Impact of Land Cover Composition and Structure on Air Temperature Based on the Local Climate Zone Scheme in Hangzhou, China

atmosphere

Article Impact of Land Cover Composition and Structure on Air Temperature Based on the Local Climate Zone Scheme in Hangzhou, China

Hai Yan 1, Shimin Yang 1, Xiaohui Guo 1, Fan Wu 2, Renwu Wu 1, Feng Shao 1 and Zhiyi Bao 1,*

1 School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China; yanhai@zafu.edu.cn (H.Y.); [email protected] (S.Y.); [email protected] (X.G.); [email protected] (R.W.); [email protected] (F.S.) 2 School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China; [email protected] * Correspondence: [email protected]

Abstract: At present, conﬂicts between urban development and the climate environment are becom- ing increasingly apparent under rapid urbanization in China. Revealing the dynamic mechanism and controlling factors of the urban outdoor thermal environment is the necessary theoretical preparation for regulating and improving the urban climate environment. Taking Hangzhou as an example and based on the local climate zones classiﬁcation system, we investigated the effects of land cover composition and structure on temperature variability at the local scale. The measurement campaign was conducted within four local climate zones (LCZ 2, 4, 5, and LCZ 9) during 7 days in the summer

of 2018. The results showed that the temperature difference within the respective LCZ was always below 1.1 ◦C and the mean temperature difference between LCZs caused by different surface physical ◦ Citation: Yan, H.; Yang, S.; Guo, X.; properties was as high as 1.6 C at night. Among four LCZs, LCZ 2 was always the hottest, and LCZ 9 Wu, F.; Wu, R.; Shao, F.; Bao, Z. was the coolest at night. In particular, the percentage of pervious surface was the most important land Impact of Land Cover Composition cover feature in explaining the air temperature difference. For both daytime and nighttime, increasing and Structure on Air Temperature the percentage of pervious surface as well as decreasing the percentage of impervious surface and Based on the Local Climate Zone the percentage of building surface could lower the local temperature, with the strongest inﬂuence Scheme in Hangzhou, China. radius range from 120 m to 150 m. Besides, the temperature increased with the SVF increased at day Atmosphere 2021, 12, 936. https:// and opposite at night. doi.org/10.3390/atmos12080936 Keywords: urban heat island; local climate zones; air temperature; land cover composition; sky Academic Editor: Edward Wolf view factor

Received: 20 June 2021 Accepted: 19 July 2021 Published: 21 July 2021 1. Introduction

Publisher’s Note: MDPI stays neutral A distinct characteristic of the urban climate is the urban heat island (UHI), which with regard to jurisdictional claims in refers to the phenomenon that air/surface temperatures in urban areas are higher than published maps and institutional affil- that in the surrounding rural areas due to urbanization [1]. The UHI effect has been iations. widely observed in big-, medium-, and small-sized cities in the world, regardless of their latitude, location, topography, and environment, and the intensity of UHI increases with the development of cities [2–4]. The higher temperature caused by UHI not only affects the living environment and health of city residents, but also increases energy consumption and consequently increases the emission of pollutants and greenhouse gases [5–9]. With global Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. warming and a new round of urbanization, the UHI will affect a larger number of urban This article is an open access article dwellers [10]. Therefore, it is of great significance to study the main influencing factors of distributed under the terms and the UHI effect for improving the urban thermal environment and urban livability. conditions of the Creative Commons The urban air temperature is influenced by the urban land cover features, which has Attribution (CC BY) license (https:// long been recognized by urban climatologists in practice. The relationship between air creativecommons.org/licenses/by/ temperature and land cover composition is one of the key issues in urban climate research. 4.0/). A number of studies have reported that vegetation and some other types of land cover,

Atmosphere 2021, 12, 936. https://doi.org/10.3390/atmos12080936 https://www.mdpi.com/journal/atmosphere Atmosphere 2021, 12, 936 2 of 13

such as built-up surface, impervious surface, and water body, substantially affect intra- urban air temperature distributions and urban heat island intensity [11–14]. Higher air temperatures usually result from higher building densities, increased impervious surface, and reduced permeable natural vegetation covers [15,16]. Yan et al. [17], for example, found that land cover features have a great influence on the magnitude and spatial characteristic of the urban air temperature variations. Increasing the amount of greenspace can be an effective means to decrease air temperature, while the increase of building surface would significantly increase it. In Xi’an, a similar result was observed by Xu et al. [18], an indication that around 66% of the urban air temperature variability could be explained by the building footprint ratio and that approximately 50% of the intra-urban heat island intensity variability could be explained by the green land cover. In addition to the land cover composition, the three-dimensional geometric structure of the land cover also has an impact on the urban outdoor thermal environment. The urban land cover structure largely determines air flow, solar radiation, and long-wave radiation of the ground, so that the climate of the urban canopy becomes extremely complex [19–21]. An important measure of the structural characteristics of the urban underlying surface is the sky view factor (SVF), which refers to the fraction of the overlying hemisphere occupied by the sky [22]. Lots of studies have shown that SVF has a great influence on the intra-urban air temperature as well as the intensity and spatial pattern of UHI [23–25]. However, there are various types of land use in our cities, forming different urban forms and underlying surface types. Therefore, the primary task of studying the urban heat island problem is dividing urban surface types reasonably. The local climate zone (LCZ) classification system developed by Stewart and Oke [26], which aims at facilitating consistent relevant classifications of urban areas for air temperature measurements, explic- itly assigns specific classes to areas of similar land cover features and provides a proper framework for fully understanding local-scale climate studies. The LCZ classification system comprises 17 classes of area at the local scale by certain standards. Each class is based on several surface structure, morphological, and anthropogenic parameters. The applicability of the LCZ classification system for different urban climate study domains has been well established by various researchers. In most of these studies, which included both fixed and mobile methods, data were collected in calm and clear conditions [27–33]. The effectiveness and universality of this classification system was verified in multiple cities by field measurements and temperature simulations. Leconte et al. [34] found that the classification of local climate zones can be properly used for elementary information of neighborhoods around the climate station. Geletiˇcet al. [35] analyzed the differences of land surface air temperatures in several European cities based on the LCZ theory using traverse measurements. Different LCZs have general temperature patterns, and some researchers have confirmed that thermal contrasts between day and night exist among all LCZs. Such contrasts are governed primarily by the pervious surface fraction, street aspect ratio, tree canopy coverage, and soil wetness [30,36–38]. According to a simulation of different LCZs during both day and night, the LCZ 1 (compact high-rise), which showed a higher nocturnal temperature than other LCZs, has the lowest temperature difference between day and night, and LCZ D has the highest temperature difference [26,34]. Additionally, LCZ 1 has a lower diurnal temperature than other LCZs and has a diurnal cool island effect. Moreover, the same LCZs within a city have similar temperature conditions [34]. However, most of the existing field monitoring research of the LCZ scheme has been conducted from cites in North America and Europe. These cities are very different from the cities of China in terms of city size, land cover features, and climate characteristics. We chose a residential zone as the study area, which is one of the important daily living environments for urban residents. Besides, considering the influence of heat events in summer, we chose the summer condition as our study season. This study aimed to provide insights into this subject by focusing on the quantitative relationship between the land cover features and air temperature of several LCZ classes in Hangzhou, China. Specifically, Atmosphere 2021, 12, x FOR PEER REVIEW 3 of 15 Atmosphere 2021, 12, 936 3 of 13

We chose a residential zone as the study area, which is one of the important daily living environments for urban residents. Besides, considering the influence of heat events in wesummer, aimed we to chose elucidate: the summer (1) Howcondition large as our is the study air season. temperature This study differences aimed to pro- between each LCZ, asvide well insights as within into this the subject respective by focusing environment; on the quantitative and (2) relationship how do thebetween land the cover composition andland structurecover features features and air differentially temperature of contribute several LCZ to explainingclasses in Hangzhou, the air temperatureChina. difference? Specifically, we aimed to elucidate: (1) How large is the air temperature differences be- 2.tween Methodology each LCZ, as well as within the respective environment; and (2) how do the land 2.1.cover Study composition Area and structure features differentially contribute to explaining the air temperature difference? Hangzhou, the capital of Zhejiang Province, is located in the east of China. The study was2. Methodology conducted in Lin’an district (30◦230 N, 119◦720 E) located in the west of Hangzhou (Figure2.1. Study1 ).Area According to Köppern–Geiger climate classiﬁcation, the climate of Lin’an is Cfa (C:Hangzhou, Mild temperate,the capital of f:Zhejiang Fully Province, humid, is a: located Hot summer)in the east [of39 China.,40]. The It has a subtropical monsoonstudy was climateconducted characterized in Lin’an district by (30°23 hot summers′ N, 119°72′ andE) located cold wintersin the west with of an annual mean Hangzhou (Figure 1). According◦ to Köppern–Geiger climate classification, the climate of airLin’an temperature is Cfa (C: Mild of temperate, 16.2 C (1981–2010). f: Fully humid, Julya: Hot is summer) the warmest [39,40]. monthIt has a sub- with an average air ◦ temperaturetropical monsoon of climate about characterized 28.9 C, while by hot summers January and is thecold winters coolest with month an annual with an average air temperaturemean air temperature of about of 16.2 4.6 ◦°CC. (1981–2010). As one of July the is central the warmest cities month in the with Yangtze an average River Delta Economic Circle,air temperature urban spaceof about has 28.9 expanded °C, while January fast with is the the coolest rapid month economic with an developmentaverage air of Hangzhou. temperature of about 4.6 °C. As one of the central cities in the Yangtze River Delta Eco- Alongnomic Circle, with theurban change space has of landexpanded use andfast with land the cover, rapid the economic urban development thermal environment of has also changed.Hangzhou. InAlong recent with years, the change the summerof land use high and land temperature cover, the urban in Hangzhou thermal envi- has been extremely obvious.ronment has The also urban changed. heat In island recent years, effect the has summer further high aggravated temperature the in high-temperatureHangzhou weather, leadinghas been toextremely deterioration obvious. ofThe the urban urban heat thermal island effect environment. has further aggravated the high-temperature weather, leading to deterioration of the urban thermal environment.

Figure 1. Location of the study area and measurement points. Figure 1. Location of the study area and measurement points.

The measurements were performed in the downtown of Lin’an district, which can be seen as a typical area urbanizing rapidly in Hangzhou. The LCZ classification of Lin’an was based on field surveys and calculations from aerial photographs. Based on the classification results of the LCZ, we chose four types of different landscape areas with various underlying land cover surfaces, including three residential areas and one university since they represent the most prevalent LCZs in Lin’an (Table1). According to the LCZ classification standards [26], Ping Shan Xin Cun (PSXC) can be classified as LCZ 2 (compact midrise), Zhu Jing Hua Yuan (ZJHY) can be classified as LCZ 4 (open high-rise), Lin Shui Shan Ju (LSSJ) can be classified as LCZ 5 (open midrise), and the Zhejiang Agriculture and Forestry University (ZAFU) can be classified as LCZ 9 (sparsely built). We selected 5 measurement points in each LCZs, resulting in 20 points in total (Figure1). Atmosphere 2021, 12, x FOR PEER REVIEW 4 of 15 Atmosphere 20212021,, 1212,, xx FORFOR PEERPEER REVIEWREVIEW 44 ofof 1515 Atmosphere 2021, 12, x FOR PEER REVIEW 4 of 15

The measurements were performed in the downtown of Lin’an district, which can The measurements were performed in the downtown of Lin’an district, which can be seenThe as measurements a typical area were urbanizing performed rapi indly the in downtown Hangzhou. of TheLin’an LCZ district, classification which can of Atmosphere 2021, 12, 936 be seen as a typical area urbanizing rapidly in Hangzhou. The LCZ classification of 4 of 13 Lin’anbe seen was as baseda typical on field area surveys urbanizing and calculationsrapidly in Hangzhou.from aerial photographs.The LCZ classification Based on the of Lin’an was based on field surveys and calculations from aerial photographs. Based on the classificationLin’an was based results on offield the surveys LCZ, we and chose calculations four types from of aerial different photographs. landscape Based areas on with the classificationclassification resultsresults ofof thethe LCZ,LCZ, wewe chosechose fourfour typestypes ofof differentdifferent landscapelandscape areasareas withwith variousclassification underlying results land of the cover LCZ, surfaces, we chose including four types three of residentialdifferent landscape areas and areas one withuni- various underlying land cover surfaces, including three residential areas and one uni- versityvarious since underlying they represent land cover the most surfaces, prevalent including LCZs three in Lin’an residential (Table 1).areas According and one to uni- the 2.2.versity Field since Measurements they represent the most prevalent LCZs in Lin’an (Table 1). According to the LCZversity classification since they representstandards the [26], most Ping prevalent Shan Xin LCZs Cun in (PSXC) Lin’an can(Table be 1).classified According as LCZ to the 2 LCZ classification standards [26], Ping Shan Xin Cun (PSXC) can be classified as LCZ 2 (compactLCZWe classification usedmidrise), mobile standards Zhu methods Jing [26], Hua Ping to Yuan conduct Shan (Z XinJHY) air Cun temperaturecan (PSXC) be classified can measurementbe classified as LCZ as4 LCZ(open in each 2 local (compact(compact midrise),midrise), ZhuZhu JingJing HuaHua YuanYuan (Z(ZJHY)JHY) cancan bebe classifiedclassified asas LCZLCZ 44 (open(open climatehigh-rise),(compact zone midrise),Lin onShui 7 Shan typicalZhu JuJing days(LSSJ) Hua during canYuan be classified(Z theJHY) summer can as LCZbe of classified 20185 (open (15t midrise),asto LCZ 27 June,4and (open the clear and high-rise), Lin Shui Shan Ju (LSSJ) can be classified as LCZ 5 (open midrise), and the smoothZhejianghigh-rise), day Agriculture Lin with Shui minimum Shanand Forestry Ju (LSSJ) cloud University can and be windclassified (ZAFU) speed canas LCZ wasbe classified 5 less (open than as midrise), LCZ 3 m/s). 9 (sparsely and On the each day, Zhejiang Agriculture and Forestry University (ZAFU)(ZAFU) cancan bebe classifiedclassified asas LCZLCZ 99 (sparsely(sparsely measurementsbuilt).Zhejiang We Agriculture selected were 5 conductedandmeasurement Forestry at University 14:00points (daytime)in (ZAFU)each LCZs,and can be 21:00resulting classified (nighttime) in as20 LCZ points along 9 (sparsely in atotal ﬁxed route built). We selected 5 measurement points in each LCZs, resulting in 20 points in total with(Figurebuilt). a distanceWe 1). selected of 7.1 5 measurement km, and each points measurement in each LCZs, lasted resulting about 1 h.in During20 points the in study total period, (Figure(Figure 1).1). the(Figure maximum 1). and minimum air temperatures were 37.1 and 23.8 ◦C, respectively, within Table 1. Landscape parameters of each study site. the studyTable area. 1. Landscape parameters of each study site. Table 1. Landscape parameters of each study site. Land Cover Composition (%) Land Cover Composition (%) Table 1. Landscape parameters of each study site. Land Cover Composition (%)

PerPS PerIS PerBS PerPS LandPerIS Cover CompositionPerBS (%) Site LCZ Built Types Area (ha2) MBH (m) Plant Species PerPS PerIS PerBS PerPS PerIS PerBS 17.87 41.31 40.82 Osmanthus fragrans17.8717.87 41.3141.31 40.8240.82 PSXC LCZ 2 12.7 14 17.87 17.8741.31 41.3140.82 40.82 Iris tectorum

Liriodendron chinense38.52 33.76 27.72 ZJHY LCZ 4 14.6 27 Ginkgo biloba 38.5238.52 38.5233.7633.76 33.7627.7227.72 27.72 38.52 33.76 27.72 Cinnamomum camphora

Magnolia grandiﬂora42.14 27.32 30.54 LSSJ LCZ 5 10.9 15 Ginkgo biloba 42.1442.14 42.1427.3227.32 27.3230.5430.54 30.54 Prunus mume 42.14 27.32 30.54

Ginkgo biloba ZAFU LCZ 9 16.0 12 Michelia chapensis 60.29 60.2926.89 26.8912.82 12.82 60.2960.29 26.8926.89 12.8212.82 Magnolia grandiﬂora60.29 26.89 12.82

Note: MBH, PerPS, PerIS, and PerBS refer to the mean building height, the percentage of pervious surface, the percentage of impervious surface, and the percentage of building surface, respectively. 2.2. Field Measurements 2.2.2.2. FieldField MeasurementsMeasurements 2.2. FieldAirWe temperaturesusedMeasurements mobile methods were measured to conduct by thermistorair temperature temperature measurement sensors in each (TES-1365, local sensor We used mobile methods to◦ conduct air temperature measurement in each local accuracy:climateWe zone used air on temperaturemobile 7 typical methods days±0.5 toduring C,conduct relative the airsummer humiditytemperature of 2018±3 measurement −(15t5%) to connected 27 June, in eachclear to a localand data logger climateclimate zonezone onon 77 typicaltypical daysdays duringduring thethe summersummer ofof 20182018 (15t(15t toto 2727 June,June, clearclear andand shadedsmoothclimate withdayzone with aon radiation 7minimum typical shield. days cloud during The and instrument windthe summer speed waswas of mounted2018less than(15t to3 onto m/s).27 June, the On front eachclear of day,and an electro- smoothsmooth dayday withwith minimumminimum cloudcloud andand windwind spspeedeed waswas lessless thanthan 33 m/s).m/s). OnOn eacheach day,day, measurementssmooth day with were minimum conducted cloud at 14:00and wind (daytime) speed and was 21:00 less than(nighttime) 3 m/s). alongOn each a fixed day, mobilemeasurements (15 km/h) were at conducted 1.5 m above at 14:00 ground. (daytime) The and instant 21:00 information, (nighttime) along including a fixed latitude, routemeasurements with a distance were conducted of 7.1 km, at and 14:00 each (daytime) measurement and 21:00 lasted (nighttime) about 1 h. along During a fixed the longitude,routeroute withwith andaa distancedistance time, wasofof 7.17.1 recorded km,km, andand by eacheach a GPS measurementmeasurement travel recorder lastedlasted (Holuxaboutabout 11 M-241A).h.h. DuringDuring Thethethe HOBO studyroute period,with a distancethe maximum of 7.1 andkm, minimumand each measurementair temperatures lasted were about 37.1 1and h. 23.8During °C, there- weatherstudystudy period,period, station thethe used maximummaximum for ﬁxed andand measurementsminimumminimum airair temperaturestemperatures was installed werewere in 37.137.1 each andand LCZ 23.823.8 region. °C,°C, re-re- These spectively, within the study area. re- dataspectively,studyspectively, were period, withinwithin mainly the thethe maximum used studystudy area.area. to correctand minimum the data air of temperatures mobile measurements were 37.1 and because 23.8 °C, the mobile spectively,Air temperatures within the study were area. measured by thermistor temperature sensors (TES-1365, Air temperatures were measured by thermistor temperature sensors (TES-1365, measurementssensorAir accuracy: temperatures at air different temperature were pointsmeasured ±0.5 were °C, by notrelative ther instantaneous.mistor humidity temperature ±3−5%) connectedsensors (TES-1365, to a data sensorsensor accuracy:accuracy: airair temperaturetemperature ±0.5±0.5 °C,°C, relativerelative humidityhumidity ±3±3−−5%)5%) connectedconnected toto aa datadata loggersensor shadedaccuracy: with air a temperature radiation shield. ±0.5 The°C, relativeinstrument humidity was mounted ±3−5%) ontoconnected the front to a of data an 2.3.loggerlogger Land shadedshaded Cover withwith Parameters aa radiationradiation Measurement shield.shield. TheTheand instrumentinstrument Calculation waswas mountedmounted ontoonto thethe frontfront ofof anan electro-mobilelogger shaded (15with km/h) a radiation at 1.5 mshield. above The grou instrumentnd. The instant was mounted information, onto includingthe front of lati- an electro-mobileelectro-mobile (15(15 km/h)km/h) atat 1.51.5 mm aboveabove grouground. The instant information, including latitude,electro-mobileConsidering longitude, (15 and km/h) the time, potential at was 1.5 mrecorded effectsabove grouby of a land nd.GPS The covertravel instant onrecorder urbaninformation, (Holux temperatures, includingM-241A). weThelati- selected tude,tude, longitude,longitude, andand time,time, waswas recordedrecorded byby aa GPSGPS traveltravel recorderrecorder (Holux(Holux M-241A).M-241A). TheThe twoHOBOtude, categories longitude, weather of stationand land time, used cover was for parameters, recorded fixed measurements by includinga GPS travel was land recorder installed cover (Holux compositionin each M-241A). LCZ region. and The structure, HOBO weather station used for fixed measurements was installed in each LCZ region. TheseHOBO data weather were station mainly used used for to fixedcorrect measurements the data of mobile was installed measurements in each LCZbecause region. the toThese measure data thewere characteristics mainly used to of correct the local the environmentse datadata ofof mobilemobile (Figure measurementsmeasurements2). Land coverbecausebecause composition thethe mobileThese datameasurements were mainly at differentused to pointscorrect were the data not instantaneous.of mobile measurements because the includesmobile measurements the percentage at different of pervious points surfacewere not (PerPS), instantaneous. percentage of impervious surface mobile measurements at different points were not instantaneous. (PerIS),2.3. Land and Cover percentage Parameters ofMeasurement built-up surface and Calculation (PerBS). The sky view factor (SVF) was used to quantify2.3.2.3. LandLand landCoverCover cover ParametersParameters structure. MeasurementMeasurement andand CalculationCalculation 2.3. Land Cover Parameters Measurement and Calculation We used satellite photos downloaded from Google Maps (18 May 2017) to evaluate the land cover features in the measurement area. According to previous research, the air

temperature in an urban site is mainly affected by its surrounding land cover within a few hundred meters [41,42]. Thus, we chose buffer zones with radii of 15, 30, 60, 90, 120, 150, and 180 m in our study. By drawing corresponding areas on the aerial photographs with AutoCAD 2018 and on-site investigation, we calculated the percentages of vegetation cover, impervious area, and build-up area for each site (Figure2b). The SVF images were taken by a Sigma 8 mm circular ﬁsheye lens coupled to a Cannon EOS 6D Mark II (Canon Inc., Japan) digital camera (Figure2c). Then, we used RayMan1.2 software for the SVF calculation. Atmosphere 2021, 12, 936 5 of 13

2.4. Data Analysis We ﬁrst calculated the average air temperature of four study areas as the mean temperature of each time. For each LCZ, the air temperature differences were calculated as follows: Air temperature difference (◦C):

dAT = Tw,t − Ta,t (1) Atmosphere 2021, 12, x FOR PEER REVIEW 5 of 15

where w represents the average air temperature of 7 days in one site (◦C); t represents the time of day, including daytime and nighttime; and a represents the average air temperature in the fourConsidering sites. Positive the potential or negative effects dAT of land indicates cover on whether urban thetemperatures, site was a we hot selected or cold spot, respectively.two categories A one-way of land cover ANOVA parameters, F-test (inclp

FigureFigure 2. Landscape 2. Landscape features features of oneof one measurement measurement point point (ZJ1).(ZJ1). ( a)) Site Site photograph, photograph, (b) ( bland) land cover cover features features in the in buffer the buffer zone, andzone, (c and) ﬁsh-eye (c) fish-eye photograph. photograph.

3. ResultsWe used satellite photos downloaded from Google Maps (18 May 2017) to evaluate 3.1.the Thermal land cover Performance features withinin the measurement and between Differentarea. According LCZs to previous research, the air temperatureThe temperature in an urban distribution site is mainly patterns affected of different by its surrounding local climate land zones cover in thewithin afternoon a few hundred meters [41,42]. Thus, we chose buffer zones with radii of 15, 30, 60, 90, 120, and night are presented in Figure3. Within each local climate zone, in the afternoon, the 150, and 180 m in our study. By drawing corresponding areas on the aerial photographs intra-LCZ differentiation of the air temperature ranged from 0.4 ◦C (LCZ 5 and 9) to 1.1 ◦C with AutoCAD 2018 and on-site investigation, we calculated the percentages of vegeta- ◦ (LCZtion 4). cover, Additionally, impervious the area, air and temperature build-up area difference for each site ranged (Figure from 2b). 0.3 The (LCZ SVF images 5) to 1.0 C (LCZwere 9) intaken the by nighttime. a Sigma 8 mm circular fisheye lens coupled to a Cannon EOS 6D Mark II (CanonFigure Inc.,4 shows Japan) the digital mean camera temperature (Figure 2c). of Then, all measured we used RayMan1.2 days in different software local for the climate zones.SVF calculation. It can easily be seen that inter-LCZ differentiation of the air temperature was obvious. In the daytime, the microscale variability in AT between PSXC (LCZ 2) and ZJHY (LCZ2.4. 4) Data was Analysis 0.8 ◦C. The order of LCZ temperature was LCZ 2 > 5 > 9 > 4. The dAT in site ZJHY (LCZWe first 4) was calculated always the below average zero; air however, temperature the dAT of four in site study PSXC areas (LCZ as the 2) wasmean always abovetemperature zero. While of each site time. PSXC For was each usually LCZ, the the air hottest temperature spot duringdifferences the were day, calculated site ZJHY was shownas follows: to be the daytime cold spot. During the night, the temperature contrast between PSXC (LCZAir temperature 2) and ZAFU difference (LCZ 9)(°C): was as high as 1.6 ◦C. The order of LCZ temperatures was LCZ 2 > 4 > 5 > 9. The dAT in sitedAT ZAFU = Tw,t (LCZ − Ta,t 9) was always below zero; however, (1) the dATs in site PSXC (LCZ 2) and ZJHY (LCZ 4) were always above zero. Regarding all where w represents the average air temperature of 7 days in one site (°C); t represents the measurement days, the thermal contrast of LCZs during the daytime was larger than that time of day, including daytime and nighttime; and a represents the average air temper- at nighttime.ature in the four sites. Positive or negative dAT indicates whether the site was a hot or coldThe spot, one-way respectively. ANOVA A one-way F-test (p ANOVA< 0.05) results F-test ( showedp < 0.05) thatwas performed the mean ATsto compare among LCZ at eachthe mean time air of daytemperature had significant (AT) among differences. LCZs at both Specifically, day and innight. the daytime,Then, we analyzed the mean ATs betweenthe relationship PSXC (LCZ between 2) and the all air the temperature other LCZs an wered the significant percentage inof allpervious mobile surface, measurements, the butpercentage the mean of ATs impervious between surface, ZJHY and (LCZ the 4),percentage LSSJ (LCZ of building 5), and surface ZAFU and (LCZ the SVF 9) were by not significant.a simple linear In the regression nighttime, in different the mean time ATs and between radius. PSXC (LCZ 2) and other LCZs were significant in most cases. 3. Results 3.1. Thermal Performance within and between Different LCZs The temperature distribution patterns of different local climate zones in the afternoon and night are presented in Figure 3. Within each local climate zone, in the afternoon, the intra-LCZ differentiation of the air temperature ranged from 0.4 ° (LCZ 5 and 9)

Atmosphere 2021, 12, 936 6 of 13

In terms of the order of LCZs at different times, it is worth noting that LCZ 2 always had the highest temperature. This is probably because the percentage of impervious surface of LCZ 2 was higher than the others (Table1). The ATs of LCZ 4 were much similar to LCZ 5 in our study. Additionally, LCZ 9 always had the lowest temperature in the nighttime between the four LCZs due to the high percentage of pervious surface and the high sky view factor value.

3.2. The Relationship between Air Temperature and Land Cover Composition Each LCZ is individually different from each other in land cover composition, which has a different effect on the outdoor thermal performance. Figure5 shows the degree of correlation of AT during the two periods of the day with a pervious surface fraction at various spatial extent scales. During the day, PerPS was mainly negatively correlated with AT. During the daytime, there was a weak correlation for the 60 m radius (R2 = 0.328, p < 0.01), while the correlation was significant for the 30 m to 60 m radius. In the nighttime, AT was strongly correlated with PerPS at the spatial scale of 60 m to 180 m in radius. Additionally, the greatest correlation was in the 120 m radius (R2 = 0.807, p < 0.01). Every 10% increase in the PerPS decreased AT by 0.245 ◦C in the night when the buffer radius was 120 m. Figure6 shows the correlation of AT during two periods in a day with an impervious surface fraction. During the two time periods of the day, PerIS was mainly correlated positively with AT. During the nighttime, the strongest correlation was in the 150 m radius Atmosphere 2021, 12, x FOR PEER REVIEW2 6 of 15 (R = 0.587, p < 0.01), while in a radius of 60 m to 180 m, the correlations were all significant. Every 10% increase in PerIS increased the AT by 0.107 ◦C in the nighttime when the buffer radius was 150 m. However, the strongest correlation in the daytime was not significant evento 1.1 in°C the(LCZ 60 4). m Additionally, radius (R2 =the 0.121, air temperp = 0.133).ature difference ranged from 0.3 (LCZ 5) to 1.0 °C (LCZ 9) in the nighttime.

FigureFigure 3. 3. TheThe mean mean air airtemperature temperature (°C) in (◦ eachC) in LCZ each at LCZdifferent at differenttimes: (a) daytime, times: ( a(b)) daytime, nighttime. ( b) nighttime.

Figure 47 showsshows the the mean correlation temperature of AT of all during measured the twodays periodsin different of thelocal day climate with a building zones. It can easily be seen that inter-LCZ differentiation of the air temperature was ob- surface fraction. During the two periods of the day, PerBS mainly correlated positively with vious. In the daytime, the microscale variability in AT between PSXC (LCZ 2) and2 ZJHY AT.(LCZ In 4) the was daytime, 0.8 °C. The the order greatest of LCZcorrelation temperature was LCZ in the 2 > 305 > m9 > radius4. The dAT (R in= site 0.293, p < 0.05), ZJHY (LCZ 4) was always below zero; however, the dAT in site PSXC (LCZ 2) was always above zero. While site PSXC was usually the hottest spot during the day, site ZJHY was shown to be the daytime cold spot. During the night, the temperature contrast between PSXC (LCZ 2) and ZAFU (LCZ 9) was as high as 1.6 °C. The order of LCZ temperatures was LCZ 2 > 4 > 5 > 9. The dAT in site ZAFU (LCZ 9) was always below zero; however, the dATs in site PSXC (LCZ 2) and ZJHY (LCZ 4) were always above zero. Regarding all measurement days, the thermal contrast of LCZs during the daytime was larger than that at nighttime.

Atmosphere 2021, 12, x FOR PEER REVIEW 6 of 15

to 1.1 °C (LCZ 4). Additionally, the air temperature difference ranged from 0.3 (LCZ 5) to 1.0 °C (LCZ 9) in the nighttime.

Figure 3. The mean air temperature (°C) in each LCZ at different times: (a) daytime, (b) nighttime.

Figure 4 shows the mean temperature of all measured days in different local climate zones. It can easily be seen that inter-LCZ differentiation of the air temperature was obvious. In the daytime, the microscale variability in AT between PSXC (LCZ 2) and ZJHY (LCZ 4) was 0.8 °C. The order of LCZ temperature was LCZ 2 > 5 > 9 > 4. The dAT in site Atmosphere 2021, 12, 936 ZJHY (LCZ 4) was always below zero; however, the dAT in site PSXC (LCZ 2) was al- 7 of 13 ways above zero. While site PSXC was usually the hottest spot during the day, site ZJHY was shown to be the daytime cold spot. During the night, the temperature contrast between PSXC (LCZ 2) and ZAFU (LCZ 9) was as high as 1.6 °C. The order of LCZ tem- whileperatures in the was 30 LCZ m to 2 90> 4 m> 5 radius, > 9. The the dAT correlations in site ZAFU were (LCZ all signiﬁcant.9) was always During below thezero; nighttime, thehowever, strongest the correlationdATs in site was PSXC in the(LCZ 120 2) mand radius ZJHY (R (LCZ2 = 0.696, 4) werep < always 0.01), whileabove inzero. the 30 m to 180Regarding m radius, all measurement the correlations days, were the thermal all signiﬁcant. contrast Everyof LCZs 10% during increase the daytime in PerBS was increased ATlarger by than 0.157 that◦C at in nighttime. the evening when the buffer radius was 120 m.

Atmosphere 2021, 12Figure, x FOR PEER 4. TheREVIEW box plots of the air temperature in each LCZ at different times. (a) Daytime,8 of 15 (b) night-

time. The bulging dots in the middle illustrate the median values.

Figure Figure5. Relationships 5. Relationships between air temperature between and airthe percentage temperature of pervious and the surface percentage in different of perviousradii. surface in dif-

feren radii. Figure 6 shows the correlation of AT during two periods in a day with an impervious surface fraction. During the two time periods of the day, PerIS was mainly correlated The correlationpositively with between AT. During the the land nighttime, cover the compositionstrongest correlation features was in andthe 150 outdoor m ra- thermal performancedius showed (R² = 0.587, the P different< 0.01), while contributions in a radius of 60 of m these to 180 three m, the factors correlations to the were air all temperature significant. Every 10% increase in PerIS increased the AT by 0.107 °C in the nighttime contrasts. Inwhen particular, the buffer PerPSradius was was 150 the m. However, most important the strongest land correlation cover in composition the daytime was in explaining the air temperaturenot significant contrast, even in the followed 60 m radius by (R² PerBS = 0.121, and P = 0.133). the PerIS. In this case, the assumption that LCZ 9, which has a high PerPS, would be the cool one in four LCZs during the night is consistent with the actual measurement result.

3.3. The Relationship between Air Temperature and Land Cover Structure Each LCZ has a speciﬁc interval of SVF and aspect ratio, and different structural characteristics have an impact on the urban outdoor thermal environment. During the daytime in summer, the correlation between AT and SVF was positive, and it became negative during the night (Figure8). However, there was no signiﬁcant relationship between AT and SVF both in the daytime (R2 = 0.003, p = 0.814) and in the nighttime (R2 = 0.130, p = 0.118).

Atmosphere 2021, 12, 936 8 of 13 Atmosphere 2021, 12, x FOR PEER REVIEW 9 of 15

Atmosphere 2021, 12, x FOR PEER REVIEW 10 of 15 FigureFigure 6. Relationships 6. Relationships between air betweentemperature air and temperature the percentage and of the impervious percentage surface of impervious in different surface radii. in different radii. Figure 7 shows the correlation of AT during the two periods of the day with a building surface fraction. During the two periods of the day, PerBS mainly correlated positively with AT. In the daytime, the greatest correlation was in the 30 m radius (R² = 0.293, P < 0.05), while in the 30 m to 90 m radius, the correlations were all significant. During the nighttime, the strongest correlation was in the 120 m radius (R² = 0.696, P < 0.01), while in the 30 m to 180 m radius, the correlations were all significant. Every 10% increase in PerBS increased AT by 0.157 °C in the evening when the buffer radius was 120 m.

FigureFigure 7. Relationships 7. Relationships between air between temperature air and temperature the percentage and of the building percentage surface of buildingin different surfaceradii. in different radii. The correlation between the land cover composition features and outdoor thermal performance showed the different contributions of these three factors to the air temperature contrasts. In particular, PerPS was the most important land cover composition in explaining the air temperature contrast, followed by PerBS and the PerIS. In this case, the assumption that LCZ 9, which has a high PerPS, would be the cool one in four LCZs during the night is consistent with the actual measurement result.

3.3. The Relationship between Air Temperature and Land Cover Structure Each LCZ has a specific interval of SVF and aspect ratio, and different structural characteristics have an impact on the urban outdoor thermal environment. During the daytime in summer, the correlation between AT and SVF was positive, and it became negative during the night (Figure 8). However, there was no significant relationship between AT and SVF both in the daytime (R² = 0.003, P = 0.814) and in the nighttime (R² = 0.130, P = 0.118).

Atmosphere 2021, 12, x FOR PEER REVIEW 11 of 15 Atmosphere 2021, 12, 936 9 of 13

Figure 8. Figure 8. RelationshipsRelationships between between the the air air temperature temperature an andd sky sky view view factor factor at at different different times. times. 4. Discussion 4.4.1. Discussion Thermal Performance of Different LCZs 4.1. ThermalIt has beenPerformance widely of confirmed Different thatLCZs spatial diversity could influence the outdoor thermal environmentIt has been in manywidely cities confirmed [43–46]. that The spatial results ofdiversity our study could indicated influence that allthe tested outdoor LCZ thermalclasses environment exhibited a temperature in many ci differenceties [43–46]. in summerThe results [47 ].of The our maximum study indicated air temperature that all testedcontrast LCZ (1.6 classes◦C) duringexhibited the asummer temperature night difference was larger in than summer during [47]. the The day maximum (0.8 ◦C). Thisair temperaturewas similar contrast to the results (1.6 °C) of manyduring previous the summer UHI andnight LCZ was studies larger conductedthan during in the different day (0.8cities. °C). The This result was thatsimilar the spatialto the results variability of many of AT previous within the UHI respective and LCZ LCZ studies was always con- ductedlower in than different the differences cities. The between result eachthat th LCZe spatial confirms variability the application of AT within of the LCZthe respec- concept. tive LCZAccording was always to the lower LCZ than theory, the thermal differenc contrastses between are driveneach LCZ largely confirms by building the applica- geome- tiontry of and the land LCZ cover concept. under ideal conditions, which are calm and cloudless [26]. Contrasts betweenAccording classes to withthe LCZ significant theory, differences thermal contra in surfacests are physical driven propertieslargely by exceededbuilding 5ge-◦C, ometrywhereas and contrasts land cover between under classesideal conditions, with fewer which physical are differencescalm and cloudless were less [26]. than Con- 2 ◦C trastsin Uppsala, between Nagano, classes with and significant Vancouver differenc [48]. In particular,es in surface our physical results indicatedproperties that exceeded during 5 the°C, hotwhereas summer contrasts night, between the temperature classes with contrast fewer between physical LCZ differences 2 and LCZ were 9 wasless than as high 2 ◦ °Cas in 1.6 Uppsala,C. Thseason Nagano, might and be Vancouver the reason [48]. why In the particular, temperature our contrast results ofindicated our measure- that ◦ duringment the was hot not summer close to night, 5 C. Winterthe temperatur was hypothesizede contrast between to be the LCZ season 2 and with LCZ the 9 was highest as hightemperature as 1.6 °C. contrasts Th season in Beijing, might China be the [42 reason]. Gál etwhy al. [ 49the] found temperature that the ordercontrast of LCZof our tem- measurementperature varied was with not seasons,close to and5 °C. the Winter results wa ofs Thomashypothesized et al. [ 30to] be indicated the season that with pre-dawn the highestUHI intensity temperature in winter contrasts was strongerin Beijing, than China that [42]. in summer. Gál et al. Additionally, [49] found that the the temperature order of LCZcontrasts temperature between varied LCZ with 4 and seasons, LCZ 5, whichand the had results fewer of physical Thomas differenceset al. [30] indicated than the that other ◦ pre-dawnsites, did UHI not intensity exceed 1.1 in andwinter 0.7 wasC, respectively,stronger than at that the in two summer. different Additionally, times of the the day. temperatureNevertheless, contrasts comparing between LCZ LCZ temperature 4 and LCZ contrasts 5, which across had fewer all of thephysical studies differences seemed to thanbe difficultthe other because sites, did of not the variousexceed 1.1 sample and 0.7 sizes. °C, Orderingrespectively, the at LCZs the two by temperature different times was ofthought the day. to Nevertheless, be more valuable comparing [33]. LCZ temperature contrasts across all of the studies seemedGenerally, to be difficult LCZ 1because or 2 had of thethe highestvarious nocturnalsample sizes. temperature Ordering [ 50the]. LCZs However, by tem- LCZ perature1 was not was involved thought into thebe more present valuable study. [33]. AT of PSXC (LCZ 2) was the highest of all the LCZs in all mobile measurement campaigns, which was consistent with the studies of Generally, LCZ 1 or 2 had the highest nocturnal temperature [50]. However, LCZ 1 Lehnert et al. [33]. In agreement with the assumption of Stewart and Oke [26], the nocturnal was not involved in the present study. AT of PSXC (LCZ 2) was the highest of all the temperatures of LSSJ, classified as LCZ 5, were, on average, lower than that of PSXC (LCZ 2). LCZs in all mobile measurement campaigns, which was consistent with the studies of Similarly, LCZ 2 had higher nocturnal AT than LCZ 5 in Dublin and Nancy [28,34]. In Lehnert et al. [33]. In agreement with the assumption of Stewart and Oke [26], the noc- addition, the ATs of site ZJHY (LCZ 4) were approximate to LCZ 5 because of the similar turnal temperatures of LSSJ, classified as LCZ 5, were, on average, lower than that of surface physical properties. The results of the present study showed that ZAFU (LCZ 9) PSXC (LCZ 2). Similarly, LCZ 2 had higher nocturnal AT than LCZ 5 in Dublin and was always the coolest built types of LCZs at night, which agrees with the findings in the Nancy [28,34]. In addition, the ATs of site ZJHY (LCZ 4) were approximate to LCZ 5 be- reviewed papers [33,51]. cause of the similar surface physical properties. The results of the present study showed According to the LCZ classification, other factors influencing the temperature in urban that ZAFU (LCZ 9) was always the coolest built types of LCZs at night, which agrees with areas (such as elevation, altitude, terrain morphology, general climatic conditions) are not the findings in the reviewed papers [33,51]. included in the LCZ concept [26]. It is worth noting that some studies revealed that the AT

Atmosphere 2021, 12, 936 10 of 13

measured within a certain LCZ may be inﬂuenced by its internal structure, position within (or beyond) the city, microclimatic effects, or pattern of circulation systems [29,34,46,52]. Besides, due to the existence of a buffer of mutually permeating types [33], the temperature might be inﬂuenced by the boundary between each LCZ, and its surroundings may be fuzzy.

4.2. Effects of Land Cover Features on Outdoor Thermal Performance Temperature differences and distribution patterns in cities are mainly due to the thermal properties and energy balance of different underlying surfaces, which leads to the difference of AT near the ground [53]. Additionally, the influence of the underlying surface composition on the urban environmental temperature has been widely verified. In the present study, the observed AT significantly decreased when the PerPS increased during the summer nighttime, and the strongest effect radius was 120 m. However, during the daytime, AT decreased with PerPS in a radius of 30 to 60 m. According to the theory put forward by Oke [54], the three main factors of urban heat energy balance are anthropogenic heat, latent heat, and sensible heat. Summer is the growing season of vegetation. During the daytime, vegetative cover mainly cools down the temperature in two ways [55–57]. First, the solar radiation absorbed by vegetation is partitioned into latent heat rather than sensible heat, reducing the solar radiation energy, which should be absorbed by the atmosphere. Second, the underlying surface receives less solar radiation because of the vegetation barrier. In this way, higher PerPS leads to lower AT. The shade provided by trees during the daytime produces an effect on a small scale. During the nighttime, the natural surface has a faster cooling rate than the artificial one, so site ZAFU (LCZ 9), which was covered by bush and trees, was the coldest spot with an AT lower than the average temperature. However, an increase in PerIS would significantly increase AT. During the daytime, the solar radiation absorbed by the impervious surface is partitioned into sensible heat, resulting in a fast rise of AT. Lin et al. [58] showed that the surface temperature difference between pavements and vegetation at around midday could reach 10 ◦C. At night, the higher heat capacity of the artificial impervious surface slowed cooling. Consistent with PerIS, the increase in PerBS would significantly increase AT. Besides, the increased building cover meant an increase of anthropogenic heat production, such as space heating, industrial operations, and automobile use. When it came to 14:00 in the afternoon, the temperature was the highest in one day. Additionally, the buildings became a big heat source, which released long-wave radiation. In the evening, the high building density and poor ventilation were not conducive to outward diffusion of heat. On a large scale in the study (in 60 to 180 m radius), AT increased with the increase of building coverage. In the evening, PSXC (LCZ 2), which was covered by the highest percentage of artificial surface, including PerIS and PerBS of all the LCZ types, always had ATs higher than the average temperature. The increase of SVF made the increase of the radiation reception during the daytime and increased AT, while the increase of the out-going long-wave radiation during the nighttime decreased AT [59]. In our study, AT increased with the increase of SVF during the daytime, while a contrary tendency was observed during nighttime. We found no significant relationship between AT and SVF during both daytime and nighttime. One of the reasons that may explain the results was the position of visible sky [60]. Besides, we found that when testing the effects of SVF on air temperature, it is important to control for the potential effects from other landscape variables, particularly the land cover composition. In conclusion, we found that AT was comprehensively influenced by the land coverage characteristics and urban structure. The sky view factor was affected by both surrounding buildings and trees. During the daytime, trees could decrease ATs by evaporation and the provision of shade. However, buildings blocked long-wave radiation from the ground, thus the remaining heat resulted in increased ATs [22]. Atmosphere 2021, 12, 936 11 of 13

5. Conclusions With the LCZ concept, the effects of land cover composition and structure on the urban thermal environment in the high population density city of Hangzhou in the summer were investigated. The results showed that the maximum mean nighttime air temperature difference was 1.6 ◦C. Furthermore, among the different LCZs, LCZs ordered by temperature that showed significance were LCZ 2 > 5 > 9 > 4 during the daytime, and LCZ 2 > 4 > 5 > 9 during the nighttime. The land cover features differentially contributed to the air temperature variability. On the one hand, the land cover composition could change the urban environmental temperature by influencing the factors of urban heat energy balance. Additionally, the most important landscape characteristic in explaining AT in different situations was PerPS. During the summer daytime and nighttime, increasing PerPS and decreasing PerIS and PerBS ameliorated the local thermal environment, with the strongest influence radius ranging from 120 to 150 m. On the other hand, the land cover structure feature was important in the absorption and loss of radiation. It was observed that AT increased with SVF increasing in the day and the opposite was shown during the night. These results confirm that the LCZ concept can be applied to urban temperature research. This kind of application will be beneficial to systematical and quantitative exploration of AT in different kinds of LCZs, and to further predict the urban climate. We suppose that a landscape design balancing the controllable land cover composition, such as adequate pervious surface, reasonable impervious surface, and building surface fraction in a certain range, should be taken into consideration to improve the urban thermal environment.

Author Contributions: Conceptualization, H.Y., S.Y., X.G., F.W., R.W., F.S. and Z.B.; methodology, H.Y., S.Y. and R.W.; investigation, S.Y., X.G. and R.W.; software, S.Y. and F.S.; resources, X.G., F.W. and F.S.; writing—original draft preparation, H.Y. and S.Y.; writing—review and editing, H.Y., S.Y. and Z.B. visualization, S.Y., X.G. and F.S. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Zhejiang Provincial Natural Science Foundation of China under Grant No. LGF21E080001 and National Natural Science Foundation of China under Grant No. 51508515. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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