","author":"H Hersbach","year":"2020","unstructured":"Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Hor\u00e1nyi, A., Mu\u00f1oz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., \u2026 Th\u00e9paut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999\u20132049. https:\/\/doi.org\/10.1002\/qj.3803","journal-title":"Quarterly Journal of the Royal Meteorological Society"},{"key":"15384_CR27","doi-asserted-by":"publisher","unstructured":"Huang, G., Li, X., Chen, Y., et al. (2019). Applications and performance analysis of CatBoost algorithm. Journal of Big Data, 6(1), Article 89. https:\/\/doi.org\/10.1186\/s40537-019-0239-5","DOI":"10.1186\/s40537-019-0239-5"},{"issue":"12","key":"15384_CR28","doi-asserted-by":"publisher","first-page":"8853","DOI":"10.5194\/acp-21-8853-2021","volume":"21","author":"A Inness","year":"2021","unstructured":"Inness, A., Ades, M., Agust\u00ed-Panareda, A., Barr\u00e9, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., & Suttie, M. (2021). The CAMS reanalysis of atmospheric composition. Atmospheric Chemistry and Physics, 21(12), 8853\u20138911. https:\/\/doi.org\/10.5194\/acp-21-8853-2021","journal-title":"Atmospheric Chemistry and Physics"},{"issue":"4","key":"15384_CR29","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.5194\/acp-5-1053-2005","volume":"5","author":"M Kanakidou","year":"2005","unstructured":"Kanakidou, M., Seinfeld, J. H., Pandis, S. N., Barnes, I., Dentener, F. J., Facchini, M. C., Van Dingenen, R., Ervens, B., Nenes, A., Nielsen, C. J., Swietlicki, E., Putaud, J. P., Balkanski, Y., Fuzzi, S., Horth, J., Moortgat, G. K., Winterhalter, R., Myhre, C. E. L., Tsigaridis, K., \u2026 Vignati, E. (2005). Organic aerosol and global climate modelling: A review. Atmospheric Chemistry and Physics, 5(4), 1053\u20131123. https:\/\/doi.org\/10.5194\/acp-5-1053-2005","journal-title":"Atmospheric Chemistry and Physics"},{"issue":"2","key":"15384_CR30","doi-asserted-by":"publisher","DOI":"10.3390\/atmos12020182","volume":"12","author":"M Kebe","year":"2021","unstructured":"Kebe, M., Traore, A., Manousakas, M. I., Vasilatou, V., Ndao, A. S., Wague, A., & Eleftheriadis, K. (2021). Source apportionment and assessment of air quality index of PM2.5\u201310 and PM2.5 in at two different sites in urban background area in Senegal. Atmosphere, 12(2), Article 182. https:\/\/doi.org\/10.3390\/atmos12020182","journal-title":"Atmosphere"},{"issue":"1","key":"15384_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s44273-025-00056-1","volume":"19","author":"M Kebe","year":"2025","unstructured":"Kebe, M., Traore, A., Sow, M., Gueye, M., Gueye, A., Dia, A., & Wague, A. (2025). Human health risk evaluation of particle air pollution (PM10 and PM2.5) and heavy metals in Dakar\u2019s two urban areas. Asian Journal of Atmospheric Environment, 19(1), Article Article 7. https:\/\/doi.org\/10.1007\/s44273-025-00056-1","journal-title":"Asian Journal of Atmospheric Environment"},{"issue":"9","key":"15384_CR32","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1038\/nclimate2727","volume":"5","author":"P Knippertz","year":"2015","unstructured":"Knippertz, P., Evans, M. J., Field, P. R., Fink, A. H., Liousse, C., & Marsham, J. H. (2015). The possible role of local air pollution in climate change in West Africa. Nature Climate Change, 5(9), 815\u2013822. https:\/\/doi.org\/10.1038\/nclimate2727","journal-title":"Nature Climate Change"},{"issue":"17","key":"15384_CR33","doi-asserted-by":"publisher","DOI":"10.3390\/en15176428","volume":"15","author":"J Kujawska","year":"2022","unstructured":"Kujawska, J., Kulisz, M., Oleszczuk, P., & Cel, W. (2022). Machine learning methods to forecast the concentration of PM\u2081\u2080 in Lublin, Poland. Energies, 15(17), Article 6428. https:\/\/doi.org\/10.3390\/en15176428","journal-title":"Energies"},{"key":"15384_CR34","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.envint.2014.11.019","volume":"75","author":"P Kumar","year":"2015","unstructured":"Kumar, P., Morawska, L., Martani, C., Biskos, G., Mihalopoulos, N., Harrison, R. M., & Norford, L. (2015). The rise of low-cost sensing for managing air pollution in cities. Environment International, 75, 199\u2013205. https:\/\/doi.org\/10.1016\/j.envint.2014.11.019","journal-title":"Environment International"},{"issue":"12","key":"15384_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2020.e05618","volume":"6","author":"S Kumar","year":"2020","unstructured":"Kumar, S., Mishra, S., & Singh, S. K. (2020). A machine learning-based model to estimate PM2.5 concentration levels in Delhi\u2019s atmosphere. Heliyon, 6(12), Article e05618. https:\/\/doi.org\/10.1016\/j.heliyon.2020.e05618","journal-title":"Heliyon"},{"issue":"2182","key":"15384_CR36","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2015.0257","volume":"471","author":"X Liang","year":"2015","unstructured":"Liang, X., Zou, T., Guo, B., Li, S., Zhang, H., Zhang, S., Huang, H., & Chen, S. X. (2015). Assessing Beijing\u2019s PM2.5 pollution: Severity, weather impact, APEC and winter heating. Proceedings of the Royal Society a: Mathematical, Physical and Engineering Sciences, 471(2182), Article 20150257. https:\/\/doi.org\/10.1098\/rspa.2015.0257","journal-title":"Proceedings of the Royal Society a: Mathematical, Physical and Engineering Sciences"},{"issue":"3","key":"15384_CR37","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/9\/3\/035003","volume":"9","author":"C Liousse","year":"2014","unstructured":"Liousse, C., Assamoi, E., Criqui, P., Granier, C., & Rosset, R. (2014). Explosive growth in African combustion emissions from 2005 to 2030. Environmental Research Letters, 9(3), Article 035003. https:\/\/doi.org\/10.1088\/1748-9326\/9\/3\/035003","journal-title":"Environmental Research Letters"},{"issue":"1","key":"15384_CR38","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56\u201367. https:\/\/doi.org\/10.1038\/s42256-019-0138-9","journal-title":"Nature Machine Intelligence"},{"key":"15384_CR39","first-page":"4768","volume":"30","author":"SM Lundberg","year":"2017","unstructured":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4768\u20134777.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"11","key":"15384_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/rs13112179","volume":"13","author":"P Mateus","year":"2021","unstructured":"Mateus, P., Mendes, V. B., & Plecha, S. M. (2021). HGPT2: An ERA5-based global model to estimate relative humidity. Remote Sensing, 13(11), Article Article 2179. https:\/\/doi.org\/10.3390\/rs13112179","journal-title":"Remote Sensing"},{"issue":"10","key":"15384_CR41","doi-asserted-by":"publisher","DOI":"10.1002\/gch2.202500108","volume":"9","author":"VN Matthaios","year":"2025","unstructured":"Matthaios, V. N., Pope, D., Koutrakis, P., Olopade, C. O., & North, C. M. (2025). The struggle against air pollution in African megacities and the hidden problems for the estimation of the burden of disease. Global Challenges, 9(10), Article Article 202500108. https:\/\/doi.org\/10.1002\/gch2.202500108","journal-title":"Global Challenges"},{"key":"15384_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2022.105637","volume":"161","author":"A McCarron","year":"2023","unstructured":"McCarron, A., Heimann, M., & Williams, M. L. (2023). Machine learning model choice is secondary to data quality in environmental prediction. Environmental Modelling & Software, 161, Article Article 105637. https:\/\/doi.org\/10.1016\/j.envsoft.2022.105637","journal-title":"Environmental Modelling & Software"},{"issue":"23","key":"15384_CR43","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17239151","volume":"17","author":"C Mir Alvarez","year":"2020","unstructured":"Mir Alvarez, C., Hourcade, R., Lefebvre, B., & Pilot, E. (2020). A scoping review on air quality monitoring, policy and health in West African cities. International Journal of Environmental Research and Public Health, 17(23), Article 9151. https:\/\/doi.org\/10.3390\/ijerph17239151","journal-title":"International Journal of Environmental Research and Public Health"},{"issue":"3","key":"15384_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/s10661-020-8115-5","volume":"192","author":"S Muhammad","year":"2020","unstructured":"Muhammad, S., Shah, M. T., & Khan, S. (2020). Health risk assessment of heavy metals in soils and vegetables from wastewater irrigated area, Peshawar, Pakistan. Environmental Monitoring and Assessment, 192(3), Article 157. https:\/\/doi.org\/10.1007\/s10661-020-8115-5","journal-title":"Environmental Monitoring and Assessment"},{"issue":"9","key":"15384_CR45","doi-asserted-by":"publisher","first-page":"4349","DOI":"10.5194\/essd-13-4349-2021","volume":"13","author":"J Mu\u00f1oz-Sabater","year":"2021","unstructured":"Mu\u00f1oz-Sabater, J. (2021). ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13(9), 4349\u20134383. https:\/\/doi.org\/10.5194\/essd-13-4349-2021","journal-title":"Earth System Science Data"},{"issue":"4","key":"15384_CR46","doi-asserted-by":"publisher","first-page":"292","DOI":"10.3846\/jeelm.2024.22361","volume":"32","author":"TNT Nguyen","year":"2024","unstructured":"Nguyen, T. N. T., et al. (2024). Statistical and machine learning approaches for estimating pollution of fine particulate matter (PM2.5) in Vietnam. Journal of Environmental Engineering and Landscape Management, 32(4), 292\u2013304. https:\/\/doi.org\/10.3846\/jeelm.2024.22361","journal-title":"Journal of Environmental Engineering and Landscape Management"},{"issue":"5","key":"15384_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s10661-021-09049-3","volume":"193","author":"N Onyeuwaoma","year":"2021","unstructured":"Onyeuwaoma, N., Okoh, D., & Okere, B. (2021). A neural network-based method for modeling PM 2.5 measurements obtained from the surface particulate matter network. Environmental Monitoring and Assessment, 193(5), Article 315. https:\/\/doi.org\/10.1007\/s10661-021-09049-3","journal-title":"Environmental Monitoring and Assessment"},{"key":"15384_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/s11270-025-08122-8","volume":"236","author":"Y \u00d6z\u00fcpak","year":"2025","unstructured":"\u00d6z\u00fcpak, Y., Alpsalaz, F., & Aslan, E. (2025). Air quality forecasting using machine learning: Comparative analysis and ensemble strategies for enhanced prediction. Water, Air, & Soil Pollution, 236, Article 464. https:\/\/doi.org\/10.1007\/s11270-025-08122-8","journal-title":"Water, Air, & Soil Pollution"},{"key":"15384_CR49","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.5555\/1953048.2078195","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, \u00c9. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825\u20132830. https:\/\/doi.org\/10.5555\/1953048.2078195","journal-title":"Journal of Machine Learning Research"},{"issue":"428","key":"15384_CR50","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1080\/01621459.1994.10476870","volume":"89","author":"DN Politis","year":"1994","unstructured":"Politis, D. N., & Romano, J. P. (1994). The stationary bootstrap. Journal of the American Statistical Association, 89(428), 1303\u20131313. https:\/\/doi.org\/10.1080\/01621459.1994.10476870","journal-title":"Journal of the American Statistical Association"},{"issue":"12","key":"15384_CR51","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.1109\/TKDE.2018.2823740","volume":"30","author":"Z Qi","year":"2018","unstructured":"Qi, Z., Wang, T., Song, A., Zhou, H., Shi, Z., & Li, T. (2018). Deep air learning: Interpolation, prediction, and feature analysis of fine-grained air quality. IEEE Transactions on Knowledge and Data Engineering, 30(12), 2285\u20132297. https:\/\/doi.org\/10.1109\/TKDE.2018.2823740","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"15384_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2019.104909","volume":"130","author":"D Qian","year":"2019","unstructured":"Qian, D., Amini, H., Shi, L., Kloog, I., Silvern, R., Kelly, J., Sabath, M. B., Choirat, C., Koutrakis, P., Lyapustin, A., Wang, Y., Mickley, L. J., & Schwartz, J. (2019). An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environment International, 130, Article 104909. https:\/\/doi.org\/10.1016\/j.envint.2019.104909","journal-title":"Environment International"},{"key":"15384_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.uclim.2022.101315","volume":"46","author":"R Rakholia","year":"2022","unstructured":"Rakholia, R., et al. (2022). AI-based air quality PM2.5 forecasting models for developing countries: A case study of Ho Chi Minh City, Vietnam. Urban Climate, 46, Article 101315. https:\/\/doi.org\/10.1016\/j.uclim.2022.101315","journal-title":"Urban Climate"},{"issue":"7743","key":"15384_CR54","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","volume":"566","author":"M Reichstein","year":"2019","unstructured":"Reichstein, M., Camps-Valls, G., Stevens, B., et al. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195\u2013204. https:\/\/doi.org\/10.1038\/s41586-019-0912-1","journal-title":"Nature"},{"issue":"8","key":"15384_CR55","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1111\/ecog.02881","volume":"40","author":"DR Roberts","year":"2017","unstructured":"Roberts, D. R., Bahn, V., Ciuti, S., et al. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913\u2013929. https:\/\/doi.org\/10.1111\/ecog.02881","journal-title":"Ecography"},{"key":"15384_CR56","unstructured":"Seinfeld, J. H., & Pandis, S. N. (2016). Atmospheric chemistry and physics: From air pollution to climate change (3rd ed.). John Wiley & Sons."},{"issue":"16","key":"15384_CR57","doi-asserted-by":"publisher","first-page":"9069","DOI":"10.1021\/acs.est.8b02864","volume":"52","author":"G Shaddick","year":"2018","unstructured":"Shaddick, G., Thomas, M. L., Green, A., Brauer, M., van Donkelaar, A., Burnett, R., Chang, H. H., Cohen, A., van Dingenen, R., Dora, C., Duncan, B., Forouzanfar, M., Frostad, J., Gapens, L., Gatari, M., Guttikunda, S. K., Hagan, J., Amini, H., Broday, D., \u2026 Cohen, A. (2018). Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environmental Science & Technology, 52(16), 9069\u20139078. https:\/\/doi.org\/10.1021\/acs.est.8b02864","journal-title":"Environmental Science & Technology"},{"issue":"20","key":"15384_CR58","doi-asserted-by":"publisher","first-page":"11369","DOI":"10.1021\/es4022602","volume":"47","author":"EG Snyder","year":"2013","unstructured":"Snyder, E. G., Watkins, T. H., Solomon, P. A., Thoma, E. D., Williams, R. W., Hagler, G. S. W., Shelow, D., Hindin, D. A., Kilaru, V. J., & Preuss, P. W. (2013). The changing paradigm of air pollution monitoring. Environmental Science & Technology, 47(20), 11369\u201311377. https:\/\/doi.org\/10.1021\/es4022602","journal-title":"Environmental Science & Technology"},{"key":"15384_CR59","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.envint.2019.01.016","volume":"124","author":"M Stafoggia","year":"2019","unstructured":"Stafoggia, M., Schneider, A., Cyrys, J., Forastiere, F., Haupt, P., Hampel, R., Hennig, F., Jacquemin, B., Katsouyanni, K., Peters, A., Weinmayr, G., & Hoek, G. (2019). Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013\u20132015, using a spatiotemporal land-use random-forest model. Environment International, 124, 170\u2013179. https:\/\/doi.org\/10.1016\/j.envint.2019.01.016","journal-title":"Environment International"},{"issue":"5","key":"15384_CR60","doi-asserted-by":"publisher","first-page":"2527","DOI":"10.5194\/hess-24-2527-2020","volume":"24","author":"M Tarek","year":"2020","unstructured":"Tarek, M., Brissette, F. P., & Arsenault, R. (2020). Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America. Hydrology and Earth System Sciences, 24(5), 2527\u20132544. https:\/\/doi.org\/10.5194\/hess-24-2527-2020","journal-title":"Hydrology and Earth System Sciences"},{"issue":"9","key":"15384_CR61","doi-asserted-by":"publisher","first-page":"780","DOI":"10.17485\/IJST\/v17i9.2969","volume":"17","author":"A Traore","year":"2024","unstructured":"Traore, A., Kebe, M., Sow, M., Vasilatou, V., & Ndao, A. S. (2024). Comparative receptor models using principal component analysis\/absolute principal component scores and positive matrix factorization to assess source apportionment of PM2.5\u201310 and PM2.5 in urban cities. Indian Journal of Science and Technology, 17(9), 780\u2013786. https:\/\/doi.org\/10.17485\/IJST\/v17i9.2969","journal-title":"Indian Journal of Science and Technology"},{"key":"15384_CR62","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.rse.2011.09.027","volume":"120","author":"JP Veefkind","year":"2012","unstructured":"Veefkind, J. P., Aben, I., McMullan, K., F\u00f6rster, H., de Vries, J., Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., \u2026 Levelt, P. F. (2012). TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sensing of Environment, 120, 70\u201383. https:\/\/doi.org\/10.1016\/j.rse.2011.09.027","journal-title":"Remote Sensing of Environment"},{"issue":"19","key":"15384_CR63","doi-asserted-by":"publisher","first-page":"11377","DOI":"10.1029\/2019JD030674","volume":"124","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Zhang, Q., Jiang, J., Zhou, W., Wang, B., He, K., & Hao, J. (2019). Enhanced sulfate formation during China\u2019s severe winter haze episode in January 2013 missing from current chemical transport models. Journal of Geophysical Research: Atmospheres, 124(19), 11377\u201311400. https:\/\/doi.org\/10.1029\/2019JD030674","journal-title":"Journal of Geophysical Research: Atmospheres"},{"issue":"1","key":"15384_CR64","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1890\/10-0340.1","volume":"92","author":"DI Warton","year":"2011","unstructured":"Warton, D. I., & Hui, F. K. (2011). The arcsine is asinine: The analysis of proportions in ecology. Ecology, 92(1), 3\u201310. https:\/\/doi.org\/10.1890\/10-0340.1","journal-title":"Ecology"},{"issue":"1","key":"15384_CR65","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1021\/acsestair.4c00366","volume":"2","author":"DM Westervelt","year":"2025","unstructured":"Westervelt, D. M., Amooli, J. A., & Anand, A. (2025). Twenty years of high spatiotemporal resolution estimates of daily PM2.5 in West Africa using satellite data, surface monitors, and machine learning. ACS ES&T Air, 2(1), 1\u201312. https:\/\/doi.org\/10.1021\/acsestair.4c00366","journal-title":"ACS ES&T Air"},{"issue":"1","key":"15384_CR66","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3354\/cr030079","volume":"30","author":"CJ Willmott","year":"2005","unstructured":"Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79\u201382. https:\/\/doi.org\/10.3354\/cr030079","journal-title":"Climate Research"},{"key":"15384_CR67","unstructured":"World Health Organization. (2021). WHO global air quality guidelines: Particulate matter (PM\u2082.\u2085 and PM\u2081\u2080), ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide. Geneva: World Health Organization. ISBN 978\u201392\u20134\u2013003422\u20138. https:\/\/www.who.int\/publications\/i\/item\/9789240034228"},{"key":"15384_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2023.107832","volume":"173","author":"Y Wu","year":"2023","unstructured":"Wu, Y., Zhang, W., Wang, J., Hu, M., & Zheng, M. (2023). Gradient boosting machines outperform deep learning in short-term PM\u2082.\u2085 forecasting under sparse monitoring: Evidence from eight Chinese megacities. Environment International, 173, Article 107832. https:\/\/doi.org\/10.1016\/j.envint.2023.107832","journal-title":"Environment International"},{"key":"15384_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2022.119411","volume":"292","author":"D Yang","year":"2023","unstructured":"Yang, D., Li, T., Zhang, H., & Liu, J. (2023). Input representation dominates performance in air pollution forecasting: A comparative study across 12 cities. Atmospheric Environment, 292, Article 119411. https:\/\/doi.org\/10.1016\/j.atmosenv.2022.119411","journal-title":"Atmospheric Environment"},{"issue":"3","key":"15384_CR70","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1016\/S0167-9473(03)00015-0","volume":"45","author":"S Ye","year":"2004","unstructured":"Ye, S., & Zhang, Z. (2004). K-nearest neighbor imputation for missing values in high-dimensional data. Computational Statistics & Data Analysis, 45(3), 571\u2013586. https:\/\/doi.org\/10.1016\/S0167-9473(03)00015-0","journal-title":"Computational Statistics & Data Analysis"},{"issue":"4","key":"15384_CR71","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1093\/biomet\/87.4.954","volume":"87","author":"IK Yeo","year":"2000","unstructured":"Yeo, I. K., & Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry. Biometrika, 87(4), 954\u2013959. https:\/\/doi.org\/10.1093\/biomet\/87.4.954","journal-title":"Biometrika"},{"key":"15384_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2021.112713","volume":"266","author":"D Zhang","year":"2021","unstructured":"Zhang, D., Du, L., Wang, W., Zhu, Q., Bi, J., Scovronick, N., Naidoo, M., Garland, R. M., & Liu, Y. (2021). A machine learning model to estimate ambient PM\u2082.\u2085 concentrations in the industrialised Highveld region of South Africa. Remote Sensing of Environment, 266, Article 112713. https:\/\/doi.org\/10.1016\/j.rse.2021.112713","journal-title":"Remote Sensing of Environment"}],"container-title":["Environmental Monitoring and Assessment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10661-026-15384-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10661-026-15384-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10661-026-15384-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T01:43:54Z","timestamp":1781315034000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10661-026-15384-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,13]]},"references-count":72,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["15384"],"URL":"https:\/\/doi.org\/10.1007\/s10661-026-15384-0","relation":{},"ISSN":["1573-2959"],"issn-type":[{"value":"1573-2959","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,13]]},"assertion":[{"value":"24 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors have read, understood, and complied, as applicable, with the \u201cEthical responsibilities of Authors\u201d statement as found in the Instructions for Authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"581"}}