Journal of Research in Environmental Health

Journal of Research in Environmental Health

Statistical Analysis and Forecast Modeling of Particles Concentration Using Artificial Intelligence Based on Machine Learning in Mashhad

Document Type : Research article

Authors
Ahmad Makhdoomi 1
Somayyeh Ziaei 2
Maryam Sarkhosh 3
1 Master's Student in Environmental Health Engineering, Member of the Student Research Committee, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
2 Postdoctoral Researcher, School of Health, Member of the Student Research Committee, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
3 Department of Environmental Health, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
10.22038/jreh.2025.25727
Abstract
Background and Purpose: This study aims to forecast PM concentrations using four non-linear Machine Learning (ML) models.

Materials and Methods: The ML techniques employed include Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting Regressor (XGBR), Random Forest (RF), and Gradient Boosting Regressor (GBR). Meteorological and pollutant data were collected to predict the Air Quality Index (AQI) in Mashhad, Khorasan Razavi Province, Iran.

Results: Based on the MAE and MAPE metrics, RF demonstrated the best performance, while according to the MSE and RMSE metrics, GBR model was more robust.
Conclusion: This study proposes a high-accuracy PM prediction method using ML, which can be beneficial for global air quality monitoring and improving acute exposure assessments in epidemiological research.
 
Open Access Policy: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/
Keywords
Air quality index
PM
Machine learning
Non-linear Models
  1. Verma P, Verma R, Mallet M, Sisodiya S, Zare A, Dwivedi G, Ristovski Z. Assessment of human and meteorological influences on PM10 concentrations: Insights from machine learning algorithms. Atmospheric Pollution Research. 2024 Jun 1;15(6):102123. https://doi.org/10.1016/j.apr.2024.102123
  2. Li X, Jia Y, Nan A, Zhang N, Zhou H, Chen L, et al. CircRNA104250 and lncRNAuc001. dgp. 1 promote the PM2. 5-induced inflammatory response by co-targeting miR-3607-5p in BEAS-2B cells. Environmental Pollution. 2020;258:113749. https://doi.org/10.1016/j.envpol.2019.113749
  3. Kleine Deters J, Zalakeviciute R, Gonzalez M, Rybarczyk Y. Modeling PM2. 5 urban pollution using machine learning and selected meteorological parameters. Journal of Electrical and Computer Engineering. 2017;2017(1):5106045. https://doi.org/10.1155/2017/5106045
  4. Yang H, Wang W, Li G. Prediction method of PM2.5 concentration based on decomposition and integration. Measurement. 2023;216:112954. https://doi.org/10.1016/j.measurement.2023.112954
  5. Sarkar N, Gupta R, Keserwani PK, Govil MC. Air Quality Index prediction using an effective hybrid deep learning model. Environmental Pollution. 2022 Dec 15;315:120404. https://doi.org/10.1016/j.envpol.2022.120404
  6. Wu C-l, He H-d, Song R-f, Zhu X-h, Peng Z-r, Fu Q-y, et al. A hybrid deep learning model for regional O3 and NO2 concentrations prediction based on spatiotemporal dependencies in air quality monitoring network. Environmental Pollution. 2023;320:121075. https://doi.org/10.1016/j.envpol.2023.121075 PMid:36641063
  7. Doan QC, Chen C, He S, Zhang X. How urban air quality affects land values: Exploring non-linear and threshold mechanism using explainable artificial intelligence. Journal of Cleaner Production. 2024;434:140340. https://doi.org/10.1016/j.jclepro.2023.140340
  8. Sun J, Gong J, Zhou J. Estimating hourly PM2.5 concentrations in Beijing with satellite aerosol optical depth and a random forest approach. Science of The Total Environment. 2021;762:144502. https://doi.org/10.1016/j.scitotenv.2020.144502 PMid:33360341
  9. Yang L, Xu H, Yu S. Estimating PM2.5 concentrations in Yangtze River Delta region of China using random forest model and the Top-of-Atmosphere reflectance. Journal of Environmental Management. 2020;272:111061. https://doi.org/10.1016/j.jenvman.2020.111061 PMid:32669259
  10. Kim B-Y, Lim Y-K, Cha JW. Short-term prediction of particulate matter (PM10 and PM2.5) in Seoul, South Korea using tree-based machine learning algorithms. Atmospheric Pollution Research. 2022;13(10):101547. https://doi.org/10.1016/j.apr.2022.101547
  11. Gardner MW, Dorling SR. Statistical surface ozone models: an improved methodology to account for non-linear behaviour. Atmospheric Environment. 2000;34(1):21-34. https://doi.org/10.1016/S1352-2310(99)00359-3
  12. Berrocal VJ, Guan Y, Muyskens A, Wang H, Reich BJ, Mulholland JA, et al. A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration. Atmospheric Environment. 2020;222:117130. https://doi.org/10.1016/j.atmosenv.2019.117130 PMid:32863727 PMCid:PMC7451200
  13. Ghahremanloo M, Choi Y, Sayeed A, Salman AK, Pan S, Amani M. Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach. Atmospheric Environment. 2021;247:118209. https://doi.org/10.1016/j.atmosenv.2021.118209
  14. Tang D, Liu D, Tang Y, Seyler BC, Deng X, Zhan Y. Comparison of GOCI and Himawari-8 aerosol optical depth for deriving full-coverage hourly PM2.5 across the Yangtze River Delta. Atmospheric Environment. 2019;217:116973. https://doi.org/10.1016/j.atmosenv.2019.116973
  15. Wang W, Mao F, Du L, Pan Z, Gong W, Fang S. Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing-Tianjin-Hebei in China. Remote Sensing [Internet]. 2017; 9(8). https://doi.org/10.3390/rs9080858
  16. Williams DR, Rast P. Back to the basics: Rethinking partial correlation network methodology. British Journal of Mathematical and Statistical Psychology. 2020;73(2):187-212. https://doi.org/10.1111/bmsp.12173 PMid:31206621 PMCid:PMC8572131
  17. Demir E, Bilgin MH, Karabulut G, Doker AC. The relationship between cryptocurrencies and COVID-19 pandemic. Eurasian Economic Review. 2020;10(3):349-60. https://doi.org/10.1007/s40822-020-00154-1 PMCid:PMC7388435
  18. Chen H, Li X, Feng Z, Wang L, Qin Y, Skibniewski MJ, et al. Shield attitude prediction based on Bayesian-LGBM machine learning. Information Sciences. 2023;632:105-29. https://doi.org/10.1016/j.ins.2023.03.004
  19. Asselman A, Khaldi M, Aammou S. Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments. 2023;31(6):3360-79. https://doi.org/10.1080/10494820.2021.1928235
  20. Tran DA, Tsujimura M, Ha NT, Nguyen VT, Binh DV, Dang TD, et al. Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam. Ecological Indicators. 2021;127:107790. https://doi.org/10.1016/j.ecolind.2021.107790
  21. T R P. A Comparative Study on Decision Tree and Random Forest Using R Tool. IJARCCE. 2015:196-9. https://doi.org/10.17148/IJARCCE.2015.4142
  22. Otchere DA, Ganat TOA, Ojero JO, Tackie-Otoo BN, Taki MY. Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions. Journal of Petroleum Science and Engineering. 2022;208:109244. https://doi.org/10.1016/j.petrol.2021.109244
  23. Chicco D, Warrens M, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science. 2021;7:e623. https://doi.org/10.7717/peerj-cs.623 PMid:34307865 PMCid:PMC8279135
  24. Althoff D, Rodrigues LN. Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment. Journal of Hydrology. 2021;600:126674. https://doi.org/10.1016/j.jhydrol.2021.126674
  25. Liu X, Zou B, Feng H, Liu N, Zhang H. Anthropogenic factors of PM2.5 distributions in China's major urban agglomerations: A spatial-temporal analysis. Journal of Cleaner Production. 2020;264:121709. https://doi.org/10.1016/j.jclepro.2020.121709
  26. Mohammadi M, Hatami M, Esmaeli R, Gohari S, Mohammadi M, khayami E. Relationships between Ambient Air Pollution, Meteorological Parameters and Respiratory Mortality in Mashhad, Iran: a Time Series Analysis. Pollution. 2022;8(4):1250-65.)Persian)
  27. Harrison R. Airborne particulate matter. Philosophical transactions Series A, Mathematical, physical, and engineering sciences. 2020;378:20190319. https://doi.org/10.1098/rsta.2019.0319 PMid:32981435 PMCid:PMC7536032
  28. Aminiyan MM, Kalantzi O-I, Etesami H, Khamoshi SE, Hajiali Begloo R, Aminiyan FM. Occurrence and source apportionment of polycyclic aromatic hydrocarbons (PAHs) in dust of an emerging industrial city in Iran: implications for human health. Environmental Science and Pollution Research. 2021;28(44):63359-76. https://doi.org/10.1007/s11356-021-14839-w PMid:34231139
  29. Maciejczyk P, Chen L-C, Thurston G. The Role of Fossil Fuel Combustion Metals in PM2.5 Air Pollution Health Associations. Atmosphere [Internet]. 2021; 12(9). https://doi.org/10.3390/atmos12091086
  30. Bilal M, Hassan M, Tahir DBT, Iqbal MS, Shahid I. Understanding the role of atmospheric circulations and dispersion of air pollution associated with extreme smog events over South Asian megacity. Environmental Monitoring and Assessment. 2022;194(2):82. https://doi.org/10.1007/s10661-021-09674-y PMid:35013892
  31. Pal S, Das P, Mandal I, Sarda R, Mahato S, Nguyen K-A, et al. Effects of lockdown due to COVID-19 outbreak on air quality and anthropogenic heat in an industrial belt of India. Journal of Cleaner Production. 2021;297:126674. https://doi.org/10.1016/j.jclepro.2021.126674 PMid:34975233 PMCid:PMC8714179
  32. Kanawade VP, Srivastava AK, Ram K, Asmi E, Vakkari V, Soni VK, et al. What caused severe air pollution episode of November 2016 in New Delhi? Atmospheric Environment. 2020;222:117125. https://doi.org/10.1016/j.atmosenv.2019.117125
  33. Hamzeh NH, Karami S, Kaskaoutis DG, Tegen I, Moradi M, Opp C. Atmospheric Dynamics and Numerical Simulations of Six Frontal Dust Storms in the Middle East Region. Atmosphere [Internet]. 2021; 12(1). https://doi.org/10.3390/atmos12010125