Abstract. On-road vehicle emissions are a major contributor to elevated air pollution levels in populous metropolitan areas. We developed a link-level emissions inventory of vehicular pollutants, called EMBEV-Link (Link-level Emission factor Model for the BEijing Vehicle fleet), based on multiple datasets extracted from the extensive road traffic monitoring network that covers the entire municipality of Beijing, China (16 400 km2). We employed the EMBEV-Link model under various traffic scenarios to capture the significant variability in vehicle emissions, temporally and spatially, due to the real-world traffic dynamics and the traffic restrictions implemented by the local government. The results revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in the urban area (i.e., within the Fifth Ring Road) and during rush hours, both associated with the passenger vehicle traffic. By contrast, considerable fractions of nitrogen oxides (NOx), fine particulate matter (PM2.5) and black carbon (BC) emissions were present beyond the urban area, as heavy-duty trucks (HDTs) were not allowed to drive through the urban area during daytime. The EMBEV-Link model indicates that nonlocal HDTs could account for 29 % and 38 % of estimated total on-road emissions of NOx and PM2.5, which were ignored in previous conventional emission inventories. We further combined the EMBEV-Link emission inventory and a computationally efficient dispersion model, RapidAir®, to simulate vehicular NOx concentrations at fine resolutions (10 m × 10 m in the entire municipality and 1 m × 1 m in the hotspots). The simulated results indicated a close agreement with ground observations and captured sharp concentration gradients from line sources to ambient areas. During the nighttime when the HDT traffic restrictions are lifted, HDTs could be responsible for approximately 10 µg m−3 of NOx in the urban area. The uncertainties of conventional top-down allocation methods, which were widely used to enhance the spatial resolution of vehicle emissions, are also discussed by comparison with the EMBEV-Link emission inventory.

Abstract. A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring 2006. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility and dust loading retrieval from the Chinese geostationary satellite FY-2C. The results show that a major improvement to the capability of CUACE/Dust in forecasting the short-term variability in the spatial distribution and intensity of dust concentrations has been achieved, especially in those areas far from the source regions. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The assimilation results usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful for the unification of observation and numerical modeling results.

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The source region of the Yellow River, China, experienced degradation during the 1980s and 1990s, but effective ecological restoration projects have restored the alpine grassland ecosystem. The local government has taken action to restore the grassland area since 1996. Remote sensing monitoring results show an initial restoration of this alpine grassland ecosystem with the structural transformation of land cover from 2000 to 2009 as low- and high-coverage grassland recovered. From 2000 to 2009, the low-coverage grassland area expanded by over 25% and the bare soil area decreased by approximately 15%. To examine the relationship between ecological structure and function, surface temperature (Ts) and evapotranspiration (ET) levels were estimated to study the dynamics of the hydro-heat pattern. The results show a turning point in approximately the year 2000 from a declining ET to a rising ET, eventually reaching the 1990 level of approximately 1.5 cm/day. We conclude that grassland coverage expansion has improved the regional hydrologic cycle as a consequence of ecological restoration. Thus, we suggest that long-term restoration and monitoring efforts would help maintain the climatic adjustment functions of this alpine grassland ecosystem.

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