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ORIGINAL RESEARCH
Trend and Association Between Particulate
Matters and Meteorological Factors: A Prospect
for Prediction of PM2.5 in Southern Thailand
1
Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University,
Pattani Campus, 94000, Thailand
2
Air Pollution and Health Effects Research Center, Prince of Songkla University, Hat Yai Campus, 90110, Thailand
3
Multidisciplinary Research and Innovation Centre, Kumasi, Ghana
4
Division of Digital Innovation and Data Analytics, Faculty of Medicine, Prince of Songkla University,
Hat Yai Campus, 90110, Thailand
5
College of Digital Science, Prince of Songkla University, Hat Yai Campus, Songkhla 90110, Thailand
6
Department of Sciences and Humanities, National University of Computer and Emerging Sciences, Chiniot-Faisalabad
Campus, Chiniot, Pakistan
7
Department of Family and Preventive Medicine, Faculty of Medicine, Prince of Songkla University,
Hat Yai Campus, 90110, Thailand
8
Research Center for Cancer Control in Southern Thailand, Prince of Songkla University, Hat Yai Campus,
Songkhla 90110, Thailand
9
School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology,
Thammasat University, Pathum Thani, Thailand
Submission date: 2024-04-26
Final revision date: 2024-06-12
Acceptance date: 2024-07-03
Online publication date: 2024-11-05
Publication date: 2025-07-05
Corresponding author
Haris Khurram
Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, 94000, Thailand
Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, 94000, Thailand
Pol. J. Environ. Stud. 2025;34(5):5215-5223
KEYWORDS
TOPICS
ABSTRACT
Particulate matter (PM) concentration in southern Thailand has increased significantly due to
open crop burning within the southeast Asian sub-region. This study aimed to explore the trend and
relationship between PM and meteorological features in southern Thailand. Also, estimate the PM2.5
when monitoring sites measure PM10. Data on PM concentration and meteorological features were taken
from air monitoring stations within southern Thailand from 2012 to 2021. Descriptive statistics were
used to explore the data, and then a spline model was used to examine the trends and seasonal patterns
of PM concentration and meteorological features. A scatter plot matrix and correlation analysis were
used to assess the relation between PM and meteorological features. Machine learning models were
used to predict PM2.5 concentration. The highest annual average concentration of PM2.5 and PM10 in
southern Thailand was 18.9±8.24 μg/m3 and 36.3±14.2 μg/m3 in Songkhla Province, and the lowest
concentration of PM2.5 and PM10 was 13.9±7.65 μg/m3 and 27.5±12.2 μg/m3 at Phuket. The Multiple Linear Regression (MLR) and Artifical Neural Network (ANN) almost perform best for the prediction
of PM2.5 at each station, with 13.6% average Mean Absoulte Percentage Error (MAPE). Songkhla and
Phuket need significant attention from local government officials and policymakers. PM2.5 can be better
predicted using the MLR model when it has missing values at some stations. The results help scientists
and policymakers to better understand the condition and find the best possible solution to overcome the
health issue that arises due to exposure.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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