EN中文

地理科学进展 >

2025 , Vol. 44 >Issue 5: 1021 - 1035

DOI: https://doi.org/10.18306/dlkxjz.2025.05.011

研究论文

LCZ框架在PM2.5浓度模拟中的适用性研究——以南昌市主城区为例

  • 阳海鸥 , 1, 2 ,
  • 肖延芳 1 ,
  • 冷清明 , 3, * ,
  • 陈文波 4
展开
  • 1.九江学院旅游与地理学院，江西 九江 332005
  • 2.江西长江经济带研究院，江西 九江 332005
  • 3.九江学院资源环境学院，江西 九江 332005
  • 4.东华理工大学测绘与信息工程学院，南昌 330013
*冷清明(1987— )，男，江西九江人，教授，硕士生导师，主要从事遥感信息处理研究。E-mail:

阳海鸥(1988— )，女，湖南衡阳人，副教授，硕士生导师，主要从事土地利用及其生态环境效应研究。E-mail:

收稿日期: 2024-07-24

  修回日期: 2025-05-05

  网络出版日期: 2025-05-26

基金资助

国家自然科学基金项目(42361015)

江西省社会科学基金项目(22GL46)

江西省高校人文社会科学基金项目(JC22222)

收起

Applicability of the local climate zone framework in PM2.5 concentration simulation: A case study of Nanchang City’s main urban area

  • YANG Hai'ou , 1, 2 ,
  • XIAO Yanfang 1 ,
  • LENG Qingming , 3, * ,
  • CHEN Wenbo 4
Expand
  • 1. College of Tourism and Geography, Jiujiang University, Jiujiang 332005, Jiangxi, China
  • 2. Jiangxi Yangtze River Economic Zone Research Institute, Jiujiang University, Jiujiang 332005, Jiangxi, China
  • 3. School of Resources and Environment, Jiujiang University, Jiujiang 332005, Jiangxi, China
  • 4. School of Surveying and Spatial Information Engineering, East China University of Technology, Nanchang 330013, China

Received date: 2024-07-24

  Revised date: 2025-05-05

  Online published: 2025-05-26

Supported by

National Natural Science Foundation of China(42361015)

Jiangxi Social Science Foundation of China(22GL46)

Humanities and Social Science Foundation of Universities in Jiangxi Province(JC22222)

Fold

摘要

景观格局演变的大气环境效应是城市大气污染的主要成因，但相关研究尚未形成普适性的景观分类方案。论文引入热岛研究中的局地气候区(local climate zone，LCZ)框架，利用土地利用回归(land use regression，LUR)模型对南昌市主城区进行PM2.5浓度模拟，探索LCZ框架在PM2.5浓度模拟中的适用性。对比传统LUR建模和3种融合LCZ的LUR建模发现：① 融合LCZ的LUR模型调整R²普遍提高，标准估计误差降低，春、夏季效果尤为明显。其中，同时替代土地利用和人口密度变量的建模效果最佳，说明融合LCZ进行PM2.5浓度模拟是可行的，四季均可解释80%以上的PM2.5浓度空间变化。② LCZ 8(大型低层)和LCZ 10(重工业)是替代工业用地的最佳变量，LCZ G(水域)和LCZ F(裸地/沙地)是替代生态用地的最佳变量，LCZ 1~3(紧凑型建筑)和LCZ 5(开敞中层建筑)是替代人口密度的最佳变量，说明LCZ能在一定程度上解释城市PM2.5空间分布的驱动机制。③ 气象因子的季节变化是导致PM2.5季节变化的主要原因，不同季节影响PM2.5浓度的主导气象因子不同，春、秋季节主要受风速的影响，夏季受相对湿度的影响，冬季受温度和降水的影响。研究探索了LCZ框架在PM2.5浓度模拟中的适用性，结果可为城市大气污染评估与治理提供参考。

关键词： LCZ; 景观分类; LUR; PM2.5; 南昌市主城区

本文引用格式

阳海鸥 , 肖延芳 , 冷清明 , 陈文波 . LCZ框架在PM2.5浓度模拟中的适用性研究——以南昌市主城区为例[J]. 地理科学进展, 2025 , 44(5) : 1021 -1035 . DOI: 10.18306/dlkxjz.2025.05.011

Abstract

The atmospheric environmental effect of landscape pattern evolution is considered the main cause of urban air pollution. However, there is no universal landscape classification for relevant research. In this study, the local climate zone (LCZ) framework was introduced to simulate PM2.5 concentration in Nanchang City's main urban area by using the land use regression (LUR) model. The applicability of the LCZ framework in PM2.5 concentration simulation was tested. The results indicate that adjusted R2 of the LUR models with LCZ variables is improved compared with the traditional LUR models, and the standard estimation errors have decreased. The improvement is especially significant in spring and summer. The LUR models with LCZ variables substituting for both land use and population density variables are the best fitting models, indicating that it is feasible to integrate LCZ for PM2.5 concentration simulation, and more than 80% of the variations in PM2.5 can be explained in four seasons. We also found that LCZ 8 (large low-rise) and LCZ 10 (heavy industry) are the best variables to replace industrial land, LCZ G (water) and LCZ F (bare soil or sand) are the best variables to replace ecological land, LCZ 1-3 (compact buildings) and LCZ 5 (open mid-rise) are the best variables to replace population density. The LCZs can explain the driving mechanism of urban atmospheric particles' spatial distribution to a certain extent. The seasonal variation of meteorological factors is the main reason for the seasonal variation of PM2.5 concentration. The PM2.5 concentration of spring and autumn are mainly affected by wind speed, that of summer is affected by relative humidity, and that of winter is affected by temperature and precipitation. This study explored the applicability of the LCZ framework in PM2.5 concentration simulation, and the results can provide a reference for urban air pollution assessment and control.

Key words： local climate zone (LCZ); landscape classification; land use regression (LUR); PM2.5; Nanchang City's main urban area

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
[1]
韩立建. 城市化与PM2.5时空格局演变及其影响因素的研究进展[J]. 地理科学进展, 2018, 37(8): 1011-1021.

DOI

[Han Lijian. Relationship between urbanization and urban air quality: An insight on fine particulate dynamics in China. Progress in Geography, 2018, 37(8): 1011-1021.]

DOI

[2]
周宏浩, 谷国锋. 外部性视角下中国城市网络演化及其环境效应研究[J]. 地理研究, 2022, 41(1): 268-285.

DOI

[Zhou Honghao, Gu Guofeng. The evolution of China's city network and its environmental effects from the perspective of externalities. Geographical Research, 2022, 41(1): 268-285.]

[3]
曲艺, 龙花楼, 曹林森. 城镇化背景下土地利用转型的环境与经济效应耦合研究框架构建[J]. 地理科学进展, 2024, 43(4): 799-809.

DOI

[Qu Yi, Long Hualou, Cao Linsen. Construction of a research framework for coupling environmental and economic effects of land use transition under urbanization. Progress in Geography, 2024, 43(4): 799-809.]

DOI

[4]
World Health Organization (WHO). World health statistics overview 2019: Monitoring health for the SDGs, sustainable development goals[R]. Geneva, Switzerland: WHO, 2019.

[5]
Murray C J L, Aravkin A Y, Zheng P, et al. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019[J]. The Lancet, 2020, 396: 1223-1249.

[6]
陈镘, 黄柏石, 刘晔. PM2.5污染对中国人口死亡率的影响: 基于346个城市面板数据的实证分析[J]. 地理科学进展, 2022, 41(6): 1028-1040.

DOI

[Chen Man, Huang Baishi, Liu Ye. Effects of PM2.5 concentration on mortality in China: A study based on city-level panel data. Progress in Geography, 2022, 41(6): 1028-1040.]

[7]
王少剑, 高爽, 陈静. 基于GWR模型的中国城市雾霾污染影响因素的空间异质性研究[J]. 地理研究, 2020, 39(3): 651-668.

DOI

[Wang Shaojian, Gao Shuang, Chen Jing. Spatial heterogeneity of driving factors of urban haze pollution in China based on GWR model. Geographical Research, 2020, 39(3): 651-668.]

[8]
陈文波, 刘士余, 余敦, 等. 城市建成区土地利用的环境影响评价: 以南昌市主城区为例[J]. 应用生态学报, 2009, 20(7): 1747-1752.

[Chen Wenbo, Liu Shiyu, Yu Dun, et al. Assessment of land use environmental impacts in urban built-up area: A case study in main built-up area of Nanchang City. Chinese Journal of Applied Ecology, 2009, 20(7): 1747-1752.]

PMID

[9]
栾庆祖, 李波, 张新时. 面向大气环境影响的城市景观分类系统: 以北京为例[J]. 生态学杂志, 2019, 38(5): 1482-1490.

[Luan Qingzu, Li Bo, Zhang Xinshi. Urban landscape classification system under influences of atmospheric environment: A case study in Beijing. Chinese Journal of Ecology, 2019, 38(5): 1482-1490.]

[10]
赵文斐, 于占江, 王让会, 等. 石家庄市PM2.5时空特征及其对土地利用变化的响应[J]. 生态环境学报, 2020, 29(12): 2404-2413.

DOI

[Zhao Wenfei, Yu Zhanjiang, Wang Ranghui, et al. Spatial and temporal characteristics of PM2.5 and its response to land use change in Shijiazhuang. Ecology and Environmental Sciences, 2020, 29(12): 2404-2413.]

[11]
刘海猛, 方创琳, 黄解军, 等. 京津冀城市群大气污染的时空特征与影响因素解析[J]. 地理学报, 2018, 73(1): 177-191.

DOI

[Liu Haimeng, Fang Chuanglin, Huang Jiejun, et al. The spatial-temporal characteristics and influencing factors of air pollution in Beijing-Tianjin-Hebei urban agglomeration. Acta Geographica Sinica, 2018, 73(1): 177-191.]

DOI

[12]
阳海鸥, 陈文波, 梁照凤. LUR模型模拟的南昌市PM2.5浓度与土地利用类型的关系[J]. 农业工程学报, 2017, 33(6): 232-239.

[Yang Haiou, Chen Wenbo, Liang Zhaofeng. Relationship of PM2.5 concentration and land use type in Nanchang City based on LUR simulation. Transactions of the CSAE, 2017, 33(6): 232-239.]

[13]
Stewart I D, Oke T R. Local climate zones for urban temperature studies[J]. Bulletin of the American Meteorological Society, 2012, 93(12): 1879-1900.

[14]
Ching J, Mills G, Bechtel B, et al. WUDAPT: An urban weather, climate, and environmental modeling infrastructure for the anthropocene[J]. Bulletin of the American Meteorological Society, 2018, 99(9): 1907-1924.

[15]
Bechtel B, Alexander P J, Böhner J, et al. Mapping local climate zones for a worldwide database of the form and function of cities[J]. ISPRS International Journal of Geo-Information, 2015, 4(1): 199-219.

[16]
Leconte F, Bouyer J, Claverie R, et al. Using local climate zone scheme for UHI assessment: Evaluation of the method using mobile measurements[J]. Building and Environment, 2015, 83: 39-49.

[17]
Bechtel B, Demuzere M, Mills G, et al. SUHI analysis using local climate zones: Comparison of 50 cities[J]. Urban Climate, 2019, 28: 100451. doi: 10.1016/j.uclim.2019.01.005.

[18]
江斯达, 占文凤, 杨俊, 等. 局地气候分区框架下城市热岛时空分异特征研究进展[J]. 地理学报, 2020, 75(9): 1860-1878.

DOI

[Jiang Sida, Zhan Wenfeng, Yang Jun, et al. Urban heat island studies based on local climate zones: A systematic overview. Acta Geographica Sinica, 2020, 75(9): 1860-1878.]

DOI

[19]
Cilek M U, Cilek A. Analyses of land surface temperature (LST) variability among local climate zones (LCZs) comparing Landsat-8 and ENVI-met model data [J]. Sustainable Cities and Society, 2021, 69: 102877. doi:10.1016/j.scs.2021.102877.

[20]
Hou X Y, Xie X, Bagan H S, et al. Exploring spatiotemporal variations in land surface temperature based on local climate zones in Shanghai from 2008 to 2020[J]. Remote Sensing, 2023, 15(12): 3106. doi: 10.3390/rs15123106.

[21]
Chen G, Chen Y Q, He H, et al. Assessing the synergies between heat waves and urban heat islands of different local climate zones in Guangzhou, China[J]. Building and Environment, 2023, 240: 110434. doi: 10.1016/j.buildenv.2023.110434.

[22]
赵恩灵, 邓帆, 李志远, 等. 基于局地气候区的武汉市地表热环境研究[J]. 长江流域资源与环境, 2023, 32(5): 1030-1041.

[Zhao Enling, Deng Fan, Li Zhiyuan, et al. Study on the thermal environment of Wuhan City based on local climate zones. Resources and Environment in the Yangtze Basin, 2023, 32(5): 1030-1041.]

[23]
Zheng B H, Chen Y P, Hu Y Z. Analysis of land cover and SUHII pattern using local climate zone framework: A case study of Chang-Zhu-Tan main urban area[J]. Urban Climate, 2022, 43: 101153. doi: 10.1016/j.uclim.2022.101153.

[24]
耿树丰, 任嘉义, 杨俊, 等. 局地气候区视角下的城市热环境研究[J]. 生态学报, 2022, 42(6): 2221-2227.

[Geng Shufeng, Ren Jiayi, Yang Jun, et al. Exploration of urban thermal environment based on local climate zone. Acta Ecologica Sinica, 2022, 42(6): 2221-2227.]

[25]
Wang R F, Wang M M. Multi-scale analysis of surface thermal environment in relation to urban form: A case study of the Guangdong-Hong Kong-Macao Greater Bay Area[J]. Sustainable Cities and Society, 2023, 99: 104953. doi: 10.1016/j.scs.2023.104953.

[26]
娄彩荣, 刘红玉, 李玉玲, 等. 大气颗粒物(PM2.5、PM10)对地表景观结构的响应研究进展[J]. 生态学报, 2016, 36(21): 6719-6729.

[Lou Cairong, Liu Hongyu, Li Yuling, et al. Research on the response of air particles (PM2.5, PM10) to landscape structure: A review. Acta Ecologica Sinica, 2016, 36(21): 6719-6729.]

[27]
Shi Y, Ren C, Lau K K-L, et al. Investigating the influence of urban land use and landscape pattern on PM2.5 spatial variation using mobile monitoring and WUDAPT[J]. Landscape and Urban Planning, 2019, 189: 15-26.

[28]
Yang H O, Leng Q M, Xiao Y F, et al. Investigating the impact of urban landscape composition and configuration on PM2.5 concentration under the LCZ scheme: A case study in Nanchang, China[J]. Sustainable Cities and Society, 2022, 84: 104006. doi: 10.1016/j.scs.2022.104006.

[29]
Jiang R Y, Xie C K, Man Z H, et al. LCZ method is more effective than traditional LUCC method in interpreting the relationship between urban landscape and atmospheric particles[J]. Science of the Total Environment, 2023, 869: 161677. doi: 10.1016/j.scitotenv.2023.161677.

[30]
白杨, 杨剑, 陈鹏, 等. 基于空间插值的西安市重空气污染期间主要污染物时空变化特征及相关性分析[J]. 环境科学研究, 2020, 33(4): 809-819.

[Bai Yang, Yang Jian, Chen Peng, et al. Spatiotemporal characteristics and relationships of main air-pollutants during a typical heavy air pollution in Xi'an City based on a spatial interpolation method. Research of Environmental Sciences, 2020, 33(4): 809-819.]

[31]
张猛, 张博. 基于机器学习和卫星遥感的PM2.5/10空间连续分布反演方法研究[J]. 地球环境学报, 2020, 11(4): 447-455.

[Zhang Meng, Zhang Bo. Research on the methods to retrieve continuous spatial distribution of PM2.5/10 based on machine learning and satellite imagery. Journal of Earth Environment, 2020, 11(4): 447-455.]

[32]
曹惠玲, 李玉铭, 晏嘉伟, 等. 基于修正Gaussian Puff模型的飞机LTO循环PM2.5排放及扩散特性研究[J]. 环境科学学报, 2022, 42(3): 352-361.

[Cao Huiling, Li Yuming, Yan Jiawei, et al. Research on PM2.5 emission and diffusion characteristics of aircraft LTO cycle based on modified Gaussian Puff Model. Acta Scientiae Circumstantiae, 2022, 42(3): 352-361.]

[33]
Xu H, Bechle M J, Wang M, et al. National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging[J]. Science of the Total Environment, 2019, 655: 423-433.

[34]
Xu X Y, Qin N, Qi L, et al. Development of season-dependent land use regression models to estimate BC and PM1exposure[J]. Science of the Total Environment, 2021, 793: 148540. doi: 10.1016/j.scitotenv.2021.148540.

[35]
Briggs D J, Collins S, Elliott P, et al. Mapping urban air pollution using GIS: A regression-based approach[J]. International Journal of Geographical Information Science, 1997, 11(7): 699-718.

[36]
Henderson S B, Beckerman B, Jerrett M, et al. Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter[J]. Environmental Science & Technology, 2007, 41(7): 2422-2428.

[37]
陈曦, 冯建喜. 基于步行性与污染物暴露空间格局比较的建成环境健康效应: 以南京为例[J]. 地理科学进展, 2019, 38(2): 296-304.

DOI

[Chen Xi, Feng Jianxi. Health effects of built environment based on a comparison of walkability and air pollution: A case study of Nanjing City. Progress in Geography, 2019, 38(2): 296-304.]

[38]
陈莉, 白志鹏, 苏笛, 等. 利用LUR模型模拟天津市大气污染物浓度的空间分布[J]. 中国环境科学, 2009, 29(7): 685-691.

[Chen Li, Bai Zhipeng, Su Di, et al. Application of land use regression to simulate ambient air PM10 and NO2 concentration in Tianjin City. China Environmental Science, 2009, 29(7): 685-691.]

[39]
焦利民, 许刚, 赵素丽, 等. 基于LUR的武汉市PM2.5浓度空间分布模拟[J]. 武汉大学学报(信息科学版), 2015, 40(8): 1088-1094.

[Jiao Limin, Xu Gang, Zhao Suli, et al. LUR-based simulation of the spatial distribution of PM2.5 of Wuhan. Geomatics and Information Science of Wuhan University, 2015, 40(8): 1088-1094.]

[40]
吴健生, 谢舞丹, 李嘉诚. 土地利用回归模型在大气污染时空分异研究中的应用[J]. 环境科学, 2016, 37(2): 413-419.

[Wu Jiansheng, Xie Wudan, Li Jiacheng. Application of land-use regression models in spatial-temporal differentiation of air pollution. Environmental Science, 2016, 37(2): 413-419.]

[41]
Hoek G, Beelen R, De Hoogh K, et al. A review of land-use regression models to assess spatial variation of outdoor air pollution[J]. Atmospheric Environment, 2008, 42(33): 7561-7578.

[42]
Ma X Y, Zou B, Deng J, et al. A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023[J]. Environment International, 2024: 108430. doi: 10.1016/j.envint.2024.108430.

[43]
梁照凤, 陈文波, 郑蕉, 等. 南昌市中心城区主要大气污染物分布模拟及土地利用对其影响[J]. 应用生态学报, 2019, 30(3): 1005-1014.

[Liang Zhaofeng, Chen Wenbo, Zheng Jiao, et al. Simulation of the distribution of main atmospheric pollutants and the influence of land use on them in central urban area of Nanchang City, China. Chinese Journal of Applied Ecology, 2019, 30(3): 1005-1014.]

[44]
陈文波, 谢涛, 郑蕉, 等. 地表植被景观对PM2.5浓度空间分布的影响研究[J]. 生态学报, 2020, 40(19): 7044-7053.

[Chen Wenbo, Xie Tao, Zheng Jiao, et al. The impacts of vegetation landscape on PM2.5 spatial distribution. Acta Ecologica Sinica, 2020, 40(19): 7044-7053.]

[45]
Liu W, Li X D, Chen Z, et al. Land use regression models coupled with meteorology to model spatial and temporal variability of NO2 and PM10 in Changsha, China[J]. Atmospheric Environment, 2015, 116: 272-280.

[46]
European Centre for Medium-Range Weather Forecasts (ECMWF). IFS documentation Cy40r1 part IV: Physical processes[M]. Sheffield, UK: ECMWF, 2014.

[47]
Buck A L. New equations for computing vapor pressure and enhancement factor[J]. Journal of Applied Meteorology and Climatology, 1981, 20(12): 1527-1532.

[48]
张紫越, 郭威, 朱仁果, 等. 南昌经开区冬季PM2.5中正构烷烃污染特征及来源[J]. 环境科学与技术, 2023, 46(1): 180-190.

[Zhang Ziyue, Guo Wei, Zhu Renguo, et al. Pollution characteristics and sources of n-alkanes in PM2.5 in Nanchang economic development zone in winter. Environmental Science & Technology, 2023, 46(1):180-190.]

[49]
何宗健, 袁胜林, 肖美. 夏季南昌市大气颗粒物PM10·PM2.5污染水平研究[J]. 安徽农业科学, 2010, 38(3): 1336-1338, 1384.

[He Zongjian, Yuan Shenglin, Xiao Mei. Pollution levels of airborne particulate matter PM10 and PM2.5 in summer in Nanchang City. Journal of Anhui Agricultural Sciences, 2010, 38(3): 1336-1338, 1384.]

[50]
彭希珑, 何宗健, 刘小真, 等. 南昌市大气PM2.5中多环芳烃的来源解析[J]. 环境污染与防治, 2009, 31(10): 28-32.

[Peng Xilong, He Zongjian, Liu Xiaozhen, et al. Source apportionment of PAHs in fine particulate matter (PM2.5) of Nanchang City. Environmental Pollution & Control, 2009, 31(10): 28-32.]

[51]
房佳兴, 李少宁, 赵娜, 等. 植物吸滞PM2.5研究进展: 从宏观到微观[J]. 生态学报, 2024, 44(6): 2632-2645.

[Fang Jiaxing, Li Shaoning, Zhao Na, et al. Research progress on PM2.5 absorption by plants: From mac to micro. Acta Ecologica Sinica, 2024, 44(6): 2632-2645.]

[52]
刘航. 黄淮海地区典型湿地对大气PM2.5的影响[D]. 徐州: 江苏师范大学, 2019.

[Liu Hang. Influence of typical wetland in Huang-Huai region on atmospheric PM2.5. Xuzhou, China: Jiangsu Normal University, 2019.]

[53]
苗元露, 耿春梅, 纪元, 等. 天山北坡城市群颗粒物及臭氧时空分布及气象因素的影响[J]. 北京大学学报(自然科学版), 2025, 61(2): 240-252.

[Miao Yuanlu, Geng Chunmei, Ji Yuan, et al. The spatial and temporal distribution of particulate matter and ozone in the urban agglomeration on the northern slope of Tianshan Mountains and the influence of meteorological factors. Acta Scientiarum Naturalium Universitatis Pekinensis, 2025, 61(2): 240-252.]

[54]
张振华. PM2.5浓度时空变化特性、影响因素及来源解析研究[D]. 杭州: 浙江大学, 2014.

[Zhang Zhenhua. Research on spatial and temporal variation characterstics, factors, and source apportionment of PM2.5. Hangzhou, China: Zhejiang University, 2014.]

[55]
黄小刚, 赵景波, 辛未冬. 基于网格的长三角PM2.5分布影响因素及交互效应[J]. 环境科学, 2021, 42(7): 3107-3117.

[Huang Xiaogang, Zhao jingbo, Xin Weidong. Factors and their interaction effects on the distribution of PM2.5 in the Yangtze River Delta based on grids. Environmental Science, 2021, 42(7): 3107-3117.]

[56]
方云皓, 赵丽元, 窦碧莹, 等. 长江中游城市群建成区绿色基础设施对PM2.5的消减效应[J]. 生态学报, 2024, 44(1): 117-128.

[Fang Yunhao, Zhao Liyuan, Dou Biying, et al. Reduction effect of green infrastructure on PM2.5 in the built-up areas of urban agglomerations in the Yangtze River's middle reaches. Acta Ecologica Sinica, 2024, 44(1): 117-128.]

[57]
Eeftens M, Beekhuizen J, Beelen R, et al. Quantifying urban street configuration for improvements in air pollution models[J]. Atmospheric Environment, 2013, 72: 1-9.doi: 10.1016/j.atmosenv.2013.02.007.

[58]
Liu M, Chen H, Wei D, et al. Nonlinear relationship between urban form and street-level PM2.5 and CO based on mobile measurements and gradient boosting decision tree models[J]. Building and Environment, 2021, 205: 108265. doi: 10.1016/j.buildenv.2021.108265.

[59]
周伟奇, 田韫钰. 城市三维空间形态的热环境效应研究进展[J]. 生态学报, 2020, 40(2): 416-427.

[Zhou Weiqi, Tian Yunyu. Effects of urban three-dimensional morphology on thermal environment: A review. Acta Ecologica Sinica, 2020, 40(2): 416-427.]

[60]
Yang F, Qian F, Lau S S Y. Urban form and density as indicators for summertime outdoor ventilation potential: A case study on high-rise housing in Shanghai[J]. Building and Environment, 2013, 70: 122-137.

Options
文章导航

/

〈 〉