异戊二烯
环境科学
大气科学
微量气体
挥发性有机化合物
化学输运模型
灌木丛
纬度
排放清单
焊剂(冶金)
生物量(生态学)
生态系统
气候学
空气质量指数
气象学
对流层
生态学
化学
地理
有机化学
大地测量学
共聚物
生物
地质学
聚合物
作者
Alex Guenther,C. N. Hewitt,David J. Erickson,Ray Fall,Chris Geron,Tom Graedel,P. C. Harley,Lee F. Klinger,Manuel Lerdau,W.A. McKay,Thomas Pierce,Bob Scholes,R. Steinbrecher,Raja Krishna Mohan Rao Tallamraju,John Taylor,Pat Zimmerman
摘要
Numerical assessments of global air quality and potential changes in atmospheric chemical constituents require estimates of the surface fluxes of a variety of trace gas species. We have developed a global model to estimate emissions of volatile organic compounds from natural sources (NVOC). Methane is not considered here and has been reviewed in detail elsewhere. The model has a highly resolved spatial grid (0.5°×0.5° latitude/longitude) and generates hourly average emission estimates. Chemical species are grouped into four categories: isoprene, monoterpenes, other reactive VOC (ORVOC), and other VOC (OVOC). NVOC emissions from oceans are estimated as a function of geophysical variables from a general circulation model and ocean color satellite data. Emissions from plant foliage are estimated from ecosystem specific biomass and emission factors and algorithms describing light and temperature dependence of NVOC emissions. Foliar density estimates are based on climatic variables and satellite data. Temporal variations in the model are driven by monthly estimates of biomass and temperature and hourly light estimates. The annual global VOC flux is estimated to be 1150 Tg C, composed of 44% isoprene, 11% monoterpenes, 22.5% other reactive VOC, and 22.5% other VOC. Large uncertainties exist for each of these estimates and particularly for compounds other than isoprene and monoterpenes. Tropical woodlands (rain forest, seasonal, drought‐deciduous, and savanna) contribute about half of all global natural VOC emissions. Croplands, shrublands and other woodlands contribute 10–20% apiece. Isoprene emissions calculated for temperate regions are as much as a factor of 5 higher than previous estimates.
科研通智能强力驱动
Strongly Powered by AbleSci AI