Mining PM2.5 and traffic conditions for air quality

Fine particle pollution is related to road traffic conditions. In this work, we analyze Particulate Matter with a diameter less than 2.5 micrometers, called PM2.5, along with traffic conditions. This is done for multicity data to study the relationships in the context of environmental modeling. The goal behind this modeling is to support prediction of PM2.5 concentration and resulting air quality. We deploy data mining algorithms in association rules, clustering and classification to discover knowledge from the concerned data sets. The results are used to develop a prototype tool for the prediction of PM2.5 and hence air quality for public health and safety. This paper describes our approach and experiments with examples of PM2.5 prediction that would be helpful for decision support to potential users in a smart cities context. These users include city dwellers, environmental scientists and urban planners. Novel aspects of this work are multicity PM2.5 analysis by data mining and the resulting air quality prediction tool, the first of its kind, to the best of our knowledge.