A new approach combining a simplified FLEXPART model and a Bayesian-RAT method for forecasting PM10 and PM2.5-Reference-Cited by-同舟云学术

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A new approach combining a simplified FLEXPART model and a Bayesian-RAT method for forecasting PM10 and PM2.5

Published:2019-11-26 Issue:2 Volume:27 Page:2165-2183
ISSN:0944-1344
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Guo Lifeng,Chen Baozhang,Zhang Huifang,Zhang Yanhu

Funder

National Key R&D Program of China

International partnership program of Chinese Academy of Sciences

State Key Laboratory of Resources and Environmental Information System

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

http://link.springer.com/content/pdf/10.1007/s11356-019-06605-w.pdf

Reference69 articles.

1. Angevine WM, Brioude J, McKeen S et al (2013) Pollutant transport among California regions[J]. J Geophys Res Atmos 118(12):6750–6763

2. Barmpadimos I, Hueglin C, Keller J, Henne S, Prévôt ASH (2011) Influence of meteorology on PM10 5 trends and variability in Switzerland from 1991 to 2008. Atmos Chem Phys 11:1813–1835. https://doi.org/10.5194/acp-11-1813-2011

3. Barmpadimos I, Keller J, Oderbolz D et al (2012) One decade of parallel PM10 and PM2.5 measurements in Europe: trends and variability[J]. Atmos Chem Phys Discuss 12(1):1–43

4. Bei N, Xiao B, Meng N, Feng T (2016) Critical role of meteorological conditions in a persistent haze episode in the Guanzhong basin, China[J]. Sci Total Environ 550:273–284

5. Biancofiore F, Busilacchio M, Verdecchia M et al (2017) Recursive neural network model for analysis and forecast of PM10 and PM2.5[J]. Atmos Pollut Res 8(4):652–659

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