Discovering pollution sources and propagation patterns in urban area

Air quality is one of the most important environmental concerns in the world, and it has deteriorated substantially over the past years in many countries. For example, Chinese Academy of Social Sciences reports that the problem of haze and fog in China is hitting a record level, and China is currently suffering from the worst air pollution. Among the various causal factors of air quality, particulate matter with a diameter of 2.5 micrometers or less (i.e., PM2.5) is a very important factor; governments and people are increasingly concerned with the concentration of PM2.5. In many cities, stations for monitoring PM2.5 concentration have been built by governments or companies to monitor urban air quality. Apart from monitoring, there is a rising demand for finding pollution sources of PM2.5 and discovering the transmission of PM2.5 based on the data from PM2.5 monitoring stations. 

However, to the best of our knowledge, none of previous work proposes a solution to the problem of detecting pollution sources and mining pollution propagation patterns from such monitoring data. In this work, we propose the first solution for the problem, which comprises two steps. The first step is to extract the uptrend intervals and calculate the causal strengths among spatially distributed sensors; The second step is to construct causality graphs and perform frequent subgraphs mining on these causality graphs to find pollution sources and propagation patterns. We use real-life monitoring data collected by a company in our experiments. Our experimental results demonstrate significant findings regarding pollution sources and pollutant propagations in Beijing, which will be useful for governments to make policy and govern pollution sources.