Design and Implementation of a Crowdsensing-Based Air Quality Monitoring Open and FAIR Data Infrastructure

Abstract

This work reports on the development of a real-time vehicle sensor network (VSN) system and infrastructure devised to monitor particulate matter (PM) in urban areas within a participatory paradigm. The approach is based on the use of multiple vehicles where sensors, acquisition and transmission devices are installed. PM values are measured and transmitted using standard mobile phone networks. Given the large number of acquisition platforms needed in crowdsensing, sensors need to be low-cost (LCS). This sets limitations in the precision and accuracy of measurements that can be mitigated using statistical methods on redundant data. Once data are received, they are automatically quality controlled, processed and mapped geographically to produce easy-to-understand visualizations that are made available in almost real time through a dedicated web portal. There, end users can access current and historic data and data products. The system has been operational since 2021 and has collected over 50 billion measurements, highlighting several hotspots and trends of air pollution in the city of Trieste (north-east Italy). The study concludes that (i) this perspective allows for drastically reduced costs and considerably improves the coverage of measurements; (ii) for an urban area of approximately 100,000 square meters and 200,000 inhabitants, a large quantity of measurements can be obtained with a relatively low number (5) of public buses; (iii) a small number of private cars, although less easy to organize, can be very important to provide infills in areas where buses are not available; (iv) appropriate corrections for LCS limitations in accuracy can be calculated and applied using reference measurements taken with high-quality standardized devices and methods; and that (v) analyzing the dispersion of measurements in the designated area, it is possible to highlight trends of air pollution and possibly associate them with traffic directions. Crowdsensing and open access to air quality data can provide very useful data to the scientific community but also have great potential in fostering environmental awareness and the adoption of correct practices by the general public.

Improving Air Quality Zoning Through Deep Learning and Hyperlocal Measurements

“…In addition, accurate models can provide simulations closer to the real measurements, allowing to estimate air quality concentrations in locations where it is not possible to install sensors or reference stations, improving the resolution of the network [52]. Last, better data increases citizens' trustability in air quality measurements, a key aspect in air quality regulations , allowing them to gain insights into the air quality in individual countries, regions and cities, and become more engaged regarding climate change mitigation [53].…”

Editorial Overview of the Special Issue “Air Quality Monitoring for Smart Cities and Industrial Applications”

“…Another paper explores the evolution of research trends in ozone formation sensitivity using bibliometric analysis [6]. Among the studies presented in this issue, there is one that reports a crowdsensing-based vehicle sensor network system for real-time monitoring of urban PM levels, discussing its costeffectiveness and benefits [7]. The impact of poor indoor air quality on work efficiency is examined through a case study of PM2.5 levels in a large shopping mall in Macao [8].…”