Abstract
Abstract. The development of new tools that allow continuous monitoring of air quality is essential for the study of actions, in order to improve the levels of pollutants in the air that are harmful to the health of citizens. Cardiovascular and respiratory diseases have been identified as risk factors for death in patients with COVID-19; at the same time, exposure to air pollution is associated with these diseases. In this article, we present the pilot tests of the Crowdsourced Air Quality Monitoring (C-AQM) system, which allows the generation of reliable air pollution maps, using data provided by low-cost sensor nodes. The results verify that the system is correct after performing a data calibration; an improvement in NO2 pollution has been observed on weekends, as well as a situation of less air pollution by NO2 between the first and second pandemic waves in Spain.
Highlights
INTRODUCTIONTechnicians are working hard to improve cities’ air quality, and traffic restrictions in the main European cities are a clear example
A recent study of the Institute of Global Health of Barcelona (ISGlobal) reveals that, only in Spain, NO2 is responsible for more than 9,150 premature deaths (Khomenko et al, 2021)
Almost at the same time, last November 2020, the European Commission announced that 30% of the EU funds for 20212027 will be spent to fight climate change, the highest share ever of the largest European budget ever
Summary
Technicians are working hard to improve cities’ air quality, and traffic restrictions in the main European cities are a clear example. The European Commission provides information on the current air quality situation, based on measurements carried out at more than 2000 air quality measurement stations across Europe. Optical sensors: detect gases such as carbon monoxide and carbon dioxide by measuring the absorption of infrared light. In our first work (Pares and Vazquez-Gallego, 2018), we proposed the Crowdsourced Air Quality Monitoring (C-AQM) system, which relies on the measurements obtained by reference stations, and a cluster of lowcost and low-energy sensor nodes to generate high-resolution air quality maps. Our interpretation of the results, together with project conclusions are presented
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