Prediction of hourly ground-level PM2.5 concentrations 3 days in advance using neural networks with satellite data in eastern China

Atmospheric Pollution Research

Volumn 8, Issue 6, 2017, Pages 1005-1015

  Mao, Xi ^a   Shen, Tao ^a   Feng, Xiao ^b  

^a CHINESE ACADEMY OF SURVEYING AND MAPPING   (China)

^b PEKING UNIVERSITY   (China)

Author keywords

Backward air mass trajectory; Eastern China; MODIS AOD; Neural networks; Optimal averaging periods; PM2.5 forecasting

Indexed keywords

EID: 85017438053     PISSN: 13091042     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apr.2017.04.002     Document Type: Article

References (53)

1. Anenberg, S.C., Horowitz, L.W., Tong, D.Q., West, J.J., An estimate of the global burden of anthropogenic ozone and fine particulate matter on premature human mortality using atmospheric modeling. Environ. Health Perspect. 118:9 (2010), 1189–1195.
2. 10 concentration, based on MODIS and MERIS/AATSR synergistic observations. Atmos. Environ. 79 (2013), 448–454.
3. Bishop, C.M., Neural Networks for Pattern Recognition. 1995, Clarendon Press, Oxford.
4. China Environmental Protection Agency, Technical Regulation on Ambient Air Quality Index (On Trial). 2012, China Environmental Science Press.
5. Chu, D.A., Kaufman, Y.J., Zibordi, G., Chern, J.D., Mao, J., Li, C., Holben, B.N., Global monitoring of air pollution over land from the earth observing system—terra moderate resolution imaging spectroradiometer (MODIS). J. Geophys. Res., 108(D21), 2003, 4661.
6. 2.5 for columnar AOD measurements. Atmos. Environ. 79 (2013), 172–187.
7. Chuang, M.-T., Zhang, Y., Kang, D., Application of WRF/Chem-MADRID for real-time air quality forecasting over the Southeastern United States. Atmos. Environ. 45:34 (2011), 6241–6250.
8. 2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations. Atmos. Environ. 44:25 (2010), 3015–3023.
9. Coman, A., Ionescu, A., Candau, Y., Hourly ozone prediction for a 24-h horizon using neural networks. Environ. Model. Softw. 23:12 (2008), 1407–1421.
10. Donkelaar, A.V., Martin, R.V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., Villeneuve, P.J., Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ. Health Perspect. 118 (2010), 847–855.
11. Draxler, R.R., Rolph, G.D., HYSPLIT (HYbrid Single-particle Lagrangian Integrated Trajectory) Model Access via NOAA ARL READY Website. 2003, NOAA Air Resources Laboratory, Silver Spring, MD http://www.arl.noaa.gov/ready/hysplit4.html.
12. Drury, E., Jacob, D.J., Spurr, R.J., Wang, J., Shinozuka, Y., Anderson, B.E., Clarke, A.D., Dibb, J., McNaughton, C., Weber, R., Synthesis of satellite (MODIS), aircraft (ICARTT), and surface (IMPROVE, EPA-AQS, AERONET) aerosol observations over eastern North America to improve MODIS aerosol retrievals and constrain surface aerosol concentrations and sources. J. Geophys. Res., 115(D14), 2010, D14204.
13. 10 remote sensing from geostationary SEVIRI and polar-orbiting MODIS sensors over the complex terrain of the European Alpine region. Remote Sens. Environ. 114:11 (2010), 2485–2499.
14. Feng, X., Li, Q., Zhu, Y., Wang, J., Liang, H., Xu, R., Formation and dominant factors of haze pollution over Beijing and its peripheral areas in winter. Atmos. Pollut. Res. 5:3 (2014), 1–11.
15. 2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos. Environ. 107 (2015), 118–128.
16. Guo, J., Zhang, X., Che, H., Gong, S., An, X., Cao, C., Guang, J., Zhang, H., Wang, Y., Zhang, X., Xue, M., Li, X., Correlation between PM concentrations and aerosol optical depth in eastern China. Atmos. Environ. 43 (2009), 5876–5886.
17. Guo, J., Zhang, X., Wu, Y., Zhaxi, Y., Che, H., La, B., Wang, W., Li, X., Spatio-temporal variation trends of satellite-based aerosol optical depth in China during 1980–2008. Atmos. Environ. 45:37 (2011), 6802–6811.
18. Gupta, P., Christopher, S.A., Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: 2. A neural network approach. J. Geophys. Res., 114(D20), 2009, D20205.
19. Haykin, S., Neural Networks: a Comprehensive Foundation. second ed., 1999, Prentice Hall, Upper Saddle River, NJ, 237–239.
20. Hoff, R.M., Christopher, S.A., Remote sensing of particulate pollution from space: have we reached the promised land?. J. Air & Waste Manag. Assoc. 59:6 (2009), 645–675.
21. Hsu, N.C., Tsay, S.-C., King, M.D., Herman, J.R., Aerosol properties over bright-reflecting source regions. IEEE Trans. Geosci. Remote Sens. 42 (2004), 557–569.
22. Hubanks, P.A., King, M.D., Platnick, S., Pincus, R., MODIS Atmosphere L3 Gridded Product Algorithm Theoretical Basis Document, No. ATBD-MOD-30, December, 2008. 2008.
23. Jiang, D., Zhang, Y., Hu, X., Zeng, Y., Tan, J., Shao, D., Progress in developing an ANN model for air pollution index forecast. Atmos. Environ. 38 (2004), 7055–7064.
24. Kaufman, Y., Tanré D., Remer, L., Vermote, E., Chu, A., Holben, B., Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. J. Geophys. Res., 102(D14), 1997, 17051.
25. Koelemeijer, R.B.A., Homan, C.D., Matthijsen, J., Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmos. Environ. 40:27 (2006), 5304–5315.
26. 2 retrievals from the ozone monitoring instrument over NE China. J. Geophys. Res., 113, 2008, D16S40.
27. 2.5 concentrations. Atmos. Chem. Phys. 11 (2011), 7991–8002.
28. Lepeule, J., Laden, F., Dockery, D., Schwartz, J., Chronic exposure to fine particles and mortality: an extended follow-up of the harvard six cities study from 1974 to 2009. Environ. Health Perspect. 120:7 (2012), 965–970.
29. Li, C., Hsu, N.C., Tsay, S.-C., A study on the potential applications of satellite data in air quality monitoring and forecasting. Atmos. Environ. 45:22 (2011), 3663–3675.
30. 2.5. Remote Sens. Environ. 156 (2015), 117–128.
31. Liu, Z., Liu, D., Huang, J., Vaughan, M., Uno, I., Sugimoto, N., Kittaka, C., Trepte, C., Wang, Z., Hostetler, C., Winker, D., Airborne dust distributions over the Tibetan Plateau and surrounding areas derived from the first year of CALIPSO lidar observations. Atmos. Chem. Phys. 8 (2008), 5045–5060.
32. 2.5 during haze episodes: a case study of Delhi, India. Atmos. Environ. 102 (2015), 239–248.
33. Niska, H., Rantamäki, M., Hiltuinen, T., Karpinen, A., Kukkonen, J., Ruuskanen, J., Kolehmainen, M., Evaluation of an integrated modeling system containing a multi-layer perceptron model and the numerical weather prediction model HIRLAM for the forecasting of urban airborne pollutant concentrations. Atmos. Environ. 39 (2005), 6524–6536.
34. 2.5. Environ. Sci. Technol. 42 (2008), 5800–5806.
35. Prakash, A., Kumar, U., Kumar, K., Jain, V.K., A wavelet-based neural network model to predict ambient air pollutants’ concentration. Environ. Model. Assess. 16:5 (2011), 503–517.
36. Sarle, W.S., Stopped training and other remedies for overfitting. Proceedings of the 27th Symposium on the Interface of Computer Science and Statistics, 1995, 352–360.
37. 2.5 at Cabauw, The Netherlands. Atmos. Chem. Phys. 9:3 (2009), 909–925.
38. Schlink, U., Dorling, S., Pelikan, E., Nunnari, G., Cawley, G., Junninen, H., Greig, A., Foxall, R., Eben, K., Chatterton, T., Vondracek, J., Richter, M., Dostal, M., Bertucco, L., Kolehmainen, M., Doyle, M., A rigorous inter-comparison of ground-level ozone predictions. Atmos. Environ. 37 (2003), 3237–3253.
39. Schlink, U., Herbarth, O., Richter, M., Dorling, S., Nunnari, G., Cawley, G., Pelikan, E., Statistical models to assess the health effects and to forecast ground-level ozone. Environ. Model. Softw. 21 (2006), 547–558.
40. Shepherd, A.J., Second-order Methods for Neural Networks (New York). 1997.
41. 10 concentrations. Atmos. Environ. 42 (2008), 4567–4588.
42. 2.5 concentrations using a hidden Markov model with different emission distributions in Northern California. Sci. Total Environ. 443 (2013), 93–103.
43. Tao, J., Zhang, M., Chen, L., Wang, Z., Su, L., Ge, C., Han, X., Zou, M., A method to estimate concentrations of surface-level particulate matter using satellite-based aerosol optical thickness. Sci. China Earth Sci. 56:8 (2013), 1422–1433.
44. Tao, M., Chen, L., Wang, Z., Ma, P., Tao, J., Jia, S., A study of urban pollution and haze clouds over northern China during the dusty season based on satellite and surface observations. Atmos. Environ. 82 (2014), 183–192.
45. 2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements. Remote Sens. Environ. 114:2 (2010), 221–229.
46. van Donkelaar, A., Martin, R.V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., Villeneuve, P.J., Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ. Health Perspect. 118:6 (2010), 847–855.
47. 2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. Sci. Total Environ. 409:7 (2011), 1266–1276.
48. Wang, Z., Chen, L., Tao, J., Liu, Y., Hu, X., Tao, M., An empirical method of RH correction for satellite estimation of ground-level PM concentrations. Atmos. Environ. 95 (2014), 71–81.
49. Wu, Y., Guo, J., Zhang, X., Tian, X., Zhang, J., Wang, Y., Duan, J., Li, X., Synergy of satellite and ground based observations in estimation of particulate matter in eastern China. Sci. Total Environ. 433 (2012), 20–30.
50. Yahya, K., Zhang, Y., Vukovich, J.M., Real-time air quality forecasting over the southeastern United States using WRF/Chem-MADRID: multiple-year assessment and sensitivity studies. Atmos. Environ. 92 (2014), 318–338.
51. 10 prediction from MODIS aerosol optical depth. Atmos. Chem. Phys. 13 (2013), 3517–3526.
52. Zhao, X., Zhao, P., Xu, J., Meng, W., Pu, W., Dong, F., He, D., Shi, Q., Analysis of a winter regional haze event and its formation mechanism in the North China Plain. Atmos. Chem. Phys. 13:11 (2013), 5685–5696.
53. 2.5 concentrations with a multifactoral radial basis function neural network. Environ. Sci. Pollut. Res. 22:14 (2015), 10395–10404.