Effects of micro scale underlying surface type and pattern of urban residential area on microclimate: taking Beijing as a case study
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摘要:
目的 城市人居环境中小微尺度是人们日常使用最频繁的基本空间尺度。探究小微尺度下垫面类型及其格局特征对环境微气候的影响机制，是借助风景园林规划设计改善城市热环境的理论基础。 方法 本研究基于实地测量，针对城市居住区绿地不同类型下垫面日均空气温度、湿度的差异，以及小微尺度下垫面格局对环境微气候的影响开展定量研究。 结果 四季7种下垫面类型的日均温度、湿度均存在显著差异，且7种下垫面类型的排序有季节性变化。高郁闭度植被、水体与中郁闭度植被的降温、增湿效应明显。借助景观格局指数量化不同类型下垫面的格局特征。景观指数与日均温度、湿度的相关性随不同季节而变化。各类下垫面的面积占比、斑块面积是影响小微尺度环境温度、湿度的关键格局特征，破碎度与聚集度也有一定影响。 结论 未来进行热舒适型小微尺度户外空间设计时，提高高郁闭度与中郁闭度植被的面积占比和斑块面积，控制其破碎程度，采用聚集式布局形式可有效提高环境相对湿度，降低空气温度。同时应避免配置成片的硬质铺装（占比率高，面积大，分布聚集），降低其热调节负效应。 Abstract:Objective The micro-scale environment is a very important human-scale outdoor space unit. Exploring the influence mechanism of micro-scale underlying surface type and pattern on microclimate is the theoretical basis of improving urban thermal environment with the help of landscape design strategies. Method Through field measurements, differences of daily air temperature (AT) and relative humidity (RH) among seven typical underlying surface types of urban residential green space, and correlations between daily AT and RH and various micro-scale underlying surface patterns as explained by landscape metrics were analyzed. Result During the four seasons, there were various differences in daily AT and RH among the seven underlying surface types, and the order of seven types varied seasonally. Overall, high canopy-density vegetation and water body type always had prominent cooling and humidifying effects, whereas highest AT and lowest RH were always found in pavement type. Correlations between landscape metrics and daily AT and RH varied by season. Metrics reflecting the dominance and distribution of underlying surface classifications had closer relationships with microclimate level in the micro-scale environment. The proportion and average patch area of underlying surface classifications were the critical pattern characteristics affecting the daily AT and RH. And the fragmentation and aggregation also had certain influence. Conclusion When designing micro-scale thermal comfort outdoor space, increasing the proportion and patch area of high and moderate canopy-density vegetation, controlling the fragmentation and adopting aggregating layout can significantly reduce AT and increase RH. The impervious pavement with high proportion, large area and concentrated distribution should be avoided to reduce its thermal regulation negative effect. -
图 1 21个下垫面类型样方和7个复合样方的位置分布示意图
图像来源：2014年9月谷歌地图。Picture source: Google maps, September 2014.Site A ~ G：样方A ~ G；HVC1 ~ 3：高郁闭度植被垫面1 ~ 3；MVC1 ~ 3：中郁闭度植被垫面1 ~ 3；LVC1 ~ 3：低郁闭度植被垫面1 ~ 3；WB1 ~ 3：水体垫面样点1 ~ 3；BS1 ~ 3：裸土垫面样点1 ~ 3；BA1 ~ 3：建筑垫面样点1 ~ 3；PA1 ~ 3：不透水铺装垫面样点。下同。Site A−G: quadrat A−G; HVC1−3: high canopy density vegetation 1−3; MVC1−3: moderate canopy density vegetation 1−3; LVC1−3: low canopy density vegetation 1−3; WB1−3: water-body 1−3; BS1−3: bare soil 1−3; BA1−3: building area 1−3; PA1−3: paved area 1−3. The same below.
Figure 1. Locations of study area, 21 type quadrats and 7 pattern quadrats
图 2 格局试验复合样方范围内测点示例(样方E、G)
Figure 2. Arrangement example of three measuring points for pattern site (quadrat E and G)
图 3 四季7种下垫面类型的日平均空气温度
不同小写字母表示多重比较的差异。下同。Different lowercase letters indicate the differences of multiple comparisons. The same below.
Figure 3. Daily average air temperature (AT) among seven underlying surface types in the four seasons
图 4 四季7种下垫面类型的日平均相对湿度
Figure 4. Daily average relative humidity (RH) among seven underlying surface types in the four seasons
图 5 四季不同下垫面格局样方的日平均空气温度、湿度分布
Figure 5. Distribution of daily average AT and RH at seven underlying surface pattern quadrats in four seasons
表 1 3类植被垫面的基本信息
Table 1. Details of the sampling vegetation underlying surface
群落编号
Community No.群落类型
Community type郁闭度
Canopy density/%平均株高
Average plant height/m平均冠幅
Average crown diameter/m平均胸径(地径)
Average DBH (GD)/cmHVC1 针阔混交−乔灌草型
Coniferous broadleaved mixed-arbor shrub grass type (M-TSG)89 6.21 4.30 11.25 HVC2 针阔混交−乔灌草型 M-TSG 83 6.93 2.22 15.35 HVC3 针阔混交−乔灌草型 M-TSG 92 4.79 3.91 8.73 MVC1 阔叶−乔灌草型
Broadleaved trees, shrubs and grasses (B-TSG)61 5.40 3.95 16.00 MVC2 针阔混交−乔灌草型 M-TSG 48 8.84 3.26 10.18 MVC3 针阔混交−乔草型
Mixed coniferous and broadleaved
trees and grasses (M-TG)53 9.29 4.46 14.53 LVC1 针阔混交−乔灌草型 M-TSG 38 5.32 1.66 7.23 LVC2 针阔混交−乔草型 M-TG 19 3.98 4.08 7.60 LVC3 针阔混交−乔灌草型 M-TSG 32 8.84 3.26 8.18 
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表 2 所选景观指数
Table 2. List of selected landscape indexes
景观指数 Landscape index 水平 Level 描述 Description 单位 Unit 范围 Range 斑块所占景观面积比例
Percent of landscape (PLAND)类型 Type 某一斑块类型占景观的面积比率
Area ratio of a certain patch type to the landscape% 0 ~ 100 斑块密度
Patch density (PD)类型 Type 每公顷某一斑块类型斑块的个数
Number of a certain patch type per hectare个/hm2
number/ha> 0 平均斑块面积
Mean patch size (MPS)类型 Type 某一斑块类型的平均斑块面积
Mean patch size of a certain patch typehm2
ha> 0 破碎度指数
Fragmentation index (FI)类型 Type 某一斑块类型被分割的破碎程度
Degree of fragmentation of a certain patch type> 0 聚集度指数
Aggregation index (AI)类型 Type 某一斑块类型相互聚集的程度
Degree of aggregation of a certain patch type% 0 ~ 100
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表 3 所选格局样方的景观指数信息
Table 3. Landscape index information of selected pattern quadrats
景观指数
Landscape index样方编号 Quadrat No. Site A Site B Site C Site D Site E Site F Site G PA PLAND 50.130 33.523 33.334 58.203 31.113 25.268 50.814 PD 5.391 5.375 4.030 2.695 2.687 2.696 2.694 MPS 0.093 0.062 0.083 0.216 0.116 0.094 0.189 FI 32.258 48.077 24.184 4.630 8.636 10.672 5.302 AI 97.391 95.903 97.281 98.456 96.532 96.076 98.500 BA PLAND 24.661 22.902 48.178 26.951 20.857 18.920 33.127 PD 2.696 4.031 4.030 5.390 8.060 6.739 5.389 MPS 0.092 0.057 0.120 0.050 0.026 0.028 0.062 FI 10.929 35.211 16.722 60.000 193.050 142.349 48.780 AI 99.099 98.592 98.263 97.892 97.103 97.487 97.700 LVC PLAND 3.101 20.618 15.427 9.468 24.448 30.602 10.278 PD 2.696 10.749 13.433 12.128 13.433 9.434 10.778 MPS 0.012 0.019 0.012 0.008 0.018 0.032 0.010 FI 86.957 364.583 782.609 1 025.641 494.505 185.185 736.842 AI 97.909 95.606 93.370 93.152 95.612 96.290 94.260 MVC PLAND 20.903 8.247 2.005 1.919 13.952 17.551 3.700 PD 8.087 4.031 2.687 2.695 9.403 8.087 4.042 MPS 0.026 0.021 0.008 0.007 0.015 0.022 0.009 FI 193.798 97.561 133.333 140.845 405.405 230.415 217.391 AI 97.075 97.003 95.497 94.404 94.949 96.280 96.133 HVC PLAND 1.205 14.710 1.056 3.459 9.631 7.660 2.080 PD 1.348 2.687 1.343 2.695 4.030 2.696 2.694 MPS 0.009 0.055 0.008 0.013 0.024 0.028 0.008 FI 0.000 18.282 0.000 78.125 83.682 35.211 129.870 AI 99.085 98.132 96.856 98.218 97.184 注：HVC：高郁闭度植被垫面；MVC：中郁闭度植被垫面；LVC：低郁闭度植被垫面；BA：建筑垫面样点；PA：不透水铺装垫面样点。下同。Notes: HVC, high canopy density vegetation；MVC, moderate canopy density vegetation; LVC, low canopy density vegetation; BA, building area；PA, paved area. Same as below.
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表 4 四季各景观指数与日平均空气温度与相对湿度的相关性
Table 4. Spearman correlations between landscape indexes and daily average AT and RH in four seasons
景观指数
Landscape index日均空气温度 Daily average AT/℃ 日均相对湿度 Daily average RH/% 冬季 Winter 春季 Spring 夏季 Summer 秋季 Autumn 冬季 Winter 春季 Spring 夏季 Summer 秋季 Autumn PA PLAND 0.852* 0.895** 0.761* 0.808* −0.844* −0.794* −0.823* −0.809* PD 0.089 0.093 −0.245 −0.158 0.076 0.175 0.055 −0.045 MPS 0.616 0.639 0.740 0.736 −0.703 −0.735 −0.675 −0.619 FI −0.040 −0.060 −0.379 −0.281 0.219 0.292 0.193 0.067 AI 0.784* 0.842* 0.933** 0.898** −0.908** 0.897** −0.888** −0.825* BA PLAND 0.362 0.445 0.647 0.564 −0.572 −0.583 0.610 −0.56 PD −0.561 −0.547 −0.236 −0.271 0.427 0.343 0.413 0.484 MPS 0.382 0.453 0.422 0.380 −0.462 −0.408 −0.491 −0.473 FI −0.745 −0.730 −0.489 −0.510 0.655 0.596 0.637 0.708 AI 0.421 0.411 0.041 0.105 −0.25 −0.154 −0.245 −0.322 LVC PLAND −0.759* −0.833* −0.692 −0.72 0.773* 0.684 0.771* 0.759* PD −0.143 −0.065 0.242 0.245 −0.081 −0.174 −0.149 −0.106 MPS −0.709 −0.854* −0.875** −0.917** 0.860* 0.801* 0.907** 0.868 FI 0.536 0.633 0.820* 0.830* −0.745 −0.800* −0.788* −0.724* AI −0.443 −0.507 −0.711 −0.701 0.637 0.715 0.675 0.629 MVC PLAND −0.579 −0.631* −0.767* −0.780* 0.729 0.783* 0.760* 0.763* PD −0.758* −0.758* −0.705 −0.733 0.785* 0.818* 0.791* 0.835* MPS −0.413 −0.511 −0.799* −0.767* 0.669 0.734 0.712 0.635 FI −0.681 −0.598 −0.278 −0.325 0.507 0.504 0.476 0.574 AI −0.035 0.174 −0.429 −0.433 0.291 0.357 0.359 0.211 HVC PLAND −0.485 −0.536 −0.658 −0.539 0.624 0.605 0.599 0.488 PD −0.429 −0.408 −0.228 −0.174 0.367 0.320 0.342 0.345 MPS −0.352 −0.439 −0.656 −0.536 0.556 0.543 0.548 0.407 FI 0.214 0.216 0.473 0.451 −0.326 −0.367 −0.298 −0.287 AI −0.120 −0.119 −0.519 −0.409 0.314 0.364 0.296 0.302 注：粗体为显著变量，**表示极显著相关(P < 0.01)，*表示显著相关(P < 0.05)。Notes: significant variables are marked in bold, ** stands for extremely significant correlation (P < 0.01); * stands for significant correlation (P < 0.05).
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