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Modelling Malaysia air quality data using Bayesian Structural Time Series models

Air pollution poses a significant threat to human health and the environment, especially in developing nations facing rapid industrialization, urbanization, and increased vehicle emissions. As cities and factories continue to grow, the air quality problem worsens, making it crucial to enhance the mo...

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Main Authors: Aeshah Mohammed,, Mohd Aftar Abu Bakar,, Mahayaudin M. Mansor,, Noratiqah Mohd Ariff,
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2024
Online Access:http://journalarticle.ukm.my/24659/1/SS%2023.pdf
http://journalarticle.ukm.my/24659/
https://www.ukm.my/jsm/english_journals/vol53num11_2024/contentsVol53num11_2024.html
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spelling my-ukm.journal.246592025-01-06T06:57:34Z http://journalarticle.ukm.my/24659/ Modelling Malaysia air quality data using Bayesian Structural Time Series models Aeshah Mohammed, Mohd Aftar Abu Bakar, Mahayaudin M. Mansor, Noratiqah Mohd Ariff, Air pollution poses a significant threat to human health and the environment, especially in developing nations facing rapid industrialization, urbanization, and increased vehicle emissions. As cities and factories continue to grow, the air quality problem worsens, making it crucial to enhance the monitoring, testing, and forecasting of air quality. In this context, this study focuses on building air quality models using Bayesian Structural Time Series (BSTS) models to predict air quality levels in Malaysia. The BSTS model integrates three main techniques: The structural model, which employs the Kalman filter approach to model trend and seasonality components; spike and slab regression for variable selection; and Bayesian model averaging to estimate the best-performing prediction model while accounting for uncertainty. The study utilized air quality time-series data spanning two years, from June 2017 to July 2019, obtained from the Malaysian Department of Environment (DOE). The primary objective of this study was to forecast air quality and assess the effectiveness of the Bayesian structural time series analysis on air quality time-series data. The results indicated that the BSTS technique is capable of modeling air quality time-series data with high accuracy, effectively capturing seasonal and trend components. The seasonal component showed a repetition of weekly concentration patterns, while the local linear trend component showed a steady decline in PM10 and PM2.5 concentration levels in most stations. Regression analysis demonstrated that humidity and ambient temperature significantly affected air quality in most locations in Malaysia. Penerbit Universiti Kebangsaan Malaysia 2024 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/24659/1/SS%2023.pdf Aeshah Mohammed, and Mohd Aftar Abu Bakar, and Mahayaudin M. Mansor, and Noratiqah Mohd Ariff, (2024) Modelling Malaysia air quality data using Bayesian Structural Time Series models. Sains Malaysiana, 53 (11). pp. 3817-3829. ISSN 0126-6039 https://www.ukm.my/jsm/english_journals/vol53num11_2024/contentsVol53num11_2024.html
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description Air pollution poses a significant threat to human health and the environment, especially in developing nations facing rapid industrialization, urbanization, and increased vehicle emissions. As cities and factories continue to grow, the air quality problem worsens, making it crucial to enhance the monitoring, testing, and forecasting of air quality. In this context, this study focuses on building air quality models using Bayesian Structural Time Series (BSTS) models to predict air quality levels in Malaysia. The BSTS model integrates three main techniques: The structural model, which employs the Kalman filter approach to model trend and seasonality components; spike and slab regression for variable selection; and Bayesian model averaging to estimate the best-performing prediction model while accounting for uncertainty. The study utilized air quality time-series data spanning two years, from June 2017 to July 2019, obtained from the Malaysian Department of Environment (DOE). The primary objective of this study was to forecast air quality and assess the effectiveness of the Bayesian structural time series analysis on air quality time-series data. The results indicated that the BSTS technique is capable of modeling air quality time-series data with high accuracy, effectively capturing seasonal and trend components. The seasonal component showed a repetition of weekly concentration patterns, while the local linear trend component showed a steady decline in PM10 and PM2.5 concentration levels in most stations. Regression analysis demonstrated that humidity and ambient temperature significantly affected air quality in most locations in Malaysia.
format Article
author Aeshah Mohammed,
Mohd Aftar Abu Bakar,
Mahayaudin M. Mansor,
Noratiqah Mohd Ariff,
spellingShingle Aeshah Mohammed,
Mohd Aftar Abu Bakar,
Mahayaudin M. Mansor,
Noratiqah Mohd Ariff,
Modelling Malaysia air quality data using Bayesian Structural Time Series models
author_facet Aeshah Mohammed,
Mohd Aftar Abu Bakar,
Mahayaudin M. Mansor,
Noratiqah Mohd Ariff,
author_sort Aeshah Mohammed,
title Modelling Malaysia air quality data using Bayesian Structural Time Series models
title_short Modelling Malaysia air quality data using Bayesian Structural Time Series models
title_full Modelling Malaysia air quality data using Bayesian Structural Time Series models
title_fullStr Modelling Malaysia air quality data using Bayesian Structural Time Series models
title_full_unstemmed Modelling Malaysia air quality data using Bayesian Structural Time Series models
title_sort modelling malaysia air quality data using bayesian structural time series models
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2024
url http://journalarticle.ukm.my/24659/1/SS%2023.pdf
http://journalarticle.ukm.my/24659/
https://www.ukm.my/jsm/english_journals/vol53num11_2024/contentsVol53num11_2024.html
_version_ 1821002976271007744
score 13.244413

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