Spatial ensemble prediction of hourly PM2.5 concentrations around Beijing railway station in China-Reference-Cited by-同舟云学术

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Spatial ensemble prediction of hourly PM2.5 concentrations around Beijing railway station in China

Published:2020-04-11 Issue:5 Volume:13 Page:563-573
ISSN:1873-9318
Container-title:Air Quality, Atmosphere & Health
language:en
Short-container-title:Air Qual Atmos Health

Author:

Xu Yinan,Liu Hui

Funder

National Natural Science Foundation of China

Science and Technology Bureau, Changsha

Central South University

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Management, Monitoring, Policy and Law,Atmospheric Science,Pollution

Link

https://link.springer.com/content/pdf/10.1007/s11869-020-00817-7.pdf

Reference32 articles.

1. Alimissis A, Philippopoulos K, Tzanis C, Deligiorgi D (2018) Spatial estimation of urban air pollution with the use of artificial neural network models. Atmos Environ 191:205–213

2. Bai Y, Zeng B, Li C, Zhang J (2019) An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting. Chemosphere 222:286–294. https://doi.org/10.1016/j.chemosphere.2019.01.121

3. Błaszczyk E, Rogula-Kozłowska W, Klejnowski K, Kubiesa P, Fulara I, Mielżyńska-Švach D (2017) Indoor air quality in urban and rural kindergartens: short-term studies in Silesia, Poland. Air Qual Atmos Health 10:1207–1220. https://doi.org/10.1007/s11869-017-0505-9

4. Cabaneros SM, Calautit JK, Hughes BR (2019) A review of artificial neural network models for ambient air pollution prediction. Environ Model Softw 119:285–304. https://doi.org/10.1016/j.envsoft.2019.06.014

5. Corfa E, Maury F, Segers P, Fresneau A, Albergel A (2004) Short-range evaluation of air pollution near bus and railway stations. Sci Total Environ 334-335:223–230. https://doi.org/10.1016/j.scitotenv.2004.04.077

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