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报告题目:
全球变化科学紫荆论坛——Nitrous Oxide (N2O) Emissions from California based on 2010 CalNex Airborne Measurements
 报告人:
Dr. Bin XIANG(项彬)
报告时间:
2013-01-09 10:00
报告地点:
伟清楼610会议室
主办单位:
地球系统科学研究中心
  简介:
主讲人简介:
Dr. Bin XIANG received her B.S. in Environ Science from Fudan University in 2004 and Ph.D. in Environmental Chemistry from State University of New York at Albany in 2009. She was a Postdoctoral Fellow working with Prof. Steve Wofsy in School of Engineering and Applied Sciences, Harvard University from 2009 -2011 and is currently a Research Associate at Harvard University.
 
讲座简介:
N2O is an important gas for climate and for stratospheric chemistry, with a lifetime exceeding 100 years. Global concentrations have increased steadily since the 18th century, apparently due to human-associated emissions, principally from the application of nitrogen fertilizers. However, quantitative studies of agricultural emissions at large spatial scales are lacking. Here we derive regional emission rates for N2O in the agricultural heartland of California, based on analysis of in-situ airborne atmospheric observations collected using a new quantum cascade laser spectrometer on board the NOAA WP-3 research aircraft during the CalNex (California Research at the Nexus of Air Quality and Climate Change) program in late spring, 2010. We coupled the WRF model with the STILT (Stochastic Time-Inverted Lagrangian Transport) model to link our in-situ airborne observations to surface emissions. We then used a variety of statistical methods to identify source areas and to optimize emission rates. Our results are consistent with the view that fertilizer application is the largest source of N2O in the Central Valley. The spatial distribution of surface emissions, based on California land use and activity maps, was very different than indicated in the leading emissions inventory (EDGAR 4.0). Our estimated total emission flux of N2O for California in May and June was 3 - 4 times larger than the annual mean given for the state by EDGAR and other inventories, indicating a strong seasonal variation.
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