When big data meets big smog: a big spatio-temporal data framework for China severe smog analysis

Abstract

Recently, the appearing disaster of severe smog has been attacking many cities in China such as the capital Beijing. The chief culprit of China smog, namely PM2.5, is affected by various factors including air pollutants, weather, climate, geographical location, urbanization, etc. To analyze the factors, we collect about 35,000,000 air quality records and about 30,000,000 weather records from the sensors in 77 China's cities in 2013. Moreover, two big data sets named Geoname and DBPedia are also combined for the data of climate, geographical location and urbanization. To deal with big spatio-temporal data for big smog analysis, we propose a MapReduce-based framework named BigSmog. It mainly conducts parallel correlation analysis of the factors and scalable training of artificial neural networks for spatio-temporal approximation of the concentration of PM2.5. In the experiments, BigSmog displays high scalability for big smog analysis with big spatio-temporal data. The analysis result shows that the air pollutants influence the short-term concentration of PM2.5 more than the weather and the factors of geographical location and climate rather than urbanization play a major role in determining a city's long-term pollution level of PM2.5. Moreover, the trained ANNs can accurately approximate the concentration of PM2.5.