Effects of urban and landscape elements on air temperature in a high-density subtropical city

Building and Environment 164 (2019) 106362

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Effects of urban and landscape elements on air temperature in a high-density subtropical city

T

Pui Kwan Cheung, C.Y. Jim∗ Department of Social Sciences, Education University of Hong Kong, Lo Ping Road, Tai Po, Hong Kong, China

ARTICLE INFO

ABSTRACT

Keywords: Landscape design Urban park Urban vegetation Tree cover Air temperature Cooling effect

In this study, we examined the effects of key urban (road cover, building volume ratio, and proximity to sea) and landscape (water body, tree cover, shrub cover, turf cover, park area, and sky view factor) parameters on air temperature, and the impacts of weather conditions on landscape-temperature relationship. One hundred temperature sensors were installed in fourteen urban parks in Hong Kong during summer season to collect continuous air temperature data. Linear mixed-effect models showed that the effects of weather (cloud amount, solar radiation and wind speed) on landscape-temperature relationships were minor (< 0.2 °C). Therefore, the landscape effects were further investigated using the entire dataset regardless of weather conditions. In a circular buffer zone with a 20-m radius, a 10% increase in road density caused a 0.059 °C rise in daytime mean air temperature while the same increase in tree cover and shrub cover led to a 0.052 and 0.041 °C drop in temperature, respectively. A 0.849 °C rise could be expected when sky view factor increased from 0 to 1. The proximity to the sea also had a significant daytime cooling effect (0.784 °C/1000 m). The night-time landscape effects were similar to the daytime except that the strengths of the effects on air temperature were weaker. The obtained results can be used by landscape designers and urban planners for modifying the landscape to bring cooling effects and tackle heat-island and climate-change impacts.

1. Introduction Urbanisation is associated with large-scale conversion of vegetative surface to impervious surface and buildings. Such changes alter the energy balance in the urban canopy layer and in turn affect air temperature. Air temperature in an urban area is often higher than its surrounding rural area, and this phenomenon is known as urban heat island (UHI) effect [1]. The rise in air temperature in cities can increase space cooling demand [2] and lead to thermal discomfort and associated heat-related health issues [3,4]. Construction materials and urban geometry are the two key factors in modifying energy balance and increasing urban temperature [1]. Common construction materials, such as concrete and asphalt, have high thermal admittances and absorb and store more solar radiation than vegetative surfaces [5]. As dry construction materials have no evaporative capability, they release the heat mostly in the form of longwave radiation and sensible heat instead of latent heat, thereby effectively warming the adjoining air [6]. Complex urban geometry and tall buildings lower the sky view factor (SVF) at the pedestrian level and reduce long-wave radiation loss from the ground, especially at night [7]. Night-time radiative cooling is therefore strongly suppressed in the urban core, contributing to nocturnal UHI [8]. ∗

There has been a sustained interest in understanding the effects of different urban landscape designs on air temperature in the past few decades aimed at mitigating the UHI effect [9–22]. Some studies only considered the effect of a single landscape parameter, such as SVF [23,24], tree [25,26], lawn [6,27], proximity to water body [28,29], building density [30,31], and proximity to a park or park area [32–34]. Others adopted a more comprehensive approach and considered a basket of landscape parameters using multiple linear regression analysis to predict air temperature [17,34–40]. Evaluating a group of parameters simultaneously can permit more accurate estimate of the effects of each parameter whilst controlling other parameters. The established procedures, e.g. stepwise regression, can eliminate insignificant parameters in the model to further improve its accuracy and predictive power [41,42]. Giridharan et al. [36] used mobile measurements to study the correlation between six urban design parameters and air temperature in 17 housing estates in Hong Kong's summer. It was concluded that SVF, shrub cover, and tree cover were the most crucial factors in controlling air temperature. Jusuf and Wong [37] collected air temperature data at 46 fixed stations in Singapore to examine its relationship with landscape characteristics, namely pavement area ratio, height to building area ratio, total wall surface area, green plot ratio, SVF and surface albedo.

Corresponding author. E-mail addresses: [email protected] (P.K. Cheung), [email protected] (C.Y. Jim).

https://doi.org/10.1016/j.buildenv.2019.106362 Received 18 June 2019; Received in revised form 20 August 2019; Accepted 22 August 2019 Available online 22 August 2019 0360-1323/ © 2019 Elsevier Ltd. All rights reserved.

Building and Environment 164 (2019) 106362

P.K. Cheung and C.Y. Jim

Fig. 1. Histograms of (a) amount of cloud (1989–2018), (b) daily total solar radiation (1993–2018), and (c) wind speed (1993–2018). Data of the three hottest months (June, July and August) were used. Solar radiation and wind speed data were collected at the King's Park weather station and amount of cloud data at the Hong Kong Observatory station.

They showed that all six parameters were significant predictors of mean air temperature. Petralli et al. [43] investigated the relationship between six urban planning indicators (green cover ratio, lawn cover ratio, tree cover ratio, street cover ratio, building cover ratio and building volume density) and air temperature in Florence. Maximum air temperature was significantly affected by green cover ratio and tree cover ratio. Konarska et al. [17] monitored ten sites in the parks and streets in Gothenburg continuously for two years. SVF was the primary factor controlling daytime and night-time air temperature in the warm and cold seasons under two different weather conditions (eight cases in total). Tree cover was only significant in one of the eight air temperature models. Other parameters, namely tree volume, building cover and permeable surface cover, were not significant predictors of air temperature. This empirical modelling approach presents an efficient method to identify important landscape parameters having an impact on air temperature, but the developed models are only applicable in the local context. UHI research usually focuses on clear and calm days during which UHI intensity is the strongest [44–46]. Cloud cover and wind speed are the major weather parameters that correlate with UHI intensity [47,48]. High cloud cover reduces UHI intensity because it restricts radiative cooling in the rural area; high wind speed also limits the urban-rural temperature contrast because it enhances atmospheric transport and mixing [49]. For this reason, some studies only examined the landscapetemperature relationship on clear calm days [37–39,50–54]. However, clear and calm days may not be the most common weather conditions in

some places. In Melbourne, low wind speed (< 3 m/s) and low cloud amount (0–3 octas) accounted for only 53% and 33% of its weather conditions (1972–1991) [46]. Similarly, clear calm days only occurred in 25% and 32% of the time in Athens in February and July (1961–1990) respectively [45]. It is thus necessary to study the effects of landscape elements on air temperature under different weather conditions and identify parameters that can contribute to city cooling more effectively by considering the general climate of a given place as a whole. There are three major deficiencies in previous studies that investigated landscape-temperature relationships in the urban environment. First, most studies used mobile measurement to collect air temperature data, which severely limited the representativeness of the data for a day or a season. Second, most studies only focused on the effect of one landscape feature, e.g. tree or SVF. The lack of control on the confounding factors in the vicinity, such as the presence of lawn, building and shrub, reduced the accuracy of estimating the effects. Third, the landscape effects on air temperature in cloudy or windy days were rarely studied because most investigations focused on clear and calm days when urban microclimatic differences were most pronounced. In view of these two deficiencies, this study aimed to quantify and compare the urban landscape effects on air temperature under different weather conditions (cloud amount, solar radiation, and wind speed) in Hong Kong's summer using a network of 100 fixed weather stations. The results can be used to predict cooling potentials for a new landscape design or a proposed modification of existing landscape in 2

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Hong Kong's urban core and new towns, with practical implications on improving human thermal comfort and reducing space cooling demand.

and management limitations. A complete dataset with 91 sensors and 66 days were obtained after accounting for sensor failures.

2. Methods and materials

2.3. Landscape parameters

2.1. Study area

Ten urban and landscape parameters (hereinafter referred to as ‘landscape parameters’) were considered in this study (Table 1): (1) pavement cover (PAVEMENT, %), (2) road cover (ROAD, %), (3) water body cover (WATER, %), (4) tree cover (TREE, %), (5) shrub cover (SHRUB, %), (6) turf cover (TURF, %), (7) building volume ratio (BV, m), (8) park area (AREA, m2), (9) distance from sea (SEA, m), and (10) sky view factor (SVF, unitless). These parameters were the most relevant to the urban park design in Hong Kong with impacts on air temperature. Park designers and urban planners can consider modifying these elements to improve the thermal environment of the outdoor recreational venues. The first seven parameters were calculated based on a circular buffer zone with a 20-m radius (area = 1257 m2) centred at the sensor location. This radius was proven to be sufficiently large for the detection of landscape effects on air temperature [17,36], and it was also the most relevant to park design and renovation aimed at improving the thermal comfort of the park. PAVEMENT, WATER, TREE, SHRUB, TURF and ROAD were the ratios of the respective surface cover area to buffer zone area, except that tree crown area was used for TREE. BV was the ratio of building volume to buffer zone area. PAVEMENT, WATER, TREE, SHRUB and TURF were obtained by field measurement. ROAD, BV, AREA and SEA were calculated using iB1000 digital maps with building and road information produced by the government's Lands Department (https://www.landsd.gov.hk/mapping/en/digital_map/digital_map.htm) in ArcMap 10.5. SVF was determined by importing fisheye photos taken at each monitoring station into Rayman 1.2 (http://www.mif.unifreiburg.de/rayman/intro.htm) [60]. Except WATER and TURF, the variabilities of the landscape characteristics were sufficiently large, and the distribution was sufficiently even to allow the development of accurate and unbiased empirical models (Table 1) (c.f. Section 2.4). Pond and turf were rare landscape elements in Hong Kong because most parks were too small to accommodate a pond, and hard paving was widely preferred over turf to minimise management burden.

Hong Kong, located at the south coast of China (22° N, 114° E), has a population of 7.5 million and a land area of about 1106 km2. It has a hothumid summer (May to October) and a mild-dry winter (November to April) (Köppen climate classification: Cwa). The mean air temperatures (Tmean) in the three hottest months (June, July and August) are 27.9, 28.6 and 28.3 °C respectively [55]. The daily maximum temperature (Tmax) in these months can easily exceed 30 °C, with a minimum of (Tmin) over 26.0 °C. The high level of average relative humidity (RH) (> 80%), strong solar radiation (G) (15.77 MJ/m2) and low wind speed (υ) (~2.3 m/s) further aggravate thermal discomfort, which limits the use of outdoor green space and decrease the quality of life [3,56]. Long-term weather data showed that days with low cloud cover only accounted for less than 10% of the time in June, July, and August (1989–2018) (Fig. 1a). Nearly half of the time in these summer months were cloudy days (> 80% cloud cover). The distribution of daily total solar radiation was quite even and about 30% of the days only received < 10 MJ/m2 (Fig. 1b). Low wind speed (< 3 m/s) occurred in approximately 80% of the time in Hong Kong's summer (Fig. 1c). The data suggested that Hong Kong's summer has rather diverse weather. The landscape effects on air temperature should be considered with respect to different weather conditions. 2.2. Data collection Yau Tsim Mong is one of the most densely developed districts in Hong Kong (Fig. 2). It can be designated as the Compact High-rise Zone (BCZ1) according to the local climate zone classification scheme proposed by Stewart and Oke [57]. It occupies an area of 7 km2 with a population of about 340,000 [58]. Fourteen urban parks in this district were selected for the monitoring of air temperature (Figs. 2 and 3). Kowloon Park (Kow), King George V Memorial Park Kowloon (King) and Lok Kwan Street Park (Lok) were the largest parks with areas of 130,671, 13,831 and 9843 m2, respectively. The large parks allowed for the investigation of the effects of different landscape elements, such as pond, lawn, urban forest, and shrub. Two waterfront parks, namely Hoi Fai Road Promenade (Hoi) and Mody Road Rest Garden (Mody), were included to examine the effect of proximity to sea. Six tiny parks (< 1100 m2) were monitored, including Arran Street Sitting-out Area (Arr), Canton Road/Soy Street Sitting-out Area (Can), Dundas Street Sitting-out Area (Dun), Lai Chi Kok Road/Canton Road Garden (Lai), Man Ming Lane Rest Garden (Man) and Lai Chi Kok Road/Tai Nam Street Sitting-out Area (Tai). They are known as ‘pocket parks’ in Hong Kong, developed on small plots left between buildings, or derived from the demolition of old buildings [59]. These parks are usually surrounded by buildings or closed to busy roads. HOBO® MX2303 temperature sensors with built-in data loggers (accuracy: ± 0.2 °C from 0 to 70 °C) were installed at different locations of each park to measure the temperature effects of different landscape features. Air temperature was measured every 10 s and averaged over a 15-min period. The measurement locations in each park were selected to cover the widest range of the landscape features available in that park (c.f. Section 2.3). The sensors were tied to existing pole-like structures at 2.3 m to prevent disturbance and vandalism [39]. The number of sensors installed in a park was approximately proportional to the park area. Three sensors were used in smaller parks, e.g. Arr and Lai, and 27 sensors were used in the largest park, i.e. Kow. A total of 100 sensors were deployed. Air temperature was continuously monitored in the three hottest months of the year (June, July, and August) in 2018. A longer monitoring period was infeasible owing to logistic

2.4. Data analysis The Pearson's correlation coefficients between the ten landscape parameters were first examined to identify highly correlated (r > 0.7) parameters (Table 2). Because PAVEMENT was highly correlated with TREE and SHRUB, it was excluded from further analysis. Any increase in WATER, TREE, SHRUB or TURF could imply a reduction in PAVEMENT from a park design perspective as most surfaces in the measurement sites were paved. Mixed effect linear model was an appropriate algorithm to estimate the effects of landscape parameters on air temperature with repeated measurement of air temperate at the same location [61]. This model includes two types of variables, namely random variable, and fixed variable. Random variables are usually grouping variables or repeated measurements [62]. Fixed variables are investigated to understand their effects. The random variables in this study were date and monitoring station nested under park; the fixed variables were the nine landscape parameters. Daytime and night-time mean air temperatures were the dependent variables in the models. Full models were first developed to examine the effects of all considered landscape parameters their statistical significances using the lmer function in the lme4 package [63] in R Studio [64]. lmer was a function developed to perform mixed effect linear regression. If necessary, a stepwise procedure was performed to eliminate insignificant fixed variables (p < 0.05) using the step function in the lmerTest package [42]. Eliminating insignificant variables can allow a more accurate estimation of the regression coefficients. The data were divided into three cases (‘high’, ‘low’, and ‘others’) considering the effects of cloud amount, solar radiation, and wind speed 3

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Fig. 2. Locations of the 14 monitored parks and the two government weather stations (Hong Kong Observatory and King's Park) in the core urban area of Hong Kong.

‘other’ weather conditions (0.060 °C/10%) were between the high and low cloud cases. The patterns were similar in SHRUB, SVF and SEA. The cooling effect of TREE was slightly higher on cloudy days. The same five parameters remained significant when solar radiation was considered (Table 5), namely ROAD, TREE, SHRUB, SVF and SEA. Their trends (high vs low) were essentially reversed compared to the cloud amount cases because low cloud amount often implied high solar radiation, and vice versa. The coefficient estimates of these significant parameters in the high solar radiation case were similar to the corresponding ones in the low cloud amount case. The results suggested that either the cloud amount or solar radiation could be used to estimate the landscape effects on daytime mean air temperature. Compared to the cloud amount and solar radiation cases, WATER was an additional significant variable in the high wind speed case (Table 5). Except WATER and SVF, increased wind speed generally reduced the landscape effects on daytime mean air temperature (as indicated by the ‘-’ sign in the last column). The high and low cases of all three weather conditions demonstrated minor difference in the coefficients. Even if the significant land cover types (ROAD, TREE or SHRUB) were increased by 100%, the temperature changes between the high and low cases would be less than 0.2 °C. With reference to increase in SVF from 0 to 1, or increase in SEA from 0 to 1000 m, the temperature changes were similarly small. The more notable landscape effects in the ‘others’ case were mostly between the high and low cases, and therefore, the practical temperature changes for a given modification in landscape trait was even smaller.

(Table 3). The classification criteria were based on the weather statistics in the monitoring period (Table 4). The primary criteria were based on the first quartile (Q1) and third quartile (Q3) values of the target weather parameters and other criteria considered the means of the other two parameters. As the Q1-Q3 differences were insignificant in cloud amount (14%) and wind speed (1 m/s), more stringent criteria were applied to ‘low’ case of cloud amount and the two wind speed cases to increase the contrast between the two cases. Any day that did not meet the ‘high’ or ‘low’ criteria was classified as ‘others’. The wind speed data for classification were collected from the government's weather station (King's Park) instead of the temperature measurement sites, because it was financially and logistically infeasible to install anemometers at all the sites. The wind speed at the street-level is likely to be lower than that measured at King's Park weather station, where wind speed was measured at 90 m above mean seal level. The weather station's wind speed data only serve as a proxy of the wind speed at the street-level in the urban environment. 3. Results 3.1. Landscape effects on air temperature under different weather conditions 3.1.1. Daytime There were five significant landscape parameters in the daytime mean air temperature models of cloud amount (Table 5), namely ROAD, TREE, SHRUB, SVF and SEA. The levels of significance were roughly the same across the three cases (high, low and others) except that TREE was not significant (p < 0.05) in the low cloud amount case. Considering only the significant parameters, a high cloud amount dampened the landscape effects on air temperature (as shown by the ‘-’ sign in the last column) except TREE. In the low cloud case, daytime mean air temperature would increase by 0.072 °C for every 10% increase in ROAD, while the warming effect reduced to 0.052 °C in the high cloud case. The ROAD effects under

3.1.2. Night-time Only four parameters were significant in the night-time mean air temperature models of cloud amount (Table 6). Compared to the daytime cases, TREE and SVF became insignificant parameters, while AREA gained a significant effect in the low cloud case. The strength of the significant effects (ROAD, SHRUB and SEA) weakened compared to their daytime counterparts. Cloudy conditions dampened the landscape 4

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Fig. 3. Photos of the study parks, namely Arran Street Sitting-out Area (Arr), Canton Road/Soy Street Sitting-out Area (Can), Dundas Street Sitting-out Area (Dun), Hoi Fai Road Promenade (Hoi), Ivy Street Rest Garden (Ivy), King George V Memorial Park (King), Kowloon Park (Kow), Lai Chi Kok Road/Canton Road Garden (Lai), Lok Kwan Street Park (Lok), Man Ming Lane Rest Garden (Man), Mody Road Garden (Mody), Lai Chi Kok Road/Tai Nam Street Sitting-out Area (Lai), Thistle Street Rest Garden (This), Chui Yu Road Rest Garden (Yu).

effects (ROAD, SHRUB, AREA and SEA) on night-time air temperature (as indicated by the ‘-’ sign in the last column). It was attributed to the negative correlation between cloud amount and solar radiation (Pearson's correlation coefficient = −0.666, p < 0.001). Strong solar radiation during the day strengthened the significant landscape effects on night-time air temperature (as indicated by the ‘+’ sign in the last column). However, the practical difference (air temperature change) in the strength of the effects was almost undetectable (< 0.1 °C) for a 100% change (increase or decrease) in land cover (ROAD or SHRUB), 10 ha change in AREA or 1000 m change in SEA. The same four landscape parameters (ROAD, SHRUB, AREA and SEA) were significant in the wind speed models (Table 6). High wind speed enhanced the effects of ROAD and SHRUB, but weakened the effects of AREA and SEA. The temperature implication in the case of SEA was more notable; a waterfront location was 0.581 °C cooler than an inner-city (1000 m from sea) location in the low wind condition, but it was only 0.464 °C cooler in the high wind condition.

The differences in the coefficients between high and low cases in the night-time air temperature models were even smaller compared to the daytime models. The coefficients in the ‘others’ cases were also close to the high and low cases. The results showed that the impacts of weather conditions on landscape effects on air temperature within the urban area of Hong Kong were relatively minor. 3.2. Summer empirical model for all weather conditions As the impacts of weather conditions were negligible, it was appropriate to develop air temperature models based on the whole summer dataset. The stepwise elimination procedure was applied to the overall summer models to eliminate the insignificant landscape parameters. ROAD, TREE, SHRUB, SVF and SEA were included in the reduced daytime mean air temperature model (Table 7), implying that their effects on temperature were statistically significant. A 10% increase in 5

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Table 1 Mean values of the landscape parameters of the monitoring stations in each park and the summary statistics of the parameters of all 91 stations. Parka

Pavement cover (PAVEMENT) (%)

Road cover (ROAD) (%)

Water body (WATER) (%)

Tree cover (TREE) (%)

Shrub cover (SHRUB) (%)

Arr Can Dun Hoi Ivy King Kow Lai Lok Man Mody Tai This Yu

90 80 85 51 84 55 43 88 42 75 74 92 88 62

59 17 0 15 8 6 3 50 6 26 6 16 5 2

0 0 0 19 0 0 3 0 20 0 0 0 0 0

35 20 18 17 31 60 62 35 60 30 31 20 23 45

10 20 14 10 16 37 44 12 36 25 26 8 13 30

Mean SD Median Min. Max

62 23 65 12 95

10 16 0 0 59

4 10 0 0 53

44 26 35 0 95

28 18 25 0 75

a

Turf cover (TURF) (%)

Park area (AREA) (m2)

Building volume ratio (BV) (m)

Distance from sea (SEA) (m)

Sky view factor (SVF)

0 0 0 7 0 7 5 0 0 0 0 0 0 8

147 213 1048 4326 3792 13,831 130,671 213 9843 390 3300 1061 2240 4200

0 12 14 0 4 5 0 2 0 4 0 5 8 0

1080 840 1115 10 619 462 305 1051 638 992 80 1112 755 527

0.165 0.088 0.162 0.475 0.276 0.185 0.295 0.152 0.154 0.160 0.261 0.264 0.195 0.363

3 7 0 0 40

38,203 55,799 4326 147 130,671

3 5 0 0 22

561 333 522 5 1131

0.244 0.196 0.212 0.000 0.901

The full names and locations of the 14 monitored parks can be found in Fig. 2.

ROAD in a 20-m circular buffer zone would lead to a 0.059 °C increase in daytime mean air temperature. Increasing TREE and SHRUB by 10% in the same area could reduce temperature by 0.052 and 0.041 °C, respectively. A 0.849 °C rise in mean temperature could be expected if SVF increased from 0 to 1. A waterfront location could be 0.784 °C cooler than an inner-city location (1000 m from shore). ROAD, TREE, SHRUB and SEA remained significant landscape parameters that controlled night-time mean air temperature (Table 7). However, the strength of their effects weakened compared to their daytime counterparts. The night-time coefficient of ROAD was similar (0.056 °C/10%) to that of the daytime. The coefficients of TREE and SHRUB decreased by 0.023 and 0.016 °C/10%, respectively. The thermal benefit of proximity to sea reduced to 0.433 °C/1000 m. SVF became an insignificant parameter in the night-time model. AREA was included in the reduced model; a 0.039 °C reduction in night-time mean air temperature could be realized for every 10,000 m2 increase in park area.

Table 3 Criteria adopted in weather classification in this study. Weather parameter

Case

Cloud amount

High Low High Low High Low

Solar radiation Wind speed

No. of day

Criteria Cloud amount (%)

Solar radiation (MJ/m2)

Wind speed (m/s)

5 3 10 6 3 4

≥ 88 < 55 < 83 > 83 < 83 < 83

< 10.40 > 10.40 ≥ 22.88 < 10.40 > 10.40 > 10.40

< 2.5 < 2.5 < 2.5 < 2.5 ≥ 4.0 < 1.6

Note: Primary criteria are shown in bold font.

research (canopy layer heat island) always involved at least one reference site in the rural area and the dependent variable was the urban-rural temperature difference. In contrast, studies on the landscape-temperature relationship could focus on the intra-urban temperature difference alone [17,24,38,39,43,50,52,53,68–71]. The influence of weather conditions on landscape-temperature relationships may be weaker if the rural temperature is not considered in the equation. This study showed that weather conditions could, to a limited extent, alter the effects of landscape elements on air temperature. A cloudy and windy condition generally weakened the landscape effects, whereas strong solar radiation promoted the effects. However, the practical implication of such weather effects on air temperature was

4. Discussion The idea of studying urban microclimate under ideal (calm and clear) weather conditions originated from UHI research that aimed at measuring the maximum temperature contrast, i.e. maximum UHI intensity, between the urban area and its surrounding rural area [65–67]. UHI intensity is stronger in a low wind condition because it retains the warm air in the urban core by suppressing advection; UHI is further promoted in a clear sky condition because it facilitates the cooling of the rural area with high SVF while the cooling of the urban area is retarded by low SVF [49]. UHI Table 2 Pearson correlation coefficients between the landscape parameters.

PAVEMENT ROAD WATER TREE SHRUB TURF AREA BV SVF SEA

PAVEMENT

ROAD

WATER

TREE

SHRUB

TURF

AREA

BV

SVF

SEA

1.00 0.45 −0.44 −0.71 −0.79 −0.26 −0.53 0.43 0.01 0.56

0.45 1.00 −0.16 −0.30 −0.37 −0.17 −0.30 −0.11 −0.08 0.41

−0.44 −0.16 1.00 0.05 −0.04 −0.09 −0.07 −0.21 0.17 −0.17

−0.71 −0.30 0.05 1.00 0.76 0.15 0.45 −0.33 −0.45 −0.30

−0.79 −0.37 −0.04 0.76 1.00 −0.01 0.54 −0.27 −0.19 −0.39

−0.26 −0.17 −0.09 0.15 −0.01 1.00 0.19 −0.22 0.16 −0.27

−0.53 −0.30 −0.07 0.45 0.54 0.19 1.00 −0.29 0.15 −0.49

0.43 −0.11 −0.21 −0.33 −0.27 −0.22 −0.29 1.00 −0.20 0.40

0.01 −0.08 0.17 −0.45 −0.19 0.16 0.15 −0.20 1.00 −0.28

0.56 0.41 −0.17 −0.30 −0.39 −0.27 −0.49 0.40 −0.28 1.00

6

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Table 4 General weather of the study period at the King's Park weather station. Statistic

Tmean (°C)

Tmin (°C)

Tmax (°C)

Rainfall (mm)

RH (%)

Wind speed, υ (m/s)

Solar radiation, G (MJ/m2)

Cloud amount (%)a

Min. First quartile (Q1) Median Mean Third quartile (Q3) Max

26.2 27.8 28.9 28.6 29.6 30.3

24.0 25.5 26.5 26.4 27.3 28.4

27.7 30.6 31.8 31.4 32.6 33.5

0.0 0.5 6.0 14.5 25.9 72.9

73 77 82 82 85 93

1.3 2.0 2.5 2.6 3.0 5.1

3.71 10.40 16.13 16.27 22.88 27.26

38 74 83 79 88 92

a

Measured at the Hong Kong Observatory weather station.

minuscule under the range of weather conditions and the size of the buffer zone considered in this study. Because the data was collected only over the three hottest months of summer in one year, it is likely that the variability of weather was too insignificant for revealing the weather effects. It is possible that the low cloud condition (< 55% or 4 octas) defined in this study was still too cloudy for studying the effect of cloud amount (Table 3). The lowest cloud amount was 38% (3 octas) (Table 4), which was probably not sufficient for studying the effect of a clear-sky condition. The UHI intensity in Szeged was found to be a function of cloudiness [48]; it increased from 0.5 °C for an overcast condition (8 octas) to 3.0 °C for a cloudless condition (0 octa). Morris et al. [46] also showed that the average UHI intensity in Melbourne reduced from 1.6 to 1.1 °C when cloud cover increase from 0 to 4–6 octas. Unfortunately, it was impossible to analyse the cloudless condition (0 octa) owing to the lack of such weather condition in the sampling period. The wind speed recorded in the study period was low (Table 4). The Q3 wind speed was only 3.0 m/s, which was considered a calm condition in other landscape-temperature relationship studies [17,37,38,53]. The highest wind speed recorded in this study was only 5.1 m/s. Some studies were able to analyse the UHI intensity in a more windy condition (> 5 m/s) [45,46]; it was suggested that the effects of high wind speed on UHI intensity were substantial.

Although this study did not cover a wide range of weather conditions, it considered the collection of data such that it was representative of the summer climate of Hong Kong. The wind speed, solar radiation, and cloud amount data distributions of the study period (Fig. 4) highly resembled the long-term distributions (Fig. 1). Thus, it was appropriate to use the entire dataset to develop the two models (daytime and nighttime) for representing the summer period, considering the fact that the impacts of weather on the regression coefficients were minor in Hong Kong's climate. The models for the entire study period showed that ROAD, TREE, SHRUB, SVF and SEA significantly controlled daytime mean temperature (Table 7). A study conducted in Florence indicated that daily minimum air temperature would increase by 0.29 °C for every 10% increase in street cover ratio in a circular buffer with a 25-m radius [43]. The coefficient was about five times higher than the daytime (0.059 °C/10%) and the night-time (0.056 °C/10%) ones obtained in this study. The strong impact of street cover in the Florence may be due to the use of a simple linear regression instead of multiple regression. Therefore, the influence of other factors was muted, and the effect of street cover was amplified. However, the results may not be directly comparable because the temperature indices and climates were different. Anthropogenic heat production from road vehicles was likely to substantially contribute to temperature rise in the urban parks,

Table 5 Coefficient estimates of the daytime mean air temperature models under different weather conditions. Weather parameter

Landscape parameter

Unit of comparison

Low

High

Others

Trend ( ± )

Cloud amount (low: < 55%; high: ≥ 88%)

ROAD WATER TREE SHRUB TURF AREA BV SVF SEA

10% 10% 10% 10% 10% 10,000 m2 10% 1 1000 m

0.072** −0.030 −0.038 −0.064** −0.059 −0.015 0.014 1.026*** 0.761***

0.052* −0.029 −0.044* −0.045* −0.013 −0.017 0.047 0.781*** 0.627**

0.060** −0.044 −0.036* −0.052* −0.023 −0.019 0.060 0.998*** 0.698***

– – + – – + + – –

Solar radiation (low: < 10.40 MJ/m2; high: ≥ 22.88 MJ/m2)

ROAD WATER TREE SHRUB TURF AREA BV SVF SEA

10% 10% 10% 10% 10% 10,000 m2 10% 1 1000 m

0.049* −0.026 −0.043* −0.049* −0.018 −0.019 0.049 0.819*** 0.628**

0.066** −0.047 −0.033 −0.050* −0.027 −0.020 0.038 1.108*** 0.739***

0.060** −0.044 −0.036* −0.053* −0.024 −0.018 0.063 0.982*** 0.696***

+ + – + + + – + +

Wind speed (low: < 1.6 m/s; high: ≥ 4 m/s)

ROAD WATER TREE SHRUB TURF AREA BV SVF SEA

10% 10% 10% 10% 10% 10,000 m2 10% 1 1000 m

0.061* −0.052 −0.042* −0.060* −0.040 −0.017 0.079 1.082*** 0.942***

0.054* −0.078* −0.039* −0.050* −0.020 −0.016 0.066 1.095*** 0.680***

0.06** −0.040 −0.036* −0.051* −0.023 −0.019 0.056 0.972*** 0.681***

– + – – – – – + –

Note: the trend sign ( ± ) compares the strength of the coefficients between the ‘high’ and ‘low’ cases. *p < 0.05, **p < 0.01 and ***p < 0.001. 7

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Table 6 Coefficient estimates of the night-time mean air temperature models under different weather conditions. Weather parameter

Landscape parameter

Unit of comparison

Low

High

Others

Trend ( ± )

Cloud amount (low: < 55%; high: ≥ 88%)

ROAD WATER TREE SHRUB TURF AREA BV SVF SEA

10% 10% 10% 10% 10% 10,000 m2 10% 1 1000 m

0.054** −0.004 −0.018 −0.035* −0.034 −0.044* 0.005 0.050 0.469**

0.054** −0.013 −0.024 −0.031* −0.026 −0.033 0.029 0.084 0.437*

0.055*** −0.016 −0.021 −0.034* −0.030 −0.039* 0.022 0.078 0.426**

– + + – – – + + –

Solar radiation (low: < 10.40 MJ/m2; high: ≥ 22.88 MJ/m2)

ROAD WATER TREE SHRUB TURF AREA BV SVF SEA

10% 10% 10% 10% 10% 10,000 m2 10% 1 1000 m

0.050** −0.009 −0.024* −0.028* −0.019 −0.030 0.026 0.066 0.423**

0.057*** −0.014 −0.020 −0.034* −0.029 −0.039* 0.018 0.081 0.424**

0.055*** −0.017 −0.021 −0.035* −0.032 −0.039* 0.022 0.077 0.430**

+ + – + + + – + +

Wind speed (low: < 1.6 m/s; high: ≥ 4 m/s)

ROAD WATER TREE SHRUB TURF AREA BV SVF SEA

10% 10% 10% 10% 10% 10,000 m2 10% 1 1000 m

0.054*** −0.017 −0.015 −0.033* −0.046 −0.045** 0.025 0.004 0.581***

0.055** −0.029 −0.015 −0.041** −0.037 −0.045* 0.027 0.028 0.464**

0.055*** −0.015 −0.022 −0.034* −0.029 −0.038* 0.021 0.084 0.416**

+ + – + – – + + –

Note: the trend sign ( ± ) compares the strength of the coefficients between the ‘high’ and ‘low’ cases. *p < 0.05, **p < 0.01 and ***p < 0.001.

particularly those close to busy roads (Arr, Ivy and Lai) (Fig. 3). Urban parks, especially small ones, should be avoided in areas with busy traffic to evade the relatively significant thermal impacts of the heatretaining paved surfaces and heat-generating vehicles in relation to the limited park area, to improve the thermal environment and air quality of the public space. TREE and SHRUB were proven to have significant impacts of daytime and night-time mean air temperatures (Table 7). A 50% increase in TREE would lead to an approximately 0.26 °C reduction in daytime mean air temperature. Another study conducted in Hong Kong's

summer showed that a similar increase in TREE led to a 0.6 °C decrease in daytime mean air temperature [25]; daily irrigation probably accounted for the observed stronger cooling effect than the model suggested because evaporative cooling can be substantially prompted if water supply is abundant [72]. However, there was no water outlet in 17 of the 92 sites, meaning that irrigation might be insufficient and evaporative cooling could be limited in these sites. TREE always had a stronger impact on air temperature than SHRUB owing to the former's shading effect. A controlled experiment showed that shading effect contributed to 67% of the temperature reduction within canopy while

Table 7 Daytime and night-time mean air temperature models (full and reduced) of the whole study period. Time of day

Landscape parameter

Unit of comparison

Full model

Reduced model

Estimate

SE

df

t

p

Estimate

SE

df

t

p

Daytime

(Intercept) ROAD WATER TREE SHRUB TURF AREA BV SVF SEA

NA 10% 10% 10% 10% 10% 10,000 m2 10% 1 1000 m

29.853 0.060 −0.043 −0.037 −0.052 −0.024 −0.018 0.058 0.984 0.697

0.177 0.021 0.028 0.017 0.020 0.037 0.015 0.056 0.158 0.149

72.0 68.3 80.9 76.8 76.0 75.7 7.5 78.3 75.0 14.8

168.8 2.8 −1.5 −2.1 −2.5 −0.6 −1.2 1.0 6.2 4.7

< 0.001 < 0.01 > 0.05 < 0.05 < 0.05 > 0.05 > 0.05 > 0.05 < 0.001 < 0.001

29.892 0.059 NA −0.052 −0.041 NA NA NA 0.849 0.784

0.176 0.020 NA 0.015 0.018 NA NA NA 0.144 0.152

75.2 75.1 NA 83.7 77.5 NA NA NA 81.4 17.3

169.8 3.0 NA −3.4 −2.3 NA NA NA 5.9 5.2

< 0.001 < 0.01 NA < 0.01 < 0.05 NA NA NA < 0.001 < 0.001

Nighttime

(Intercept) ROAD WATER TREE SHRUB TURF AREA BV SVF SEA

NA 10% 10% 10% 10% 10% 10,000 m2 10% 1 1000 m

28.669 0.055 −0.016 −0.021 −0.034 −0.030 −0.038 0.022 0.077 0.428

0.129 0.015 0.020 0.012 0.014 0.025 0.014 0.039 0.109 0.124

71.9 77.9 79.7 75.1 74.1 73.9 9.4 76.2 73.5 17.1

222.6 3.6 −0.8 −1.8 −2.4 −1.2 −2.8 0.6 0.7 3.5

< 0.001 < 0.001 > 0.05 > 0.05 < 0.05 > 0.05 < 0.05 > 0.05 > 0.05 < 0.01

28.670 0.056 NA −0.029 −0.025 NA −0.039 NA NA 0.433

0.130 0.014 NA 0.008 0.012 NA 0.014 NA NA 0.126

71.7 82.0 NA 78.3 76.4 NA 9.7 NA NA 16.6

220.5 4.1 NA −3.6 −2.0 NA −2.7 NA NA 3.4

< 0.001 < 0.001 NA < 0.001 < 0.05 NA < 0.05 NA NA < 0.01

8

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Fig. 4. Histograms of (a) amount of cloud, (b) daily total solar radiation, and (c) wind speed of the study period. Solar radiation and wind speed data were collected at the King's Park weather station and the amount of cloud data at the Hong Kong Observatory station.

evaporation accounted for the rest [73]. The cooling benefits of TREE and SHRUB were able to extend to the night because they reduced daytime heat storage and limited its release at night in the form of sensible heat [8]. Reducing SVF was widely recognized as an effective way to intercept solar radiation and lower daytime air temperature in urban areas [17,18,23,24,74]. The empirical models in this study showed that lowering SVF by 0.5 could reduce daytime mean air temperature by 0.425 °C, but SVF had no significant impact on night-time temperature (Table 7). Unger [75] conducted a comprehensive review on the SVF-temperature relationship and revealed that the findings reported in the literature were rather inconsistent. The author attributed the inconsistency to large microvariations in the immediate environs of the measurement points; measurement height, time of year and background climate also had impacts on the regression coefficients. Nevertheless, there is a consensus in the literature that controlling SVF is crucial to improve daytime thermal comfort especially in subtropical and tropical areas where solar radiation is strong [76–79]. Although low SVF can accentuate nocturnal UHI because it restricts radiative cooling [80], this study did not find a significant relationship between SVF and night-time urban air temperature. AREA was only significantly associated with night-time air temperature (Table 7). Park area itself does not constitute a warming or cooling mechanism, but a large park implies that its centre is less affected by the advection of warm air from its surrounding built-up areas. The mobile temperature measurements conducted in Tel Aviv [69] and Beijing [34] showed that air temperature within a park increased with decreasing distance from the park boundary. These findings suggested that urban parks should be large enough to buffer the advection of warm air from their surrounding built-up areas.

Although SEA had significant impacts on both daytime and nighttime air temperatures in this study (Table 7), the literature suggested that the temperature effects of SEA were unclear and dependent on time of the day and season. A study conducted in Korea showed that summer daytime mean UHI was 1.557 °C weaker in sites near the coast than in the rural area [61]. However, the cooling effect reversed to warming in summer night-time (0.013 °C), winter daytime (0.072 °C) and winter night-time (2.324 °C). Giridharan et al. [15] monitored the air temperature in 17 residential areas in Hong Kong. On summer clear days, the coefficient of proximity to sea changed from negative (cooling) in the daytime to positive (warming) in the night-time. Under other weather conditions, the coefficients remained negative during the day and night. More elaborate studies of this kind are required to ascertain the effects of proximity to sea on air temperature. The effects of urban and landscape parameters on air temperature have been extensively investigated [6,23,28,50,81]; yet few studies have evaluated simultaneously a comprehensive set of parameters and identified the ones with significant effects using temporally continuous data. The results of this study can inform the design and planning of urban parks because the estimated temperature changes with landscape modification can help prioritise heat mitigation strategies. The regression results indicated that increasing tree cover and lowering SVF were critical to reducing air temperature (Table 7), which corroborated with previous studies [25,76,82]. It is recommended that an urban woodland can be established in certain areas of the urban parks with native and diverse tree species forming an interlocking crown structure to provide sufficient shading [83]. The results also suggested that shrubs are effective cooling elements to be included in the parks when tree planting is infeasible. Small roadside parks should be avoided due to proximity 9

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to anthropogenic heat source and the lack of buffering distance. Instead, waterfront parks are preferred to tap the cooling benefits of sea breeze [84]. Although lakes and turf had no significant cooling effect (Table 7), they could be included in parks for aesthetic and functional purposes. The increase in the cover of tree, shrub, turf, and water body implicitly implied a reduction in dominant pavement surface (62%) type in the sampled parks (Table 1) and allowed the conversion to green and natural elements. Apart from high air temperature, high levels of humidity can also lead to thermal discomfort [85], especially in the humid tropical and subtropical regions [86–88]. Whereas synoptic weather is an important control on air moisture content on the city and regional scales, urban landscape can generate water vapour and increase humidity. An empirical study conducted in Shenzhen, China, showed that small-scale vegetation communities could increase relative humidity by 6.2–8.3% compared to a paved open site [89]. A simulation study suggested that the water vapour mass fraction in the air could increase by 14% at the waterfront comparing to a location 300 m from the water body [90]. The thermal benefits of vegetation cover and proximity to sea may therefore be compromised by the increase of humidity. Future studies are required to consider air moisture content when assessing the microclimatic effects of landscape elements. Since relative humidity is a function of air temperature, mixing ratio (mass of water vapour/mass of dry air) should be used as the dependent variable in the regression model.

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5. Conclusion This study showed that the impacts of weather conditions on landscape-temperature relationships are minor. Under the range of landscape characteristics examined in this study, the landscape effects on temperature did not differ more than 0.2 °C in different weather conditions. Given that the weather of the study period was representative of that of Hong Kong's summer, it was unnecessary to consider weather impacts when the landscape-temperature relationship was investigated. Without considering the effects of weather, the daytime mean air temperature model of the whole study period demonstrated that ROAD, SVF and SEA were positively associated with temperature while TREE and SHRUB were negatively associated. The landscape parameters that entered the night-time model were same as those of the daytime except that SVF was excluded, and AREA was included with a significant cooling effect. The strengths of other parameters weakened during the night. UHI has become a fundamental problem for several cities worldwide as the cities continue to expand and densify to accommodate the everincreasing urban population. UHI can be mitigated by careful landscape design and planning. Urban vegetation, particularly trees, has been proven to be effective in reducing urban temperature. Empirical studies of this type can provide information to landscape designers and urban planners regarding the temperature effects of landscape modification. However, such empirical results are often only applicable to the study location. Local studies should be conducted to understand the unique urban and climate features of a city and to obtain objective findings to optimize landscape design with a view to bring nature-based and effective cooling. Acknowledgements We are grateful for the technical and logistic support provided by Ms Jeannette Liu, Mr Siu Chun To and Mr Wong Wing Yiu. This project was financially supported by the Dr Stanley Ho Alumni Challenge Fund. Permission from the Leisure and Cultural Services Department of the HKSAR Government to install temperature sensors in the parks is greatly appreciated. 10

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