Spatially fine-grained urban air quality estimation using ensemble semi-supervised learning and pruning

Air pollution has adverse effects on humans and ecosystem, and spatially fine-grained air quality information (i.e., the air quality information of every fine-grained area) can help people to avoid unhealthy outdoor activities. However, the number of air quality monitoring stations is usually limited, and thus spatially fine-grained air quality estimation is a challenging task. This paper proposes a method for inferring spatially fine-grained air quality information throughout a city. On one hand, since air quality is affected by multiple factors (e.g., factory waste gases and automobile exhaust fumes), this method employs various data sources, including traffic, road network, point of interests (POIs), and check-ins from social network services, which are related to air quality, to conduct the estimation. On the other hand, since the labeled data are highly limited due to the sparseness of monitoring stations, this method uses an improved ensemble semi-supervised learning (Semi-EP) to establish the relationship between the various data sources and urban air quality. Semi-EP firstly generates multiple classifiers from the original labeled data set and these classifiers are retrained in the iterative co-training process. Then, ensemble pruning technique is used to select the most-diverse subset from these multiple classifiers. This method is evaluated on the real-world dataset of Hangzhou city, China, and the experimental results have demonstrated its advantages over state-of-the-art methods.