Needs of global tropospheric composition observations

Extracted from Chimot, J., Global mapping of atmospheric composition from space – Retrieving aerosol height and tropospheric NO2 from OMI, PhD book, Delft University of Technology (TU Delft), The Royal Netherlands Meteorological Institute (KNMI), July 2018.

 

• Since gases and particles can be transported over long distance, air quality “nowcasting” and forecasting must be supported and their capabilities enhanced. This can be done by developing a comprehensive system describing the dynamics and chemistry evolution of air mass, such as the Copernicus Atmospheric Monitoring System (CAMS) (Eskes et al., 2015; Huijnen et al., 2016). Tropospheric composition observations can help by evaluating Earth-System model outputs or by providing them with inputs (the so-called boundary conditions) (Ciais et al., 2010; Burrows et al., 2011; IPCC, 2014). This contributes to monitor effects of health regulations and anticipate protection measures of highly populated areas.
• Exceptional events and their pollutant releases must be monitored, and their subsequent plumes should be tracked from day to day. The American Clean Air Act defines them as air quality episodes that are not reasonably controllable or preventable, caused by human activity that is unlikely to recur at a particular location or a natural event (Duncan et al., 2014). Major forest fires such as in South-America or Russia or volcanic eruptions are considered as exceptional events.
• Long-time series (i.e. concentration trends) and knowledge of the sources and sinks of gases and particles need to be updated. This is important not only to verify the effectiveness and compliance of implemented policies and new technologies in cities (e.g. new generation of transport vehicles, electricity sources, denitrification systems on power plants etc.), monitor the impact of economic growth such as in South-Asian in densely urban and large industrial areas but also to anticipate the non-linear response of natural fluxes (ocean and forest) in a warming climate together with interannual sensitivity (Lu et al., 2011; Lu and Streets, 2012; Worldbank, 2015; Duncan et al., 2016). Anthropogenic emissions calculated by the bottom-up approach introduces large uncertainties in emission inventories as it uses statistics on land-use and sector specific emissions factors that can be quite outdated (Mijling and van der A, 2012; Ding et al., 2017b,a; van der A et al., 2017). Moreover, they are not independent of national declarations and their potential inaccuracies. Concentration observations, sensitive to the surface, can be used in a top-down approach through inverse modeling or data assimilation techniques to adjust emission estimates in the model, and even detect unknown sources (Martin et al., 2003; Streets et al., 2013). Trend monitoring and surface flux estimations need homogeneous long-time series of observation data.

 

In addition, because of the multiple interactions occurring in the troposphere, air quality and climate change characterization requires the monitoring of the ensemble components (i.e. not only one) that form the integrated part of our tropospheric composition to elude some open research questions. For example.