Improved Fine Particles Monitoring in Smart Cities by Means of Advanced Data Concentrator

Abstract

Traffic reduction and air-quality improvement are among the main goals of several projects worldwide. This paper presents a fine particle monitoring based on heterogeneous air quality mobile sensors and an advanced data concentrator, AdDC, so that the level of pollution in the urban area, where few accurate fixed measurement stations are present, can be assessed with better accuracy. Some urban buses are used to carry low-cost sensors, thus implementing a mobile sensor network and increasing the time and space resolution of air quality information. The data obtained by these low-cost sensors is significantly affected by uncertainties, also due to atmospheric factors, such as humidity. The proposed AdDC processes all the obtained measurements and exploits the information obtained by the accurate fixed stations to improve the accuracy of the low-cost mobile sensors. In particular, a new compensation methodology, specifically targeted to the fine particles monitoring, is proposed. The monitoring of relative humidity is added, with the relevant on-the-fly calibration, so that the measured values can be used to correct the effects of humidity on PM2.5 sensors. The validity of the proposed system is proved by means of simulations performed on an appropriate set-up.

Effects of Gas and Steam Humidity on Particulate Matter Measurements Obtained Using Light-Scattering Sensors

“…Notably, the demand for air quality monitoring techniques corresponding to the recent advancement of smart technology that can be applied in smart cities has been increasing. Consequently, PM sensors that can be employed in the light-scattering method for outdoor monitoring should be developed [12].…”

Drive-by Air Pollution Sensing Systems: Challenges and Future Directions

“…At the same time, drive-by sensing has introduced a range of novel research challenges in terms of sensor deployment [11] [12], spatiotemporal coverage [13], data collection strategies [14][15] [16], calibration models [10] [17], and data analysis [18] [19]. For example, the predictable nature of bus routes and schedules presents new opportunities that could be exploited for optimizing spatial coverage.…”

Drive-by Air Pollution Sensing Systems: Challenges and Future Directions

“…At the same time, vehicularbased sensing has introduced a range of novel research challenges in terms of sensor deployment [11] [12], spatiotemporal coverage [13], data collection strategies [14] [15] Hassan Zarrar is with the Institute for Research in Applicable Computing (IRAC), University of Bedfordshire, University Square, Luton, LU1 3JU, United Kingdom (e-mail: Hassan.Zarrar@study.beds.ac.uk). [16], calibration models [10] [17], and data analysis [18] [19]. For example, the predictable nature of bus routes and schedules presents new opportunities that could be exploited for optimizing spatial coverage.…”