Towards Sustainable Smart City by Particulate Matter Prediction Using Urban Big Data, Excluding Expensive Air Pollution Infrastructures

Living in the age of data and the new era of digitization of cities have created a large volume of data sets and data flows associated with the urban environments. It is significantly vital to capture and analyze the data from various resources in smart cities. For instance, the real-time air pollution data are remarkably important in controlling air pollution for urban sustainability and protecting humans against the air pollution damages. However, in reality, the average construction investment and maintenance costs in the air pollution stations are too high. This paper intends to investigate whether and how we can measure air pollution using cost effective means and without using the expensive pollution sensors and facilities. In order to realize such a goal, a predective model for particulate matter prediction was developed. The proposed model consists of multiple components to integrate heterogeneous multiple sources of urban data and predict the particulate matter based on transfer learning perspective in which neural network and regression was leveraged as the core of the prediction. The results of the particulate matter prediction exposed that while these data sources are capable of proper prediction of the particulate matter, they can also yield better results over the models, which were based only on the features of the air pollution sensors. This work provides an opportunity for evaluation of the model with the urban data from the city of Ahussar, in Denmark, and comparison of the model performance against various specified baselines. The superiority of the model over the baselines shows the practicality of the model.