Abstract
Air pollutant monitoring is a basic issue in contemporary urban life. This paper describes an approach based on the diffused use of low-cost sensors that can be mounted on board urban vehicles for more abundant and distributed measures. The system exchanges data, exploiting a “Smart Road” infrastructure, with a central computing facility, the CIPCast platform, a GIS-based Decision Support System designed to perform real-time monitoring and interpolation of data with the aim of possibly issuing alarms with respect to different town areas. Experimental data gathering in the Rome urban area and subsequent processing results are presented. Algorithms for data fusion among different simulated monitoring systems and interpolation of data for a geographically denser map were utilised. Thus, in the framework of the Smart Road, protocols for data exchange were designed. Finally, air pollutant distribution maps were produced and integrated into the CIPCast platform. The feasibility of a full system architecture from the sensors to the real-time pollutant maps is shown.
Highlights
Human health is adversely affected by exposure to air pollutants with chronic and long-term ailments ranging from upper respiratory irritation to deadly morbidities such as lung cancer and heart diseases [1]
The aim of this paper is to describe an air pollutant monitoring system composed of three different elements: a mobile small-sized device accommodating low-cost sensors (LCSs), a road infrastructure for the communication of data, and a central computing facility [6]
We have presented a system composed of a lightweight device equipped with a LCS for the measurement of particulate matter, an urban infrastructure for the relay of data among end users, either vehicles or humans, a central facility, and a GIS-based Decision Support
Summary
Human health is adversely affected by exposure to air pollutants with chronic and long-term ailments ranging from upper respiratory irritation to deadly morbidities such as lung cancer and heart diseases [1]. The underlining cause is represented by the everincreasing consumption of energy, most of it in the form of the burning of fossil fuels This produces huge amounts of carbon dioxide (CO2 ), contributing to the warming of our planet, and, at the same time, outputs a series of air pollutants that have a direct and harmful influence on human health: carbon monoxide (CO), sulphur dioxide (SO2 ), nitrogen oxides (NOx ), and particulate matters of several sizes (PM-1, PM-2.5, and PM-10). Long-term exposure has been linked to premature mortality [2] This problem has been tackled at a legislative level with the installation of monitoring systems in urban areas. If an average radius of 9 km is assumed for the GRA, each station covers a surface of 25 km if evenly distributed. The sparseness of the data has produced, as a consequence, a need for estimates of the pollutants in unmeasured areas using a variety of methods, e.g., spatial averaging, nearest neighbour, Inverse Distance Weighting (IDW), Spline interpolation, Kriging, Land-Use
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