About the Journal
In Memory of Professor Jerzy Radecki
Ownership and Management Statement
Editorial Office
Editorial Board
Scope
Impact Factor
Indexed by
Journal Impact Factor contributing items
Publication Frequency
Revenue Sources & Business Model
Contact
Subscription
Journals Statistics
Current issue
Online first
Instructions for Authors
Copyright & Licensing
Publication Fees
Editorial policies
Publication Policy Overview
Peer Review Policy
Research Ethics and Malpractice Policy
Plagiarism Policy
Conflict of Interest Policy
Open Access Policy
Instructions for Reviewers
Generative AI and AI-Assisted Technologies Policy
Advertising Policy
Direct Marketing & Manuscript Solicitation
Digital Archiving and Preservation Policy
CrossMark, Correction, and Retraction Policy
Complaints and Appeals Procedure
Archive
Publication Fees
Copyright & Licensing
Editorial policies
Publication Policy Overview
Peer Review Policy
Research Ethics and Malpractice Policy
Plagiarism Policy
Conflict of Interest Policy
Open Access Policy
Instructions for Reviewers
Generative AI and AI-Assisted Technologies Policy
Advertising Policy
Direct Marketing & Manuscript Solicitation
Digital Archiving and Preservation Policy
CrossMark, Correction, and Retraction Policy
Complaints and Appeals Procedure
eISSN: 2083-5906
ISSN: 1230-1485
ISSN: 1230-1485
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.
REFERENCES (32)
1.
QU H., LU X., LIU L., YE Y. Effects of traffic and urban parks on PM10 and PM2.5 mass concentrations. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 45 (2), 5635, 2023. https://doi.org/10.1080/155670....
2.
COLETTE A., GRANIER C., HODNEBROG Ø., JAKOBS H., MAURIZI A., NYIRI A., BESSAGNET B., D'ANGIOLA A., D'ISIDORO M., GAUSS M. Air quality trends in Europe over the past decade: a first multi-model assessment. Atmospheric Chemistry and Physics. 11 (22), 11657, 2011. https://doi.org/10.5194/acp-11....
3.
SHI Y., MATSUNAGA T., YAMAGUCHI Y., LI Z., GU X., CHEN X. Long-term trends and spatial patterns of satellite-retrieved PM2.5 concentrations in South and Southeast Asia from 1999 to 2014. Science of the Total Environment. 615, 177, 2018. https://doi.org/10.1016/j.scit... PMid:28968579.
4.
BHATTI U.A., NIZAMANI M.M., MENGXING H. Climate change threatens Pakistan's snow leopards. Science. 377 (6606), 585, 2022. https://doi.org/10.1126/scienc... PMid:35926049.
5.
CHUANG H.C., HSIAO T.C., WANG S.H., TSAY S.C., LIN N.H. Characterization of particulate matter profiling and alveolar deposition from biomass burning in Northern Thailand: The 7-SEAS study. Aerosol and Air Quality Research. 16 (11), 2897, 2016. https://doi.org/10.4209/aaqr.2....
6.
PHUENGSAMRAN P., LALITAPORN P. Estimating particulate matter concentrations in central Thailand using satellite data. Thai Environmental Engineering Journal. 35 (3), 1, 2021.
7.
AMNUAYLOJAROEN T., MACATANGAY R.C., KHODMANEE S. Modeling the effect of VOCs from biomass burning emissions on ozone pollution in upper Southeast Asia. Heliyon. 5 (10), 2019. https://doi.org/10.1016/j.heli... PMid:31692647 PMCid:PMC6806393.
8.
SAHANAVIN N., PRUEKSASIT T., TANTRAKARNAPA K. Relationship between PM10 and PM2.5 levels in high-traffic area determined using path analysis and linear regression. Journal of Environmental Sciences. 69, 105, 2018. https://doi.org/10.1016/j.jes.... PMid:29941245.
9.
CHALERMPONG S., THAITHATKUL P., ANUCHITCHANCHAI O., SANGHATAWATANA P. Land use regression modeling for fine particulate matters in Bangkok, Thailand, using time-variant predictors: Effects of seasonal factors, open biomass burning, and traffic-related factors. Atmospheric Environment. 246, 118128, 2021. https://doi.org/10.1016/j.atmo....
10.
GAUTAM S., BREMA J. Spatio-temporal variation in the concentration of atmospheric particulate matter: A study in fourth largest urban agglomeration in India. Environmental Technology & Innovation. 17, 100546, 2020. https://doi.org/10.1016/j.eti.....
11.
BUYA S., LIM A., SAELIM R., MUSIKASUWAN S., CHOOSONG T., TANEEPANICHSKUL N. Impact of air pollution on cardiorespiratory morbidities in Southern Thailand. Clinical Epidemiology and Global Health. 25, 101501, 2024. https://doi.org/10.1016/j.cegh....
12.
SAHANAVIN N., TANTRAKARNAPA K., PRUEKSASIT T. Ambient PM10 and PM2.5 concentrations at different high traffic-related street configurations in Bangkok, Thailand. Southeast Asian J Trop Med Public Health. 47 (3), 528, 2016.
13.
SUKKHUM S., LIM A., INGVIYA T., SAELIM R. Seasonal patterns and trends of air pollution in the upper northern Thailand from 2004 to 2018. Aerosol and Air Quality Research. 22 (5), 210318, 2022. https://doi.org/10.4209/aaqr.2....
14.
KHODMANEE S., AMNUAYLOJAROEN T. Impact of biomass burning on ozone, carbon monoxide, and nitrogen dioxide in Northern Thailand. Frontiers in Environmental Science. 9, 641877, 2021. https://doi.org/10.3389/fenvs.....
15.
AMNUAYLOJAROEN T., PARASIN N., LIMSAKUL A. Health risk assessment of exposure near-future PM2.5 in Northern Thailand. Air Quality, Atmosphere & Health. 15 (11), 1963, 2022. https://doi.org/10.1007/s11869....
16.
CHOMANEE J., THONGBOON K., TEKASAKUL S., FURUUCHI M., DEJCHANCHAIWONG R., TEKASAKUL P. Physicochemical and toxicological characteristics of nanoparticles in aerosols in southern Thailand during recent haze episodes in lower southeast Asia. Journal of Environmental Sciences. 94, 72, 2020. https://doi.org/10.1016/j.jes.... PMid:32563489.
17.
PAOIN K., UEDA K., VATHESATOGKIT P., INGVIYA T., BUYA S., DEJCHANCHAIWONG R., PHOSRI A., SEPOSO X.T., KITIYAKARA C., THONGMUNG N. Long-term air pollution exposure and decreased kidney function: A longitudinal cohort study in Bangkok Metropolitan Region, Thailand from 2002 to 2012. Chemosphere. 287, 132117, 2022. https://doi.org/10.1016/j.chem... PMid:34523443.
18.
PAOIN K., UEDA K., INGVIYA T., BUYA S., PHOSRI A., SEPOSO X.T., SEUBSMAN S.A., KELLY M., SLEIGH A., HONDA A. Long-term air pollution exposure and self-reported morbidity: A longitudinal analysis from the Thai cohort study (TCS). Environmental Research. 192, 10330, 2021. https://doi.org/10.1016/j.envr... PMid:33068582 PMCid:PMC7768181.
19.
BHATTI U.A., YAN Y., ZHOU M., ALI S., HUSSAIN A., QINGSONG H., YU Z., YUAN L. Time series analysis and forecasting of air pollution particulate matter (PM2.5): an SARIMA and factor analysis approach. IEEE Access. 9, 41019, 2021. https://doi.org/10.1109/ACCESS....
20.
HASNAIN A., HASHMI M.Z., KHAN S., BHATTI U.A., MIN X., YUE Y., HE Y., WEI G. Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China. Environmental Monitoring and Assessment. 196 (5), 487, 2024. https://doi.org/10.1007/s10661... PMid:38687422.
21.
ERDEN C. Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction. International Journal of Environmental Science and Technology. 20 (3), 2959, 2023. https://doi.org/10.1007/s13762....
22.
JIANG L., TAO Z., ZHU J., ZHANG J., CHEN H. Exploiting PSO-SVM and sample entropy in BEMD for the prediction of interval-valued time series and its application to daily PM2.5 concentration forecasting. Applied Intelligence. 53 (7), 7599, 2023. https://doi.org/10.1007/s10489....
23.
CORTES C., VAPNIK V. Support-vector networks. Machine Learning. 20, 273, 1995. https://doi.org/10.1023/A:1022....
24.
DRUCKER H., BURGES C., KAUFMAN L., SMOLA A., VAPNIK V. Support Vector Regression Machines. Advances in Neural Information Processing Systems. 9, 155–161, 1997.
26.
CHA G.W., MOON H.J., KIM Y.C. Comparison of random forest and gradient boosting machine models for predicting demolition waste based on small datasets and categorical variables. International Journal of Environmental Research and Public Health. 18 (16), 8530, 2021. https://doi.org/10.3390/ijerph... PMid:34444277 PMCid:PMC8392226.
27.
JAIN A.K., MAO J., MOHIUDDIN K.M. Artificial neural networks: A tutorial. Computer. 29 (3), 31, 1996. https://doi.org/10.1109/2.4858....
28.
SIRITHIAN D., THANATRAKOLSRI P. Relationships between meteorological and particulate matter concentrations (PM2.5 and PM10) during the haze period in urban and rural areas, northern Thailand. Air, Soil and Water Research. 15, 11786221221117264, 2022. https://doi.org/10.1177/117862....
29.
DAHARI N., LATIF M.T., MUDA K., NORELYZA N. Influence of meteorological variables on suburban atmospheric PM2.5 in the southern region of peninsular Malaysia. Aerosol and Air Quality Research. 20 (1), 14, 2020. https://doi.org/10.4209/aaqr.2....
30.
ECK T.F., HOLBEN B., KIM J., BEYERSDORF A., CHOI M., LEE S., KOO J.-H., GILES D., SCHAFER J., SINYUK A. Influence of cloud, fog, and high relative humidity during pollution transport events in South Korea: Aerosol properties and PM2.5 variability. Atmospheric Environment. 232, 117530, 2020. https://doi.org/10.1016/j.atmo....
31.
SAWUT R., LI Y., KASIMU A., ABLAT X. Examining the spatially varying effects of climatic and environmental pollution factors on the NDVI based on their spatially heterogeneous relationships in Bohai Rim, China. Journal of Hydrology. 617, 128815, 2023. https://doi.org/10.1016/j.jhyd....
32.
WANG L., ZHAO B., ZHANG Y., HU H. Correlation between surface PM2.5 and O3 in eastern China during 2015–2019: Spatiotemporal variations and meteorological impacts. Atmospheric Environment. 294, 119520, 2023. https://doi.org/10.1016/j.atmo....
CITATIONS (3):
1.
Spatial and Temporal Heterogeneity of Human-
Air-Ground Coupling Relationships at Fine Scale
Huaizhen Peng, Huachao Lou, Ying Yang, Qingying He, Yifan Liu, Enqing Chen, Maomao Zhang
Polish Journal of Environmental Studies
Huaizhen Peng, Huachao Lou, Ying Yang, Qingying He, Yifan Liu, Enqing Chen, Maomao Zhang
Polish Journal of Environmental Studies
2.
Using Gaussian processes for spatial prediction of PM2.5 concentration based on calibrated data from distributed low-cost sensor networks
Lillian Muyama, Richard Sserunjogi, Deo Okure, Engineer Bainomugisha
Environmental Data Science
Lillian Muyama, Richard Sserunjogi, Deo Okure, Engineer Bainomugisha
Environmental Data Science
3.
Principal component-based predictive modelling of particulate matters using air pollutants and meteorological variables in Bangkok, Thailand
Sattawat Saeyang, Khairil Anwar Notodiputro, Haris Khurram, Apiradee Lim, Boonorm Chomtee, Wandee Wanishsakpong, Jing Chen
PLOS Climate
Sattawat Saeyang, Khairil Anwar Notodiputro, Haris Khurram, Apiradee Lim, Boonorm Chomtee, Wandee Wanishsakpong, Jing Chen
PLOS Climate
Share
RELATED ARTICLE
| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
