基于ENVI-met的城市居住区空间形态与PM_(2.5)浓度关联性研究
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摘要
探讨城市居住区空间形态与PM_(2.5)浓度关联性,对改善城市居住区空气质量具有重要意义。将居住区空间形态指标归纳为住宅群体组合形式、风向角度、容积率、绿地率等要素,采用ENVI-met软件模拟不同居住区空间形态指标下的PM_(2.5)浓度,探讨城市居住区空间形态与PM_(2.5)浓度关联性。结果表明,不同住宅群体平面组合形式的居住区PM_(2.5)浓度分布呈现出较大的差异,周边式和混合式较其他形式居住区内部PM_(2.5)难以扩散,行列式与点群式的居住区内部没有出现PM_(2.5)聚集的现象,但是点群式居住区外西南角较行列式PM_(2.5)积聚的面积大,其平均浓度分别为:171.78、170.021、169.255、172.365μg·m~(-3),综合分析,合肥市居住区住宅群体平面组合形式的最佳方式为行列式,其次为点群式,混合式和周边式不利于居住区PM_(2.5)扩散;不同风向角度的居住区PM_(2.5)浓度分布呈现较为复杂的变化状况,当风向角度为15°-30°时,PM_(2.5)浓度随着风向角度的增大而增大,十分显著,平均浓度从136.796μg·m~(-3)增至140.796μg·m~(-3),但是增至45度时,PM_(2.5)浓度有所减小,平均浓度为135.605μg·m~(-3),当风向角度为60°-90°时,PM_(2.5)浓度随着风向角度的增大而增大,变化较小,其平均浓度依次为132.025、133.87、141.334μg·m~(-3),综合分析,风向角度最优方案为60°,而45°、75°为可选方案,15°、30°、90°为不可选方案;PM_(2.5)浓度较低的为容积率0.8、其次为1.2,容积率为1.6和2的居住区PM_(2.5)浓度较高,容积率由小到大其平均浓度分别为131.678、139.402、159.906、154.638μg·m~(-3),可以基本认为居住区整体空间内的PM_(2.5)浓度随着居住区容积率的增大而逐渐增大;增加绿地率可缓解PM_(2.5)污染,当绿地率大小超过35%,PM_(2.5)浓度随着绿地率的增大而减小的趋势逐渐减缓,综合分析,绿地率应该在35%以上,才能有效降低居住区的PM_(2.5)浓度。
Exploring the correlation between space form of urban residential areas and PM_(2.5) concentration is of great significance for improving the air quality of urban residential areas. In this paper, the space form index of residential area is summarized as the combination form of residential group, wind direction angle, floor area ratio, green rate. The PM_(2.5) concentration under different space form index of residential area is simulated by ENVI-met software, and the correlation between the space form of urban residential area and PM_(2.5) concentration is studied. The experimental results are as follows: the concentration distribution of PM_(2.5) in different combination forms of residential group shows great difference. The PM_(2.5) concentration in peripheral and mixed residential areas is more difficult to diffuse than that in other residential areas. There is no PM_(2.5) aggregation in determinant and point-group residential areas. However, the aggregation area of PM_(2.5) in the southwest corner of the point-group residential area is larger than that in determinant residential area. The average concentration of PM_(2.5) is 171.78, 170.021, 169.255, 172.365 μg·m~(-3), respectively. Comprehensive analysis shows that the best way of the combination form of residential groups in residential areas in Hefei is determinant, followed by point-group, mixed type and peripheral type are not conducive to PM_(2.5) diffusion in residential area. Meanwhile, the distribution of PM_(2.5) concentration in residential areas with different wind direction presents complex changes. When the wind direction angle is 15°-30°, the PM_(2.5) concentration increases with the increase of the wind direction angle, which is very significant. The average concentration increases from 136.796 μg·m~(-3) to 140.796 μg·m~(-3). However, when the wind direction angle increases to 45°, the PM_(2.5) concentration decreases, and the average concentration is 135.605 μg·m~(-3). When the wind direction angle is 60°-90°, the PM_(2.5) concentration increases with the increase of the wind direction angle, and the average concentration is 132.025, 133.87, 141.334 μg·m~(-3), respectively. Comprehensive analysis shows that the optimal scheme of wind direction angle is 60°, while 45° and 75° are optional, and 15°, 30° and 90° are not available. In addition, when the floor area ratio is 0.8, the concentration of PM_(2.5) is lower, followed by 1.2. The concentration of PM_(2.5) in residential area with floor area ratio of 1.6 and 2 is higher. When the floor area ratio changes from small to large, the average concentrations are 131.678, 139.402, 159.906, 154.638 μg·m~(-3), respectively. It can be basically considered that the PM_(2.5) concentration in the whole space of residential area increases gradually with the increase of the floor area ratio of residential area. Moreover, PM_(2.5) pollution can be alleviated by increasing the green space. When green rate exceeds 35%, PM_(2.5) concentration decreases lowly with the increase of green space rate. Comprehensive analysis shows that the green rate should be above 35% in order to effectively reduce the PM_(2.5) concentration in residential areas.
Exploring the correlation between space form of urban residential areas and PM_(2.5) concentration is of great significance for improving the air quality of urban residential areas. In this paper, the space form index of residential area is summarized as the combination form of residential group, wind direction angle, floor area ratio, green rate. The PM_(2.5) concentration under different space form index of residential area is simulated by ENVI-met software, and the correlation between the space form of urban residential area and PM_(2.5) concentration is studied. The experimental results are as follows: the concentration distribution of PM_(2.5) in different combination forms of residential group shows great difference. The PM_(2.5) concentration in peripheral and mixed residential areas is more difficult to diffuse than that in other residential areas. There is no PM_(2.5) aggregation in determinant and point-group residential areas. However, the aggregation area of PM_(2.5) in the southwest corner of the point-group residential area is larger than that in determinant residential area. The average concentration of PM_(2.5) is 171.78, 170.021, 169.255, 172.365 μg·m~(-3), respectively. Comprehensive analysis shows that the best way of the combination form of residential groups in residential areas in Hefei is determinant, followed by point-group, mixed type and peripheral type are not conducive to PM_(2.5) diffusion in residential area. Meanwhile, the distribution of PM_(2.5) concentration in residential areas with different wind direction presents complex changes. When the wind direction angle is 15°-30°, the PM_(2.5) concentration increases with the increase of the wind direction angle, which is very significant. The average concentration increases from 136.796 μg·m~(-3) to 140.796 μg·m~(-3). However, when the wind direction angle increases to 45°, the PM_(2.5) concentration decreases, and the average concentration is 135.605 μg·m~(-3). When the wind direction angle is 60°-90°, the PM_(2.5) concentration increases with the increase of the wind direction angle, and the average concentration is 132.025, 133.87, 141.334 μg·m~(-3), respectively. Comprehensive analysis shows that the optimal scheme of wind direction angle is 60°, while 45° and 75° are optional, and 15°, 30° and 90° are not available. In addition, when the floor area ratio is 0.8, the concentration of PM_(2.5) is lower, followed by 1.2. The concentration of PM_(2.5) in residential area with floor area ratio of 1.6 and 2 is higher. When the floor area ratio changes from small to large, the average concentrations are 131.678, 139.402, 159.906, 154.638 μg·m~(-3), respectively. It can be basically considered that the PM_(2.5) concentration in the whole space of residential area increases gradually with the increase of the floor area ratio of residential area. Moreover, PM_(2.5) pollution can be alleviated by increasing the green space. When green rate exceeds 35%, PM_(2.5) concentration decreases lowly with the increase of green space rate. Comprehensive analysis shows that the green rate should be above 35% in order to effectively reduce the PM_(2.5) concentration in residential areas.
引文
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SKELHORN C,LINDLEY S,LEVERMORE G,2014.The impact of vegetation types on air and surface temperatures in a temperate city:Afine scale assessment in Manchester,UK[J].Landscape and Urban Planning,121:129-140.
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葛跃,王明新,孙向武,等,2017.基于增强回归树的城市PM2.5日均值变化分析:以常州为例[J].环境科学,38(2):485-494.GE Y,WANG M X,SUN X W,et al.,2017.Variation analysis of daily PM2.5 concentrations based on boosted regression tree:A case study in Changzhou[J].Environmental Science,38(2):485-494.
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马杰,李晓锋,朱颖心,2013.住区微气候的数值模拟方法研究[J].太阳能学报,34(12):2132-2138.MA J,LI X F,ZHU Y X,2013.Numerical simulation of microclimate around residential buildings[J].Acta Energiae Solaris Sinica,34(12):2133-2138.
马西娜,马方,赵敬源,等,2017.庭院式居住组团平面形态对PM2.5扩散的影响[J].建筑科学与工程学报,34(4):120-126.MA X N,MA F,ZHAO J Y,et al.,2017.Effect of Courtyard Housing Group Plane Shape on PM2.5 Diffusion[J].Journal of Architecture and Civil Engineering,34(4):120-126.
牛慧敏,涂建军,姚作林,等,2016.中国城市空气质量时空分布特征[J].河南科学,34(8):1317-1321.NIU H M,TU J J,YAO Z L,et al.,2016.Spatial and Temporal Distrubution Characteristics of Air Quality in Chain Cities[J].Henan Sciences,34(8):1317-1321.
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裴晶晶,2006.废气污染对建筑小区空气质量影响的研究[D].天津:天津大学:1-72.PEI J J,2006.Research on the influence of waste gas pollution on residential area air quality[D].Tianjin:Tianjin University:1-72.
乔冠皓,陈警伟,刘肖瑜,等,2017.两种常见绿化树种对大气颗粒物的滞留与再悬浮[J].应用生态学报,28(1):266-272.QIAO G H,CHEN J W,LIU X Y,et al.,2017.Retention and resuspension of atmospheric particles with two common urban greening trees[J].Chinese Journal of Applied Ecology,28(1):266-272.
王佳,吕春东,牛利伟,等,2018.道路植被结构对大气可吸入颗粒物扩散影响的模拟与验证[J].农业工程学报,34(20):225-232.WANG J,LU C D,NIU L W,et al.,2018.Simulation and verification of influence of different street vegetation structure on diffusion of atmosphere particulates[J].Transactions of the Chinese Society of Agricultural Engineering,34(20):225-232.
吴正旺,韩宇婷,吴彦强,2016.PM2.5在北京几种典型居住区中的分布及扩散比较[J].华中建筑,34(8):38-41.WU Z W,HANG Y T,WU Y Q,2016.Distribution and diffusion comparison of PM2.5 in several typical residential areas in Beijing[J].Huazhong Architecture,34(8):38-41.
吴正旺,王岩慧,单海楠,2016.基于PM2.5分布不均现象的城市居住区景观格局分析[J].华中建筑,34(2):52-56.WU Z W,WANG Y H,SHAN H N,2016.Residential area landscape pattern optimization analysis on the based of uneven distribution of PM2.5[J].Huazhong Architecture,34(2):52-56.
于静,张志伟,蔡文婷,2011.城市规划与空气质量关系研究[J].城市规划,35(12):51-56.YU J,ZHANG Z W,CAI W T,2011.Research on relationship between urban planning and air quality[J].City Planning Review,35(12):51-56.
袁磊,宛杨,何成,2019.基于CFD模拟的高密度街区交通污染物分布[J].深圳大学学报,36(3):274-280.YUAN L,YUAN Y,HE C,2019.Distribution of traffic pollutants in high-density blocks based on CFD simulation[J].Journal of Shenzhen University,36(3):274-280.
LI X B,LU Q C,LU S J,et al.,2016.The impacts of roadside vegetation barriers on the dispersion of gaseous traffic pollution in urban street canyons[J].Urban Forestry&Urban Greening,17:80-91.
LU S W,YANG X B,LI S N,et al.,2018.Effects of plant leaf surface and different pollution levels on PM2.5,adsorption capacity[J].Urban Forestry&Urban Greening,34:64-70.
MORAKINYO T E,LAM Y F,2016.Simulation study of dispersion and removal of particulate matter from traffic by road-side vegetation barrier[J].Environmental Science and Pollution Research,23(7):6709-6722.
SANCHEZ I A,MCCOLLIN D,2015.A comparison of microclimate and environmental modification produced by hedgerows and dehesa in the Mediterranean region:A study in the Guadarrama region,Spain[J].Landscape and Urban Planning,143(1):230-237.
SKELHORN C,LINDLEY S,LEVERMORE G,2014.The impact of vegetation types on air and surface temperatures in a temperate city:Afine scale assessment in Manchester,UK[J].Landscape and Urban Planning,121:129-140.
XIE C,KAN L,GUO J,et al.,2018.A dynamic processes study of PMretention by trees under different wind conditions[J].Environmental Pollution,233:315-322.
顾康康,祝玲玲,2018.合肥市主城区PM2.5时空分布特征研究[J].生态环境学报,27(6):1107-1112.GU K K,ZHU L L,2018.Study on the spatial-temporal distribution characteristics of PM2.5 in Hefei urban areas[J].Ecology and Environmental Sciences,27(6):1107-1112.
葛跃,王明新,孙向武,等,2017.基于增强回归树的城市PM2.5日均值变化分析:以常州为例[J].环境科学,38(2):485-494.GE Y,WANG M X,SUN X W,et al.,2017.Variation analysis of daily PM2.5 concentrations based on boosted regression tree:A case study in Changzhou[J].Environmental Science,38(2):485-494.
贾小芳,颜鹏,孟昭阳,等,2019.2016年11-12月北京及周边重污染过程PM2.5特征[J].应用气象学报,30(3):302-315.JIA X F,YAN P,MENG Z Y,et al.,2019.Characteristics of PM2.5 in heavy pollution events in Beijing and surrounding areas from November to December in 2016[J].Journal of Applied Meteorological Science,30(3):302-315.
李名升,任晓霞,于洋,等,2016.中国大陆城市PM2.5污染时空分布规律[J].中国环境科学,36(3):641-650.LI M S,REN X X,YU Y,et al.,2016.Spatiotemporal pattern of ground-level fine particulate matter(PM2.5)pollution in mainland China[J].China Environmental Science,36(3):641-650.
马杰,李晓锋,朱颖心,2013.住区微气候的数值模拟方法研究[J].太阳能学报,34(12):2132-2138.MA J,LI X F,ZHU Y X,2013.Numerical simulation of microclimate around residential buildings[J].Acta Energiae Solaris Sinica,34(12):2133-2138.
马西娜,马方,赵敬源,等,2017.庭院式居住组团平面形态对PM2.5扩散的影响[J].建筑科学与工程学报,34(4):120-126.MA X N,MA F,ZHAO J Y,et al.,2017.Effect of Courtyard Housing Group Plane Shape on PM2.5 Diffusion[J].Journal of Architecture and Civil Engineering,34(4):120-126.
牛慧敏,涂建军,姚作林,等,2016.中国城市空气质量时空分布特征[J].河南科学,34(8):1317-1321.NIU H M,TU J J,YAO Z L,et al.,2016.Spatial and Temporal Distrubution Characteristics of Air Quality in Chain Cities[J].Henan Sciences,34(8):1317-1321.
裴会义,2016.石家庄雾霾与高层建筑规划布局探索[D].石家庄:河北科技大学:1-86.PEI H Y,2016.The Study on the Relationship between City[D].Shijiazhuang:Hebei University of Science and Technology:1-86.
裴晶晶,2006.废气污染对建筑小区空气质量影响的研究[D].天津:天津大学:1-72.PEI J J,2006.Research on the influence of waste gas pollution on residential area air quality[D].Tianjin:Tianjin University:1-72.
乔冠皓,陈警伟,刘肖瑜,等,2017.两种常见绿化树种对大气颗粒物的滞留与再悬浮[J].应用生态学报,28(1):266-272.QIAO G H,CHEN J W,LIU X Y,et al.,2017.Retention and resuspension of atmospheric particles with two common urban greening trees[J].Chinese Journal of Applied Ecology,28(1):266-272.
王佳,吕春东,牛利伟,等,2018.道路植被结构对大气可吸入颗粒物扩散影响的模拟与验证[J].农业工程学报,34(20):225-232.WANG J,LU C D,NIU L W,et al.,2018.Simulation and verification of influence of different street vegetation structure on diffusion of atmosphere particulates[J].Transactions of the Chinese Society of Agricultural Engineering,34(20):225-232.
吴正旺,韩宇婷,吴彦强,2016.PM2.5在北京几种典型居住区中的分布及扩散比较[J].华中建筑,34(8):38-41.WU Z W,HANG Y T,WU Y Q,2016.Distribution and diffusion comparison of PM2.5 in several typical residential areas in Beijing[J].Huazhong Architecture,34(8):38-41.
吴正旺,王岩慧,单海楠,2016.基于PM2.5分布不均现象的城市居住区景观格局分析[J].华中建筑,34(2):52-56.WU Z W,WANG Y H,SHAN H N,2016.Residential area landscape pattern optimization analysis on the based of uneven distribution of PM2.5[J].Huazhong Architecture,34(2):52-56.
于静,张志伟,蔡文婷,2011.城市规划与空气质量关系研究[J].城市规划,35(12):51-56.YU J,ZHANG Z W,CAI W T,2011.Research on relationship between urban planning and air quality[J].City Planning Review,35(12):51-56.
袁磊,宛杨,何成,2019.基于CFD模拟的高密度街区交通污染物分布[J].深圳大学学报,36(3):274-280.YUAN L,YUAN Y,HE C,2019.Distribution of traffic pollutants in high-density blocks based on CFD simulation[J].Journal of Shenzhen University,36(3):274-280.