Air quality forecast through integrated data assimilation and machine learning

Hai Xiang Lin, Jianbing Jin, Jaap Van Den Herik

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

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 PM 10 concentrations during a dust storm is performed. It is known that the PM 10 concentrations are caused by multiple emission sources, e.g., dust from desert and anthropogenic emissions. An accurate modeling of the PM 10 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 PM 10 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.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Agents and Artificial Intelligence, ICAART 2019
EditorsJaap van den Herik, Luc Steels, Ana Rocha
PublisherSciTePress
Pages787-793
Number of pages7
Volume2
ISBN (Electronic)978*989-758-350-6
DOIs
Publication statusPublished - 2019
EventICAART 2019: 11th International Conference on Agents and Artificial Intelligence - Prague, Czech Republic
Duration: 19 Feb 201921 Feb 2019
Conference number: 11
http://www.icaart.org/?y=2019

Conference

ConferenceICAART 2019
CountryCzech Republic
CityPrague
Period19/02/1921/02/19
Internet address

Keywords

  • Chemical Transport Model
  • Data-driven Machine Learning
  • Physics-based Machine Learning

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