{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T07:34:32Z","timestamp":1775028872274,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-18102-x","type":"journal-article","created":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T03:02:24Z","timestamp":1705114944000},"page":"64157-64175","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["ML based assessment and prediction of air pollution from satellite images during COVID-19 pandemic"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8548-0044","authenticated-orcid":false,"given":"Priyanka","family":"Biswas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7371-232X","authenticated-orcid":false,"given":"Nirmalya","family":"Kar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subhrajyoti","family":"Deb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,13]]},"reference":[{"issue":"69","key":"18102_CR1","first-page":"2394","volume":"40","author":"Y Kambalagere","year":"2020","unstructured":"Kambalagere Y (2020) A study on air quality index (aqi) of bengaluru, karnataka during lockdown period to combat coronavirus disease (covid-19): air quality turns \u2018better\u2019 from \u2018hazardous\u2019. Stud Indian Place Names 40(69):2394\u20133114","journal-title":"Stud Indian Place Names"},{"issue":"1\u20132","key":"18102_CR2","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1504\/IJEP.2020.11967","volume":"68","author":"Q Jiang","year":"2020","unstructured":"Jiang Q, Wang F, Ying C, Zhu B (2020) Seasonal variations of aerosol number concentration and spectrum distribution in nanjing. Int J Environ Pollut 68(1\u20132):100\u2013120. https:\/\/doi.org\/10.1504\/IJEP.2020.11967","journal-title":"Int J Environ Pollut"},{"key":"18102_CR3","doi-asserted-by":"publisher","unstructured":"Asadi A, Goharnejad H, Niri MZ (2019) Regression modelling of air quality based on meteorological parameters and satellite data. J Elementol 24(1). https:\/\/doi.org\/10.5601\/jelem.2018.23.1.1599","DOI":"10.5601\/jelem.2018.23.1.1599"},{"key":"18102_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosres.2021.10573","volume":"261","author":"S Munir","year":"2021","unstructured":"Munir S, Luo Z, Dixon T (2021) Comparing different approaches for assessing the impact of covid-19 lockdown on urban air quality in reading, uk. Atmos Res 261:105730. https:\/\/doi.org\/10.1016\/j.atmosres.2021.10573","journal-title":"Atmos Res"},{"issue":"9","key":"18102_CR5","doi-asserted-by":"publisher","first-page":"7373","DOI":"10.5194\/acp-21-7373-202","volume":"21","author":"J Barr\u00e9","year":"2021","unstructured":"Barr\u00e9 J, Petetin H, Colette A, Guevara M, Peuch V-H, Rouil L, Engelen R, Inness A, Flemming J, P\u00e9rez Garc\u00eda-Pando C et al (2021) Estimating lockdown-induced european no 2 changes using satellite and surface observations and air quality models. Atmos Chem Phys 21(9):7373\u20137394. https:\/\/doi.org\/10.5194\/acp-21-7373-202","journal-title":"Atmos Chem Phys"},{"key":"18102_CR6","doi-asserted-by":"publisher","unstructured":"Benchrif A, Wheida A, Tahri M, Shubbar RM, Biswas B (2021) Air quality during three covid-19 lockdown phases: Aqi, pm2. 5 and no2 assessment in cities with more than 1 million inhabitants. Sustain Cities Soc 74:103170. https:\/\/doi.org\/10.1016\/j.scs.2021.10317","DOI":"10.1016\/j.scs.2021.10317"},{"key":"18102_CR7","doi-asserted-by":"publisher","unstructured":"Muthukumar P, Cocom E, Nagrecha K, Comer D, Burga I, Taub J, Calvert CF, Holm J, Pourhomayoun M (2022) Predicting pm2. 5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data. Air Quality, Atmos Health 15(7), 1221\u20131234. https:\/\/doi.org\/10.1007\/s11869-021-01126-","DOI":"10.1007\/s11869-021-01126-"},{"issue":"1\u20133","key":"18102_CR8","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1504\/IJEP.2019.104521","volume":"66","author":"N Rawal","year":"2019","unstructured":"Rawal N (2019) An approach for selection of solid waste disposal sites by rapid impact assessment matrix and environmental performance index analysis. Int J Environ Pollut 66(1\u20133):127\u2013142","journal-title":"Int J Environ Pollut"},{"issue":"19","key":"18102_CR9","doi-asserted-by":"publisher","first-page":"5190","DOI":"10.3390\/su1119519","volume":"11","author":"NN Zakaria","year":"2019","unstructured":"Zakaria NN, Othman M, Sokkalingam R, Daud H, Abdullah L, Abdul Kadir E (2019) Markov chain model development for forecasting air pollution index of miri, sarawak. Sustainability 11(19):5190. https:\/\/doi.org\/10.3390\/su1119519","journal-title":"Sustainability"},{"key":"18102_CR10","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.trd.2018.05.00","volume":"63","author":"A Orun","year":"2018","unstructured":"Orun A, Elizondo D, Goodyer E, Paluszczyszyn D (2018) Use of bayesian inference method to model vehicular air pollution in local urban areas. Transportation Research Part D: Transport and Environment. 63:236\u2013243. https:\/\/doi.org\/10.1016\/j.trd.2018.05.00","journal-title":"Transportation Research Part D: Transport and Environment."},{"key":"18102_CR11","doi-asserted-by":"publisher","unstructured":"Fu M, Kelly JA, Clinch JP (2020) Prediction of pm2. 5 daily concentrations for grid points throughout a vast area using remote sensing data and an improved dynamic spatial panel model. Atmos Environ 237: 117667. https:\/\/doi.org\/10.1016\/j.atmosenv.2020.11766","DOI":"10.1016\/j.atmosenv.2020.11766"},{"issue":"4","key":"18102_CR12","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1007\/s10640-020-00483-","volume":"76","author":"MA Cole","year":"2020","unstructured":"Cole MA, Elliott RJ, Liu B (2020) The impact of the wuhan covid-19 lockdown on air pollution and health: a machine learning and augmented synthetic control approach. Environ Resour Econ 76(4):553\u2013580. https:\/\/doi.org\/10.1007\/s10640-020-00483-","journal-title":"Environ Resour Econ"},{"issue":"1\u20132","key":"18102_CR13","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1504\/IJEP.2020.119670","volume":"68","author":"HO Ikhumhen","year":"2020","unstructured":"Ikhumhen HO, Li T, Chen ZX, Difei A (2020) Particulate matter emission prediction of beijing\u2019s fengtai district through the box model application and statistical analysis of spatial layout grid. Int J Environ Pollut 68(1\u20132):13\u201340","journal-title":"Int J Environ Pollut"},{"key":"18102_CR14","doi-asserted-by":"crossref","unstructured":"Li S, Ning X, Yu L, Zhang L, Dong X, Shi Y, He W (2020) Multi-angle head pose classification when wearing the mask for face recognition under the covid-19 coronavirus epidemic. In: 2020 International conference on high performance big data and intelligent systems (HPBD &IS), pp. 1\u20135. IEEE","DOI":"10.1109\/HPBDIS49115.2020.9130585"},{"key":"18102_CR15","doi-asserted-by":"crossref","unstructured":"Goswami T, Sarma H (2020) Intelligent computing for air pollution monitoring using gis, remote sensing and machine learning. In: Emerging Trends in Electrical, Communications, and Information Technologies, pp. 125\u2013133","DOI":"10.1007\/978-981-13-8942-9_12"},{"key":"18102_CR16","doi-asserted-by":"publisher","unstructured":"Kaplan G, Avdan ZY (2020) Space-borne air pollution observation from sentinel-5p tropomi: Relationship between pollutants, geographical and demographic data. Int J Eng Geosci 5(3):130\u2013137. https:\/\/doi.org\/10.26833\/ijeg.64408","DOI":"10.26833\/ijeg.64408"},{"key":"18102_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2019.11697","volume":"218","author":"R Smit","year":"2019","unstructured":"Smit R, Kingston P, Neale D, Brown M, Verran B, Nolan T (2019) Monitoring on-road air quality and measuring vehicle emissions with remote sensing in an urban area. Atmos Environ 218:116978. https:\/\/doi.org\/10.1016\/j.atmosenv.2019.11697","journal-title":"Atmos Environ"},{"key":"18102_CR18","doi-asserted-by":"publisher","unstructured":"Chen J, Dobbie G, Koh YS, Somervell E, Olivares G (2018) Vehicle emission prediction using remote sensing data and machine learning techniques. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp 444\u2013451. https:\/\/doi.org\/10.1145\/3167132.316718","DOI":"10.1145\/3167132.316718"},{"key":"18102_CR19","doi-asserted-by":"crossref","unstructured":"Ning E, Zhang C, Wang C, Ning X, Chen H, Bai X (2023) Pedestrian re-id based on feature consistency and contrast enhancement. Displays 102467","DOI":"10.1016\/j.displa.2023.102467"},{"key":"18102_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A (2020) Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena. 404:132306","journal-title":"Physica D: Nonlinear Phenomena."},{"issue":"7","key":"18102_CR21","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu Y, Si X, Hu C, Zhang J (2019) A review of recurrent neural networks: Lstm cells and network architectures. Neural Comput 31(7):1235\u20131270","journal-title":"Neural Comput"},{"issue":"sup2","key":"18102_CR22","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1080\/22797254.2020.175599","volume":"54","author":"W Wang","year":"2021","unstructured":"Wang W, Jackson Samuel RD, Hsu C-H (2021) Prediction architecture of deep learning assisted short long term neural network for advanced traffic critical prediction system using remote sensing data. Eur J Remote Sens 54(sup2):65\u201376. https:\/\/doi.org\/10.1080\/22797254.2020.175599","journal-title":"Eur J Remote Sens"},{"key":"18102_CR23","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1016\/j.scitotenv.2018.11.08","volume":"654","author":"C Wen","year":"2019","unstructured":"Wen C, Liu S, Yao X, Peng L, Li X, Hu Y, Chi T (2019) A novel spatiotemporal convolutional long short-term neural network for air pollution prediction. Sci Total Environ 654:1091\u20131099. https:\/\/doi.org\/10.1016\/j.scitotenv.2018.11.08","journal-title":"Sci Total Environ"},{"key":"18102_CR24","doi-asserted-by":"publisher","first-page":"15","DOI":"10.5194\/isprs-annals-IV-4-W2-1","volume":"4","author":"J Fan","year":"2017","unstructured":"Fan J, Li Q, Hou J, Feng X, Karimian H, Lin S (2017) A spatiotemporal prediction framework for air pollution based on deep rnn. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 4:15. https:\/\/doi.org\/10.5194\/isprs-annals-IV-4-W2-1","journal-title":"ISPRS Ann Photogramm Remote Sens Spat Inf Sci"},{"key":"18102_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2020.11741","volume":"237","author":"W Zhai","year":"2020","unstructured":"Zhai W, Cheng C (2020) A long short-term memory approach to predicting air quality based on social media data. Atmos Environ 237:117411. https:\/\/doi.org\/10.1016\/j.atmosenv.2020.11741","journal-title":"Atmos Environ"},{"key":"18102_CR26","doi-asserted-by":"publisher","unstructured":"Tsai Y-T, Zeng Y-R, Chang Y-S (2018) Air pollution forecasting using rnn with lstm. In: 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC\/PiCom\/DataCom\/CyberSciTech), pp. 1074\u20131079. https:\/\/doi.org\/10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.0017 . IEEE","DOI":"10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.0017"},{"key":"18102_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2020.11193","volume":"247","author":"Y Loozen","year":"2020","unstructured":"Loozen Y, Rebel KT, Jong SM, Lu M, Ollinger SV, Wassen MJ, Karssenberg D (2020) Mapping canopy nitrogen in european forests using remote sensing and environmental variables with the random forests method. Remote Sens Environ 247:111933. https:\/\/doi.org\/10.1016\/j.rse.2020.11193","journal-title":"Remote Sens Environ"},{"issue":"3","key":"18102_CR28","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1111\/cag.1227","volume":"60","author":"W Yuchi","year":"2016","unstructured":"Yuchi W, Knudby A, Cowper J, Gombojav E, Amram O, Walker BB, Allen RW (2016) A description of methods for deriving air pollution land use regression model predictor variables from remote sensing data in ulaanbaatar, mongolia. The Canadian Geographer\/Le G\u00e9ographe canadien 60(3):333\u2013345. https:\/\/doi.org\/10.1111\/cag.1227","journal-title":"The Canadian Geographer\/Le G\u00e9ographe canadien"},{"key":"18102_CR29","doi-asserted-by":"crossref","unstructured":"Bose R, Dey RK, Roy S, Sarddar D (2020) Time series forecasting using double exponential smoothing for predicting the major ambient air pollutants. In: Information and Communication Technology for Sustainable Development, pp. 603\u2013613","DOI":"10.1007\/978-981-13-7166-0_60"},{"key":"18102_CR30","first-page":"51","volume":"2019","author":"R Kumar","year":"2019","unstructured":"Kumar R, Ghude SD, Jena C, Alessandrini S, Biswas M, Soni V, Singh S, Nanjundaiah RS, Rajeevan M (2019) Improving air quality predictions in new delhi during the crop-residue burning season via chemical data assimilation. AGU Fall Meeting Abstracts 2019:51\u201310","journal-title":"AGU Fall Meeting Abstracts"},{"key":"18102_CR31","doi-asserted-by":"publisher","unstructured":"Somvanshi SS, Vashisht A, Chandra U, Kaushik G (2019) Delhi air pollution modeling using remote sensing technique. Handbook of Environmental Materials Management 1\u201327. https:\/\/doi.org\/10.1007\/978-3-319-58538-3_174-","DOI":"10.1007\/978-3-319-58538-3_174-"},{"key":"18102_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.rsase.2019.05.00","volume":"15","author":"D Saha","year":"2019","unstructured":"Saha D, Soni K, Mohanan M, Singh M (2019) Long-term trend of ventilation coefficient over delhi and its potential impacts on air quality. Remote Sensing Applications: Society and Environment 15:100234. https:\/\/doi.org\/10.1016\/j.rsase.2019.05.00","journal-title":"Remote Sensing Applications: Society and Environment"},{"key":"18102_CR33","doi-asserted-by":"crossref","unstructured":"Lokeshwari N, Joshi K, Srinikethan G, Hegde V (2017) Urban air pollution impact and strategic plans\u2013a case study of a tier-ii city. In: Materials, Energy and Environment Engineering, pp 291\u2013297","DOI":"10.1007\/978-981-10-2675-1_34"},{"issue":"6","key":"18102_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12517-020-5214-2","volume":"13","author":"M Imran","year":"2020","unstructured":"Imran M, Mehmood A (2020) Analysis and mapping of present and future drivers of local urban climate using remote sensing: a case of lahore, pakistan. Arab J Geosci 13(6):1\u201314","journal-title":"Arab J Geosci"},{"issue":"1","key":"18102_CR35","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1080\/03736245.2016.123162","volume":"100","author":"M Appalasamy","year":"2018","unstructured":"Appalasamy M, Varghese B, Sershen Ismail R (2018) Examining the utility of hyperspectral remote sensing and partial least squares to predict plant stress responses to sulphur dioxide pollution: a case study of trichilia dregeana sond. S Afr Geogr J 100(1):22\u201340. https:\/\/doi.org\/10.1080\/03736245.2016.123162","journal-title":"S Afr Geogr J"},{"key":"18102_CR36","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.rse.2017.12.02","volume":"206","author":"J Yao","year":"2018","unstructured":"Yao J, Raffuse SM, Brauer M, Williamson GJ, Bowman DM, Johnston FH, Henderson SB (2018) Predicting the minimum height of forest fire smoke within the atmosphere using machine learning and data from the calipso satellite. Remote Sens Environ 206:98\u2013106. https:\/\/doi.org\/10.1016\/j.rse.2017.12.02","journal-title":"Remote Sens Environ"},{"key":"18102_CR37","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1016\/j.envpol.2019.03.06","volume":"249","author":"X Li","year":"2019","unstructured":"Li X, Zhang X (2019) Predicting ground-level pm2. 5 concentrations in the beijing-tianjin-hebei region: a hybrid remote sensing and machine learning approach. Environ Pollut 249:735\u2013749. https:\/\/doi.org\/10.1016\/j.envpol.2019.03.06","journal-title":"Environ Pollut"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-18102-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-18102-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-18102-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T17:39:34Z","timestamp":1720460374000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-18102-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,13]]},"references-count":37,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["18102"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-18102-x","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,13]]},"assertion":[{"value":"12 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This manuscript has no conflict of interest declared by the authors","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}