UAV-based PM₂.₅ monitoring system for small scale urban areas

In urban areas, air particle pollution is of precise interest because of its impact on health. Air quality data collection near the ground surface is difficult, particularly in small complex regions, and the usage of satellites image may not suffice and do not achieve the required accuracy. A variet...

Full description

Saved in:

Bibliographic Details
Main Author:	Jumaah, Huda Jamal
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Air - Pollution Particles - Health aspects Drone aircraft
Online Access:	http://psasir.upm.edu.my/id/eprint/76073/1/FK%202018%20152%20-%20IR.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-upm-ir.76073
record_format	uketd_dc
spelling	my-upm-ir.760732020-01-09T07:16:07Z UAV-based PM₂.₅ monitoring system for small scale urban areas 2018-07 Jumaah, Huda Jamal In urban areas, air particle pollution is of precise interest because of its impact on health. Air quality data collection near the ground surface is difficult, particularly in small complex regions, and the usage of satellites image may not suffice and do not achieve the required accuracy. A variety of Unmanned Aerial Vehicles (UAVs) based on remote sensing technology enables data collection in these particular regions and overcoming obstacles and the difficulties obtaining required data. Remote sensing can be considered the best significant tool to assist in data monitoring for estimating and predicting air quality parameters. The recent monitoring stations are fixed stations and are not designed to denote exposure on a small scale adequate. Most of the studies rely on satellite observations from Aerosol Optical Depth (AOD) and have used lower resolution AOD to estimate PM2.5 levels. In general, this used resolution of AOD products is often insufficient to define exposure estimations in urban areas. In this manner evaluation at different altitudes can offer extra information to assess air quality. The research aims to introduce a PM2.5 prediction algorithm based on PM2.5 measurements from a developed a system capable of measuring PM2.5 concentrations in small-scale areas and validate the model at specified low altitudes. Observations based on UAV-based PM2.5 monitoring sensors were applied around 1.6 km² area for collecting data at low altitude. Meteorological parameters including temperature and humidity were collected. This study uses an empirical method via applying amassed records of PM2.5 concentrations and meteorological par ameters to create a geographically weighted regression (GWR) model to estimate PM2.5 concentrations in a small-scale area. For the predicted model, an accuracy value is computed from the probability value given by the regression analysis model of each parameter. To validate our method, we have utilized two types of data, training, and testing. To evaluate and validate the suggested GWR model, we applied the model using testing measured points. Results showed a relatively good fit of the model to the observed data. Where the maximum accuracy obtained was set as 65% in July and 73% in August. Also, the obtained results showed that there is a good statistical correlation between the measured in situ data and testing data, the maximum accuracy was set as 93% in July and 94% in August. The developed tool can be considered as an independent method for sample collection demonstrated that the characteristics obtained by analysis are able to monitor and predict the concentrations of PM2.5 in small-scale areas with high accuracy. This suggested approach is useful to cover the area within a short amount of time, with low cost and limitless flexibility. Air - Pollution Particles - Health aspects Drone aircraft 2018-07 Thesis http://psasir.upm.edu.my/id/eprint/76073/ http://psasir.upm.edu.my/id/eprint/76073/1/FK%202018%20152%20-%20IR.pdf text en public masters Universiti Putra Malaysia Air - Pollution Particles - Health aspects Drone aircraft
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Air - Pollution Particles - Health aspects Drone aircraft
spellingShingle	Air - Pollution Particles - Health aspects Drone aircraft Jumaah, Huda Jamal UAV-based PM₂.₅ monitoring system for small scale urban areas
description	In urban areas, air particle pollution is of precise interest because of its impact on health. Air quality data collection near the ground surface is difficult, particularly in small complex regions, and the usage of satellites image may not suffice and do not achieve the required accuracy. A variety of Unmanned Aerial Vehicles (UAVs) based on remote sensing technology enables data collection in these particular regions and overcoming obstacles and the difficulties obtaining required data. Remote sensing can be considered the best significant tool to assist in data monitoring for estimating and predicting air quality parameters. The recent monitoring stations are fixed stations and are not designed to denote exposure on a small scale adequate. Most of the studies rely on satellite observations from Aerosol Optical Depth (AOD) and have used lower resolution AOD to estimate PM2.5 levels. In general, this used resolution of AOD products is often insufficient to define exposure estimations in urban areas. In this manner evaluation at different altitudes can offer extra information to assess air quality. The research aims to introduce a PM2.5 prediction algorithm based on PM2.5 measurements from a developed a system capable of measuring PM2.5 concentrations in small-scale areas and validate the model at specified low altitudes. Observations based on UAV-based PM2.5 monitoring sensors were applied around 1.6 km² area for collecting data at low altitude. Meteorological parameters including temperature and humidity were collected. This study uses an empirical method via applying amassed records of PM2.5 concentrations and meteorological par ameters to create a geographically weighted regression (GWR) model to estimate PM2.5 concentrations in a small-scale area. For the predicted model, an accuracy value is computed from the probability value given by the regression analysis model of each parameter. To validate our method, we have utilized two types of data, training, and testing. To evaluate and validate the suggested GWR model, we applied the model using testing measured points. Results showed a relatively good fit of the model to the observed data. Where the maximum accuracy obtained was set as 65% in July and 73% in August. Also, the obtained results showed that there is a good statistical correlation between the measured in situ data and testing data, the maximum accuracy was set as 93% in July and 94% in August. The developed tool can be considered as an independent method for sample collection demonstrated that the characteristics obtained by analysis are able to monitor and predict the concentrations of PM2.5 in small-scale areas with high accuracy. This suggested approach is useful to cover the area within a short amount of time, with low cost and limitless flexibility.
format	Thesis
qualification_level	Master's degree
author	Jumaah, Huda Jamal
author_facet	Jumaah, Huda Jamal
author_sort	Jumaah, Huda Jamal
title	UAV-based PM₂.₅ monitoring system for small scale urban areas
title_short	UAV-based PM₂.₅ monitoring system for small scale urban areas
title_full	UAV-based PM₂.₅ monitoring system for small scale urban areas
title_fullStr	UAV-based PM₂.₅ monitoring system for small scale urban areas
title_full_unstemmed	UAV-based PM₂.₅ monitoring system for small scale urban areas
title_sort	uav-based pm₂.₅ monitoring system for small scale urban areas
granting_institution	Universiti Putra Malaysia
publishDate	2018
url	http://psasir.upm.edu.my/id/eprint/76073/1/FK%202018%20152%20-%20IR.pdf
_version_	1747813112751325184

UAV-based PM₂.₅ monitoring system for small scale urban areas

Similar Items