The effect of land cover and land use on urban heat island in Taiwan

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1 The effect of land cover and land use on urban heat island in Taiwan Feng-Chi Liao 1 ;Ming-Jen Cheng 2 ;Reuy-Lung Hwang 3 ;Wen-Shan Yang 4 1 Program in Civil and Hydraulic Engineering, Feng Chia University, Taichung, Taiwan 2 Department of Architecture, Feng Chia University, Taichung, Taiwan 3 Department of Architecture, National United University, Miaoli, Taiwan 4 Program in Civil and Hydraulic Engineering, Feng Chia University, Taichung, Taiwan Abstract The global warming get worse as problems on highly land developed, other artificial heat influences, and especially it causes that urban heat island effect with highly intensive population and makes the living environment worse. Finally, we mainly investigate the influence of land use on urban heat island (UHI) and study impact factors of urban microclimate in Taiwan. In the study, we established 20 different measuring points in Taichung, Taiwan ( E, N) with fixed-point monitoring. We analyzed the correlation of land use pattern factors and variations in UHI strength by buffering analysis, ANOVA, multiple regression analysis, and then we established air temperature regression model. Finally, we found that it had the highest correlation between the building area and UHI strength, and the results shows that the average UHI strength during the period of experiment is about 0.96 and the maximum vaule is Keywords: land use, urban heat island (UHI), fixed-point monitoring, multiple regression analysis 1. Introduction After the Industrial Revolution, urbanization is an apparent appearance in every country, and the environment has become different from the past decades.all the research indicate that the microclimate in urban has become more and moreheater. The urban heat island effect has happened in some city in Europe, such as London. Other Cities in different country also have the same problem. For example, the temperature in Tokyo city has risen up since 1920, and the upward trendexceeds the average of the Tokyo County. Therefore, Sciences had attached importance to the urban microclimate since the 20th century. Surveyed all the reverences in the world, the factor which affect the urban microclimate includedthe sky view factor (SVF), surface albedo, ratio of green cover, building height/street width (H/W), and land use/land cover (LCLU). The LCLU composition includes the buildings, streets, plants, and water, which can rise or decrease the temperature. It might be the improvement factors of the urban microclimate. Because of the references on urban climate didn t take the types of LCLU as the main factor, therefore the purpose of this study will includeconstructing the database of urban microclimate in midland of Taiwan, surveying the correlation between the Land Use and the 1

2 urban microclimate, analyzing how the Land Use effects the urban heat island effect in Taiwan. 2. Materials and Methods 2.1. Definition of land cover and land use As Fig.1 shows, the LCLU patterns around the survey point could be classed into two categories, the hard or the soft pavement.and it also could be classed into five factors: (1)the building area(ba), which included all the structures; (2)the paved area(pa), which included the asphalt road, the sidewalk, and the impermeable pavement; (3)the free area(fa), which included the back yard, the grassun-shade by trees, and theun-paved soil surface; (4)the green area(ga), which shaded by trees; (5)the water area(wa), which included the rivers, the lakes, and the ponds Field measurement Fig.1 Description of LCLU patterns. Fig.2 Location of Taichung City and 20 measuring spots. Table 1 Locations description on LCLU patterns.(radius:150 meters) A B C D E F G H I J K L M N O 2

3 P Q R S T In this study, the fieldwork is surveying in fixed-point monitoring. The measurement spots had to be consistent in the impact of traffic, street orientation, street and building aspect ratio, and green trees. In order to obtain the variationof the temperature and enhance its credibility, we chose 20measurement spots, which were distributed in the axis of downtown to the outskirts of Taichung City and around the dense population and activities areas, as Fig. 2 shows. We carry out the fieldwork and analyze the aerial photographs of themeasurement spots to diagram the LCLU patterns. And then we differentiate and quantify the composition of LCLU patterns around the measurement spots, as Table 1 shows. Then, we monitored the temperature and humidity in July to September 2008 and 2009 with the ilog sensor. We set a well-ventilated white cover on top of the sensor, in order to avoid direct exposure to the sun and rain. And we also used wooden structure to set the sensor on a light pole at 3 meters high and at least 1.5 meters far away the buildings to avoid the artificial damage, and reduce the effect of radiant heat from the buildings and grounds. In addition, sensors were all located on the north side of streets as the different of sunshine duration. 3. Results and Discussion Fig.3 Description of setting i-log sensor The contrast between local temperature After surveying, we had 68 valid data by excluding cloudy and rainy days, and we took the climatic data from Taichung meteorological station to be the reference. Based on the related research and the buffering analyzing with a 50m/100m/150m/200m/300m radius of the measurement locations, it indicates that the parameters of the microclimate is most related to the Land Use patterns within a 150m radius, therefore, we take i as the criterion for this study. The Fig. 4 shows that all the data of each measuring spots and the Land Use patterns. 3

4 (1) The daily average temperatures The different LCLU patterns and the artificial heats primarily affected the daily average temperature of each measurement spot. We found that the daily average temperature of spot K is 31.47, which is the highest among all. The daily average temperature of spot F, N, and Q wereover 31.3, and the ratio of hard pavement in these three spots was more than 80%. Furthermore, the daily average temperature of spot J is 29.94, which is the lowest, and the ratio of soft pavement in J spot is 53%. Besides, the data indicated that the maximum temperature of each day is affected not only by the different LCLU patterns, but also by the size of surface heating area around the measuring location and the artificial heats. Fig.4 Collection of the climatic data and LCLU patterns for each spot. (2) The average daytime temperatures In daytime, we found that the building is an endothermic factor in daytime, but its shadow will reduce the temperature around. The more Pa will accelerate the absorption of heat in the surface, and then increase the temperature. The Fa could heat up the air faster, even if it wasn t an endothermic factor. On the contrary, the Ga can reduce the temperature of measuring spots, and the Wa could cooldown around. (3) The average nighttime temperatures The temperature of each measuring location had the same variation phenomenon at nighttime. The difference in the average of nighttime temperature of each measuring spot is 1.9. On the contrary, the building and the artificial pavement would radiate the heat slowly, which 4

5 they absorb in daytime. On the contrary, the free area,green area, and water could cool down the temperature of measuring location. (4) Temperature differences To explore the difference of the temperature between downtown and outskirts, we analyzed the data of spot B and J first, and we discovered that the difference of the daily average temperature was 1.54, and the maximum temperature difference was 2.23 occurred at 20:00. As Fig. 5 shows, the difference of the temperature had the same variation phenomenon at night. The average difference between spot B and J was Secondly, exploring the overall temperature difference between the whole cityand the outskirts, we analyzed the data of location J and the highest temperature spot of all, then we measured the UHI strength by the average of temperature difference between spot J and others. The results indicated that the temperaturedifferences of spot Balmost consistent with spot J. The temperature difference was lower as 1.18 in the morning, but it significantly increased in the afternoon, especially at the measurement spot with larger ratio of the Pa and Ba. And the maximum temperature difference was 3.85 occurred at 19:00. In brief, all the maximum temperature of every measurement spots were higher than spot J, and the average temperature differences was 2.1. The maximum UHI was 1.49 occurred at 19: Quantitative analysis Fig.5 The daily mean temperature difference (1) One way ANOVA In this study, we analyzed the relationship between the climate data and the LCLU patterns by ANOVA for regression. Table 2 shows that the daily average temperature was obvious related 5

6 to the Pa, Fa, and Ga. The area of Pa was obvious related to the average daytime temperature, and the average nighttime temperature was obvious related to the Ba, Fa, Ga, and Wa. by differential thermal analysis,we found that the daily average temperature difference was obvious related to the Pa and Ga. The Pa was obvious related to the average daytime temperature difference, and the average nighttime temperature difference was obvious related to the Ba, Fa, and Ga. (2) Multiple Regression With stepwise regression, we confirmed the significant factor by buffering analysis and oneway analysis of variance. And then we analyzed the LCLU patterns in accordance withthe average temperature of the whole day, the daytime, and the nighttimeby multiple regression analysis. And Table 3 shows the results. Table 2 The P value of the significance for LCLU factors to air temperature(α=.05) Factors Ta avg Ta avg daily day night daily day night UHI strength ( Ta at 19:00) Ba Pa Fa Ga Wa Table 3 The multiple regression model for air temperature and LCLU Parameter Formula R 2 Daily_Ta avg Ba-0.32 Pa-4.39 Fa-2.56 Ga 0.54 Day_Ta avg Pa-2.37 Fa-0.1 Ga+6.14 Wa 0.27 Night_Ta avg Ba-3.47 Fa-2.18 Ga-3.78 Wa 0.86 Daily_ Ta avg Ba-2.35 Pa-4.52 Fa-4.87 Ga 0.48 Day_ Ta avg Ba+1.77 Pa-0.99 Ga+9.74 Wa 0.30 Night_ Ta avg Ba+3.26 Pa+0.69 Fa+0.98 Ga 0.81 UHI strength avg(19:00) (Σ Ba+0.83 Pa-2.84 Fa-1.31 Ga)/n Conclusions In this study, we analyzed the temperature by fixed-point field measurement and regression analysis. We explored that the hard pavement, such as the Ba, and Pa,would absorb heat at daytime and radiate the absorbed heat at nighttime. Resulting in the spot with more ratio of Pa cooled down slower than with more ratio of soft pavement, such as Fa, Ga, and Wa. More Ga and Fa area would cool down the urban microclimate more, especially at night. On the contrary, more Ba and Pa area would heat up the urban microclimate, especially at daytime. By analyzing the temperature difference of each spot, the LCLU pattern wasn t related to the temperature difference of daytime. We confirmed the Pa and Ga were the main impact factor, but the shadow of buildings could due to the downtown temperature higher than the outskirts in the morning. The Ba, the Fa, and Ga obvious related to the average temperature difference of the nighttime. During the measurement period, the average UHI is The maximum 6

7 UHI was 0.96 occurred at 19:00 because of the heat absorption and radiation of the Pa, and the outskirts cooled down faster than downtown. Finally, we established the correlation model of the LCLU patterns and the temperature by multiple regressions. And we explored that the LCLU pattern obvious related to the average temperature of the nighttime and the average nighttime temperature difference of each measuring spot. The maximum temperature difference of whole day was 4 because of the different LCLU patterns. The difference of maximum average temperature was 2.52, and the difference of maximum UHI was 3.7. In brief, the LCLU pattern was an important impact factor to the urban microclimate. 5. References 1. Haider Taha. (1997). Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energy and Buildings, 25: , doi: /S (96) Hadas Saaroni. Eyal Ben-Dor. Arieh Bitan. Oded Potchter. (2000). Spatial distribution and microscale characteristics of the urban heat island in Tel-Aviv, Israel. Landscape and Urban Planning, 48: 1-18, doi: /S (99) H, Akbari. M, Pomerantz. H, Taha. (2001). Cool Surfaces and Shade Trees to Reduce Energy Use and Improve Air Quality in Urban Areas. Solar Energy, 70(3): , doi: /S X(00)00089-X. 4. Z, Bottya n. J, Unger. (2003). A multiple linear statistical model for estimating the mean maximum urban heat island. Theoretical and Applied Climatology, 75: , doi: /s Janet Nichol. (2005). Man Sing Wong, Modeling urban environmental quality in a tropical city. Landscape and Urban Planning, 73: 49-58, doi: /j.landurbplan Alexander Velazquez-Lozada. Jorge E. Gonzalez. Amos Winter. (2006). Urban heat island effect analysis for San Juan, Puerto Rico. Atmospheric and Environment, 40: , doi: /j.atmosenv N,H, Wong. Steve Kardinal Jusuf. Aung Aung La Win. Htun Kyaw Thu. To Syatia Negara. Wu Xuchao. (2007). Environmental study of the impact of greenery in an institutional campus in the tropics. Building and Environment, 42: , doi: /j.buildenv Chuan-Yao Lin. Fei Chen. J,-C, Huang. W,-C, Chen. Y,-A, Liou. W,-N, Chen. Shaw-C, Liu. (2008). Urban heat island effect and its impact on boundary layer development and land-sea circulation over northern Taiwan. Atmospheric Environment, 42: , doi: /j.atmosenv

8 9. Hwang, R.L.. Lin, T.P.. Matzarakis, A.. (2011). Seasonal effect of urban street shading on long-term outdoor thermal comfort. Building and Environment, 46(4): , doi: /j.buildenv Sun, C.Y.. Lee, K.P.. Lin, T.P.. Lee, S.H.. (2012). Vegetation as a material of roof and city to cool down the temperature. Advanced Materials Research, 461: , doi: / 8