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...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/176366 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-176366 |
---|---|
record_format |
dspace |
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 |