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Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect atmospheric chemistry and secondary aerosol formation that ultimately influences air quality and aerosol radiative forcing. These uncertainties result from many factors, including uncertainties in land surface processes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (BVOCs). In this study, the latest version of Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1) is coupled within the land surface scheme CLM4 (Community Land Model version 4.0) in the Weather Research and Forecasting model with chemistry (WRF-Chem). In this implementation, MEGAN v2.1 shares a consistent vegetation map with CLM4 for estimating BVOC emissions. This is unlike MEGAN v2.0 in the public version of WRF-Chem that uses a stand-alone vegetation map that differs from what is used by land surface schemes. This improved modeling framework is used to investigate the impact of two land surface schemes, CLM4 and Noah, on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. The measurements collected during the Carbonaceous Aerosol and Radiative Effects Study (CARES) and the California Nexus of Air Quality and Climate Experiment (CalNex) conducted in June of 2010 provided an opportunity to evaluate the simulated BVOCs. Sensitivity experiments show that land surface schemes do influence the simulated BVOCs, but the impact is much smaller than that of vegetation distributions. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models in terms of simulating BVOCs, oxidant chemistry and, consequently, secondary organic aerosol formation.

Using a GC/FID/MS system, we analyzed the mixing ratio of 16 halocarbon species in more than 100 air samples collected in 2004 from the Pearl River Delta (PRD) region of southern China. The results revealed that there are elevated mixing ratios for most of halocarbons, especially for HClC = CCl2 (trichloroethylene, TCE), CH2Cl2 (dichloromethane, DCM), CH3 Br (bromomethane), HCFC-22, CHCl3 (trichloromethane), CCl4 (tetrachloromethane), Cl2C = CCl2 (perchloroethylene, PCE), CH3CCl3 (methyl chloroform, MCF), and CFC-12. Comparisons were done with the data from TRACE-P and ALE/GAGE/AGAGE experiments, we found that the large variability in mixing ratios (relative standard deviation ranged from 9.31 % to 96.55 %) of the halocarbons suggested substantial local emissions from the PRD region in 2004. Correlations between the mixing ratio of each species and carbon monoxide (CO) was examined, and then the emission of each halocarbon was quantified based on scaling the optimized CO emission inventory with the slope of the regression line fitted to each species relative to CO. The calculated results revealed that mass of CH2Cl2 (7.0 Gg), CH3CCl3 (6.7 Gg), and Cl2C = CCl2 (2.3 Gg) accounted for about 62.9 % of total halocarbon emissions, it suggested a significant contribution from solvent use in the PRD region. Emissions of HCFC-22 (3.5 Gg), an alternative refrigerant to chlorofluorocarbons (CFCs), were about 2.3 times greater than those of CFC-12 (1.6 Gg). CFC-12 and HCFC-22 accounted for 21.5 % of total emissions of halocarbons, so that the refrigerant would be the second largest source of halocarbons. However, the ratio approach found only minor emissions of CFCs, such as CFC-11, and the emission of CFC-114 and CFC-113 were close to zero. Emissions of other anthropogenic halocarbons, such as CCl4, CHCl3, CH3Br, and CH3Cl, were also estimated. Where possible, the emissions estimated from the measured ratios were compared with results from source inventory techniques, we found that both approaches gave emissions at similar magnitude for most of the halocarbons, except CFC-11. The comparison suggested that the ratio method may be a useful tool for assessing regional halocarbon emissions, and emission uncertainty could be further reduced by incorporating both longer-term and higher-frequency observations, as well as improving the accuracy of the CO inventory.