Polychlorinated biphenyl (PCB) concentrations in surface soil samples (0-20 cm) from 52 sites (4 background, 39 rural, and 9 urban) across China in 2005 are presented. The average concentration of total PCBs among all the sites was 515 pg/g dry weight (dw), approximately one-tenth of that in global background soil in 1998. Differences of less than 1 order of magnitude were found between all background and rural sites, indicating a generally uniform distribution of PCBs in Chinese background/rural surface soil. While the major PCB homologue group in global background soil is hexa-PCB followed by penta-PCB, the major PCB homologue group in Chinese background/rural soil is tri-PCB followed by di-PCB, indicating a rather fresh signature in comparison to the much weathered PCBs in global surface soil. The correlation between sigmaPCBs and also each PCB homologue group per soil organic carbon (SOC) content in background/rural soil with longitude from 80 degrees to 122 degrees East was studied. The results indicated the strong influence of PCBs concentrations in Chinese background/rural soil by proximity to source region and SOC content, and also provided evidence for urban fractionation effect of PCBs in soil in the city of Shanghai and the longitudinal fractionation of PCBs in Chinese background/ rural soil from east to west. This work is the first comprehensive and spatial study of its kind for PCBs in Chinese surface soil on a national scale, and the data presented in this study can provide baseline information for establishing a long-term PCBs monitoring program in China.

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[1] Statistical modeling techniques and the Vaganov-Shashkin (VS) forward model of tree ring formation were used to investigate tree growth response of Pinus tabulaeformis to climate variations in semi-arid north central China. Both statistical and process-based modeling techniques were shown to be capable of simulating and evaluating climate-tree growth relationships for the study area, but the process-based VS model produced results that were more physically interpretable. Statistical modeling results indicate that both moisture and temperature have significant effects on tree growth during the growing season, with the most important months being May-August. The VS modeled results validated the above statistical modeling results, and further clarified the effects on tree growth of the seasonal distribution of temperature and soil moisture, soil moisture status prior to the growing season, and the start and end dates of the growing season. Under current and projected climate scenarios, our modeling results suggest significant tree growth reduction in north central China, and the possibility that regional forests may reduce their capacity to sequester carbon.