Data driven air pollutant concentration forecast system

People can stay alive for days without water and even for weeks without food, but without air, one cannot survive for more than a few minutes. Therefore, air quality plays a key role in our health and well-being, as poor air quality contributes to several serious respiratory diseases, as well as...

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Main Author: Ong, Li Xuan
Other Authors: Wong Kin Shun, Terence
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/176366
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1763662024-05-17T15:45:45Z Data driven air pollutant concentration forecast system Ong, Li Xuan Wong Kin Shun, Terence School of Electrical and Electronic Engineering EKSWONG@ntu.edu.sg Engineering Air quality Forecast system People can stay alive for days without water and even for weeks without food, but without air, one cannot survive for more than a few minutes. Therefore, air quality plays a key role in our health and well-being, as poor air quality contributes to several serious respiratory diseases, as well as other detrimental health effects. Pollutants such as nitrogen oxides (NOx) which includes nitrogen oxide (NO) and nitrogen dioxides (NO2), sulfur dioxide (SO2) pose consequential threat to our health as they are responsible for the formation of particulate and ground level ozone (O3). At the same time, meteorological data such as wind speed and relative humidity intricately influence the dynamics of the air pollutants as they are involved with pollutant emissions and dispersions which adds complexity to the overall air quality. This project focuses on developing an appropriate data-driven air pollutant concentration forecasting system where it will be considering most of the essential air pollutants, aiming to provide reliable predictions of air quality health index levels by categorising them into different health risk categories over specific time periods and stations such as Shatin, Causeway Bay and Mong Kok. The forecasting system make use of historical air quality data, meteorological parameters, and various machine learning techniques. However, there are also limitations to the dataset itself and machine learning models used which caused some complications to the results obtained. And for that reason, insightful information into future pollutant concentrations is attained. Hence, the findings of this project highlight the critical importance of accurate air quality forecasting in addressing the health impacts of air pollution. Bachelor's degree 2024-05-16T04:43:14Z 2024-05-16T04:43:14Z 2024 Final Year Project (FYP) Ong, L. X. (2024). Data driven air pollutant concentration forecast system. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176366 https://hdl.handle.net/10356/176366 en A2244-231 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Air quality
Forecast system
spellingShingle Engineering
Air quality
Forecast system
Ong, Li Xuan
Data driven air pollutant concentration forecast system
description People can stay alive for days without water and even for weeks without food, but without air, one cannot survive for more than a few minutes. Therefore, air quality plays a key role in our health and well-being, as poor air quality contributes to several serious respiratory diseases, as well as other detrimental health effects. Pollutants such as nitrogen oxides (NOx) which includes nitrogen oxide (NO) and nitrogen dioxides (NO2), sulfur dioxide (SO2) pose consequential threat to our health as they are responsible for the formation of particulate and ground level ozone (O3). At the same time, meteorological data such as wind speed and relative humidity intricately influence the dynamics of the air pollutants as they are involved with pollutant emissions and dispersions which adds complexity to the overall air quality. This project focuses on developing an appropriate data-driven air pollutant concentration forecasting system where it will be considering most of the essential air pollutants, aiming to provide reliable predictions of air quality health index levels by categorising them into different health risk categories over specific time periods and stations such as Shatin, Causeway Bay and Mong Kok. The forecasting system make use of historical air quality data, meteorological parameters, and various machine learning techniques. However, there are also limitations to the dataset itself and machine learning models used which caused some complications to the results obtained. And for that reason, insightful information into future pollutant concentrations is attained. Hence, the findings of this project highlight the critical importance of accurate air quality forecasting in addressing the health impacts of air pollution.
author2 Wong Kin Shun, Terence
author_facet Wong Kin Shun, Terence
Ong, Li Xuan
format Final Year Project
author Ong, Li Xuan
author_sort Ong, Li Xuan
title Data driven air pollutant concentration forecast system
title_short Data driven air pollutant concentration forecast system
title_full Data driven air pollutant concentration forecast system
title_fullStr Data driven air pollutant concentration forecast system
title_full_unstemmed Data driven air pollutant concentration forecast system
title_sort data driven air pollutant concentration forecast system
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176366
_version_ 1814047187378634752