Using a diversity of big data to infer and predict fine-grained air quality throughout a city, and finally tackle air pollutions.
Many countries are suffering from air pollutions. Cities have built a few air quality monitoring stations to inform people urban air quality every hour. Influenced by multiple complex factors, however, urban air quality is highly skewed in a city, varying by locations significantly and changing over time differently in different places. Thus, we cannot infer the air quality of a location without a monitoring station by simply using a linear interpolation.
The first step of this project is to infer the real-time and fine-grained air quality of arbitrary location by using two parts of data. One is the real-time and historical air quality data from existing monitoring stations. The other is five additional data sources we observed in a city, consisting of meteorological data, traffic, human mobility, POIs, and road network data. 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. Read the related publications for more details.
The research has been publicly available through a "cloud + client" framework, where the cloud continuously collect real-time data, such meteorological data and air quality data. A user can access the air quality information through using a mobile client or web client.
The second step is to predict the fine-grained air quality a few hours ahead. Specifically, for each monitoring station, we predict the probability of air quality status being good, normal and bad in the next a few hours. We also predict the trend of air quality being the same as current, or becoming worse, or better.
1) Suggesting the locations for building additional monitoring stations;
2) Identifying the root cause of air pollutions;
3) Study the impact of air pollution to people's life and health.
There are a few interns who have worked with us in the urban air project. We may not be able to list all of them here.
Yubiao Chen, Xuxu Chen, Hsun-Ping Hsieh, Furui Li, Zhenni Feng.
- Xuxu Chen, Yu Zheng, Yubiao Chen, Qiwei Jin, Weiwei Sun, Eric Chang, and Wei-Ying Ma, Indoor Air Quality Monitoring System for Smart Buildings, in UbiComp 2014, ACM, September 2014
- Yu Zheng, Xuxu Chen, Qiwei Jin, Yubiao Chen, Xiangyun Qu, Xin Liu, Eric Chang, Wei-Ying Ma, Yong Rui, and Weiwei Sun, A Cloud-Based Knowledge Discovery System for Monitoring Fine-Grained Air Quality , no. MSR-TR-2014-40, March 2014
- Yu Zheng, Furui Liu, and Hsun-Ping Hsieh, U-Air: When Urban Air Quality Inference Meets Big Data, in KDD 2013, ACM, August 2013