Internet of things solution for intelligent air pollution prediction and visualization

Abstract

Air pollution monitoring and control is becoming a key priority in urban areas due to its substantial effect on human morbidity and mortality. This paper presents a system architecture for intelligent pollution visualization and future pollution prediction by encompassing pollution measurements and meteorological parameters. First, a pollution model using spatial interpolation is built. By adding meteorological parameters this model is further used to identify the pollution field evolution and the position of potential sources of air pollution. Using deep learning techniques, the system provides predictions for future pollution levels as well as times to reaching alarming thresholds. The whole system is encompassed in a fast, easy to use web service and a client that visually renders the system responses. The system is built and tested on data for the city of Skopje. Although the spatial resolution of the system data is low, the results are satisfactory and promising. Since the system can be seamlessly deployed on an Internet of Things sensing architecture, the improved data spatial resolution will improve performance.