{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T18:10:52Z","timestamp":1787767852015,"version":"build-2784847793"},"reference-count":92,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,22]],"date-time":"2024-12-22T00:00:00Z","timestamp":1734825600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,22]],"date-time":"2024-12-22T00:00:00Z","timestamp":1734825600000},"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":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s12145-024-01648-1","type":"journal-article","created":{"date-parts":[[2024,12,22]],"date-time":"2024-12-22T12:22:33Z","timestamp":1734870153000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Machine learning and deep learning approaches for PM2.5 prediction: a study on urban air quality in Jaipur, India"],"prefix":"10.1007","volume":"18","author":[{"given":"Saurabh","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gourav","family":"Suthar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,22]]},"reference":[{"key":"1648_CR1","doi-asserted-by":"publisher","unstructured":"Abdolrasol MG, Hussain SS, Ustun, TS, Sarker, MR, Hannan MA, Mohamed R et al (2021). Artificial neural networks based optimization techniques: A review. Electronics 10(21):2689. https:\/\/doi.org\/10.3390\/electronics10212689","DOI":"10.3390\/electronics10212689"},{"key":"1648_CR2","doi-asserted-by":"publisher","first-page":"14899","DOI":"10.1007\/s00521-020-04845-3","volume":"32","author":"AA Akinpelu","year":"2020","unstructured":"Akinpelu AA, Ali ME, Owolabi TO, Johan MR, Saidur R, Olatunji SO, Chowdbury Z (2020) A support vector regression model for the prediction of total polyaromatic hydrocarbons in soil: an artificial intelligent system for mapping environmental pollution. Neural Comput Appl 32:14899\u201314908. https:\/\/doi.org\/10.1007\/s00521-020-04845-3","journal-title":"Neural Comput Appl"},{"key":"1648_CR3","doi-asserted-by":"publisher","unstructured":"Amnuaylojaroen T, Parasin N (2024) Pathogenesis of PM2.5-related disorders in different age groups: children, adults, and the elderly. Epigenomes 8(2):13. https:\/\/doi.org\/10.3390\/epigenomes8020013","DOI":"10.3390\/epigenomes8020013"},{"key":"1648_CR4","doi-asserted-by":"publisher","unstructured":"Araujo LN, Belotti JT, Alves TA, de Souza Tadano Y, Siqueira H (2020) Ensemble method based on artificial neural networks to estimate air pollution health risks. Environ Model Softw 123. https:\/\/doi.org\/10.1016\/j.envsoft.2019.104567","DOI":"10.1016\/j.envsoft.2019.104567"},{"key":"1648_CR5","doi-asserted-by":"publisher","unstructured":"Bedi S, Katiyar A, Krishnan NA, and Kota SH (2024) Utilizing LSTM models to predict PM2.5 levels during critical episodes in Delhi, the world's most polluted capital city. Urban Clim 53:101835. https:\/\/doi.org\/10.1016\/j.uclim.2024.101835","DOI":"10.1016\/j.uclim.2024.101835"},{"key":"1648_CR6","doi-asserted-by":"publisher","unstructured":"Castelli M, Clemente FM, Popovi\u010d A, Silva S, and Vanneschi L (2020) A machine learning approach to predict air quality in California. Complexity.\u00a0https:\/\/doi.org\/10.1155\/2020\/8049504","DOI":"10.1155\/2020\/8049504"},{"key":"1648_CR7","doi-asserted-by":"publisher","unstructured":"Cesler-Maloney M, Simpson W, Kuhn J, Stutz J, Thomas J, Roberts T, and Cooperdock S (2024) Shallow boundary layer heights controlled by the surface-based temperature inversion strength are responsible for trapping home heating emissions near the ground level in Fairbanks, Alaska. EGUsphere 1-51. https:\/\/doi.org\/10.5194\/egusphere-2023-3082","DOI":"10.5194\/egusphere-2023-3082"},{"key":"1648_CR8","doi-asserted-by":"publisher","unstructured":"Chu B, Ma Q, Liu J, Ma J, Zhang P, Chen T, and He H (2020) Air pollutant correlations in China: secondary air pollutant responses to NO x and SO2 control. Environ Technol Lett 7(10):695\u2013700. https:\/\/doi.org\/10.1021\/acs.estlett.0c00403","DOI":"10.1021\/acs.estlett.0c00403"},{"key":"1648_CR9","unstructured":"CPCB (2009) National Ambient Air Quality Standards. Retrieved from https:\/\/cpcb.nic.in\/uploads\/National_Ambient_Air_Quality_Standards.pdf (Accessed on April 2024)."},{"key":"1648_CR10","doi-asserted-by":"publisher","unstructured":"Du XX, Shi GM, Zhao TL, Yang FM, Zheng XB, Zhang YJ, Tan QW (2020). Contribution of secondary particles to wintertime PM2.5 during 2015\u20132018 in a major urban area of the Sichuan Basin, Southwest China. ESS 7(6):e2020EA001194. https:\/\/doi.org\/10.1029\/2020EA001194","DOI":"10.1029\/2020EA001194"},{"key":"1648_CR11","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s41810-020-00087-x","volume":"5","author":"S Dutta","year":"2021","unstructured":"Dutta S, Ghosh S, Dinda S (2021) Urban air-quality assessment and inferring the association between different factors: A comparative study among Delhi, Kolkata and Chennai megacity of India. ASE 5:93\u2013111. https:\/\/doi.org\/10.1007\/s41810-020-00087-x","journal-title":"ASE"},{"key":"1648_CR12","doi-asserted-by":"publisher","unstructured":"Fang C, Zhang Z, Jin M, Zou P, Wang J. (2017) Pollution characteristics of PM2. 5 aerosol during haze periods in Changchun, China. AAQR 17(4):888\u2013895. https:\/\/doi.org\/10.4209\/aaqr.2016.09.0407","DOI":"10.4209\/aaqr.2016.09.0407"},{"key":"1648_CR13","doi-asserted-by":"publisher","unstructured":"Faraji M, Nadi S, Ghaffarpasand O, Homayoni S, and Downey K (2022) An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2. 5 concentration in urban environment. Sci Total Environ 834:155324. https:\/\/doi.org\/10.3390\/app10061953","DOI":"10.3390\/app10061953"},{"key":"1648_CR14","doi-asserted-by":"publisher","unstructured":"Gao, Z., Do, K., Li, Z., Jiang, X., Maji, K. J., Ivey, C. E., & Russell, A. G. 2024. Predicting PM2. 5 levels and exceedance days using machine learning methods. Atmos. Environ 120396. https:\/\/doi.org\/10.1016\/j.atmosenv.2024.120396","DOI":"10.1016\/j.atmosenv.2024.120396"},{"key":"1648_CR15","doi-asserted-by":"publisher","unstructured":"Gerges F, Llaguno-Munitxa M, Zondlo MA, Boufadel MC, Bou-Zeid E (2024). Weather and the city: machine learning for predicting and attributing fine scale air quality to meteorological and\u00a0Urban determinants. Environ Sci Tech.\u00a0https:\/\/doi.org\/10.1021\/acs.est.4c00783","DOI":"10.1021\/acs.est.4c00783"},{"key":"1648_CR16","doi-asserted-by":"publisher","unstructured":"Gokul PR, Mathew A, Bhosale A, Nair AT (2023) Spatio-temporal air quality analysis and PM2.5 prediction over Hyderabad City, India using artificial intelligence techniques. Ecol. Inform 76:102067. https:\/\/doi.org\/10.1016\/j.ecoinf.2023.102067","DOI":"10.1016\/j.ecoinf.2023.102067"},{"key":"1648_CR17","doi-asserted-by":"publisher","unstructured":"Goudarzi G, Hopke PK, Yazdani M (2021) Forecasting PM2.5 concentration using artificial neural network and its health effects in Ahvaz, Iran. Chemosphere 283:131285. https:\/\/doi.org\/10.1016\/j.chemosphere.2021.131285","DOI":"10.1016\/j.chemosphere.2021.131285"},{"key":"1648_CR18","doi-asserted-by":"publisher","unstructured":"Guo X, Zhang M, Gao Z, Zhang J, Buccolieri R (2023). Neighborhood-scale dispersion of traffic-related PM2.5: Simulations of nine typical residential cases from Nanjing. Sustain Cities Soc 90:104393. https:\/\/doi.org\/10.1016\/j.scs.2023.104393","DOI":"10.1016\/j.scs.2023.104393"},{"key":"1648_CR19","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1016\/j.procs.2020.04.221","volume":"171","author":"KS Harishkumar","year":"2020","unstructured":"Harishkumar KS, Yogesh KM, Gad I (2020) Forecasting air pollution particulate matter (PM2.5) using machine learning regression models. Procedia Comput Sci 171:2057\u20132066. https:\/\/doi.org\/10.1016\/j.procs.2020.04.221","journal-title":"Procedia Comput Sci"},{"key":"1648_CR20","doi-asserted-by":"publisher","unstructured":"Hope TM (2020).\u00a0Chapter 4 - Linear regression. Mach Learn 67\u201381.\u00a0https:\/\/doi.org\/10.1016\/B978-0-12-815739-8.00004-3","DOI":"10.1016\/B978-0-12-815739-8.00004-3"},{"key":"1648_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5194\/egusphere-2024-343","volume":"2024","author":"PC Huang","year":"2024","unstructured":"Huang PC, Hung HM, Lai HC, Chou CCK (2024) Assessing the Effectiveness of SO2, NOx, and NH3 Emission Reductions in Mitigating Winter PM2.5 in Taiwan Using CMAQ Model. Egusphere 2024:1\u201330. https:\/\/doi.org\/10.5194\/egusphere-2024-343","journal-title":"Egusphere"},{"key":"1648_CR22","doi-asserted-by":"publisher","unstructured":"Humbal, A., Chaudhary, N., & Pathak, B. 2023. Urbanization trends, climate change, and environmental sustainability. In Climate Change and Urban Environment Sustainability (pp. 151\u2013166). Singapore: Springer Nature Singapore. https:\/\/doi.org\/10.1007\/978-981-19-7618-6_9","DOI":"10.1007\/978-981-19-7618-6_9"},{"key":"1648_CR23","doi-asserted-by":"publisher","unstructured":"Istiana T, Kurniawan B, Soekirno S, Nahas A, Wihono A, Nuryanto DE, Hakim ML (2023) Causality analysis of air quality and meteorological parameters for PM2.5 characteristics determination: evidence from Jakarta. AS&T 23(9):230014. https:\/\/doi.org\/10.4209\/aaqr.230014","DOI":"10.4209\/aaqr.230014"},{"issue":"17","key":"1648_CR24","doi-asserted-by":"publisher","first-page":"10587","DOI":"10.3390\/ijerph191710587","volume":"19","author":"A Izzotti","year":"2022","unstructured":"Izzotti A, Spatera P, Khalid Z, Pulliero A (2022) Importance of punctual monitoring to evaluate the health effects of airborne particulate matter. Int J Environ Res Public Health 19(17):10587. https:\/\/doi.org\/10.3390\/ijerph191710587","journal-title":"Int J Environ Res Public Health"},{"key":"1648_CR25","doi-asserted-by":"publisher","unstructured":"Jat R, Gurjar BR (2021) Contribution of different source sectors and source regions of Indo-Gangetic Plain in India to PM2. 5 pollution and its short-term health impacts during peak polluted winter. Atmos. Pollut Res 12(4):89\u2013100. https:\/\/doi.org\/10.1016\/j.apr.2021.02.016","DOI":"10.1016\/j.apr.2021.02.016"},{"issue":"1","key":"1648_CR26","doi-asserted-by":"publisher","first-page":"4275","DOI":"10.1038\/s41467-022-31962-5","volume":"13","author":"R Jha","year":"2022","unstructured":"Jha R, Mondal A, Devanand A, Roxy MK, Ghosh S (2022) Limited influence of irrigation on pre-monsoon heat stress in the Indo-Gangetic Plain. Nat Commun 13(1):4275. https:\/\/doi.org\/10.1038\/s41467-022-31962-5","journal-title":"Nat Commun"},{"key":"1648_CR27","doi-asserted-by":"publisher","unstructured":"Kermani M, Jafari AJ, Gholami M, Fanaei F, Arfaeinia H. (2020) Association between meteorological parameter and PM2.5 concentration in Karaj, Iran. Int J Environ Health Eng 9(1):4. https:\/\/doi.org\/10.4103\/ijehe.ijehe_14_20","DOI":"10.4103\/ijehe.ijehe_14_20"},{"issue":"7","key":"1648_CR28","doi-asserted-by":"publisher","first-page":"713","DOI":"10.3390\/machines11070713","volume":"11","author":"MZ Khaneghah","year":"2023","unstructured":"Khaneghah MZ, Alzayed M, Chaoui H (2023) Fault detection and diagnosis of the electric motor drive and battery system of electric vehicles. Machines 11(7):713. https:\/\/doi.org\/10.3390\/machines11070713","journal-title":"Machines"},{"issue":"2","key":"1648_CR29","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.apr.2020.10.007","volume":"12","author":"DN Khojasteh","year":"2021","unstructured":"Khojasteh DN, Goudarzi G, Taghizadeh-Mehrjardi R, Asumadu-Sakyi AB, Fehresti-Sani M (2021) Long-term effects of outdoor air pollution on mortality and morbidity\u2013prediction using nonlinear autoregressive and artificial neural networks models. Atmos Pollut Res 12(2):46\u201356. https:\/\/doi.org\/10.1016\/j.apr.2020.10.007","journal-title":"Atmos Pollut Res"},{"issue":"1","key":"1648_CR30","doi-asserted-by":"publisher","first-page":"18479","DOI":"10.1149\/10701.18479ecst","volume":"107","author":"K Kuldeep","year":"2022","unstructured":"Kuldeep K, Kumar P, Kamboj P, Mathur AK (2022) Air quality decrement after lockdown in major cities of Rajasthan. India ECS Trans 107(1):18479. https:\/\/doi.org\/10.1149\/10701.18479ecst","journal-title":"India ECS Trans"},{"key":"1648_CR31","doi-asserted-by":"publisher","unstructured":"Kulkarni S, Bali HS, Krishna R (2023) Estimation of air quality index in delhi by merging neural networks and multiple regression techniques with principal components analysis. In 2023 Winter Summit on Smart Computing and Networks (WiSSCoN) (pp. 1\u20136). IEEE. https:\/\/doi.org\/10.1109\/WiSSCoN56857.2023.10133846","DOI":"10.1109\/WiSSCoN56857.2023.10133846"},{"key":"1648_CR32","doi-asserted-by":"publisher","unstructured":"Kumshe UMM, Abdulhamid ZM, Mala BA, Muazu T, Muhammad AU, Sangary O, Bala MM (2024) Improving short-term daily streamflow forecasting using an autoencoder based CNN-LSTM model. Water Resour Manag 1\u201317. https:\/\/doi.org\/10.1007\/s11269-024-03937-2","DOI":"10.1007\/s11269-024-03937-2"},{"issue":"7","key":"1648_CR33","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.3390\/atmos14071056","volume":"14","author":"T Li","year":"2023","unstructured":"Li T, Wang Z (2023a) Increasing NH3 emissions in high emission seasons and its Spatiotemporal evolution characteristics during 1850\u20132060. Atmos 14(7):1056. https:\/\/doi.org\/10.3390\/atmos14071056","journal-title":"Atmos"},{"key":"1648_CR34","doi-asserted-by":"publisher","unstructured":"Li Z, Yim SHL, Ho KF (2020) High temporal resolution prediction of street-level PM2. 5 and NOx concentrations using machine learning approach. J Clean Prod 268:121975. https:\/\/doi.org\/10.1016\/j.jclepro.2020.121975","DOI":"10.1016\/j.jclepro.2020.121975"},{"key":"1648_CR35","doi-asserted-by":"publisher","unstructured":"Li Z, Tong X, Ho JMW, Kwok TC, Dong G, Ho KF Yim SHL (2021) A practical framework for predicting residential indoor PM2.5 concentration using land-use regression and machine learning methods. Chemosphere 265:129140. https:\/\/doi.org\/10.1016\/j.chemosphere.2020.129140","DOI":"10.1016\/j.chemosphere.2020.129140"},{"key":"1648_CR36","doi-asserted-by":"publisher","unstructured":"Li J, Ho SC, Griffith SM, Huang Y, Cheung RK, Hallquist M, Yu JZ (2023b) Concurrent measurements of nitrate at urban and suburban sites identify local nitrate formation as a driver for urban episodic PM2. 5 pollution. Sci Total Environ 897:165351. https:\/\/doi.org\/10.1016\/j.scitotenv.2023.165351","DOI":"10.1016\/j.scitotenv.2023.165351"},{"key":"1648_CR37","doi-asserted-by":"publisher","unstructured":"Li N, Li Y, Xu D, Liu Z, Li N, Chartier R, Xu C (2024a). Predicting personal exposure to PM 2.5 using different determinants and machine learning algorithms in two megacities, China. Indoor Air.\u00a0https:\/\/doi.org\/10.1155\/2024\/5589891","DOI":"10.1155\/2024\/5589891"},{"key":"1648_CR38","doi-asserted-by":"publisher","unstructured":"Li Y, Wang T, Wang QG, Li M, Qu Y, Wu H, Xie M (2024b). The impact of aerosol-radiation interaction and heterogeneous chemistry on the winter decreasing PM2. 5 and increasing O3 in Eastern China 2014\u20132020. J Environ Sci\u00a0https:\/\/doi.org\/10.1016\/j.jes.2024.04.010","DOI":"10.1016\/j.jes.2024.04.010"},{"key":"1648_CR39","doi-asserted-by":"publisher","unstructured":"Lin C, Wang Y, Ooka R, Flageul C, Kim Y, Kikumoto H, Sartelet K (2022). Modelling of street-scale pollutant dispersion by coupled simulation of chemical reaction, aerosol dynamics, and CFD. Atmos Chem Phys Discuss 1\u201332. https:\/\/doi.org\/10.5194\/acp-23-1421-2023, 2023","DOI":"10.5194\/acp-23-1421-2023"},{"key":"1648_CR40","doi-asserted-by":"publisher","unstructured":"Liu Y, Song M, Liu X, Zhang Y, Hui L, Kong L, Feng M (2020). Characterization and sources of volatile organic compounds (VOCs) and their related changes during ozone pollution days in 2016 in Beijing, China. Environ Pollut 257:113599. https:\/\/doi.org\/10.1016\/j.envpol.2019.113599","DOI":"10.1016\/j.envpol.2019.113599"},{"issue":"4","key":"1648_CR41","doi-asserted-by":"publisher","first-page":"2124","DOI":"10.1021\/acs.est.1c06157","volume":"56","author":"X Liu","year":"2022","unstructured":"Liu X, Lu D, Zhang A, Liu Q, Jiang G (2022) Data-driven machine learning in environmental pollution: gains and problems. Environ Sci Tech 56(4):2124\u20132133. https:\/\/doi.org\/10.1021\/acs.est.1c06157","journal-title":"Environ Sci Tech"},{"key":"1648_CR42","doi-asserted-by":"publisher","unstructured":"Lu Z, Guan Y, Shao C, Niu R (2023) Assessing the health impacts of PM2. 5 and ozone pollution and their comprehensive correlation in Chinese cities based on extended correlation coefficient. Ecotoxicol Environ Saf 2621:15125. https:\/\/doi.org\/10.1016\/j.ecoenv.2023.115125","DOI":"10.1016\/j.ecoenv.2023.115125"},{"key":"1648_CR43","doi-asserted-by":"publisher","unstructured":"Maciejczyk P, Chen LC, Thurston G (2021) The role of fossil fuel combustion metals in PM2. 5 air pollution health associations. Atmos 12(9):1086. https:\/\/doi.org\/10.3390\/atmos12091086","DOI":"10.3390\/atmos12091086"},{"issue":"14","key":"1648_CR44","doi-asserted-by":"publisher","first-page":"8752","DOI":"10.3390\/ijerph19148752","volume":"19","author":"TZ Maung","year":"2022","unstructured":"Maung TZ, Bishop JE, Holt E, Turner AM, Pfrang C (2022) Indoor air pollution and the health of vulnerable groups: a systematic review focused on particulate matter (PM), volatile organic compounds (VOCs) and their effects on children and people with pre-existing lung disease. Int J Environ Res Public Health 19(14):8752. https:\/\/doi.org\/10.3390\/ijerph19148752","journal-title":"Int J Environ Res Public Health"},{"issue":"2","key":"1648_CR45","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1007\/s12145-023-00952-6","volume":"16","author":"AU Muhammad","year":"2023","unstructured":"Muhammad AU, Abba SI (2023) Transfer learning for streamflow forecasting using unguaged MOPEX basins data set. Earth Sci Inform 16(2):1241\u20131264. https:\/\/doi.org\/10.1007\/s12145-023-00952-6","journal-title":"Earth Sci Inform"},{"key":"1648_CR46","doi-asserted-by":"publisher","unstructured":"Muhammad AU, Muazu T, Ying H, Ba AF, Tijjani S, Adam JM, Yahaya MS (2024) Enhanced streamflow forecasting using attention-based neural network models: a comparative study in MOPEX basins. Model Earth Syst Env 10(4):5717\u20135734. https:\/\/doi.org\/10.1007\/s40808-024-02088-y","DOI":"10.1007\/s40808-024-02088-y"},{"key":"1648_CR47","doi-asserted-by":"publisher","unstructured":"Mukta TA, Hoque MMM, Sarker ME, Hossain MN, Biswas GK (2020) Seasonal variations of gaseous air pollutants (SO2, NO2, O3, CO) and particulates (PM2. 5, PM10) in Gazipur: an industrial city in Bangladesh. Adv Environ Technol 6(4):195\u2013209. https:\/\/doi.org\/10.22104\/aet.2021.4890.1320","DOI":"10.22104\/aet.2021.4890.1320"},{"issue":"738","key":"1648_CR48","doi-asserted-by":"publisher","first-page":"2834","DOI":"10.1002\/qj.4102","volume":"147","author":"M Musiolkov\u00e1","year":"2021","unstructured":"Musiolkov\u00e1 M, Husz\u00e1r P, Navr\u00e1til M, \u0160punda V (2021) Impact of season, cloud cover, and air pollution on different spectral regions of ultraviolet and visible incident solar radiation at the surface. Q J R Meteorol Soc 147(738):2834\u20132849. https:\/\/doi.org\/10.1002\/qj.4102","journal-title":"Q J R Meteorol Soc"},{"key":"1648_CR49","doi-asserted-by":"publisher","unstructured":"Nan N, Yan Z, Zhang Y, Chen R, Qin G, Sang N (2023) Overview of PM2. 5 and health outcomes: focusing on components, sources, and pollutant mixture co-exposure. Chemosphere 323:138181. https:\/\/doi.org\/10.1016\/j.chemosphere.2023.138181","DOI":"10.1016\/j.chemosphere.2023.138181"},{"issue":"1","key":"1648_CR50","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s44274-023-00018-w","volume":"1","author":"MT Nejad","year":"2023","unstructured":"Nejad MT, Ghalehteimouri KJ, Talkhabi H, Dolatshahi Z (2023) The relationship between atmospheric temperature inversion and urban air pollution characteristics: a case study of Tehran. Iran Discover Environment 1(1):17. https:\/\/doi.org\/10.1007\/s44274-023-00018-w","journal-title":"Iran Discover Environment"},{"key":"1648_CR51","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.jes.2023.07.020","volume":"145","author":"GTH Nguyen","year":"2024","unstructured":"Nguyen GTH, La LT, Hoang-Cong H, Le AH (2024) An exploration of meteorological effects on PM2.5 air quality in several provinces and cities in Vietnam. J Environ Sci 145:139\u2013151. https:\/\/doi.org\/10.1016\/j.jes.2023.07.020","journal-title":"J Environ Sci"},{"key":"1648_CR52","doi-asserted-by":"publisher","unstructured":"Park SY, Woo SH, Lim C (2023). Predicting PM10 and PM2. 5 concentration in container ports: A deep learning approach. Transp Res Part D: Transp Environ 115:103601. https:\/\/doi.org\/10.1016\/j.trd.2022.103601","DOI":"10.1016\/j.trd.2022.103601"},{"key":"1648_CR53","doi-asserted-by":"publisher","unstructured":"Ping L, Wang Y, Lu Y, Lee LC, Liang C (2023) Tracing the sources of PM2. 5-related health burden in China. Environ Pollut 327:121544. https:\/\/doi.org\/10.1016\/j.envpol.2023.121544","DOI":"10.1016\/j.envpol.2023.121544"},{"key":"1648_CR54","doi-asserted-by":"publisher","unstructured":"Qiu Y, Wu Z, Man R, Zong T, Liu Y, Meng X, Hu M. (2023). Secondary aerosol formation drives atmospheric particulate matter pollution over megacities (Beijing and Seoul) in East Asia. Atmos Environ 301:119702. https:\/\/doi.org\/10.1016\/j.atmosenv.2023.119702","DOI":"10.1016\/j.atmosenv.2023.119702"},{"key":"1648_CR55","doi-asserted-by":"publisher","unstructured":"Saini VK, Kumar R, Al-Sumaiti AS, Sujil A, Heydarian-Forushani E (2023) Learning based short term wind speed forecasting models for smart grid applications: An extensive review and case study. Electric Power Syst Res 222. https:\/\/doi.org\/10.1016\/j.epsr.2023.109502","DOI":"10.1016\/j.epsr.2023.109502"},{"issue":"6","key":"1648_CR56","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/s10661-022-09927-4","volume":"194","author":"N Savio","year":"2022","unstructured":"Savio N, Lone FA, Bhat JIA, Kirmani NA, Nazir N (2022) Study on the effect of vehicular pollution on the ambient concentrations of particulate matter and carbon dioxide in Srinagar City. Environ Monit Assess 194(6):393. https:\/\/doi.org\/10.1007\/s10661-022-09927-4","journal-title":"Environ Monit Assess"},{"key":"1648_CR57","doi-asserted-by":"publisher","unstructured":"Seong C, Kim D, Jeong R, Qiu Y, Wu Z, Lee JY, Song M (2024). Influence of relative humidity and composition on PM2. 5 phases in Northeast Asia. ACS Earth Space Chem.\u00a0https:\/\/doi.org\/10.1021\/acsearthspacechem.4c00019","DOI":"10.1021\/acsearthspacechem.4c00019"},{"key":"1648_CR58","doi-asserted-by":"publisher","unstructured":"Shakya D, Deshpande V, Goyal MK, Agarwal M (2023) PM2. 5 air pollution prediction through deep learning using meteorological, vehicular, and emission data: a case study of New Delhi, India. J Clean Prod 427:139278. https:\/\/doi.org\/10.1016\/j.jclepro.2023.139278","DOI":"10.1016\/j.jclepro.2023.139278"},{"key":"1648_CR59","doi-asserted-by":"publisher","unstructured":"Shan M, Wang Y, Lu Y, Liang C, Wang T, Li L, Li RYM (2023). Uncovering PM2. 5 transport trajectories and sources at district within city scale. J Clean Prod 423:138608. https:\/\/doi.org\/10.1016\/j.jclepro.2023.138608","DOI":"10.1016\/j.jclepro.2023.138608"},{"key":"1648_CR60","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.ijcce.2021.03.001","volume":"2","author":"S Sharma","year":"2021","unstructured":"Sharma S, Gupta R, Bhatia R, Toor AP, Setia H (2021) Predicting microbial response to anthropogenic environmental disturbances using artificial neural network and multiple linear regression. Int J Cogn Comput Eng 2:65\u201370. https:\/\/doi.org\/10.1016\/j.ijcce.2021.03.001","journal-title":"Int J Cogn Comput Eng"},{"key":"1648_CR61","unstructured":"Shezi, L. 2020. The assessment of cardiopulmonary health risks associated with PM\u2081\u2080 and PM\u2082. \u2085 exposure on the community of Kriel Town and Thubelihle Township in the province of Mpumalanga (Doctoral dissertation). Retrieved from https:\/\/openscholar.dut.ac.za\/bitstream\/10321\/3892\/3\/Shezi%20L_2020.pdf (Accessed on April 2024)"},{"key":"1648_CR62","doi-asserted-by":"publisher","unstructured":"Singh RP Chauhan A (2022b). Sources of atmospheric pollution in India. In Asian J. Atmos. Environ. (pp. 1\u201337). Elsevier. https:\/\/doi.org\/10.1016\/B978-0-12-816693-2.00029-9","DOI":"10.1016\/B978-0-12-816693-2.00029-9"},{"key":"1648_CR63","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s10874-021-09419-8","volume":"78","author":"BP Singh","year":"2021","unstructured":"Singh BP, Singh D, Kumar K, Jain VK (2021) Study of seasonal variation of PM 2.5 concentration associated with meteorological parameters at residential sites in Delhi. India J Atmos Chem 78:161\u2013176. https:\/\/doi.org\/10.1007\/s10874-021-09419-8","journal-title":"India J Atmos Chem"},{"key":"1648_CR64","doi-asserted-by":"publisher","unstructured":"Singh S, Kulshreshtha NM, Goyal S, Brighu U, Bezbaruah AN, Gupta AB (2022a) Performance prediction of horizontal flow constructed wetlands by employing machine learning. J Water Proc.engineering 50:103264. https:\/\/doi.org\/10.1016\/j.jwpe.2022.103264","DOI":"10.1016\/j.jwpe.2022.103264"},{"key":"1648_CR65","doi-asserted-by":"publisher","unstructured":"Singh S, Maithani C, Malyan SK, Soti A, Kulshreshtha NM, Singh R, Goyal VC (2023). Comparative performance and 16S amplicon sequencing analysis of deep and shallow cells of a full scale HFCW having sequentially decreasing depths reveals vast enhancement potential. Bioresource Technol Reports\u00a022:101404. https:\/\/doi.org\/10.1016\/j.biteb.2023.101404","DOI":"10.1016\/j.biteb.2023.101404"},{"key":"1648_CR66","doi-asserted-by":"publisher","unstructured":"Singh S, Soti A, Kulshreshtha NM, Samaria A, Brighu U, Gupta AB, Bezbaruah AN (2024a) Machine learning application for nutrient removal rate coefficient analyses in horizontal flow constructed wetlands. ACS ES&T Water. https:\/\/doi.org\/10.1021\/acsestwater.4c00121","DOI":"10.1021\/acsestwater.4c00121"},{"key":"1648_CR67","doi-asserted-by":"publisher","unstructured":"Singh S, Suthar G, Kulshreshtha NM, Brighu U, Bezbaruah AN, Gupta AB (2024b) A futuristic approach to subsurface-constructed wetland design for the South-East Asian region using machine learning. ACS ES&T Water 4(9):4061\u20134074. https:\/\/doi.org\/10.1021\/acsestwater.4c00346","DOI":"10.1021\/acsestwater.4c00346"},{"key":"1648_CR68","doi-asserted-by":"publisher","unstructured":"Sinha BRK (2024) Introduction: a broad perspective on the concepts of urban dynamics, environment, and health. in urban dynamics, environment and health: an international Perspective (pp. 3\u201379). Singapore: Springer Nature Singapore. https:\/\/doi.org\/10.1007\/978-981-99-5744-6_1","DOI":"10.1007\/978-981-99-5744-6_1"},{"key":"1648_CR69","doi-asserted-by":"publisher","unstructured":"Sobrinho OM, Martins LD, Pedruzzi R, Vizuete W, de Almeida Albuquerque TT (2024) From mining to fire outbreaks: the relative impact of pollutants sources on air quality in the metropolitan area of Belo Horizonte. Atmos Pollut Res 102118. https:\/\/doi.org\/10.1016\/j.apr.2024.102118","DOI":"10.1016\/j.apr.2024.102118"},{"key":"1648_CR70","doi-asserted-by":"publisher","unstructured":"Sousa AC, Pastorinho MR, Masjedi MR, Urrutia-Pereira M, Arrais M, Nunes E, Taborda-Barata L (2022) Issue 1-\u201cUpdate on adverse respiratory effects of outdoor air pollution\u201d Part 2): Outdoor air pollution and respiratory diseases: Perspectives from Angola, Brazil, Canada, Iran, Mozambique and Portugal. Pulmonol 28(5):376\u2013395. https:\/\/doi.org\/10.1016\/j.pulmoe.2021.12.007","DOI":"10.1016\/j.pulmoe.2021.12.007"},{"key":"1648_CR71","doi-asserted-by":"publisher","unstructured":"Suthar G, Singhal RP, Khandelwal S, Kaul N, Parmar V, Singh AP (2022) Four-year spatiotemporal distribution & analysis of PM2. 5 and its precursor air pollutant SO2, NO2 & NH3 and their impact on LST in Bengaluru city, India. In IOP Conference Series: Environ. Earth Sci. (Vol. 1084, No. 1, p. 012036). IOP Publishing. https:\/\/doi.org\/10.1088\/1755-1315\/1084\/1\/012036","DOI":"10.1088\/1755-1315\/1084\/1\/012036"},{"key":"1648_CR72","doi-asserted-by":"publisher","unstructured":"Suthar G, Singh S, Kaul N, Khandelwal S, Singhal RP (2023a) Prediction of maximum air temperature for defining heat wave in Rajasthan and Karnataka states of India using machine learning approach. Remote Sens Appl: Soc Environ 32:101048. https:\/\/doi.org\/10.1016\/j.rsase.2023.101048","DOI":"10.1016\/j.rsase.2023.101048"},{"key":"1648_CR73","doi-asserted-by":"publisher","unstructured":"Suthar G, Singhal RP, Khandelwal S, Kaul N (2023b) Spatiotemporal variation of air pollutants and their relationship with land surface temperature in Bengaluru, India. Remote Sensing Applications: Society and Environment 32. https:\/\/doi.org\/10.1016\/j.rsase.2023.101011","DOI":"10.1016\/j.rsase.2023.101011"},{"key":"1648_CR74","doi-asserted-by":"publisher","unstructured":"Suthar G, Singhal RP, Khandelwal S, Kaul N, Parmar V, Singh AP (2023c) Annual and seasonal assessment of spatiotemporal variation in PM2.5 and gaseous air pollutants in Bengaluru, India. Environ Dev Sustain 1\u201324. https:\/\/doi.org\/10.1007\/s10668-023-03495-4","DOI":"10.1007\/s10668-023-03495-4"},{"key":"1648_CR75","doi-asserted-by":"publisher","unstructured":"Suthar G, Singh S, Kaul N, Khandelwal S (2024) Prediction of land surface temperature using spectral indices, air pollutants, and urbanization parameters for Hyderabad City of India using six machine learning approaches. Remote Sens Appl: Soc Environ 101265. https:\/\/doi.org\/10.1016\/j.rsase.2024.101265","DOI":"10.1016\/j.rsase.2024.101265"},{"key":"1648_CR76","doi-asserted-by":"publisher","unstructured":"Tella A, Balogun AL, Adebisi N, Abdullah S (2021) Spatial assessment of PM10 hotspots using random forest, K-nearest neighbour and Na\u00efve Bayes. Atmos Pollut Res 12(10). https:\/\/doi.org\/10.1016\/j.apr.2021.101202","DOI":"10.1016\/j.apr.2021.101202"},{"key":"1648_CR77","doi-asserted-by":"publisher","unstructured":"Thangavel P, Park D, Lee YC (2022) Recent insights into particulate matter (PM2.5)-mediated toxicity in humans: an overview. Int J Environ Res Public Health 19(12):7511. https:\/\/doi.org\/10.3390\/ijerph19127511","DOI":"10.3390\/ijerph19127511"},{"key":"1648_CR78","doi-asserted-by":"publisher","unstructured":"Utku A, Can \u00dc, Kamal M, Das N, Cifuentes-Faura J, Barut A (2023). A long short-term memory-based hybrid model optimized using a genetic algorithm for particulate matter 2.5 prediction. Atmos Pollut Res 14(8):101836. https:\/\/doi.org\/10.1016\/j.apr.2023.101836","DOI":"10.1016\/j.apr.2023.101836"},{"key":"1648_CR79","doi-asserted-by":"publisher","unstructured":"Vignesh PP, Jiang JH, Kishore P (2023) Predicting PM2. 5 concentrations across USA using machine learning. Earth Space Sci 10(10):e2023EA002911. https:\/\/doi.org\/10.1029\/2023EA002911","DOI":"10.1029\/2023EA002911"},{"key":"1648_CR80","doi-asserted-by":"publisher","unstructured":"Wang R, Chen B, Qiu S, Zhu Z, Ma L, Qiu X, Duan W (2017). Real-time data driven simulation of air contaminant dispersion using particle filter and UAV sensory system. In 2017 IEEE\/ACM 21st International Symposium on Distributed Simulation and Real Time Applications (DS-RT) (pp. 1\u20134). IEEE. https:\/\/doi.org\/10.1109\/DISTRA.2017.8167688","DOI":"10.1109\/DISTRA.2017.8167688"},{"key":"1648_CR81","doi-asserted-by":"publisher","unstructured":"Wang Y, Wen Y, Zhang S, Zheng G, Zheng H, Chang X, Hao J (2023). Vehicular ammonia emissions significantly contribute to urban PM2.5 pollution in two Chinese megacities. Environ. Sci Tech 57(7):2698\u20132705. https:\/\/doi.org\/10.1021\/acs.est.2c06198","DOI":"10.1021\/acs.est.2c06198"},{"key":"1648_CR82","unstructured":"WHO. 2021. World Health Organization (WHO) air quality guidelines (AQGs) and estimated reference levels (RLs). Retrieved from https:\/\/www.eea.europa.eu\/publications\/status-of-air-quality-in-Europe-2022\/europes-air-quality-status-2022\/world-health-organization-who-air. (Accessed on April 2024)."},{"key":"1648_CR83","doi-asserted-by":"publisher","unstructured":"William P, Paithankar DN, Yawalkar PM, Korde SK, Rajendra A, Rakshe DS (2023) Divination of air quality assessment using ensembling machine learning approach. In 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) (pp. 1\u201310). IEEE. https:\/\/doi.org\/10.1109\/ICECONF57129.2023.10083751","DOI":"10.1109\/ICECONF57129.2023.10083751"},{"key":"1648_CR84","doi-asserted-by":"publisher","unstructured":"Wood DA (2022) Trend decomposition aids forecasts of air particulate matter (PM2.5) assisted by machine and deep learning without recourse to exogenous data. Atmos Pollut Res 13(3):101352. https:\/\/doi.org\/10.1016\/j.apr.2022.101352","DOI":"10.1016\/j.apr.2022.101352"},{"issue":"8","key":"1648_CR85","doi-asserted-by":"publisher","first-page":"8350","DOI":"10.1007\/s11356-019-07493-w","volume":"27","author":"Y Xie","year":"2020","unstructured":"Xie Y (2020) Yearly changes of the sulfate-nitrate-ammonium aerosols and the relationship with their precursors from 1999 to 2016 in Beijing. Environ Sci Pollut Res 27(8):8350\u20138358. https:\/\/doi.org\/10.1007\/s11356-019-07493-w","journal-title":"Environ Sci Pollut Res"},{"key":"1648_CR86","doi-asserted-by":"publisher","unstructured":"Xie Y, Zhao B, Zhang L, Luo R (2015). Spatiotemporal variations of PM2.5 and PM10 concentrations between 31 Chinese cities and their relationships with SO2, NO2, CO and O3. Particuology 20:141\u2013149. https:\/\/doi.org\/10.1016\/j.partic.2015.01.003","DOI":"10.1016\/j.partic.2015.01.003"},{"key":"1648_CR87","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107573","volume":"129","author":"Z Xu","year":"2024","unstructured":"Xu Z, Lv Z, Chu B, Sheng Z, Li J (2024) Progress and prospects of future urban health status prediction. Eng Appl Artif Intell 129. https:\/\/doi.org\/10.1016\/j.engappai.2023.107573","journal-title":"Eng Appl Artif Intell"},{"key":"1648_CR88","doi-asserted-by":"publisher","unstructured":"Ye P, Li J, Ma W, Zhang H (2022) Impact of collaborative agglomeration of manufacturing and producer services on air quality: evidence from the emission reduction of PM2.5, NOx and SO2 in China. Atmos 13(6):966. https:\/\/doi.org\/10.3390\/atmos13060966","DOI":"10.3390\/atmos13060966"},{"key":"1648_CR89","doi-asserted-by":"publisher","unstructured":"Yu HR, Zhang YL, Cao F, Yang XY, Xie T, Zhang YX, Xue Y (2024) Gas-to-particle partitioning of atmospheric water-soluble organic aerosols: Indications from high-resolution observations of stable carbon isotope. Atmos Environ 120494. https:\/\/doi.org\/10.1016\/j.atmosenv.2024.120494","DOI":"10.1016\/j.atmosenv.2024.120494"},{"key":"1648_CR90","doi-asserted-by":"publisher","unstructured":"Zaman NAFK, Kanniah KD, Kaskaoutis DG, Latif MT (2024) Improving the quantification of fine particulates (PM2.5) concentrations in Malaysia using simplified and computationally efficient models. J Clean Prod 448:141559. https:\/\/doi.org\/10.1016\/j.jclepro.2024.141559","DOI":"10.1016\/j.jclepro.2024.141559"},{"key":"1648_CR91","doi-asserted-by":"publisher","unstructured":"Zhang S, Li D, Ge S, Liu S, Wu C, Wang Y, Wang G (2021) Rapid sulfate formation from synergetic oxidation of SO2 by O3 and NO2 under ammonia-rich conditions: Implications for the explosive growth of atmospheric PM2.5 during haze events in China. Sci Total Environ 772:144897. https:\/\/doi.org\/10.1016\/j.scitotenv.2020.144897","DOI":"10.1016\/j.scitotenv.2020.144897"},{"key":"1648_CR92","doi-asserted-by":"publisher","unstructured":"Zhou L, Wu T, Pu L, Meadows M, Jiang G, Zhang J, Xie X. (2023) Spatially heterogeneous relationships of PM2.5 concentrations with natural and land use factors in the Niger River Watershed, West Africa. J Clean Prod 394:136406.\u00a0https:\/\/doi.org\/10.1016\/j.jclepro.2023.136406","DOI":"10.1016\/j.jclepro.2023.136406"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01648-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01648-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01648-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T04:04:55Z","timestamp":1745640295000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01648-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,22]]},"references-count":92,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1648"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01648-1","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,22]]},"assertion":[{"value":"19 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2024","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 the authors of the present work have read and reviewed, understood and compiled with the \u2018Ethical Responsibilities of Authors\u2019 as mentioned in the Journal guidelines.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"97"}}