IoT-based decision support system for monitoring and mitigating atmospheric pollution in smart cities

Rapid increases in the world’s population, increased urban density and increased congestion have created upwards pressure that has seen traffic-related pollution growing at a rapid pace. As atmospheric pollution has a proven detrimental effect on human health and decreases the ambience and general liveability of the world’s cities. Developing, deciding and implementing effective atmospheric pollution and mitigation strategies has become of the utmost importance to policy-makers around the world. Alongside the increase in urban densification, there has been a rapid increase in Smart City infrastructure, made possible by harnessing data from low-cost sensors that can report information in a timely, dependable and accurate manner. This paper proposes a decision support system (DSS) that uses an underlying traffic model to inform an atmospheric dispersion model. Mitigation strategies can then be tested within the DSS through simulation of strategies in the underlying traffic model and analysing the effect on the forecasted atmospheric pollution levels. The proposed DSS is used to detect a critical level of atmospheric pollution and then may respond via the implementation of full road closures. While a partial road closure is not incorporated in this paper it is a trivial extension and diverting a subsection of the polluting traffic (e.g. heavy trucks) may be an easier policy to implement. The paper demonstrates the ability of the DSS to prevent atmospheric pollution from reaching hazardous levels and inform policy-makers as to when and where mitigation treatments should be implemented for the best outcome.