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ABSTRACT
With six kinds of plants in Shenyang as the research object, we selected five rainfalls and applied an aerosol generator to conduct a dynamic observation retention of PM2.5 by plant leaves; the resultant research explores the effect retention of PM2.5 by plant leaves during rainfall. The results show that the adsorption capacity of PM2.5 per unit of leaf area can effectively remove different tree species by rainfall, and removals were between 0.04 and 0.23 μg·cm−2. The removal rate was 24.02–46.15%, and the PM2.5 capacity of broad leaves was easier to be removed; the removal rate was greater for broadleaf trees (37.69%) than for conifers (27.76%). This was positively related to rainfall, and PM2.5 removal amount of plant leaves that was three times the curve model between rainfall and removal volume of PM2.5 per unit leaf area (R2 ＞ 0.62). Each tree species of PM2.5 adsorption quantity per unit of leaf area was rapidly increased on the second day after a rainfall, with per unit leaf area of PM2.5 adsorption capacity restored to 6.07–43.92% on the fourth day after rainfall. This was due to the fact that the PM2.5 content of the plant leaves was washed by the rain, and it was approximately 16 days before it once again reached saturation capacity. The increased speed and multiples of the capacity of PM2.5 content that was cleaned by rain were larger for broadleaf than for conifer, and this was related to the atmospheric PM2.5 concentration and different species per unit of leaf area of PM2.5 adsorption quantity, and it was inversely correlated with PM2.5 removal capacity. The research results are helpful to reveal the mechanism and process of leaf retention of atmospheric particulate matter and can provide a scientific basis for the quantitative evaluation of leaf retention dust.
Acknowledgments
This work was financially supported by the Hunan Provincial Natural Science Foundation (2017JJ3151), Changsha science and technology project (kq1706029), Hunan forestry science and technology project (XLK201704), Key R & D Plan of Hunan Province (2017NK2223), Hunan forestry science and technology project (XLKPT201710), National Science and Technology Plan in Rural areas during the Twelfth Five-Year Plan (2015BAD07B04), National Key R&D Program of China (2017YFC0505506), Hunan forestry science and technology project [XLC201701-2], and CFERN & BEIJING TECHNO SOLUTIONS Award Funds on excellent academic achievements.
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Jia Luo
Jia Luo (1983–), Femail, Ph.D. of Biology, Assistant Researcher, Doctor of Biology, Research direction: Ecology.