EN中文

Journal of Geo-information Science >

2017 , Vol. 19 >Issue 1: 39 - 49

DOI: https://doi.org/10.3724/SP.J.1047.2017.00039

Orginal Article

Study on the Security Pattern of the Heat Environment and the Influence of Land Use Change in the Yangtze River Delta Urban Agglomeration

  • HAN Dongrui , 1 ,
  • XU Xinliang , 2, * ,
  • LI Jing 3 ,
  • SUN Xihua 1 ,
  • QIAO Zhi 4
Expand
  • 1. College of Geography and Environment, Shandong Normal University, Jinan 250014,China
  • 2. State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 3. Satellite Environment Center, Ministry of Environmental Protection,Beijing 100094, China
  • 4. Key Laboratory of Indoor Air Environment Quality Control, School ofEnvironmental Science and Engineering, Tianjin University, Tianjin 300072,China
*Corresponding author: XU Xinliang, E-mail:

Received date: 2016-06-30

  Request revised date: 2016-08-20

  Online published: 2017-01-13

Copyright

《地球信息科学学报》编辑部 所有
Fold

Abstract

The acceleration of urbanization plays an important role in regional heat environment, whose changes may lead to a series of ecological problems. A scientific evaluation on the heat environment of urban agglomeration is essential to urban planning and construction. Based on the construction of a standard of heat environment security levels, we analyzed the spatio-temporal variation of the heat environment security pattern and its causes from land use changes in the Yangtze River Delta urban agglomeration using the MODIS land surface temperature products. The conclusions are as follows: (1) in 2015, the dangerous zones of the heat environment in the Yangtze River Delta urban agglomeration were mostly located in or closed to the urban built-up regions. For example, the “Z” region of Nanjing, Shanghai, Hangzhou, Ningbo and other cities were most obvious. The critical security zones were mostly located in suburbs while the relative security zones were mainly distributed in the northern plains of the Yangtze River. The security zones were mainly located in Hangzhou and its southern mountain and hilly region, most of the region of Tai Lake and the north of the Yangtze River Delta urban agglomerations; (2) the dangerous zone, the critical security zone, the relative security zone and the security zone showed an upward trend, a slight upward trend, a downward trend and a first downward then upward trend, respectively; (3) the primary reason of the decline in security levels of the heat environment was the high ratio of the build-up areas and the low ratio of woodlands. Additionally, the large quantity of croplands occupied by build-up areas was also the reason of the expansion of dangerous zones.

Key words： urbanization; urban agglomeration; the heat environment; security pattern

Cite this article

HAN Dongrui , XU Xinliang , LI Jing , SUN Xihua , QIAO Zhi . Study on the Security Pattern of the Heat Environment and the Influence of Land Use Change in the Yangtze River Delta Urban Agglomeration[J]. Journal of Geo-information Science, 2017 , 19(1) : 39 -49 . DOI: 10.3724/SP.J.1047.2017.00039

1 引言

城市化是世界各国发展的共同趋势,也是人类文明和进步的标志[1]。随着城市化进程的加快,热岛效应、环境污染、植被退化等一系列生态环境问题不断出现,严重制约了区域的持续与健康发展。此外,城市的快速扩张使城市下垫面环境发生了剧烈的变化,而土地利用/土地覆盖变化（LUCC）作为人类活动与自然环境相互作用最直接的表现形 式[2],对区域热环境产生了重要影响。城市热岛UHI（Urban Heat Island）作为城市热环境特征的集中体现[3],自19世纪初Howard提出“热岛效应”以来,国内外学者对城市热岛的形态与结构[4-6]、过程与变化[7-8]和机制与模拟[9-11]等方面开展了大量的研究。在当前城市快速向外扩张、人口不断向城市聚集,加之全球气候变暖的影响下[12],城市群热环境安全状况面临前所未有的挑战,因此研究城市群热环境安全格局的时空变化特征以及成因对区域气候研究十分重要。
近年来,大量学者对城市化进程中城市热环境改变引发的生态环境问题进行了研究。郑祚芳等[13]研究了20世纪70年代以来城市化进程对北京区域气温的影响,结果表明城市化进程带来的热岛效应是导致局地增温的主要因子,占总增温比重的47.5%~61.2%。林学椿等[14]利用气象数据研究发现1960-2000年北京由于城市化,城乡间局地平均气温差扩大接近1 ℃。陈云浩等[15]研究了上海不同热力背景对城市局地降雨的影响,发现在高温背景区下,年降雨量、年暴雨量、年暴雨次数均最大。陈燕等[16]采用数值模拟手段研究了珠江三角洲城市群发展对城市污染物分布以及城市间污染物传送的影响,发现城市群热岛效应致使局地空气污染严重。此外,一些学者也分析了土地利用/覆被变化对城市热岛效应的影响,如宫阿都等[17]研究了北京市土地利用/覆盖变化与对城市地表温度的影响。岳文泽等[18]分析了上海市地表温度在不同土地利用类型之间的差异,并揭示了土地利用格局与城市地表温度之间的关系。牟雪洁等[19]探讨了珠江三角洲地区土地利用类型与地表温度之间的关系。从整体来看,已有研究对城市化进程中热环境改变所引发的生态环境问题进行了详尽的分析,但对于热环境安全格局状况的研究比较缺乏;另外,当前研究多以单个城市为研究对象,而对城市群尺度的研究还较为欠缺。城市的快速发展所带来的高强度人为热排放在原本相对离散的单个城市内部已难以消解,而城市群的出现又使得各城市之间的热安全空间进一步缩减,城市之间不可避免地会出现热岛效应叠加等新型热环境问题。如何科学地评价城市群尺度的热环境安全状况及其产生的原因,是当前亟需解决的问题。
长江三角洲城市群作为世界六大城市群之一,快速城市化导致的城市群热环境安全问题十分突出。本研究在分析2015年长江三角洲城市群热环境空间分布格局的基础上,通过热岛足迹与地表温度等级2个指标对城市群热环境安全等级进行划分,进而分析长江三角洲城市群热环境安全格局的时空变化特征与区域差异,以及土地利用/覆被变化对热环境安全格局的影响,对改善城市群热环境安全格局,制订城市发展的规划布局和建设舒适的人居环境有重要的现实意义。

2 研究区概况与数据来源

2.1 研究区概况

长江三角洲城市群作为六大世界级城市群之一,是中国城市化水平最高、城市体系最完备的地区。长江三角洲城市群位于中国大陆东部沿海,是一个以上海为中心、包括江苏省的南京、无锡、常州、苏州、南通、扬州、镇江和泰州以及浙江省的杭州、宁波、绍兴、湖州、嘉兴、台州和舟山共16个城市所组成的城市群（图1）。其具有完备的城镇体系,面积约为10万km2,约占全国总面积的1%,经济总量却约占全国的20%。该区属于亚热带季风气候,全年温暖湿润,年平均气温为18~23 ℃,年平均降水量为1000~2000 mm。地貌以平原为主,西南部有部分丘陵和山地。
长江三角洲城市群依托其优越的地理位置以及良好的经济基础等优势,以上海、南京和杭州等核心城市带动了长江三角洲地区经济的快速发展,其快速城市化的同时,大量耕地被建设用地所取代,单个城市在原来以市辖区单核心扩展模式发展的基础上逐步转变为现在以市辖区和外围县域多核心扩展的发展模式。城市间建设用地多连片分布,且相连后继续填充式发展,下垫面性质的改变使得局地热环境效应进一步加强,加之人口高度集聚等因素,导致该区域热环境安全状况进一步恶化。
Fig. 1 The location of the Yangtze River Delta and its administrative map

图1 长江三角洲城市群地理位置与行政区划图

2.2 数据来源

本研究所使用的数据主要包括地表温度数据和土地利用数据。
地表温度数据来源于MODIS地表温度（LST-Land Surface Temperature）1 km L3产品(MOD11)[20-21],该产品是基于分裂窗算法通过反演获取的[22]。大量研究表明,分裂窗算法反演得到的LST产品达到了1 K的精度[23-24],可以满足城市群热环境安全格局研究的需要。为研究长江三角洲城市群热环境安全格局,选用了2005、2010和2015年地表温度数据。
土地利用数据包含2005、2010和2015年,来源于中国科学院资源环境数据中心的1：10万比例尺土地利用数据库[25-28]。该数据库是以美国陆地卫星Landsat长时间序列遥感影像为主要数据源,通过人机交互目视遥感解译生成。土地利用类型包括6个一级类型以及25个二级类型,其中6个一级类型为耕地、林地、草地、水域、居民地和未利用土地。通过大量样本验证分析,全国土地利用一级类型综合评价精度达到94.3%以上,能够满足1:10万比例尺用户制图精度要求[29]。本研究利用GIS技术,从3期土地利用现状数据集中提取长江三角洲城市群地区空间分布数据,用于开展2005-2015年长江三角洲城市群热环境安全格局以及土地利用变化对热环境安全格局的影响研究。

3 研究方法

3.1 城市热岛足迹

城市热岛足迹（UHI footprint）表征热岛效应发生的空间范围。为了保持热力景观的完整性,减少人为主观的干扰,本研究通过参考城市形态分形理论[30],采用改进的半径法[31],测算长江三角洲城市群各城市热岛足迹。因城市热岛效应表现为城市中心热岛效应强,向周围逐渐减弱,故选取城市建成区的重心作为城市热场分布的同心圆环带的中心,以第1个圆环的面积为基准,使每个圆环内的面积相等,并使同心圆环覆盖所有的建成区。分别统计各圆环内的平均地表温度,并建立环数和平均地表温度之间的关系,以判定内外地表温度分异的边界,确定为热岛足迹[32]。其计算方法如式（1）所示。
S 1 = π r 1 2 S 2 = π r 2 2 - π r 1 2 = S 1 r 2 = 2 r 1 S 3 = π r 3 2 - π r 2 2 = S 1 r 3 = 3 r 1 ⋮ S i = π r i 2 - π r i - 1 2 = S 1 r i = i r 1 （1）
式中： S i 表示第 i 个圆环的面积; r i 表示第 i 个圆环的半径, i =1,2,3,…,n。
以苏州市为例,首先提取建成区重心,初始半径为5 km向外叠加同心圆,使每个圆环面积与第一个圆环面积相等,建立环数与圆环内地表温度平均值之间对应关系（图2）。初始圆环内平均地表温度最高,且随着环数的增加,环内平均地表温度逐渐降低。当到18环时,平均地表温度又发生明显变化,且在第18环后,地表温度平均值趋于平缓,故将前18环的面积作为热岛足迹。
Fig. 2 Average land surface temperature of eachring of Suzhou in 2005

图2 2005年苏州市各环地表温度平均值散点图

3.2 地表温度等级划分

本研究采用密度分割技术对长江三角洲城市群地表温度进行分级[33],以反映不同时相的长江三角洲城市群热环境空间分布特征,首先利用式（2）将地表温度范围归为0与1之间。
T normal = T - T min T max - T min （2）
式中： T normal 表示归一化后的像元值; T 表示地表温度值; T max 表示长江三角洲城市群地表温度的最大值; T min 表示长江三角洲城市群地表温度的最小值。
如表1所示,根据地表温度热力等级划分标准,将长江三角洲城市群地表温度划分为低温、次低温、中温、次高温和高温5个热力等级。
Tab. 1 Division standard for LST thermodynamic levels

表1 地表温度热力等级划分标准

地表温度等级 温度范围
低温 Tnormal<Tmean–1.5Tstd
次低温 Tmean–1.5Tstd≤Tnormal<Tmean–0.5Tstd
中温 Tmean–0.5Tstd≤Tnormal<Tmean+0.5Tstd
次高温 Tmean+0.5Tstd≤Tnormal<Tmean+1.5Tstd
高温 Tnormal≥Tmean+1.5Tstd

注：Tmean表示归一化后所有像元的平均值;Tstd表示归一化后所有像元的标准差

3.3 热环境安全等级划分

为表征城市群热环境安全等级程度,本研究通过热岛足迹和地表温度等级2个指标,制定了城市群热环境安全等级分级标准,对城市群热环境安全程度进行分级。首先利用热岛足迹来判别城市热岛效应发生的空间范围,然后采用密度分割技术对城市群地表温度进行分级。在综合考虑热岛足迹和地表温度等级的基础上将热环境安全程度分为4个等级：不安全、临界安全、较安全和安全（表2）。
Tab. 2 Division standard for heat environmentsecurity levels

表2 热环境安全等级划分标准

安全等级 划分标准
不安全
临界安全		 区域内的高温区与次高温区
热岛足迹内部的中温区、次低温区与低温区
较安全
安全		 热岛足迹外部的中温区
热岛足迹外部的次低温区与低温区

4 结果与分析

4.1 2015年热环境安全格局分布特征

通过分析2015年长江三角洲城市群不同地表温度等级面积及比例和不同安全等级面积及比例（表3、4）,以及2015年地表温度和热环境安全格局空间分布（图3）可知,2015年长江三角洲城市群热环境安全格局特征。
Fig. 3 Spatial distribution of LST and security pattern of the Yangtze River Delta in 2015

图3 2015年长江三角洲城市群地表温度与安全格局空间分布图

Tab. 3 Area and its proportion of different LST thermodynamic levels in the Yangtze River Delta in 2015

表3 2015年长江三角洲城市群不同地表温度热力等级面积及比例

地表温度等级 面积/km2 比例/%
低温 5925.59 5.96
次低温 21 422.25 21.54
中温 36 791.99 36.99
次高温 26 348.76 26.49
高温 8969.99 9.02
Tab. 4 Area and its proportion of different security level regions in the Yangtze River Delta from 2005 to 2015

表4 2005-2015年长江三角洲城市群不同安全等级区域面积（km2）及比例（%）

区域 2005年 2010年 2015年
面积 比例 面积 比例 面积 比例
不安全 23 278.62 23.41 29 361.92 29.52 35 222.33 35.41
临界安全 5059.76 5.09 6578.00 6.61 7196.30 7.24
较安全 46 319.21 46.57 39 262.04 39.48 31 583.56 31.76
安全 24 801.00 24.93 24 256.63 24.39 25 456.40 25.59
（1）2015年长江三角洲城市群地表温度空间分布特征
高温区与次高温区多分布于城市建成区及建成区周围;中温区多分布于长江以北及太湖西北部平原区域;次低温区多于杭州及杭州以南山地、丘陵区分布;低温区则主要分布于太湖以及高邮湖等水域（图3）。2015年不同地表温度等级面积从大到小分别为：中温区、次高温区、次低温区、高温区与低温区（表3）,其中,中温区面积高达36 791.99 km2,所占比例最高,为36.99%;次高温区与次低温区面积也较高,占比分别为26.49%和21.54%;高温区占比较低,为9.02%;低温区占比最低,为5.96%。
（2）2015年长江三角洲城市群热环境安全格局空间分布特征
不安全区域多分布于建成区及建成区周围;临界安全区域则多于郊区分布;较安全区域集中分布于长江以北平原区域;安全区域则主要分布于杭州及杭州以南山地、丘陵区域,太湖大部分区域以及长江三角洲城市群北部区域（图3）。不同安全等级面积从大到小分别为：不安全区域、较安全区域、安全区域和临界安全区域（表4）。① 不安全区域面积最大,为35 222.33 km2,占比最高,为35.41%,主要分布于城市建成区及建成区周围,因城市建成区及建成区周围多为建设用地,人群密集,其热环境安全水平最低,其中以南京、镇江、常州、无锡、苏州、上海、嘉兴、杭州、绍兴和宁波形成的“Z”型是长江三角洲城市群不安全区域分布最为明显的区域,已基本连片分布;② 较安全区域占比为31.76%,多分布于长江以北平原区域,因该区域地势以平原为主,耕地较多,所以其热环境安全水平较高;③ 安全区域比为25.59%,主要分布于杭州及杭州以南山地、丘陵区,太湖大部分区域以及长江三角洲城市群北部区域,杭州及杭州以南山地、丘陵区植被较多,此外太湖及长江三角洲城市群北部区域多水域及水田分布,因为水的比热容大,热传导率小,升温缓慢,所以其热环境安全水平最高;④ 临界安全区域占比最低,为7.24%,主要分布于郊区,因城市内部地表温度较高,而郊区地表温度较低,部分郊区会受到城市内部温度扩散的影响,导致其热环境安全水平较低。

4.2 热环境安全格局变化特征

通过分析长江三角洲城市群各个时期的不同安全等级区域面积及比例和不同城市各安全等级面积变化及比例（表4、5）,以及不同时期热环境安全格局空间分布（图3、4）可知,2005-2015年长江三角洲城市群热环境空间格局变化特征。
Fig. 4 Spatial distribution of heat environment security pattern in the Yangtze River Delta from 2005 to 2010

图4 2005-2010年长江三角洲城市群热环境安全格局空间分布图

Tab. 5 Change area and its proportion of different security level regions in the Yangtze River Delta from 2005 to 2015

表5 2005-2015年长江三角洲城市群不同安全等级区域变化面积（km2）及比例（%）

城市 2005-2010年变化面积 2010-2015年变化面积 2005-2015年变化比例
不安全 临界安全 较安全 安全 不安全 临界安全 较安全 安全 不安全 临界安全 较安全 安全
上海 350.84 19.66 -521.60 151.10 878.17 22.55 -658.45 -242.27 21.13 0.73 -20.29 -1.57
南京 752.39 429.49 -1043.21 -138.68 -32.66 263.77 -312.07 80.96 11.78 11.35 -22.18 -0.94
无锡 356.02 204.92 -413.97 -146.96 154.53 27.46 -99.35 -82.65 11.60 5.28 -11.66 -5.22
常州 421.22 158.34 -467.79 -111.77 0.74 50.34 -118.45 67.38 10.23 5.06 -14.22 -1.08
苏州 458.47 292.88 -495.73 -255.63 1013.61 -38.84 -681.22 -293.55 19.30 3.33 -15.43 -7.20
南通 -1574.12 200.78 -509.18 1882.53 1382.36 -247.83 846.66 -1981.19 -2.39 -0.59 4.21 -1.23
扬州 274.26 183.18 -861.06 403.62 107.58 47.85 359.69 -515.12 5.90 3.57 -7.74 -1.72
镇江 302.20 -1.03 -231.82 -69.34 93.35 100.78 -317.04 122.91 11.49 2.90 -15.94 1.56
泰州 112.81 107.63 -1018.37 797.93 48.53 154.13 32.47 -235.14 2.94 4.77 -17.98 10.26
杭州 1613.45 -232.86 712.03 -2092.62 -470.62 184.19 -3348.07 3634.49 7.45 -0.32 -17.19 10.06
宁波 446.05 -39.33 -303.23 -103.49 561.44 -25.86 -523.05 -12.52 13.44 -0.87 -11.02 -1.55
绍兴 875.55 -70.38 -12.42 -792.75 318.29 203.33 -1402.99 881.37 15.60 1.74 -18.49 1.16
湖州 978.01 91.07 -719.27 -349.81 48.30 50.87 -390.17 290.99 19.40 2.68 -20.97 -1.11
嘉兴 1216.04 -8.28 -1172.57 -35.19 287.35 0.03 -247.39 -40.00 42.14 -0.23 -39.80 -2.11
台州 -498.83 185.25 49.68 263.91 1425.83 -183.67 -821.85 -420.32 11.31 0.02 -9.42 -1.91
舟山 -1.03 -3.10 -48.64 52.78 43.61 9.19 2.78 -55.58 10.11 1.44 -10.89 -0.66
2005-2015年长江三角洲城市群热环境空间格局变化特征为不安全区域呈扩张趋势,临界安全区域呈小幅扩张趋势,较安全区域呈缩减趋势,安全区域呈先缩减后扩张趋势（表4、5）;不安全区域、临界安全区域以及较安全区域以“Z”型区域变化最为明显,长江以北平原区域较安全区域缩减明显,杭州及杭州以南山地丘陵区则安全区域扩展明显 （图3、4）。
2005年不安全区域总面积为23 278.62 km2,2010年和2015年分别为29 361.92 km2和35 222.33 km2,比例分别上升6.11%和5.89%,其中,前5 a以杭州和嘉兴扩张最为明显,分别扩张1613.45 km2和1216.04 km2,后5 a以台州和南通扩张最为明显,分别扩张1425.83 km2和1382.36 km2。同时,10年间嘉兴和上海不安全区域扩张在各自城市占比中上升最为明显,分别上升42.14%和21.13%。2005年临界安全区域总面积为5059.76 km2,2010年和2015年分别为6578.00 km2和7196.30 km2,比例分别上升1.52%和0.63%。2005年较安全区域总面积为 46 319.21 km2,2010年和2015年分别为39 262.04 km2和31 583.56 km2,比例分别下降7.09%和7.72%,其中,前5年以嘉兴和南京缩减最为明显,分别缩减1172.57 km2和1043.21 km2,后5年以杭州和绍兴扩张最为明显,分别缩减3348.07 km2和1402.99 km2。同时,10年间嘉兴和南京较安全区域缩减在各自城市占比中下降最为明显,分别下降39.80%和22.18%。不安全区域、临界安全区与较安全区域以“Z”型区域变化最明显,其中,“Z”型区域10年间 不安全区域与临界安全区域面积分别增长 9596.43 km2与1541.18 km2,分别占整个长江三角洲城市群不安全区域与临界安全区域扩展总面积的80.35%和72.13%,均侵占大部分较安全区域。不安全区域与临界安全区域的扩张与该区域铁路、公路网密集,城市分布相对密集,经济往来密切,城市化水平高有直接联系。随着城市化发展进程的加快,单个城市建设用地不断扩张,城市间距离进一步缩小。进而导致该区域较安全区域与安全区域面积进一步缩小;长江以北平原区10年间除较安全区域面积缩减1149.79 km2以外,不安全区域、临界安全区域与安全区域面积均有小幅增长,这与该区域相邻城市间距离较远且多耕地有关。2005年安全区域总面积为24 801.00 km2,2010年和2015年分别为24 256.63 km2和25 456.40 km2,比例先下降0.54%后上升1.20%。杭州及杭州以南山地丘陵区10年间安全区域扩展1358.06 km2,主要来源于较安全区域,这与该区域城区多沿海分布,且多山地、丘陵,受地形影响较大,安全区域有所扩展。

5 土地利用变化对城市群热环境安全格局的影响

5.1 土地利用结构的影响

通过分析长江三角洲城市群不同安全等级区域内土地利用类型面积比例（图5）,可以得到土地利用结构的差异对城市群热环境安全格局的影响。
建设用地比例过高以及林地比例过低是导致热环境安全等级下降的主要原因。不安全区域内,各土地利用类型面积比例从高到低为耕地、建设用地、林地、水域和草地,比例分别为50.07%、35.79%、10.34%、3.18%和0.54%（图5）。其中,建设用地比例高达35.79%,远高于临界安全区域、较安全区域和安全区域建设用地比例,其中安全区域建设用地比例最低,为2.57%;而不安全区域林地比例为10.34%,远低于临界安全区域、较安全区域和安全区域林地比例,其中安全区域林地比例最高,高达57.31%。这是由于不安全区域内多城市建成区,多建设用地而少林地。建设用地下垫面又多由建筑、沥青或水泥马路等不透水下垫面构成,其热容量较小,在接受太阳辐射后快速向周围大气扩散,加之人类活动剧烈,使得局地地表温度升高;林地则多植被覆盖,土壤湿度相对较大,加之植被的蒸腾作用可以有效地降低地表温度。而在不同安全等级区域中,安全区域内建设用地比例最低以及林地比例最高,与不安全区域恰恰相反。上述分析充分说明建设用地比例过高和林地比例过低是导致热环境安全等级下降的主要原因。
Fig. 5 Proportion of land use in different security level regions in the Yangtze River Delta in 2015

图5 2015年长江三角洲城市群不同区域内土地利用构成比例

5.2 土地利用类型变化的影响

通过分析2005-2015年长江三角洲城市群不同安全等级区域土地利用转移矩阵（表6）,可以得到土地利用类型变化对城市群热环境安全格局的影响。主要结论如下：
（1）不安全区域、临界安全区域与较安全区域2005-2015年土地利用类型的转化主要集中在耕地的转出和建设用地的转入（表6）。从不安全区域来看,耕地转出面积为56.43×102 km2,主要转为建设用地,占88.02%;建设用地转入面积为53.93×102 km2,主要来源于耕地,占92.10%;从临界安全区域来看,耕地转出面积为6.50×102 km2,主要转为建设用地,占68.92%,另外水域和林地也占不少比例,分别占18.15%和12.00%;建设用地转入面积为5.24×102 km2,主要来源于耕地,占85.50%;从较安全区域来看,耕地转出面积为20.62×102 km2,主要转为建设用地,占57.90%,另外林地和水体分别占24.98%和16.05%;建设用地转入面积为13.47×102 km2,主要来源于耕地,占88.64%。
（2）安全区域2005-2015年土地利用类型的转化主要集中在耕地的转出、建设用地的转入以及耕地的转入（表6）。耕地转出面积为8.37×102 km2,主要转为林地,占40.02%,另外水域和建设用地分别占33.57%和24.97%;建设用地转入面积为 3.04×102 km2,主要来源于耕地,占49.26%;耕地转入面积为4.33×102 km2,来源于林地和建设用地,分别占72.29%和27.71%。
（3）从建设用地的转入来看（表6）,不安全区域、临界安全区域、较安全区域与安全区域均来源于耕地比例最大,且远高于林地、草地和水体,其中不安全区域建设用地占用耕地最多,为49.67×102 km2,占整个区域建设用地占据耕地总面积的72.85%,远高于较安全区域、临界安全区域和安全区域,这与不安全区域城市扩张有直接联系。
综上所述,建设用地侵占大量耕地是导致城市群热环境不安全区域扩张主要原因。
Tab. 6 Change area of dominated land change types in the different security grading regions in theYangtze River Delta from 2005 to 2015 （102 km2）

表6 2005-2015年长江三角洲城市群不同安全等级区域土地利用转移矩阵（102 km2）

区域 耕地-林地 耕地-草地 耕地-水域 耕地-建设 林地-耕地 林地-建设 草地-建设 水域-建设 建设-耕地
不安全 3.57 0.24 2.95 49.67 3.33 2.07 0.12 2.07 9.41
临界安全 0.78 0.06 1.18 4.48 0.67 0.39 0.04 0.33 1.32
较安全 5.15 0.22 3.31 11.94 4.92 0.87 0.11 0.55 5.68
安全 3.35 0.12 2.81 2.09 3.13 0.70 0.07 0.18 1.20
合计 12.85 0.64 10.25 68.18 12.05 4.03 0.34 3.13 17.61

6 结论与讨论

本文以长江三角洲城市群为例,基于MODIS地表温度数据产品,通过构建热环境安全分级标准,将热环境安全水平分为不安全、临界安全、较安全与安全4个等级,进而对长江三角洲城市群热环境安全格局时空变化以及土地利用变化对热环境安全格局的影响进行了研究,结论如下：
（1）2015年长江三角洲城市群热环境安全格局分布特征为：① 不安全区域多分布于城市建成区及建成区周围,以南京、镇江、常州、无锡、苏州、上海、嘉兴、杭州、绍兴和宁波形成的“Z”型区域最为明显;② 临界安全区域则多分布于郊区;③ 较安全区域集中分布于长江以北平原区域;④ 安全区域则主要分布于杭州及杭州以南山地、丘陵区,太湖大部分区域以及长江三角洲城市群北部区域。
（2）2005年以来长江三角洲城市群热环境不安全区域呈扩张趋势,临界安全区域呈小幅增长趋势,较安全区域呈缩减趋势,安全区域呈现先缩减后扩张趋势。10年间不安全区域与临界安全区区域面积分别扩张11 943.71 km2和2136.54 km2,较安全区域面积缩减14 735.7 km2,安全区域面积扩张655.40 km2,其中以“Z”型区域变化最为明显,该区域10 a间不安全区域和临界安全区域面积分别增长9596.43 km2和1541.18 km2,分别占整个长江三角洲城市群不安全区域与临街安全区域增长总面积的80.35%和72.13%,占据大部分较安全区域;杭州及杭州以南山地、丘陵区域10年间安全区域扩展1358.07 km2,主要来源于较安全区域;长江以北平原区域10年间除较安全区域面积缩减1149.79 km2以外,不安全区域、临界安全区域与安全区域面积均有小幅增长。
（3）区域土地利用结构中,建设用地比例过高以及林地比例过低是导致热环境安全等级下降的主要原因,其中,2015年不安全区域内建设用地比例高达35.79%,远高于临界安全区域、较安全区域以及安全区域,而林地比例低至10.34%,远低于安全区域的57.31%。此外,建设用地侵占大量耕地也是导致城市热环境不安全区域扩张的主要原因,10年间不安全区域内建设用地占用耕地多达49.67×102 km2,占整个区域建设用地侵占耕地总面积的72.85%。
从研究结果来看,2005-2015年长江三角洲城市群热环境不安全区域与临界安全区域不断扩张。热环境的改变严重影响区域生态环境,可能造成局地气候异常、加重城市空气污染程度和加剧城市能源消耗等[34],科学地改善城市群热环境安全状况是目前城市规划发展工作的重中之重。今后长江三角洲城市群热环境的改善可以从3个方面入手：① 应提高区域植被覆盖度。区域内植被覆盖度的增加意味着可以增强遮蔽、蒸散等地表降温过程,辅以合理设计群落结构将显著强化区域内植被的温度调控效果[35]。② 合理规划城市空间布局。地表温度与建筑物密度呈明显正相关,首先,城市规划过程中,道路、高层建筑以及工厂应充分考虑风向;其次,合理布局道路系统,留出通风廊道,加快空气流通;最后,合理布局城市建设空间,严格控制建筑物密度与高度[36]。③ 尽量减少人为热排放。首先通过提高能源利用效率合理控制工业人为热排放,其次提倡使用太阳能等清洁能源以减少居民人为热排放,最后提倡绿色出行以减少机动车人为热排 放[37]。基于热岛足迹与地表温度等级2个指标构建热环境安全分级标准,对城市群热环境安全格局进行评价,能够较好地反映城市群热环境安全格局的时空变化特征,对于研究城市群热环境安全状况有积极的意义。此外,热环境的改变是人类活动与自然环境相互作用的结果,本研究从热岛足迹与地表温度等级2个方面来进行了热环境安全等级划分,如何综合考虑人地关系、构建多指标体系的城市群热环境安全格局划分标准是未来研究的重要方向。

The authors have declared that no competing interests exist.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Li B C.Problems of Municipal Ecological Environment and the Sustainable Development in China[J].Journal of Arid Land Resources and Environment, 2006,20(2):1-6.The problem of sustainable development of the city is an important subject in its construction and plan.The strategy of the sustainable development has been brought into the long-term national economy and the plan of our country's development of 2010.The ecological environment and environmental protection of the city are the uppermost premise and guarantee for the strategy of sustainable development.Based on the world standpoint,this text summarizes the dialectical relation between the city's entironment and the sustainable development,human social economy activities,natural ecosystem and environmental protection.It discusses the existent problems of the Chinese city's ecological environment and sustainable development in detail and analyzes the case example. It puts forward countermeasures for the sustainable development confronted with serious ecological environment problems.

DOI

[2]
Liu J Y, Kuang W H, Zhang Z X, et al.Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980s[J].Journal of Geographical Sciences, 2014,69(1):3-14.Land-use/land-cover changes(LUCCs) have links to both human and nature interactions. China's Land-Use/cover Datasets(CLUDs) were updated regularly at 5-year intervals from the late 1980s to 2010, with standard procedures based on Landsat TM\ETM+ images. A land-use dynamic regionalization method was proposed to analyze major land-use conversions. The spatiotemporal characteristics, differences, and causes of land-use changes at a national scale were then examined. The main findings are summarized as follows. Land-use changes(LUCs) across China indicated a significant variation in spatial and temporal characteristics in the last 20 years(1990鈥2010). The area of cropland change decreased in the south and increased in the north, but the total area remained almost unchanged. The reclaimed cropland was shifted from the northeast to the northwest. The built-up lands expanded rapidly, were mainly distributed in the east, and gradually spread out to central and western China. Woodland decreased first, and then increased, but desert area was the opposite. Grassland continued decreasing. Different spatial patterns of LUC in China were found between the late 20th century and the early 21st century. The original 13 LUC zones were replaced by 15 units with changes of boundaries in some zones. The main spatial characteristics of these changes included(1) an accelerated expansion of built-up land in theHuang-Huai-Hai region, the southeastern coastal areas, the midstream area of the Yangtze River, and the Sichuan Basin;(2) shifted land reclamation in the north from northeast China and eastern Inner Mongolia to the oasis agricultural areas in northwest China;(3) continuous transformation from rain-fed farmlands in northeast China to paddy fields; and(4) effectiveness of the "Grain for Green" project in the southern agricultural鈥損astoral ecotones of Inner Mongolia, the Loess Plateau, and southwestern mountainous areas. In the last two decades, although climate change in the north affected the change in cropland, policy regulation and economic driving forces were still the primary causes of LUC across China. During the first decade of the 21st century, the anthropogenic factors that drove variations in land-use patterns have shifted the emphasis from one-way land development to both development and conservation. The "dynamic regionalization method" was used to analyze changes in the spatial patterns of zoning boundaries, the internal characteristics of zones, and the growth and decrease of units. The results revealed "the pattern of the change process," namely the process of LUC and regional differences in characteristics at different stages. The growth and decrease of zones during this dynamic LUC zoning, variations in unit boundaries, and the characteristics of change intensities between the former and latter decades were examined. The patterns of alternative transformation between the "pattern" and "process" of land use and the causes for changes in different types and different regions of land use were explored.

DOI

[3]
Rizwan A M, Dennis Y.C. L, Liu C H. A Review on the gerneration, dertermination and mitigation of Urban Heat Island[J].Journal of Environmental Sciences, 2008,20(1):120-128.<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">Urban Heat Island (UHI) is considered as one of the major problems in the 21st century posed to human beings as a result of urbanization and industrialization of human civilization. The large amount of heat generated from urban structures, as they consume and re-radiate solar radiations, and from the anthropogenic heat sources are the main causes of UHI. The two heat sources increase the temperatures of an urban area as compared to its surroundings, which is known as Urban Heat Island Intensity (UHII). The problem is even worse in cities or metropolises with large population and extensive economic activities. The estimated three billion people living in the urban areas in the world are directly exposed to the problem, which will be increased significantly in the near future. Due to the severity of the problem, vast research effort has been dedicated and a wide range of literature is available for the subject. The literature available in this area includes the latest research approaches, concepts, methodologies, latest investigation tools and mitigation measures. This study was carried out to review and summarize this research area through an investigation of the most important feature of UHI. It was concluded that the heat re-radiated by the urban structures plays the most important role which should be investigated in details to study urban heating especially the UHI. It was also concluded that the future research should be focused on design and planning parameters for reducing the effects of urban heat island and ultimately living in a better environment.</p>

DOI

[4]
Oke T R.City size and urban heat island[J].Atmospheric Environment, 1973,7(8):769-779.ABSTRACT The paper demonstrates the relationship existing between the size of a village, town or city (as measured by its population), and the magnitude of the urban heat island it produces. This is accomplished by analyzing data gathered by automobile traverses in 10 settlements on the St. Lawrence Lowland, whose populations range from 1000 to 2 million inhabitants. The locations of these settlements effectively eliminate all non-urban climatic influences. The results are compared with previously published data.The analysis shows the heat island intensity under cloudless skies to be related to the inverse of the regional windspeed, and the logarithm of the population. A simple model is derived which incorporates these controls. In agreement with an extension of Summers' model the heat island appears to be approximately proportional to the fourth root of the population. With calm and clear conditions the relation is shown to hold remarkably well for North American settlements, and in a slightly modified form, for European towns and cities.

DOI

[5]
周荣卫,蒋维楣,何晓凤.城市冠层结构热力效应对城市热岛形成及强度影响的模拟研究[J].地球物理学报,2008,51(3):715-726.本文在城市边界层预报模式中耦合了一个单层冠层模式，此模式能够体现城市冠层结构和人为热源对城市热岛的共同作用.通过传统平板模式和城市冠层模式的模拟结果与自动气象站观测资料对比发现，耦合了城市冠层模式的模拟结果与观测资料更为吻合，尤其能够较好地模拟出城市地区夜间地面的气温变化情况. 对北京城市区域的模拟结果进行分析，白家庄地区冠层建筑物使得城市地区气温白天下降，夜晚上升，不考虑人为热源作用时，城市冠层使得白家庄站地面气温白天最低下降2.5℃，夜间气温最大升高为4.7℃.针对模拟区域较小的理想算例模拟结果分析表明，城市冠层模式能够很好地模拟城市地区地表能量平衡关系，体现城市冠层对长短波辐射的封截以及热量存储能力，全天平均净辐射通量由传统模式的43.38W/m2变为84.19W/m2，热存储通量白天最大值为278.04W/m2，夜晚最大释放热存储通量为160.35W/m2.冠层建筑物和人为热源对夜间城市热岛强度的贡献分别为70.65％和29.35％.城市冠层建筑物对夜间城市热岛的形成起决定性作用.

DOI

[ Zhou R W, Jiang W M, He X F.Numerical simulation of the impacts of the thermal effects of urban canopy structure on the formation and the intensity of the urban heat island[J]. Chinese Journal of Geophysics, 2008,51(3):715-726. ]

[6]
戴晓燕,张利权,过仲阳,等.上海城市热岛效应形成机制及空间格局[J].生态学报,2009,29(7):3995-4004.城市热岛效应的产生及演变与城市地表覆被变化、人类社会经济活动密切相关，是城市生态环境状况的综合概括与体现，目前对城市热岛形成、演变的驱动机制、热岛效应与地表覆被变化的定量关系研究大多还是从对某些影响因子的测定入手，缺乏对区域热环境系统全面、综合的评价与分析。近年来，在城市化过程中，人类社会、经济活动的加剧使城市地表热力景观呈现出高度的空间异质性，在利用landsat7etm+热波段数据反演上海地区地表温度的基础上，应用地统计学方法揭示了不同尺度下上海城市地表温度场空间变异特征及其不同的驱动因子。进而，采用决策树方法构造城市热环境系统的分类和预测模型，建立中心城区地表温度场空间分布及其驱动因素之间的定量关系，挖掘上海城市热岛效应的形成机制，揭示出多种影响因素综合作用下中心城区热环境空间格局差异。研究结果表明，城市热环境形成的驱动因子在空间上呈现出明显的分异性特征，各种影响因素在空间上不同的组合方式将决定城市热岛效应的时空演变趋势。运用决策树方法可以有效地确定在城市内部不同区域影响热环境形成的主导因素，揭示城市热岛形成与演变的成因机制及其空间差异，并可以进一步用来预测分析未来城市地表温度场动态变化的空间分布格局。

DOI

[ Dai X Y, Zhang L Q, Guo Z Y, et al.Mechanism of formation of urban heat island effect and its spatial pattern in Shanghai[J]. Acta Ecologica Sinica, 2009,29(7):3995-4004. ]

[7]
Oke T R.Boundary layer climate[M]. Cambridge: Great Britain at the University Press, 1987:471-508.

[8]
Zhang Y, Bao W J, Yu Q, et al.Study on seasonal variations of the urban heat island and its inte-annual changes in a typical Chinese megacity[J]. Chinese Journal of Geophysics, 2012,55(4):1121-1128.The differences between the urban and rural temperatures in the past 50 years were analyzed based on the seasonal averaged data from 1960 to 2006 at two meteorological stations in Shanghai megacity.Moreover,the characteristics of the urban heat island(UHI) in Shanghai for the last two decades were studied based on air temperatures measured four times per day at nine stations in 1987,1990,1997 and 2004 in urbanization process.The corresponding seasonal variations of UHI in Shanghai were discussed.The results showed that the temperature differences between urban area and suburb were increasing in fluctuations,UHI occurred in 86.0% days of a year with an average magnitude of 1.17 ℃,and the frequency and magnitude of UHI in autumn were higher than those in other seasons,with the highest accumulated UHI.The accumulated intensities of night-time UHI(2 ∶ 00,20 ∶ 00) were significant in all four seasons,but that at 14 ∶ 00 were more significant in spring and summer,and that at 8 ∶ 00 is more evident in autumn and winter.It can be inferred that the seasonal variations could mainly be attributed to the frequency difference between mid-intensity UHI and high-intensity UHI.Besides,the F-level stability condition occurred more frequently in autumn,which could account for the enhancement of autumn UHIs.Also the differences among 4 years were discussed.The accumulated intensity of UHI was the strongest in autumn,while weakest in summer before 1997.However,the accumulated intensities of UHI of four seasons tended to be equalized during 1997—2004.In summer,low-intensity UHI tended to change into mid-magnitude and high-magnitude UHI,which could be caused by anthropogenic heating in some degree.

DOI

[9]
岳文泽,徐丽华,徐建华.20世纪90年代上海市热环境变化及其社会经济驱动力[J].生态学报,2010,30(1):155-164.城市化过程导致地表水分蒸腾减少、径流加速、显热的存储和传输增 加以及水质降低等一系列生态环境效应,其中最典型的特征就是城市热岛的出现.城市热环境动态主要驱动力可以慨括为两大方面:地表覆被的变化与人类社会经济 活动.论文采用Landsat的TM/ETM+为基本数据源,先定量反演了每个像元内的陆地表面温度,以此探讨了20世纪90年代上海市主城区热环境的动 态演化和社会经济驱动力.研究结果揭示,2O世纪90年代上海市主城区热岛范围显著扩大,但中心城区的热岛强度在下降,空间格局也趋于复杂;通过分析,城 市热环境空间格局动态演变是不同尺度上、相互嵌套、相互影响的多种驱动力的综合作用结果.其中,城市建成区的快速扩展是热岛范围不断扩展的最显性驱动力; 中心城区人口密度显著下降是热岛中心强度降低的一个综合性驱动因子,而以重化工业为代表的能耗布局改变则是中心城区热岛强度降低的一个主要驱动力;通过增 加绿化面积布局等人为措施改变热辐射和存储模式,对缓解城市热环境来说是最有意义的因素.由于多种驱动力相互嵌套、交互作用,因此建市城市热环境演变驱动 机制的定量模型还十分困难,但是研究结果对于制定城市热环境改善政策和编制合理的城市规划等都具有一定的借鉴意义.

[ Yue W Z, Xu L H, Xu J H.Thermal environment change and its socio-economic drivers in Shanghai City during the 1990s[J]. Acta Ecologica Sinica, 2010,30(1):155-164. ]

[10]
蒙伟光,张艳霞,李江南,等.WRF/UCM在广州高温天气及城市热岛模拟研究中的应用[J].热带气象学报,2010,26(3):273-282.应用WRF及其耦合的城市冠层模式(UCM,Urban Canopy Model),对2004年6月底—7月初受副热带高压和台风外围气流影响下发生在广州地区的一次高温天气过程进行了数值模拟。考察了WRF/UCM对"城市热岛"及城市高温天气模拟的应用效果。三个不同设计的模拟试验表明,E-UCM试验(新土地利用资料,耦合UCM模式)比E-BPA试验(新土地利用资料,BPA方法)和E-NOU试验(旧土地利用资料,耦合UCM模式)更好地模拟出了城区2 m高度温度的演变,平均绝对误差最小。尤其在夜间,E-UCM试验成功地再现了夜间热岛的形成及分布。城区及郊区地表能量平衡差异的分析表明,日间城区高温与低反射率引起的短波辐射吸收增加有关,由于城区缺少水汽蒸发蒸腾冷却过程,大部分能量收入被分配为感热加热大气。夜间,地表能量收入来自土壤热通量的向上输送,收入能量除部分用于长波辐射之外,由于城区潜热通量小,其余部分仍主要以感热形式加热大气。夜间热岛的形成与感热加热的持续有关,有利于夜间高温的维持。

DOI

[ Meng W G, Zhang Y X, Li J N, et al.Application of WRF/UCM in the simulation of a heat wave event and urban heat island around Guangzhou City[J].Journal of Tropical Meteorology, 2010,26(3):273-282. ]

[11]
陈燕,蒋维楣,吴涧,等.利用区域边界层模式对杭州市热岛的模拟研究[J].高原气象,2004,23(4):519-528.利用一个三维非静力区域边界层数值模式,对杭州地区城市热岛现象进行了数值模拟.数值模拟结果表明,杭州地区存在明显的城市热岛现象.夏季城市热岛现象较强,春秋季次之,冬季较弱.利用资源卫星遥感资料反演所得的地表温度,以及与地面气象站观测资料和数值模拟结果相比较,数值模拟结果和实测结果吻合得较好.在此基础上,模拟并探讨了人为热源、风速、地面粗糙度等因素对城市热岛强度的影响.

DOI

[ Chen Y, Jiang W M, Wu J, et al.A numerical simulation on Hangzhou City heat island using region boundary model[J].Plateau Meteorology, 2004,23(4):519-528. ]

[12]
Kuang W H, Chi W F, Shi W J.Spatio-temporal characteristics of intra-urban land cover in the cities of China and USA from 1978 to 2010[J]. Journal of Geographical Sciences, 2014,69(7):883-895.Urban land cover has major impacts on a city's ecosystem services and the inherent quality of its urban residential environment. The spatio-temporal distribution of impervious surface area and green areas in Chinese cities has exhibited a significantly marked difference in comparison with USA cities. This study focused on monitoring and comparing the spatio-temporal dynamics, land cover patterns and characteristics of functional regions in six Chinese(n=3) and USA(n=3) cities. The study data were collated from Landsat TM/MSS imagery during the period 1978鈥2010. Results indicate that Chinese cities have developed compactly over the past three decades, while development has been notably dispersed among USA cities. Mean vegetation coverage in USA cities is approximately 2.2 times that found amongst Chinese urban agglomerations. Land use types within Chinese cities are significantly more complex, with a higher density of impervious surface area. Conversely, the central business district(CBD) and residential areas within USA cities were comprised of a lower proportion of impervious surface area and a higher proportion of green land. Results may be used to contribute to future urban planning and administration efforts in both China and the USA.

DOI

[13]
郑祚芳,郑艳,李青春.近30年来城市化进程对北京区域气温的影响[J].中国生态农业学报, 2007,15(4):26-29. 分析了20世纪70年代以来北京市气温指数的变化规律，发现其与城市化进程有良好对应关系。 并应用主成分分析方法，将影响北京城区及郊县各站气温变化的因子分为区域因子及局地因子。计算表明，城市化带来的热岛效应是导致局地增暖的主要因子，占总 增暖比重的47．5％～61．2％。在众多表征城市发展的指标中，气温与人口总量的相关性最好。以上结果有助于深入探讨城市化对区域气候的影响机制。

[ Zheng Z F, Zheng Y, Li Q C. Effect of urbanization on the temperature of Beijing metropolis in recent 30 years[J]. Chinese Journal of Eco-Agriculture, 2007,15(4):26-29. ]

[14]
林学椿,于淑秋,唐国利.北京京城市化进程与热岛强度关系的研究[J].自然科学进展,2005,15(7):882-886.利用北京市统计局编的中 的三类城市发展指数:人口、基本建设投资总额和城市基础设施投资总额、房屋竣工面积和住房竣工面积及北京地区20个站近40年年平均气温资料,研究了北京 城市发展指数与城市热岛效应变化之间的关系,认为:(1)北京城市发展指数,在改革开放前发展缓慢.改革开放后,北京城市发展指数增长率比改革开放前成几 十倍乃至百倍的增长;(2)近40年,北京市郊区的增温率为0.04℃/10a,城市中心区的增温率为0.35℃/10a,而热岛强度的增温率为 0.31℃/10 a.城市热岛强度(或市中心区)的增温率是城市郊区的8倍(9倍);(3)热岛强度的增温率在改革开放前为0.001℃/10 a;改革开放后跃变升为0.2286℃/10a.它与城市发展指数的变化颇为一致,两者之间的相关系数都超过了0.1%信度.说明北京城市人类活动引起的 增温己经超过了自然因素引起的增温,能改变城市中的温度变化.

DOI

[ Lin X C, Yu S Q, Tang G L.Study on the relation between urbanization and UHI intensity in Beijing[J]. Journal of Natural Sciences, 2005,15(7):882-886. ]

[15]
陈云浩,史培军,李晓兵.不同热力背景对城市降雨(暴雨)的影响(I)——降雨分布的空间差异[J].自然灾害学报,2001,10(2):37-42.如何准确描述和预测降雨（暴雨）的空间分布，已成为城市减灾防灾的重要内容，本文探讨了利用热信息研究城市降雨空间分布差异的方法。在遥感、GIS技术支持下，利用对没的期夏、冬季热图像的主成分析和图像分割，将上海市1958－1998年热环境划分为自然、低温、高温3种不同的热力背景。在此基础上，就不同热力背景对城市降雨（暴雨）分布空间差异的影响规律进行了研究，为建立城市降雨空间差异预报模型奠定了基础。

DOI

[ Chen Y H, Shi P J, Li X B.Effect of different thermal background on urban rainfall (rainstorm) (I): spatial difference of rainfall distribution[J]. Journal of Natural Disasters, 2001,10(2):37-42. ]

[16]
陈燕,蒋维楣,郭文利,等.珠江三角洲地区城市群发展对局地大气污染物扩散的影响[J].环境科学学报,2005,25(5):700-710.为探讨城市群发展对局地气象环境和污染物输送的影响,以珠江三角洲不同时期的下垫面为例,选取有利于和不利于污染扩散的较典型的气象条件,采用数值模拟手段,模拟并分析比较该地区城市群的形成与发展对城市气象环境、污染物分布、城市问污染物输送的影响.结果表明,重污染气象条件下出现长时间逆温现象,凌晨3:00到6:00间逆温最强,强度约为2.1℃·hm-1,逆温层厚度达300m.城市群的发展使得城市夜间的逆温强度增强,逆温持续时间增长;城市群的发展使得城市地区风速减小,重污染气象条件下广州小风区面积约增加28%,佛山约增加45.2%,轻污染气象条件下增加较小;重污染气象条件时广州和佛山二氧化硫浓度一般大于45 μg·m-3,城市群的发展使污染物扩散范围变小,对本地贡献率增大,对其它地区的贡献率减小;地区间氮氧化物和二氧化硫的输送基本量级为100t·d-1到数101t·d-1,城市群的发展使污染物不易向外输送,在重污染气象条件时广州二氧化硫输出量由51.37t·d-1减小为42.81t·d-1.

DOI

[ Chen Y, Jiang W M, Guo W L, et al.Study on the effect of the city group development in Pearl River Delta on local air pollutant dispersion by numerical modeling[J]. Acta Scientiae Circumstantiae, 2005,25(5):700-710. ]

[17]
宫阿都,陈云浩,李京,等.北京市城市热岛与土地利用/覆盖变化的关系研究[J].中国图象图形学报,2007,12(8):1476-1481.本文以北京市为例,在遥感和GIS技术的支持下,以TM热红外遥感影像定量反演的城市地表温度为基础,分析了城市热岛效应与城市土地利用/覆盖变化的关系,以期为缓解城市热岛效应提供科学依据.

DOI

[ Gong A D, Chen Y H, Li J, et al.Study on relationship between urban heat island and urban land use and cover change in Beijing[J]. Journal of Image and Graphics, 2007,12(8):1476-1481. ]

[18]
岳文泽,徐丽华.城市土地利用类型及格局的热环境效应研究——以上海市中心城区为例[J].地理科学,2007,27(2):243-248.以上海市中心城区为例,首先采用SPOT影象,人工解译出城市土地利用的类型,同时利用Landsat7卫星,ETM+影象的热红外波段反演每个像元内的陆地表面温度,分析陆地表面温度在不同土地利用类型之间的差异,进一步分析不同土地空间分布格局与地面温度之间的关系。研究结果显示上海市地表温度具有明显中心城区高、郊区低的热岛效应存在;在城市不同土地利用类型上的表面温度具有显著性差异,两两之间的比较揭示,城市地面温度在大多数土地利用类型之间的差异是显著的。城市内部不同土地类型所产生的热环境效应不同;城市土地类型在空间布局上越复杂,其产生的热岛效应越明显。

DOI

[ Yue W Z, Xu L H.Thermal environment effect of urban land use type and pattern: A case study of central area of Shanghai City[J]. Scientia Geographica Sinica, 2007,27(2):243-248. ]

[19]
牟雪洁,赵昕奕.珠江三角洲地区地表温度与土地利用类型关系[J].地理研究,2012,31(9):1589-1597.随着城市化进程的加快,城市气候与环境问题日益显现,尤以城市热岛效应最为突出。通过监督分类方法对TM遥感影像进行了土地利用分类,并运用TM热红外波段线性拟合模型进行近地表气温反演,分析城市热岛的空间分布及地域性差异,以及与土地利用类型的关系。结果表明:珠三角地区近地表气温与土地利用类型紧密相关,城市建设区形成高温中心,是热岛的主要贡献因子,植被和水体则有明显的冷岛效应;研究区热岛具有区域性集中与分散分布的特点,且以区域性热岛为中心向周边扩展;分析热岛强度剖面线发现,由于地形、植被覆盖度等因素影响,研究区热岛强度的南北差异较大,而东西差异较小;热岛分布与土地利用类型分布格局较为一致,但也有分布不一致性的区域,表现为城市热岛向非城市建设用地扩展。

DOI

[ Mou X J, Zhao X Y.Study on the relationship between surface temperature and land use in Pearl River Delta[J]. Geographical Research, 2012,31(9):1589-1597. ]

[20]
乔治,田光进.北京市热环境时空分异与区划[J].遥感学报,2014,18(3):715-734.城市热环境空间区划是采用分区管理的思路来缓解城市社会经济发展与热环境之间矛盾的技术基础.本文构建城市热环境区划模型的思路为:(1)将不同时相的MODIS地表温度数据产品进行正规化、分级,分析2008年北京城市热环境时空分布特征.(2)构建城市热环境影响因素评价体系,并通过空间主成分分析计算得到热环境影响主成分因子.(3)通过自组织映射神经网络,利用热环境影响主因子,进一步对热环境进行空间区划.结果表明,北京夜间较白天城市热岛分布层次感明显,夏季白天较其他季节高温区聚合程度高.区域下垫面组成要素直接影响热环境,北京城市热环境的主成分因子依次为植被覆盖、地形地貌、城市下垫面建设规模和人为热排放,并依此将北京划为7个热环境区域,根据各个分区热环境成因机制差异分别提出热环境改善和调控措施.

DOI

[ Qiao Z, Tian G J.Spatiotemporal diversity and regionalization of the urban thermal environment in Beijing[J]. Journal of Remote Sensing, 2014,18(3):725-734. ]

[21]
王建凯,王开存,王普才.基于MODIS地表温度产品的北京城市热岛(冷岛)强度分析[J].遥感学报,2007,11(3):330-339.城市热岛是影响城市及其周边地区天气气候和空气质量的重要因素。利用2000-2005年MODIS（Moderate Resolution Imaging Spectroradiometer,中分辨率成像光谱仪）分裂窗算法反演得到的1km分辨率地表温度产品分析了北京的城市热岛效应,发现白天城市热岛强度具有明显的季节变化,夏季最大值可以达到10℃以上,冬季变为冷岛,即城市地表温度低于乡村,最低值可以达到-5℃;模拟结果表明冬季城市冷岛的存在主要是城市和乡村地表热特性（热惯量）的差异引起的。夜间热岛强度的季节变化较小,全年稳定在5℃左右。选择北京周边地区比较典型的乡村耕地、山区森林以及永定河流域来研究乡村的选择对热岛强度的影响。发现选择不同的邻近区域作为乡村时,不仅城市热岛（冷岛）强度有较大变化,而且热岛强度的季节变化情况也有较大差异。冬季白天北京城市冷岛增加了近地层的大气稳定度,可能会降低城市空气污染物的扩散能力,加剧了北京冬季的空气污染。

DOI

[ Wang J K, Wang K C, Wang P C.Urban heat (or Cool) island over Beijing from MODIS land surface temperature[J]. Journal of Remote Sensing, 2007,11(3):330-339. ]

[22]
Wan Z M, Dozier J.A generalized Split-window algorithm for retrieving Land-surface temperature from space[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996,34(4):892-905Not Available

DOI

[23]
Wan Z, Zhang Y, Zhang Q, et al.Quality assessment and validation of the MODIS global land surface temperature[J]. International Journal of Remote Sensing, 2004,25(1):261-274This paper presents an evaluation of the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared bands and the status of land surface temperature (LST) version-3 standard products retrieved from Terra MODIS data. The accuracy of daily MODIS LST products has been validated in more than 20 clear-sky cases with measurement data collected in field campaigns in 2000-2002. The MODIS LST accuracy is better than 1°C in the range from 6110 to 50°C. Refinements and improvements were made to the new version of MODIS LST product generation executive code. Using both Terra and Aqua MODIS data for LST retrieval improves the quality of the LST product and the diurnal feature in the product due to better temporal, spatial and angular coverage of clear-sky observations.

DOI

[24]
Wang K, Wan Z, Wang P, et al.Evaluation and improvement of the MODIS land surface temperature /emissivity products using ground-based measurements at a semi-desert site on the western Tibetan Plateau[J]. International Journal of Remote Sensing, 2007,28(11):2549-2565Current MODerate-resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST, surface skin temperature)/emissivity products are evaluated and improvements are investigated. The ground-based measurements of LST at Gaize (32.30掳 N, 84.06掳 E, 4420 m) on the western Tibetan Plateau from January 2001 to December 2002 agree well (mean and standard deviation of differences of 0.27 K and 0.84 K) with the 1-km Version 004 (V4) Terra MODIS LST product (MOD11A1) generated by the split-window algorithm. Spectral emissivities measured from surface soil samples collected at and around the Gaize site are in close agreement with the landcover-based emissivities in bands 31 and 32 used by the split-window algorithm. The LSTs in the V4 MODIS LST/emissivity products (MYD11B1 for Aqua and MOD11B1 for Terra) from the day/night LST algorithm are higher by 1-1.7 K (standard deviation around 0.6 K) in comparisons to the 5-km grid aggregated values of the LSTs in the 1-km products, which is consistent with the results of a comparison of emissivities. On average, the emissivity in MYD11B1 (MOD11B1) is 0.0107 (0.0167) less than the ground-based measurements, which is equivalent to a 0.64 K (1.25 K) overestimation of LST around the average value of 285 K. Knowledge obtained from the evaluation of MODIS LST/emissivity retrievals provides useful information for the improvement of the MODIS LST day/night algorithm. Improved performance of the refined (V5) day/night algorithm was demonstrated with the Terra MODIS data in May-June 2004.

DOI

[25]
Liu J Y, Liu M L, Tian H Q, et al.Spatial and temporal patterns of China's cropland during 1990-2000: Analysis based on Landsat TM data[J]. Remote Sensing of Environment, 2005,98(4):442-456.There are large discrepancies among estimates of the cropland area in China due to the lack of reliable data. In this study, we used Landsat TM/ETM data at a spatial resolution of 30 m to reconstruct spatial and temporal patterns of cropland across China for the time period of 1990鈥2000. Our estimate has indicated that total cropland area in China in 2000 was 141.1 million hectares (ha), including 35.6 million ha paddy land and 105.5 million ha dry farming land. The distribution of cropland is uneven across the regions of China. The North-East region of China shows more cropland area per capita than the South-East and North regions of China. During 1990鈥2000, cropland increased by 2.79 million ha, including 0.25 million ha of paddy land and 2.53 million ha of dry farming land. The North-East and North-West regions of China gained cropland area, while the North and South-East regions showed a loss of cropland area. Urbanization accounted for more than half of the transformation from cropland to other land uses, and the increase in cropland was primarily due to reclamation of grassland and deforestation. Most of the lost cropland had good quality with high productivity, but most gained cropland was poor quality land with less suitability for crop production. The globalization as well as changing environment in China is affecting land-use change. Coordinating the conflict between environmental conservation and land demands for food will continue to be a primary challenge for China in the future.

DOI

[26]
Liu J Y, Zhang Z X, Zhuang D F, et al.A study on the spatial-temporal dynamic changes of land-use and driving forces analyses of China in the 1990s[J]. Geographical Research, 2003,22(1):1-12.Supported by the key knowledge innovation projects,i.e., a preliminary study on the theories and techniques of the remotely sensed temporal-spatial information and digital Earth; and a study on the integration of national resources and environment and data sharing, the authors have set up a spatial-temporal information platform by the integration of the corresponding scientific and research achievements during the periods of the 8th- and 9th-Five Year Plan, which comprehensively reflected the features of land-use change, designed a series of technical frameworks on the spatial-temporal database construction based on remote sensing techniques, e.g., the construction of remotely sensed database and land-use spatial database of the mid-1980s, the mid-1990s and the end of the 1990s, which laid a foundation for the dynamic monitoring of land-use change and the corresponding studies. In this paper,the authors have analyzed comprehensively the features of land-use change in the 1990s, revealed the spatial-temporal change of land use supported by remote sensing and GIS technologies as well as analyzed the geophysical and socio-economic driving factors.The findings are as follows: the arable land has been increased in total amount, the balance of decrease in the south and increase in the north was resulted from the reclamations of grassland and forest land. On the whole, the forest land area had a process of decrease, and the decreased area was mainly distributed in the traditional forest areas. Areas with plentiful precipitation and heat in the south, however, had distinct effects of reforestation. The rural-urban construction land had a situation of persistent expansion, and the general expansion speed has been slowed down during the last five years of the 1990s with the exception of the Western China where the expansion speed has been accelerated. The land use change in China in the 1990s had distinct temporal and spatial differences due to two main reasons, which were policy control and economic driving. Hereby, conclusions and proposals brought forward by the authors were as follows: the spatial diversity rules of the modern land use change in China must be fully considered in the future land use planning. At the same time, the pertinence of physical geographical zones must be considered during the planning of eco-environment construction. And, based on the increasingly maturity of the infrastructure, the traditional thoughts on planning and management of resources must be shifted so as to fully realize the optimized allocation of land resources at regional scale.

[27]
Liu J Y, Buhe A.Study on spatial-temporal feature or modern land-use change in China: Using remote sensing techniques[J]. Quaternary Sciences, 2000,20(3):229-239.lt;p>In China, a country with great discrepancy between dense population, rapideconomic increase and the fragile environmenL the study on the modem process ofland-use change is significantly important for better understanding land-use changeand environmental management for sustainable development. Since the 1960' s, thedevelopment of remote sensing has put researches about our planet into a brand-newstage. With the support of remote sensing techniques, which make possible the dataacqtusihon at completed spahal and temporal series, the spahal feature of the Earth'ssurface and its evolvement modern process can be analyzed in quanhtahve andpararneterized methods. And it enables the study on historical construchon of landuse /cover change over temporal scales. Understanding the mechanisms which operated in the past to determine the main land-use/cover change is a crucial input to analyzing the current changes and predichng future ones. From this point, acompleted hme series spahal data platform derived from remotely sensed data isindispensable because on the basis of thes platform. Then effective studies on themodern process of land-use change, including the temporal feature and spahal featureof this process, can be approached by means of process analysis and quantitativemodeling.</p>

DOI

[28]
Liu J Y, Zhang Z X, Xu X L, et al.Spatial patterns and driving forces of land use change in China during the early 21st century[J]. Journal of Geographical Sciences, 2010,20(4):483-494.Land use and land cover change as the core of coupled human-environment systems has become a potential field of land change science (LCS) in the study of global environmental change. Based on remotely sensed data of land use change with a spatial resolution of 1 km 脳 1 km on national scale among every 5 years, this paper designed a new dynamic regionalization according to the comprehensive characteristics of land use change including regional differentiation, physical, economic, and macro-policy factors as well. Spatial pattern of land use change and its driving forces were investigated in China in the early 21st century. To sum up, land use change pattern of this period was characterized by rapid changes in the whole country. Over the agricultural zones, e.g., Huang-Huai-Hai Plain, the southeast coastal areas and Sichuan Basin, a great proportion of fine arable land were engrossed owing to considerable expansion of the built-up and residential areas, resulting in decrease of paddy land area in southern China. The development of oasis agriculture in Northwest China and the reclamation in Northeast China led to a slight increase in arable land area in northern China. Due to the "Grain for Green" policy, forest area was significantly increased in the middle and western developing regions, where the vegetation coverage was substantially enlarged, likewise. This paper argued the main driving forces as the implementation of the strategy on land use and regional development, such as policies of "Western Development", "Revitalization of Northeast", coupled with rapidly economic development during this period.

DOI

[29]
Liu J Y, Liu M L, Zhuang D F, et al.Study on spatial pattern of land-use change in China during 1995-2000[J]. Science in China Series D: Earth Sciences, 2003,46(4):373-384.It is more and more acknowledged that land-use/cover dynamic change has become a key subject urgently to be dealt with in the study of global environmental change. Supported by the Landsat TM digital images, spatial patterns and temporal variation of land-use change during 1995 鈥2000 are studied in the paper. According to the land-use dynamic degree model, supported by the 1km GRID data of land-use change and the comprehensive characters of physical, economic and social features, a dynamic regionalization of land-use change is designed to disclose the spatial pattern of land-use change processes. Generally speaking, in the traditional agricultural zones, e.g., Huang-Huai-Hai Plains, Yangtze River Delta and Sichuan Basin, the built-up and residential areas occupy a great proportion of arable land, and in the interlock area of farming and pasturing of northern China and the oases agricultural zones, the reclamation of arable land is conspicuously driven by changes of production conditions, economic benefits and climatic conditions. The implementation of 鈥渞eturning arable land into woodland or grassland鈥 policies has won initial success in some areas, but it is too early to say that the trend of deforestation has been effectively reversed across China. In this paper, the division of dynamic regionalization of land-use change is designed, for the sake of revealing the temporal and spatial features of land-use change and laying the foundation for the study of regional scale land-use changes. Moreover, an integrated study, including studies of spatial pattern and temporal process of land-use change, is carried out in this paper, which is an interesting try on the comparative studies of spatial pattern on change process and the change process of spatial pattern of land-use change.

DOI

[30]
肖汉,李志鹏.基于分形理论的北京城市形态结构遥感分析[J].科技导报,2010,28(16):57-62.城市形态结构分析对北京市的长远规划具有重要意义,运用分形思想 规划城市对人居环境的改良和人地关系的协调也有着重要意义.本研究基于分形理论,运用GIS空间分析等方法,对北京城市形态结构和变化进行了定量的分析与 评价,预测了北京市未来城市发展范围.利用TM遥感影像监督分类,提取1992、1999和2006年北京市建筑用地面积,以城市中心为圆心,取几十个半 径递增的同心圆进行剪裁.得出各个同心圆内的城市建筑用地面积.通过反复试验确定双标度区并得到城市建筑用地面积折线图,并对面积进行基于乘幂关系的函数 拟合.拟合优度R2≥0.995.并得到分维数D以及北京市范围及其变化.在得到这些数据后,进行了深入的分析以解释这些现象,同时对北京市与其他大城市 的城市形态进行对比.最后,对北京市的城市建筑用地的未来发展以及可能出现的情况做出预测.

[ Xiao H, Li Z P.Remote sensing analyses of the urban morphology of Beijing based on Fractal Theory[J]. Science and Technology Review, 2010,28(16):57-62. ]

[31]
查良松,王莹莹.一种城市热岛强度的计算方法——以合肥市为例[J].科技导报,2009,27(20):76-79.城市热岛是指城市化过程中城区高空以及近地面大气温度高于城区以外郊区的现象。城市热岛强度是城市热场分布的重要表达手段,目前,对专门研究城市热岛强度的计算方法讨论较少。本文利用Landsat5数据,基于遥感相关理论,以合肥市为例,反演出城市地表亮温,同时引入城市形态分维理论,使用改进的半径法对合肥市的城市热场状况进行研究,发现亮温的水平分布在城市建成区与非城市建成区间有突变现象,并以此分析城市热力场的空间分布,得到热岛强度计算公式,从而得出城市热岛强度的表达方式。在城市热岛强度计算过程中,着重考虑了城市热场分布特征,克服了在研究城市热岛时寻找郊区气温值的困惑,这对于城市热岛强度的表达研究具有一定的指导意义。

DOI

[ Zha L S, Wang Y Y.A calculation method for urban heat island intensity: A case study on Hefei city[J]. Science and Technology Review, 2009,27(20):76-79. ]

[32]
乔治,田光进.基于MODIS的2001-2012年北京热岛足迹及容量动态监测[J].遥感学报,2015,19(3):476-484.利用2001年—2012年MODIS分裂窗算法反演得到的1 km分辨率地表温度产品分析了北京城市热岛效应。首先计算北京2001年—2012年地表温度年平均值,其次利用半径法确定热岛足迹并计算热岛容量。结论如下:(1)热岛足迹及热岛容量昼夜差异明显,2012年白天热岛足迹是夜间的1.5倍,这是由于城市下垫面热特性差异及人为活动的综合影响。(2)2001年—2012年北京城市高温区在空间上向南北扩展,热岛足迹和热岛容量呈阶段性增长。2010年白天热岛足迹最大,半径为28 km,面积是2001年的2.4倍。当热岛足迹相同时,城市绿地和水体功能区的分布和布局方式等因素能够影响热岛容量。城市建设用地和农村居民点对城市热环境贡献率明显高于其他土地利用类型。当建设用地面积比例超过50%时,区域会产生显著的热岛现象。(3)根据北京热岛足迹及容量时空动态变化特征,提出改善城市热岛的措施。

DOI

[ Qiao Z, Tian G J.Dynamic monitoring of the footprint and capacity for urban heat island in Beijing between 2001 and 2012 based on MODIS[J]. Journal of Remote Sensing, 2015,19(3):476-484. ]

[33]
蒋晶,乔治.北京市土地利用变化对地表温度的影响分析[J].遥感信息,2012,27(3):105-111.运用遥感(RS)和地理信息系统(GIS)技术,对北京1995 年至2005年的土地利用时空变化特征进行了分析,并分析了土地利用变化对区域内地表温度的影响.北京在1995年至2000年的土地利用变化较小,随后 五年城镇面积大量扩张,占用了大量的耕地和林地.北京地表温度等级较高的区域在数量上呈逐年增加的趋势,同时在空间分布上呈现从分散向城市中心集中的趋 势.构建了TVX空间研究土地利用变化对地表温度的影响,计算了不同土地利用类型转变为建设用地的变化向量长度,并选取未变化的土地利用类型的平均温度作 为控制点,消除不确定性因素,分析得出变化向量的幅度依次是:有林地＞水域＞其他林地＞灌木林地＞草地＞耕地,由此可以看出,林地和水域对缓解地表热环境 作用较强,在城市规划中,要尽量保护林地和水域.

DOI

[ Jiang J, Qiao Z.Impact analysis of Land Surface Temperature(LST)Land Use Change on Beijing[J]. Remote Sensing Information, 2012,27(3):105-111. ]

[34]
康文星,吴耀兴,何介南,等.城市热岛效应的研究进展[J].中南林业科技大学学报, 2011,31(1):70-76.随着世界性城市化、工业进程的加快,城市热岛效应越来越强烈,并极大地影响城市生态环境的安全和居民的日常生活,缓解城市热岛已是迫切需要解决的问题.综述了目前城市热岛的生态环境效应、热岛的形成机制、时空分布特征、研究方法的模型和治理与对策等研究的最新进展动态.在总结当前研究的基础上,探讨了城市热岛未来的研究重点和方向.

DOI

[ Kang W X, Wu Y X, He J N, et al. Research progress of urban heat island effect[J]. Journal of Central South University of Forestry&Technology, 2011,31(1):70-76. ]

[35]
程好好,曾辉,汪自书,等.城市绿地类型及格局特征与地表温度的关系——以深圳特区为例[J].北京大学学报(自然科学版),2009,45(3):495-501.以深圳特区为例,利用2005年11月份的TM和Quickbird影像数据,在进行地表温度反演和城市绿地详细解译的基础上,研究了城市绿地类型、属性特征与地表热环境分异之间的关系。研究结果显示,城市绿地不同类型的地表温度差异较大,各类人工绿地的地表温度普遍高于自然绿地类型。分样区平均NDVI和聚集度指数与热岛强度指数之间呈显著的正相关关系,而均匀度和碎裂化指数则与热岛强度指数之间呈显著的负相关关系。上述结果表明,城市绿地的类型、结构及其格局特征均对城市热环境空间分异具有显著影响,在城市绿地建设工作中,应充分重视绿地景观的这种热环境效应特征的指导意义,以期使城市绿地建设能够更好地担负其必要的生态服务功能。

DOI

[ Cheng H H, Zeng H, Wang Z S, et al.Green Space and Land Surface Temperature: A Case Study in Shenzhen Special Economic Zone[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2009,45(3):495-501. ]

[36]
高吉喜,张惠远.构建城市生态安全格局从源头防控区域大气污染[J].环境保护,2014,42(6):20-22.城市规模过大和布局不合理是导 致大气环境问题的主要原因之一,破解当前以及今后的区域性大气污染问题,除了要调整产业结构和减少污染排放外,还必须控制城市自身建设规模,合理测算和确 定城市群、城市带之间的生态安全距离,为空气污染净化预留空间,从而达到从源头控制空气污染的目的。

DOI

[ Gao J X, Zhang H Y.Constructing urban ecological security pattern to control the regional air pollution at the source[J]. Environment Protection, 2014,42(6):20-22. ]

[37]
顾莹,束炯.上海近30年人为热变化及与气温的关系研究[J].长江流域资源与环境,2014,23(8):1105-1110.为研究上海人为热,根据统计年鉴资料,估算了1978～2008 年工业、民用和机动车人为热的排放量及其总和,并从时间变化和空间分布两个方面进行分析.鉴于人为热对温度的影响,根据上海地区11个区县气象观测站近 30 a的年平均气温资料,研究城郊气温及其差值与人为热变化的关系.结果表明:(1)近30 a来,上海工业、民用和机动车人为热排放量及其总和呈现逐年上升的趋势.2000年前上海人为热缓慢增长;而2000年后(含2000年)则呈现快速增长 的趋势.(2)工业是上海人为热的主要来源,但自1978年以来,工业人为热比重逐年下降.2000年起,机动车人为热比重超过民用人为热.(3)上海工 业人为热排放主要分布在以宝钢为主的宝山长江沿岸地区、黄浦江沿岸市区段地区、以吴泾为主的闵行南部地区,以及金山石化地区.而民用和机动车人为热排放主 要集中在市区.(4)自20世纪90年代以后,市中心与郊区的气温差加大,城市热岛效应强度日趋增强.同时,上海地区温度的空间分布特征与人为热的空间分 布有很好的一致性.人为热的大量排放对气温的增加也是不可忽视的重要因素.

DOI

[ Gu Y, Shu J.Variation of anthropogenic heat in Shanghai in recent 30 years and its relation to air temperature[J]. Resources and Environment in the Yangtze Basin, 2014,23(8):1105-1110. ]

Options
Outlines

/

〈 〉