{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T21:03:30Z","timestamp":1762376610888,"version":"3.41.0"},"reference-count":33,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2019,9,24]],"date-time":"2019-09-24T00:00:00Z","timestamp":1569283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61572041"],"award-info":[{"award-number":["61572041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2019,10,31]]},"abstract":"<jats:p>Recently, real-time air quality estimation has attracted more and more attention from all over the world, which is close to our daily life. With the prevalence of mobile sensors, there is an emerging way to monitor the air quality with mobile sensors on vehicles. Compared with traditional expensive monitor stations, mobile sensors are cheaper and more abundant, but observations from these sensors have unstable spatial and temporal distributions, which results in the existing model could not work very well on this type of data. In this article, taking advantage of air quality data from mobile sensors, we propose an real-time urban air quality estimation method based on the Gaussian Process Regression for air pollution of the unmonitored areas, pivoting on the diffusion effect and the accumulation effect of air pollution. In order to meet the real-time demands, we propose a two-layer ensemble learning framework and a self-adaptivity mechanism to improve computational efficiency and adaptivity. We evaluate our model with real data from mobile sensor system located in Beijing, China. And the experiments show that our proposed model is superior to the state-of-the-art spatial regression methods in both precision and time performances.<\/jats:p>","DOI":"10.1145\/3356584","type":"journal-article","created":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T12:57:52Z","timestamp":1569416272000},"page":"11-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Real-Time Estimation of the Urban Air Quality with Mobile Sensor System"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1632-7273","authenticated-orcid":false,"given":"Yun","family":"Wang","sequence":"first","affiliation":[{"name":"Peking University, Beijing, P. R. China"}]},{"given":"Guojie","family":"Song","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, P. R. China"}]},{"given":"Lun","family":"Du","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, P. R. China"}]},{"given":"Zhicong","family":"Lu","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, P. R. China"}]}],"member":"320","published-online":{"date-parts":[[2019,9,24]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Carl Edward Rasmussen, and Marc Peter Deisenroth","author":"Calandra Roberto","year":"2014","unstructured":"Roberto Calandra , Jan Peters , Carl Edward Rasmussen, and Marc Peter Deisenroth . 2014 . Manifold gaussian processes for regression. arXiv:1402.5876. Roberto Calandra, Jan Peters, Carl Edward Rasmussen, and Marc Peter Deisenroth. 2014. Manifold gaussian processes for regression. arXiv:1402.5876."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00058655"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"J. A. Brian S. P. Tipper B. Weaver and S. E. Bryson. 2009. An Experimental Evaluation of ZCS-DM for the Prediction of Urban Air Quality. Springer Berlin 291--304.  J. A. Brian S. P. Tipper B. Weaver and S. E. Bryson. 2009. An Experimental Evaluation of ZCS-DM for the Prediction of Urban Air Quality. Springer Berlin 291--304.","DOI":"10.1007\/978-3-540-88351-7_22"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971725"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-12640-1_16"},{"volume-title":"IEEE International Conference on Industrial Informatics (INDIN\u201916)","author":"Tapiwa","key":"e_1_2_1_6_1","unstructured":"Tapiwa M. Chiwewe and Jeofrey Ditsela. 2016. Machine learning based estimation of ozone using spatio-temporal data from air quality monitoring stations . In IEEE International Conference on Industrial Informatics (INDIN\u201916) . Tapiwa M. Chiwewe and Jeofrey Ditsela. 2016. Machine learning based estimation of ozone using spatio-temporal data from air quality monitoring stations. In IEEE International Conference on Industrial Informatics (INDIN\u201916)."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolmodel.2005.01.008"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783275"},{"key":"e_1_2_1_9_1","volume-title":"29th AAAI Conference on Artificial Intelligence (AAAI\u201915)","author":"Guizilini Vitor Campanholo","year":"2015","unstructured":"Vitor Campanholo Guizilini and Fabio Tozeto Ramos . 2015 . A nonparametric online model for air quality prediction . In 29th AAAI Conference on Artificial Intelligence (AAAI\u201915) . 651--657. Vitor Campanholo Guizilini and Fabio Tozeto Ramos. 2015. A nonparametric online model for air quality prediction. In 29th AAAI Conference on Artificial Intelligence (AAAI\u201915). 651--657."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2014.11.008"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1097\/00001648-200105000-00017"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783344"},{"key":"e_1_2_1_14_1","volume-title":"National Conference on Artificial Intelligence.","author":"Jutzeler Arnaud","year":"2014","unstructured":"Arnaud Jutzeler , Jason Jingshi Li , B. Faltings . 2014 . A region-based model for estimating urban air pollution . In National Conference on Artificial Intelligence. Arnaud Jutzeler,Jason Jingshi Li, B. Faltings. 2014. A region-based model for estimating urban air pollution. In National Conference on Artificial Intelligence."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2808492.2808564"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2008.08.018"},{"volume-title":"Towards Air Quality Estimation Using Collected Multimodal Environmental Data","author":"Moumtzidou Anastasia","key":"e_1_2_1_17_1","unstructured":"Anastasia Moumtzidou , Symeon Papadopoulos , Stefanos Vrochidis , Ioannis Kompatsiaris , Konstantinos Kourtidis , George Hloupis , Ilias Stavrakas , Konstantina Papachristopoulou , and Christodoulos Keratidis . 2016. Towards Air Quality Estimation Using Collected Multimodal Environmental Data . Springer International Publishing . Anastasia Moumtzidou, Symeon Papadopoulos, Stefanos Vrochidis, Ioannis Kompatsiaris, Konstantinos Kourtidis, George Hloupis, Ilias Stavrakas, Konstantina Papachristopoulou, and Christodoulos Keratidis. 2016. Towards Air Quality Estimation Using Collected Multimodal Environmental Data. Springer International Publishing."},{"key":"e_1_2_1_18_1","volume-title":"Local Gaussian process regression for real time online model learning.Advances in Neural Information Processing Systems 22","author":"Nguyen-Tuong Duy","year":"2009","unstructured":"Duy Nguyen-Tuong , Matthias Seeger , and Jan Peters . 2008. Local Gaussian process regression for real time online model learning.Advances in Neural Information Processing Systems 22 , 2009 (2008), 1193--1200. Duy Nguyen-Tuong, Matthias Seeger, and Jan Peters. 2008. Local Gaussian process regression for real time online model learning.Advances in Neural Information Processing Systems 22, 2009 (2008), 1193--1200."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/0016-7061(95)00007-B"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1080\/02693799008941549"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2006.06.007"},{"volume-title":"Gaussian Processes for Machine Learning","author":"Rasmussen Carl Edward","key":"e_1_2_1_22_1","unstructured":"Carl Edward Rasmussen . 2006. Gaussian Processes for Machine Learning . Springer . Carl Edward Rasmussen. 2006. Gaussian Processes for Machine Learning. Springer."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756006.1953029"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2013.07.072"},{"key":"e_1_2_1_25_1","volume-title":"International Conference on Neural Information Processing Systems. 1257--1264","author":"Snelson Edward","year":"2005","unstructured":"Edward Snelson and Zoubin Ghahramani . 2005 . Sparse Gaussian processes using pseudo-inputs . In International Conference on Neural Information Processing Systems. 1257--1264 . Edward Snelson and Zoubin Ghahramani. 2005. Sparse Gaussian processes using pseudo-inputs. In International Conference on Neural Information Processing Systems. 1257--1264."},{"volume-title":"Introduction to Ensemble Learning","author":"Steinki Oliver","key":"e_1_2_1_26_1","unstructured":"Oliver Steinki and Ziad Mohammad . 2015. Introduction to Ensemble Learning . Social Science Electronic Publishing . Oliver Steinki and Ziad Mohammad. 2015. Introduction to Ensemble Learning. Social Science Electronic Publishing."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623367"},{"volume-title":"Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian Processes","author":"Wilson Andrew Gordon","key":"e_1_2_1_28_1","unstructured":"Andrew Gordon Wilson . 2014. Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian Processes . University of Cambridge . Andrew Gordon Wilson. 2014. Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian Processes. University of Cambridge."},{"key":"e_1_2_1_29_1","first-page":"129","article-title":"Gaussian process kernels for pattern discovery and extrapolation","volume":"13","author":"Wilson Andrew Gordon","year":"2013","unstructured":"Andrew Gordon Wilson and Ryan Prescott Adams . 2013 . Gaussian process kernels for pattern discovery and extrapolation . Microtome Publishing 13 , 4 (2013), 129 -- 135 . Andrew Gordon Wilson and Ryan Prescott Adams. 2013. Gaussian process kernels for pattern discovery and extrapolation. Microtome Publishing 13, 4 (2013), 129--135.","journal-title":"Microtome Publishing"},{"volume-title":"Mobile Sensor Network Design and Optimization for Air Quality Monitoring","author":"Xiang Yun","key":"e_1_2_1_30_1","unstructured":"Yun Xiang . 2014. Mobile Sensor Network Design and Optimization for Air Quality Monitoring . The University of Michigan . Yun Xiang. 2014. Mobile Sensor Network Design and Optimization for Air Quality Monitoring. The University of Michigan."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.5194\/acp-8-2895-2008"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2012.06.031"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788573"},{"volume-title":"Computer Communications Workshops. 612--617","author":"Zhu Julie Yixuan","key":"e_1_2_1_34_1","unstructured":"Julie Yixuan Zhu , Chenxi Sun , and Victor O. K. Li . 2015. Granger-causality-based air quality estimation with spatio-temporal (S-T) heterogeneous big data . In Computer Communications Workshops. 612--617 . Julie Yixuan Zhu, Chenxi Sun, and Victor O. K. Li. 2015. Granger-causality-based air quality estimation with spatio-temporal (S-T) heterogeneous big data. In Computer Communications Workshops. 612--617."}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3356584","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3356584","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:22:54Z","timestamp":1750202574000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3356584"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,24]]},"references-count":33,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2019,10,31]]}},"alternative-id":["10.1145\/3356584"],"URL":"https:\/\/doi.org\/10.1145\/3356584","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2019,9,24]]},"assertion":[{"value":"2017-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-06-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-09-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}