{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T14:50:42Z","timestamp":1776351042047,"version":"3.51.2"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T00:00:00Z","timestamp":1609632000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100004359","name":"Vetenskapsr\u00e5det","doi-asserted-by":"crossref","award":["2017-04543"],"award-info":[{"award-number":["2017-04543"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM\/IMS Trans. Data Sci."],"published-print":{"date-parts":[[2021,2,28]]},"abstract":"<jats:p>Air pollution is one of the major concerns in global urbanization. Data science can help to understand the dynamics of air pollution and build reliable statistical models to forecast air pollution levels. To achieve these goals, one needs to learn the statistical models which can capture the dynamics from the historical data and predict air pollution in the future. Furthermore, the large size and heterogeneity of today\u2019s big urban data pose significant challenges on the scalability and flexibility of the statistical models. In this work, we present a scalable belief updating framework that is able to produce reliable predictions, using over millions of historical hourly air pollutant and meteorology records. We also present a non-parametric approach to learn the statistical model which reveals interesting periodical dynamics and correlations of the dataset. Based on the scalable belief update framework and the non-parametric model learning approach, we propose an iterative update algorithm to accelerate Gaussian process, which is notorious for its prohibitive computation with large input data. Finally, we demonstrate how to integrate information from heterogeneous data by regarding the beliefs produced by other models as the informative prior. Numerical examples and experimental results are presented to validate the proposed method.<\/jats:p>","DOI":"10.1145\/3402903","type":"journal-article","created":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T12:06:42Z","timestamp":1609675602000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Scalable Belief Updating for Urban Air Quality Modeling and Prediction"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6783-9351","authenticated-orcid":false,"given":"Xiuming","family":"Liu","sequence":"first","affiliation":[{"name":"Uppsala University, Uppsala, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3454-8731","authenticated-orcid":false,"given":"Edith","family":"Ngai","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Pokfulam Road, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dave","family":"Zachariah","sequence":"additional","affiliation":[{"name":"Uppsala University, Uppsala, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,1,3]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"U. S. Environmental Protection Agency. 1999. Nitrogen Oxides (NOx) Why and How They Are Controlled."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.05.068"},{"key":"e_1_2_1_3_1","unstructured":"Matthias Bauer Mark van der Wilk and Carl Edward Rasmussen. 2016. Understanding probabilistic sparse Gaussian process approximations. In Advances in Neural Information Processing Systems. 1533--1541."},{"key":"e_1_2_1_4_1","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop Christopher M.","unstructured":"Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning. Springer."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12158"},{"key":"e_1_2_1_6_1","volume-title":"Robustness in Statistics","author":"Box George E. P.","unstructured":"George E. P. Box. 1979. Robustness in the strategy of scientific model building. In Robustness in Statistics. Elsevier, 201--236."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1964.tb00553.x"},{"key":"e_1_2_1_8_1","volume-title":"A deep learning approach for forecasting air pollution in south korea using LSTM. Arxiv Preprint Arxiv:1804.07891","author":"Bui Tien-Cuong","year":"2018","unstructured":"Tien-Cuong Bui, Van-Duc Le, and Sang-Kyun Cha. 2018. A deep learning approach for forecasting air pollution in south korea using LSTM. Arxiv Preprint Arxiv:1804.07891 (2018)."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.279188"},{"key":"e_1_2_1_10_1","volume-title":"Netto","author":"Diniz Paulo S. R.","year":"2010","unstructured":"Paulo S. R. Diniz, Eduardo A. B, Da Silva, and Sergio L. Netto. 2010. Digital Signal Processing: System Analysis and Design. Cambridge University Press."},{"key":"e_1_2_1_11_1","volume-title":"2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 1053--1058","author":"Duan Yanjie","year":"2016","unstructured":"Yanjie Duan, Yisheng Lv, and Fei-Yue Wang. 2016. Travel time prediction with LSTM neural network. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 1053--1058."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2200694"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2004.837418"},{"key":"e_1_2_1_14_1","doi-asserted-by":"crossref","first-page":"44004","DOI":"10.1038\/srep44004","article-title":"The interaction effects of meteorological factors and air pollution on the development of acute coronary syndrome","volume":"7","author":"Huang Ching-Hui","year":"2017","unstructured":"Ching-Hui Huang, Heng-Cheng Lin, Chen-Dao Tsai, Hung-Kai Huang, Ie-Bin Lian, and Chia-Chu Chang. 2017. The interaction effects of meteorological factors and air pollution on the development of acute coronary syndrome. Scientific Reports 7 (2017), 44004.","journal-title":"Scientific Reports"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2014.2330357"},{"key":"e_1_2_1_16_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. Arxiv Preprint Arxiv:1412.6980 (2014)."},{"key":"e_1_2_1_17_1","first-page":"235","article-title":"Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies","author":"Krause Andreas","year":"2008","unstructured":"Andreas Krause, Ajit Singh, and Carlos Guestrin. 2008. Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies. Journal of Machine Learning Research 9, Feb. (2008), 235--284.","journal-title":"Journal of Machine Learning Research 9"},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of IJCAI. 3428--3434","author":"Liang Yuxuan","year":"2018","unstructured":"Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, and Yu Zheng. 2018. Geoman: Multi-level attention networks for geo-sensory time series prediction. In Proceedings of IJCAI. 3428--3434."},{"key":"e_1_2_1_19_1","volume-title":"When Gaussian process meets big data: A review of scalable GPs. Arxiv Preprint Arxiv:1807.01065","author":"Liu Haitao","year":"2018","unstructured":"Haitao Liu, Yew-Soon Ong, Xiaobo Shen, and Jianfei Cai. 2018. When Gaussian process meets big data: A review of scalable GPs. Arxiv Preprint Arxiv:1807.01065 (2018)."},{"key":"e_1_2_1_20_1","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s00703-017-0508-y","article-title":"A note on the correlation between circular and linear variables with an application to wind direction and air temperature data in a mediterranean climate","volume":"130","author":"Lototzis M.","year":"2018","unstructured":"M. Lototzis, G. K. Papadopoulos, F. Droulia, A. Tseliou, and I. X. Tsiros. 2018. A note on the correlation between circular and linear variables with an application to wind direction and air temperature data in a mediterranean climate. Meteorology and Atmospheric Physics 130, 2 (2018), 259--264.","journal-title":"Meteorology and Atmospheric Physics"},{"key":"e_1_2_1_21_1","unstructured":"World Health Organization. 2015. Economic cost of the health impact of air pollution in Europe: Clean air health and wealth."},{"key":"e_1_2_1_22_1","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1164\/ajrccm\/145.5.1123","article-title":"Acute health effects of PM10 pollution on symptomatic and asymptomatic children","volume":"145","author":"Arden C.","year":"1992","unstructured":"C. Arden Pope III and Douglas W. Dockery. 1992. Acute health effects of PM10 pollution on symptomatic and asymptomatic children. American Review of Respiratory Disease 145, 5 (1992), 1123--1128.","journal-title":"American Review of Respiratory Disease"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2823740"},{"key":"e_1_2_1_24_1","first-page":"1939","article-title":"A unifying view of sparse approximate Gaussian process regression","author":"Qui\u00f1onero-Candela Joaquin","year":"2005","unstructured":"Joaquin Qui\u00f1onero-Candela and Carl Edward Rasmussen. 2005. A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research 6, Dec (2005), 1939--1959.","journal-title":"Journal of Machine Learning Research 6"},{"key":"e_1_2_1_25_1","volume-title":"Gaussian Processes for Machine Learning","author":"Rasmussen Carl Edward","unstructured":"Carl Edward Rasmussen and Christopher Williams. 2006. Gaussian Processes for Machine Learning. The MIT Press."},{"key":"e_1_2_1_26_1","unstructured":"Matthias Seeger Christopher Williams and Neil Lawrence. 2003. Fast Forward Selection to Speed up Sparse Gaussian Process Regression. Technical Report."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2016.2514378"},{"key":"e_1_2_1_28_1","volume-title":"Shumway and David Stoffer","author":"Robert","year":"2017","unstructured":"Robert H. Shumway and David Stoffer. 2017. Time Series Analysis and Its Applications: with R Examples. Springer."},{"key":"e_1_2_1_29_1","unstructured":"Edward Snelson and Zoubin Ghahramani. 2006. Sparse Gaussian processes using pseudo-inputs. In Advances in Neural Information Processing Systems. 1257--1264."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/87.4.954"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219822"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2629592"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2488188"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788573"}],"container-title":["ACM\/IMS Transactions on Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3402903","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3402903","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T13:56:53Z","timestamp":1776347813000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3402903"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,3]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,2,28]]}},"alternative-id":["10.1145\/3402903"],"URL":"https:\/\/doi.org\/10.1145\/3402903","relation":{},"ISSN":["2691-1922"],"issn-type":[{"value":"2691-1922","type":"print"}],"subject":[],"published":{"date-parts":[[2021,1,3]]},"assertion":[{"value":"2019-06-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-05-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-01-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}