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 language | English |
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Title of host publication | Proceedings of the 11th International Conference on Agents and Artificial Intelligence, ICAART 2019 |
Editors | Ana Rocha, Luc Steels, Jaap van den Herik |
Publisher | SciTePress |
Pages | 787-793 |
Number of pages | 7 |
Volume | 2 |
ISBN (Electronic) | 978*989-758-350-6 |
DOIs | |
Publication status | Published - 2019 |
Event | ICAART 2019: 11th International Conference on Agents and Artificial Intelligence - Prague, Czech Republic Duration: 19 Feb 2019 → 21 Feb 2019 Conference number: 11 http://www.icaart.org/?y=2019 |
Conference
Conference | ICAART 2019 |
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Country/Territory | Czech Republic |
City | Prague |
Period | 19/02/19 → 21/02/19 |
Internet address |
Keywords
- Chemical Transport Model
- Data-driven Machine Learning
- Physics-based Machine Learning