Due to the strong economic growth in China in the past decade, air pollution has become a serious issue in many parts of the country. For this reason up-to-date regional air pollution information and means for emission control of the main pollutants are becoming more and more important. Within the EU FP7 programme two collaborative research projects have started in which Chinese and European partners co-operate to study the air quality in China by using space observations. These two projects, MarcoPolo and Panda, have joint their forces and present their results on this web-portal.
In the MarcoPolo project the focus is placed on emission estimates from space and the refinement of these emission estimates by spatial downscaling and by source sector apportionment. A wide range of data is used from various satellite instruments. From these satellite data, emission estimates are made for anthropogenic and biogenic sources. With various state-of-the-art techniques, up-to-date emission inventories will be created. By combining these emission data with known information from the ground, a new emission database for MarcoPolo will be constructed. The new emission inventory is input to air quality models and is expected to improve the existing air quality modelling and forecasts considerably. We demonstrate the resulting air quality information by running models on both regional and urban scale. [download leaflet]
The objective of the PANDA project is to establish a team of European and Chinese scientists who will jointly use space observations and in-situ data as well as advanced numerical models to monitor, analyse and forecast global and regional air quality. PANDA will provide to users and stakeholders knowledge, methodologies and toolboxes that will serve as a basis for global and regional air quality analysis and forecasts. It will provide science-based information that will improve air quality management by regional and local authorities. Through turorials, workshops and summerschools, users and stakeholders will be trained to use the key products and data generated by the project.