{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:40:18Z","timestamp":1726044018643},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030308582"},{"type":"electronic","value":"9783030308599"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30859-9_7","type":"book-chapter","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T12:03:26Z","timestamp":1568203406000},"page":"77-90","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Context- and Situation Prediction for the MyAQI Urban Air Quality Monitoring System"],"prefix":"10.1007","author":[{"given":"Daniel","family":"Sch\u00fcrholz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arkady","family":"Zaslavsky","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sylvain","family":"Kubler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,12]]},"reference":[{"key":"7_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1007\/3-540-48157-5_29","volume-title":"Handheld and Ubiquitous Computing","author":"GD Abowd","year":"1999","unstructured":"Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304\u2013307. Springer, Heidelberg (1999). \n                      https:\/\/doi.org\/10.1007\/3-540-48157-5_29"},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1016\/j.procs.2018.05.068","volume":"132","author":"V Athira","year":"2018","unstructured":"Athira, V., Geetha, P., Vinayakumar, R., Soman, K.P.: Deepairnet: applying recurrent networks for air quality prediction. Procedia Comput. Sci. 132, 1394\u20131403 (2018)","journal-title":"Procedia Comput. Sci."},{"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 \u201916, pp. 1076\u20131087 (2016)","key":"7_CR3","DOI":"10.1145\/2971648.2971725"},{"issue":"10082","key":"7_CR4","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., et al.: 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. Lancet 389(10082), 1907\u20131918 (2017)","journal-title":"Lancet"},{"unstructured":"EEA: Air quality in Europe - 2017 report. Technical report 13, European Environmental Agency (EEA) (2017)","key":"7_CR5"},{"unstructured":"EPA Victoria: Future air quality in victoria - final report future air quality in victoria - final report. Technical report. Environmental Protection Agency Victoria Australia, Melbourne (2013)","key":"7_CR6"},{"issue":"8","key":"7_CR7","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(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"7","key":"7_CR8","doi-asserted-by":"publisher","first-page":"2220","DOI":"10.3390\/s18072220","volume":"18","author":"CJ Huang","year":"2018","unstructured":"Huang, C.J., Kuo, P.H.: A deep cnn-lstm model for particulate matter (pm2.5) forecasting in smart cities. Sensors 18(7), 2220 (2018). Switzerland","journal-title":"Sensors"},{"key":"7_CR9","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.scs.2018.08.033","volume":"43","author":"E Kalisa","year":"2018","unstructured":"Kalisa, E., Fadlallah, S., Amani, M., Nahayo, L., Habiyaremye, G.: Temperature and air pollution relationship during heatwaves in Birmingham, UK. Sustain. Cities Soc. 43, 111\u2013120 (2018)","journal-title":"Sustain. Cities Soc."},{"doi-asserted-by":"crossref","unstructured":"Klimova, A., Porras, J., Andersson, K., Rondeau, E., Ahmed, S.: PERCCOM: A master program in pervasive computing and communications for sustainable development, April 2016","key":"7_CR10","DOI":"10.1109\/CSEET.2016.39"},{"key":"7_CR11","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."},{"doi-asserted-by":"crossref","unstructured":"Nurgazy, M., Zaslavsky, A., Jayaraman, P., Kubler, S., Mitra, K., Saguna, S.: CAVisAP: Context-aware visualization of outdoor air pollution with IoT platforms. In: International Conference on High Performance Computing and Simulation (HPCS) (2019)","key":"7_CR12","DOI":"10.29007\/9ld4"},{"issue":"6","key":"7_CR13","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.1007\/s00521-015-1955-3","volume":"27","author":"BT Ong","year":"2016","unstructured":"Ong, B.T., Sugiura, K., Zettsu, K.: Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting pm2.5. Neural Comput. Appl. 27(6), 1553\u20131566 (2016)","journal-title":"Neural Comput. Appl."},{"unstructured":"Padovitz, A., Wai Loke, S., Zaslavsky, A.: Towards a theory of context. In: Second IEEE Annual Conference on Pervasive Computing and Communications (Workshops, PerCom), pp. 38\u201342 (2010)","key":"7_CR14"},{"issue":"1","key":"7_CR15","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1109\/SURV.2013.042313.00197","volume":"16","author":"C Perera","year":"2014","unstructured":"Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the internet of things: a survey. IEEE Commun. Surv. Tutor. 16(1), 414\u2013454 (2014)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"7_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.scitotenv.2019.01.333","volume":"664","author":"Y Qi","year":"2019","unstructured":"Qi, Y., Li, Q., Karimian, H., Liu, D.: A hybrid model for spatiotemporal forecasting of pm2.5 based on graph convolutional neural network and long short-term memory. Sci. Total Environ. 664, 1\u201310 (2019)","journal-title":"Sci. Total Environ."},{"key":"7_CR17","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.atmosenv.2012.07.026","volume":"76","author":"H Qiu","year":"2013","unstructured":"Qiu, H., Tak, I., Yu, S., Wang, X., Tian, L., Tse, L.A.: Season and humidity dependence of the effects of air pollution on COPD hospitalizations in Hong Kong. Atmos. Environ. 76, 74\u201380 (2013)","journal-title":"Atmos. Environ."},{"issue":"6","key":"7_CR18","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1109\/TMC.2011.170","volume":"11","author":"S Sigg","year":"2012","unstructured":"Sigg, S., Gordon, D., Zengen, G., Beigl, M., Haseloff, S., David, K.: Investigation of context prediction accuracy for different context abstraction levels. IEEE Trans. Mob. Comput. 11(6), 1047\u20131059 (2012)","journal-title":"IEEE Trans. Mob. Comput."},{"unstructured":"USEPA: Technical assistance document for the reporting of daily air quality - the air quality index (AQI). Environmental Protection, pp. 1\u201328, May 2013","key":"7_CR19"},{"key":"7_CR20","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.neucom.2018.06.049","volume":"314","author":"J Wang","year":"2018","unstructured":"Wang, J., Song, G.: A deep spatial-temporal ensemble model for air quality prediction. Neurocomputing 314, 198\u2013206 (2018)","journal-title":"Neurocomputing"},{"key":"7_CR21","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1016\/j.scitotenv.2018.11.086","volume":"654","author":"C Wen","year":"2019","unstructured":"Wen, C., et al.: A novel spatiotemporal convolutional long short-term neural network for air pollution prediction. Sci. Total Environ. 654, 1091\u20131099 (2019)","journal-title":"Sci. Total Environ."},{"key":"7_CR22","doi-asserted-by":"publisher","first-page":"j667","DOI":"10.1136\/bmj.j667","volume":"667","author":"P Yin","year":"2017","unstructured":"Yin, P., et al.: Particulate air pollution and mortality in 38 of china\u2019s largest cities: time series analysis. Bmj 667, j667 (2017)","journal-title":"Bmj"},{"key":"7_CR23","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.jclepro.2018.10.243","volume":"209","author":"Y Zhou","year":"2019","unstructured":"Zhou, Y., Chang, F.J., Chang, L.C., Kao, I.F., Wang, Y.S.: Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J. Clean. Prod. 209, 134\u2013145 (2019)","journal-title":"J. Clean. Prod."},{"key":"7_CR24","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.atmosenv.2018.04.004","volume":"183","author":"S Zhu","year":"2018","unstructured":"Zhu, S., et al.: PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors. Atmos. Environ. 183, 20\u201332 (2018)","journal-title":"Atmos. Environ."}],"container-title":["Lecture Notes in Computer Science","Internet of Things, Smart Spaces, and Next Generation Networks and Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30859-9_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T12:04:54Z","timestamp":1568203494000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30859-9_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030308582","9783030308599"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30859-9_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"12 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ruSMART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference on Internet of Things and Smart Spaces","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"St. Petersburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rusmart2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/rusmart.e-werest.org\/2019.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EDAS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50","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":"17","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":"0","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":"34% - 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","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","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}