Abstract
BackgroundHow to quickly predict and evaluate urban dust deposition is the key to the control of urban atmospheric environment. Here, we focus on changes of plant reflectance and plant functional traits due to dust deposition, and develop a prediction model of dust deposition based on these traits.ResultsThe results showed that (1) The average dust deposition per unit area of Ligustrum quihoui leaves was significantly different among urban environments (street (18.1001 g/m2), community (14.5597 g/m2) and park (9.7661 g/m2)). Among different urban environments, leaf reflectance curves tends to be consistent, but there were significant differences in leaf reflectance values (park (0.052–0.585) > community (0.028–0.477) > street (0.025–0.203)). (2) There were five major reflection peaks and five major absorption valleys. (3) The spectral reflectances before and after dust removal were significantly different (clean leaves > dust-stagnant leaves). 695 ~ 1400 nm was the sensitive range of spectral response. (4) Dust deposition has significant influence on slope and position of red edge. Red edge slope was park > community > street. After dust deposition, the red edge position has obviously “blue shift”. The moving distance of the red edge position increases with the increase of dust deposition. The forecast model of dust deposition amount established by simple ratio index (y = 2.517x + 0.381, R2 = 0.787, RMSE (root-mean-square error) = 0.187. In the model, y refers to dust retention, x refers to simple ratio index.) has an average accuracy of 99.98%. (5) With the increase of dust deposition, the specific leaf area and chlorophyll content index decreased gradually. The leaf dry matter content, leaf tissue density and leaf thickness increased gradually.ConclusionIn the dust-polluted environment, L. quihoui generally presents a combination of characters with lower specific leaf area, chlorophyll content index, and higher leaf dry matter content, leaf tissue density and leaf thickness. Leaf reflectance spectroscopy and functional traits have been proved to be effective in evaluating the changes of urban dust deposition.
Highlights
How to quickly predict and evaluate urban dust deposition is the key to the control of urban atmospheric environment
The forecast model of dust deposition amount established by simple ratio index
Y refers to dust retention, x refers to simple ratio index.) has an average accuracy of 99.98%. (5) With the increase of dust deposition, the specific leaf area and chlorophyll content index decreased gradually
Summary
How to quickly predict and evaluate urban dust deposition is the key to the control of urban atmospheric environment. According to the data of Beijing Environmental Monitoring Center, the primary pollutant in Beijing is inhalable particulate matter. In order to further strengthen the dust control work, the Beijing Municipal Government established an atmospheric coarse particulate matter monitoring network of atmospheric coarse particles covering all streets and towns in the city in 2018 [4, 5]. Dust particulate air pollutants seriously poison the respiratory system of urban residents, and threaten the normal growth of urban vegetation [6]. Studies show that the influence of dust on trees is mainly through dust deposition on leaves and stomatal blockage [4,5,6]. It is urgent to monitor and control air pollution
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