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Pollution from Highways Detection Using Winter UAV Data
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AI
DOI：10.3390/drones7030178.png)
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Abstract
En 中文
This study identified and evaluated the association between metal content and UAV data to monitor pollution from roadways. A total of 18 mixed snow samples were collected at the end of winter, utilizing a 1 m long and 10 cm wide snow collection tube, from either side of the Caspian Highway (Moscow-Tambo-Astrakhan) in Moscow. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to examine the chemical composition of the samples, yielding 35 chemical elements (metals). UAV data and laboratory findings were calculated and examined. Regression estimates demonstrated the possibility of using remote sensing data to identify Al, Ba, Fe, K, and Na metals in snow cover near roadways due to dust dispersal. This discovery supports the argument that UAV sensing data can be utilized to monitor air pollution from roadways.
Keywords:
pearson correlation analysis
independent variable
metal
concentration
street dust
pollution
unmanned aerial vehicle
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