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Abstract

The prediction of PM2.5 is important for environmental forecasting and air pollution control. In this study, four machine learning methods, ground-based LiDAR data and meteorological data were used to predict the ground-level PM2.5 concentrations in Beijing. Among the four methods, the random forest (RF) method was the most effective in predicting ground-level PM2.5 concentrations. Compared with BP neural network, support vector machine (SVM), and various linear fitting methods, the accuracy of the RF method was superior by 10%. The method can describe the spatial and temporal variation in PM2.5 concentrations under different meteorological conditions, with low root mean square error (RMSE) and mean square deviation (MD), and the consistency index (IA) reached 99.69%. Under different weather conditions, the hourly variation in PM2.5 concentrations has a good descriptive ability. In this paper, we analyzed the weights of input variables in the RF method, constructed a pollution case to correspond to the relationship between input variables and PM2.5, and analyzed the sources of pollutants via HYSPLIT backward trajectory. This method can study the interaction between PM2.5 and air pollution variables, and provide new ideas for preventing and forecasting air pollution.
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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
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Authors and Affiliations

Zhiyuan Fang
1 2 3
Hao Yang
1 2 3
Cheng Li
1 2 3
Liangliang Cheng
1 2 3
Ming Zhao
1 2
Chenbo Xie
1 2

  1. Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics,Chinese Academy of Sciences, Hefei 230031, China
  2. Science Island Branch of Graduate School, University of Science and Technology of China,Hefei 230026, China
  3. Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, Chin
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Abstract

This study attempts to find a fuzzy logic system for assessing the quality of water in water treatment plants (WTPs) providing water for irrigation purposes in the Basrah Governorate (South of Iraq). Each month, samples are taken in each of six major WTPs to measure electrical conductivity ( EC), and the content of sodium, magnesium and calcium. The calculated value which is the sodium adsorption ratio ( SAR) is plotted with EC on the Richard diagram. SAR and EC values are combined together in a fuzzy inference system (FIS) to find out a quality number called the fuzzy irrigation water quality index number ( FIWQI) which ranges from zero to one. The higher the value of the index, the better water quality. The Richard diagram, which helps to classify irrigation water, is used to adjust FIS components. Results show that the FIWQI for all WTPs changes depending on location and season. It ranges between 0.114–0.170, 0.120–0.190, 0.114–0.170, 0.114–0.202, 0.118–0.500 and 0.46–0.500 for Al-Bradhaia 1, Al-Jubaila 1, Shatt Al-Arab, Garmmah 1, Al-Rebat, and Old Shauaibah WTPs, respectively. The results indicate that WTPs effluent drawn from the Shatt Al-Arab River has poor water quality for irrigation purposes, except for an Old Shauaibah which receives water from another source called a sweet water canal. FIS results are compared with values obtained from the Richard diagram and 96% degree of compatibility between the two methods is attained. This indicates that FIS is an acceptable method for water quality classification.
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Authors and Affiliations

Ahmed N.A. Hamdan
1
ORCID: ORCID
Zainb A.A. Al Saad
1
ORCID: ORCID
Saad Abu-Alhail
1
ORCID: ORCID

  1. University of Basrah, Engineering College, Civil Engineering Department, Basrah 61004, Iraq

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