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

Sci Total Environ. 2017 Sep 1:593-594:713-721. doi: 10.1016/j.scitotenv.2017.03.206. Epub 2017 Mar 29.

Abstract

Atmospheric removal of PM2.5 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 PM2.5 dry 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 PM2.5 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 increasing deposition velocity due to forest expansion and PM2.5 atmospheric level contributed to the increasing deposition flux of PM2.5. The highest atmospheric deposition flux of PM2.5 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 collection of PM2.5 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 PM2.5 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 2.85×107t PM2.5 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 PM2.5 dry deposition fluxes. Our results suggest that the TNRSF plays a moderate role in PM2.5 uptake, but enhances PM2.5 atmospheric removal by 30% in 2010 than in 1980.

Keywords: Dry deposition; Model intercomparison; Trend analysis; Vegetation collection.