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Atmospheric removal of PM2.5 by man-made Three Northern Regions Shelter Forest in Northern China estimated using satellite retrieved PM2.5 concentration

机译:利用卫星获取的PM2.5浓度估算中国北方北部三个人工林的大气中PM2.5的去除量

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摘要

Atmospheric removal of PM_(2.5) by the Three Northern Regions Shelter Forest (TNRSF) - the so called Green Great Wall (GGW) in northern China through dry deposition process was estimated using a bulk big-leaf model and a vegetation collection model. Decadal trend of PM_(2.5) diy deposition flux from 1999 to 2010 was calculated from modeled dry deposition velocity and air concentration retrieved from the satellite remote sensing. Dry deposition velocities of PM_(2.5) calculated using the two deposition models increased in many places of the TNRSF over the last decade due to increasing vegetation coverage of the TNRSF. Both inaeasing deposition velocity due to forest expansion and PM_(2.5) atmospheric level contributed to the increasing deposition flux of PM_(2.5). The highest atmospheric deposition flux of PM_(2.5) was found in the Central-north region covering Beijing-Tianjin-Hebei area, followed by the Northwestern and the Northeastern regions of the TNRSF. While greater coïlection of PM_(2.5) by vegetation was identified in the Northeastern region of the TNRSF due to higher forest coverage over this region, the most significant incline of the PM_(2.5) atmospheric removal due to vegetation collection was discerned in the Central-north region because of the most rapid increase in the vegetation coverage in this region. A total mass of 2.85 x 10~71 PM_(2.5) was estimated to be removed from the atmosphere through dry deposition process over the TNRSF from 1999 to 2010. The two deposition models simulated similar magnitude and spatial patterns of PM_(2.5) dry deposition fluxes. Our results suggest that the TNRSF plays a moderate role in PM_(2.5) uptake, but enhances PM2.5 atmospheric removal by 30% in 2010 than in 1980.
机译:使用大叶大块模型和植被收集模型估算了北部三大防护林(TNRSF)通过干沉降过程在大气中对PM_(2.5)的大气去除作用。根据模拟的干沉降速度和卫星遥感反演的空气浓度,计算了1999年至2010年PM_(2.5)diy沉降通量的年代际变化趋势。在过去十年中,由于TNRSF的植被覆盖率增加,使用这两种沉积模型计算出的PM_(2.5)的干沉降速度在TNRSF的许多地方有所增加。由于森林扩张而增加的沉积速度和大气中的PM_(2.5)两者都促进了PM_(2.5)的沉积通量的增加。在北京-天津-河北地区的中北部地区发现了最高的PM_(2.5)大气沉积通量,其次是TNRSF的西北和东北地区。尽管由于该地区较高的森林覆盖率,TNRSF东北部地区的植被对PM_(2.5)的吸收更大,但在中部地区却发现了由于植被收集而导致的PM_(2.5)大气清除的最大趋势。北部地区,因为该地区的植被覆盖率增长最快。据估计,从1999年到2010年,通过TNRSF的干沉降过程从大气中去除了2.85 x 10〜71 PM_(2.5)的总质量。这两个沉积模型模拟了PM_(2.5)干沉降的相似大小和空间模式通量。我们的结果表明,TNRSF在PM_(2.5)吸收中起中等作用,但与1980年相比,2010年PM2.5大气去除量增加了30%。

著录项

  • 来源
    《The Science of the Total Environment》 |2017年第1期|713-721|共9页
  • 作者单位

    Key Laboratory for Environmental Pollution Prediction and Control Gansu Province College of Earth and Environmental Saences, Lanzhou University, Lanzhou 730000, China;

    Key Laboratory for Environmental Pollution Prediction and Control Gansu Province College of Earth and Environmental Saences, Lanzhou University, Lanzhou 730000, China;

    Key Laboratory for Environmental Pollution Prediction and Control Gansu Province College of Earth and Environmental Saences, Lanzhou University, Lanzhou 730000, China;

    Air Quality Research Division, Environment and Climate Change Canada, Toronto, Canada;

    Key Laboratory for Environmental Pollution Prediction and Control Gansu Province College of Earth and Environmental Saences, Lanzhou University, Lanzhou 730000, China;

    Key Laboratory for Environmental Pollution Prediction and Control Gansu Province College of Earth and Environmental Saences, Lanzhou University, Lanzhou 730000, China;

    Key Laboratory for Environmental Pollution Prediction and Control Gansu Province College of Earth and Environmental Saences, Lanzhou University, Lanzhou 730000, China,CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Dry deposition; Model intercomparison; Trend analysis; Vegetation collection;

    机译:干法沉积;模型比对;趋势分析;植被收集;

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