ABSTRACT
Information about urban air quality, e.g., the concentration of PM2.5, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this paper, we infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location. We evaluated our approach with extensive experiments based on five real data sources obtained in Beijing and Shanghai. The results show the advantages of our method over four categories of baselines, including linear/Gaussian interpolations, classical dispersion models, well-known classification models like decision tree and CRF, and ANN.
- A. V. Donkelaar, R. V. Martin, and R. J. Park (2006), Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing, J. Geophys. Res., 111, D21201.Google ScholarCross Ref
- D. Hasenfratz, O. Saukh, S. Sturzenegger, and L. Thiele. Participatory Air Pollution Monitoring Using Smartphones. In the 2nd International Workshop on Mobile Sensing.Google Scholar
- Y. Jiang, K. Li, L. Tian, R. Piedrahita, X. Yun, O. Mansata, Q. Lv, R. P. Dick, M. Hannigan, and L. Shang. Maqs: A personalized mobile sensing system for indoor air quality. In Proc. of UbiComp 2011. Google ScholarDigital Library
- J. Lafferty, A. McCallum, F. Pereira (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. of 18th International Conf. on Machine Learning. Google ScholarDigital Library
- S. Ma, Y. Zheng, O. Wolfson. T-Share: A Large-Scale Dynamic Taxi Ridesharing Service. In Proc. of ICDE 2013. Google ScholarDigital Library
- L. N. Lamsal, R. V. Martin, A. V. Donkelaar, M. Steinbacher, E. A. Celarier, E. Bucsela, E. J. Dunlea, and J. P. Pinto (2008), Ground-level nitrogen dioxide concentrations inferred from the satellite-borne Ozone Monitoring Instrument, J. Geophys. Res., 113, D1630.Google ScholarCross Ref
- R. V. Martin. Satellite remote sensing of surface air quality, Atmospheric Environment (2008), doi:10.1016.Google ScholarCross Ref
- K. Nigam, R. Ghani. Analyzing the Effectiveness and Applicability of Co-Training. In Proc. of CIKM 2000. Google ScholarDigital Library
- S. Vardoulakis, B. E. A. Fisher, K. Pericleous, N. Gonzalez-Flesca. Modelling air quality in street canyons: a review. Atmospheric Environment 37 (2003) 155--182.Google Scholar
- J.S. Scire, D.G. Strimaitis and R.J. Yamartino, 2000b: User's Guide for the CALPUFF Dispersion Model, (Version 5.0), Earth Tech, Inc.Google Scholar
- J. Yuan, Y. Zheng, X. Xie. Discovering regions of different functions in a city using human mobility and POIs. In Proc. of KDD 2012. Google ScholarDigital Library
- J. Yuan, Y. Zheng, C. Zhang, X. Xie, G. Sun. An Interactive-Voting based Map Matching Algorithm. In Proc. of MDM 2010. Google ScholarDigital Library
- J. Yuan, Y. Zheng, X. Xie, G. Sun. Driving with Knowledge from the Physical World. In Proc. of KDD 2011. Google ScholarDigital Library
- Y. Zheng, Y. Liu, J. Yuan, X. Xie. Urban Computing with Taxicabs. In Proc. of UbiComp 2011. Google ScholarDigital Library
- F. Zhang, D. Wilkie, Y. Zheng, X. Xie. Sensing the Pulse of Urban Refueling Behavior. In Proc. of UbiComp 2013.Google ScholarDigital Library
Index Terms
- U-Air: when urban air quality inference meets big data
Recommendations
Urban Computing: Concepts, Methodologies, and Applications
Special Section on Urban ComputingUrbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in ...
Telemetric systems and the assessment of the air quality in the city area
EE'09: Proceedings of the 4th IASME/WSEAS international conference on Energy & environmentIn this paper, the air quality network that will be analyzed are the Bucharest Network, which provide the real time data on current level of air pollution in representative high-traffic, residential, industrial and urban-background locations in ...
Context Aware City Air Quality Monitoring Estimation
ICDCN '23: Proceedings of the 24th International Conference on Distributed Computing and Networking“Save the environment for the people by the people”
Plethora of works have been carried out with meteorological data like humidity, temperature, barometer pressure, wind speed, and weather conditions, which can be mined from different web sources. Air ...
Comments