The Impact of Microclimate on the Reproductive Phenology of Female Populus tomentosa in a Micro-Scale Urban Green Space in Beijing
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Site
2.2. Research Object
2.3. Selection of Sample Poplars and Setting of Sampling Points
2.4. Setting of the Buffer Area
2.5. Data Collection
2.5.1. Reproductive Phenology Data Collection
2.5.2. Data Collection of Microclimate Factors with ENVI-MET Simulation
3D Model Construction
Microclimate Simulation
Data Extraction and Calculation of Microclimate Parameters
The Accuracy Evaluation of ENVI-MET Microclimate Simulation
2.6. Statistical Analysis
2.6.1. MANOVA (Multivariate Analysis of Variance) of the Reproductive Phenology among Different Sampling Points
2.6.2. Pearson Correlation Analysis between Reproductive Phenology and Microclimate Factors
2.6.3. Multiple Regression Analysis of Reproductive Phenology in Relation to Multiple Microclimate Factors
3. Results
3.1. Simulated Microclimate Conditions in Taoranting Park
3.2. Spatial Variation of the Reproductive Phenology of Female P. tomentosa in Taoranting Park
3.3. Key Microclimate Factors Affecting the Reproductive Phenology of Female P. tomentosa in Taoranting Park
3.3.1. Air Temperature vs. Reproductive Phenology
3.3.2. Heat Transfer Coefficient vs. Reproductive Phenology
3.4. Multiple Regression of Reproductive Phenology in Relation to Microclimate Factors
4. Discussion
4.1. Testing of Hypothesis 1: There Was a Significant Difference between the Reproductive Phenology of P. tomentosa at Different Sampling Points in Taoranting Park (H1)
4.2. Testing of Hypothesis 2: The Spatial Variation of Reproductive Phenology Had a Significant Correlation with at Least One of the Microclimate Factors (H1)
4.2.1. Air Temperature
4.2.2. Heat Transfer Coefficient
4.2.3. Wind Speed
4.3. Some Insights into the Alleviation of Catkin Fiber Pollution from Female Poplars
4.4. Research Prospect—Other Possible Influential Factors Besides the Microclimate?
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Sampling Point | Time/h | O (Observed Value)/°C | P (Simulated Value)/°C |
---|---|---|---|
1 | 9 | 7.900 | 7.359 |
1 | 11 | 10.200 | 9.891 |
1 | 13 | 10.700 | 11.938 |
2 | 9 | 9.100 | 7.521 |
2 | 11 | 11.300 | 10.204 |
2 | 13 | 11.500 | 12.050 |
3 | 9 | 8.900 | 7.088 |
3 | 11 | 10.000 | 9.599 |
3 | 13 | 11.300 | 11.655 |
4 | 9 | 7.300 | 7.162 |
4 | 11 | 11.700 | 10.077 |
4 | 13 | 12.300 | 12.449 |
5 | 9 | 9.200 | 7.454 |
5 | 11 | 11.000 | 10.207 |
5 | 13 | 12.200 | 12.322 |
6 | 9 | 8.400 | 7.243 |
6 | 11 | 10.400 | 9.742 |
6 | 13 | 13.500 | 11.999 |
7 | 9 | 8.700 | 7.467 |
7 | 11 | 10.000 | 10.184 |
7 | 13 | 12.600 | 12.128 |
8 | 9 | 9.100 | 6.955 |
8 | 11 | 9.900 | 9.234 |
8 | 13 | 11.600 | 11.257 |
Sampling Point | Height/m | Grid | Air Temperature (°C) | Heat Transfer Coefficient (m2/s) | Wind Speed (m/s) |
---|---|---|---|---|---|
1 | 7 | (25,25) | 4.94411 | 0.73853 | 0.15746 |
1 | 7 | (25,26) | 4.95718 | 0.68387 | 0.15649 |
1 | 7 | (26,25) | 4.93272 | 0.71091 | 0.09793 |
1 | 7 | (26,26) | 4.95432 | 0.65436 | 0.10095 |
1 | 9 | (25,25) | 4.82478 | 0.921 | 0.15724 |
1 | 9 | (25,26) | 4.83755 | 0.86584 | 0.15776 |
1 | 9 | (26,25) | 4.79132 | 0.88426 | 0.11612 |
1 | 9 | (26,26) | 4.81473 | 0.82837 | 0.12184 |
2 | 7 | (25,25) | 4.96934 | 0.86473 | 0.01732 |
2 | 7 | (25,26) | 4.98826 | 0.83966 | 0.01732 |
2 | 7 | (26,25) | 4.97558 | 0.78301 | 0.01732 |
2 | 7 | (26,26) | 4.98805 | 0.76064 | 0.01732 |
2 | 9 | (25,25) | 4.85373 | 0.96057 | 0.01732 |
2 | 9 | (25,26) | 4.86631 | 0.94578 | 0.01732 |
2 | 9 | (26,25) | 4.85275 | 0.85853 | 0.01732 |
2 | 9 | (26,26) | 4.85959 | 0.8466 | 0.01732 |
3 | 7 | (25,25) | 4.7894 | 0.80526 | 0.0855 |
3 | 7 | (25,26) | 4.90766 | 0.62121 | 0.04893 |
3 | 7 | (26,25) | 4.72684 | 0.84954 | 0.10222 |
3 | 7 | (26,26) | 4.82913 | 0.7035 | 0.05171 |
3 | 9 | (25,25) | 4.75257 | 0.94812 | 0.15999 |
3 | 9 | (25,26) | 4.85382 | 0.75199 | 0.098 |
3 | 9 | (26,25) | 4.69495 | 0.99634 | 0.15359 |
3 | 9 | (26,26) | 4.7795 | 0.84172 | 0.0987 |
4 | 7 | (25,25) | 4.86222 | 0.66136 | 0.19295 |
4 | 7 | (25,26) | 4.88697 | 0.62847 | 0.17919 |
4 | 7 | (26,25) | 4.80913 | 0.67229 | 0.15741 |
4 | 7 | (26,26) | 4.82516 | 0.65482 | 0.14189 |
4 | 9 | (25,25) | 4.76722 | 0.83137 | 0.17774 |
4 | 9 | (25,26) | 4.78572 | 0.80715 | 0.15244 |
4 | 9 | (26,25) | 4.72532 | 0.83442 | 0.15273 |
4 | 9 | (26,26) | 4.73824 | 0.81826 | 0.12612 |
5 | 7 | (25,25) | 4.74393 | 1.16586 | 0.08481 |
5 | 7 | (25,26) | 4.76468 | 1.09453 | 0.09385 |
5 | 7 | (26,25) | 4.75177 | 1.11631 | 0.08857 |
5 | 7 | (26,26) | 4.77499 | 1.05017 | 0.09886 |
5 | 9 | (25,25) | 4.62369 | 1.41415 | 0.02509 |
5 | 9 | (25,26) | 4.63422 | 1.34605 | 0.03397 |
5 | 9 | (26,25) | 4.63427 | 1.36947 | 0.02808 |
5 | 9 | (26,26) | 4.64675 | 1.30627 | 0.03778 |
6 | 7 | (25,25) | 5.07931 | 0.37814 | 0.13877 |
6 | 7 | (25,26) | 5.14171 | 0.31286 | 0.10161 |
6 | 7 | (26,25) | 5.11634 | 0.31783 | 0.09593 |
6 | 7 | (26,26) | 5.1789 | 0.27043 | 0.10044 |
6 | 9 | (25,25) | 4.97752 | 0.62436 | 0.11991 |
6 | 9 | (25,26) | 5.01796 | 0.5542 | 0.13044 |
6 | 9 | (26,25) | 5.00566 | 0.56704 | 0.11992 |
6 | 9 | (26,26) | 5.04808 | 0.49504 | 0.12892 |
7 | 7 | (25,25) | 4.87942 | 0.92416 | 0.03561 |
7 | 7 | (25,26) | 4.90284 | 0.88042 | 0.05461 |
7 | 7 | (26,25) | 4.86616 | 0.87325 | 0.01906 |
7 | 7 | (26,26) | 4.88696 | 0.8336 | 0.02451 |
7 | 9 | (25,25) | 4.74441 | 1.10841 | 0.01732 |
7 | 9 | (25,26) | 4.76104 | 1.08415 | 0.01793 |
7 | 9 | (25,26) | 4.74054 | 1.05178 | 0.01732 |
7 | 9 | (26,26) | 4.75756 | 1.02931 | 0.01732 |
8 | 7 | (25,25) | 4.60172 | 1.38658 | 0.01732 |
8 | 7 | (25,26) | 4.59001 | 1.33089 | 0.01732 |
8 | 7 | (26,25) | 4.60631 | 1.34551 | 0.01732 |
8 | 7 | (26,26) | 4.5964 | 1.2865 | 0.01732 |
8 | 9 | (25,25) | 4.56218 | 1.51627 | 0.01732 |
8 | 9 | (25,26) | 4.55053 | 1.45792 | 0.01732 |
8 | 9 | (26,25) | 4.56434 | 1.48148 | 0.01732 |
8 | 9 | (26,26) | 4.5539 | 1.4205 | 0.01732 |
Sampling Point | Phenophase | Sample Tree 1 | Sample Tree 2 | Mean | SD | ||||
---|---|---|---|---|---|---|---|---|---|
Sample Branch 1 | Sample Branch 2 | Sample Branch 3 | Sample Branch 1 | Sample Branch 2 | Sample Branch 3 | ||||
1 | BBB | 59 | 59 | 60 | 60 | 60 | 61 | 59.8 | 0.753 |
1 | BF | 65 | 66 | 66 | 66 | 66 | 67 | 66.0 | 0.632 |
1 | FF | 67 | 68 | 68 | 68 | 68 | 69 | 68.0 | 0.632 |
1 | FS | 70 | 70 | 70 | 70 | 70 | 71 | 70.2 | 0.408 |
1 | BSD | 94 | 95 | 95 | 95 | 95 | 96 | 95.0 | 0.632 |
1 | ESD | 103 | 104 | 104 | 104 | 105 | 104 | 104.0 | 0.632 |
1 | DSD | 9 | 9 | 9 | 9 | 10 | 8 | 9.0 | 0.632 |
2 | BBB | 59 | 58 | 59 | 59 | 59 | 59 | 58.8 | 0.408 |
2 | BF | 65 | 64 | 65 | 65 | 65 | 65 | 64.8 | 0.408 |
2 | FF | 67 | 66 | 67 | 67 | 67 | 67 | 66.8 | 0.408 |
2 | FS | 70 | 69 | 70 | 71 | 70 | 70 | 70.0 | 0.632 |
2 | BSD | 93 | 92 | 93 | 94 | 93 | 93 | 93.0 | 0.632 |
2 | ESD | 103 | 103 | 103 | 104 | 103 | 103 | 103.2 | 0.408 |
2 | DSD | 10 | 11 | 10 | 10 | 10 | 10 | 10.2 | 0.408 |
3 | BBB | 61 | 62 | 61 | 60 | 61 | 61 | 61.0 | 0.632 |
3 | BF | 68 | 69 | 68 | 67 | 68 | 68 | 68.0 | 0.632 |
3 | FF | 70 | 71 | 70 | 70 | 70 | 70 | 70.2 | 0.408 |
3 | FS | 72 | 73 | 72 | 72 | 72 | 72 | 72.2 | 0.408 |
3 | BSD | 95 | 96 | 95 | 94 | 95 | 95 | 95.0 | 0.632 |
3 | ESD | 103 | 103 | 103 | 102 | 103 | 103 | 102.8 | 0.408 |
3 | DSD | 8 | 7 | 8 | 8 | 8 | 8 | 7.8 | 0.408 |
4 | BBB | 61 | 61 | 60 | 61 | 61 | 62 | 61.0 | 0.632 |
4 | BF | 68 | 68 | 68 | 68 | 68 | 67 | 67.8 | 0.408 |
4 | FF | 69 | 69 | 69 | 69 | 69 | 69 | 69.0 | 0.000 |
4 | FS | 71 | 71 | 70 | 71 | 71 | 71 | 70.8 | 0.408 |
4 | BSD | 95 | 95 | 94 | 95 | 95 | 96 | 95.0 | 0.632 |
4 | ESD | 105 | 105 | 104 | 105 | 105 | 105 | 104.8 | 0.408 |
4 | DSD | 10 | 10 | 10 | 10 | 10 | 9 | 9.8 | 0.408 |
5 | BBB | 62 | 63 | 62 | 62 | 62 | 61 | 62.0 | 0.632 |
5 | BF | 68 | 69 | 68 | 68 | 68 | 67 | 68.0 | 0.632 |
5 | FF | 70 | 70 | 70 | 70 | 70 | 69 | 69.8 | 0.408 |
5 | FS | 72 | 72 | 73 | 72 | 72 | 71 | 72.0 | 0.632 |
5 | BSD | 95 | 96 | 95 | 95 | 95 | 95 | 95.2 | 0.408 |
5 | ESD | 102 | 103 | 102 | 102 | 102 | 101 | 102.0 | 0.632 |
5 | DSD | 7 | 7 | 7 | 7 | 7 | 6 | 6.8 | 0.408 |
6 | BBB | 58 | 58 | 57 | 58 | 59 | 58 | 58.0 | 0.632 |
6 | BF | 65 | 65 | 65 | 65 | 65 | 64 | 64.8 | 0.408 |
6 | FF | 67 | 67 | 67 | 67 | 67 | 66 | 66.8 | 0.408 |
6 | FS | 70 | 70 | 69 | 70 | 70 | 70 | 69.8 | 0.408 |
6 | BSD | 94 | 94 | 93 | 94 | 95 | 93 | 93.8 | 0.753 |
6 | ESD | 106 | 107 | 106 | 106 | 106 | 105 | 106.0 | 0.632 |
6 | DSD | 12 | 13 | 13 | 12 | 11 | 12 | 12.2 | 0.753 |
7 | BBB | 61 | 61 | 61 | 61 | 61 | 60 | 60.8 | 0.408 |
7 | BF | 67 | 66 | 67 | 67 | 66 | 67 | 66.7 | 0.516 |
7 | FF | 69 | 69 | 69 | 69 | 68 | 69 | 68.8 | 0.408 |
7 | FS | 71 | 71 | 71 | 71 | 70 | 71 | 70.8 | 0.408 |
7 | BSD | 94 | 94 | 94 | 94 | 93 | 93 | 93.7 | 0.516 |
7 | ESD | 102 | 103 | 102 | 102 | 102 | 101 | 102.0 | 0.632 |
7 | DSD | 8 | 9 | 8 | 8 | 9 | 8 | 8.3 | 0.516 |
8 | BBB | 63 | 62 | 63 | 64 | 63 | 63 | 63.0 | 0.632 |
8 | BF | 71 | 70 | 71 | 71 | 71 | 71 | 70.8 | 0.408 |
8 | FF | 72 | 72 | 73 | 72 | 72 | 72 | 72.2 | 0.408 |
8 | FS | 73 | 73 | 74 | 73 | 73 | 73 | 73.2 | 0.408 |
8 | BSD | 96 | 96 | 97 | 96 | 96 | 96 | 96.2 | 0.408 |
8 | ESD | 102 | 102 | 103 | 102 | 102 | 102 | 102.2 | 0.408 |
8 | DSD | 6 | 6 | 6 | 6 | 6 | 6 | 6.0 | 0.000 |
References
- Li, X.; Zhou, Y.; Asrar, G.R.; Mao, J.; Li, X.; Li, W. Response of vegetation phenology to urbanization in the conterminous United States. Glob. Chang. Biol. 2016, 23, 2818–2830. [Google Scholar] [CrossRef] [PubMed]
- Oke, T.R. The energetic basis of the urban heat island. Q. J. R. Meteorol. Soc. 1982, 108, 1–24. [Google Scholar] [CrossRef]
- Wohlfahrt, G.; Tomelleri, E.; Hammerle, A. The urban imprint on plant phenology. Nat. Ecol. Evol. 2019, 3, 1668–1674. [Google Scholar] [CrossRef] [PubMed]
- Zhou, D.; Zhao, S.; Zhang, L.; Liu, S. Remotely sensed assessment of urbanization effects on vegetation phenology in China’s 32 major cities. Remote Sens. Environ. 2016, 176, 272–281. [Google Scholar] [CrossRef] [Green Version]
- Neil, K.; Wu, J. Effects of urbanization on plant flowering phenology: A review. Urban Ecosyst. 2006, 9, 243–257. [Google Scholar] [CrossRef]
- World Health Organization. Regional Office for Europe. Phenology and Human Health: Allergic Disorders: Report on a WHO Meeting—Rome, Italy, 16–17 January 2003; Report No. 108750; WHO Regional Office for Europe: Copenhagen, Denmark, 2003. [Google Scholar]
- Arnstein, T. Curse of the Catkins: A Brief History of Cottonwood Poplars in Beijing. 31 March 2019. Available online: https://www.thebeijinger.com/blog/2014/04/17/curse-catkins-brief-history-cottonwood-poplars-beijing (accessed on 31 December 2020).
- Wu, X.; Shi, T.; Chen, H.; Wang, H.; Sun, M.; Zhang, J. The microstructure and mechanical properties of poplar catkin fibers evaluated by atomic force microscope (AFM) and nanoindentation. Forestry 2019, 10, 938. [Google Scholar] [CrossRef] [Green Version]
- Columbus, J.T.; Wu, Z.; Raven, P.H.; Hong, D. Flora of China. TAXON 2008, 57, 682. [Google Scholar] [CrossRef]
- Hu, Y.; Ferguson, D.K.; Bera, S.; Li, C. Seed hairs of poplar trees as natural airborne pollen trap for allergenic pollen grains. Grana 2008, 47, 241–245. [Google Scholar] [CrossRef]
- Wan, X.; Gu, G.; Lei, M.; Zeng, W. Bioaccessibility of metals/metalloids in willow catkins collected in urban parks of Beijing and their health risks to human beings. Sci. Total Environ. 2020, 717, 137240. [Google Scholar] [CrossRef]
- Athar, P. Coronavirus bane for thousands of poplar trees in Kashmir. The local administration issues axing orders on female poplar trees fearing their cottony seeds may worsen COVID-19 symptoms. 2020. Available online: https://www.natureasia.com/en/nindia/article/10.1038/nindia.2020.63(accessed on 15 February 2021). [CrossRef]
- Bashaarat, M. Do ‘Russian Poplar’ Seeds Cause May Illness in Kashmir? The Fears, the Science. 13 May 2019. Available online: https://indianexpress.com/article/explained/do-russian-poplar-seeds-cause-may-illness-in-kashmir-the-fears-the-science-5724268/ (accessed on 31 December 2020).
- Naqvi, S. Poplar Trees Creating Respiratory Problems in Abbottabad. 11 May 2019. Available online: https://www.thenews.com.pk/print/469879-poplar-trees-creating-respiratory-problems-in-abbottabad (accessed on 31 December 2020).
- Du, J. City Will Stop Planting Certain Trees to Curtail Catkins. 16 April 2019. Available online: http://www.chinadaily.com.cn/a/201904/16/WS5cb52f56a3104842260b664e.html (accessed on 31 December 2020).
- Wong, L. Fluff Off: More than 300,000 Willow Trees to be Injected with Birth Control. 18 April 2018. Available online: http://www.timeoutbeijing.com/features/Blogs-Beijing_News/164015/Fluff-off-More-than-300,000-willow-trees-to-be-injected-with-birth-control.html (accessed on 31 December 2020).
- Beijing Daily. The Flying-Catkin Season of Poplars and Willows in Beijing Will Begin This Week. 9 April 2020. Available online: http://bjrb.bjd.com.cn/html/2020-04/09/content_12455449.htm (accessed on 31 December 2020).
- Jochner, S.C.; Sparks, T.H.; Estrella, N.; Menzel, A. The influence of altitude and urbanization on trends and mean dates in phenology (1980–2009). Int. J. Biometeorol. 2011, 56, 387–394. [Google Scholar] [CrossRef]
- Parece, T.E.; Campbell, J.B. Intra-urban microclimate effects on phenology. Urban Sci. 2018, 2, 26. [Google Scholar] [CrossRef] [Green Version]
- Qiu, T.; Song, C.; Li, J. Impacts of urbanization on vegetation phenology over the past three decades in Shanghai, China. Remote Sens. 2017, 9, 970. [Google Scholar] [CrossRef] [Green Version]
- Zipper, S.C.; Schatz, J.; Singh, A.; Kucharik, C.J.; A Townsend, P.; Ii, S.P.L. Urban heat island impacts on plant phenology: Intra-urban variability and response to land cover. Environ. Res. Lett. 2016, 11, 054023. [Google Scholar] [CrossRef]
- Mimet, A.; Pellissier, V.; Quénol, H.; Aguejdad, R.; Dubreuil, V.; Rozé, F. Urbanisation induces early flowering: Evidence from Platanus acerifolia and Prunus cerasus. Int. J. Biometeorol. 2009, 53, 287–298. [Google Scholar] [CrossRef]
- Amani-Beni, M.; Zhang, B.; Xie, G.-D.; Xu, J. Impact of urban park’s tree, grass and waterbody on microclimate in hot summer days: A case study of Olympic Park in Beijing, China. Urban For. Urban Green. 2018, 32, 1–6. [Google Scholar] [CrossRef]
- Yan, H. The Effect of Urban Green Area on Urban Microclimate; Beijing Forestry University: Beijing, China, 2014. [Google Scholar]
- Pellis, A.; Laureysens, I.; Ceulemans, R. Genetic variation of the bud and leaf phenology of seventeen poplar clones in a short rotation coppice culture. Plant Biol. 2004, 6, 38–46. [Google Scholar] [CrossRef] [PubMed]
- Tarayre, M.; Bowman, G.; Schermann-Legionnet, A.; Barat, M.; Atlan, A. Flowering phenology of Ulex europaeus: Ecological consequences of variation within and among populations. Evol. Ecol. 2007, 21, 395–409. [Google Scholar] [CrossRef]
- Coder, K.D. Tree Anatomy: Shoots and Growth Patterns; Warnell School of Forestry & Natural Resources, University of Georgia, Outreach Publication: Athens, GA, USA, 2018; Available online: https://www.warnell.uga.edu/sites/default/files/publications/WSFNR-19-36_Coder.pdf#:~:text=Tree%20Anatomy:%20SHOOTS%20&%20GROWTH%20PATTERNS%20Tree%20shoots,,there%20are%20many%20meanings%20for%20the%20word%20%E2%80%9Cshoot.%E2%80%9D (accessed on 31 December 2020).
- Stewart, I.D.; Oke, T.R. Local climate zones for urban temperature studies. Bull. Am. Meteorol. Soc. 2012, 93, 1879–1900. [Google Scholar] [CrossRef]
- Yokobori, T.; Ohta, S. Effect of land cover on air temperatures involved in the development of an intra-urban heat island. Clim. Res. 2009, 39, 61–73. [Google Scholar] [CrossRef]
- Wan, M.W.; Liu, X.Z. Chinese Phenology Observation Method; Science Press: Beijing, China, 1979. [Google Scholar]
- Cornelius, C.; Petermeier, H.; Estrella, N.; Menzel, A. A comparison of methods to estimate seasonal phenological development from BBCH scale recording. Int. J. Biometeorol. 2011, 55, 867–877. [Google Scholar] [CrossRef] [PubMed]
- Unwin, D.M. Microclimate Measurement for Ecologists; Academic Press: London, UK, 1980. [Google Scholar]
- Bruse, M.; Fleer, H. Simulating surface-plant-air interactions inside urban environments with a three dimensional numerical model. Environ. Model. Softw. 1998, 13, 373–384. [Google Scholar] [CrossRef]
- Liu, D.; Hu, S.; Liu, J. Contrasting the performance capabilities of urban radiation field between three microclimate simulation tools. Build. Environ. 2020, 175, 106789. [Google Scholar] [CrossRef]
- Rui, L.; Buccolieri, R.; Gao, Z.; Gatto, E.; Ding, W. Study of the effect of green quantity and structure on thermal comfort and air quality in an urban-like residential district by ENVI-met modelling. Build. Simul. 2019, 12, 183–194. [Google Scholar] [CrossRef]
- Salata, F.; Golasi, I.; Vollaro, R.D.L.; Vollaro, A.D.L. Urban microclimate and outdoor thermal comfort. A proper procedure to fit ENVI-met simulation outputs to experimental data. Sustain. Cities Soc. 2016, 26, 318–343. [Google Scholar] [CrossRef]
- Sharmin, T.; Steemers, K.; Matzarakis, A. Microclimatic modelling in assessing the impact of urban geometry on urban thermal environment. Sustain. Cities Soc. 2017, 34, 293–308. [Google Scholar] [CrossRef]
- Tseliou, A.; Tsiros, I.X. Modeling urban microclimate to ameliorate thermal sensation conditions in outdoor areas in Athens (Greece). Build. Simul. 2016, 9, 251–267. [Google Scholar] [CrossRef]
- Tsoka, S.; Tsikaloudaki, A.; Theodosiou, T. Analyzing the ENVI-met microclimate model’s performance and assessing cool materials and urban vegetation applications—A review. Sustain. Cities Soc. 2018, 43, 55–76. [Google Scholar] [CrossRef]
- Zhao, Q.; Sailor, D.J.; Wentz, E.A. Impact of tree locations and arrangements on outdoor microclimates and human thermal comfort in an urban residential environment. Urban For. Urban Green. 2018, 32, 81–91. [Google Scholar] [CrossRef] [Green Version]
- Penfield, S. Temperature perception and signal transduction in plants. New Phytol. 2008, 179, 615–628. [Google Scholar] [CrossRef]
- Ng, E.; Chen, L.; Wang, Y.; Yuan, C. A study on the cooling effects of greening in a high-density city: An experience from Hong Kong. Build. Environ. 2012, 47, 256–271. [Google Scholar] [CrossRef]
- Taleb, D.; Abu-Hijleh, B. Urban heat islands: Potential effect of organic and structured urban configurations on temperature variations in Dubai, UAE. Renew. Energy 2013, 50, 747–762. [Google Scholar] [CrossRef]
- Ambrosini, D.; Galli, G.; Mancini, B.; Nardi, I.; Sfarra, S. Evaluating mitigation effects of urban heat islands in a historical small center with the ENVI-Met® climate model. Sustainability 2014, 6, 7013–7029. [Google Scholar] [CrossRef] [Green Version]
- Middel, A.; Häb, K.; Brazel, A.J.; Martin, C.A.; Guhathakurta, S. Impact of urban form and design on mid-afternoon microclimate in Phoenix local climate zones. Landsc. Urban Plan. 2014, 122, 16–28. [Google Scholar] [CrossRef]
- Chen, X.; Zhang, W.; Ren, S.; Lang, W.; Liang, B.; Liu, G. Temporal coherence of phenological and climatic rhythmicity in Beijing. Int. J. Biometeorol. 2017, 61, 1733–1748. [Google Scholar] [CrossRef] [PubMed]
- Mudelsee, M. Statistical Analysis of Climate Extremes; Cambridge University Press (CUP): Cambridge, UK, 2020. [Google Scholar]
- Xu, M.Z.; Ren, G.Y. Change in growing season over China: 1961. J. Appl. Meteorol. Sci. 2004, 15, 306–312. (In Chinese) [Google Scholar]
- Erell, E.; Pearlmutter, D.; Williamson, T. Urban Microclimate; Routledge: London, UK, 2012. [Google Scholar]
- Moreira, T.A.; Colmanetti, A.R.A.; Tibiriçá, C.B. Heat transfer coefficient: A review of measurement techniques. J. Braz. Soc. Mech. Sci. Eng. 2019, 41, 264. [Google Scholar] [CrossRef]
- Duarte, D.H.; Shinzato, P.; Gusson, C.D.S.; Alves, C.A. The impact of vegetation on urban microclimate to counterbalance built density in a subtropical changing climate. Urban Clim. 2015, 14, 224–239. [Google Scholar] [CrossRef]
- Yang, P.; Ren, G.; Liu, W. Spatial and temporal characteristics of Beijing urban heat island intensity. J. Appl. Meteorol. Clim. 2013, 52, 1803–1816. [Google Scholar] [CrossRef]
- Willmott, C.J. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 1982, 63, 1309–1313. [Google Scholar] [CrossRef] [Green Version]
- Stunder, M.; Sethuraman, S. A statistical evaluation and comparison of coastal point source Dispersion Models. Atmos. Environ. 1967 1986, 20, 301–315. [Google Scholar] [CrossRef]
- Yang, X.; Zhao, L.; Bruse, M.; Meng, Q. Evaluation of a microclimate model for predicting the thermal behavior of different ground surfaces. Build. Environ. 2013, 60, 93–104. [Google Scholar] [CrossRef]
- Hoerl, A.E.; Kennard, R.W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 1970, 12, 55–67. [Google Scholar] [CrossRef]
- McDonald, G.C. Ridge regression. Wiley Interdiscip. Rev. Comput. Stat. 2009, 1, 93–100. [Google Scholar] [CrossRef]
- Dahlgren, J.P.; Zeipel, H.V.; Ehrlén, J. Variation in vegetative and flowering phenology in a forest herb caused by environmental heterogeneity. Am. J. Bot. 2007, 94, 1570–1576. [Google Scholar] [CrossRef] [PubMed]
- Gehrmann, F.; Hänninen, H.; Liu, C.; Saarinen, T. Phenological responses to small-scale spatial variation in snowmelt timing reveal compensatory and conservative strategies in subarctic-alpine plants. Plant Ecol. Divers. 2017, 10, 453–468. [Google Scholar] [CrossRef] [Green Version]
- Jackson, M.T. Effects of microclimate on spring flowering phenology. Ecology 1966, 47, 407–415. [Google Scholar] [CrossRef]
- Aono, Y.; Kazui, K. Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century. Int. J. Clim. 2008, 28, 905–914. [Google Scholar] [CrossRef] [Green Version]
- Guo, L.; Dai, J.; Wang, M.; Xu, J.; Luedeling, E. Responses of spring phenology in temperate zone trees to climate warming: A case study of apricot flowering in China. Agric. For. Meteorol. 2015, 201, 1–7. [Google Scholar] [CrossRef] [Green Version]
- Luo, Z.; Sun, O.J.; Ge, Q.; Xu, W.; Zheng, J. Phenological responses of plants to climate change in an urban environment. Ecol. Res. 2006, 22, 507–514. [Google Scholar] [CrossRef]
- Tooke, F.; Battey, N.H. Temperate flowering phenology. J. Exp. Bot. 2010, 61, 2853–2862. [Google Scholar] [CrossRef] [Green Version]
- Beaubien, E.G.; Freeland, H.J. Spring phenology trends in Alberta, Canada: Links to ocean temperature. Int. J. Biometeorol. 2000, 44, 53–59. [Google Scholar] [CrossRef] [PubMed]
- Schwartz, M.D. Phenology: An Integrative Environmental Science, 2nd ed.; Springer: New York, NY, USA, 2013. [Google Scholar]
- Thompson, R.; Clark, R.M. Spatio-temporal modelling and assessment of within-species phenological variability using thermal time methods. Int. J. Biometeorol. 2006, 50, 312–322. [Google Scholar] [CrossRef]
- Memon, R.A.; Leung, D.Y.; Liu, C.-H. Effects of building aspect ratio and wind speed on air temperatures in urban-like street canyons. Build. Environ. 2010, 45, 176–188. [Google Scholar] [CrossRef]
- Memon, R.A.; Leung, D.Y. Impacts of environmental factors on urban heating. J. Environ. Sci. 2010, 22, 1903–1909. [Google Scholar] [CrossRef]
- Hagishima, A.; Tanimoto, J. Field measurements for estimating the convective heat transfer coefficient at building surfaces. Build. Environ. 2003, 38, 873–881. [Google Scholar] [CrossRef]
- Eliasson, I.; Offerle, B.; Grimmond, C.; Lindqvist, S. Wind fields and turbulence statistics in an urban street canyon. Atmos. Environ. 2006, 40, 1–16. [Google Scholar] [CrossRef]
- Giometto, M.; Christen, A.; Egli, P.; Schmid, M.; Tooke, R.; Coops, N.; Parlange, M. Effects of trees on mean wind, turbulence and momentum exchange within and above a real urban environment. Adv. Water Resour. 2017, 106, 154–168. [Google Scholar] [CrossRef]
- Finn, G.; Straszewski, A.; Peterson, V. A general growth stage key for describing trees and woody plants. Ann. Appl. Biol. 2007, 151, 127–131. [Google Scholar] [CrossRef]
- Acero, J.A.; Arrizabalaga, J. Evaluating the performance of ENVI-met model in diurnal cycles for different meteorological conditions. Theor. Appl. Clim. 2018, 131, 455–469. [Google Scholar] [CrossRef]
- Song, B.-G.; Park, K.-H.; Jung, S.-G. Validation of ENVI-met model with in situ measurements considering spatial characteristics of land use types. J. Korean Assoc. Geogr. Inf. Stud. 2014, 17, 156–172. [Google Scholar] [CrossRef]
- Konarska, J.; Holmer, B.; Lindberg, F.; Thorsson, S. Influence of vegetation and building geometry on the spatial variations of air temperature and cooling rates in a high-latitude city. Int. J. Clim. 2015, 36, 2379–2395. [Google Scholar] [CrossRef] [Green Version]
Parameters | Wind Speed Measured at 10 m Height (m/s) | Wind Direction (deg) | Minimum Temperature of the Atmosphere (°C) | Maximum Temperature of the Atmosphere (°C) | Minimum Relative Humidity at 2 m (%) | Maximum Relative Humidity at 2 m (%) |
---|---|---|---|---|---|---|
Set value | 1.89 | 45 | −2 | 12 | 17 | 47 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Xing, X.; Dong, L.; Konijnendijk, C.; Hao, P.; Fan, S.; Niu, W. The Impact of Microclimate on the Reproductive Phenology of Female Populus tomentosa in a Micro-Scale Urban Green Space in Beijing. Sustainability 2021, 13, 3518. https://0-doi-org.brum.beds.ac.uk/10.3390/su13063518
Xing X, Dong L, Konijnendijk C, Hao P, Fan S, Niu W. The Impact of Microclimate on the Reproductive Phenology of Female Populus tomentosa in a Micro-Scale Urban Green Space in Beijing. Sustainability. 2021; 13(6):3518. https://0-doi-org.brum.beds.ac.uk/10.3390/su13063518
Chicago/Turabian StyleXing, Xiaoyi, Li Dong, Cecil Konijnendijk, Peiyao Hao, Shuxin Fan, and Wei Niu. 2021. "The Impact of Microclimate on the Reproductive Phenology of Female Populus tomentosa in a Micro-Scale Urban Green Space in Beijing" Sustainability 13, no. 6: 3518. https://0-doi-org.brum.beds.ac.uk/10.3390/su13063518