Ground data analysis for PM2.5 Prediction using predictive modeling techniques
Abstract
Introduction: Air quality forecasting, particularly predicting Particulate Matter (PM2.5 ) concentrations, has gained significant attention due to its critical implications for public health and environmental management.
Accurately predicting PM2.5 , a harmful air pollutant associated with respiratory and cardiovascular diseases, is vital for effective air quality management in densely populated urban areas.
Materials and methods: This study uses various meteorological and environmental data combinations in Tehran, Iran, this study investigates the efficacy of three predictive modeling techniques Auto Regressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) in forecasting daily and monthly PM2.5 levels. The models were evaluated based on performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R² scores.
Results: Results indicate that XGBoost excelled in daily predictions when using solely meteorological data, achieving an R² score of 0.998674, while ARIMA demonstrated strong predictive capacity but struggled with added complexity. LSTM maintained reasonable performance amidst increased data input but faced challenges in both daily and monthly forecasts. Monthly predictions from all models proved less reliable, particularly with ARIMA yielding negative R² values, indicating suboptimal performance compared to simplistic models.
Conclusion: The findings highlight the importance of model selection and feature engineering in accurately predicting PM2.5 levels. The study suggests a shift towards hybrid modeling approaches and incorporating diverse environmental data to enhance forecasting accuracy in air quality management, particularly for long-term predictions.
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22. Chu Y, Liu Y, Li X, Liu Z, Lu H, Lu Y, et al. A review on predicting ground PM2.5 concentration using satellite aerosol optical depth. Atmosphere. 2016;7(10):129.
23. Li X, Zhang X. Predicting ground-level PM2. 5 concentrations in the Beijing-Tianjin-Hebei region: A hybrid remote sensing and machine learning approach. Environmental pollution. 2019;249:735-49.
24. Li T, Shen H, Yuan Q, Zhang L. Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5. ISPRS Journal of Photogrammetry and Remote Sensing. 2020;167:178-88.
25. Huang K, Xiao Q, Meng X, Geng G, Wang Y, Lyapustin A, et al. Predicting monthly high-resolution PM2.5 concentrations with random forest model in the North China Plain. Environmental pollution. 2018;242:675-83.
26. He Z, Guo Q, Wang Z, Li X. Prediction of monthly PM2.5 concentration in Liaocheng in China employing artificial neural network. Atmosphere. 2022;13(8):1221.
27. Guo B, Wang X, Pei L, Su Y, Zhang D, Wang Y. Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018. Science of The Total Environment. 2021;751:141765.
28. Chen B, Song Z, Pan F, Huang Y. Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms. Science of The Total Environment. 2022;805:150338.
29. Zhang Y, Sun Q, Liu J, Petrosian O. Long-Term Forecasting of Air Pollution Particulate Matter (PM2.5) and Analysis of Influencing Factors. Sustainability. 2023;16(1):19.
30. Ma J, Yu Z, Qu Y, Xu J, Cao Y. Application of the XGBoost machine learning method in PM2. 5 prediction: a case study of Shanghai. Aerosol and Air Quality Research. 2020;20(1):128-38.
31. Zamani Joharestani M, Cao C, Ni X, Bashir B, Talebiesfandarani S. PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data. Atmosphere. 2019;10(7):373.
32. Xiao F, Yang M, Fan H, Fan G, Al-Qaness MA. An improved deep learning model for predicting daily PM2.5 concentration. Scientific reports. 2020;10(1):20988.
33. Ho C-H, Park I, Kim J, Lee J-B. PM2.5 forecast in korea using the long short-term memory (LSTM) model. Asia-Pacific Journal of Atmospheric Sciences. 2023;59(5):563-76.
2. Nabizadeh R, Yousefian F, Moghadam VK, Hadei M. Characteristics of cohort studies of long-term exposure to PM2.5: a systematic review. Environmental science and pollution research. 2019;26:30755-71.
3. Hadei M, Naddafi K. Cardiovascular effects of airborne particulate matter: a review of rodent model studies. Chemosphere. 2020;242:125204.
4. Landrigan PJ, Fuller R, Acosta NJ, Adeyi O, Arnold R, Baldé AB, et al. The Lancet Commission on pollution and health. The lancet. 2018;391(10119):462-512.
5. Li T, Shen H, Yuan Q, Zhang X, Zhang L. Estimating ground‐level PM2.5 by fusing satellite and station observations: a geo‐intelligent deep learning approach. Geophysical Research Letters. 2017;44(23):11,985-11,93.
6. Bodor K, Szép R, Bodor Z. The human health risk assessment of particulate air pollution (PM2. 5 and PM10) in Romania. Toxicology Reports. 2022;9:556-62.
7. Faraji Ghasemi F, Dobaradaran S, Saeedi R, Nabipour I, Nazmara S, Ranjbar Vakil Abadi D, et al. Levels and ecological and health risk assessment of PM2.5-bound heavy metals in the northern part of the Persian Gulf. Environmental Science and Pollution Research. 2020;27:5305-13.
8. Cao S, Guo Q, Xue T, Wang B, Wang L, Duan X, et al. Long-term exposure to ambient PM2.5 increase obesity risk in Chinese adults: A cross-sectional study based on a nationwide survey in China. Science of the Total Environment. 2021;778:145812.
9. Wu J, Zheng H, Zhe F, Xie W, Song J. Study on the relationship between urbanization and fine particulate matter (PM2.5) concentration and its implication in China. Journal of cleaner production. 2018;182:872-82.
10. Bu X, Xie Z, Liu J, Wei L, Wang X, Chen M, et al. Global PM2.5-attributable health burden from 1990 to 2017: Estimates from the Global Burden of disease study 2017. Environmental Research. 2021;197:111123.
11. Kim S-Y, Olives C, Sheppard L, Sampson PD, Larson TV, Keller JP, et al. Historical prediction modeling approach for estimating long-term concentrations of PM2.5 in cohort studies before the 1999 implementation of widespread monitoring. Environmental health perspectives. 2017;125(1):38-46.
12. Naddafi K, Hassanvand MS, Yunesian M, Momeniha F, Nabizadeh R, Faridi S, et al. Health impact assessment of air pollution in megacity of Tehran, Iran. Iranian journal of environmental health science & engineering. 2012;9:1-7.
13. Pardakhti A, Baheeraei H, Dehhaghi S. Forecasting and Seasonal Investigation of PM10 Concentration Trend: a Time Series and Trend Analysis Study in Tehran. Pollution. 2023;9(4):1579-88.
14. Gao W, Xiao T, Zou L, Li H, Gu S. Analysis and Prediction of Atmospheric Environmental Quality Based on the Autoregressive Integrated Moving Average Model (ARIMA Model) in Hunan Province, China. Sustainability. 2024;16(19):8471.
15. Ramadan MS, Abuelgasim A, Al Hosani N. Advancing air quality forecasting in Abu Dhabi, UAE using time series models. Frontiers in Environmental Science. 2024;12:1393878.
16. Zaini Na, Ean LW, Ahmed AN, Abdul Malek M, Chow MF. PM2.5 forecasting for an urban area based on deep learning and decomposition method. Scientific Reports. 2022;12(1):17565.
17. Saminathan S, Malathy C. Ensemble-based classification approach for PM2.5 concentration forecasting using meteorological data. Frontiers in big Data. 2023;6:1175259.
18. Habibi R, Alesheikh AA, Mohammadinia A, Sharif M. An assessment of spatial pattern characterization of air pollution: A case study of CO and PM2.5 in Tehran, Iran. ISPRS international journal of Geo-information. 2017;6(9):270.
19. Arhami M, Hosseini V, Shahne MZ, Bigdeli M, Lai A, Schauer JJ. Seasonal trends, chemical speciation and source apportionment of fine PM in Tehran. Atmospheric Environment. 2017;153:70-82.
20. Shahbazi H, Reyhanian M, Hosseini V, Afshin H. The relative contributions of mobile sources to air pollutant emissions in Tehran, Iran: an emission inventory approach. Emission control science and technology. 2016;2:44-56.
21. Atash F. The deterioration of urban environments in developing countries: Mitigating the air pollution crisis in Tehran, Iran. Cities. 2007;24(6):399-409.
22. Chu Y, Liu Y, Li X, Liu Z, Lu H, Lu Y, et al. A review on predicting ground PM2.5 concentration using satellite aerosol optical depth. Atmosphere. 2016;7(10):129.
23. Li X, Zhang X. Predicting ground-level PM2. 5 concentrations in the Beijing-Tianjin-Hebei region: A hybrid remote sensing and machine learning approach. Environmental pollution. 2019;249:735-49.
24. Li T, Shen H, Yuan Q, Zhang L. Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5. ISPRS Journal of Photogrammetry and Remote Sensing. 2020;167:178-88.
25. Huang K, Xiao Q, Meng X, Geng G, Wang Y, Lyapustin A, et al. Predicting monthly high-resolution PM2.5 concentrations with random forest model in the North China Plain. Environmental pollution. 2018;242:675-83.
26. He Z, Guo Q, Wang Z, Li X. Prediction of monthly PM2.5 concentration in Liaocheng in China employing artificial neural network. Atmosphere. 2022;13(8):1221.
27. Guo B, Wang X, Pei L, Su Y, Zhang D, Wang Y. Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018. Science of The Total Environment. 2021;751:141765.
28. Chen B, Song Z, Pan F, Huang Y. Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms. Science of The Total Environment. 2022;805:150338.
29. Zhang Y, Sun Q, Liu J, Petrosian O. Long-Term Forecasting of Air Pollution Particulate Matter (PM2.5) and Analysis of Influencing Factors. Sustainability. 2023;16(1):19.
30. Ma J, Yu Z, Qu Y, Xu J, Cao Y. Application of the XGBoost machine learning method in PM2. 5 prediction: a case study of Shanghai. Aerosol and Air Quality Research. 2020;20(1):128-38.
31. Zamani Joharestani M, Cao C, Ni X, Bashir B, Talebiesfandarani S. PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data. Atmosphere. 2019;10(7):373.
32. Xiao F, Yang M, Fan H, Fan G, Al-Qaness MA. An improved deep learning model for predicting daily PM2.5 concentration. Scientific reports. 2020;10(1):20988.
33. Ho C-H, Park I, Kim J, Lee J-B. PM2.5 forecast in korea using the long short-term memory (LSTM) model. Asia-Pacific Journal of Atmospheric Sciences. 2023;59(5):563-76.
| Files | ||
| Issue | Vol 10 No 1 (2025): Winter 2025 | |
| Section | Original Research | |
| Keywords | ||
| Particulate matters (PM2.5) prediction; Ground data; Meteorological data; Traffic emissions; Air quality management | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Nourmohammad E, Rashidi Y. Ground data analysis for PM2.5 Prediction using predictive modeling techniques. JAPH. 2025;10(1):61-82.
