Multiple linear regression (MLR) models for PM2.5 concentration forecasting in Greater Klang Valley

Siti Hazimah Ayu Ismain; Siti Aekbal Salleh; Wan Nurul Farah Wan Azmi; Zulkiflee Abd Latif; Noraishah Mohammad Sham

doi:10.51489/tuzal.1707890

Multiple linear regression (MLR) models for PM2.5 concentration forecasting in Greater Klang Valley

Abstract

Prolonged exposure to fine particulate matter (PM2.5), which is less than 2.5 micrometers in diameter, has been linked to significant health risks, including increased mortality from cardiopulmonary diseases and lung cancer. Developing effective local-level forecasting models is crucial, as these models allow authorities and the public to anticipate and respond to unhealthy air quality conditions. This study aims to develop Multiple Linear Regression (MLR) models to estimate PM2.5 concentrations diurnally across nine different locations in the Greater Klang Valley (GKV) and to examine the influence of meteorological factors, namely temperature and humidity. Model validation using ground-based data yielded R-squared values ranging from 0.3558 to 0.7188 and RMSE values from 0.093 to 1.7687, indicating that temperature and humidity significantly affect PM2.5 levels. Additionally, a correlation analysis between Landsat 8 satellite data and ground-based PM2.5 measurements revealed that Band 8 had the highest correlation coefficient of 0.574, with an R-squared value of 0.329 and a highly significant p-value of 0.005. Spatial distribution map of PM2.5. temperature and humidity have been generated and it showed that PM2.5 levels were lower in northern GKV from July to December 2022 but increased across central and southern regions in early 2023, especially between February and May. These patterns indicate seasonal variation and suggest that higher temperatures and lower humidity may influence pollutant concentration.

Keywords

Supporting Institution

Universiti Teknologi MARA, Environmental Health Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health, Malaysia

Ethical Statement

All authors declare that they have no conflicts of interest

Thanks

Authors acknowledge the Universiti Teknologi MARA for enabling this research to be carried out. The authors would like to extend the appreciation to the Director General of Health Malaysia for his permission to publish this article. We thank the staff of Institute for Medical Research for helping us in data cleaning and endless support. Also, sincere appreciation to the reviewers for spending their time reading and suggestions to improve the manuscript.

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Details

Primary Language

English

Subjects

Remote Sensing

Journal Section

Research Article

Authors

Siti Hazimah Ayu Ismain This is me
0009-0004-6246-8458
Malaysia

Siti Aekbal Salleh ^*
0000-0002-2812-1897
Malaysia

Wan Nurul Farah Wan Azmi This is me
0000-0001-5229-7289
Malaysia

Zulkiflee Abd Latif This is me
0000-0002-6005-5551
Malaysia

Noraishah Mohammad Sham This is me
0000-0001-5921-1599
Malaysia

Publication Date

March 25, 2026

Submission Date

May 29, 2025

Acceptance Date

September 3, 2025

Published in Issue

Year 2026 Volume: 8

DOI

https://doi.org/10.51489/tuzal.1707890

IZ

https://izlik.org/JA98US56LH

Cite

RIS / Bibtex

APA

Ismain, S. H. A., Salleh, S. A., Wan Azmi, W. N. F., Abd Latif, Z., & Mohammad Sham, N. (2026). Multiple linear regression (MLR) models for PM2.5 concentration forecasting in Greater Klang Valley. Turkish Journal of Remote Sensing, 8, 1-22. https://doi.org/10.51489/tuzal.1707890

AMA

1.Ismain SHA, Salleh SA, Wan Azmi WNF, Abd Latif Z, Mohammad Sham N. Multiple linear regression (MLR) models for PM2.5 concentration forecasting in Greater Klang Valley. TJRS. 2026;8:1-22. doi:10.51489/tuzal.1707890

Chicago

Ismain, Siti Hazimah Ayu, Siti Aekbal Salleh, Wan Nurul Farah Wan Azmi, Zulkiflee Abd Latif, and Noraishah Mohammad Sham. 2026. “Multiple Linear Regression (MLR) Models for PM2.5 Concentration Forecasting in Greater Klang Valley”. Turkish Journal of Remote Sensing 8 (March): 1-22. https://doi.org/10.51489/tuzal.1707890.

EndNote

Ismain SHA, Salleh SA, Wan Azmi WNF, Abd Latif Z, Mohammad Sham N (March 1, 2026) Multiple linear regression (MLR) models for PM2.5 concentration forecasting in Greater Klang Valley. Turkish Journal of Remote Sensing 8 1–22.

IEEE

[1]S. H. A. Ismain, S. A. Salleh, W. N. F. Wan Azmi, Z. Abd Latif, and N. Mohammad Sham, “Multiple linear regression (MLR) models for PM2.5 concentration forecasting in Greater Klang Valley”, TJRS, vol. 8, pp. 1–22, Mar. 2026, doi: 10.51489/tuzal.1707890.

ISNAD

Ismain, Siti Hazimah Ayu - Salleh, Siti Aekbal - Wan Azmi, Wan Nurul Farah - Abd Latif, Zulkiflee - Mohammad Sham, Noraishah. “Multiple Linear Regression (MLR) Models for PM2.5 Concentration Forecasting in Greater Klang Valley”. Turkish Journal of Remote Sensing 8 (March 1, 2026): 1-22. https://doi.org/10.51489/tuzal.1707890.

JAMA

1.Ismain SHA, Salleh SA, Wan Azmi WNF, Abd Latif Z, Mohammad Sham N. Multiple linear regression (MLR) models for PM2.5 concentration forecasting in Greater Klang Valley. TJRS. 2026;8:1–22.

MLA

Ismain, Siti Hazimah Ayu, et al. “Multiple Linear Regression (MLR) Models for PM2.5 Concentration Forecasting in Greater Klang Valley”. Turkish Journal of Remote Sensing, vol. 8, Mar. 2026, pp. 1-22, doi:10.51489/tuzal.1707890.

Vancouver

1.Siti Hazimah Ayu Ismain, Siti Aekbal Salleh, Wan Nurul Farah Wan Azmi, Zulkiflee Abd Latif, Noraishah Mohammad Sham. Multiple linear regression (MLR) models for PM2.5 concentration forecasting in Greater Klang Valley. TJRS. 2026 Mar. 1;8:1-22. doi:10.51489/tuzal.1707890