Advances in air quality modeling and forecasting
Autor
Baklanov, Alexander
Zhang, Yang
Institución
Resumen
The importance of and interest to research and investigations of atmospheric composition and its
modeling for different applications are substantially increased. Air quality forecast (AQF) and assessment
systems help decision makers to improve air quality and public health, mitigate the occurrence of acute
air pollution episodes, particularly in urban areas, and reduce the associated impacts on agriculture,
ecosystems and climate. Advanced approaches in AQF combine an ensemble of state-of-the-art models,
high-resolution emission inventories, satellite observations, and surface measurements of most relevant
chemical species to provide hindcasts, analyses, and forecasts from global to regional air pollution and
downscaling for selected countries, regions, and urban areas. Based on published reviews and recent
analyses, the article discusses main gaps, challenges, applications and advances, main trends and
research needs in further advancements of atmospheric composition and air quality modeling and
forecasting.