A Domain-Specific Bayesian Deep-learning Approach for Air Pollution Forecast

2020 ◽  
pp. 1-1
Author(s):  
Yang Han ◽  
Jacqueline C.K. Lam ◽  
Victor OK Li ◽  
Qi Zhang
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Learning Approach ◽  
Domain Specific ◽  
Air Pollution Forecast ◽  
Pollution Forecast

Air pollution forecast for Ho Chi Minh City, Vietnam in 2015 and 2020

2010 ◽  
Vol 4 (2) ◽  
pp. 145-158 ◽  
Author(s):  
Bang Quoc Ho ◽  
Alain Clappier ◽  
Golay François
Keyword(s):  
Air Pollution ◽  
Ho Chi Minh City ◽  
Air Pollution Forecast ◽  
Pollution Forecast

Air pollution forecast in Portugal: a demand from the new air quality framework directive

2005 ◽  
Vol 25 (1/2/3/4) ◽  
pp. 4 ◽  
Author(s):  
Alexandra Monteiro ◽  
Myriam Lopes ◽  
Ana Isabel Miranda ◽  
Carlos Borrego ◽  
Robert Vautard
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Framework ◽  
Air Pollution Forecast ◽  
Pollution Forecast

A nonnegativity preserved efficient chemical solver applied to the air pollution forecast

2017 ◽  
Vol 314 ◽  
pp. 44-57 ◽  
Author(s):  
Fan Feng ◽  
Xuebin Chi ◽  
Zifa Wang ◽  
Jie Li ◽  
Jinrong Jiang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Pollution Forecast ◽  
Pollution Forecast

Development of a New Operational Air Pollution Forecast System on Regional and Urban Scale

2004 ◽  
pp. 573-581
Author(s):  
Jørgen Brandt ◽  
Jesper H. Christensen ◽  
Lise M. Frohn ◽  
Gary Geernaert ◽  
Ruwim Berkowicz
Keyword(s):  
Air Pollution ◽  
Forecast System ◽  
Air Pollution Forecast ◽  
Pollution Forecast

scholarly journals Implementation and testing of a simple data assimilation algorithm in the regional air pollution forecast model, DEOM

2009 ◽  
Vol 9 (2) ◽  
pp. 7645-7680
Author(s):  
J. Frydendall ◽  
J. Brandt ◽  
J. H. Christensen
Keyword(s):  
Air Pollution ◽  
Data Assimilation ◽  
Transport Model ◽  
Surface Ozone ◽  
Monitoring And Evaluation ◽  
Forecast Model ◽  
Environmental Research ◽  
Air Pollution Forecast ◽  
National Environmental ◽  
Pollution Forecast

Abstract. A simple data assimilation algorithm based on statistical interpolation has been developed and coupled to a long-range chemistry transport model, the Danish Eulerian Operational Model (DEOM), applied for air pollution forecasting at the National Environmental Research Institute (NERI), Denmark. In this paper, the algorithm and the results from experiments designed to find the optimal setup of the algorithm are described. The algorithm has been developed and optimized via eight different experiments where the results from different model setups have been tested against measurements from the EMEP (European Monitoring and Evaluation Programme) network covering a half-year period, April–September 1999. The best performing setup of the data assimilation algorithm for surface ozone concentrations has been found, including the combination of determining the covariances using the Hollingsworth method, varying the correlation length according to the number of adjacent observation stations and applying the assimilation routine at three successive hours during the morning. Improvements in the correlation coefficient in the range of 0.1 to 0.21 between the results from the reference and the optimal configuration of the data assimilation algorithm, were found. The data assimilation algorithm will in the future be used in the operational THOR integrated air pollution forecast system, which includes the DEOM.

Air Pollution Forecast Model Based on LSTM

2021 ◽  
Author(s):  
Ziyu Wei ◽  
Lirong Yan ◽  
Xiaodong Zhu ◽  
Jiayi Chen ◽  
Yuanyuan Chen
Keyword(s):  
Air Pollution ◽  
Forecast Model ◽  
Model Based ◽  
Air Pollution Forecast ◽  
Pollution Forecast
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