ESTIMATION OF PM_2.5 VERTICAL DISTRIBUTION USING CUSTOMIZED UAV AND MOBILE SENSORS IN BRGY. UP CAMPUS, DILIMAN, QUEZON CITY

• Main Authors: J. B. Babaan, J. P. Ballori, A. M. Tamondong, R. V. Ramos, P. M. Ostrea
• Format: Article
• Language: English
• Published: Copernicus Publications 2018-10-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W9/89/2018/isprs-archives-XLII-4-W9-89-2018.pdf

As the unmanned aerial vehicle (UAV) technology has gained popularity over the years, it has been introduced for air quality monitoring. This study demonstrates the feasibility of customized UAV with mobile monitoring devices as an effective, flexible, and alternative means to collect three-dimensional air pollutant concentration data. This also shows the vertical distribution of PM concentration and the relationship between the PM_2.5 vertical distribution and the meteorological parameters within 500 m altitude on a single flight in UP Diliman, Quezon City. Measurement and mapping of the vertical distribution of particulate matter (PM)_2.5 concentration is demonstrated in this research using integrated air quality sensors and customized Unmanned Aerial Vehicle. The flight covers an area with a radius of 80 meters, following a cylindrical path with 40-meter interval vertically. The PM_2.5 concentration values are analyzed relative to the meteorological parameters including air speed, pressure, temperature, and relative humidity up to a 500 meter-flying height in a single flight in Barangay UP Campus, UP Diliman, Quezon City. The study shows that generally, the PM_2.5 concentration decreases as the height increases with an exception in the 200–280 m above ground height interval due to a sudden change of atmospheric conditions at the time of the flight. Using correlation and regression analysis, the statistics shows that PM_2.5 concentration has a positive relationship with temperature and a negative relationship with relative humidity and wind speed. As relative humidity and wind speed increases, PM_2.5 decreases, while as temperature increases, PM_2.5 also increases.