Authors:
Hai Xiang Lin
1
;
Jianbing Jin
2
and
Jaap van den Herik
3
Affiliations:
1
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands, Leiden University, Leiden and The Netherlands
;
2
Delft Institute of Applied Mathematics, Delft University of Technology, Delft and The Netherlands
;
3
Leiden University, Leiden and The Netherlands
Keyword(s):
Chemical Transport Model, Data-driven Machine Learning, Physics-based Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Big Data
;
Computational Intelligence
;
Data Engineering
;
Data Management and Quality
;
Evolutionary Computing
;
Industrial Applications of AI
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Model-Based Reasoning
;
Soft Computing
;
Symbolic Systems
Abstract:
Numerical models of chemical transport have been used to simulate the complex processes involved in the formation and transport of air pollutants. Although these models can predict the spatiotemporal variability of a variety of chemical species, the accuracy of these models is often limited. Therefore, in the past two decades, data assimilation methods have been applied to use the available measurements for improving the forecast. Nowadays, machine learning techniques provide new opportunities for improving the air quality forecast. A case study on PM10 concentrations during a dust storm is performed. It is known that the PM10 concentrations are caused by multiple emission sources, e.g., dust from desert and anthropogenic emissions. An accurate modeling of the PM10 concentration levels owing to the local anthropogenic emissions is essential for an adequate evaluation of the dust level. However, real-time measurement of local emissions is not possible, so no direct data is available.
Actually, the lack of in-time emission inventories is one of the main reasons that current numerical chemical transport models cannot produce accurate anthropogenic PM10 simulations. Using machine learning techniques to generate local emissions based on real-time observations is a promising approach. We report how it can be combined with data assimilation to improve the accuracy of air quality forecast considerably.
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