Prediction Tool on Fine Particle Pollutants and Air Quality for Environmental Engineering

This article focuses on the research, design and implementation of a prediction tool for air quality to estimate pollutant concentrations, contributing to environmental engineering. It addresses prediction of fine particle air pollutants of diameter less than 2.5 µm (particulate matter 2.5), their concentration being substantially influenced by urban traffic. We collect worldwide multicity data from health-related public sources on which mining is performed using classical data mining/machine learning paradigms: association rules, clustering and classification. Challenges include adapting appropriate techniques based on data, and capturing subtle domain-specific aspects. The prediction tool is built using knowledge discovered by mining, leveraging health standards, catering to novice, intermediate and expert users. The prediction output is accurate, efficient, interpretable and useful as evident from our experiments. The tool is helpful for urban decision support. This work is beneficial in developing software systems such as intelligent tutors, mobile device apps and smart city tools. It contributes to smart environment, mobility and living, making a positive impact on smart cities and sustainability. In this work, we claim that classical computational paradigms in their fundamental form can be adapted to solve environmental engineering problems, with easy comprehension, as per the Occam's razor principle that advocates simplicity. This article constitutes applied research: using computational techniques to solve domain-specific problems. Future work includes exploring models in deep learning such as CNN and Bi-LSTM, and considering different types of pollutants as well as other sources besides multicity traffic data, to conduct further studies. This would address additional challenges with enhancements.