Yijun Lin
ABSTRACT
Air quality models are important for studying the impact of air pollutant on health conditions at a fine spatiotemporal scale. Existing work typically relies on area-specific, expert-selected attributes of pollution emissions (e,g., transportation) and dispersion (e.g., meteorology) for building the model for each combination of study areas, pollutant types, and spatiotemporal scales. In this paper, we present a data mining approach that utilizes publicly available OpenStreetMap (OSM) data to automatically generate an air quality model for the concentrations of fine particulate matter less than 2.5 μm in aerodynamic diameter at various temporal scales. Our experiment shows that our (domain-) expert-free model could generate accurate PM2.5 concentration predictions, which can be used to improve air quality models that traditionally rely on expert-selected input. Our approach also quantifies the impact on air quality from a variety of geographic features (i.e., how various types of geographic features such as parking lots and commercial buildings affect air quality and from what distance) representing mobile, stationary and area natural and anthropogenic air pollution sources. This approach is particularly important for enabling the construction of context-specific spatiotemporal models of air pollution, allowing investigations of the impact of air pollution exposures on sensitive populations such as children with asthma at scale.
- S. Bertazzon, M.Johnson, K. Eccles, and G. G. Kaplan. 2015. Accounting for spatial effects in land use regression for urban air pollution modeling. Spatial and spatio-temporal epidemiology 14 (2015), 9--21.Google Scholar
- D. J. Briggs, S. Collins, P. Elliott, P. Fischer, S. Kingham, E. Lebret, K. Pryl, H. van Reeuwijk, K. Smallbone, and A. Van Der Veen. 1997. Mapping urban air pollution using GIS: a regression-based approach. International Journal of Geographical Information Science 11, 7 (1997), 699--718.Google ScholarCross Ref
- C. Brokamp, R. Jandarov, M. B. Rao, G. LeMasters, and P. Ryan. 2017. Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches. Atmospheric Environment 151 (2017), 1--11.Google ScholarCross Ref
- D. W. Dockery, C. A. Pope, X. Xu, J. D. Spengler, J. H. Ware, M. E. Fay, B. G. Ferris Jr, and F. E. Speizer. 1993. An association between air pollution and mortality in six US cities. New England journal of medicine 329, 24 (1993), 1753--1759.Google Scholar
- J. Fan, S. Li, C. Fan, Z. Bai, and K. Yang. 2016. The impact of PM2.5 on asthma emergency department visits: a systematic review and meta-analysis. Environmental Science and Pollution Research 23, 1 (2016), 843--850.Google ScholarCross Ref
- M. F. Goodchild and L. Li. 2012. Assuring the quality of volunteered geographic information. Spatial statistics 1 (2012), 110--120.Google Scholar
- M. Guarnieri and J. R. Balmes. 2014. Outdoor air pollution and asthma. The Lancet 383, 9928 (2014), 1581--1592.Google Scholar
- D. Hasenfratz, O. Saukh, S. Sturzenegger, and L. Thiele. 2012. Participatory air pollution monitoring using smartphones. Mobile Sensing (2012), 1--5.Google Scholar
- D. Hasenfratz, O. Saukh, C. Walser, C. Hueglin, M. Fierz, and L. Thiele. 2014. Pushing the spatio-temporal resolution limit of urban air pollution maps. In IEEE International Conference Pervasive Computing and Communications. 69--77.Google Scholar
- G. Hoek, R. Beelen, K. De Hoogh, D. Vienneau, J. Gulliver, P. Fischer, and D. Briggs. 2008. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric environment 42, 33 (2008), 7561--7578.Google Scholar
- J. Jokar Arsanjani, M. Helbich, M. Bakillah, J. Hagenauer, and A. Zipf. 2013. Toward mapping land-use patterns from volunteered geographic information. International Journal of Geographical Information Science 27, 12 (2013), 2264--2278. Google ScholarDigital Library
- L. Li, F. Lurmann, R. Habre, R. Urman, E. Rappaport, B. Ritz, J.-C. Chen, F. D. Gilliland, and J. Wu. 2017. Constrained Mixed-Effect Models with Ensemble Learning for Prediction of Nitrogen Oxides Concentrations at High Spatiotemporal Resolution. Environmental Science & Technology 51, 17 (2017), 9920--9929.Google ScholarCross Ref
- C. Liu, B. H. Henderson, D. Wang, X. Yang, and Z.-R. Peng. 2016. A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO 2) concentrations in City of Shanghai, China. Science of The Total Environment 565 (2016), 607--615.Google ScholarCross Ref
- D. K. Moore, M. Jerrett, W. J. Mack, and N. Künzli. 2007. A land use regression model for predicting ambient fine particulate matter across Los Angeles, CA. Journal of Environmental Monitoring 9, 3 (2007), 246--252.Google ScholarCross Ref
- Z. Ross, P. B. English, R. Scalf, R. Gunier, S. Smorodinsky, S. Wall, and M. Jerrett. 2006. Nitrogen dioxide prediction in Southern California using land use regression modeling: potential for environmental health analyses. Journal of Exposure Science and Environmental Epidemiology 16, 2 (2006), 106--114.Google ScholarCross Ref
- Z. Ross, M. Jerrett, K. Ito, B. Tempalski, and G. D. Thurston. 2007. A land use regression for predicting fine particulate matter concentrations in the New York City region. Atmospheric Environment 41, 11 (2007), 2255--2269.Google ScholarCross Ref
- J. M. Samet, F. Dominici, F. C. Curriero, I. Coursac, and S. L. Zeger. 2000. Fine particulate air pollution and mortality in 20 US cities, 1987--1994. New England journal of medicine 343, 24 (2000), 1742--1749.Google Scholar
- D. Stripelis, J. Ambite, Y.-Y. Chiang, S. P. Eckel, and R Habre. 2017. A Scalable Data Integration and Analysis Architecture for Sensor Data of Pediatric Asthma. In Data Engineering. 1407--1408.Google Scholar
- W. Sun, H. Zhang, A. Palazoglu, A. Singh, W. Zhang, and S. Liu. 2013. Prediction of 24-hour-average PM2.5 concentrations using a hidden Markov model with different emission distributions in Northern California. Science of the total environment 443 (2013), 93--103.Google Scholar
- D. Wilton, A. Szpiro, T. Gould, and T. Larson. 2010. Improving spatial concentration estimates for nitrogen oxides using a hybrid meteorological dispersion/land use regression model in Los Angeles, CA and Seattle, WA. Science of the total environment 408, 5 (2010), 1120--1130.Google Scholar
- X. Xi, Z. Wei, X. Rui, Y. Wang, X. Bai, W. Yin, and J. Don. 2015. A comprehensive evaluation of air pollution prediction improvement by a machine learning method. In Service Operations And Logistics, And Informatics. 176--181.Google Scholar
- Y.-F. Xing, Y.-H. Xu, M.-H. Shi, and Y.-X. Lian. 2016. The impact of PM2.5 on the human respiratory system. Journal of thoracic disease 8, 1 (2016), E69.Google Scholar
- W. Xu, C. Cheng, D. Guo, X. Chen, H. Yuan, R. Yang, and Y. Liu. 2014. PM2.5 Air Quality Index Prediction Using an Ensemble Learning Model. In International Conference on Web-Age Information Management. Springer, 119--129.Google Scholar
- X. Yang, Y. Zheng, G. Geng, H. Liu, H. Man, Z. Lv, K. He, and K. De Hoogh. 2017. Development of PM2.5 and NO2 models in a LUR framework incorporating satellite remote sensing and air quality model data in Pearl River Delta region, China. Environmental Pollution 226 (2017), 143--153.Google ScholarCross Ref
- L. Zhai, B. Zou, X. Fang, Y. Luo, N. Wan, and S. Li. 2016. Land Use Regression Modeling of PM2.5 Concentrations at Optimized Spatial Scales. Atmosphere 8, 1 (2016), 1.Google ScholarCross Ref
- Y. Zhang, M. Bocquet, V Mallet, C. Seigneur, and A. Baklanov. 2012. Real-time air quality forecasting, part I: History, techniques, and current status. Atmospheric Environment 60 (2012), 632--655.Google ScholarCross Ref
- Y. Zhang, M. Bocquet, V. Mallet, C. Seigneur, and A. Baklanov. 2012. Real-time air quality forecasting, part II: State of the science, current research needs, and future prospects. Atmospheric Environment 60 (2012), 656--676.Google ScholarCross Ref
- Y. Zheng, F. Liu, and H.-P. Hsieh. 2013. U-air: When urban air quality inference meets big data. In Proceedings of the 19th ACM international conference on Knowledge discovery and data mining. 1436--1444. Google ScholarDigital Library
- Y. Zheng, X. Yi, M. Li, R. Li, Z. Shan, E. Chang, and T. Li. 2015. Forecasting fine-grained air quality based on big data. In Proceedings of the 21th ACM International Conference on Knowledge Discovery and Data Mining. 2267--2276. Google ScholarDigital Library
Index Terms
- Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution
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