{"Affiliation":[{"label":"Affiliation","value":"Forestry, Faculty of","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."},{"label":"Affiliation","value":"Non UBC","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."},{"label":"Affiliation","value":"Forest Resources Management, Department of","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."}],"AggregatedSourceRepository":[{"label":"Aggregated Source Repository","value":"DSpace","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","classmap":"ore:Aggregation","property":"edm:dataProvider"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","explain":"A Europeana Data Model Property; The name or identifier of the organization who contributes data indirectly to an aggregation service (e.g. Europeana)"}],"Citation":[{"label":"Citation","value":"Sustainability 13 (6): 3518 (2021)","attrs":{"lang":"en","ns":"https:\/\/open.library.ubc.ca\/terms#identifierCitation","classmap":"oc:PublicationDescription","property":"oc:identifierCitation"},"iri":"https:\/\/open.library.ubc.ca\/terms#identifierCitation","explain":"UBC Open Collections Metadata Components; Local Field; Indicates a bibliographic reference for the resource if it has been previously published."}],"Creator":[{"label":"Creator","value":"Xing, Xiaoyi","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"Dong, Li","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"Konijnendijk, C. C. (Cecil C.)","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"Hao, Peiyao","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"Fan, Shuxin","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"Niu, Wei","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."}],"DateAvailable":[{"label":"Date Available","value":"2021-04-09T22:01:00Z","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/issued","classmap":"edm:WebResource","property":"dcterms:issued"},"iri":"http:\/\/purl.org\/dc\/terms\/issued","explain":"A Dublin Core Terms Property; Date of formal issuance (e.g., publication) of the resource."}],"DateIssued":[{"label":"Date Issued","value":"2021-03-22","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/issued","classmap":"oc:SourceResource","property":"dcterms:issued"},"iri":"http:\/\/purl.org\/dc\/terms\/issued","explain":"A Dublin Core Terms Property; Date of formal issuance (e.g., publication) of the resource."}],"Description":[{"label":"Description","value":"The spatial variation of poplars\u2019 reproductive phenology in Beijing\u2019s urban area has aggravated the threat of poplar fluff (cotton-like flying seeds) to public health. This research explored\r\nthe impact of microclimate conditions on the reproductive phenology of female Populus tomentosa\r\nin Taoranting Park, a micro-scale green space in Beijing (range <1 km). The observed phenophases\r\ncovered flowering, fruiting, and seed dispersal, and ENVI-MET was applied to simulate the effect\r\nof the microclimate on SGS (start day of the growing season). The results showed that a significant spatial variation in poplar reproductive phenology existed at the research site. The variation\r\nwas significantly affected by the microclimate factors DMT (daily mean temperature) and DMH\r\n(daily mean heat transfer coefficient), with air temperature playing a primary role. Specifically,\r\nthe phenology of flowering and fruiting phenophases (BBB, BF, FF, FS) was negatively correlated\r\nwith DMT (\u22120.983 \u2264 r \u2264 \u22120.908, p <0.01) and positively correlated with DMH (0.769 \u2264 r \u2264 0.864,\r\np < 0.05). In contrast, DSD (duration of seed dispersal) showed a positive correlation with DMT\r\n(r = 0.946, p < 0.01) and a negative correlation with DMH (r = \u22120.922, p < 0.01). Based on the findings,\r\nthe increase in air convection with lower air temperature and decrease in microclimate variation in\r\ngreen space can be an effective way to shorten the seed-flying duration to tackle poplar fluff pollution\r\nin Beijing\u2019s early spring.","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/description","classmap":"dpla:SourceResource","property":"dcterms:description"},"iri":"http:\/\/purl.org\/dc\/terms\/description","explain":"A Dublin Core Terms Property; An account of the resource.; Description may include but is not limited to: an abstract, a table of contents, a graphical representation, or a free-text account of the resource."}],"DigitalResourceOriginalRecord":[{"label":"Digital Resource Original Record","value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/77736?expand=metadata","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","classmap":"ore:Aggregation","property":"edm:aggregatedCHO"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","explain":"A Europeana Data Model Property; The identifier of the source object, e.g. the Mona Lisa itself. This could be a full linked open date URI or an internal identifier"}],"FullText":[{"label":"Full Text","value":"sustainabilityArticleThe Impact of Microclimate on the Reproductive Phenology ofFemale Populus tomentosa in a Micro-Scale Urban Green Spacein BeijingXiaoyi Xing 1,2,3 , Li Dong 1,2,3,*, Cecil Konijnendijk 4 , Peiyao Hao 1,2,3, Shuxin Fan 1,2,3 and Wei Niu 1\u0001\u0002\u0003\u0001\u0004\u0005\u0006\u0007\b\u0001\u0001\u0002\u0003\u0004\u0005\u0006\u0007Citation: Xing, X.; Dong, L.;Konijnendijk, C.; Hao, P.; Fan, S.; Niu,W. The Impact of Microclimate on theReproductive Phenology of FemalePopulus tomentosa in a Micro-ScaleUrban Green Space in Beijing.Sustainability 2021, 13, 3518.https:\/\/doi.org\/10.3390\/su13063518Academic Editor: \u00c5sa GrenReceived: 31 December 2020Accepted: 23 February 2021Published: 22 March 2021Publisher\u2019s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: \u00a9 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https:\/\/creativecommons.org\/licenses\/by\/4.0\/).1 School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China;xiaoyixing1993@gmail.com (X.X.); peiyaohao@gmail.com (P.H.); fanshuxin_09@bjfu.edu.cn (S.F.);niuwei_bjfu_2019@163.com (W.N.)2 Laboratory of Beijing Urban and Rural Ecological Environment, Beijing Forestry University,Beijing 100083, China3 National Engineering Research Center for Floriculture, Beijing Forestry University, Beijing 100083, China4 Department of Forest Resources Management, The University of British Columbia,Vancouver, BC V6T 1Z4, Canada; cecil.konijnendijk@ubc.ca* Correspondence: dongli@bjfu.edu.cnAbstract: The spatial variation of poplars\u2019 reproductive phenology in Beijing\u2019s urban area has ag-gravated the threat of poplar fluff (cotton-like flying seeds) to public health. This research exploredthe impact of microclimate conditions on the reproductive phenology of female Populus tomentosain Taoranting Park, a micro-scale green space in Beijing (range <1 km). The observed phenophasescovered flowering, fruiting, and seed dispersal, and ENVI-MET was applied to simulate the effectof the microclimate on SGS (start day of the growing season). The results showed that a signifi-cant spatial variation in poplar reproductive phenology existed at the research site. The variationwas significantly affected by the microclimate factors DMT (daily mean temperature) and DMH(daily mean heat transfer coefficient), with air temperature playing a primary role. Specifically,the phenology of flowering and fruiting phenophases (BBB, BF, FF, FS) was negatively correlatedwith DMT (\u22120.983 \u2264 r \u2264 \u22120.908, p <0.01) and positively correlated with DMH (0.769 \u2264 r \u2264 0.864,p < 0.05). In contrast, DSD (duration of seed dispersal) showed a positive correlation with DMT(r = 0.946, p < 0.01) and a negative correlation with DMH (r = \u22120.922, p < 0.01). Based on the findings,the increase in air convection with lower air temperature and decrease in microclimate variation ingreen space can be an effective way to shorten the seed-flying duration to tackle poplar fluff pollutionin Beijing\u2019s early spring.Keywords: ENVI-MET simulation; microclimate; micro-scale space; Populus tomentosa; reproductivephenology; spatial variation1. IntroductionThe rapid changes in urban environments driven by the massive landscape reshapingduring urbanization have dramatically shifted the growth rhythm of city plants [1\u20134].The variation of the phenological temporal pattern can affect public health by influencingthe occurrence period of phenology-triggered diseases [5,6].A long-term phenology-correlated public health problem in Beijing is the seasonalrespiratory diseases caused by the cotton-like flying seeds from Populus tomentosa (Chinesewhite poplar) and other Salicaceae species in early spring [7,8]. P. tomentosa is one of themost common and widespread deciduous woody species native to Beijing. It is dioeciousand wind-pollinated, blooming in early spring before leafing [9]. In the 1960s, 0.2 millioncloned female poplars were planted in Beijing city in a large-scale city greening action,in consideration of their rapid propagation and fast growth at a young age. Since then,the catkin fibers have become a major trigger of respiratory ailments, skin anaphylaxis,Sustainability 2021, 13, 3518. https:\/\/doi.org\/10.3390\/su13063518 https:\/\/www.mdpi.com\/journal\/sustainabilitySustainability 2021, 13, 3518 2 of 20inflammation, and other diseases in Beijing\u2019s early spring, posing a great risk to publichealth [10,11]. Beyond China, some other countries also face similar health problems causedby poplar fluff. For example, in Kashmir, India, the flying seeds of introduced EasternCottonwood (Populus deltoides) have given significant rise to the respiratory disease cases oflocal people in recent years [12,13]. In Abbottabad, Pakistan, as one of the main triggers ofallergic asthma in spring, poplar fluff has caused severe psychological stress [14]. In Beijing,several strategies have been put forward by the Beijing Gardening and Greening Bureau tocontrol poplar seed pollution, such as breeding sterile triploid female poplars, replacingfemale plants with male ones, and inhibiting catkin formation [15,16]. However, the heavyworkload, high economic cost, and limited budget for treating such a huge number oftargeted poplars made the implementation very difficult. Furthermore, the interindividualdifference in the seed dispersal time between different regions or within green spacesextended the overall flying-seed period in Beijing\u2019s urban area [17], aggravating the threatof catkin fibers to public health. A profound understanding of the temporal pattern ofpoplars\u2019 reproductive phenology and the impact mechanism behind the spatial variation isthe basic step to tackle this problem.In recent years, a prominent intra-urban spatial variation of plant spring phenologyhas been found in many metropolises at both an urban scale (<50 km) [18\u201321] and micro-scale (<1 km) [19]. With regard to the impact mechanism associated with the intra-urbanphenological variation, most of the research was conducted at an urban scale, revealingthat the urban-scale variation in spring phenology was mainly driven by environmentalfactors, especially the near-surface air temperature that indicates urban heat [19,21,22].However, the key influential factors of the phenological variation at the micro-scale are stilllargely unknown, even though a spatially uneven microclimate has been found in urbangreen spaces [23,24]. For urban-scale research, a significant phenological variation withina green space can affect the overall spatial pattern of studied phenology [22]. Therefore,more micro-scale studies need to be done.In this research, we focused on the spatial variation of the reproductive phenologyof female P. tomentosa in Taoranting Park, a micro-scale green space in Beijing, aiming toexplore the key influential factors of this phenological variation. We attempted to answertwo questions: (1) Is there a significant spatial variation in the reproductive phenologyof female P. tomentosa in Taoranting Park? (2) What are the key influential factors for thisvariation? Assuming the impact mechanism at the micro-scale is similar to what has beenwidely revealed for the urban scale, we proposed two hypotheses to be tested.Hypothesis 1 (H1): There was no significant difference between the reproductive phenology of P.tomentosa in different sampling points of Taoranting Park (p > 0.05). H1: There was a significantdifference between the reproductive phenology of P. tomentosa in different sampling points ofTaoranting Park (p < 0.05).Hypothesis 2 (H2): The spatial variation in reproductive phenology had no significant correlationwith any microclimate factor (p > 0.05). H1: The spatial variation in reproductive phenology had asignificant correlation with at least one of the microclimate factors (p < 0.05).Based on the revealed impact, this study attempted to gain some insight into moreeffective control and management of poplars\u2019 catkin fiber pollution in Beijing\u2019s early spring.2. Materials and Methods2.1. Research SiteThe research was conducted in Taoranting Park, Xicheng District, Beijing, China.Beijing (39\u25e654\u2032 N 116\u25e624\u2032 E), the capital city of China, is located at the northern tip ofthe North China Plain. Beijing has a monsoon-influenced humid continental climate,characterized by hot, humid summers and cold, dry winters. Taoranting Park (39\u25e652\u203221\u201d N116\u25e622\u203232\u201d E) is situated in the central downtown area of the dense capital city. As one of theSustainability 2021, 13, 3518 3 of 20largest urban public gardens in Beijing, with a 65-year development history, Taoranting Parkis famous for the flourishing vegetation with rich diversity and healthy growth conditions.The dense evergreen forest dominated by Platycladus orientalis (L.) and Juniperus chinensis L.together with deciduous groves has formed various green spaces in the garden, and theuneven landscape structure of the underlying surface has resulted in a highly diversemicroclimate environment.2.2. Research ObjectFemale Populus tomentosa (Chinese white poplar) trees growing in Taoranting Parkwere the research objects for phenological observation. According to the garden con-struction record, the adult female white poplars in Taoranting Park were planted in 1952.The seedlings were a batch of clones that were propagated with the cutting of root suckersfrom a stock plant. Therefore, the influence of genetic variation on the interindividualphenological variation [25,26] could be excluded in this research.2.3. Selection of Sample Poplars and Setting of Sampling PointsThere were eight female poplar populations distributed in the green space, with eachpopulation composed of 3\u20135 individuals. From each population, we selected two adjacentadult individuals with good growth conditions (that performed well in yearly growthand reproduction, with no sign of diseases or being subject to serious environmentalstress) as the sample poplars of this population; their canopy height ranged from 5 to11 m. The central growing point of the two sampling trees was set as the sampling point;the distribution of eight sampling points is shown in Figure 1.Figure 1. The distribution of sampling points and setting of the buffer area in Taoranting Park.The background image is the Worldview-3 satellite image of Taoranting Park taken in February 2019.From each sampled individual, three branchlets at the height of 7\u20139 m above groundwere selected for phenological observation, with 20\u201330 catkins growing on each branchlet.A branchlet refers to a 2-year-old branch formed in the previous year [27]. The phenologydata of each branchlet were obtained from the earliest catkin, and the data for each samplingpoint were the values of six branchlets (three branchlets\/individual \u00d7 two individuals).Sustainability 2021, 13, 3518 4 of 202.4. Setting of the Buffer AreaA buffer area was set around each sampling point for microclimate simulation.Each buffer area was a 100 m \u00d7 100 m square centered on the sampling point, as shownin Figure 1. The size of the buffer area was determined with reference to the findings ofprevious research, i.e., that the microclimate of a point in urban green land can interact withthe thermal environment of its surrounding area within a range of 100\u2013200 m [24,28,29].Considering that the distance between any two sampling points was less than 100 min this research, the side length of the square buffer area was set to 100 m to preventconsiderable overlap.2.5. Data Collection2.5.1. Reproductive Phenology Data CollectionThe reproductive phenology of female P. tomentosa was observed from February20 until April 18 in 2019, covering the entire reproduction process from the beginningof bud break to the end of seed dispersal. The observation was conducted every dayat 13:00\u201314:00, when the air temperature reaches its daily maximum and often drives aphenological change [30].The BBCH (Biologische Bundesanstalt, Bundessortenamt and Chemical Industry)scale was used to identify the phenological development stages, i.e., phenophases [31],and the phenology data of each phenophase were recorded in Julian Day (the day of theyear). The observed phenophases covered flowering, fruiting, and seed dispersal, includingthe Beginning of Bud Break (BBB, 07)\u2014bud scales spread open with the catkin top visible,Beginning of Flowering (BF, 60)\u2014the first few flowers bloom, Full Flowering (FF, 65)\u2014morethan 95% flowers bloom, Fruit Set Visible (FS, 69)\u2014green fruits appear behind flowers,Beginning of Seed Dispersal (BSD, 89a)\u2014pericarps crack and the first few seeds coveredwith white fibers detach, End of Seed Dispersal (ESD, 89b)\u2014all the seeds detach frompeduncles, and Duration of Seed Dispersal (DSD, N\/A)\u2014The period between BSD andESD. The number in the parentheses is the BBCH code for each phenophase (Finn et al.,2007). The detailed images of the reproductive phenophases are shown in Figure 2.Figure 2. Detailed images of the reproductive phenophases of female P. tomentosa. (a) Beginning of bud break (BBB),(b) Beginning of flowering (BF), (c) Full flowering (FF), (d) Fruit set visible (FS), (e) End of flowering, (f) Beginning of seeddispersal (BSD), (g) Peak of seed dispersal, (h) End of seed dispersal (ESD).2.5.2. Data Collection of Microclimate Factors with ENVI-MET SimulationWe needed to evaluate the microclimate environment at the height of the sampledbranches (7\u20139 m). Field data measurements are generally accepted and widely applied inthe data collection of the microclimate condition, including using fixed equipment [32].However, this method was not feasible in this research, subject to the management regu-lation of the Park Administrative Office, that no equipment was allowed to be attachedto tree branches to prevent potential damage caused to the plants. Unable to measure themicroclimate with field data, we applied ENVI-MET software (ENVI-MET 4.0, sourcedfrom ENVI_met GmbH, Essen, Germany) for microclimate simulation in the micro-scalegreen space.ENVI-MET is holistic, three-dimensional and non-hydrostatic modeling software, de-veloped by the research team of Professor Michael Bruse at Johannes Gutenberg UniversityMainz [33]. Based on computational fluid dynamics, ENVI-MET is often used to simulateSustainability 2021, 13, 3518 5 of 20urban microclimate environments and evaluate the effects of small-scale variations inurban design (e.g., atmosphere, vegetation, architecture, and materials) on the microcli-mate [34\u201340]. ENVI-MET has some functions in favor of phenological research. Firstly,the 3D modeling can simulate meteorological conditions at a specific height, a favorablepoint for the phenological research of tree species, whose reproductive phenology canbe directly affected by the microclimate condition at canopy height via bud temperatureperception [41]. Secondly, ENVI-MET can simulate multiple microclimate parameters, e.g.,air temperature, convection, and wind, which can help explore the key microclimate factorsthat affect plant phenology. These characteristics provide evidence for its applicability inphenological research.The microclimate simulation in ENVI-MET mainly includes the following severalsteps: (1) Create a Workspace in Manage Projects and Workspaces, (2) Construct a 3D modelin SPACES, (3) Run the Simulation in ENVIGuide, (4) Extract data in LEONARDO.3D Model ConstructionThe satellite images of the eight 100 m \u00d7 100 m buffer areas were extracted from theWorldview\u22123 image of Taoranting Park in February 2019 as the background image formodel construction. A network of 50 grids (x)\u00d7 50 grids (y)\u00d7 30 grids (z) with a resolutionratio of 2 m\/grid was constructed for each buffer area in ENVI-MET.The basic model elements include architecture, vegetation, DEM (topography), soil,and surface (including water). The height of landscape elements (e.g., evergreen trees andarchitecture) was measured with Nikon Forestry Pro, i.e., laser rangefinder equipment.The height of the architecture and single trees was measured one by one, while the heightof evergreen forest was measured using 3\u20135 sample trees at the forest edge surroundingthe sampling point. The topography data were extracted from Google Earth Pro. The vege-tation and architecture layer of the constructed models is shown in Figure 3, which canreflect the various landscape structures of the underlying surface of the eight buffer areas.Figure 3. The vegetation and architecture layer of the ENVI-MET model for the eight buffer areas.Microclimate SimulationSimulation time. The number of days for microclimate simulation varied betweendifferent studies, ranging from one day [42,43] to three days [44] or more [33,45]. Generally,the simulation of the microclimate for each hour would take 0.5\u20131.5 h in ENVI-MET (whenrunning on equipment with a Core i7 processor and 8 Gb RAM), which varied dependingon the complexity of the models and the running speed of the equipment. In this research,the ENVI-MET simulation for one-day microclimate of the eight models (eight buffer areas)would take more than 10 days with tests and final running time taken into account.Sustainability 2021, 13, 3518 6 of 20Spring phenology in temperate zones is affected by the microclimate of the develop-ment period prior to the phenophase occurrence [46]; therefore, the microclimate duringthis period was expected to be simulated in this research. Considering the high timeconsumption of ENVI-MET simulation for the eight buffer areas, we chose to simulate themicroclimate condition on 25 February 2019, the start of the growing season (SGS) for P.tomentosa in Beijing. SGS was chosen for simulation considering that the above-thresholdair temperature at the onset of the growing season could be an effective stimulus to ini-tiating spring phenology, such as sprouting and flowering [46]. SGS is defined as thebeginning of the first six consecutive days with local daily mean air temperature > 5 \u25e6C,the general threshold temperature for tree growth in temperate zones [47,48]. The date ofSGS was calculated based on the dynamics of daily mean temperature in Beijing\u2019s earlyspring (shown in Appendix A, Figure A1). The simulation extended a period of 24 h from0:00 to 24:00. Each simulation had been repeated twice before to ensure a steady runningcondition in the final round.Initial meteorological condition preset. We preset the initial meteorological conditionincluding wind, temperature, and humidity. We used min\/max temperature bounds forforcing in ENVI-MET. The data for the meteorology preset came from the Hourly ObservationDataset of Surface Meteorological Stations in Beijing Station (54,511, 39\u25e648\u2032 N,116\u25e628\u2032 E,31.5 m elevation above sea level), retrieved from the Chinese Meteorological Data Network(http:\/\/data.cma.cn\/) (accessed on 15 March 2021). The detailed setting of the backgroundmeteorological condition is shown in Table 1.Table 1. The setting of background parameters for microclimate simulation in ENVI-MET.ParametersWind SpeedMeasured at10 m Height(m\/s)WindDirection (deg)MinimumTemperature ofthe Atmosphere(\u25e6C)MaximumTemperature ofthe Atmosphere(\u25e6C)MinimumRelativeHumidity at2 m (%)MaximumRelativeHumidity at2 m (%)Set value 1.89 45 \u22122 12 17 47Simulation. The microclimate simulation was run in ENVIGuide. The output datawere stored in SIMX files.Data Extraction and Calculation of Microclimate ParametersData extraction. The output of the ENVI-MET simulation provided hourly data ofmultiple microclimate parameters. In this research, the air temperature (\u25e6C), heat transfercoefficient (m2\/s), and wind speed (m\/s) were selected as the microclimate factors tobe analyzed. The heat transfer coefficient\/heat exchange coefficient is a proportionalityconstant between the heat flux and the thermodynamic driving force for the flow ofheat [49]. In thermodynamics, the heat transfer coefficient usually indicates the overall heattransfer rate, reflecting the efficiency and activeness of air convection, and is considerablyaffected by the pattern of air-flow and the geometry of solid space [49,50]. In this study,besides the two basic factors, air temperature and wind speed, the heat transfer coefficientwas also used to explore the impact of convection efficiency on reproductive phenology.Calculation of microclimate parameters. Considering that the reproductive phenologyof sampling branchlets could be directly affected by the surrounding microclimate condi-tion, the microclimate data of each sampling point was extracted from the central eight gridsin the 4 m (x-axis) \u00d7 4 m (y-axis) \u00d7 2 m (h-axis) model space. The 4 m \u00d7 4 m horizontalarea was constituted by four grids (25,25),(25,26),(26,25),(26,26) at each height, the verti-cal range of 2 m covered the height of 7 m and 9 m (the height of sampled branchlets).The values of the daily mean air temperature (DMT), daily mean heat transfer coefficient(DMH), and daily mean wind speed (DMW) for each grid were calculated with the hourlySustainability 2021, 13, 3518 7 of 20simulated data. Taking air temperature as an example, the DMT for each sampling pointwas calculated with Formula (1).DMTn =\u03a3x=25,26\u03a3y=25,26\u03a3h=7,9(DMTn)xyh4 \u2217 2 , n = 1, 2, 3 . . . . . . 8 (1)where DMTn refers to the DMT of sampling point n; x,y,h refer to the respective x-ordinate,y-ordinate, and h-ordinate of a grid in the model; (DMTn)xyh refers to the DMT on the grid(x,y,h) of sampling point n.The Accuracy Evaluation of ENVI-MET Microclimate SimulationThe measured and simulated air temperature 1.5 m above ground was used for theaccuracy evaluation. The air temperature of 1.5 m above ground (Ta) is one of the standardindexes for the performance evaluation of ENVI-MET microclimate simulation [39,51,52].The field data of air temperature at eight sampling points were simultaneously collected at9 a.m., 11 a.m., and 13 p.m. on February 25, with TES\u22121314, a high-accuracy handhelddigital hygrothermograph.A suite of quantitative indices for model evaluation recommended by Willmott(1982) [53], was used to evaluate the model performance, including the root mean squareerror (RMSE), systematic errors (RMSEs), unsystematic errors (RMSEu), and the indexof agreement (d). With a purpose to obtain an accurate simulation outcome, the magni-tude of RMSEs should approach 0, the value of RMSEu should approach RMSE, and thevalue of d should approach 1. RMSE, RMSEs, RMSEu, and d were calculated withFormulas (2)\u2013(8) [54,55].RMSE =\u221aRMSES2 + RMSEu2 (2)RMSES =\u221a\u221a\u221a\u221aN\u22121 N\u2211i=1(P\u02c6i \u2212Oi)2 (3)RMSEu =\u221a\u221a\u221a\u221aN\u22121 N\u2211i=1(Pi \u2212 P\u02c6i)2 (4)P\u02c6i = f (Oi) = E(Oi|Pi) = a + bOi (5)d = 1\u2212[\u03a3Ni=1(Pi \u2212Oi)2\u03a3Ni=1(\u2223\u2223P\u2032i \u2223\u2223+ \u2223\u2223O\u2032i\u2223\u2223)2], 0 \u2264 d \u2264 1 (6)P\u2032i = Pi \u2212O (7)O\u2032i = Oi \u2212O (8)where Oi refers to the observed value, Pi refers to the simulated value, P\u02c6i refers to the fittedvalue from the linear regression equation between the observed value and simulated value,and Oi refers to the average of observed values.2.6. Statistical Analysis2.6.1. MANOVA (Multivariate Analysis of Variance) of the Reproductive Phenologyamong Different Sampling PointsMultivariate analysis of variance (MANOVA) was applied to explore whether therewas a significant variation in the reproductive phenology of female P. tomentosa amongthe eight sampling points within the green space. The dependent variables for MANOVAincluded the phenology of various reproductive phenophases\u2014BBB (beginning of budbreak), BF (beginning of flowering), FF (full flowering), FS (fruit set visible), BSD (beginningof seed dispersal), ESD (end of seed dispersal), DSD (duration of seed dispersal).Sustainability 2021, 13, 3518 8 of 202.6.2. Pearson Correlation Analysis between Reproductive Phenology andMicroclimate FactorsPearson correlation between the microclimate parameters and reproductive phenologywas analyzed to explore the key microclimate factors that had a significant influence on thespatial variation of studied phenology and to reveal the quantitative impact. The indepen-dent variables for Pearson correlation analysis included DMT (daily mean temperature),DMH (daily mean heat transfer coefficient) and DMW (Daily mean wind speed); the ana-lyzed dependent variables included the phenology of the reproductive phenophases\u2014BBB,BF, FF, FS, BSD, ESD, DSD.2.6.3. Multiple Regression Analysis of Reproductive Phenology in Relation to MultipleMicroclimate FactorsMultiple regression analysis was performed to explore the overall correlation patternbetween the phenology of each reproductive phenophase and multiple microclimate factors(DMT, DMH, DMW). According to the primary multiple regression analysis, there was ahigh-degree multicollinearity between DMT and DMH with VIF (variance inflation) >10.In response to the problem of multicollinearity, we applied Ridge Regression in the multipleregression analysis [56,57].3. Results3.1. Simulated Microclimate Conditions in Taoranting ParkWith respect to the accuracy evaluation of the air temperature simulation in ENVI-MET, RMSE = 1.057 \u25e6C, RMSEs = 0.684 \u25e6C, RMSEu = 0.806 \u25e6C, d = 0.962. With RMSEuapproaching RMSE and d approaching 1, the model performance can be evaluated as \u2018good\u2019,which means the ENVI-MET simulation could reflect the actual thermal environment inthis research. The detailed data of the accuracy evaluation are shown in Appendix A,Table A1 and Figure A2.Among the eight sampling points, DMT (daily mean temperature) ranged from 4.578to 5.071 \u25e6C, SD = 0.147 \u25e6C; DMH (daily mean heat transfer coefficient) ranged from 0.440to 1.403 m2\/s, SD = 0.300 m2\/s; DMW (daily mean wind speed) ranged from 0.017 to0.160 m\/s, SD = 0.079 m\/s. A significant correlation was found between DMT and DMH(r = \u22120.920, p <0.01), which means a higher air temperature was associated with a lowerconvection rate. The simulated data of the microclimate factors are shown in Appendix A,Table A2.3.2. Spatial Variation of the Reproductive Phenology of Female P. tomentosa in Taoranting ParkThe multivariate tests (MANOVA) showed that there was a statistically significant dif-ference in the phenology of analyzed reproductive phenophases among the eight samplingpoints (p < 0.01). As seen in Figure 4, the phenology of BBB showed the most significantspatial difference, and the between-point phenological variation in flowering phenophases(BF, FF) was more significant than that in the fruiting and seed dispersal phenophases(FS, BSD).In the micro-scale green space, the entire reproduction process of female P. tomentosaoccurred from Julian day 58 (27 February)\u2013Julian day 106 (16 April), from the earliestflower bud breaking to the latest time of seed dispersal. The beginning of bud break (BBB)ranged from Julian day 58 to day 63, the beginning of flowering (BF) ranged from day 65 today 71, full flowering (FF) ranged from day 67 to day 72, fruit set visible (FS) ranged fromday 70 to day 73, beginning of seed dispersal (BSD) ranged from day 93 to day 96, end ofseed dispersal (ESD) ranged from day 102 to day 106, and duration of seed dispersal (DSD)ranged from 6 days to 12 days.Sustainability 2021, 13, 3518 9 of 20Figure 4. The reproductive phenology of female P. tomentosa at eight sampling points.A significant positive correlation was found between BBB, BF, FF, FS, BSD,the phenophases at flowering and fruiting stage (0.781 \u2264 r \u2264 0.986, p < 0.05), while theyshowed a significantly negative correlation with DSD (\u22120.930 \u2264 r \u2264 \u22120.840, p < 0.01).This contrast indicated that the seed dispersal of female P. tomentosa could be a differentprocess inconsistent with the flowering and fruiting development. The raw phenologydata are presented in Appendix A, Table A3.3.3. Key Microclimate Factors Affecting the Reproductive Phenology of Female P. tomentosa inTaoranting ParkAccording to the Pearson correlation analysis between reproductive phenology andthe daily mean value of three microclimate parameters associated with SGS, we found thatthe phenology of the reproductive phenophases was heavily dependent on air temperatureand the heat transfer coefficient, while wind speed showed no significant correlation.3.3.1. Air Temperature vs. Reproductive PhenologyAs shown in Figure 5, the DMT (daily mean temperature) associated with SGS wassignificantly negatively correlated with the phenology of BBB, BF, FF, FS, BSD, which meansa higher air temperature at the onset of the growing season could drive an earlier occurrenceof flowering and fruiting in female P. tomentosa. For BBB, BF, FF, and FS that happenedsuccessively within 15 days from the beginning of reproduction, the correlation wasextremely significant (\u22120.983 \u2264 r \u2264 \u22120.908, p < 0.01), while the phenology of BSD thathappened over 20 days after the appearance of fruit set showed a weaker correlationwith DMT (r = \u22120.754, p < 0.05). Besides, according to the linear regression analysis,the absolute slope of the regression decreased with the occurrence time of the phenophases,with a change rate of \u221212.64 day\/\u25e6C for BF to \u22125.23 days\/\u25e6C for BSD. This differencedemonstrated that the \u2018driving effect\u2019 of the air temperature at the onset of the growingseason could become weaker for those phenophases occurring at later reproduction stages.With respect to ESD and DSD, the correlation pattern reversed. Their positive cor-relation with DMT (r = 0.749, p < 0.05; r = 0.946, p < 0.01) indicated a different impactmechanism behind the seed dispersal process.Sustainability 2021, 13, 3518 10 of 20Figure 5. The correlation between microclimate factors (DMT, DMH on SGS) and the reproduc-tive phenology of female P. tomentosa. DMT\u2014daily mean temperature, DMH\u2014daily mean heattransfer coefficient, BBB \u2013 beginning of bud break, BF\u2014beginning of flowering, FF\u2014full flowering,FS\u2014fruit set visible, BSD\u2014beginning of seed dispersal, ESD\u2014end of seed dispersal, DSD\u2014durationof seed dispersal.Sustainability 2021, 13, 3518 11 of 203.3.2. Heat Transfer Coefficient vs. Reproductive PhenologyAs shown in Figure 5, the DMH (daily mean heat transfer coefficient) associated withSGS was significantly positively correlated with BBB (r = 0.864, p < 0.01), BF (r = 0.769,p < 0.05), FF (r = 0.788, p < 0.05), and FS (r = 0.799, p < 0.05). The positive correlationmeans a lower air convection rate could advance the occurrence of flowering and fruitingphenophases, especially in the early development stage. According to the linear regressionbetween phenology and DMH, the slope of the regression decreased with the phenophaseoccurrence time, from 5.11 day\/(m2\/s) for BF to 1.88 day\/(m2\/s) for BSD, which indicated adeclining influence of the heat transfer coefficient at SGS on the late-occurring phenophases.In addition, similar to DMT, the correlation between DMH and phenology ceased to benegative with respect to DSD (r =\u22120.922, p < 0.01).Compared with air temperature, the heat transfer coefficient had a less significant im-pact on the reproductive phenology, reflecting on a lower absolute r (correlation coefficient)for most analyzed phenophases.3.4. Multiple Regression of Reproductive Phenology in Relation to Microclimate FactorsBased on Ridge Regression analysis, the regression models of the phenology forvarious reproductive phenophases (BBB, BF, FF, FS, BSD and DSD) are shown as follows.DBBB = 96.230\u2212 7.781 XDMT\u2217\u2217 + 1.607 XDMH\u2217 + 4.797 XDMW(R2 = 0.962, p < 0.01, k = 0.1)DBF = 120.203\u2212 11.257 XDMT\u2217 + 0.744 XDMH + 6.335 XDMW(R2 = 0.887, p < 0.05, k = 0.08)DFF = 120.381 \u2212 10.759 XDMT\u2217 + 0.213 XDMH + 3.025 XDMW(R2 = 0.875, p < 0.05, k = 0.06)DFS = 104.534\u2212 6.942 XDMT\u2217 + 0.055 XDMH(R2 = 0.821, p < 0.05, k = 0.05)DBSD = 110.402 \u2212 3.816 XDMT + 1.731XDMH + 13.010XDMW(R2 = 0.843, p < 0.05, k = 0.01)DDSD = \u221223.747 + 7.255XDMT\u2217 \u2212 2.658 XDMH\u2217 \u2212 0.489 XDMW(R2 = 0.911, p < 0.05, k = 0.07)where D refers to the phenology (Julian day) or duration (days) of phenophases;XDMT refers to the value of the daily mean temperature at SGS, XDMH refers to the valueof the daily mean heat transfer coefficient at SGS, XDMW refers to the value of the dailymean wind speed at SGS; k refers to the ridge parameter; * refers to the factor that had asignificant influence on the analyzed phenology in the ridge regression model (* p < 0.05,** p < 0.01).In general, the regression models for various phenophases showed a satisfactory fit(R2 = 0.883, p < 0.05) especially for BBB and DSD, which means the three microclimatefactors could explain more than 88% of the spatial variation in reproductive phenology.In the regression of flowering and fruiting phenophases, BBB, BF, FF, FS, and DMT had asignificantly negative influence on the phenology, while DMH showed a weaker positivecorrelation. As regards to the regression of DSD, the correlation pattern shifted, where DMTexerted a significantly positive impact on seed dispersal duration while DMH showed anegative effect.4. Discussion4.1. Testing of Hypothesis 1: There Was a Significant Difference between the ReproductivePhenology of P. tomentosa at Different Sampling Points in Taoranting Park (H1)H1 was true based on the MANOVA results, i.e., that the phenology of the analyzedreproductive phenophases all showed a statistically significant difference among the eightsampling points (p < 0.05). This is consistent with the finding that a significant spatialunevenness of plant phenology could appear in a small-scale urban space [19] or naturalspace [58\u201360]. Affected by this spatial variation, the duration of seed dispersal for femaleP. tomentosa in the green space extended from the average of 8.8 days for an individual to13 days in total, aggravating the threat of poplar fluff to public health, let alone the impactat the urban scale, which highlights the need for more micro-scale studies in this field.Sustainability 2021, 13, 3518 12 of 204.2. Testing of Hypothesis 2: The Spatial Variation of Reproductive Phenology Had a SignificantCorrelation with at Least One of the Microclimate Factors (H1)H1 was true based on the results of the correlation and regression analyses, i.e., that theDMT and DMH at SGS were significantly correlated with the reproductive phenology offemale P. tomentosa (p < 0.05). Besides, air temperature played a key role in the multipleregression of reproductive phenology for various phenophases, showing a more significantimpact on phenology than other factors. This also illustrated that in the studied garden area,the primary influence on poplar\u2019s reproductive phenology was air temperature, and othermicroclimate factors might exert their effects by affecting or interacting with temperature.This significant correlation showed that a slight microclimate variation could lead toan obvious spatial unevenness of reproductive phenology in such a micro-scale green space.4.2.1. Air TemperatureThe significant linear correlation between the air temperature and phenology ofthe reproductive phenophases demonstrated the prominent influence of the micro-scalethermal environment on poplar\u2019s reproductive development in Beijing\u2019s early spring.This finding accords with the widely accepted conclusion that air temperature is the criticalclimate driver of spring flowering and sprouting phenology in temperate zones [60\u201364],including poplar\u2019s reproduction [65].The simulated air temperature at SGS performed well in the correlation and regressionanalyses, but the limitation of the one-day simulated data should be mentioned as well.In this research, the R2 (coefficient of determination) of the linear regression between airtemperature and phenology decreased as the phenophases occurred later, which meansthe ability of the temperature data at SGS to explain the spatial variation in late-occurringphenophases (e.g., FS, BSD) declined. Some studies revealed that multi-day thermalaccumulation, e.g., Growing Degree Days, is the decisive initiator of spring phenology andhas been widely applied in phenology modeling [66,67]. From this perspective, the one-day simulated microclimate data may not be robust enough for phenology prediction,and multi-day data simulation or observation is needed in future research.4.2.2. Heat Transfer CoefficientIn addition to air temperature, the heat transfer coefficient was also found to havea significant impact on the reproductive phenology of female poplars. In Beijing in lateFebruary, a much lower heat transfer coefficient in an urban microclimate usually indicatesa less frequent air disturbance from the outer cold airflow (e.g., Skimming flow regime),which is conductive to the establishment of a stable flow pattern with independent cir-culatory vortexes and a steady thermal field [49] that can provide a favorable conditionfor reproductive development. As a micro-scale parameter, the role of the heat transfercoefficient in affecting phenological variation was rarely explored before and deservesmore attention.4.2.3. Wind SpeedAs an important impact factor of air temperature [68,69] and the heat transfer coeffi-cient [70] in urban microclimates, wind speed is expected to show a clear correlation withthe studied phenology. However, in this research, the correlation between wind speed andthe phenology of reproductive phenophases was not significant, even though wind speedhad a weak positive impact on the flowering and fruiting phenophases and a negativeinfluence on the duration of seed dispersal in the multiple regression models.One possible reason for this discrepancy could be the poor simulation accuracy ofwind speed in ENVI-MET. Due to the lack of measurement data for comparison andvalidation, the performance of wind simulation could not be evaluated in this research.A challenge in the wind simulation is due to the unique wind environment in the micro-scale green space. Different from open urban spaces characterized with strong windfields (e.g., urban canyons) [71], the large area of evergreen woods in the research siteSustainability 2021, 13, 3518 13 of 20could function as a wind obstacle, decreasing the turbulence of high-momentum fluid [72],forming small vortexes with changeable airflow [73]. Further, the weak wind field in Beijingcity on the simulated day (1.89 m\/s at 10 m height), the changeable wind pattern, and lowwind speed could bring a large challenge to the wind simulation in ENVI-MET [74,75].The unexpected correlation pattern between wind speed and reproductive phenologyshowed the limitation of ENVI-MET in wind environment simulation, highlighting theneed for field data as a support.4.3. Some Insights into the Alleviation of Catkin Fiber Pollution from Female PoplarsBased on the revealed impact of microclimate factors on the reproductive phenologyof female P. tomentosa, some potential strategies to alleviate the catkin fiber pollution canbe proposed.(1) Shorten the seed-flying period by increasing air convection and decreasing spatialvariation of the microclimate in the green space.The phenological response of seed dispersal to microclimate factors showed thatthe duration of seed dispersal (DSD) was significantly correlated with air temperature(r = 0.946) and the heat transfer coefficient (r = \u22120.922). Considering the heat transfercoefficient, the indicator of air convection, has been proved to be positively correlated withthe spatial openness [49], to improve the air convection by increasing spatial opennesscan be an effective way to shorten the duration of seed dispersal for each individualpoplar. In addition, the decrease in microclimate variation in the green space can helpstandardize the thermal environment around target trees and reduce the spatial variation ofreproductive phenology, so as to shorten the overall duration of seed dispersal. Of course,the implementation of this potential strategy can be subject to reality and a comprehensivetrade-off consideration, which deserves further discussion.(2) Time arrangement of flying seed control based on the phenological response toair temperature.According to the linear regression, with an increase in the daily mean temperature atSGS of 1 \u25e6C, BSD could advance 5.23 days and DSD could extend over 10 days. Combininglocal meteorological data, the phenological response of seed dispersal to air temperaturevariation can provide a reference to the timing of flying-seed control in Beijing\u2019s differenturban heat environments (Yang et al., 2013).4.4. Research Prospect\u2014Other Possible Influential Factors Besides the Microclimate?As shown in Figure 4, among the eight sampling points, the female P. tomentosaat point 8 showed a significantly later phenology than at other points for most analyzedphenophases (BBB, BF, FF, FS, BSD), and the poplars at points 2 and 6 showed a significantlyearlier phenology than that at other points. According to the landscape pattern of theeight buffer areas shown in Figure 3, point 8 was situated in a highly open space witha wide wind corridor, while points 2 and point 6 were highly enclosed by evergreengroves. This indicates a possible correlation between the spatial structure (e.g., spatialopenness) and reproductive phenology. Besides, the spatial structure of the underlyingsurface has been proved to have a significant influence on the air temperature in urbanmicroclimates [24,49,76]. Therefore, a possible impact mechanism of the spatial structure\u2014microclimate\u2013 reproductive phenology should be explored in follow-up research.5. ConclusionsThis research investigated the impact of microclimate factors on SGS (start of thegrowing season) on the spatial variation in the reproductive phenology of P. tomentosain a micro-scale green space in Beijing. We found that the phenology of the floweringand fruiting phenophases of female poplars was significantly negatively correlated withDMT (daily mean temperature) and positively correlated with DMH (daily mean heattransfer coefficient), while the duration of seed dispersal was positively affected by DMTand negatively affected by DMH. Based on the findings, an increase in air convectionSustainability 2021, 13, 3518 14 of 20with lower air temperature and a decrease in the spatial variation of microclimates ingreen spaces can be an effective way to shorten the seed-flying duration, so as to helptackle poplar\u2019s catkin fiber pollution in Beijing. The discovery of this research can helpfill the knowledge gap in the impact mechanism of the microclimate with respect toplant phenology in a micro-scale urban green space. It can also provide some empiricalguidance for the alleviation of catkin fiber pollution in Beijing and other countries facing asimilar problem.Meanwhile, some limitations of this research need to be noted. ENVI-MET microcli-mate simulation (especially thermal environment simulation) was suitable for this phe-nological study, but field data measurements are still strongly advocated for the datacollection for microclimate conditions to ensure data accuracy. Subject to the managementregulation of the Park Administrative Office, we were unable to collect microclimate datawith field measurements in this research. Regarding this, we will try to improve the accessto field data in our future work. Another limitation was that the one-day simulated micro-climate was not robust enough for phenology prediction, which addressed the importanceof multi-day data collection in follow-up work.Author Contributions: Conceptualization, X.X., L.D., C.K.; Funding acquisition, L.D.; Project admin-istration, L.D.; Data acquisition, X.X., W.N., Formal analysis, X.X., P.H., S.F.; Drafting the manuscript,X.X., L.D., C.K.; Revising the manuscript critically for important intellectual content, X.X., L.D., C.K.All authors have read and agreed to the published version of the manuscript.Funding: This study was funded by the Beijing Municipal Science and Technology Commission(D171100007117001).Institutional Review Board Statement: Not applicable.Informed Consent Statement: Not applicable.Data Availability Statement: Data is contained within the article.Acknowledgments: This research was funded by Beijing Municipal Science and Technology Project:Establishing Evaluation System for Ecological Function of Multi-scale Green Spaces in the NorthernUrban Area (D171100007117001) and was supported by The Study on the Plant Resources Collection,Rapid Propogation, and Applied Technology of Fine Species with Richer Coloration and ProlongedGreen Period in Beijing Urban Greening (CEG2018). We would like to thank Sijia Wu, Yilun Li,Kun Li and Mengyuan Zhang for their support in the field work and research methods. We sincerelythank the anonymous reviewers for their valuable comments.Conflicts of Interest: The authors declare no conflict of interest.Appendix AFigure A1. The Daily Mean Air Temperature of Beijing City in the Spring (10 February\u201430 April\/Julian Day 41\u2013120) of2019. Data Came from the Hourly Observation Dataset of Surface Meteorological Stations in Beijing Station (54511).Sustainability 2021, 13, 3518 15 of 20Table A1. The Observed and Simulated Data for the Accuracy Evaluation of Air TemperatureSimulation with ENVI-MET.Sampling Point Time\/h O (Observed Value)\/\u25e6C P (Simulated Value)\/\u25e6C1 9 7.900 7.3591 11 10.200 9.8911 13 10.700 11.9382 9 9.100 7.5212 11 11.300 10.2042 13 11.500 12.0503 9 8.900 7.0883 11 10.000 9.5993 13 11.300 11.6554 9 7.300 7.1624 11 11.700 10.0774 13 12.300 12.4495 9 9.200 7.4545 11 11.000 10.2075 13 12.200 12.3226 9 8.400 7.2436 11 10.400 9.7426 13 13.500 11.9997 9 8.700 7.4677 11 10.000 10.1847 13 12.600 12.1288 9 9.100 6.9558 11 9.900 9.2348 13 11.600 11.257Figure A2. The linear regression between the observed and simulated air temperature at height of1.5 m.Sustainability 2021, 13, 3518 16 of 20Table A2. The Simulated Values of Microclimate Parameters.Sampling Point Height\/m Grid Air Temperature (\u25e6C) Heat Transfer Coefficient (m2\/s) Wind Speed (m\/s)1 7 (25,25) 4.94411 0.73853 0.157461 7 (25,26) 4.95718 0.68387 0.156491 7 (26,25) 4.93272 0.71091 0.097931 7 (26,26) 4.95432 0.65436 0.100951 9 (25,25) 4.82478 0.921 0.157241 9 (25,26) 4.83755 0.86584 0.157761 9 (26,25) 4.79132 0.88426 0.116121 9 (26,26) 4.81473 0.82837 0.121842 7 (25,25) 4.96934 0.86473 0.017322 7 (25,26) 4.98826 0.83966 0.017322 7 (26,25) 4.97558 0.78301 0.017322 7 (26,26) 4.98805 0.76064 0.017322 9 (25,25) 4.85373 0.96057 0.017322 9 (25,26) 4.86631 0.94578 0.017322 9 (26,25) 4.85275 0.85853 0.017322 9 (26,26) 4.85959 0.8466 0.017323 7 (25,25) 4.7894 0.80526 0.08553 7 (25,26) 4.90766 0.62121 0.048933 7 (26,25) 4.72684 0.84954 0.102223 7 (26,26) 4.82913 0.7035 0.051713 9 (25,25) 4.75257 0.94812 0.159993 9 (25,26) 4.85382 0.75199 0.0983 9 (26,25) 4.69495 0.99634 0.153593 9 (26,26) 4.7795 0.84172 0.09874 7 (25,25) 4.86222 0.66136 0.192954 7 (25,26) 4.88697 0.62847 0.179194 7 (26,25) 4.80913 0.67229 0.157414 7 (26,26) 4.82516 0.65482 0.141894 9 (25,25) 4.76722 0.83137 0.177744 9 (25,26) 4.78572 0.80715 0.152444 9 (26,25) 4.72532 0.83442 0.152734 9 (26,26) 4.73824 0.81826 0.126125 7 (25,25) 4.74393 1.16586 0.084815 7 (25,26) 4.76468 1.09453 0.093855 7 (26,25) 4.75177 1.11631 0.088575 7 (26,26) 4.77499 1.05017 0.098865 9 (25,25) 4.62369 1.41415 0.025095 9 (25,26) 4.63422 1.34605 0.033975 9 (26,25) 4.63427 1.36947 0.028085 9 (26,26) 4.64675 1.30627 0.037786 7 (25,25) 5.07931 0.37814 0.138776 7 (25,26) 5.14171 0.31286 0.101616 7 (26,25) 5.11634 0.31783 0.095936 7 (26,26) 5.1789 0.27043 0.100446 9 (25,25) 4.97752 0.62436 0.119916 9 (25,26) 5.01796 0.5542 0.130446 9 (26,25) 5.00566 0.56704 0.119926 9 (26,26) 5.04808 0.49504 0.128927 7 (25,25) 4.87942 0.92416 0.035617 7 (25,26) 4.90284 0.88042 0.054617 7 (26,25) 4.86616 0.87325 0.019067 7 (26,26) 4.88696 0.8336 0.024517 9 (25,25) 4.74441 1.10841 0.017327 9 (25,26) 4.76104 1.08415 0.017937 9 (25,26) 4.74054 1.05178 0.017327 9 (26,26) 4.75756 1.02931 0.01732Sustainability 2021, 13, 3518 17 of 20Table A2. Cont.Sampling Point Height\/m Grid Air Temperature (\u25e6C) Heat Transfer Coefficient (m2\/s) Wind Speed (m\/s)8 7 (25,25) 4.60172 1.38658 0.017328 7 (25,26) 4.59001 1.33089 0.017328 7 (26,25) 4.60631 1.34551 0.017328 7 (26,26) 4.5964 1.2865 0.017328 9 (25,25) 4.56218 1.51627 0.017328 9 (25,26) 4.55053 1.45792 0.017328 9 (26,25) 4.56434 1.48148 0.017328 9 (26,26) 4.5539 1.4205 0.01732Table A3. The Raw Phenology Data of Reproductive Phenophases of Female P. tomentosa.Sampling Point PhenophaseSample Tree 1 Sample Tree 2Mean SDSampleBranch 1SampleBranch 2SampleBranch 3SampleBranch 1SampleBranch 2SampleBranch 31 BBB 59 59 60 60 60 61 59.8 0.7531 BF 65 66 66 66 66 67 66.0 0.6321 FF 67 68 68 68 68 69 68.0 0.6321 FS 70 70 70 70 70 71 70.2 0.4081 BSD 94 95 95 95 95 96 95.0 0.6321 ESD 103 104 104 104 105 104 104.0 0.6321 DSD 9 9 9 9 10 8 9.0 0.6322 BBB 59 58 59 59 59 59 58.8 0.4082 BF 65 64 65 65 65 65 64.8 0.4082 FF 67 66 67 67 67 67 66.8 0.4082 FS 70 69 70 71 70 70 70.0 0.6322 BSD 93 92 93 94 93 93 93.0 0.6322 ESD 103 103 103 104 103 103 103.2 0.4082 DSD 10 11 10 10 10 10 10.2 0.4083 BBB 61 62 61 60 61 61 61.0 0.6323 BF 68 69 68 67 68 68 68.0 0.6323 FF 70 71 70 70 70 70 70.2 0.4083 FS 72 73 72 72 72 72 72.2 0.4083 BSD 95 96 95 94 95 95 95.0 0.6323 ESD 103 103 103 102 103 103 102.8 0.4083 DSD 8 7 8 8 8 8 7.8 0.4084 BBB 61 61 60 61 61 62 61.0 0.6324 BF 68 68 68 68 68 67 67.8 0.4084 FF 69 69 69 69 69 69 69.0 0.0004 FS 71 71 70 71 71 71 70.8 0.4084 BSD 95 95 94 95 95 96 95.0 0.6324 ESD 105 105 104 105 105 105 104.8 0.4084 DSD 10 10 10 10 10 9 9.8 0.4085 BBB 62 63 62 62 62 61 62.0 0.6325 BF 68 69 68 68 68 67 68.0 0.6325 FF 70 70 70 70 70 69 69.8 0.4085 FS 72 72 73 72 72 71 72.0 0.6325 BSD 95 96 95 95 95 95 95.2 0.4085 ESD 102 103 102 102 102 101 102.0 0.6325 DSD 7 7 7 7 7 6 6.8 0.408Sustainability 2021, 13, 3518 18 of 20Table A3. Cont.Sampling Point PhenophaseSample Tree 1 Sample Tree 2Mean SDSampleBranch 1SampleBranch 2SampleBranch 3SampleBranch 1SampleBranch 2SampleBranch 36 BBB 58 58 57 58 59 58 58.0 0.6326 BF 65 65 65 65 65 64 64.8 0.4086 FF 67 67 67 67 67 66 66.8 0.4086 FS 70 70 69 70 70 70 69.8 0.4086 BSD 94 94 93 94 95 93 93.8 0.7536 ESD 106 107 106 106 106 105 106.0 0.6326 DSD 12 13 13 12 11 12 12.2 0.7537 BBB 61 61 61 61 61 60 60.8 0.4087 BF 67 66 67 67 66 67 66.7 0.5167 FF 69 69 69 69 68 69 68.8 0.4087 FS 71 71 71 71 70 71 70.8 0.4087 BSD 94 94 94 94 93 93 93.7 0.5167 ESD 102 103 102 102 102 101 102.0 0.6327 DSD 8 9 8 8 9 8 8.3 0.5168 BBB 63 62 63 64 63 63 63.0 0.6328 BF 71 70 71 71 71 71 70.8 0.4088 FF 72 72 73 72 72 72 72.2 0.4088 FS 73 73 74 73 73 73 73.2 0.4088 BSD 96 96 97 96 96 96 96.2 0.4088 ESD 102 102 103 102 102 102 102.2 0.4088 DSD 6 6 6 6 6 6 6.0 0.000References1. Li, X.; Zhou, Y.; Asrar, G.R.; Mao, J.; Li, X.; Li, W. 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Recommended best practice is to use a controlled vocabulary."},{"label":"Subject","value":"spatial variation","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/subject","classmap":"dpla:SourceResource","property":"dcterms:subject"},"iri":"http:\/\/purl.org\/dc\/terms\/subject","explain":"A Dublin Core Terms Property; The topic of the resource.; Typically, the subject will be represented using keywords, key phrases, or classification codes. Recommended best practice is to use a controlled vocabulary."}],"Title":[{"label":"Title ","value":"The Impact of Microclimate on the Reproductive Phenology of Female Populus tomentosa in a Micro-Scale Urban Green Space in Beijing","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/title","classmap":"dpla:SourceResource","property":"dcterms:title"},"iri":"http:\/\/purl.org\/dc\/terms\/title","explain":"A Dublin Core Terms Property; The name given to the resource."}],"Type":[{"label":"Type","value":"Text","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/type","classmap":"dpla:SourceResource","property":"dcterms:type"},"iri":"http:\/\/purl.org\/dc\/terms\/type","explain":"A Dublin Core Terms Property; The nature or genre of the resource.; Recommended best practice is to use a controlled vocabulary such as the DCMI Type Vocabulary [DCMITYPE]. To describe the file format, physical medium, or dimensions of the resource, use the Format element."}],"URI":[{"label":"URI","value":"http:\/\/hdl.handle.net\/2429\/77736","attrs":{"lang":"","ns":"https:\/\/open.library.ubc.ca\/terms#identifierURI","classmap":"oc:PublicationDescription","property":"oc:identifierURI"},"iri":"https:\/\/open.library.ubc.ca\/terms#identifierURI","explain":"UBC Open Collections Metadata Components; Local Field; Indicates the handle for item record."}],"SortDate":[{"label":"Sort Date","value":"2021-03-22 AD","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/date","classmap":"oc:InternalResource","property":"dcterms:date"},"iri":"http:\/\/purl.org\/dc\/terms\/date","explain":"A Dublin Core Elements Property; A point or period of time associated with an event in the lifecycle of the resource.; Date may be used to express temporal information at any level of granularity. Recommended best practice is to use an encoding scheme, such as the W3CDTF profile of ISO 8601 [W3CDTF].; A point or period of time associated with an event in the lifecycle of the resource.; Date may be used to express temporal information at any level of granularity. Recommended best practice is to use an encoding scheme, such as the W3CDTF profile of ISO 8601 [W3CDTF]."}]}