{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:04:42Z","timestamp":1775066682141,"version":"3.50.1"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031959103","type":"print"},{"value":"9783031959110","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-95911-0_6","type":"book-chapter","created":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T06:07:53Z","timestamp":1750486073000},"page":"74-87","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Spatio-Temporal Neural Network for\u00a0Air Quality Reanalysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2306-1223","authenticated-orcid":false,"given":"Ammar","family":"Kheder","sequence":"first","affiliation":[]},{"given":"Benjamin","family":"Foreback","sequence":"additional","affiliation":[]},{"given":"Lili","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4507-3097","authenticated-orcid":false,"given":"Zhi-Song","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Boy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Appel, K., et\u00a0al.: Description and evaluation of the community multiscale air quality (cmaq) modeling system version 5.1. Geoscientific Model Dev. 10(4), 1703\u20131732 (2017)","DOI":"10.5194\/gmd-10-1703-2017"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Baklanov, A., et\u00a0al.: Enviro-hirlam online integrated meteorology\u2013chemistry modelling system: strategy, methodology, developments and applications (v7.2). Geoscientific Model Dev. 10(8), 2971\u20132999 (2017)","DOI":"10.5194\/gmd-10-2971-2017"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Baklanov, A.: Introduction \u2013 Integrated Systems: On-line and Off-line Coupling of Meteorological and Air Quality Models, Advantages and Disadvantages, pp. 1\u201311 (2011)","DOI":"10.1007\/978-3-642-13980-2_1"},{"key":"6_CR4","volume-title":"Machine learning and deep learning for air pollution forecasting: A review","author":"O Bashir","year":"2024","unstructured":"Bashir, O., et al.: Machine learning and deep learning for air pollution forecasting: A review. Agriculture, and Ecosystem Modeling, Sustainable Agriculture (2024)"},{"key":"6_CR5","first-page":"8834699","volume":"2020","author":"L Bingchun","year":"2020","unstructured":"Bingchun, L., et al.: Air pollutant concentration forecasting using long short-term memory based on wavelet transform and information gain: a case study of beijing. Comput. Intell. Neurosci. 2020, 8834699 (2020)","journal-title":"Comput. Intell. Neurosci."},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Chen, T., et\u00a0al.: Xgboost: A scalable tree boosting system, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Cho, K., et\u00a0al.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. In: Proc. of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724\u20131734 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"6_CR8","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.envpol.2017.04.075","volume":"227","author":"S Congbo","year":"2017","unstructured":"Congbo, S., et al.: Air pollution in china: status and spatiotemporal variations. Environ. Pollut. 227, 334\u2013347 (2017)","journal-title":"Environ. Pollut."},{"key":"6_CR9","doi-asserted-by":"publisher","first-page":"121054","DOI":"10.1016\/j.atmosenv.2025.121054","volume":"346","author":"Z Du","year":"2025","unstructured":"Du, Z., et al.: Advancements in machine learning for spatiotemporal urban on-road traffic-air quality study: a review. Atmos. Environ. 346, 121054 (2025)","journal-title":"Atmos. Environ."},{"issue":"1","key":"6_CR10","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.atmosenv.2008.09.064","volume":"43","author":"M Fang","year":"2009","unstructured":"Fang, M., Chan, C.K., Yao, X.: Managing air quality in a rapidly developing nation: China. Atmos. Environ. 43(1), 79\u201386 (2009)","journal-title":"Atmos. Environ."},{"issue":"2","key":"6_CR11","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1080\/20964471.2024.2316320","volume":"8","author":"B Foreback","year":"2024","unstructured":"Foreback, B., et al.: A new implementation of flexpart with enviro-hirlam meteorological input, and a case study during a heavy air pollution event. Big Earth Data 8(2), 397\u2013434 (2024)","journal-title":"Big Earth Data"},{"key":"6_CR12","unstructured":"Foreback, B., et\u00a0al.: Severe haze episodes in beijing may be influenced by emissions in far western china. Air quality, atmosphere & health (dec 2024)"},{"issue":"8","key":"6_CR13","doi-asserted-by":"publisher","first-page":"5265","DOI":"10.5194\/acp-22-5265-2022","volume":"22","author":"C Gao","year":"2022","unstructured":"Gao, C., et al.: Two-way coupled meteorology and air quality models in asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality. Atmos. Chem. Phys. 22(8), 5265\u20135329 (2022)","journal-title":"Atmos. Chem. Phys."},{"key":"6_CR14","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"6_CR15","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.atmosenv.2011.11.053","volume":"50","author":"D Ji","year":"2012","unstructured":"Ji, D., et al.: Analysis of heavy pollution episodes in selected cities of northern china. Atmos. Environ. 50, 338\u2013348 (2012)","journal-title":"Atmos. Environ."},{"issue":"7574","key":"6_CR16","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1038\/526497a","volume":"526","author":"M Kulmala","year":"2015","unstructured":"Kulmala, M.: Atmospheric chemistry: China\u2019s choking cocktail. Nature 526(7574), 497\u2013499 (2015)","journal-title":"Nature"},{"issue":"11","key":"6_CR17","doi-asserted-by":"publisher","first-page":"1910","DOI":"10.1093\/cvr\/cvaa025","volume":"116","author":"J Lelieveld","year":"2020","unstructured":"Lelieveld, J., et al.: Loss of life expectancy from air pollution compared to other risk factors: a worldwide perspective. Cardiovasc. Res. 116(11), 1910\u20131917 (2020)","journal-title":"Cardiovasc. Res."},{"key":"6_CR18","unstructured":"Li, Y., et\u00a0al.: Diffusion convolutional recurrent neural network: Data-driven traffic forecasting (2017)"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z.S., et\u00a0al.: Image super-resolution via attention based back projection networks. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3517\u20133525 (2019)","DOI":"10.1109\/ICCVW.2019.00436"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Z.S., et\u00a0al.: Unsupervised real image super-resolution via generative variational autoencoder. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020)","DOI":"10.1109\/CVPRW50498.2020.00229"},{"issue":"4","key":"6_CR21","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1109\/TCSVT.2020.3003832","volume":"31","author":"ZS Liu","year":"2021","unstructured":"Liu, Z.S., et al.: Photo-realistic image super-resolution via variational autoencoders. IEEE Trans. Circuits Syst. Video Technol. 31(4), 1351\u20131365 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Z.S., et\u00a0al.: Variational autoencoder for reference based image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 516\u2013525 (2021)","DOI":"10.1109\/CVPRW53098.2021.00063"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Luo, M., et\u00a0al.: Characteristics and health risk assessment of pm2.5-bound pahs during heavy air pollution episodes in winter in urban area of beijing, china. Atmosphere 12(3), 323 (2021)","DOI":"10.3390\/atmos12030323"},{"key":"6_CR24","unstructured":"Nie, Y., et\u00a0al.: A time series is worth 64 words: Long-term forecasting with transformers. arXiv preprint arXiv:2211.14730 (2022)"},{"key":"6_CR25","unstructured":"Peckham, S.E.S.E.: Wrf\/chem version 3.3 user\u2019s guide, technical Memorandum (2012)"},{"key":"6_CR26","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1039\/D4EA00054D","volume":"4","author":"L Pichelstorfer","year":"2024","unstructured":"Pichelstorfer, L., et al.: Towards automated inclusion of autoxidation chemistry in models: from precursors to atmospheric implications. Environ. Sci. Atmos. 4, 879\u2013896 (2024)","journal-title":"Environ. Sci. Atmos."},{"issue":"21","key":"6_CR27","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1080\/10643389.2023.2190315","volume":"53","author":"M Qingxin","year":"2023","unstructured":"Qingxin, M., et al.: A review on the heterogeneous oxidation of so2 on solid atmospheric particles: implications for sulfate formation in haze chemistry. Crit. Rev. Environ. Sci. Technol. 53(21), 1888\u20131911 (2023)","journal-title":"Crit. Rev. Environ. Sci. Technol."},{"key":"6_CR28","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c.: Convolutional lstm network: a machine learning approach for precipitation nowcasting. Adv. Neural Inf. Proc. Syst. 28, 802\u2013810 (2015)"},{"issue":"4","key":"6_CR29","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1016\/j.atmosenv.2005.09.069","volume":"40","author":"M Sofiev","year":"2006","unstructured":"Sofiev, M., et al.: A dispersion modelling system silam and its evaluation against etex data. Atmos. Environ. 40(4), 674\u2013685 (2006)","journal-title":"Atmos. Environ."},{"key":"6_CR30","unstructured":"Vaswani, A., et\u00a0al.: Attention is all you need. In: Proc. of NeurIPS, pp. 5998\u20136008 (2017)"},{"issue":"1","key":"6_CR31","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/S1001-0742(11)60724-9","volume":"24","author":"S Wang","year":"2012","unstructured":"Wang, S., Hao, J.: Air quality management in china: issues, challenges, and options. J. Environ. Sci. 24(1), 2\u201313 (2012)","journal-title":"J. Environ. Sci."},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Wei, M., et\u00a0al.: Apply a deep learning hybrid model optimized by an improved chimp optimization algorithm in pm2.5 prediction. Mach. Learn. Appl. 19, 100624 (2025)","DOI":"10.1016\/j.mlwa.2025.100624"},{"key":"6_CR33","unstructured":"WHO (World Health Organization): Air quality, energy and health: Health impacts. Accessed 27 Jan 2025"},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"Wu, H., et al.: Probabilistic automatic outlier detection for surface air quality measurements from the china national environmental monitoring network. Adv. Atmos. Sci. 35(12), 1522\u20131532 (2018)","DOI":"10.1007\/s00376-018-8067-9"},{"key":"6_CR35","unstructured":"Xu, M., et\u00a0al.: Spatial-temporal transformer networks for traffic flow forecasting. arXiv preprint arXiv:2001.02908 (2020)"},{"key":"6_CR36","doi-asserted-by":"crossref","unstructured":"Yan, S., et\u00a0al.: Spatial temporal graph convolutional networks for skeleton-based action recognition. Proc. of AAAI (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"6_CR37","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.scitotenv.2019.01.262","volume":"663","author":"Z Yingying","year":"2019","unstructured":"Yingying, Z., et al.: Air pollution reduction in china: recent success but great challenge for the future. Sci. Total Environ. 663, 329\u2013337 (2019)","journal-title":"Sci. Total Environ."},{"key":"6_CR38","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1016\/j.envpol.2018.01.069","volume":"236","author":"N Zhi-zhen","year":"2018","unstructured":"Zhi-zhen, N., et al.: Assessment of winter air pollution episodes using long-range transport modeling in hangzhou, China, during world internet conference. Environ. Pollut. 236, 550\u2013561 (2018)","journal-title":"Environ. Pollut."},{"key":"6_CR39","doi-asserted-by":"crossref","unstructured":"Zhou, S., et\u00a0al.: Deep-learning architecture for pm2.5 concentration prediction: a review. Environ. Sci. Ecotechnology 22, 1000140 (2024)","DOI":"10.1016\/j.ese.2024.100400"}],"container-title":["Lecture Notes in Computer Science","Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-95911-0_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T21:16:58Z","timestamp":1757193418000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-95911-0_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031959103","9783031959110"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-95911-0_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"16 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"SCIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Scandinavian Conference on Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Reykjavik","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iceland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scia2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/scia2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}