{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:44:08Z","timestamp":1743061448591,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030858988"},{"type":"electronic","value":"9783030858995"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-85899-5_28","type":"book-chapter","created":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T10:06:38Z","timestamp":1629281198000},"page":"384-398","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling Dynamic Spatial Influence for\u00a0Air Quality Prediction with\u00a0Atmospheric Prior"],"prefix":"10.1007","author":[{"given":"Dan","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qilong","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yichen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,19]]},"reference":[{"issue":"4","key":"28_CR1","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1504\/IJGW.2011.043422","volume":"3","author":"AA Abdel-Rahman","year":"2011","unstructured":"Abdel-Rahman, A.A.: On the dispersion models and atmospheric dispersion. Int. J. Glob. Warming 3(4), 257\u2013273 (2011)","journal-title":"Int. J. Glob. Warming"},{"issue":"4","key":"28_CR2","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/j.matcom.2004.06.023","volume":"67","author":"NK Arystanbekova","year":"2004","unstructured":"Arystanbekova, N.K.: Application of gaussian plume models for air pollution simulation at instantaneous emissions. Math. Comput. Simul. 67(4), 451\u2013458 (2004)","journal-title":"Math. Comput. Simul."},{"issue":"7","key":"28_CR3","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1021\/es980749y","volume":"33","author":"MS Bergin","year":"1999","unstructured":"Bergin, M.S., Noblet, G.S., Petrini, K., Dhieux, J.R., Milford, J.B., Harley, R.A.: Formal uncertainty analysis of a Lagrangian photochemical air pollution model. Environ. Sci. Technol. 33(7), 1116\u20131126 (1999)","journal-title":"Environ. Sci. Technol."},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L., Cai, Y., Ding, Y., Lv, M., Yuan, C., Chen, G.: Spatially fine-grained urban air quality estimation using ensemble semi-supervised learning and pruning. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 1076\u20131087 (2016)","DOI":"10.1145\/2971648.2971725"},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"Cheng, W., Shen, Y., Zhu, Y., Huang, L.: A neural attention model for urban air quality inference: learning the weights of monitoring stations. In: Proceedings of the 32th AAAI Conference on Artificial Intelligence (AAAI), pp. 2151\u20132158 (2018)","DOI":"10.1609\/aaai.v32i1.11871"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Guizilini, V., Ramos, F.: A nonparametric online model for air quality prediction. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), pp. 651\u2013657 (2015)","DOI":"10.1609\/aaai.v29i1.9246"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Hsieh, H., Lin, S., Zheng, Y.: Inferring air quality for station location recommendation based on urban big data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 437\u2013446 (2015)","DOI":"10.1145\/2783258.2783344"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Sun, X., Wang, W., Young, S.D.: Enhancing air quality prediction with social media and natural language processing. In: Proceedings of the 57th Conference of the Association for Computational Linguistics (ACL), pp. 2627\u20132632 (2019)","DOI":"10.18653\/v1\/P19-1251"},{"issue":"8","key":"28_CR9","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.1175\/2009JAMC2066.1","volume":"48","author":"BJ Jin","year":"2009","unstructured":"Jin, B.J., Bu, P.S., Jin, K.J.: Urban flow and dispersion simulation using a CFD model coupled to a mesoscale model. J. Appl. Meteorol. Climatol. 48(8), 1667\u20131681 (2009)","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Jutzeler, A., Li, J.J., Faltings, B.: A region-based model for estimating urban air pollution. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), pp. 424\u2013430 (2014)","DOI":"10.1609\/aaai.v28i1.8768"},{"key":"28_CR11","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations (ICLR) (2017)"},{"key":"28_CR12","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1016\/j.envpol.2017.08.114","volume":"231","author":"X Li","year":"2017","unstructured":"Li, X., et al.: Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environ. Pollut. 231, 997\u20131004 (2017)","journal-title":"Environ. Pollut."},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Liang, Y., Ke, S., Zhang, J., Yi, X., Zheng, Y.: GeoMAN: multi-level attention networks for geo-sensory time series prediction. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), pp. 3428\u20133434 (2018)","DOI":"10.24963\/ijcai.2018\/476"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Luo, Z., Huang, J., Hu, K., Li, X., Zhang, P.: AccuAir: winning solution to air quality prediction for KDD cup 2018. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1842\u20131850 (2019)","DOI":"10.1145\/3292500.3330787"},{"key":"28_CR15","doi-asserted-by":"publisher","unstructured":"Paolo, Z.: Gaussian models. In: Air Pollution Modeling, pp. 141\u2013183. Springer, Boston (1990). https:\/\/doi.org\/10.1007\/978-1-4757-4465-1_7","DOI":"10.1007\/978-1-4757-4465-1_7"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Pramanik, P., Mondal, T., Nandi, S., Saha, M.: AirCalypse: can Twitter help in urban air quality measurement and who are the influential users? In: Proceedings of the 29th International World Wide Web Conferences (WWW), pp. 540\u2013545 (2020)","DOI":"10.1145\/3366424.3382120"},{"key":"28_CR17","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.atmosenv.2014.08.073","volume":"98","author":"A Rakowska","year":"2014","unstructured":"Rakowska, A., et al.: Impact of traffic volume and composition on the air quality and pedestrian exposure in urban street canyon. Atmos. Environ. 98, 260\u2013270 (2014)","journal-title":"Atmos. Environ."},{"key":"28_CR18","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of the 28th Conference on Neural Information Processing Systems (NIPS), pp. 3104\u20133112 (2014)"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Wilson, T., Tan, P., Luo, L.: A low rank weighted graph convolutional approach to weather prediction. In: Proceeding of the 18th IEEE International Conference on Data Mining (ICDM), pp. 627\u2013636 (2018)","DOI":"10.1109\/ICDM.2018.00078"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Yi, X., Zhang, J., Wang, Z., Li, T., Zheng, Y.: Deep distributed fusion network for air quality prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 965\u2013973 (2018)","DOI":"10.1145\/3219819.3219822"},{"key":"28_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Multi-group encoder-decoder networks to fuse heterogeneous data for next-day air quality prediction. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), pp. 4341\u20134347 (2019)","DOI":"10.24963\/ijcai.2019\/603"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Zhao, X., Xu, T., Fu, Y., Chen, E., Guo, H.: Incorporating spatio-temporal smoothness for air quality inference. In: Proceeding of the 17th IEEE International Conference on Data Mining (ICDM), pp. 1177\u20131182 (2017)","DOI":"10.1109\/ICDM.2017.158"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Liu, F., Hsieh, H.: U-air: when urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1436\u20131444 (2013)","DOI":"10.1145\/2487575.2488188"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Zheng, Y., et al.: Forecasting fine-grained air quality based on big data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 2267\u20132276 (2015)","DOI":"10.1145\/2783258.2788573"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-85899-5_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T15:08:49Z","timestamp":1673104129000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-85899-5_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030858988","9783030858995"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-85899-5_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"19 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"184","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6.38","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}