Presence of atmospheric PAHs in urban and suburban region (Beijing, China) was studied in April and July 2011. Forty-four pairs of gas and particle (TSP) phase samples were collected every six day by high volume (Hi-Vol) air samplers at four sampling sites, and determined separately by GC/MS based on USEPA Method TO-13A. Average total concentration (gas + particles) of PAHs (T-PAHs) was 135.1±49.0 ng/m3 and 181.2±40.9 ng/m3 in April and July, respectively. Gas phase PAHs (G-PAHs) was the major fraction, comprising 63–92% of T-PAHs. Lighter (2-, 3-, 4-ring) and heavier (5-,6-ring) PAHs were found predominantly in gas and particle phase, respectively. 2- to 6- ring PAHs contributed 10%, 53%, 26%, 7% and 4% of T-PAHs, respectively. Five major PAHs, naphthalene (NAP), fluorene (FLU), PHE, fluoranthene (FLA), and pyrene (PYR) contributed 70 – 90% of T-PAHs. G-PAHs increased significantly while PAHs in particle phase (P-PAHs) decreased from April to July. Volatilization from soil and more emission from power generation increase might explain the increase of G-PAHs, and the washout of P-PAHs along with particles might explain the decrease of P-PAHs. Given particulate organic carbon (OC) and elemental carbon (EC) being well correlated, P-PAHs was moderately correlated with OC and EC, suggesting that there were other mechanisms contributing to P-PAHs different from those of OC/EC. Significant correlation between P-PAHs with SO2 and NO2 suggested coal combustion and automobile exhaust to be contamination contributors.

In order to control regional acid deposition pollution, it is necessary to determine scientific regional control targets for atmospheric acid deposition. This study proposed a method to conduct multi-site simulation using the VSD model and the simulation results were plotted by cumulative frequency distribution curves. Then the regional acid deposition control targets were determined based on the analysis of the restoration of the soil in the region under different deposition scenarios in the target years. The method was applied in the Guangzhou-Dongguan-Huizhou region. To analyze the control targets for acid deposition in this region, 25 sites were simulated by VSD model based on onsite soil sampling and investigation, and the results were plotted by cumulative frequency distribution curves. The results indicated that when S deposition was controlled alone and if the protection rate was 80%, the S control targets should be 7.68-12g.m-2.yr-1 in the short-term and 10.24-16g.m-2.yr-1 in the long-term; the short-term and long-term S deposition control targets should be 5.12-8g.m-2 .yr-1 和7.68-12g.m-2 .yr-1 if the protection rate was 95%. When the S and BC depositions were controlled simultaneously and if the protection rate was 80%, the S control targets should be 2.56-4 g.m-2 .yr-1 in the short-term and 5.12-8 g.m-2 .yr-1 in the long-term when BC deposition was 6.4-12.8 g.m-2 .yr -1 ; and the S control targets should be 2.56-4 g.m-2.yr-1 when BC deposition was 4.8-9.6 g.m-2.yr-1. If the protection rate was 95%, the S control targets should be 0.64-1 g.m-2.yr-1 in the short-term and 5.12-8 g.m-2.yr-1 in the long-term when BC deposition was 6.4-12.8 g.m-2.yr-1; and the S control targets should be 0.64-1g.m-2.yr-1 in the short-term and 2.56-4g.m-2.yr-1 in the long-term. When BC deposition was 2-4 g.m-2.yr-1, S deposition should be controlled to 0.64-1g.m-2.yr-1 for the protection rate of 80% and 95%, and some manual restoration measures are required at the same time

To estimate the biogenic volatile organic compound (BVOC) emissions in China, this study collected data on vegetation volume, production and distribution, converted into leaf biomass and then used BVOC emission model. In 2003, the annual BVOC emission in China was 12.83 Tg, composed of 7.45 Tg isoprene, 2.23 Tg monoterpenes, and 3.14 Tg other VOCs (OVOCs). Emissions varied significantly among plant species, with contributions ordered as follows: forests > shrubs > crops > grasslands. Southern and northeastern China were the main sources of BVOC emissions. Significant seasonal variation was found with summer contributing the most.