The China Project's atmospheric research is committed to building observationally validated, fundamental research on the physical and chemical dimensions of China’s atmospheric environment, from urban to global scales. In addition to the model-based research described below, it includes field observations described here and bottom-up emissions research described here. It is also a core component of a Project-wide interdisciplinary framework now being applied to evaluation of national GHG and pollution control policies, described here.
A primary tool is the , and a nested, high-resolution window over China developed by WANG Yuxuan (Tsinghua University Center for Earth System Science, formerly of Harvard) and China Project chair Michael B. McElroy. Validated by agreement of modeled concentrations with measurements made by ground stations and offshore aircraft, the GEOS-Chem model differentiates air transport mechanisms for individual sub-regions of China on a finer scale than previously possible ( ) and captures critical seasonal effects of meteorology—notably cold fronts in winter and monsoonal patterns in summer—on regional and urban air quality.
Applied in inverse mode—in which atmospheric concentrations observed by ground stations, aircraft, and satellites are use to derive optimized emissions—the model provides independent checks on “bottom-up” inventories of carbon monoxide (CO), nitrogen oxides (NOX), other pollutants, and greenhouse gases (; ; and ).
An inverse application to near-real-time satellite observations of atmospheric concentrations yielded encouraging evidence to officials preparing for the Beijing Olympics in 2008. It showed that measures to restrict vehicular traffic in Beijing during the Sino-African Summit of November of 2006 had the intended effect: a large reduction of NOX emissions, as much as 40%, during the period in which the restrictions were in place (other science and general news media, and were cited by Beijing authorities in news conferences prior to the Olympics.). The figure above compares model estimates and observations, illustrating the capability of the GEOS-Chem China model to capture local variation in NO2 column concentrations. The results of the Sino-African summit paper were reported on the online news of and in
A paper in Atmospheric Chemistry and Physics (ACP) used station data to investigate variations of O3 and CO in summertime in the Beijing area (). It demonstrated a decline in conditions conducive to O3 formation in August compared to June and July, attributable to increased cloudiness from monsoonal climate patterns.
adapted the GEOS-Chem China model to new assimilated meteorological data now available, improving the spatial resolution of the model to 0.5 X 0.67 degrees.
A paper in ACP () used this higher-resolution version of GEOS-Chem China and data from the Miyun station to differentiate how much of reduced ozone levels observed during the Beijing Olympics can be attributed to policy-driven restrictions of emissions, and how much to natural meteorological conditions (see above figure). A subsequent paper in Tellus B ( ) provided more expansive analyses of the year-round O3 and CO observational records at Miyun and their correlations.
A study led by Wang and Munger in ACP (Wang et al. 2010a) conducted the first analysis of the CO2 data from the Miyun station. It analyzed the "correlation slope" of observed CO2 and CO to illustrate improvement of overall combustion efficiency of energy use in a multi-province region around Beijing, consistent with official policy objectives and energy statistics during the 11th Five Year Plan. A summary of this paper for non-scientists is available at this link.
is the first analysis of the Miyun station observational record of black carbon (BC), a critical short-lived climate forcer, conducted in partnership with collaborators in Japan. It found a lower inferred average BC/CO emission ratio in the Beijing region than that suggested by available bottom-up emission inventories.
LIN Jintai (then a post-doc, now collaborating from the PKU School of Physics) led an enhancement of GEOS-Chem, reported in . The study refined representation of the boundary layer in the model, which is critical to the Project's estimation of impacts of air pollutants on health, agriculture, and ecosystems. A paper in ACP by ) developed a new approach to constraining Chinese anthropogenic emissions of NOx from four major emitting sectors. It combined tropospheric NO2 column retrievals from two satellites in July 2008, taking advantage of their different passing times over China and explicitly accounting for diurnal variations in anthropogenic emissions as well as variations in tropospheric lifetimes.
Another China Project study led by Lin indicated that satellite observations can not only shed light on the contributions of key primary and precursor pollutants to fine particle (aerosol) concentrations in China, but provide independent evidence of the state of the economy (). The black lines in the figure above are the mean "Aerosol Optical Depth" (AOD), an indicator of particulate matter (PM) in the atmosphere, and the red lines are mean "Vertical Column Densities" of nitrogen dioxide (NO2), both observed over east-central China by the NASA Aura satellite. The study explores how current emission trends and policies could lead to an increase in fine particles, even as coarse particles measured at the surface are successfully reduced. A related study by Lin and McElroy in ACP focuses specifically on trends in NO2 over China, and its close relationship to the state of its economy ( ).
The atmospheric chemistry research using GEOS-Chem China is part of an initiative integrating most of the China Project's major research capacities in assessment of the total costs and benefits of emission control and energy policy options in China. This new effort is described in a separate link here.
Acknowledgment: Some of the material summarized here is based on work supported by the National Science Foundation under Grants No. ATM-1019134 or ATM-0635548 (indicated by acknowledgments in the papers themselves). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation (NSF).