Prediction of daily air pollutants concentration and air pollutant index using machine learning approach

Nurul A’isyah Mustakim and Ahmad Zia Ul-Saufie and Wan Nur Shaziayani and Norazian Mohamad Noor and Sofianita Mutalib. (2023) Prediction of daily air pollutants concentration and air pollutant index using machine learning approach. Pertanika Journal of Science & Technology (Malaysia), 31 (1). 123 -135. ISSN 2231-8526

The major air pollutants in Malaysia that contribute to air pollution are carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter. Predicting the air pollutants concentration can help the government to monitor air quality and provide awareness to the public. Therefore, this study aims to overcome the problem by predicting the air pollutants concentration for the next day. This study focuses on an industrial, the Petaling Jaya monitoring station in Selangor. The data is obtained from the Department of Environment, which contains the dataset from 2004 to 2018. Subsequently, this study is conducted to construct predictive modeling that can predict the air pollutants concentrations for the next day using a tree-based approach. From the comparison of the three models, a random forest is a best-proposed model. The results of PM₁₀ concentration prediction for the random forest is the best performance which is shown by RMSE (15.7611–19.0153), NAE (0.6508–0.8216), and R² (0.346–0.5911). For SO₂, the RMSE was 0.0016–0.0017, the NAE was 0.7056–0.8052, and the R² was 0.3219–0.4676. The RMSE (0.0062–0.0075), the NAE (0.7892–0.9591), and the R² (0.0814–0.3609) for NO₂. The RMSE (0.3438–0.3975), NAE (0.7387–0.9015), and R²(0.2005–0.4399) for CO were all within acceptable limits. For O₃, the RMSE was 0.0051–0.0057, the NAE was 0.8386–0.9263, and the R² was 0.1379–0.2953. The API calculation results indicate that PM10 is a significant pollutant in representing the API.

Full text available from: Official URL: http://www.pertanika.upm.edu.my/resources/files/Pe...

Abstract

The major air pollutants in Malaysia that contribute to air pollution are carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter. Predicting the air pollutants concentration can help the government to monitor air quality and provide awareness to the public. Therefore, this study aims to overcome the problem by predicting the air pollutants concentration for the next day. This study focuses on an industrial, the Petaling Jaya monitoring station in Selangor. The data is obtained from the Department of Environment, which contains the dataset from 2004 to 2018. Subsequently, this study is conducted to construct predictive modeling that can predict the air pollutants concentrations for the next day using a tree-based approach. From the comparison of the three models, a random forest is a best-proposed model. The results of PM₁₀ concentration prediction for the random forest is the best performance which is shown by RMSE (15.7611–19.0153), NAE (0.6508–0.8216), and R² (0.346–0.5911). For SO₂, the RMSE was 0.0016–0.0017, the NAE was 0.7056–0.8052, and the R² was 0.3219–0.4676. The RMSE (0.0062–0.0075), the NAE (0.7892–0.9591), and the R² (0.0814–0.3609) for NO₂. The RMSE (0.3438–0.3975), NAE (0.7387–0.9015), and R²(0.2005–0.4399) for CO were all within acceptable limits. For O₃, the RMSE was 0.0051–0.0057, the NAE was 0.8386–0.9263, and the R² was 0.1379–0.2953. The API calculation results indicate that PM10 is a significant pollutant in representing the API.

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Item Type:	Article
AGROVOC Term:	air pollution
AGROVOC Term:	forecasting
AGROVOC Term:	machine learning
AGROVOC Term:	data mining
AGROVOC Term:	trends
AGROVOC Term:	air sampling
AGROVOC Term:	models
AGROVOC Term:	air quality
Geographical Term:	Malaysia
Uncontrolled Keywords:	data mining, decision tree, gradient boosted trees, Modeling, PM10 , random forest
Depositing User:	Ms. Azariah Hashim
Date Deposited:	12 Nov 2024 06:13
Last Modified:	12 Nov 2024 06:13
URI:	http://webagris.upm.edu.my/id/eprint/1847

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