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地球信息科学学报 >

2016 , Vol. 18 >Issue 8: 1094 - 1102

DOI: https://doi.org/10.3724/SP.J.1047.2016.01094

地理空间分析综合应用

厦门市高温热浪人群健康风险格局分析

  • 赵颜创 , 1, 2 ,
  • 赵小锋 , 1, 2, * ,
  • 刘乐乐 1, 2, 3
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  • 1. 中国科学院城市环境研究所,中国科学院城市环境与健康重点实验室,厦门 361021
  • 2. 厦门市城市代谢重点实验室,厦门 361021
  • 3. 中国科学院大学,北京 100049
*通讯作者：赵小锋（1981-）,男,河南洛阳人,博士,研究方向为城市环境遥感。E-mail:

作者简介：赵颜创（1988-）,男,河南郑州人,硕士生,研究方向为城市热环境。E-mail:

收稿日期: 2015-11-05

  要求修回日期: 2015-12-06

  网络出版日期: 2016-08-10

基金资助

福建省自然科学基金项目（2013J05062）

国家自然科学基金项目（41371392）

厦门市科技计划项目（3502Z20142020）

国家科技支撑计划项目（2012BAC21B03）

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Spatial Pattern Analysis on Human Health Risk of Heatwave in Xiamen City

  • ZHAO Yanchuang , 1, 2 ,
  • ZHAO Xiaofeng , 1, 2, * ,
  • LIU Lele 1, 2, 3
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  • 1. Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
  • 2. Xiamen Key Lab of Urban Metabolism, Xiamen 361021, China
  • 3. University of Chinese Academy of Sciences, Beijing 100049, China
*Corresponding author: ZHAO Xiaofeng, E-mail:

Received date: 2015-11-05

  Request revised date: 2015-12-06

  Online published: 2016-08-10

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《地球信息科学学报》编辑部 所有
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摘要

高温热浪已成为世界范围内夏季频繁发生的极端气象灾害事件,威胁着人类健康。研究高温热浪人群健康风险的空间格局,可以识别灾害高风险区域,有助于预防和应对高温热浪灾害。本文以厦门市为例,在历史气象数据分析的基础上,建立了高温热浪案例库,分析了厦门高温热浪的基本特征。通过结合卫星遥感影像和人口统计数据,选取2010年的一次高温热浪事件,进行人口因子和环境因子叠加分析,研究了厦门市高温热浪人群健康风险的空间格局,得出如下结论：（1）厦门市高温热浪强度较轻,频率较高,偶尔会发生强等级高温热浪;（2）高温热浪人群健康的高风险区域集中在厦门本岛内,沿东北-西南方向呈带状分布,较高等级的风险热点主要位于湖里区江头街道的北部与东南部和思明区夏港街道大部分区域;（3）高温热浪人群健康风险的空间格局与环境和人口的空间分布特征密不可分。本文对完善国内高温热浪人群健康风险分析的科学体系具有重要意义。

关键词： 高温热浪; 健康风险; 空间格局; 厦门市

本文引用格式

赵颜创 , 赵小锋 , 刘乐乐 . 厦门市高温热浪人群健康风险格局分析[J]. 地球信息科学学报, 2016 , 18(8) : 1094 -1102 . DOI: 10.3724/SP.J.1047.2016.01094

Abstract

Heatwave has become an extreme meteorological disaster which occurred frequently during the summer. Moreover, heatwave could evidently affect the healthy conditions of residents. Thus, study the spatial pattern of heatwave health risk would be helpful for us to prevent from and respond to the impacts of heatwaves. Using the historically meteorological datum of Xiamen, this study built a database of heatwave cases and analyzed the basic characteristics of heatwaves in Xiamen. Taking a heatwave event occurred in 2010 as a case, we analyzed the spatial pattern of heatwave health risk by using both the remote sensing data and the demographic data. It is concluded as the following statements. (1) The intensity of heatwaves in Xiamen is quite low, but its frequency is rather high. An intensive heatwave occurred occasionally. (2) The regions with high health risk are located in Xiamen Island, lying from the northeast toward the southwest. The regions with the highest healthy risk are located in the northern and southeastern Jiangtou sub-district, Huli district, and the most area of Xiagang sub-district and Siming district. (3) The human health risk pattern of Heatwave is associated with the spatial distribution of environmental and demographic factors. Generally, this study promotes and extends the scientific knowledge on the health risk of heatwaves.

Key words： heatwave; health risk; spatial pattern; Xiamen city

1 引言

全球气候变暖给人类社会和自然系统带来了诸多危害[1],高温热浪是其中之一[2-4]。联合国政府间气候变化专门委员会（IPCC）第四次气候变化评估报告指出,由于全球气候变暖和城市热岛效应的影响,自1950年以来,全球高温热浪发生频率明显增加,高温热浪已成为世界范围内夏季频繁发生的极端天气灾害事件之一[5]。
高温热浪是指具有一定持续性的高温天气[6]。目前在国际上还未形成统一的认定标准,世界气象组织（WTO）建议高温热浪的标准是持续3天以上且日最高气温大于32 ℃。而中国一般把持续3天以上且日最高气温达到或超过35 ℃的天气过程称为高温热浪[7]。许多研究表明,高温热浪会使人体不适应环境,导致人群发病率和死亡率的显著增 加[8-9]。高温热浪严重威胁人类健康,分析在高温热浪灾害天气下人群健康风险的空间格局,可以识别灾害高风险区,进而为人类预防和应对高温热浪提供决策依据。
发达国家关于高温热浪人群健康风险空间格局的研究较多。在美国,Reid等利用人口因子和地表覆被数据,在区域和城市2个尺度上分析了高温热浪人群健康风险的空间格局,为相关研究提供了范本[10];Johnson等以芝加哥1995年的热浪事件为例,结合人口、环境等数据,发现热浪高风险区的人口死亡率高于低风险区[11];Chow等对比了凤凰城2个时期的热脆弱性指数,发现了城市西北和东南的热浪风险等级变化较大[12]; Rosenthal等对纽约市高温热浪人风险空间格局的研究等[13]。在欧洲,Buscail等在社区尺度上研究了法国雷恩市的风险状况,发现在城市中心南北条带上的风险最高[14];另外,Wolf等、Tomlinson等和Dugord等分别研究了英国伦敦、伯明翰和德国柏林的风险空间格局[15-17]。在澳大利亚,Loughnan等分别基于逐步回归模型和主成分分析构建加权型热脆弱性指数,分析了堪培拉、悉尼、墨尔本等8个重要城市高温热浪人群健康风险的空间分布特征[18]。
与发达国家相比,国内关于高温热浪人群健康风险空间格局的研究相对滞后。张书娟等、黄慧琳等以高温强度和日数为基础,分别在区域和城市尺度上分析了研究区高温灾害的空间分布特征[19-20]。贺山峰等选取高温日数和热浪日数2个指标,应用PRECIS模式预估了至2100年中国高温致灾危险性的时空格局[21]。但以上研究仅分析了高温热浪的危险性,而未考虑社会人群自身的特点和组成,不能全面反映高温热浪天气下人群健康风险的空间分布特征。张校玮结合高温的危险性和各省人口密度,研究了中国高温灾害下人群健康风险的空间格局,发现高风险区域主要集中在河南、山东、四川等地,为中国高温灾害的风险管理与预防提供了重要参考[22]。但该研究仅使用人口密度衡量人群特征,很难全面反映人群特征空间分布差异。大量研究表明,高温热浪人群健康风险状况与人口的年龄、受教育程度、经济状况、种族以及生存空间环境等方面密切相关[10,12,23]。因此,有必要结合多个人口因子和环境因子对高温热浪人群健康风险进行分析,以便全面和深入刻画其空间分布特征,完善国内高温热浪人群健康风险分析的科学体系,从而为社会有效地应对和预防高温热浪灾害提供参考。
本文在厦门市历史气象数据的基础上,建立了高温热浪案例库,分析了厦门市高温热浪的基本特征。同时,选取2010年的一次高温热浪事件为例,结合卫星遥感影像和人口统计数据,研究了厦门市高温热浪人群健康风险的空间格局,以期为城市预防和应对高温热浪提供决策依据。

2 研究区和数据

厦门市位于东经118°03′~118°13′,北纬24°26′~24°28′之间,地处中国东南沿海（图1）。其属于亚热带海洋性季风气候,温和多雨,年平均降水量在1200 mm左右,年平均气温在21 ℃左右。至2013年末,全市土地面积1573.16 km²,常住人口数量373万人[24]。厦门市是中国最早建立的经济特区之一,其快速的城市化进程引起了显著的热岛现象[25-26];另外,受西太平洋副热带高压的影响,在夏季时常出现高温热浪天气。根据气象站资料统计,1974-2014年厦门共发生26次高温热浪事件,对居民生活和健康产生了很大影响。因此,分析厦门市高温热浪人群健康风险的空间格局很有必要,以便为城市预防和应对高温热浪提供决策依据。
Fig. 1 Location of the study area

图1 研究区地理位置示意图

本文采用的厦门市日最高温度数据来自中国气象科学数据共享服务网[27]。人口数据来自厦门市第六次人口普查资料,按照乡镇街道进行统计计算人口密度,并与矢量行政街道数据关联进行数字化。遥感数据来自中国资源卫星应用中心的HJ-1B星（环境与灾害监测预报小卫星星座B星）。HJ-1B星于2008年9月6日发射,配置有CCD相机和红外多光谱扫描仪（IRS）,可以为地表环境提供可见光-近红外-热红外的波段信息,其基本参数如表1所示。
Tab. 1 Specifications of HJ-1B sensors

表1 HJ-1B卫星主要载荷参数

传感器 谱段号 光谱范围/mm 空间分辨率/m 幅宽/
km		 重访周期/d
CCD相机 1 0.43~0.52 30 360 4
2 0.52~0.60
3 0.63~0.69
4 0.76~0.90
红外多光谱扫描仪（IRS） 5 0.75~1.10 150 720 4
6 1.55~1.75
7 3.50~3.90
8 10.5~12.5 300

3 研究方法

3.1 高温热浪案例库

目前国际上还没有统一的高温热浪标准,本文的高温热浪是指持续3 d及以上,日最高温度达到或高于35 ℃的天气过程[9]。基于此定义,从厦门市1974-2014年日最高气温数据集中提取高温热浪事件,建立厦门市高温热浪案例库。

3.2 环境因子

结合厦门市高温热浪案例库、天气状况和可获得卫星影像质量,选取发生于2010年7月3日至6日的一次高温热浪事件为例,使用HJ-1B星2010年7月5日的遥感影像为数据源,进行几何精校正、辐射定标等预处理之后,通过波段运算得到辐射温度、归一化植被指数和归一化建设用地指数3个环境 因子。
3.2.1 辐射温度
地表温度显著影响人群热相关的发病率和死亡率,地表温度越高,人群热相关的发病率和死亡越高[10,12-13]。但地表温度的反演受地表比辐射率、大气状况等多种因素的影响,相关参数不易获取,精度难以保证。而辐射温度可根据传感器定标参数以及普朗克方程直接计算,简单方便,且与地表温度关系密切。为反映城市热场的空间差异,本文使用辐射温度来代替地表温度[28-29]。使用HJ-1B IRS相机的热红外波段（10.5~12.5 μm）计算辐射温度,首先使用式（1）将DN值转化为大气层顶辐射亮度,然后利用式（2）将大气层顶辐射亮度转换为辐射温度。
L sensor = ( DN - bias ) / gains （1）
T sensor = c 2 λ eff ln ( c 1 L sensor λ eff 5 + 1 ) - 273.15 （2）
式中： L sensor 是大气层顶辐射亮度W/(m2·sr·μm); bias 和 gains 是转换函数的斜率系数和截距系数,可从影像头文件信息中获取; T sensor 是辐射温度/℃; λ eff 为热红外波段的有效波长[30]; c 1 和 c 2 为常数, c 1 取值1.119104×108 W·μm4/(m2·sr), c 2 取值1.143877×104 μm·K[31]。
3.2.2 归一化植被指数
植被覆盖水平与高温热浪人群健康风险密切相关,覆盖水平越高,风险越低[1,14,32]。归一化植被指数（NDVI）可以反映地表的植被信息,其值越大,说明植被覆盖度越高,计算公式如式（3）所示。
NDVI = ( D N b 4 - D N b 3 ) ( D N b 4 + D N b 3 ) （3）
式中： D N b 4 和 D N b 3 分别为HJ-1B/CCD数据第4和第3波段的亮度值。
3.2.3 归一化建设用地指数
建设用地覆盖度与人群热相关的发病率和死亡率呈显著正相关关系[11,13,33]。归一化建设用地指数（NDBI）反映地表的建设用地信息,其值越大,建设用地覆盖度越高,计算公式如式（4）所示。
NDBI = ( D N b 6 - D N b 4 ) ( D N b 6 + D N b 4 ) （4）
式中： D N b 6 和 D N b 4 分别为HJ-1B/CCD数据第6波段和第4波段亮度值。

3.3 人口因子

人口密度越高,风险发生的概率就越大。另外,不同特征的人群,受高温热浪影响程度不同。如老年人、儿童、独居以及受教育程度较低的人口更易受到伤害[10,12,21]。为此,本文选择常住人口密度、5岁以下人口密度、64岁以上人口密度、独居人口密度、64岁以上独居人口密度以及未受大学教育人口密度,综合反应人群的空间分布。

3.4 人群健康风险等级

通过环境因子和人口因子的叠加,得到高温热浪灾害天气下城市人群的健康风险指数。首先,将不同空间分辨率的图层统一重采样至30 m分辨率。其次,为消除不同变量之间计量单位的影响,对各图层进行标准化处理,变量标准化转换函数如式（5）所示。
X i * = X i - X i min X i max - X i min （5）
式中： X i * 为标准化后的变量 i ； i = 1 , 2 , ∙ ∙ ∙ , 9 分别表示辐射温度、归一化植被指数、归一化建设用地指数、常住人口密度、5岁以下人口密度、64岁以上人口密度、独居人口密度、64岁以上独居人口密度以及未受大学教育人口密度; X i min 为原始变量 i 的最大值; X i min 为原始变量 i 的最小值;各变量的基本统计信息如表2所示。人群健康风险指数的计算模型如式（6）所示。
Y = X 1 * - X 2 * + X 3 * + X 4 * + X 5 * + X 6 * + X 7 * + X 8 * + X 9 * （6）
Tab. 2 Descriptive summary of the risk factors

表2 风险因子的基本统计信息

平均值 标准差 最小值 最大值
环境
变量		 辐射温度 24.2 1.31 21.54 29.14
归一化植被指数 0.1801 0.1896 -0.4981 0.5617
归一化建设用地指数 0.497 0.0941 -0.3175 0.9045
人口
统计
变量		 常住人口密度 2267 4329 2 33 564
5岁以下人口密度 118 224 0 1900
64岁以上人口密度 101 247 0 3138
独居人口密度 228 528 0 4568
64岁以上独居人口密度 13 29 0 460
未受大学教育人口密度 1708 3123 2 25 811
式中： Y 表示高温热浪人群健康风险指数,其值越高风险越高。
根据高温热浪人群健康风险指数的统计分布,将其进行阈值分割,将风险划分为6个等级。其中所使用的阈值如表3所示。本文数据分析所采用的软件包括MATLAB R2009a、ENVI 4.5和ArcGIS 10.0。
Tab. 3 Thresholds used in the segmentation of the risk index

表3 风险等级划分中所使用的阈值

Y值范围 风险等级 代表意义
<0 1 无
0-1 2 低
1-1.5 3 较低
1.5-3 4 中
3-5 5 高
>5 6 较高

4 结果分析与讨论

4.1 高温热浪基本特征

4.1.1 时序变化特征
根据数据分析获得的厦门市高温热浪案例库,可得到高温热浪时序变化特征。图2（a）、（b）分别为1974-2014年高温热浪年际与月际变化趋势。自20世纪70年代以来,共发生26次高温热浪事件,灾害频率为1次/1.6年;其中,1977年和1988年的次数较多,分别为3次和4次。由于7-8月是厦门气温最高的月份[34],高温热浪主要发生在这2个月,分别为16次和8次,6月和9月各发生一次。
Fig. 2 Temporal change of the heatwaves in Xiamen

图2 厦门市高温热浪时序变化

注：图（b）仅列出发生高温热浪时间的月份

4.1.2 强度变化特征
高温热浪强度包括日最高气温和持续天数2个方面。图3为厦门26次高温热浪的强度变化特征。可以看出：高温热浪的平均日最高气温在36 ℃左右上下波动,其中第20次事件发生于2005年8月,平均日最高气温值最大,为37.3 ℃;而高温热浪平均持续天数为3.8 d,第9次发生于1979年7月,持续时间最长,为10 d。
Fig. 3 Intensity of the heatwaves in Xiamen

图3 厦门市高温热浪强度

综合厦门市高温热浪的时序和强度变化特征,根据高温热浪的标准（持续3 d且日最高气温大于35 ℃,为高温热浪;持续5 d且日最高气温大于35 ℃,为强高温热浪,持续3 d且日最高气温大于38 ℃,为极端高温热浪[7]）可知,厦门市高温热浪强度较轻,频率较高,偶尔会发生强等级高温热浪灾害。

4.2 高温热浪人群健康风险因子的空间分布特征

4.2.1 环境因子空间分布特征
图4（a）-（c）分别为厦门市高温热浪过程中（2010年7月5日）辐射温度、归一化植被指数和归一化建设用地指数的空间分布特征。为便于分析,分别根据3个因子的统计分布进行阈值分割,划分为6个等级,使用的阈值如表4所示。由图4（a）可以看出,厦门主要有3个高温区,分别是本岛内连片建成区、集美的杏林—灌口工业区,以及同安区的同安工业集中区-轻工食品园。其中,岛内是厦门市的主城区,商贸发达,建筑密度大,高层建筑多,居民生活和商业活动排放的大量废热难以及时扩散;而集美与同安的工业区中主要以高耗能的电子、化工、轻纺和机械制造等产业为主,工业生产的过程中会排放大量的废热。同时,如图4（b）、（c）所示,以上3大片区的植被覆盖度较低,建设用地面积较多,人造地面吸收大量的太阳辐射,故形成了大片显著的高温区。
Fig. 4 Spatial distribution of the environmental factors’ grades during the heatwave period in Xiamen

图4 厦门高温热浪期间不同等级环境因子的空间分布

Tab. 4 Thresholds used in the segmentation of the environmental factors

表4 环境因子等级划分中所使用的阈值

阈值 等级 代表
意义
辐射温度/(℃) 归一化植被指数 归一化建设用地指数
< 23 <-0.1 <0.3 1 很低
23~24 -0.1-0 0.3-0.4 2 低
24~25 0-0.1 0.4-0.5 3 较低
25~26 0.1-0.3 0.5-0.6 4 中
26~27 0.3-0.5 0.6-0.7 5 高
>27 > 0.5 > 0.7 6 较高

4.2.2 人口空间分布特征

图5（a）-（f）分别展示了厦门市常住人口密度、5岁以下人口密度、64岁以上人口密度、独居人口密度、64岁以上独居人口密度以及未受大学教育人口密度的空间分布。为了便于分析,分别根据各因子的统计分布进行阈值分割,划分为6个等级,使用的阈值如表5所示。综合图4可看出,厦门本岛内各类人群密度均明显高于岛外地区,且高密度区基本分布在本岛的东北-西南方向。尤其是本岛内的西南部和中部,各类人群密度几乎都很高。其中,西南部为厦门市的老城区,是最主要的商贸、文教中心之一;中部是新兴的商贸中心,外来人口数量庞大。
Fig. 5 Spatial distribution of the demographic factors in Xiamen

图5 厦门市人口密度空间分布

Tab. 5 Thresholds used in the segmentation of the population density

表5 人口密度等级划分中所使用的阈值

阈值/(人/km2) 等级 代表意义
常住人口
密度		 5岁以下
人口密度		 64岁以上
人口密度		 独居人口
密度		 64岁以上独居
人口密度		 未受大学教育
人口密度
<1000 <100 <100 <30 <5 <100 1 很低
1000~3000 100~300 100~300 30~100 5~10 100~1000 2 低
3000~5000 300~500 300~500 100~500 10~30 1000~2000 3 较低
5000~10 000 500~700 500~700 500~1000 30~50 2000~7000 4 中
10 000~20 000 700~1000 700~1000 1000~1500 50~150 7000~15 000 5 高
>20 000 >1000 >1000 >1500 >150 >15 000 6 较高

4.3 高温热浪人群健康风险等级的空间分布特征

通过各风险因子的叠加分析得到厦门市高温热浪人群健康风险状况。由厦门市高温热浪人群健康风险的空间格局（图6）,可以看出,人群健康高风险区域集中在作为主城区的厦门岛内,沿东北-西南方向呈带状分布;在厦门岛中部和西南部有零星较高等级风险热点,主要位于湖里区江头街道的北部与东南部和思明区夏港街道大部分区域。
Fig. 6 Map for the heatwave health risks in Xiamen

图6 厦门市高温热浪人群健康风险等级图

高温热浪人群健康风险等级的空间格局与环境和人口因子的空间分布特征密不可分。厦门市共有3个高温区。其中,岛外的2个高温区由于各类人群密度均较低,未形成高风险区。而本岛内的各类人群密度均明显较高,且高密度区基本分布在本岛的东北—西南方向上,所以在本岛的高温片区内沿人群高密度方向出现了高等级的风险。湖里区江头街道的北部与东南部均位于高温区内,其常住人口、5岁以下人口、独居人口、和未受大学教育人口的密度均是厦门市最大的区域,故形成了2个较高等级风险区。思明区夏港街道虽非高温区,但其64岁以上和64岁以上且独居的人口均是厦门市密度最大的区域,该类人群由于身体调节功能减弱、缺乏自我保护能力、社会联系较少等原因,使其更容易受到高温热浪的影响。许多研究报道了老年人在热浪中具有更高的死亡率以及更高的住院率[7,35-36],从而使该区的人群健康风险处于较高等级。

4.4 讨论

由于数据搜集方面存在的困难,本文未涉及人群经济水平和健康状况差异对高温热浪风险状况的影响,结果具有一定的不确定性。研究表明,人群的经济状况是高温相关疾病或死亡的敏感性因子。例如,在芝加哥,年收入低于9000美元的人群在高温热浪期间的死亡风险更高[36];韩国的相关研究表明,低收入人群在热浪期间的死亡率明显高于中高收入人群[37]。人群健康状况也显著影响高温热浪引起的发病或死亡效应,包括心血管疾病、糖尿病、肾脏疾病、神经紊乱、肺气肿、癫痫、脑血管疾病、肺功能相关疾病以及心理及精神方面疾病 等[36,38-39]。在未来的研究中,将进一步获取人群经济水平和健康状况等数据,以便进行更为细致深入的分析。

5 结论

本文在厦门市历史气象数据的基础上,建立了高温热浪案例库,分析了厦门市高温热浪的基本特征。同时,选取2010年的一次高温热浪事件为例,结合卫星遥感影像和人口统计数据,综合人口因子和环境因子,研究了厦门市高温热浪人群健康风险的空间分布特征,得出如下结论：
（1）厦门市高温热浪强度较轻,频率较高,偶尔会发生强等级高温热浪。
（2）厦门市高温热浪人群健康的高风险区域集中在厦门本岛内,沿东北-西南方向呈带状分布;较高等级的风险热点主要位于湖里区江头街道的北部与东南部和思明区夏港街道大部分区域。
（3）高温热浪人群健康风险的空间格局与环境和人口的空间分布特征密不可分。
本文研究结果对完善国内高温热浪人群健康风险分析的科学体系具有重要的意义,可为预防和应对高温热浪灾害提供决策依据。

The authors have declared that no competing interests exist.

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
[1]
李湉湉,杜艳君,莫杨,等.基于脆弱性的高温热浪人群健康风险评估研究进展[J].环境与健康杂志,2014(6):547-550.目的全球气候变化是21世纪人类所面临的最严重的环境问题之一,联合国政府间气候变化专门委员会（IPCC）发布预测,由于全球气候变化的影响,21世纪世界范围内的高温热浪事件无论在频率、强度还是持续时间上都将会继续增加,由于高温热浪具有高致病性和高致死性,因此评估及预测未来高温热浪人群健康风险对制定及实施高温热浪预警、预案措施具有重要意义。该文简要介绍了气候变化背景下高温热浪的健康危害,对高温热浪人群健康风险评估技术的研究方法与现状进行了总结和归纳,最后对此方面研究进行了展望。

[ Li T T, Du Y J, Mo Y, et al.Human health risk assessment of heat wave based on vulnerability: a review of recent studies[J]. Journal of Environment and Health, 2014,6:547-550. ]

[2]
Lorenzoni I, Pidgeon N F, O’Connor R E. Dangerous climate change:the role for risk research[J]. Risk Analysis, 2005,25:1387-1398.The notion of "dangerous climate change" constitutes an important development of the 1992 United Nations Framework Convention on Climate Change. It persists, however, as an ambiguous expression, sustained by multiple definitions of danger. It also implicitly contains the question of how to respond to the complex and multi-disciplinary risk issues that climate change poses. The invaluable role of the climate science community, which relies on risk assessments to characterize system uncertainties and to identify limits beyond which changes may become dangerous, is acknowledged. But this alone will not suffice to develop long-term policy. Decisions need to include other considerations, such as value judgments about potential risks, and societal and individual perceptions of "danger," which are often contested. This article explores links and cross-overs between the climate science and risk communication and perception approaches to defining danger. Drawing upon nine articles in this Special Issue of Risk Analysis, we examine a set of themes: limits of current scientific understanding; differentiated public perceptions of danger from climate change; social and cultural processes amplifying and attenuating perceptions of, and responses to, climate change; risk communication design; and new approaches to climate change decision making. The article reflects upon some of the difficulties inherent in responding to the issue in a coherent, interdisciplinary fashion, concluding nevertheless that action should be taken, while acknowledging the context-specificity of "danger." The need for new policy tools is emphasised, while research on nested solutions should be aimed at overcoming the disjunctures apparent in interpretations of climate change risks.

DOI PMID

[3]
张智,陈玉华,周红.河套及其相邻区夏半年极端高温的时空变化特征[J].自然灾害学报,2014,23(2):190-197.利用河套及其相邻地区1961-2010年81个气象台站夏半年的逐日最高气温资料,计算了各台站夏半年的极端高温阈值,对极端高温频数的时空特征和变化趋势进行了分析。结果表明:河套及其相邻地区夏半年极端高温阈值呈东南部和西北部高、西南部低的分布;夏半年极端高温频数在地域上主要呈一致性和东南—西北反位相变化;在空间上可分为河套北部区、河套东南区、河套西南区3个异常分区;3个异常分区中,河套西南区夏半年极端高温频数增加趋势显著,河套北部区增加趋势较明显,而河套东南区减少趋势较明显;河套北部区与河套西南区的年际变化趋势相近,1960年代至1980年代偏少、1990年代至2000年代偏多,而河套东南区基本相反;河套北部区与河套西南区10~14 a的周期变化比较显著;夏半年极端高温频数与同期平均气温呈正相关。

[ Zhang Z, Chen Y H, Zhou H.Spatiotemporal features of extreme high temperature in summer half year in Hetao and its vicinity[J]. Journal of Natural Disasters, 2014,23(2):190-197. ]

[4]
李敏敏,延军平.华北农牧交错带极端高温时空分布特征[J].自然灾害学报,2014,23(3):190-199.利用华北农牧交错带及其附近32个气象站点1961-2012年的逐日最高地面气温资料,采用去趋势波动分析法确定了极端高温事件的阈值,分析了华北农牧交错带极端高温日数和极端高温日平均最高气温的时空动态变化特征。结果表明:52a来,华北农牧交错带极端高温主要集中于5-8月,年均极端高温日数为1 d,极端高温日平均最高气温为36.9℃,两者分别从20世纪80和90年代由负距平转为正距平,均存在25~30 a的显著振荡周期;极端高温在空间分布上具有海拔依赖性,极端高温日数有显著的代际变化特征,并以20世纪90年代为界呈现两种不同的分布形态,极端高温日平均最高气温分布相对稳定;21世纪最初的12 a,华北农牧交错带各站点极端高温日数、极端高温日平均最高气温均呈增大的趋势。

[ Li M M, Yan J P.Spatiotemporal distribution of extreme maximum temperature in agro-pastoral zone of north China[J]. Journal of Natural Disasters, 2014,23(3):190-199. ]

[5]
IPCC. Climate change 2007: the physical science basis[M]. Cambridge, UK: Cambridge University Press, 2007.

[6]
田颖,张书余,罗斌,等.热浪对人体健康影响的研究进展[J].气象科技进展,2013,3(2):49-54.热浪对人体健康影响显著,随着 全球变暖,其发生频率、强度还会继续增加。通过对国内外研究的概述,发现热浪期间发热、中暑、心脑血管疾病、呼吸道疾病、精神病、部分传染病等疾病发病 率、热相关死亡率增加,老年人、儿童及一些慢性病患者更易受影响;影响程度与热浪特征、社会因素、行为因素及空气污染有关,并存在"滞后效应"和"收获效 应"。另外,简要介绍了主要的热浪研究方法。目前,对热浪研究主要集中于统计学方法,动物实验和人群实验研究比较少,对此,今后应更注重通过实验方法研究 热浪对人体各类疾病的影响。

DOI

[ Tian Y, Zhang Y S, Luo B, et al.Research progress in impact of heat wave on human health[J]. Advances in Meteorological Science and Technology, 2013,3(2):49-54. ]

[7]
徐金芳,邓振镛,陈敏,等.中国高温热浪危害特征的研究综述[J].干旱气象,2009,27(2):163-167.全面综述了高温热浪气象灾害在危害特点、标准与类型、气候特征、 形成原因、对全球气候变暖的响应、监测和预测与预警技术、减灾技术及应对策略等8个方面的研究成果.高温热浪是一种较短时间尺度的天气灾害,基本天气特征 是高温低湿,除高寒地带外,每年6～8月在全国各地均有发生.大气环流异常是高温热浪形成的直接原因,气候变暖变干是导致高温热浪频繁发生的重要原因.深 刻了解高温热浪的危害特征,对应对高温热浪发生发展、监测预测、安全生产、趋利弊害具有重要的指导作用.

DOI

[ Xu J F, Deng Z Y, Chen M, et al.A summary of studying on characteristics of high temperature and heat wave damage in China[J]. Journal of Arid Meteorology, 2009,27(2):163-167. ]

[8]
Luber P, Brynestad S, Topsch D, et al.Quantification of campylobacterspecies cross-contamination during handling of contaminated freshchicken parts in kitchens[J]. Apply Environment Microbiology, 2006,72:66-70.

PMID

[9]
许遐祯,郑有飞,尹继福,等.南京市高温热浪特征及其对人体健康的影响[J].生态学杂志,2011(12):2815-2820.利用1951&mdash;2009年(6&mdash;9月)气象资料、2005&mdash;2008年(6&mdash;9月)南京市逐日死亡人数、2005&mdash;2007年(6&mdash;9月)中暑相关的逐时急诊人数，采用描述性研究、回归分析等流行病学统计方法，研究南京市高温热浪特征及其对人体健康的影响。结果表明：南京是各种级别高温和热浪频发的城市，近60年来，南京市每年平均高温日数14.5 d，危害高温日数1.1 d，高温热浪1.9次，强高温热浪0.8次；高温热浪过程造成的人群超额死亡率在20%以上，其中女性超额死亡率稍大于男性，对冠心病和脑血管病患者的伤害较大，而且不存在滞后性；由于人体的适应性，发生在夏季早期的高温热浪比发生在季节中、末阶段的高温热浪危害大，但热浪持续的时间对超额死亡率的影响较小；另外，由于城市居民工作需要和出行时间的选择，导致夏季逐时高温对人体危害呈双峰型分布，2个峰值分别出现在9:00&mdash;11:00和19:00&mdash;21:00；高温热浪对人体的危害与年龄约呈三次函数关系，对0～5岁的婴幼儿和60～80岁的高龄人群危害较大。这些结果可为人体健康风险管理提供参考。

[ Xu X Z, Zheng Y F, Yin J F, et al.Characteristics of high temperature and heat wave in Nanjing city and their impacts on human health[J]. Chinese Journal of Ecology, 2011,12:2815-2820. ]

[10]
Reid C E, O’Neill M S, Gronlund C, et al. Mapping community determinants of heat vulnerability[J]. Environmental Health Perspectives, 2008,117:1730-1736.

[11]
Johnson D P, Stanforth A, Lulla V, et al.Developing an applied extreme heat vulnerability index utilizing socioeconomicand environmental data[J]. Applied Geography, 2012,35:23-31.Extreme heat is a leading cause of weather-related fatalities worldwide. Emphasis is currently being placed on the development of spatially specific vulnerability models, which are useful for decision support during extreme heat events (EHE). Research results concerning such spatially-explicit models lead efforts in preparation and mitigation of heat-related vulnerability and potential adaptation. The presented research analyzes the 1995 Chicago EHE in the context of a socio-environmental hazards approach, and fosters the development of an extreme heat vulnerability index (EHVI). The EHVI is a fused dataset consisting of census data and remotely sensed variables, which are examined in relation to geocoded mortality data. The presented analysis combines 25 well-known indicators of extreme heat-health risk into an applied index utilizing a principal components analysis. The developed EHVI presented a trend of higher rates of death in the highest risk zones to lower rates in lower zones of risk. The model explains nearly 80% of the total variance in the heat-health vulnerability variables utilized. This index could be utilized by city officials to assist in the mitigation of extreme heat events and is a further evolution of previously developed efforts. Our findings indicate extreme heat vulnerability models should likely be developed on a local level for a specific location, taking into account local variations in social and environmental vulnerability.

DOI

[12]
Chow W T L, Chuang W C, Gober P. Vulnerability to extreme heat in metropolitan Phoenix: spatial, temporaland demographic dimensions[J]. The Professional Geographer, 2012,64(2):286-302.PATRICIA GOBER is a Professor of Geography and Sustainability, as well as a co-director at the Decision Center for a Desert City, Global Institute of Sustainability, Arizona State University, Tempe, AZ 85287. E-mail: gober@asu.edu. Her current research focuses on issues of water management and environmental change in metropolitan Phoenix.

DOI

[13]
Rosenthal J K, Kinney P L, Metzger K B.Intra-urban vulnerability to heat-related mortality in New York City, 1997-2006[J]. Health and Place, 2014,30:3045-3060.The health impacts of exposure to summertime heat are a significant problem in New York City (NYC) and for many cities and are expected to increase with a warming climate. Most studies on heat-related mortality have examined risk factors at the municipal or regional scale and may have missed the intra-urban variation of vulnerability that might inform prevention strategies. We evaluated whether place-based characteristics (socioeconomic/demographic and health factors, as well as the built and biophysical environment) may be associated with greater risk of heat-related mortality for seniors during heat events in NYC. As a measure of relative vulnerability to heat, we used the natural cause mortality rate ratio among those aged 65 and over (MRR 65+ ), comparing extremely hot days (maximum heat index 10002°F+) to all warm season days, across 1997–2006 for NYC’s 59 Community Districts and 42 United Hospital Fund neighborhoods. Significant positive associations were found between the MRR 65+ and neighborhood-level characteristics: poverty, poor housing conditions, lower rates of access to air-conditioning, impervious land cover, surface temperatures aggregated to the area-level, and seniors’ hypertension. Percent Black/African American and household poverty were strong negative predictors of seniors’ air conditioning access in multivariate regression analysis.

DOI PMID

[14]
Buscail C, Upegui E, Viel J F.Mapping heatwave health risk at the community level for public health action[J]. International Journal of Health Geographics, 2012,11(1):38.

DOI

[15]
Wolf T, McGregor G. The development of a heat wave vulnerability index for London, United Kingdom[J]. Weather and Climate Extremes, 2013,1:59-68.The health impacts of heat waves are an emerging environmental health concern. This is especially so for large cities where there is a concentration of people and because of the urban heat island effect. Temperatures within cities can reach stressful levels during extreme temperature events. To better manage heat related health risks, information is required on the intra-urban variability of vulnerability to heat wave events. Accordingly a heat vulnerability index (HVI) is developed and presented for Greater London in the United Kingdom. The approach to HVI development adopted is an inductive one whereby nine proxy measures of heat risk are extracted from the 2001 London census for 4765 census districts and subject to principal components analysis. Scores for the emergent principal components are weighted according to the variance they explain and summed to form the HVI. Although mapping of the HVI shows what appears to be a heterogeneous heat 鈥渞isk-scape鈥 statistical testing reveals significant spatial clustering of areas of high heat vulnerability in central and east London which also co-occur with areas of potentially high heat exposure. Drivers of the spatial pattern of heat vulnerability are discussed as are the implications of study results for heat risk management in large cities.

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[16]
Tomlinson C J, Chapman L, Thornes J E, et al.Including the urban heat island in spatial heat health risk assessment strategies: a case study for Birmingham, UK[J]. International Journal of Health Geographics, 2011,10(1):42.BACKGROUND: Heatwaves present a significant health risk and the hazard is likely to escalate with the increased future temperatures presently predicted by climate change models. The impact of heatwaves is often felt strongest in towns and cities where populations are concentrated and where the climate is often unintentionally modified to produce an urban heat island effect; where urban areas can be significantly warmer than surrounding rural areas. The purpose of this interdisciplinary study is to integrate remotely sensed urban heat island data alongside commercial social segmentation data via a spatial risk assessment methodology in order to highlight potential heat health risk areas and build the foundations for a climate change risk assessment. This paper uses the city of Birmingham, UK as a case study area. RESULTS: When looking at vulnerable sections of the population, the analysis identifies a concentration of "very high" risk areas within the city centre, and a number of pockets of "high risk" areas scattered throughout the conurbation. Further analysis looks at household level data which yields a complicated picture with a considerable range of vulnerabilities at a neighbourhood scale. CONCLUSIONS: The results illustrate that a concentration of "very high" risk people live within the urban heat island, and this should be taken into account by urban planners and city centre environmental managers when considering climate change adaptation strategies or heatwave alert schemes. The methodology has been designed to be transparent and to make use of powerful and readily available datasets so that it can be easily replicated in other urban areas.

DOI PMID

[17]
Dugord P A, Lauf S, Schuster C, et al.Land use patterns, temperature distribution, and potential heat stressrisk - the case study Berlin, Germany[J]. Computers, Environment and Urban Systems, 2014,48:86-98.In western societies, the combined effects of climate warming, proceeding urbanization, and demographic change (e.g. population aging) increase the risk of city populations to be subjected to heat-related stress. To provide a scientific fundament for city-wide and spatially explicit adaptation planning, urban heat distribution and the population at risk need to be studied at small spatial scale. This study pursued to (a) investigate the land surface temperature (LST) distribution with regard to underlying effects of urban land use patterns, and to (b) identify areas at potential risk towards heat stress based on temperatures distribution and demographic vulnerability. We used LST maps as derived from two Landsat thermal satellite images for 10聽pm and 10聽am at two subsequent summer days and examined land use patterns through land use types, landscape metrics, and structural parameters via statistical and GIS analysis. Using linear regressions we obtained the degree of soil sealing to be the best predictor of LST-variations. However, under certain conditions, NDVI, distance to city center and floor area ratio (FAR) were better predictors. Water bodies had beneficial effects at 10聽am and inverse effects at 10聽pm, vice versa for arable land. The cooling effects of green areas were more significant in the morning than in the evening. Residential uses were among the most heat affected land use types at 10聽pm, with different intensities according to their density level. For the identification of risk areas at the building scale, we introduced a matrix to combine simulated air temperature with population age and density. Results showed higher potential risk in central inner-city areas of dense residential uses, in particular for areas with high amounts of elderly residents, and for two major residential building types. The identified building blocks of specific heat stress risk provide urban planners with useful information to mitigate adverse effects caused by future heat waves.

DOI

[18]
LoughnanM E, Tapper N J, Phan T, et al. A spatialvulnerability analysis of urban populations during extreme heat events in Australian capital cities[R]. Gold Coast, Australia: National Climate Change Adaptation Research Facility, 2013:128.

[19]
张书娟,尹占娥,刘耀龙,等.近60年我国华东地区高温灾害特征分析[J].上海师范大学学报(自然科学版),2011,40(1):95-101.高温灾害是我国华东地区频发的 自然灾害类型.基于华东7省市1951～2008年29个站点日最高气温监测数据,运用Excel、Spss进行高温(日最高气温≥35℃)和酷暑(日最 高气温≥38℃)天数的提取、统计和时间序列分析,进一步进行GIS空间分布图绘制.结果表明:高温和酷暑天数的空间分布均呈现出南北高,中间低的态势. 南部福建省、江西省、浙江省年均高温天数大于20 d,属于高度危险区;北部山东省、江苏省年均高温天数大于15 d,属于中度危险区;中部的安徽省、上海市年均高温天数在10 d左右,属于低度危险区.研究结果可为区域高温灾害风险管理和减灾提供参考依据.

DOI

[ Zhang S J, Yin Z E, Liu Y L, et al.The characteristics of high temperature disasters in east China in the past nearly 60 years[J]. Journal of Shanghai Normal University( Natural Sciences), 2011,40(1):95-101. ]

[20]
黄慧琳,缪启龙,潘文卓,等.杭州市高温致灾因子危险性风险区划[J].气象与减灾研究,2012,35(2):51-56.利用杭州市各气象观测站和自动 气象站多年气温资料,以及数理统计方法和Arc GIS空间分析技术,采用极端高温与高温日数作为高温致灾因子风险的主要评价指标,分析杭州市高温天气的气候特征,对杭州市高温灾害致灾因子危险性进行了 区划。结果表明,杭州市高温天气灾害较为严重,常出现38.0℃以上的高温,40.0℃以上也不少见,最高达到42.9℃;高温日数较长,最长持续时间超 过50 d。杭州市高温灾害危险性风险高值区大部分集中在杭州城区所在的东北部平原,包括上城区、下城区、拱墅区、江干区、滨江区、西湖区西南部、萧山区及余杭区 大部。另外,富春江、分水江等沿江两岸,西南部的河谷、盆地以及中部地势较复杂的谷地、盆地等低海拔地区也处于高温危险性中高值区域;北部天目山、西北部 昱岭及白际山、西南部千里岗山及龙门山脉高海拔地区均属于危险性风险低值区域。

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[ Huang H L, Liao Q L, Pan W Z, et al.Risk zoning of high-temperature disaster-inducing factors in Hangzhou[J]. Meteorology and Disaster Reduction Research, 2012,35(2):51-56. ]

[21]
贺山峰,戴尔阜,葛全胜,等.中国高温致灾危险性时空格局预估[J].自然灾害学报,2010,19(2):91-97.应用PRECIS模式模拟的气候情景数据,选取高温日数和热浪日数两个指标,对IPCC SRESB2情景下未来我国高温致灾危险性时空格局进行了预估。结果表明:在近期(2011-2040)、中期(2041-2070)和远期(2071-2100),全国年均高温日数从基准时段(1961-1990)的10.2d将分别增加到17.3d,22.6d和28.4d,年均热浪日数从基准时段的11.5d分别增加到22.6d,30.6d和39.0d;除了青藏高原,全国大部分地区的高温致灾危险性等级均有不同程度的提高,其中高温致灾危险性等级高于4级(包括4级)的地区在基准时段仅占全国总面积的3.8%,在近期、中期和远期将分别扩展到全国总面积的29.9%,51.3%和63.0%。

[ He S F, Dai E F, Ge Q S, et al.Pre-estimation of spatio-temporal pattern of extreme heat hazard in China[J]. Journal of Natural Disasters, 2010,19(2):91-97. ]

[22]
张校玮. 我国极端气候时空特征及风险分析-以高温为例[D].上海:上海师范大学,2012.

[ Zhang X W.Spatio-temporal pattern characteristics and risk analsis of extreme climate in China-a case of high temperature[D]. Shanghai: Shanghai Normal University, 2012. ]

[23]
Loughnan M, Nicholls N, Tapper N J.Mapping heat health risks in urban areas[J]. International Journal of Population Research, 2012:1-12.Periods of extreme heat pose a risk to the health of individuals, especially the elderly, the very young, and the chronically ill. Risk factors include housing characteristics, and socioeconomic factors, or environmental risk factors such as urban heat islands. This study developed an index of population vulnerability in an urban setting using known environmental, demographic, and health-related risk factors for heat stress. The spatial variations in risk factors were correlated with spatial variation in heat-related health outcomes in urban Melbourne. The index was weighted using measured health outcomes during heatwave periods. The index was then mapped to produce a spatial representation of risk. The key risk factors were identified as areas with aged care facilities, higher proportions of older people living alone, living in suburban rather than inner city areas, and areas with larger proportions of people who spoke a language other than English at home. The maps of spatial vulnerability provide information to target heat-related health risks by aiding policy advisors, urban planners, healthcare professionals, and ancillary services to develop heatwave preparedness plans at a local scale. 1. Introduction Climate change projections for south eastern Australia include an increase in the number of warm nights, and heatwave duration, both of which are significant for human health, potentially the impacts of climate change on the health of Australia鈥檚 population is of growing concern [1]. Recent extreme heatwaves have caused serious health, economic and social problems in Europe, USA, and southeast Australia, particularly in urban areas. Such events will continue to pose additional challenges to health risk management, emergency response systems, and to the reliability of the power supplies and other infrastructure [2]. Important lessons can be learned from many of the public health outcomes experienced during the recent American and European heatwaves, and the actions that followed. Specifically adverse health effects resulting from hot weather and heatwaves are largely preventable under current climate conditions, if heat-health preparedness plans can be implemented [3, 4]. Heatwaves in the USA over the last decade and the European heatwave in 2003 (when over 45,000 people died [5, 6]) have indicated that there are commonalities in population vulnerability during heat events. The greatest risks appear to be for urban populations, the very young, the elderly, persons with chronic disease or disability, and persons living in a built environment that

DOI

[24]
厦门市统计信息网.2014年厦门经济特区年鉴 [EB/OL]. [2015-03-20]. .

[ Statistic Information of Xiamen. Environment quality bulletin of Xiamen[EB/OL]. ]

[25]
Zhao X, Hunag J, Ye Hong, et al.Spatiotemporal changes of the urban heat island of a coastal city in the context of urbanization[J]. International Journal of Sustainable Development & World Ecology, 2010,17(4):311-316.This study quantitatively analysed the spatiotemporal changes of the urban heat island (UHI) of Xiamen City in the context of urbanisation, using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) thermal images acquired on similar dates in the winter of 1987, 1992, 1997, 2002 and 2007. UHI intensity and extent were used to quantify the changes, together with landscape metrics PLAND, PD, CA, NP, P-UHI, NP-UHI, PD-UHI, etc. The results show that the winter UHI of Xiamen has become more and more striking in the past 20 years in almost all the indices used. The UHI intensity increased to over 10掳C, and UHI extent and thermal patch number also increased remarkably. At the same time, UHI was more dominated and fragmented by high-grade thermal patches. In winter these UHI formed several hot spots and areas of significance, distributed along the coastline. This pattern was related to industrial zones and large infrastructure constructed in coastal areas during the rapid course of urbanisation, since both large impervious ground surfaces, large-sized and endothermic factory building roofs were the sources of these hot spots. A similar seasonal analysis was also carried out, which proved that autumn UHI was most intense in Xiamen and the change in season does not change the number of UHI areas of significance.

DOI

[26]
黄聚聪,赵小锋,唐立娜,等.城市化进程中城市热岛景观格局演变的时空特征—以厦门市为例[J].生态学报,2012,32(2):622-631.热岛效应作为城市化过程中产生的特有环境问题,对其形成和演变规律的研究有助于人们提出有效的应对措施。以厦门市为研究对象,利用1987-2007年等时间间隔、同时相的5景Landsat TM/ETM+遥感影像数据进行地表温度反演,在此基础上使用景观格局指数分析厦门城市热岛景观格局随城市化进程演变的趋势。结果表明:随着厦门城市化进程加深,整个热岛景观在逐渐变得更加破碎化,高等级热岛景观斑块个数、类型面积和个体面积都增大;新的高等级热岛景观斑块都出现在原有高等级斑块附近,致使高等级类型的邻近度增加而各类型之间相互接触的程度也增加;景观总体的聚合度逐渐下降,而高等级热岛景观类型的聚合度则呈上升趋势;景观水平的蔓延度总体呈下降趋势,优势度高的低等级热岛景观所占的比重下降,优势度逐渐降低;多样性指数、均匀度指数总体呈上升趋势,各热岛景观面积在各类型间的分配逐渐趋于均匀;热岛景观斑块的转化方面,在20 a间低等级斑块类型(1、2、3级)向高等级斑块类型(4、5、6级)转化的面积总体上呈增加趋势,而高等级斑块类型向低等级斑块类型转化的面积总体上呈减小趋势,且等级升高的面积明显大于同期等级降低的面积;就高等级热岛景观斑块而言,他们与3级热岛景观斑块间的相互转化最容易发生,远比高等级斑块内部各类型之间的相互转化来得容易,尤其6类和5类的转化是最为困难的热岛景观变化之一;从空间上看,各高等级热岛景观斑块都经历了数量增加、面积扩大、等级升高三个方面的变化,形成了海沧、新阳、杏林、厦门岛西北港口区和机场5个高温组团。利用景观指数分析城市热环境,可探明热岛景观随城市化演变的趋势,并为有效的热岛效应减缓措施提供直接的理论依据。

DOI

[ Huang J C, Zhao X F, Tang L N, et al.Analysis on spatiotemporal changes of urban thermal landscape pattern in the context of urbanisation: a case study of Xiamen[J]. Acta Ecologica Sinica, 2012,32(2):622-631. ]

[27]
中国气象科学数据共享服务网.中国地面气候资料日值数据集(V3.0) [EB/OL]. [2015-03-20]. .

[ China Meteorological Data Sharing Service System. China ground climate daily data set (V3.0)[EB/OL]. ]

[28]
张杨,江平,陈奕云,等.基于Landsat TM影像的武汉市热岛效应研究[J].生态环境学报, 2012,21(5):884-889.采用定量遥感与GIS相结合的方法,利用武汉市2006年TM遥 感影像,定量分析武汉市植被指数和热岛效应的关系和空间分布.在空间剖面上,研究了植被指数(NDVI)与地表辐射温度的关系并对植被指数(NDVI)与 地表辐射温度进行了回归拟合.在此基础上,研究了武汉市热岛空间分布,解释了武汉市热岛等级空间分布的特点.研究结果表明:植被指数(NDVI)与地表辐 射温度存在着明显的负相关性；随着植被覆盖的变化,城市建设用地的地表辐射温度明显比其他土地类型敏感；城市中工业区和人口活动密集的商业区热岛强度高； 水体和绿地对于分割城市热岛和缓解热岛效应有显著作用.该研究可为城市生态规划、用地合理布局等提供参考.

DOI

[ Zhang Y, Jiang P, Chen Y Y, et al.Study on heat island effect in Wuhan city based on landsat TM remote sensing[J]. Ecology and Environmental Sciences, 2012,21(5):884-889. ]

[29]
徐永明,覃志豪,朱焱.基于遥感数据的苏州市热岛效应时空变化特征分析[J].地理科学, 2009(4):529-534.城市化对于城市环境的一个重要影响是热岛效应。为了研究苏州市20多年来城市化进程对热岛效应的影响,采用遥感与GIS相结合的方法来定量的分析城市热环境的时空变化。根据1986和2004年两个时相的Landsat TM影像提取了研究区的土地覆盖以及亮温信息,在此基础上通过统计分析和GIS的缓冲区分析对这18 a来苏州地区的热岛时空变化及其与土地覆盖变化的关系进行了深入研究。分析结果表明,城市热岛与城区的空间分布之间存在明显的一致性。

DOI

[ Xu Y M, Qin Z H, Zhu Y.Spatial and temporal analysis of urban heat island in Suzhou city by remote sensing[J]. Scientia Geographica Sinica, 2009,4:529-534. ]

[30]
高文兰,李新通,石锋,等.环境一号B星热红外波段单通道算法温度反演.中国科学:信息科学,2011,41(增刊):89-98.文中在考虑环境一号B星(HJ-1B)热红外波段(infrared scanner,IRS4)光谱响应函数和有效波长的基础上,通过MODTRAN4模型模拟,对Jimenez-Munoz和Sobrino(JMS)单通道算法中的大气函数进行改进,重新计算得到了适合HJ-1B星IRS4地表温度(land surface temperature,LST)反演的3个大气函数公式,并反演了福州地区的地表温度.采用基于星上辐亮度法对反演的地表温度进行精度评价,并将反演的地表温度与JMS算法、段四波等修正的JMS算法反演的地表温度进行对比分析.结果表明:使用文中改进后的大气参数对HJ-1B星IRS4进行地表温度反演,可取得较好结果.

[ Gao W L, Li X T, Shi F, et al.Improving single-channel methods for land surface temperature retrieval from HJ-1B IRS4[J]. Scientia Sinica (Informationis), 2011,41(S1):89-98. ]

[31]
段四波,阎广建,钱永刚,等.利用HJ-1B模拟数据反演地表温度的两种单通道算法[J].自然科学进展,2008(9):1001-1008.现有的两种单通道算法(覃志豪单通道算法和Jimēnez-Mu(n)oz&Sobrino单通道算法)都是针对Landsat TM提出的,中国即将发射的HJ-1B卫星传感器也仅有一个热红外波段,要想应用这两种算法对HJ-1B数据进行地表温度的反演,需要根据HJ-1B热红外波段的通道响应函数来重新得到算法中的经验关系.文中针对HJ-1B卫星传感器对这两种算法进行了修订,通过大气辐射传输软件MODTRAN4模拟数据对算法进行了精度评价和参数的敏感性分析.并在此基础上考虑HJ-1B卫星传感器的噪声等效温差(NE△T)和各种参数的估计误差对算法进行了综合误差分析,发现在NE△T≤0.3 K的情况下,覃志豪单通道算法平均综合误差为1.14 K,而Jimēnez Mufiozoz&Sobrino单通道算法平均综合误差为0.94K.基于模拟的HJ-1B热红外波段数据,采用修订后的算法进行了地表温度的反演实验,通过对反演结果的分析,发现覃志豪单通道算法反演的地表温度比模拟的地表温度低1.2 K左右,而Jim(e)nez-Mu(n)ozoz&Sobririo单通道算法比模拟的地表温度低0.8 K左右.从算法验证和应用的结果来看,修订的这两种算法可以方便地应用到对HJ-1B热红外波段数据的地表温度反演.

[ Duan S B, Yan G J, Qian Y L, et al.Two single-channelmethods for retrieving land surface temperature by simulating data of HJ-1B[J]. Progress in Natural Science, 2008,9:1001-1008. ]

[32]
Hajat S, Kovats R S, Lachowycz K.Heat-related and cold-related deaths in England and Wales: who is at risk?[J]. Occupational and Environmental Medicine, 2007,64(2):93-100.

PMID

[33]
El-Zein A, Tonmoy F N.Assessment of vulnerability to climate change using a multi-criteria outranking approach with application to heat stress in Sydney[J]. Ecological Indicators, 2015,48:207-217.

[34]
维基. 厦门市[EB/OL]. [2015-03-20]. .

[ Wiki. Xiamen city[EB/OL]. , 2015-03-20. ]

[35]
Naughton M P, Henderson A, Mirabelli M C, et al.Heat-related mortality during a 1999 heat wave in Chicago[J]. American Journal of Preventive Medicine, 2002,22(4):221-227.

DOI

[36]
Stafoggia M, Forastiere F, Agostini D, et al.Factors affecting in-hospital heat-related mortality: a multi-city case-crossover analysis[J]. Journal of Epidemiology and Community Health, 2008,62(3):209-215.Background: Several studies have identified strong effects of high temperatures on mortality at population level; however, individual vulnerability factors associated with heat-related in-hospital mortality are largely unknown. The objective of the study was to evaluate heat-related in-hospital mortality using a multi-city case-crossover analysis. Methods: We studied residents of four Italian cities, aged 65+ years, who died during 1997-2004. For 94 944 individuals who died in hospital and were hospitalised two or more days before death, demographics, chronic conditions, primary diagnoses of last event and hospital wards were considered. A city-specific case-crossover analysis was performed to evaluate the association between apparent temperature and mortality. Pooled odds ratios (OR) of dying on a day with a temperature of 30鈩 compared to a day with a temperature of 20鈩 were estimated with a random-effects meta-analysis. Results: We estimated an overall OR of 1.32 (95% confidence interval: 1.25, 1.39). Age, marital status and hospital ward were important risk indicators. Patients in general medicine were at higher risk than those in high and intensive care units. A history of psychiatric disorders and cerebrovascular diseases gave a higher vulnerability. Mortality was greater among patients hospitalised for heart failure, stroke and chronic pulmonary diseases. Conclusions: In-hospital mortality is strongly associated with high temperatures. A comfortable temperature in hospitals and increased attention to vulnerable patients during heatwaves, especially in general medicine, are necessary preventive measures.

DOI PMID

[37]
Kim Y, Joh S.A vulnerability study of the low-income elderly in the context of high temperature and mortality in Seoul, Korea[J]. Science of the Total Environment, 2006,371(1):82-88.The relationship between high-temperature-induced excess mortality, income, and age suggests the need for a public health message, yet many results were not statistically significant: preventive and health care interventions need to be administered to the elderly and low-income group during periods of high temperature.

DOI PMID

[38]
Foroni M, Salvioli G, Rielli R, et al.A retrospective study on heatrelated mortality in an elderly population during the 2003 heat wave in Modena, Italy: the Argento Project[J]. The Journal of Gerontology, 2007,62:647-651.Abstract BACKGROUND: Summer 2003 witnessed an excess in heat-related mortality in the elderly population. The Argento Project was planned to define risk factors for heat-related death in Modena, Italy, during the hottest month of 2003 (August). METHODS: We performed a retrospective, case-control study of a cohort of 394 older persons living in Modena, 197 dead (cases) and 197 survivors (controls). A questionnaire to collect information about demographic, social, environmental, and clinical characteristics and about causes of death was completed. RESULTS: Cases were more likely to be living in a nursing home and needing assistance (p =.024, and p <.001, respectively). Survivors were living on higher level floors (p =.046). Spending the summer in Modena was significantly related to poor outcomes (p <.01). A higher number of cases were using public health services (p <.001). Individuals who died had a greater degree of comorbidity and dependence (p <.001); they were cognitively impaired (p <.001), took a larger number of drugs (p <.001), and had a greater number of hospital admissions (p <.001). Multivariate analysis showed that patients who spent the summer in Modena had a higher mortality. Other predictors of death were the use of home public-integrated assistance, a higher comorbidity and a higher degree of disability; the loss of at least 1 Activity of Daily Living (ADL) represents the strongest risk factor of heat-related death. CONCLUSIONS: Our study identifies the major risk factors of heat-related death in the elderly population. With the creation of an up-to-date database, when a new heat wave will come, it will be possible to identify frail persons for preventive targeted strategies.

DOI PMID

[39]
Kilbourne E M, Choi K, Jones T S, et al.Risk factors for heatstroke: a case-control study[J]. The Journal of the American Medical Association, 1982,247(24):3332-3336.To identify risk factors associated with heatstroke, a case-control study in St Louis and Kansas City, Mo, was conducted during July and August 1980. Questionnaire data were gathered for 156 persons with heatstroke (severe heat illness with documented hyperthermia) and 462 control subjects matched by age, sex, and neighborhood of residence. A stepwise linear logistic regression procedure was used to identify factors significantly associated with heatstroke. Alcoholism, living on the higher floors of multistory buildings, and using major tranquilizers (phenothiazines, butyrophenones, or thioxanthenes) were factors associated with increased risk. Factors associated with decreased risk were using home air conditioning, spending more time in air-conditioned places, and living in a residence well shaded by trees and shrubs. Being able to care for oneself, characteristically undertaking vigorous physical activity, but reducing such activity during the heat, and taking extra liquid were also associated with decreased risk. Our findings also suggest effective preventive measures. During a heat wave, the greatest attention should be directed toward high-risk groups, and relief efforts should include measures shown to be associated with reduced risk.

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