Abstract
The article presents the study of Particulate Matter air pollution with PM1, PM2,5 and PM10 by means of a low-cost sensors mounted on Unmanned Aerial Vehicles. The article is divided into two parts. In first part pollution measurement system is described. In second part expert system for optimization of flight parameters is described. The research was conducted over a municipal cemetery area in Poland. The obtained results were analyzed through an inductive knowledge management system (decision tree method) for classification analysis of air pollution. The decision tree mechanism would be used to optimize flight parameters taking into account the air pollution parameters. The analysis was made from the influence of PM concentration point of view, depending on the altitude. The decision tree method was used, which allowed to determine, among other aspects, which PM indicator should be measured and which altitude plays a greater role in the optimization of air pollution measurements by means of cheap sensors mounted on drones. As a result of the analysis, the optimum flight altitude of the measurement drone in the specified area was determined.
Highlights
In urban areas Particulate Matter (PM) is a key issue affecting personal pollution exposure levels (Cao et al 2020)
The first is a description of an unmanned aircraft system with a low-cost sensor to measure air quality
As a result of the conducted tests, air pollution with particulate matter in a specific area was measured and air pollution profiles were visualized for specific altitudes
Summary
In urban areas Particulate Matter (PM) is a key issue affecting personal pollution exposure levels (Cao et al 2020). To increase the resolution of air pollution measurements the use of Unmanned Aerial Vehicles, as a platform for air pollution surveys, can be applied for a wide range of research scenarios (Chilinski et al 2016). Current developments in miniaturization of chemical equipment and in low-cost small drones are catalyzing exponential growth in the use of such platforms for environmental chemical sensing applications (Javier and Marc 2020; Johnson et al 2020). The evolution of low-cost sensors (LCSs), whose prices currently range from a few to several EUR, has made the spatio-temporal mapping of the indoor and outdoor air pollution possible but the diversity of them for various applications make their optimum selection challenging (Omidvarborna et al 2021). Tracing of atmospheric pollutants release of and validation of the measurements using unmanned aerial vehicles is still difficult task from a mathematical point of view (Šmídl and Hofman 2013; Yungaicela-Naula et al 2019; Villa et al 2016)
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