Details

Title

Prediction of PM2.5 hourly concentrations in Beijing based on machine learning algorithm and ground-based LiDAR

Journal title

Archives of Environmental Protection

Yearbook

2021

Volume

47

Issue

3

Authors

Affiliation

Fang, Zhiyuan : Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences, Hefei 230031, China ; Fang, Zhiyuan : Science Island Branch of Graduate School, University of Science and Technology of China,Hefei 230026, China ; Fang, Zhiyuan : Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, Chin ; Yang, Hao : Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences, Hefei 230031, China ; Yang, Hao : Science Island Branch of Graduate School, University of Science and Technology of China,Hefei 230026, China ; Yang, Hao : Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, Chin ; Li, Cheng : Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences, Hefei 230031, China ; Li, Cheng : Science Island Branch of Graduate School, University of Science and Technology of China,Hefei 230026, China ; Li, Cheng : Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, Chin ; Cheng, Liangliang : Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences, Hefei 230031, China ; Cheng, Liangliang : Science Island Branch of Graduate School, University of Science and Technology of China,Hefei 230026, China ; Cheng, Liangliang : Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, Chin ; Zhao, Ming : Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences, Hefei 230031, China ; Zhao, Ming : Science Island Branch of Graduate School, University of Science and Technology of China,Hefei 230026, China ; Xie, Chenbo : Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences, Hefei 230031, China ; Xie, Chenbo : Science Island Branch of Graduate School, University of Science and Technology of China,Hefei 230026, China

Keywords

PM2.5 ; lidar ; machine learning ; air pollution monitoring

Divisions of PAS

Nauki Techniczne

Coverage

98-107

Publisher

Polish Academy of Sciences

Bibliography

  1. Belle, J. & Liu, Y. 2016).( Evaluation of Aqua MODIS Collection 6 AOD Parameters for Air Quality Research over the Continental United States. Remote Sensing, 8(10), pp. 815-820.
  2. Berdnik, V.V. & Loiko, V.A. (2016). Neural networks for aerosol particles characterization. Journal of Quantitative Spectroscopy & Radiative Transfer, 184.
  3. Bishop, C.M., (1995). Neural Networks for Pattern Recognition. Agricultural Engineering International the Cigr Journal of Scientific Research & Development Manuscript Pm, 12(5), pp. 1235 - 1242.
  4. Breiman & Leo, (1996). Bagging Predictors. Machine Learning, 24(2), pp. 123-140.
  5. Butt, E.W., Turnock, S. T., Rigby, R., Reddington, C. L., Yoshioka, M., Johnson, J. S., Regayre, L. A., Pringle, K. J., Mann, G. W. & Spracklen, D. V. (2017). Global and regional trends in particulate air pollution and attributable health burden over the past 50 years. Environmental Research Letters. 10 (12). DOI: 10.1088/1748-9326/aa87be
  6. Chan, P.W. (2009). Comparison of aerosol optical depth (AOD) derived from ground-based LIDAR and MODIS. Open Atmospheric Science Journal, 3(1), pp. 131-137.
  7. Chu, Y., Liu, Y., Li, X., Liu, Z., Lu, H., Lu, Y., Mao, Z., Chen, X., Li, N., Ren, M., Liu, F., Tian, L., Zhu, Z., & Xiang, H. (2016). A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth. Atmosphere, 7(10), p. 129. Doi: 10.3390/atmos7100129
  8. Fernald, F.G. (1984). Analysis of atmospheric lidar observations: some comments. Applied optics, 5, pp. 652-653.
  9. Gui, K., Che, H., Chen, Q., An, L., Zeng, Z., Guo, Z., Zheng, Y., Wang, H., Wang, Y., Yu, J., & Zhang, X. (2016)., Aerosol Optical Properties Based on Ground and Satellite Retrievals during a Serious Haze Episode in December 2015 over Beijing. Atmosphere, 7(5), pp. 70. DOI: 10.3390/atmos7050070
  10. Hu, S, Wang, Z., Xu, Q., Zhou, J. & Hu. H. (2006). Study on Lidar Measurement of Atmospheric Aerosol Optical Thickness. Journal of Quantum Electronics, 3, p. 307-310.(in Chinese)
  11. Hutchison, K.D., Faruqui, S.J. & Smi, S. (2008). The Improving correlations between MODIS aerosol optical thickness and ground-based PM2.5 observations through 3D spatial analyses. Atmosphere Environment, 3(42), pp. 530-554. DOI: 10.1016/j.atmosenv.2007.09.050
  12. Jones, R.M. (2008). Experimental evaluation of a Markov model of contaminant transport in indoor environments with application to tuberculosis transmission in commercial passenger aircraft. Dissertations & Theses - Gradworks, 2008.
  13. Kaufman, Y.J., Tanré, D., & Boucher, O. (2002). A satellite view of aerosols in the climate system. Nature, 419(6903), pp. 215-23.
  14. Li, X. & Zhang, X. (2019). Predicting ground-level PM 2.5 concentrations in the Beijing-Tianjin-Hebei region: A hybrid remote sensing and machine learning approach. Environmental Pollution, 249, pp. 735-749. DOI: 10.1016/j.envpol.2019.03.068
  15. Bing,-C.L., Binaykia, A., Chang, P-C., Tiwari, M.K. & Tsao, C-C. (2017). Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang. Plos One, 12(7), pp. e0179763. DOI: 10.1371/journal.pone.0179763
  16. Mao, X., Shen, T. & Feng, X. (2017). Prediction of hourly ground-level PM2.5 concentrations 3 days in advance using neural networks with satellite data in eastern China. Atmospheric Pollution Research, 6(8), pp. 1005-1015. S1309104217300296.
  17. Nabavi, S.O., et al., Prediction of aerosol optical depth in West Asia using deterministic models and machine learning algorithms. Aeolian Research, 2018. 35C: p. 69-84.
  18. Stein, A.F., et al., NOAA's HYSPLIT atmospheric transport and dispersion modeling system. Bulletin of the American Meteorological Society, 2016: p. 150504130527006. DOI: 10.1016/j.apr.2017.04.002
  19. Toth, T.D., Campbell, J.R., Reid, J.S., Tackett, J.L., Vaughan, M.A., Zhang, J. & Marquis, J.W. (2018). Minimum aerosol layer detection sensitivities and their subsequent impacts on aerosol optical thickness retrievals in CALIPSO level 2 data products. Atmospheric Measurement Techniques, 11, p. 499-514. DOI: 10.5194/amt-11-499-2018
  20. Yan, D., Lei, Y., Shi, Y., Zhu, Q., Li, L.& Zhang, Z. (2018). Evolution of the spatiotemporal pattern of PM2.5 concentrations in China – a 2 case study from the Beijing-Tianjin-Hebei region. Atmosphere Environment. 183, pp. 225-233. DOI: 10.1016/j.atmosenv.2018.03.041
  21. Yang, G., Lee, H. & Lee, G. (2020). A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea. Atmosphere, 11(4): pp. 348. DOI: 10.3390/atmos11040348
  22. Wang, Y., Chen, L., Li, S., Wang, X., Yu, C., Si, Y. & Zhang, Z. (2017). Interference of Heavy Aerosol Loading on the VIIRS Aerosol Optical Depth (AOD) Retrieval Algorithm. Remote Sensing, 2017. 9(4): p. 397. DOI: 10.3390/rs9040397
  23. Chen, Z., Zhang, J., Zhang, T., Liu, W. & Liu, J. (2015). Haze observations by simultaneous lidar and WPS in Beijing before and during APEC, 2014. Science China(Chemistry), 2015. 09(v.58): p. 33-40. DOI: 10.1007/s11426-015-5467-x

Date

19.09.2021

Type

Article

Identifier

DOI: 10.24425/aep.2021.138468

Abstracting & Indexing

Abstracting & Indexing


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