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The application of a high-density street-level air temperature observation network (HiSAN): Dynamic variation characteristics of urban heat island in Tainan, Taiwan
Science of the Total Environment 626 (2018) 555–566
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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
The application of a high-density street-level air temperature observation network (HiSAN): Dynamic variation characteristics of urban heat island in Tainan, Taiwan Yu-Cheng Chen a, Chun-Kuei Yao a, Tsuyoshi Honjo b, Tzu-Ping Lin a,⁎ a b
Department of Architecture, National Cheng Kung University, 1 University Rd., East Dist., Tainan 701, Taiwan Department of Landscape Architecture and Environmental Science, Chiba University, 648, Matsudo, Matsudo., Chiba 271-8510, Japan
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• The high-density street-level air temperature observation network can be helpful for evaluating urban heat island. • The urban heat island intensity can be at least 3 °C every month in Tainan and can reach up to 5 °C in hot season daytime. • Urban development pattern and geographic feature both affect thermal condition in the areas far from the coast in hot season. • The centric points of urban heat island will dynamically move from the west to the east in the daytime and opposite at night. • The impermeable surface area and the distance from the coast are the two most obvious factors on influencing UHI in Tainan.
a r t i c l e
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Article history: Received 29 October 2017 Received in revised form 7 January 2018 Accepted 7 January 2018 Available online xxxx Editor: Lidia Morawska Keywords: Urban Heat Island Observations network Urban morphology Tainan Micro climate
⁎ Corresponding author. E-mail address: [email protected] (T.-P. Lin).
https://doi.org/10.1016/j.scitotenv.2018.01.059 0048-9697/© 2018 Elsevier B.V. All rights reserved.
a b s t r a c t The effects of urban heat island (UHI) have recently become a crucial issue. This study utilized a high-density streetlevel air temperature observation network (HiSAN) to understand the UHI characteristics in Tainan City. A total of 100 measurement points were established throughout the city. The average distance between two neighboring measuring points was 1.9 km in rural areas and 0.8 km in metropolitan areas. The UHI caused a temperature differences of at least 3 °C in each month over the study period, and the UHI's centric point moved from west to east during the day and from east to west at night, mainly because of the physical effects of the different urban environment including location and the impermeable surface area (ISA), total floor area, and sky view factor in urban areas. The results also indicated that factors such as ISA and distance to the coast had the strongest influence on thermal conditions at various times, especially in the areas far from the coast during the hot season. This was mainly because of differences in how heat was retained over the study area. The HiSAN method can be used by urban planners, architects, and policymakers to mitigate the thermal stresses caused by complex urban environments. © 2018 Elsevier B.V. All rights reserved.
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1. Introduction A small number of meteorological stations are insufficient for representing the atmospheric changes and understanding the microclimate, thermal conditions, and built environment in large complex urban areas (Chen et al., 2016a, 2016b; Honjo et al., 2015; Chang et al., 2010; Merbitz et al., 2012). The urban heat island (UHI) effect indicates that the temperature differences between various environments are affected not only by natural climatic conditions, but also by different built environments (Baker et al., 2002; Kotharkar and Surawar, 2015; Lu et al., 2012). Therefore, a suitable number of meteorological stations is required in urban areas to accurately understand how different land covers, land uses, and other factors such as impermeable surface area (ISA) and total floor area (TFA) affect thermal conditions. The built environment in cities is complex. Accurately evaluating climatic conditions and understanding the relationship between an urban environment and UHI variations cannot be achieved by relying on the meteorological data provided by a few meteorological stations. A network with numerous intensive measurement points, arranged according to the type of urban typology, is required to accurately understand UHI phenomena. In this study, 100 instruments were used to obtain comprehensive meteorological information, such as air temperature and humidity, in Tainan. These instruments comprised a high-density street-level air temperature observation network (HiSAN). In the HiSAN, the measurement points were arranged in different development districts to examine the relationship between thermal conditions and urban development patterns. Using the HiSAN in this study yielded the following advantages: we were able to use numerous stations to observe the distribution of climatic conditions in the study area; an accurate representation of the thermal conditions was achieved because enough stations were distributed in the study area; all instruments recorded data at 5-min intervals every day, enabling the temporal and spatial distribution of the UHI to be presented clearly over the study period. The data of urban development patterns and geographical characteristics were used to explore the thermal environmental conditions in a large urban area to understand the microclimatic changes. Urban planners who do not have meteorological background ca use the results from this study to understand the relationship between urban development and thermal environments.
2. Method
vegetation (comprising farms, parks, and lawns in urban areas) is located on the east and south sides of Tainan (Fig. 1c). 2.2. HiSAN 2.2.1. Measurement instruments One hundred automatic recording instruments (LOGPRO TR-32, Tecpel Co., Ltd.) were distributed in the study area to collect meteorological data. The LOGPRO (Fig. 2a) was selected for the study because it is lightweight, energy efficient, and can store a large amount of data. The sensors in the LOGPROs were checked before installation to confirm accuracy. Air temperature (Ta) and RH were recorded at 5-min intervals in the study area with an accuracy of ±0.5 °C and ±5% and a measurement resolution of 0.1 °C and 1%, respectively. Radiation covers were used to protect the LOGPROs from the elements (rain, sun, and wind) and ensure that all data collected were accurate (Fig. 2b). The LOGPROs were installed on the utility poles of street lamps at a height of 2.5 m (Fig. 2c). At this height, the LOGPROs were adequately ventilated and posed no safety concerns for pedestrians or motorists. (Fig. 2d). Data were collected from the LOGPROs monthly to ensure that the batteries remained charged. At each inspection, the LOGPROs were synchronized with a standard LOGPRO that Tecpel Co. Ltd. calibrated monthly. This ensured that the field LOGPROs were accurate during the study period. 2.2.2. Measurement points The measurement locations were selected according to the following three principles (Fig. 3a): First, to achieve an average distribution of measurement points, the study area was equally divided into 58 sections, each comprising a 3 km × 3 km grid (Fig. 3b). The measurement points were scattered in each grid (each grid contained at least one measurement point). Second, the sections were then classified according to regional features, including land use, land cover, building density (such as TFA), and normalized difference vegetation index (NDVI). Doing this prevented the selection of sections with the same or similar urban patterns (Fig. 3c). Third, the comparison of homogeneity, the green areas were intentionally selected to investigate the influence of green spaces on thermal conditions in the study area (Fig. 3d). The final measurement points were selected because they represented the most significant attributes of Tainan's urban morphology (Fig. 3e). Overall, the selected measurement points accurately recorded the characteristics of UHI, including temporal temperature distribution and movement of the UHI over the study period, which revealed the impacts of urban development on the thermal conditions in the study area.
2.1. Study area The metropolitan area of Tainan (22° 59′ N, 120° 11′ E), a highdensity city in southern Taiwan (Fig. 1a), and its surrounding areas were selected as the study area. Compared with other cities in Taiwan, Tainan is rapidly expanding, which makes it more crucial to regulate urban planning in accordance with the meteorological information provided in this study. The total study area was 175.6 km2, comprising various natural and built environments. In 2010, Tainan City was upgraded to a special municipality, which subsequently led to a rise in the population and urban development. Therefore, microclimatic research is needed to influence local government urban development policies in Tainan. Tainan City has a tropical savanna climate with a mean annual temperature of 24.6 °C. The hottest month is July (mean temperature is 30.4 °C) and the coolest month is January (mean temperature of 17.6 °C). The annual mean relative humidity (RH) is 74.4%. The average wind direction in the summer is from the west at approximately 270°. Most urban development is concentrated in the central part of the city, with the density gradually decreasing outward (Fig. 1b). Most
2.2.3. Data processing The statistical analysis software R language was used to batch process and consolidate the large volume of data collected during the study period. Ta data were calculated hourly. Using meteorological information provided by the Tainan Meteorological Station, various climatic conditions could be chosen and the corresponding data could be intercepted to reveal the corresponding thermal conditions. For example, the average Ta was calculated for selected periods when the proportion of solar radiation was N75%. The inverse distance weighting interpolation method was applied to calculate the Ta distribution in the study area with a 30 m resolution. The Ta distribution and variations were generated to visualize the thermal conditions at different periods. To prevent the collection of extreme data due to measurement errors or unpredictable events such as heavy rain or a typhoon, this study compared the differences between the maximum and minimum Ta but used the 2nd and 5th percentile values instead of the minimum Ta and the 95th and 98th percentile values instead of the maximum Ta. This was done to ensure that the normal climatic conditions were accurately depicted.
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Fig. 1. Description of Study area. (1a) Map of Tainan (1b), building density and (1c) vegetation density.
For visualization and to obtain Ta values with coordinates, Quantum Geography Information System (QGIS) was used to integrate various data such as the location of the measurement points, land cover, and meteorological conditions. This data was used to map the distribution of Ta in the study area.
point of the UHI. Eq. (2) calculates the center of gravity of the points, which exhibits a positive UHI deviation. These equations can be used to describe the UHI movement and comprehensively analyze how different features, including the level of urban development and distance to the coast, affect the distribution of air temperature over the study area at various times.
2.3. Center location and UHI deviation
hi ¼ t i −t ave n 1X t ave ¼ ti n k¼1
In this study, the spatial variability of the UHI (deviation and movement) was described using the Tokyo experience method (Honjo, 2012), which has been used to understand the characteristics of UHI in urban areas. In Eq. (1), hi is defined as the UHI deviation, which is the difference in Ta at a point i; ti is the Ta at point i in a certain period; and tave is the average Ta of all the measurement points in a certain period. Eq. (2) is used to calculate the latitude of the UHI centric point Lc by using the coordinates of all measurement points, where n is the number of all measurement points and li is latitude of observation point i. The same equation can be used to calculate the longitude of the centric
Lc ¼
Pn i li hi P ðhi ¼ hi n i hi
ð1Þ
if
hi ≥0;
hi ¼ 0
if
hi b0Þ ð2Þ
2.4. Urban development parameters and the thermal environment Previous studies have shown that land use and land cover differences significantly affect urban microclimates (Johnson and Trout,
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Fig. 2. Measurement setting. (2a) Measuring instrument “LOGPRO”, (2b) radiation cover, (2c) radiation cover and poll, and (2d) installation schematic diagram.
2012; Bounoua et al., 2000; Arnold and Gibbons, 1996). In this study, two indicators were used to evaluate the influence of urban morphology and geographical characteristics on the thermal environment. The first indicator was the ISA, which reflects the type of land cover and urban development characteristics (Lin et al., 2005). A higher ISA indicates a higher area of unnatural surfaces, which contributes to higher temperatures (Xiao et al., 2007; Frazer, 2005). This study utilized satellite images with a 30-m resolution obtained from SPOT 6 in May 2017 to classify the land cover patterns in five categories using a supervised classification approach in QGIS. The five categories were water, green spaces, bare land, pavement, and buildings.
The measurement points in the HiSAN were selected to generate a 200-m buffer area and then overlaid with the ISA layer (Fig. 4a) to calculate the surface cover and determine the ISA at each point. Water, green spaces, and bare land were considered permeable, whereas pavement and buildings were considered impermeable. Therefore, if there were 30 grids with permeable surfaces and 20 grids with impermeable surfaces in the buffer area of a location, the representative ISA of the measurement location was calculated as 40%. Because Tainan is a coastal city, its climatic conditions are strongly affected by the sea (Fig. 4b). Because the sea regulates Ta and provides a sea breeze that mitigates heat, this study calculated
Fig. 3. Selection of measurement points. (3a) The flow diagram of the location arrangements, (3b) The mesh in the map to identify coordinates and for “Average distribution of measuring points”, (3c) The classification of urban developing pattern for “classification by regional features”, (3d) The parks and vegetation area distribution for “Comparison of homogeneity”, and (3e) The result of total 100 measurement points.
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the minimum distance of the HiSAN points from the coastline and divided the locations into “near the coast” and “far from the coast” according to the median value as the second indicator. This was done to investigate the impact of the sea on the coastal and inland spaces at different periods, including the hot season, cool season, daytime, and nighttime. Using these two indicators, urban development and geographical characteristics could be quantified and considered simultaneously.
cools relatively more slowly than land; therefore, the western coastal fishing area (area near the sea) was at a higher temperature. Third, in the afternoon, owing to solar radiation from the sun, the land and sea were simultaneously endothermic. The land warmed more quickly and the coastal fishing area remained at a relatively low temperature. Therefore, a higher temperature was observed in the areas with dry soil on the east side of Tainan, which are far from the sea. 3.3. Temporal variation in UHI intensity
3. Results 3.1. Temporal and spatial UHI distribution The meteorological data obtained from the HiSAN were recorded hourly. Ta distribution maps in Fig. 5 show different seasons and times, such as the hot season (from April to October), cool season (from November to February), daytime (from 6 a.m. to 6 p.m.), and nighttime (from 6 p.m. to 6 a.m.). The map corresponding to daytime in the hot season (Fig. 5a) shows that the regions with high UHI deviation were located in inland areas near the city center. Higher UHI deviation was identified in open areas such as plazas and avenues, which had numerous impermeable surfaces. The map also indicates that the thermal environment in the daytime during hot season had high thermal stress. During the daytime in the cool season (Fig. 5b), the areas with high UHI deviation were located in the inland areas of Tainan, mainly because sea breezes do not reach those areas. At nighttime in the hot season (Fig. 5c), the area with high UHI deviation was closer to the coast at night than during the daytime. One reason for this is that water retains more heat than soil does (the specific heat of water is five times higher than soil); therefore, heat was preserved near the sea. Another reason is the temperature noticeably decreased at nighttime in rural areas due to radiative cooling and was 3 °C cooler than in urban areas. During nighttime in the cool season (Fig. 5d), the areas with high UHI deviation were mainly distributed in the city center because they contained a higher ISA. Therefore, the results revealed that both, the time of the day and the urban morphology affected the UHI in the study area. The UHI deviation distribution map shows how land cover and the level of urban development affected the UHI variation and thermal conditions throughout the study area. These results improved our understanding of thermal stress and can be used to prepare mitigation strategies.
UHI intensity refers to the difference between the highest and lowest Ta in a certain period. The meteorological data obtained from the HiSAN confirmed a temporal variation in the UHI intensity, which was considerable in Tainan, especially during the night. Differences between the maximum and minimum all-day average Ta were 3.5 °C to 4.5 °C in summer and 3 °C in winter (Fig. 7a). The UHI intensity was also calculated at the 95th to 98th and 2nd to 5th percentile values of recorded Ta to prevent the influence of extreme temperatures and error values. As shown in Fig. 7a, the average UHI intensity in Tainan was 3 °C in summer and 2.5 °C in winter. These values are considered reliable because they reflect the real UHI intensity and discount extreme conditions. During the day (6 a.m. to 6 p.m.), the difference between the maximum average and minimum average Ta was as high as 5 °C (Fig. 7b). In the all-year average, the difference between the 98th and 2nd percentile values was approximately 3 °C, indicating that the UHI intensity in Tainan in the day was relatively stable for all seasons over the study period. At night (6 p.m. to 6 a.m.), the seasonal variation in Ta was significant (Fig. 7c), and the difference between the maximum and minimum average Ta was approximately 3.5 °C in summer and 2.5 °C in winter. Differences were also detected between the 98th and 2nd percentile values, with the UHI intensity difference being approximately 2.5 °C to 3 °C in summer and 1.5 °C to 2 °C in winter. The results revealed that the UHI is a serious issue in Tainan because of rapid urbanization. During the day, because the energy consumption in the city center was considerably higher than that in the suburban areas, the buildings in urban areas prevented ventilation that would otherwise mitigate the heat. A considerable amount of heat remained in urban areas at night because of the heat retained by buildings and impermeable materials, with the UHI affecting the temperature by up to 3 °C. However, because solar radiation and energy use were relatively low in the winter, the UHI intensity at nighttime was lower than it was in the summer.
3.2. Dynamic movement of UHI-centric points 3.4. Comparison of land pattern and air temperature The temporal and spatial distribution of the UHI can also be described by the dynamic movement of the UHI centric points, as discussed in Section 2.3. In this study, two representative days in the hot season and cool season with the most significant UHI distribution were selected. The 24-h paths of the UHI centric points over these days are shown in Fig. 6a and b. The paths indicate that the UHI centers moved from the west side of the city to the east side during the day and moved back to the west side during the night (6 p.m. onwards), revealing a daily pattern. The path followed the east–west direction, indicating that Ta did not change considerably in the south–north direction in Tainan. Significant differences existed between the east side and west side, including the distance to the coast and the level of urban development. In the areas near the sea, the differences in the day and night temperatures may have been caused by the following: First, the specific heat of water is five times higher than soil, therefore, water retains more heat than soil does. Furthermore, soil temperature changes more rapidly during warming or cooling. Second, there was no solar radiation early in the morning, so the sea and land in the study area were simultaneously exothermic. Sea water
ISA was separated into two categories according to the median value. Additionally, the distance to the coast was divided into “near the coast” (b5 km) and “far from the coast” (N5 km), and time of the year was divided into hot and cool seasons (Fig. 8a, b, c, and d). A high ISA indicates that an area could easily retain heat. When the ISA was N50%, the Ta distribution was at a higher temperature, especially during the hot season. In the hot season, pavement retained considerable heat, which resulted in a higher heat load. Furthermore, the Ta near the coast fluctuated less than that of areas far from the coast regardless of ISA. The average Ta difference in the areas near the coast in the hot season and cool season were 0.4 °C and 0.3 °C, respectively. The average Ta difference in the areas far from the coast was up to 1.2 °C in the hot season and 0.6 °C in the cool season. Overall, ISA had a stronger influence on Ta in areas far from the coast than in areas near the coast. As shown in Fig. 8, the areas with ISA N 50% experienced a considerably higher Ta than those with ISA b 50% in all areas in both seasons. Therefore, the ISA, which is directly related to the level of urban development, affected thermal conditions in Tainan.
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Fig. 4. Category of measurement points based on (4a) ISA with the 200 m buffer area and (4b) the distance from the coast.
Fig. 5. UHI deviation distribution in (5a) Hot season daytime (5b) Cool season daytime (5c) Hot season nighttime (5d) Cool season nighttime.
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Fig. 6. Movement path of hourly UHI centric point in (6a) 2016/5/27 and (6b) 2017/2/13.
4. Discussion 4.1. Analysis of development characteristics and geographical features Urban development patterns and geographical features were examined to evaluate their effects on thermal conditions, as shown in the HiSAN measurements. In this study, factors such as land cover and land use were insufficient to explain the microclimate of the various areas; therefore, geographical characteristics such as sea breeze and distance to the coast were also considered. The results in Section 3.4 indicate that, regardless of the season, the Ta in the areas near the coast did not vary with ISA. However, areas far from the coast (Fig. 9b) with ISA N 50% had a considerably higher Ta in the hot season than did areas with ISA b 50%, indicating that areas with higher ISA absorb more heat. Therefore, ISA must be considered when examining the influences of urban development on temperature (Weng et al., 2011; Chun and Guhathakurta, 2017). The long-term wind direction in Tainan is from the sea to land (270°). Because areas far from the coast, especially on the east side of Tainan, are not influenced by a sea breeze (Chen et al., 2016a, 2016b), heat is retained more easily in these areas. These results suggest that evaluations of thermal conditions should consider the combination of urban morphology and geographic features to establish more accurate result. 4.2. Comparison with other UHI studies The UHI effect occurs in most cities worldwide and has caused severe environmental problems. The UHI effect has been evaluated using various approaches, including urban climatic maps (UCmap) (Chen et al., 2016a, 2016b; Lin et al., 2017; Ren et al., 2010; Alcoforado et al., 2009), computational models (Ketterer et al., 2016; Bruse and Fleer, 1998; Toparlar et al., 2015), data retrieved from remote sensors (Chen et al., 2016a, 2016b; Shih, 2016; Lo and Quattrochi, 2003; Nichol, 2005), traverse measurements (Jonsson, 2004; Yang et al., 2016; van Hove et al., 2015), and observation networks (Honjo et al., 2015; Unger et al., 2015; Bassett et al., 2016). UCmaps represent microclimatic conditions by considering various urban development parameters as well as data obtained from meteorological stations. Furthermore, the data can be applied to different areas with different characteristics, but the distribution of thermal conditions remains a prediction, and it can be inaccurate (Ng and Katzschner, 2010; Ren et al., 2013).
Various models have been utilized to simulate thermal conditions and understand UHI, including SkyHelios (Matzarakis and Matuschek, 2011; Ketterer et al., 2016), SOlWEIG (Lindberg et al., 2008; Lindberg and Grimmond, 2011), and ENVI-met (Yang and Lin, 2016; Bruse and Fleer, 1998). Although modeling can easily and immediately reveal the microclimate in complex urban environments, the accuracy of these models requires further validation (Fabiyi, 2011). The thermal conditions determined from land surface temperature evaluations in satellite images are often used to discuss the relationship between changes in land cover and UHI variations (Zhou et al., 2014; Liang et al., 2012; Lo et al., 1997). The advantage of the remote sensing approach is that thermal conditions that vary widely on the urban scale can be easily estimated. However, because this method is limited by the passing time of the satellite that captures the images, thermal conditions cannot be determined for all periods. In addition, the derived air temperature results cannot always be retrieved correctly owing to the complex materials used in urban areas (Chen et al., 2016a, 2016b; Tomlinson et al., 2011). Meteorological information measurement networks have been established to quantify UHIs in numerous countries (Muller et al., 2013). Many cities are investigating hotspot distributions and UHI intensities and are creating regulations to mitigate the impacts of urban thermal stress. Tokyo is a megacity with a high intensity of UHIs (Mikami and Yamato, 2011). Honjo (2012) examined the daily movement of the UHI in the Kanto area using an observation network approach (METROS). Their study employed a total of 200 measurement points, which were instrument shelters in elementary schools. The variation in the UHI during different periods and its characteristics were illustrated, and the results revealed that its centric point moved inland from the coast during the day and returned toward the coast at night. Although Ta tends to be higher in city centers (because of a higher building density), their study showed that Ta is also influenced by seasonal winds and sea breezes (Takahashi et al., 2011). However, because the measurement points were all located in areas with the similar land use (the development intensity of schools is low, and the green cover ratio is relatively high), the findings did not accurately depict the UHI in urban areas. Consequently, the reported Ta would have been lower than the actual Ta. An observation network project named Urban-Path was established in Szeged, Hungary to monitor a UHI and obtain longterm effective climate data (URBAN-PATH Project, 2015). A thermal load distribution map was generated using the interpolation approach and the data collected from 24 measurement locations
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Fig. 7. Monthly difference of UHI intensity in (7a) All day average (7b) Daytime average (7c) Nighttime average.
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Fig. 8. Air temperature range in (8a) Hot season, near the coast (8b) Hot season, far from the coast (8c) Cool season, near the coast and (8d) Cool season, far from the coast.
equipped with Wi-Fi signal receivers. The advantage of this network was the accessibility of the data. The data were automatically collected and transmitted through the Internet, and the real-time atmospheric conditions were displayed on a website by using cloud computing (Unger et al., 2015). However, the number of measurement locations was insufficient because different land use scenarios produce different thermal conditions. The interpolation approach cannot accurately evaluate the UHI in a complex urban environment such as Tainan. Therefore, the HiSAN approach was used in this study because a large number of instruments needed to be installed in various built environments to investigate the thermal conditions on a city scale with high accuracy. 4.3. Application for other cities Using the observation network to obtain data about UHI patterns is valuable, especially because climate change has become a serious global issue. This study provides an alternative approach for visualizing and understanding the thermal conditions in a complex urban environment without using estimate and modeling methods. Other cities can apply this method and select different urban development parameters, such as the sky view factor, TFA, building cover ratio, and frontal area index as a substitute for the ISA used in this study. In addition, the distance to the coast can be replaced with other factors such as elevation, population density, and distance to green areas or large bodies of water. The method used in this study can be used to determine the most significant factors influencing a UHI, and thus provide an accurate understanding of the temporal and spatial factors that influence complex urban thermal environments. Therefore, this study can be valuable for cities that are aiming to mitigate the environmental problems caused by UHIs.
5. Conclusion This study examined the distribution and dynamic characteristics of a UHI in a complex urban environment to accurately understand and visualize the UHI during different periods. The aim was to develop a method for urban planners to use when considering thermal conditions in urban planning. This study addressed two main issues. First, the interpolation approach cannot be used to accurately understand the thermal conditions in complex urban environments because only a small number of measurement locations are used. This study used HiSAN, which consist of 100 measurement points at high density in different development districts to obtain long-term meteorological data for evaluating the UHI characteristics in Tainan. Second, because thermal conditions vary considerably over time, understanding the relationship between urban development patterns and geographical features is crucial when examining the thermal conditions in urban environments. The urban development patterns and geographical features considered in this study included ISA, a 200-m buffer area for the measurement points, and the distance to the coast. Therefore, the influence of the different urban environments on the thermal conditions could accurately be quantified, and the phenomena in various periods could be clarified. The current study obtained the following results. First, the temporal and spatial distributions of the UHI were illustrated by the UHI deviation at different periods and the movement of the UHI centric point, which was from west to east during the day and east to west at night because of the physical effects exerted by the land and sea. This finding can be used by decision makers to develop location-based mitigation strategies to manage urban thermal stress (e.g., limiting building height to street width to improve ventilation and reduce heat storage in areas far from the coast). Second, the temporal variation in UHI intensity at different
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Fig. 9. Air temperature distribution in different ISA in (9a) Hot season, near the coast (9b) Hot season, far from the coast (9c) Cool season, near the coast and (9d) Cool season, far from the coast.
periods was clarified using the UHI intensities obtained at different temperature percentiles. This approach enables researchers to focus on reasonable data ranges and understand how a UHI changes over many months. This approach can be used to reduce the UHI (e.g., by restricting electricity use in period with high UHI intensity). Third, land patterns and air temperatures were compared. This study used a 200-m buffer area for calculating surrounding ISA and the distance to the coast, and these parameters were used to quantify the influence of urban development patterns and geographic features on the thermal conditions at each measurement point. The results showed that, in the areas far from the coast, the ISA had a more significant effect on Ta and that a higher ISA contributed to a higher Ta. Urban planners ca use these findings to improve thermal conditions by changing land patterns (e.g., by reducing the use of impermeable surfaces). Because the HiSAN method can record Ta data with high accuracy for long periods and over large complex areas, the UHI characteristics in Tainan were presented in detail and the hotspots at different periods could be revealed. The results from this study can help urban planners in Tainan precisely determine the urban thermal condition and to mitigate the thermal stresses caused by urban development. Acknowledgements The authors would like to thank the Ministry of Science andTechnology of Taiwan, for financially supporting this research under Contract No. 105-2633-E-006-002- and 106-2633-E-006-001-.
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Contents lists available at ScienceDirect
Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
The application of a high-density street-level air temperature observation network (HiSAN): Dynamic variation characteristics of urban heat island in Tainan, Taiwan Yu-Cheng Chen a, Chun-Kuei Yao a, Tsuyoshi Honjo b, Tzu-Ping Lin a,⁎ a b
Department of Architecture, National Cheng Kung University, 1 University Rd., East Dist., Tainan 701, Taiwan Department of Landscape Architecture and Environmental Science, Chiba University, 648, Matsudo, Matsudo., Chiba 271-8510, Japan
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• The high-density street-level air temperature observation network can be helpful for evaluating urban heat island. • The urban heat island intensity can be at least 3 °C every month in Tainan and can reach up to 5 °C in hot season daytime. • Urban development pattern and geographic feature both affect thermal condition in the areas far from the coast in hot season. • The centric points of urban heat island will dynamically move from the west to the east in the daytime and opposite at night. • The impermeable surface area and the distance from the coast are the two most obvious factors on influencing UHI in Tainan.
a r t i c l e
i n f o
Article history: Received 29 October 2017 Received in revised form 7 January 2018 Accepted 7 January 2018 Available online xxxx Editor: Lidia Morawska Keywords: Urban Heat Island Observations network Urban morphology Tainan Micro climate
⁎ Corresponding author. E-mail address: [email protected] (T.-P. Lin).
https://doi.org/10.1016/j.scitotenv.2018.01.059 0048-9697/© 2018 Elsevier B.V. All rights reserved.
a b s t r a c t The effects of urban heat island (UHI) have recently become a crucial issue. This study utilized a high-density streetlevel air temperature observation network (HiSAN) to understand the UHI characteristics in Tainan City. A total of 100 measurement points were established throughout the city. The average distance between two neighboring measuring points was 1.9 km in rural areas and 0.8 km in metropolitan areas. The UHI caused a temperature differences of at least 3 °C in each month over the study period, and the UHI's centric point moved from west to east during the day and from east to west at night, mainly because of the physical effects of the different urban environment including location and the impermeable surface area (ISA), total floor area, and sky view factor in urban areas. The results also indicated that factors such as ISA and distance to the coast had the strongest influence on thermal conditions at various times, especially in the areas far from the coast during the hot season. This was mainly because of differences in how heat was retained over the study area. The HiSAN method can be used by urban planners, architects, and policymakers to mitigate the thermal stresses caused by complex urban environments. © 2018 Elsevier B.V. All rights reserved.
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1. Introduction A small number of meteorological stations are insufficient for representing the atmospheric changes and understanding the microclimate, thermal conditions, and built environment in large complex urban areas (Chen et al., 2016a, 2016b; Honjo et al., 2015; Chang et al., 2010; Merbitz et al., 2012). The urban heat island (UHI) effect indicates that the temperature differences between various environments are affected not only by natural climatic conditions, but also by different built environments (Baker et al., 2002; Kotharkar and Surawar, 2015; Lu et al., 2012). Therefore, a suitable number of meteorological stations is required in urban areas to accurately understand how different land covers, land uses, and other factors such as impermeable surface area (ISA) and total floor area (TFA) affect thermal conditions. The built environment in cities is complex. Accurately evaluating climatic conditions and understanding the relationship between an urban environment and UHI variations cannot be achieved by relying on the meteorological data provided by a few meteorological stations. A network with numerous intensive measurement points, arranged according to the type of urban typology, is required to accurately understand UHI phenomena. In this study, 100 instruments were used to obtain comprehensive meteorological information, such as air temperature and humidity, in Tainan. These instruments comprised a high-density street-level air temperature observation network (HiSAN). In the HiSAN, the measurement points were arranged in different development districts to examine the relationship between thermal conditions and urban development patterns. Using the HiSAN in this study yielded the following advantages: we were able to use numerous stations to observe the distribution of climatic conditions in the study area; an accurate representation of the thermal conditions was achieved because enough stations were distributed in the study area; all instruments recorded data at 5-min intervals every day, enabling the temporal and spatial distribution of the UHI to be presented clearly over the study period. The data of urban development patterns and geographical characteristics were used to explore the thermal environmental conditions in a large urban area to understand the microclimatic changes. Urban planners who do not have meteorological background ca use the results from this study to understand the relationship between urban development and thermal environments.
2. Method
vegetation (comprising farms, parks, and lawns in urban areas) is located on the east and south sides of Tainan (Fig. 1c). 2.2. HiSAN 2.2.1. Measurement instruments One hundred automatic recording instruments (LOGPRO TR-32, Tecpel Co., Ltd.) were distributed in the study area to collect meteorological data. The LOGPRO (Fig. 2a) was selected for the study because it is lightweight, energy efficient, and can store a large amount of data. The sensors in the LOGPROs were checked before installation to confirm accuracy. Air temperature (Ta) and RH were recorded at 5-min intervals in the study area with an accuracy of ±0.5 °C and ±5% and a measurement resolution of 0.1 °C and 1%, respectively. Radiation covers were used to protect the LOGPROs from the elements (rain, sun, and wind) and ensure that all data collected were accurate (Fig. 2b). The LOGPROs were installed on the utility poles of street lamps at a height of 2.5 m (Fig. 2c). At this height, the LOGPROs were adequately ventilated and posed no safety concerns for pedestrians or motorists. (Fig. 2d). Data were collected from the LOGPROs monthly to ensure that the batteries remained charged. At each inspection, the LOGPROs were synchronized with a standard LOGPRO that Tecpel Co. Ltd. calibrated monthly. This ensured that the field LOGPROs were accurate during the study period. 2.2.2. Measurement points The measurement locations were selected according to the following three principles (Fig. 3a): First, to achieve an average distribution of measurement points, the study area was equally divided into 58 sections, each comprising a 3 km × 3 km grid (Fig. 3b). The measurement points were scattered in each grid (each grid contained at least one measurement point). Second, the sections were then classified according to regional features, including land use, land cover, building density (such as TFA), and normalized difference vegetation index (NDVI). Doing this prevented the selection of sections with the same or similar urban patterns (Fig. 3c). Third, the comparison of homogeneity, the green areas were intentionally selected to investigate the influence of green spaces on thermal conditions in the study area (Fig. 3d). The final measurement points were selected because they represented the most significant attributes of Tainan's urban morphology (Fig. 3e). Overall, the selected measurement points accurately recorded the characteristics of UHI, including temporal temperature distribution and movement of the UHI over the study period, which revealed the impacts of urban development on the thermal conditions in the study area.
2.1. Study area The metropolitan area of Tainan (22° 59′ N, 120° 11′ E), a highdensity city in southern Taiwan (Fig. 1a), and its surrounding areas were selected as the study area. Compared with other cities in Taiwan, Tainan is rapidly expanding, which makes it more crucial to regulate urban planning in accordance with the meteorological information provided in this study. The total study area was 175.6 km2, comprising various natural and built environments. In 2010, Tainan City was upgraded to a special municipality, which subsequently led to a rise in the population and urban development. Therefore, microclimatic research is needed to influence local government urban development policies in Tainan. Tainan City has a tropical savanna climate with a mean annual temperature of 24.6 °C. The hottest month is July (mean temperature is 30.4 °C) and the coolest month is January (mean temperature of 17.6 °C). The annual mean relative humidity (RH) is 74.4%. The average wind direction in the summer is from the west at approximately 270°. Most urban development is concentrated in the central part of the city, with the density gradually decreasing outward (Fig. 1b). Most
2.2.3. Data processing The statistical analysis software R language was used to batch process and consolidate the large volume of data collected during the study period. Ta data were calculated hourly. Using meteorological information provided by the Tainan Meteorological Station, various climatic conditions could be chosen and the corresponding data could be intercepted to reveal the corresponding thermal conditions. For example, the average Ta was calculated for selected periods when the proportion of solar radiation was N75%. The inverse distance weighting interpolation method was applied to calculate the Ta distribution in the study area with a 30 m resolution. The Ta distribution and variations were generated to visualize the thermal conditions at different periods. To prevent the collection of extreme data due to measurement errors or unpredictable events such as heavy rain or a typhoon, this study compared the differences between the maximum and minimum Ta but used the 2nd and 5th percentile values instead of the minimum Ta and the 95th and 98th percentile values instead of the maximum Ta. This was done to ensure that the normal climatic conditions were accurately depicted.
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Fig. 1. Description of Study area. (1a) Map of Tainan (1b), building density and (1c) vegetation density.
For visualization and to obtain Ta values with coordinates, Quantum Geography Information System (QGIS) was used to integrate various data such as the location of the measurement points, land cover, and meteorological conditions. This data was used to map the distribution of Ta in the study area.
point of the UHI. Eq. (2) calculates the center of gravity of the points, which exhibits a positive UHI deviation. These equations can be used to describe the UHI movement and comprehensively analyze how different features, including the level of urban development and distance to the coast, affect the distribution of air temperature over the study area at various times.
2.3. Center location and UHI deviation
hi ¼ t i −t ave n 1X t ave ¼ ti n k¼1
In this study, the spatial variability of the UHI (deviation and movement) was described using the Tokyo experience method (Honjo, 2012), which has been used to understand the characteristics of UHI in urban areas. In Eq. (1), hi is defined as the UHI deviation, which is the difference in Ta at a point i; ti is the Ta at point i in a certain period; and tave is the average Ta of all the measurement points in a certain period. Eq. (2) is used to calculate the latitude of the UHI centric point Lc by using the coordinates of all measurement points, where n is the number of all measurement points and li is latitude of observation point i. The same equation can be used to calculate the longitude of the centric
Lc ¼
Pn i li hi P ðhi ¼ hi n i hi
ð1Þ
if
hi ≥0;
hi ¼ 0
if
hi b0Þ ð2Þ
2.4. Urban development parameters and the thermal environment Previous studies have shown that land use and land cover differences significantly affect urban microclimates (Johnson and Trout,
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Fig. 2. Measurement setting. (2a) Measuring instrument “LOGPRO”, (2b) radiation cover, (2c) radiation cover and poll, and (2d) installation schematic diagram.
2012; Bounoua et al., 2000; Arnold and Gibbons, 1996). In this study, two indicators were used to evaluate the influence of urban morphology and geographical characteristics on the thermal environment. The first indicator was the ISA, which reflects the type of land cover and urban development characteristics (Lin et al., 2005). A higher ISA indicates a higher area of unnatural surfaces, which contributes to higher temperatures (Xiao et al., 2007; Frazer, 2005). This study utilized satellite images with a 30-m resolution obtained from SPOT 6 in May 2017 to classify the land cover patterns in five categories using a supervised classification approach in QGIS. The five categories were water, green spaces, bare land, pavement, and buildings.
The measurement points in the HiSAN were selected to generate a 200-m buffer area and then overlaid with the ISA layer (Fig. 4a) to calculate the surface cover and determine the ISA at each point. Water, green spaces, and bare land were considered permeable, whereas pavement and buildings were considered impermeable. Therefore, if there were 30 grids with permeable surfaces and 20 grids with impermeable surfaces in the buffer area of a location, the representative ISA of the measurement location was calculated as 40%. Because Tainan is a coastal city, its climatic conditions are strongly affected by the sea (Fig. 4b). Because the sea regulates Ta and provides a sea breeze that mitigates heat, this study calculated
Fig. 3. Selection of measurement points. (3a) The flow diagram of the location arrangements, (3b) The mesh in the map to identify coordinates and for “Average distribution of measuring points”, (3c) The classification of urban developing pattern for “classification by regional features”, (3d) The parks and vegetation area distribution for “Comparison of homogeneity”, and (3e) The result of total 100 measurement points.
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the minimum distance of the HiSAN points from the coastline and divided the locations into “near the coast” and “far from the coast” according to the median value as the second indicator. This was done to investigate the impact of the sea on the coastal and inland spaces at different periods, including the hot season, cool season, daytime, and nighttime. Using these two indicators, urban development and geographical characteristics could be quantified and considered simultaneously.
cools relatively more slowly than land; therefore, the western coastal fishing area (area near the sea) was at a higher temperature. Third, in the afternoon, owing to solar radiation from the sun, the land and sea were simultaneously endothermic. The land warmed more quickly and the coastal fishing area remained at a relatively low temperature. Therefore, a higher temperature was observed in the areas with dry soil on the east side of Tainan, which are far from the sea. 3.3. Temporal variation in UHI intensity
3. Results 3.1. Temporal and spatial UHI distribution The meteorological data obtained from the HiSAN were recorded hourly. Ta distribution maps in Fig. 5 show different seasons and times, such as the hot season (from April to October), cool season (from November to February), daytime (from 6 a.m. to 6 p.m.), and nighttime (from 6 p.m. to 6 a.m.). The map corresponding to daytime in the hot season (Fig. 5a) shows that the regions with high UHI deviation were located in inland areas near the city center. Higher UHI deviation was identified in open areas such as plazas and avenues, which had numerous impermeable surfaces. The map also indicates that the thermal environment in the daytime during hot season had high thermal stress. During the daytime in the cool season (Fig. 5b), the areas with high UHI deviation were located in the inland areas of Tainan, mainly because sea breezes do not reach those areas. At nighttime in the hot season (Fig. 5c), the area with high UHI deviation was closer to the coast at night than during the daytime. One reason for this is that water retains more heat than soil does (the specific heat of water is five times higher than soil); therefore, heat was preserved near the sea. Another reason is the temperature noticeably decreased at nighttime in rural areas due to radiative cooling and was 3 °C cooler than in urban areas. During nighttime in the cool season (Fig. 5d), the areas with high UHI deviation were mainly distributed in the city center because they contained a higher ISA. Therefore, the results revealed that both, the time of the day and the urban morphology affected the UHI in the study area. The UHI deviation distribution map shows how land cover and the level of urban development affected the UHI variation and thermal conditions throughout the study area. These results improved our understanding of thermal stress and can be used to prepare mitigation strategies.
UHI intensity refers to the difference between the highest and lowest Ta in a certain period. The meteorological data obtained from the HiSAN confirmed a temporal variation in the UHI intensity, which was considerable in Tainan, especially during the night. Differences between the maximum and minimum all-day average Ta were 3.5 °C to 4.5 °C in summer and 3 °C in winter (Fig. 7a). The UHI intensity was also calculated at the 95th to 98th and 2nd to 5th percentile values of recorded Ta to prevent the influence of extreme temperatures and error values. As shown in Fig. 7a, the average UHI intensity in Tainan was 3 °C in summer and 2.5 °C in winter. These values are considered reliable because they reflect the real UHI intensity and discount extreme conditions. During the day (6 a.m. to 6 p.m.), the difference between the maximum average and minimum average Ta was as high as 5 °C (Fig. 7b). In the all-year average, the difference between the 98th and 2nd percentile values was approximately 3 °C, indicating that the UHI intensity in Tainan in the day was relatively stable for all seasons over the study period. At night (6 p.m. to 6 a.m.), the seasonal variation in Ta was significant (Fig. 7c), and the difference between the maximum and minimum average Ta was approximately 3.5 °C in summer and 2.5 °C in winter. Differences were also detected between the 98th and 2nd percentile values, with the UHI intensity difference being approximately 2.5 °C to 3 °C in summer and 1.5 °C to 2 °C in winter. The results revealed that the UHI is a serious issue in Tainan because of rapid urbanization. During the day, because the energy consumption in the city center was considerably higher than that in the suburban areas, the buildings in urban areas prevented ventilation that would otherwise mitigate the heat. A considerable amount of heat remained in urban areas at night because of the heat retained by buildings and impermeable materials, with the UHI affecting the temperature by up to 3 °C. However, because solar radiation and energy use were relatively low in the winter, the UHI intensity at nighttime was lower than it was in the summer.
3.2. Dynamic movement of UHI-centric points 3.4. Comparison of land pattern and air temperature The temporal and spatial distribution of the UHI can also be described by the dynamic movement of the UHI centric points, as discussed in Section 2.3. In this study, two representative days in the hot season and cool season with the most significant UHI distribution were selected. The 24-h paths of the UHI centric points over these days are shown in Fig. 6a and b. The paths indicate that the UHI centers moved from the west side of the city to the east side during the day and moved back to the west side during the night (6 p.m. onwards), revealing a daily pattern. The path followed the east–west direction, indicating that Ta did not change considerably in the south–north direction in Tainan. Significant differences existed between the east side and west side, including the distance to the coast and the level of urban development. In the areas near the sea, the differences in the day and night temperatures may have been caused by the following: First, the specific heat of water is five times higher than soil, therefore, water retains more heat than soil does. Furthermore, soil temperature changes more rapidly during warming or cooling. Second, there was no solar radiation early in the morning, so the sea and land in the study area were simultaneously exothermic. Sea water
ISA was separated into two categories according to the median value. Additionally, the distance to the coast was divided into “near the coast” (b5 km) and “far from the coast” (N5 km), and time of the year was divided into hot and cool seasons (Fig. 8a, b, c, and d). A high ISA indicates that an area could easily retain heat. When the ISA was N50%, the Ta distribution was at a higher temperature, especially during the hot season. In the hot season, pavement retained considerable heat, which resulted in a higher heat load. Furthermore, the Ta near the coast fluctuated less than that of areas far from the coast regardless of ISA. The average Ta difference in the areas near the coast in the hot season and cool season were 0.4 °C and 0.3 °C, respectively. The average Ta difference in the areas far from the coast was up to 1.2 °C in the hot season and 0.6 °C in the cool season. Overall, ISA had a stronger influence on Ta in areas far from the coast than in areas near the coast. As shown in Fig. 8, the areas with ISA N 50% experienced a considerably higher Ta than those with ISA b 50% in all areas in both seasons. Therefore, the ISA, which is directly related to the level of urban development, affected thermal conditions in Tainan.
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Fig. 4. Category of measurement points based on (4a) ISA with the 200 m buffer area and (4b) the distance from the coast.
Fig. 5. UHI deviation distribution in (5a) Hot season daytime (5b) Cool season daytime (5c) Hot season nighttime (5d) Cool season nighttime.
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Fig. 6. Movement path of hourly UHI centric point in (6a) 2016/5/27 and (6b) 2017/2/13.
4. Discussion 4.1. Analysis of development characteristics and geographical features Urban development patterns and geographical features were examined to evaluate their effects on thermal conditions, as shown in the HiSAN measurements. In this study, factors such as land cover and land use were insufficient to explain the microclimate of the various areas; therefore, geographical characteristics such as sea breeze and distance to the coast were also considered. The results in Section 3.4 indicate that, regardless of the season, the Ta in the areas near the coast did not vary with ISA. However, areas far from the coast (Fig. 9b) with ISA N 50% had a considerably higher Ta in the hot season than did areas with ISA b 50%, indicating that areas with higher ISA absorb more heat. Therefore, ISA must be considered when examining the influences of urban development on temperature (Weng et al., 2011; Chun and Guhathakurta, 2017). The long-term wind direction in Tainan is from the sea to land (270°). Because areas far from the coast, especially on the east side of Tainan, are not influenced by a sea breeze (Chen et al., 2016a, 2016b), heat is retained more easily in these areas. These results suggest that evaluations of thermal conditions should consider the combination of urban morphology and geographic features to establish more accurate result. 4.2. Comparison with other UHI studies The UHI effect occurs in most cities worldwide and has caused severe environmental problems. The UHI effect has been evaluated using various approaches, including urban climatic maps (UCmap) (Chen et al., 2016a, 2016b; Lin et al., 2017; Ren et al., 2010; Alcoforado et al., 2009), computational models (Ketterer et al., 2016; Bruse and Fleer, 1998; Toparlar et al., 2015), data retrieved from remote sensors (Chen et al., 2016a, 2016b; Shih, 2016; Lo and Quattrochi, 2003; Nichol, 2005), traverse measurements (Jonsson, 2004; Yang et al., 2016; van Hove et al., 2015), and observation networks (Honjo et al., 2015; Unger et al., 2015; Bassett et al., 2016). UCmaps represent microclimatic conditions by considering various urban development parameters as well as data obtained from meteorological stations. Furthermore, the data can be applied to different areas with different characteristics, but the distribution of thermal conditions remains a prediction, and it can be inaccurate (Ng and Katzschner, 2010; Ren et al., 2013).
Various models have been utilized to simulate thermal conditions and understand UHI, including SkyHelios (Matzarakis and Matuschek, 2011; Ketterer et al., 2016), SOlWEIG (Lindberg et al., 2008; Lindberg and Grimmond, 2011), and ENVI-met (Yang and Lin, 2016; Bruse and Fleer, 1998). Although modeling can easily and immediately reveal the microclimate in complex urban environments, the accuracy of these models requires further validation (Fabiyi, 2011). The thermal conditions determined from land surface temperature evaluations in satellite images are often used to discuss the relationship between changes in land cover and UHI variations (Zhou et al., 2014; Liang et al., 2012; Lo et al., 1997). The advantage of the remote sensing approach is that thermal conditions that vary widely on the urban scale can be easily estimated. However, because this method is limited by the passing time of the satellite that captures the images, thermal conditions cannot be determined for all periods. In addition, the derived air temperature results cannot always be retrieved correctly owing to the complex materials used in urban areas (Chen et al., 2016a, 2016b; Tomlinson et al., 2011). Meteorological information measurement networks have been established to quantify UHIs in numerous countries (Muller et al., 2013). Many cities are investigating hotspot distributions and UHI intensities and are creating regulations to mitigate the impacts of urban thermal stress. Tokyo is a megacity with a high intensity of UHIs (Mikami and Yamato, 2011). Honjo (2012) examined the daily movement of the UHI in the Kanto area using an observation network approach (METROS). Their study employed a total of 200 measurement points, which were instrument shelters in elementary schools. The variation in the UHI during different periods and its characteristics were illustrated, and the results revealed that its centric point moved inland from the coast during the day and returned toward the coast at night. Although Ta tends to be higher in city centers (because of a higher building density), their study showed that Ta is also influenced by seasonal winds and sea breezes (Takahashi et al., 2011). However, because the measurement points were all located in areas with the similar land use (the development intensity of schools is low, and the green cover ratio is relatively high), the findings did not accurately depict the UHI in urban areas. Consequently, the reported Ta would have been lower than the actual Ta. An observation network project named Urban-Path was established in Szeged, Hungary to monitor a UHI and obtain longterm effective climate data (URBAN-PATH Project, 2015). A thermal load distribution map was generated using the interpolation approach and the data collected from 24 measurement locations
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Fig. 7. Monthly difference of UHI intensity in (7a) All day average (7b) Daytime average (7c) Nighttime average.
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Fig. 8. Air temperature range in (8a) Hot season, near the coast (8b) Hot season, far from the coast (8c) Cool season, near the coast and (8d) Cool season, far from the coast.
equipped with Wi-Fi signal receivers. The advantage of this network was the accessibility of the data. The data were automatically collected and transmitted through the Internet, and the real-time atmospheric conditions were displayed on a website by using cloud computing (Unger et al., 2015). However, the number of measurement locations was insufficient because different land use scenarios produce different thermal conditions. The interpolation approach cannot accurately evaluate the UHI in a complex urban environment such as Tainan. Therefore, the HiSAN approach was used in this study because a large number of instruments needed to be installed in various built environments to investigate the thermal conditions on a city scale with high accuracy. 4.3. Application for other cities Using the observation network to obtain data about UHI patterns is valuable, especially because climate change has become a serious global issue. This study provides an alternative approach for visualizing and understanding the thermal conditions in a complex urban environment without using estimate and modeling methods. Other cities can apply this method and select different urban development parameters, such as the sky view factor, TFA, building cover ratio, and frontal area index as a substitute for the ISA used in this study. In addition, the distance to the coast can be replaced with other factors such as elevation, population density, and distance to green areas or large bodies of water. The method used in this study can be used to determine the most significant factors influencing a UHI, and thus provide an accurate understanding of the temporal and spatial factors that influence complex urban thermal environments. Therefore, this study can be valuable for cities that are aiming to mitigate the environmental problems caused by UHIs.
5. Conclusion This study examined the distribution and dynamic characteristics of a UHI in a complex urban environment to accurately understand and visualize the UHI during different periods. The aim was to develop a method for urban planners to use when considering thermal conditions in urban planning. This study addressed two main issues. First, the interpolation approach cannot be used to accurately understand the thermal conditions in complex urban environments because only a small number of measurement locations are used. This study used HiSAN, which consist of 100 measurement points at high density in different development districts to obtain long-term meteorological data for evaluating the UHI characteristics in Tainan. Second, because thermal conditions vary considerably over time, understanding the relationship between urban development patterns and geographical features is crucial when examining the thermal conditions in urban environments. The urban development patterns and geographical features considered in this study included ISA, a 200-m buffer area for the measurement points, and the distance to the coast. Therefore, the influence of the different urban environments on the thermal conditions could accurately be quantified, and the phenomena in various periods could be clarified. The current study obtained the following results. First, the temporal and spatial distributions of the UHI were illustrated by the UHI deviation at different periods and the movement of the UHI centric point, which was from west to east during the day and east to west at night because of the physical effects exerted by the land and sea. This finding can be used by decision makers to develop location-based mitigation strategies to manage urban thermal stress (e.g., limiting building height to street width to improve ventilation and reduce heat storage in areas far from the coast). Second, the temporal variation in UHI intensity at different
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Fig. 9. Air temperature distribution in different ISA in (9a) Hot season, near the coast (9b) Hot season, far from the coast (9c) Cool season, near the coast and (9d) Cool season, far from the coast.
periods was clarified using the UHI intensities obtained at different temperature percentiles. This approach enables researchers to focus on reasonable data ranges and understand how a UHI changes over many months. This approach can be used to reduce the UHI (e.g., by restricting electricity use in period with high UHI intensity). Third, land patterns and air temperatures were compared. This study used a 200-m buffer area for calculating surrounding ISA and the distance to the coast, and these parameters were used to quantify the influence of urban development patterns and geographic features on the thermal conditions at each measurement point. The results showed that, in the areas far from the coast, the ISA had a more significant effect on Ta and that a higher ISA contributed to a higher Ta. Urban planners ca use these findings to improve thermal conditions by changing land patterns (e.g., by reducing the use of impermeable surfaces). Because the HiSAN method can record Ta data with high accuracy for long periods and over large complex areas, the UHI characteristics in Tainan were presented in detail and the hotspots at different periods could be revealed. The results from this study can help urban planners in Tainan precisely determine the urban thermal condition and to mitigate the thermal stresses caused by urban development. Acknowledgements The authors would like to thank the Ministry of Science andTechnology of Taiwan, for financially supporting this research under Contract No. 105-2633-E-006-002- and 106-2633-E-006-001-.
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