Advanced searchCitation search
Skip main navigation
Close Drawer MenuOpen Drawer Menu
Previous article
Next article
Case Studies
Dec 6, 2023

Prediction of a High Concentration of PM2.5 near a Tailings Pond

Authors: Linwang Dong https://orcid.org/0009-0008-7072-3761 [email protected], Wen Nie, Ph.D. [email protected], Yang Zhu, Changhai Luo, and Zhikang Hou https://orcid.org/0009-0001-8334-4081Author Affiliations
Publication: Journal of Environmental Engineering
Volume 150, Issue 2
https://doi.org/10.1061/JOEEDU.EEENG-7445
Get Access

Abstract

In this paper, an error-correcting ensemble model combining empirical mode decomposition and reconstruction combined with statistical methods is proposed to improve the prediction accuracy of a high concentration of particulate matter with diameters of 2.5 μm or less (PM2.5) around a tailings reservoir. The proposed prediction model consists of a prediction model of a high concentration of PM2.5 and an error correction model. The optimal prediction model is obtained by comparing the two indicators of fitting effect and result error, constantly adjusting the batch size and the number of network layers. The measured data of the tailings reservoir in Ma’anshan City, China, were used for validation. The results showed that the mean absolute error (MAE) of our long short-term memory–improved empirical mode decomposition–long short-term memory (LSTM-IEMD-LSTM) mode was 0.125, 0.661, and 4.372 lower than that of LSTM, multivariable linear regression–improve empirical mode decomposition–multivariable linear regression (MR-IEMD-MR), and autoregressive integrated moving average model–improve empirical mode decomposition–autoregressive integrated moving average model (ARIMA-IEMD-ARIMA), and the root-mean-square error (RMSE) of our LSTM-IEMD-LSTM compared with LSTM, MR-IEMD-MR, and ARIMA-IEMD-ARIMA decreased by 13.2%, 45.5%, and 84.2%. The model is also used to predict other high-concentration pollutants to test their generalization ability, which also has high prediction accuracy.

Get full access to this article

View all available purchase options and get full access to this article.

Get Access

Data Availability Statement

Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

Acknowledgments

This research was funded by the Major Science and Technology Projects of Anhui Province (No. 202003a0702002), Jiangxi Provincial Natural Science Foundation (No. 20212ACB214005), and Science and Technology Service Network Initiative (No. 2022T3051). In addition, we thank the editors and the anonymous reviewers for their valuable comments and suggestions.

References

Amanollahi, J., and S. Ausati. 2020. “PM2.5 concentration forecasting using ANFIS, EEMD-GRNN, MLP, and MLR models: A case study of Tehran, Iran.” Air Qual. Atmos. Health 13 (Feb): 161–171. https://doi.org/10.1007/s11869-019-00779-5.
Google Scholar
Cai, Z.-Y., J. Hao, S.-Q. Han, Y.-X. Tang, X. Yang, W.-Y. Fan, Q. Yao, and X.-B. Qiu. 2022. “Evolution of PM2.5 mass concentration and analysis of driver factors in Tianjin in 2000–2020.” [In Chinese.] Environ. Sci. 43 (3): 1129–1139. https://doi.org/10.13227/j.hjkx.202108182.
Google Scholar
Cheng, J., et al. 2019. “Dominant role of emission reduction in PM2.5 air quality improvement in Beijing during 2013–2017: A model-based decomposition analysis.” Atmos. Chem. Phys. 19 (9): 6125–6146. https://doi.org/10.5194/acp-19-6125-2019.
Google Scholar
Dastorani, M. T., A. Moghadamnia, J. Piri, and M. Rico-Ramirez. 2010. “Application of ANN and ANFIS models for reconstructing missing flow data.” Environ. Monit. Assess 166 (Jul): 421–434. https://doi.org/10.1007/s10661-009-1012-8.
Google Scholar
Dong, Y., and C. Y. J. Peng. 2013. “Principled missing data methods for researchers.” SpringerPlus 2 (May): 222. https://doi.org/10.1186/2193-1801-2-222.
Google Scholar
Elbayoumi, M., N. A. Ramli, N. F. F. M. Yusof, A. S. B. Yahaya, W. Al Madhoun, and A. Z. Ul-Saufie. 2014. “Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings.” Atmos. Environ. 94 (Sep): 11–21. https://doi.org/10.1016/j.atmosenv.2014.05.007.
Google Scholar
Feng, X., Q. Li, Y. Zhu, J. Hou, L. Jin, and J. Wang. 2015. “Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation.” Atmos. Environ. 107 (Apr): 118–128. https://doi.org/10.1016/j.atmosenv.2015.02.030.
Google Scholar
Graves, A. 2012. Supervised sequence labelling with recurrent neural networks, studies in computational intelligence. Berlin: Springer.
Google Scholar
Hochreiter, S., and J. Schmidhuber. 1997. “Long short-term memory.” Neural Comput. 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Google Scholar
Huang, P., J. Zhang, Y. Tang, and L. Liu. 2015. “Spatial and temporal distribution of PM2.5 pollution in Xi’an City, China.” Int. J. Environ. Res. Public Health 12 (6): 6608–6625. https://doi.org/10.3390/ijerph120606608.
Google Scholar
Huang, R. J., et al. 2014. “High secondary aerosol contribution to particulate pollution during haze events in China.” Nature 514 (Oct): 218–222. https://doi.org/10.1038/nature13774.
Google Scholar
Jiang, M., W. Sun, G. Yang, and D. Zhang. 2017. “Modelling seasonal GWR of daily PM2.5 with proper auxiliary variables for the Yangtze River Delta.” Remote Sens. 9 (4): 346. https://doi.org/10.3390/rs9040346.
Google Scholar
Jiang, Y., and G. Huang. 2017. “Short-term wind speed prediction: Hybrid of ensemble empirical mode decomposition, feature selection and error correction.” Energy Convers. Manage. 144 (Jul): 340–350. https://doi.org/10.1016/j.enconman.2017.04.064.
Google Scholar
Kulesh, M., M. Holschneider, and K. Kurennaya. 2008. “Adaptive metrics in the nearest neighbours method.” Physica D 237 (3): 283–291. https://doi.org/10.1016/j.physd.2007.08.019.
Google Scholar
Lepot, M., J.-B. Aubin, and F. H. Clemens. 2017. “Interpolation in time series: An introductive overview of existing methods, their performance criteria and uncertainty assessment.” Water 9 (10): 796. https://doi.org/10.3390/w9100796.
Google Scholar
Li, G. H., L. Chen, and H. Yang. 2022. “Prediction of PM2.5 concentration based on improved secondary decomposition and CSA-KELM.” Atmos. Pollut. Res. 13 (7): 101455. https://doi.org/10.1016/j.apr.2022.101455.
Google Scholar
Li, M.-M., Y. Wang, S.-M. Yan, L. Chen, and Z.-Y. Han. 2023. “Meteorological characteristics, influence analysis and prediction of PM2.5 concentration in Taiyuan City.” Huan Jing ke Xue 44 (2): 611–625. https://doi.org/10.13227/j.hjkx.202203040.
Google Scholar
Li, S., L. Zhai, B. Zou, H. Sang, and X. Fang. 2017. “A generalized additive model combining principal component analysis for PM2.5 concentration estimation.” ISPRS Int. J. Geo-Inf. 6 (8): 248. https://doi.org/10.3390/ijgi6080248.
Google Scholar
Liang, Z., J. Liang, C. Wang, X. Dong, and X. Miao. 2016. “Short-term wind power combined forecasting based on error forecast correction.” Energy Convers. Manage. 119 (Jul): 215–226. https://doi.org/10.1016/j.enconman.2016.04.036.
Google Scholar
Liu, D. J., and L. Li. 2015. “Application study of comprehensive forecasting model based on entropy weighting method on trend of PM2.5 concentration in Guangzhou, China.” Int. J. Environ. Res. Public Health 12 (6): 7085–7099. https://doi.org/10.3390/ijerph120607085.
Google Scholar
Liu, H. P., E. Erdem, and J. Shi. 2011. “Comprehensive evaluation of ARMA–GARCH (-M) approaches for modeling the mean and volatility of wind speed.” Appl. Energy 88 (3): 724–732. https://doi.org/10.1016/j.apenergy.2010.09.028.
Google Scholar
Long, J., Y. Chen, D. Cao, P. Chen, and M. Yang. 2023. “Yield and properties prediction based on the multicondition LSTM model for the solvent deasphalting process.” ACS Omega 8 (6): 5437–5450. https://doi.org/10.1021/acsomega.2c06624.
Google Scholar
Marsha, A., and N. K. Larkin. 2019. “A statistical model for predicting PM2.5 for the western United States.” J. Air Waste Manage. Assoc. 69 (10): 1215–1229. https://doi.org/10.1080/10962247.2019.1640808.
Google Scholar
Ministry of Natural Resources of the People’s Republic of China. 2022. “National platform for common geospatial information services.” Accessed May 13, 2023. https://www.tianditu.gov.cn/.
Google Scholar
Niu, M., Y. Wang, S. Sun, and Y. Li. 2016. “A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting.” Atmos. Environ. 134 (Jun): 168–180. https://doi.org/10.1016/j.atmosenv.2016.03.056.
Google Scholar
Pan, C., and Z. M. Kang. 2022. “Effects of meteorological conditions on PM2.5 distribution in Jiangsu Province in 2001–2019.” Environ. Sci. 43 (2): 649–662. https://doi.org/10.13227/j.hjkx.202104292.
Google Scholar
Patricio, P., A. Trier, and J. Reyes. 2000. “Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile.” Atmos. Environ. 34 (8): 1189–1196. https://doi.org/10.1016/S1352-2310(99)00316-7.
Google Scholar
Schwertman, N. C., M. A. Owens, and R. Adnan. 2004. “A simple more general boxplot method for identifying outliers.” Comput. Stat. Data Anal. 47 (1): 165–174. https://doi.org/10.1016/j.csda.2003.10.012.
Google Scholar
Sun, B., H. Sun, C. Zhang, J. Shi, and Y. Zhong. 2017. “Forecast of air pollutant concentrations by BP neural network.” [In Chinese.] Acta Sci. Circumst. 37 (5): 1864–1871.
Google Scholar
Vukicevic, T. 1991. “Nonlinear and linear evolution of initial forecast errors.” Mon. Weather Rev. 119 (7): 1602–1611. https://doi.org/10.1175/1520-0493(1991)119%3C1602:NALEOI%3E2.0.CO;2.
Google Scholar
Wang, H., et al. 2020. “Spatiotemporal variability in long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 across the Yangtze River Delta (YRD) region over 2010–2016: A multistage approach.” Chemosphere 257 (Oct): 127153. https://doi.org/10.1016/j.chemosphere.2020.127153.
Google Scholar
Wang, X., R. Zhang, and W. Yu. 2019a. “The effects of PM2.5 concentrations and relative humidity on atmospheric visibility in Beijing.” J. Geophys. Res.: Atmos. 124 (4): 2235–2259. https://doi.org/10.1029/2018JD029269.
Google Scholar
Wang, Y., et al. 2019b. “Trends in particulate matter and its chemical compositions in China from 2013–2017.” Sci. China Earth Sci. 62 (12): 1857–1871. https://doi.org/10.1007/s11430-018-9373-1.
Google Scholar
Wang, Y., L. Yao, L. Wang, Z. Liu, D. Ji, G. Tang, J. Zhang, Y. Sun, B. Hu, and J. Xin. 2014. “Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China.” Sci. China Earth Sci. 57 (Jan): 14–25. https://doi.org/10.1007/s11430-013-4773-4.
Google Scholar
Wei, J., F. Yang, X. C. Ren, and S. Zou. 2021. “A short-term prediction model of PM2.5 concentration based on deep learning and mode decomposition methods.” Appl. Sci. 11 (15): 6915. https://doi.org/10.3390/app11156915.
Google Scholar
Wu, Z., and N. E. Huang. 2009. “Ensemble empirical mode decomposition: A noise-assisted data analysis method.” Adv. Adapt. Data Anal. 1 (1): 1–41. https://doi.org/10.1142/S1793536909000047.
Google Scholar
Xie, Y., H. Dai, Y. Zhang, Y. Wu, T. Hanaoka, and T. Masui. 2019. “Comparison of health and economic impacts of PM2.5 and ozone pollution in China.” Environ. Int. 130 (Sep): 104881. https://doi.org/10.1016/j.envint.2019.05.075.
Google Scholar
Yan, T., B. Chen, E.-H. Cao, and Y.-T. Liu. 2020. “Prediction of dam deformation using EEMD-ELM model.” J. Yangtze River Sci. Res. Inst. 37 (11): 70. https://doi.org/10.11988/ckyyb.20190892.
Google Scholar
Yang, S. D., Z. A. Ali, H. Kwon, and B. M. Wong. 2022. “Predicting complex erosion profiles in steam distribution headers with convolutional and recurrent neural networks.” Ind. Eng. Chem. Res. 61 (24): 8520–8529. https://doi.org/10.1021/acs.iecr.1c04712.
Google Scholar
Yang, W., M. Deng, F. Xu, and H. Wang. 2018. “Prediction of hourly PM2.5 using a space-time support vector regression model.” Atmos. Environ. 181 (May): 12–19. https://doi.org/10.1016/j.atmosenv.2018.03.015.
Google Scholar
Yu, Z., C. Yang, Z. Zhang, and J. Jiao. 2015. “Error correction method based on data transformational GM (1, 1) and application on tax forecasting.” Appl. Soft Comput. 37 (Dec): 554–560. https://doi.org/10.1016/j.asoc.2015.09.001.
Google Scholar
Zhan, S. 1988. “Introduction to Magang Nanshan iron mine.” [In Chinese.] Metal Mine 1: 64.
Google Scholar
Zhang, L., J. Lin, R. Qiu, X. Hu, H. Zhang, Q. Chen, H. Tan, D. Lin, and J. Wang. 2018. “Trend analysis and forecast of PM2.5 in Fuzhou, China using the ARIMA model.” Ecol. Indic. 95 (Dec): 702–710. https://doi.org/10.1016/j.ecolind.2018.08.032.
Google Scholar
Zhang, L., P. Liu, L. Zhao, G. Wang, W. Zhang, and J. Liu. 2021. “Air quality predictions with a semi-supervised bidirectional LSTM neural network.” Atmos. Pollut. Res. 12 (1): 328–339. https://doi.org/10.1016/j.apr.2020.09.003.
Google Scholar
Zhou, Q., H. Jiang, J. Wang, and J. Zhou. 2014. “A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network.” Sci. Total Environ. 496 (Oct): 264–274. https://doi.org/10.1016/j.scitotenv.2014.07.051.
Google Scholar
Zhu, J., P. Wu, H. Chen, L. Zhou, and Z. Tao. 2018. “A hybrid forecasting approach to air quality time series based on endpoint condition and combined forecasting model.” Int. J. Environ. Res. Public Health 15 (9): 1941. https://doi.org/10.3390/ijerph15091941.
Google Scholar
Zhu, S., X. Lian, H. Liu, J. Hu, Y. Wang, and J. Che. 2017. “Daily air quality index forecasting with hybrid models: A case in China.” Environ. Pollut. 231 (Dec): 1232–1244. https://doi.org/10.1016/j.envpol.2017.08.069.
Google Scholar
Zou, C., H. Huang, F. Yang, X. Liu, B. Zeng, F. Zhu, and Y. Liu. 2017. “Atmospheric particulate and gaseous pollutants removal efficiency by rainfall and the influencing factors.” Environ. Sci. Technol. 40 (1): 8.
Google Scholar

Information & Authors

Information

Published In

Go to Journal of Environmental Engineering
Journal of Environmental Engineering
Volume 150 • Issue 2 • February 2024

Copyright

© 2023 American Society of Civil Engineers.

History

Received: May 20, 2023
Accepted: Sep 18, 2023
Published online: Dec 6, 2023
Published in print: Feb 1, 2024
Discussion open until: May 6, 2024

Permissions

Request permissions for this article.

ASCE Technical Topics:

  1. Analysis (by type)
  2. Autoregressive models
  3. Autoregressive moving average models
  4. Biological processes
  5. Decomposition
  6. Engineering fundamentals
  7. Environmental engineering
  8. Errors (statistics)
  9. Hydraulic engineering
  10. Hydraulic structures
  11. Linear functions
  12. Mathematical functions
  13. Mathematics
  14. Mine wastes
  15. Model accuracy
  16. Models (by type)
  17. Pollutants
  18. Regression analysis
  19. Reservoirs
  20. Statistical analysis (by type)
  21. Statistics
  22. Waste management
  23. Wastes
  24. Water and water resources

Authors

Affiliations

Linwang Dong https://orcid.org/0009-0008-7072-3761 [email protected]
Postgraduate Student, School of Mathematics and Physics, Lanzhou Jiaotong Univ., Lanzhou 730070, China. ORCID: https://orcid.org/0009-0008-7072-3761. Email: [email protected]
View all articles by this author
Wen Nie, Ph.D. [email protected]
Professor, School of Mathematics and Physics, Lanzhou Jiaotong Univ., Lanzhou 730070, China (corresponding author). Email: [email protected]
View all articles by this author
Yang Zhu
Postgraduate Student, School of Mathematics and Physics, Lanzhou Jiaotong Univ., Lanzhou 730070, China.
View all articles by this author
Changhai Luo
Senior Engineer, Anhui Magang Mining Resources Group Nanshan Mining Co., Ltd., Xiangshan Town, Yushan District, Ma’anshan, Anhui 243000, China.
View all articles by this author
Zhikang Hou https://orcid.org/0009-0001-8334-4081
Postgraduate Student, School of Mathematics and Physics, Lanzhou Jiaotong Univ., Lanzhou 730070, China. ORCID: https://orcid.org/0009-0001-8334-4081
View all articles by this author

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share