Air quality forecast through integrated data assimilation and machine learning

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.