Urban air pollution is mostly due to road traffic. Conventional air quality monitoring systems use high-cost static sensor stations that can only be in limited numbers. To estimate pollution at a finer granularity, emission and dispersion models and exposure predictions are often utilised together with data from static stations. Acoustic noise associated with urban transport is even more spatially undersampled. New research is emerging on monitoring by using mobile sensor networks to better characterise spatial air and noise pollution without carrying out extensive modelling/prediction, and using low-cost measurement sensors for spatially dense, long-term, real-time monitoring.
Air and noise pollution from urban mobility should be mitigated starting with understanding its causes and dynamics. It is, thus, necessary to develop effective methods for environmental measurements and to interpret resulting data using advanced techniques and algorithms for high-resolution, accurate, and real-time analysis.
Road infrastructure problems due to inadequate planning, poor design or deterioration in time are also closely associated with urban traffic in regard to the proper functioning of transportation. There are studies to understand and detect defects in road surfaces, curvatures and the overall infrastructure, however, there is no widespread real-life use of such monitoring solutions.
Our DataMote IoT sensor/actuator nodes, SigMote edge nodes, and the Locomopt IoT/Edge GIS Data Platform together form an IoT/edge wireless/cellular sensor network to provide solutions for mobility, logistics and ITS applications as described here, as well as solutions within the smart city concept for the problems described above. For the smart city, the DataMote-SigMote-Locomopt system provides a heterogeneous multi-sensor network with mobile nodes integrated with vehicles, and static nodes deployed on buildings or infrastructure. We demonstrated air quality and noise sensing successfully at technology readiness level (TRL) of 6 by integrating air quality sensors with our sensor platforms, which already had acoustic sensing capability included in their design and development phase.
Our AQNS system contributes to monitoring, analysis and mitigation of air and noise pollution by enhancing the measurement system and methods so as to provide denser, more reliable and real-time pollution information. It suggests an environmentally responsible way of dynamic pollution monitoring as an alternative to the use of conventional under-sampled static measurement methods and environmental models. Serving reliable pollution information in finer resolution or in multiple levels of granularity will help the local authorities and policy makers take action in a more localised fashion and solve problems in pinpoint accuracy. The impact of the proposed system in granularity improvement can be directly measured or calculated, and can be compared with the conventional systems.
The AQNS measurement method using urban mobility vehicles as mobile nodes also has potential to promote clean transportation alternatives such as personal electric vehicles through subsidisation of such vehicles that carry AQNS sensor nodes. Moreover, the system has a potential impact for improving the planning of the transportation infrastructure through the intelligence it provides for authorities in terms of air and noise pollution and also certain infrastructural parameters. This in turn has economic impact in terms of infrastructure design and maintenance costs.