<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The rising levels of air pollution in the industrial environment necessitate its monitoring near the sources. The green wall around the industries is a novel and popular to mitigate the impact of Particulate Matter (PM). Such industrial mitigation cannot be monitored through a limited quantity AQ monitoring station at ground level. This paper demonstrates the application of drone aided particulate matter monitoring system to measure the above-ground level PM levels. The drone-aided AQ monitoring system was employed near the thermal power plant to calculate the attenuation factor potential due to the plantation of a green bamboo belt. The wind rose was plotted to understand the wind pattern and dispersion direction of the PM. The dust suppression due to the green wall of the bamboo is calculated using a drone-enabled PM sensor module. The PM (Particulate Matter) data at the ground level was 39 % low compared to the upper height. The PM concentration was significantly high at the height of 30 m Above Ground Level (AGL). The bamboo plant plays a crucial role in dust suppression. The overall results pointed out that the calibrated, lightweight portable drone-aided sensor monitoring system can be reliable for monitoring source-specific industries.</p>
</abstract>

The rising levels of air pollution in the industrial environment necessitate its monitoring near the sources. The green wall around the industries is a novel and popular to mitigate the impact of Particulate Matter (PM). Such industrial mitigation cannot be monitored through a limited quantity AQ monitoring station at ground level. This paper demonstrates the application of drone aided particulate matter monitoring system to measure the above-ground level PM levels. The drone-aided AQ monitoring system was employed near the thermal power plant to calculate the attenuation factor potential due to the plantation of a green bamboo belt. The wind rose was plotted to understand the wind pattern and dispersion direction of the PM. The dust suppression due to the green wall of the bamboo is calculated using a drone-enabled PM sensor module. The PM (Particulate Matter) data at the ground level was 39 % low compared to the upper height. The PM concentration was significantly high at the height of 30 m Above Ground Level (AGL). The bamboo plant plays a crucial role in dust suppression. The overall results pointed out that the calibrated, lightweight portable drone-aided sensor monitoring system can be reliable for monitoring source-specific industries.

Drone-Aided Particulate Monitoring System for Industrial Complex to Analyze the Dust Suppressing Capacity

Journal of Applied Engineering Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The rising levels of air pollution in the industrial environment necessitate its monitoring near the sources. The green wall around the industries is a novel...

Drone-Aided Particulate Monitoring System for Industrial Complex to Analyze the Dust Suppressing Capacity

Online veröffentlicht: 06. Dez. 2023

Seitenbereich: 237 - 242

Eingereicht: 08. Aug. 2023

Akzeptiert: 14. Aug. 2023

DOI: https://doi.org/10.2478/jaes-2023-0030

Schlüsselwörter
Environmental Engineering, Ecology, Remote Sensing and Electronics

© 2023 P. Kokate et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Drone-Aided Particulate Monitoring System for Industrial Complex to Analyze the Dust Suppressing Capacity

Online veröffentlicht: 06. Dez. 2023

Seitenbereich: 237 - 242

Eingereicht: 08. Aug. 2023

Akzeptiert: 14. Aug. 2023

DOI: https://doi.org/10.2478/jaes-2023-0030

SchlüsselwörterEnvironmental Engineering, Ecology, Remote Sensing and Electronics

© 2023 P. Kokate et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Environmental Engineering, Ecology, Remote Sensing and Electronics