{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:38:57Z","timestamp":1778344737932,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2016,1,9]],"date-time":"2016-01-09T00:00:00Z","timestamp":1452297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Air quality information such as the concentration of PM2.5 is of great significance for human health and city management. It affects the way of traveling, urban planning, government policies and so on. However, in major cities there is typically only a limited number of air quality monitoring stations. In the meantime, air quality varies in the urban areas and there can be large differences, even between closely neighboring regions. In this paper, a random forest approach for predicting air quality (RAQ) is proposed for urban sensing systems. The data generated by urban sensing includes meteorology data, road information, real-time traffic status and point of interest (POI) distribution. The random forest algorithm is exploited for data training and prediction. The performance of RAQ is evaluated with real city data. Compared with three other algorithms, this approach achieves better prediction precision. Exciting results are observed from the experiments that the air quality can be inferred with amazingly high accuracy from the data which are obtained from urban sensing.<\/jats:p>","DOI":"10.3390\/s16010086","type":"journal-article","created":{"date-parts":[[2016,1,11]],"date-time":"2016-01-11T10:09:38Z","timestamp":1452506978000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":164,"title":["RAQ\u2013A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems"],"prefix":"10.3390","volume":"16","author":[{"given":"Ruiyun","family":"Yu","sequence":"first","affiliation":[{"name":"Software College, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rutgers University, New Brunswick, NJ 08854, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leyou","family":"Yang","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6921-7369","authenticated-orcid":false,"given":"Guangjie","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Internet of Things Engineering, Hohai University, Changzhou 213022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oguti","family":"Move","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Devarakonda, S., Sevusu, P., Liu, H., Liu, R., Iftode, L., and Nath, B. (2013, January 11). Real-time air quality monitoring through mobile sensing in metropolitan areas. Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, New York, NY, USA.","DOI":"10.1145\/2505821.2505834"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/1352-2310(95)00219-7","article-title":"Urban air pollution in megacities of the world","volume":"30","author":"Mage","year":"1996","journal-title":"Atmos. Environ."},{"key":"ref_3","unstructured":"NASA, Available online: http:\/\/climate.nasa.gov\/causes\/."},{"key":"ref_4","unstructured":"South China Morning Post. Available online: http:\/\/www.scmp.com\/topics\/beijing-air-pollution."},{"key":"ref_5","unstructured":"People. (In Chinese)."},{"key":"ref_6","unstructured":"PM25.in. (In Chinese)."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1016\/0004-6981(73)90213-8","article-title":"Multiple Box Model for Dispersion of Air Pollutants from Area Sources","volume":"7","author":"Ragland","year":"1973","journal-title":"Atmos. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1039\/tf9363201249","article-title":"The spread of smoke and gases from chimneys","volume":"32","author":"Bosanquet","year":"1936","journal-title":"Trans. Faraday Soc."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zannetti, D.P. (1990). Air Pollution Modeling, Springer U.S.","DOI":"10.1007\/978-1-4757-4465-1"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2717","DOI":"10.1016\/S1352-2310(97)00102-7","article-title":"Simulation of the regional atmospheric transport and fate of mercury using a comprehensive eulerian model","volume":"31","author":"Pai","year":"1997","journal-title":"Atmos. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ermak, D.L. (1990). User's Manual for SLAB: An Atmospheric Dispersion Model for Denser-Than-Air-Releases, Lawrence Livermore National Laboratory.","DOI":"10.2172\/6252170"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3269","DOI":"10.1021\/es049352m","article-title":"Estimating Ground-Level PM2.5 in the Eastern United States Using Satellite Remote Sensing","volume":"39","author":"Liu","year":"2005","journal-title":"Environ. Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7823","DOI":"10.1016\/j.atmosenv.2008.07.018","article-title":"Satellite remote sensing of surface air quality","volume":"42","author":"Martin","year":"2008","journal-title":"Atmos. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5880","DOI":"10.1016\/j.atmosenv.2006.03.016","article-title":"Satellite remote sensing of particulate matter and air quality assessment over global cities","volume":"40","author":"Gupta","year":"2006","journal-title":"Atmos. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.atmosenv.2014.05.061","article-title":"Satellite data of atmospheric pollution for US air quality applications: Examples of applications, summary of data end-user resources, answers to FAQs, and common mistakes to avoid","volume":"94","author":"Duncan","year":"2014","journal-title":"Atmos. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"31","DOI":"10.5121\/ijwmn.2010.2203","article-title":"A Wireless Sensor Network Air Pollution Monitoring System","volume":"2","author":"Khedo","year":"2010","journal-title":"Int. J. Wirel. Mob. Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3601","DOI":"10.3390\/s80603601","article-title":"Air Pollution Monitoring and Mining Based on Sensor Grid in London","volume":"8","author":"Ma","year":"2008","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rajasegarar, S., Zhang, P., Zhou, Y., Karunasekera, S., Leckie, C., and Palaniswami, M. (2014, January 21\u201324). High resolution spatio-temporal monitoring of air pollutants using wireless sensor networks. Proceedings of the 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore.","DOI":"10.1109\/ISSNIP.2014.6827607"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Li, K., Tian, L., Piedrahita, R., Yun, X., Mansata, O., Lv, Q., Dick, R.P., Hannigan, M., and Shang, L. (2011, January 17\u201321). MAQS: A personalized mobile sensing system for indoor air quality monitoring. Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China.","DOI":"10.1145\/2030112.2030150"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hasenfratz, D., Saukh, O., Sturzenegger, S., and Thiele, L. (2012, January 16). Participatory air pollution monitoring using smartphones. Proceedings of the 1st International Workshop on Mobile Sensing: From Smartphones and Wearables to Big Data, Beijing, China.","DOI":"10.1145\/2536714.2536719"},{"key":"ref_21","first-page":"51","article-title":"Participatory noise pollution monitoring using mobile phones","volume":"15","author":"Maisonneuve","year":"2010","journal-title":"Inf. Polit."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sivaraman, V., Carrapetta, J., Hu, K., and Luxan, B.G. (2013, January 21\u201324). HazeWatch: A participatory sensor system for monitoring air pollution in Sydney. Proceedings of the 2013 IEEE 38th Conference on Local Computer Networks Workshops (LCN Workshops), Sydney, NSW, Australia.","DOI":"10.1109\/LCNW.2013.6758498"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., and Xie, X. (2012, January 12\u201316). Discovering regions of different functions in a city using human mobility and POIs. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China.","DOI":"10.1145\/2339530.2339561"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Liu, F., and Hsieh, H.-P. (2013, January 11\u201314). U-Air: When urban air quality inference meets big data. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2487575.2488188"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hsieh, H.-P., Lin, S.-D., and Zheng, Y. (2015, January 10\u201313). Inferring Air Quality for Station Location Recommendation Based on Urban Big Data. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia.","DOI":"10.1145\/2783258.2783344"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, H., Pan, J.Z., Wu, M., Zhang, N., and Zheng, G. (2013, January 5\u20138). When big data meets big smog: A big spatio-temporal data framework for China severe smog analysis. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, Orlando, FL, USA.","DOI":"10.1145\/2534921.2534924"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Sun, C., and Li, V.O.K. (, 2015). Granger-Causality-Based Air Quality Estimation with Spatio-Temporal (S-T) Heterogeneous Big Data. Proceedings of the 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Hong Kong, China.","DOI":"10.1109\/INFCOMW.2015.7179453"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Song, L., Pang, S., Longley, I., Olivares, G., and Sarrafzadeh, A. (2014, January 6\u201311). Spatio-temporal PM2.5 prediction by spatial data aided incremental support vector regression. Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889521"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hasenfratz, D., Saukh, O., Walser, C., Hueglin, C., Fierz, M., and Thiele, L. (2014, January 24\u201328). Pushing the spatio-temporal resolution limit of urban air pollution maps. Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom), Budapest, Hungary.","DOI":"10.1109\/PerCom.2014.6813946"},{"key":"ref_30","unstructured":"United States Environmental Protection Agency, Available online: http:\/\/www3.epa.gov\/airnow\/aqi_brochure_02_14.pdf."},{"key":"ref_31","unstructured":"China\u2019s Ministry of Environmental Protection, (In Chinese)."},{"key":"ref_32","unstructured":"Wexler, H. (1961). The Role of Meteorology in Air Pollution, Monograph Series, World Health Organization."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_34","unstructured":"Efron, B. (1992). Breakthroughs in Statistics, Springer New York."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/21.97458","article-title":"A survey of decision tree classifier methodology","volume":"21","author":"Safavian","year":"1991","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_36","unstructured":"PM25.in: Air Quality Data Provider. (In Chinese)."},{"key":"ref_37","unstructured":"RP5.ru: Weather for 243 Countries of the World. Available online: http:\/\/rp5.ru."},{"key":"ref_38","unstructured":"Baidu Map. Available online: http:\/\/map.baidu.com."},{"key":"ref_39","unstructured":"Google Map. Available online: http:\/\/map.google.com."},{"key":"ref_40","unstructured":"Breiman, L., and Cutler, A. Random Forests. Available online: https:\/\/www.stat.berkeley.edu\/~breiman\/RandomForests\/cc_home.htm#ooberr."},{"key":"ref_41","unstructured":"Weka 3: Data Mining Software in Java. Available online: http:\/\/www.cs.waikato.ac.nz\/ml\/weka\/."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Davis, J., and Goadrich, M. (2006, January 25\u201329). The Relationship between Precision-Recall and ROC Curves. Proceedings of the 23rd International Conference on Machine Learning, Corvallis, OR, USA.","DOI":"10.1145\/1143844.1143874"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/1\/86\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:17:29Z","timestamp":1760210249000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/1\/86"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,1,9]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,1]]}},"alternative-id":["s16010086"],"URL":"https:\/\/doi.org\/10.3390\/s16010086","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,1,9]]}}}