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Background: Response and adaptation strategies of plants to the environment have always been the core issues in ecological research. So far, relatively little study exists on its functional traits responses to warming, especially in an urban environment. This information is the key to help understand plant responses and trade-off strategy to urban warming. Results: We chose the common greening trees of mature age in Beijing (Fraxinus pennsylvanica, Koelreuteria paniculata, and Sophora japonica) as the research subjects, and used infrared heaters to simulate warming for three gradients of natural temperature (CK), moderate warming (T1) and severe warming (T2). Results showed that:(1) Leaf dry matter content (LDMC), chlorophyll content (CHL), leaf tissue density (LTD), and stomatal density (SD) all increased with temperature warming. Specific leaf area (SLA), stomatal size (SS), and stomatal aperture (SA) decreased with simulated warming. (2) SLA was extremely significantly negatively correlated with CHL, LDMC, LTD and SD (P < 0.01), and was extremely significantly positively correlated with SS (P < 0.01). SA was extremely negatively correlated with SD (P < 0.01), and was extremely significantly positively correlated with SS (P < 0.01). There was a significant positive correlation between LDMC and LTD (P < 0.01). This showed that urban greening trees adapted to the environment by coordinating adjustment among leaf functional traits. (3) Under the T1 treatment, the R2 and slope among the leaf traits were higher than CK, and the significance was also enhanced. The correlation between leaf traits was strengthened in this warming environment. Conversely, it will weaken the correlation between leaf traits under the T2 treatment. Conclusion: Our study demonstrated that there was a strong trade-off between leaf functional traits in the urban warming environment. Plants in the warming environment have adopted relatively consistent trade-offs and adaptation strategies. Moderate warming was more conducive to strengthening their trade-off potential. It is further verified that the global leaf economics spectrum also exists in urban ecosystems, which is generally tend to a quick-investment return type with the characteristics of thick leaves, strong photosynthetic capacity, low transpiration efficiency and long life in urban environments. [ABSTRACT FROM AUTHOR]

Urban plants can improve several environmental pollution problems in cities, especially dust prevention, noise reduction, purification of the atmosphere, etc. To explore the influence of dust deposition on the spectral characteristics of the leaf, a foliar dust deposition prediction model based on high-spectrum data was established. Taking Euonymus japonicus L., the common greening tree species in Beijing, as the research object, high (T1), medium (T2), and low (T3) dust pollution gradients were set and hyperspectral data were collected. Results showed that: (1) in the dust-contaminated environment with different concentrations, the trend of the reflectance curve of the leaves of Euonymus japonicus L. was generally consistent. The spectral reflectance of the leaf surface was positively correlated with the amount of leaf dust. (2) There were five obvious reflection peaks and five main absorption valleys with the same positions and ranges in the 350–2500 nm range. (3) The spectral reflectance of leaf flour dust particles of Euonymus japonicus L. was significantly different before and after dusting, and its size was generally clean leaves > dust-depositing leaves. The sensitive range of its spectral response was 695–1400 nm. (4) The overall trend of the first derivative spectrum was basically the same. The red edge slope and the blue edge slope appeared as T3 > T2 > T1, the red edge position and the blue edge position appeared as T1 < T2 < T3. The red edge position of the leaf surface after dust deposition had an obvious "blueshift", and the moving distance increases with the increase of dust retention on leaf surface. (5) The leaf water index (y = − 1.18x2 + 0.5424x + 0.9917, R2 = 0.8030, RMSE = 0.187) had the highest accuracy in the regression model of leaf surface dust deposition using spectral parameters. The test showed that the R2 reached 0.9019, which indicated that the model has a good fitting effect. This prediction model can effectively estimate the dust deposition of the leaf surface of Euonymus japonicus L. [ABSTRACT FROM AUTHOR]