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Journal of Geo-information Science >

2017 , Vol. 19 >Issue 11: 1475 - 1484

DOI: https://doi.org/10.3724/SP.J.1047.2017.01475

Spatially Explicit Assessment of Heat Health Risks Using Multi-source Data: A Case Study of the Yangtze River Delta Region, China

  • CHEN Qian , 1 ,
  • DING Mingjun , 1, * ,
  • YANG Xuchao 2 ,
  • HU Kejia 2
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  • 1. School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
  • 2. Ocean College, Zhejiang University, Zhoushan, 316021, China
*Corresponding author: DING Mingjun, E-mail:

Received date: 2017-07-02

  Request revised date: 2017-09-05

  Online published: 2017-11-10

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Abstract

The increase in the frequency and intensity of extreme heat events (EHEs), potentially associated with climate change in the near future, highlights the importance of heat health risks assessment, which is an important starting point for the reduction of heat-related mortality and sustainable development. However, there is a spatiotemporal mismatch between hazard data (at pixel-level) and exposure data (at census unit level) in heat health risks assessment. Based on multisensor remote sensing data and demographic and socioeconomic statistical data, we used a human settlement index to assess heat exposure. Heat health risks and its driving factors were spatially explicitly assessed and mapped at the 250 m × 250 m pixel-level across the Yangtze River Delta region (YRD). The visual inspection suggests that the high risks areas were mainly distributed in urbanized areas of YRD, including the downtown of Shanghai, Changzhou, Hangzhou, Ningbo, Wuxi, Jiaxing and Taizhou, which was mostly driven by the high human exposure and heat hazard index. In less-urbanized cities and the suburban and rural areas of mega-cities, the heat health risks come second. Even though the human exposure index was low in other less-developed areas, the heat health risks in those areas were high due to the high heat hazard and human vulnerability index. It's of great importance to identify the driving factors of high heat health risks to provide science-based support for adaptation strategies and emergency planning.

Key words： heat waves; public health; heat health risk; risk assessment; the Yangtze River Delta region

Cite this article

CHEN Qian , DING Mingjun , YANG Xuchao , HU Kejia . Spatially Explicit Assessment of Heat Health Risks Using Multi-source Data: A Case Study of the Yangtze River Delta Region, China[J]. Journal of Geo-information Science, 2017 , 19(11) : 1475 -1484 . DOI: 10.3724/SP.J.1047.2017.01475

1 引言

作为21世纪的全球性环境现象之一,以全球变暖为主要特征的气候变化已成为人类健康面临的最大挑战[1-2]。高温热浪作为全球最为严重的气象灾害之一,在气候变化背景下呈现频率增加、强度增大、持续时间增长的趋势[3]。高温天气不仅是人体心脑血管、呼吸系统等疾病的重要发病诱因之一[4],在极端情况下还可能会直接导致人体死亡[5-7]。在气候变暖导致的极端事件频发背景下,气象灾害尤其是极端温度对公共健康的威胁逐渐凸显[8],气候变化适应与灾害风险管理的关系日益密切[9]。作为高温灾害风险管理的重要组成部分,在时空尺度上进行人群健康风险评估、识别高危人群,从而为区域防灾减灾决策提供针对性信息,是未来应对和适应气候变化背景下高温热浪频发趋势的重要方向之一[10]。
相对而言,发达国家较早开始探讨高温对人群健康的影响,由早期侧重高温致灾危险性的病理研究[11],逐渐发展到综合考虑人口脆弱性等因素的高温风险评估[12-13]。就评估框架而言,目前较有代表性的为IPCC提出的基于“灾害危险性—社会脆弱性—暴露”的灾害风险评估体系[14],主要通过对各风险因子进行空间叠置来获取高温人群健康风险空间格局。从高温危险性分析来看,除基于气象数据获取的点状高温热浪信息之外[15-16],遥感反演的地表温度数据[17-19]也被用于衡量高温对人体健康的胁迫程度。目前不断有研究表明在高温期间城市居民发病率和死亡率显著增加[20-22],城市居民受到更为严重和持续的热胁迫[23-24],这与城市热岛效应的存在,尤其与其在高温期间大幅提升夜间气温密不可分[25],而以往的高温风险研究中对城市热岛效应的考虑还较少[26-27]。人口暴露度则通常由基于行政单元的统计人口密度来表征[17,28],然而统计数据不仅在数据获取上存在滞后性,且难以反映承灾体的空间分布特征,与其他高温风险评价因子存在空间尺度上的不匹配。随着遥感、GIS等空间信息技术的发展,融合多源遥感数据和统计数据是当前高温灾害风险评估的趋势[29]。然而在发展中国家尤其是中国,对于高温灾害风险的评估起步相对较晚[16,17,28,30],已有的研究多关注高温灾害的频次、强度和持续时间等高温危险信息,融合社会经济脆弱性和人口暴露的风险评价还相对较少。
基于此,本文在构建高温热浪人群健康风险评估框架的基础上,以长江三角洲地区为例,融合遥感植被指数、夜间灯光数据和高程数据对人口暴露进行空间化表征,结合分别由遥感地表温度数据和人口、社会经济统计数据获取的高温危险性和社会经济脆弱性指数,在栅格水平上评估了高温热浪人群健康风险并探究了风险的主导因子,明确其作用机制的空间差异,以期为区域防灾减灾决策、高温风险科学预警和防范及实施适应性措施提供参考。

2 研究区概况

长江三角洲地区位于中国大陆东部、长江入海口的沿海冲积平原（图1）,包括上海、杭州、宁波、嘉兴、绍兴、舟山、湖州、台州、南京、苏州、扬州、常州、南通、无锡、镇江和泰州市共16个沿海经济发达城市,经纬度范围为116.78°E~124.21°E,26.99°N~34.64°N,行政总面积112 642 km2。该区位于亚热带季风气候区,四季分明,年平均气温为18~23 ℃,最热月均温大于28 ℃,年平均降水量在1500 mm左右。其中夏季由于受西太平洋副热带高压的影响,高温天气现象十分普遍,是中国高温热浪袭击的重灾 区[31-32]。在7、8月常出现20~30天的高温天气,最高可达40天,如2013年夏季研究区高温区内的各气温指标均突破了历史记录,创近50年新高[33]。此外,作为中国目前经济发展速度最快、人口最为密集、城市化水平最高的地区之一,在过去几十年的人口聚集、城市扩张过程中,该区城市热岛效应显著增强[34],城市热环境状况及人群健康面临的风险持续增加[35-36]。
Fig. 1 Location, elevation and land cover of the study area

图1 研究区概况

3 数据源与研究方法

3.1 数据来源及预处理

（1）地表温度数据（Land Surface Temperature,LST）来源于NASA发布的MODIS每日地表温度数据集MOD11A1和MYD11A1（http://ladsweb.nascom.nasa.gov）,空间分辨率为1 km。选取研究区2013年8月7日MOD11A1白天和MYD11A1夜间地表温度数据为代表（成像时间分别为地方时10:30 am和1:30 am左右）,采用MODIS重投影工具MRT对原始影像进行镶嵌、投影和重采样处理。
（2）增强型植被指数数据（Enhanced Vegetation Index,EVI）来源于NASA发布的2013年16 d合成MOD13Q1数据集（http://ladsweb.nascom.nasa.gov）,空间分辨率为250 m。为进一步去除云污染等因素的影响,根据式（1）对多时相MODIS EVI进行年内最大值合成：
EV I max = MAX ( EV I 1 , EV I 2 , … , EV I 23 ) （1）
（3）DMSP/OLS夜间灯光数据来源于美国国家地球物理中心（NGDC）发布的2012年稳定夜间灯光数据集（http://ngdc.noaa.gov/eog/download.html）,影像的灰度值（DN值）范围为0-63,由原始数据（空间分辨率为1 km）重采样至250 m的栅格影像。
（4）DEM高程数据来源于ASTER GDEM全球数字高程模型,空间分辨率为30 m,经过投影和重采样,获取研究区250 m分辨率的栅格影像。
（5）研究区县域尺度的人口和社会经济统计数据来源于2010年中国第六次人口普查数据和2013年浙江省、江苏省和上海市统计年鉴以及各地方的统计年鉴数据。其中人口统计数据包括居民常住人口、各年龄段人口比例和60岁及以上独居人口比例,社会经济数据包括人均GDP、15岁及以上人口文盲、半文盲率、居民住房有无洗澡设施比例、每百户居民空调拥有量以及卫生机构床位数。

3.2 高温热浪人群健康风险评估方法

3.2.1 评价体系构建
自然灾害的特征及其影响程度不仅取决于灾害本身,还取决于承灾体的暴露程度及其社会经济脆弱性,是以上3个因子共同作用的结果。因此本文以2013年夏季的一次高温热浪事件为例,构建了基于“高温危险性-人口暴露度-社会经济脆弱性”的长江三角洲地区高温热浪人群健康风险评估框架。
其中,高温危险性指数由高温热浪期间的白天和夜间地表温度数据进行空间化表征;对于人口暴露度指数,为与其他风险评价因子在空间尺度上相匹配,本文融合夜间灯光、植被指数和高程数据计算能够表征人口空间分布的人居指数,从而获取栅格水平的人口暴露度指数;而社会经济脆弱性指数则通过选取和等权重叠加影响人群社会经济脆弱性的年龄、居住隔离和社会经济状况3个综合指标而成。由于在目前的高温风险评估中,各评价指标权重的确定上还尚未有统一的定论[12,37],同时考虑到根据不同决策方法或决策者不同层面的需求,均可较为快速地对进行调整[18],本文最终将研究区标准化的高温危险性、人口暴露度和社会经济脆弱性指数按等权重相乘,获取长江三角洲地区高温热浪人群健康风险空间格局。
3.2.2 高温危险性评价
高温危险性指的是人群居住或活动的空间内发生高温事件的可能性,表征了人群所处环境与高温的接近程度[38]。虽然地表温度不能够直接度量环境感知温度,但已有许多研究表明两者之间存在较强的相关性,尤其是在夜间[39]。因此,考虑到研究区遥感影像的可获得性、影像质量和云覆盖等情况,以发生在2013年8月5日至15日之间的一次典型极端高温热浪事件期间为例,选取研究区8月7日白天和夜间MODIS LST影像进行高温危险性分析,影像个别缺失像元由周边3×3像元均值代替。随后,将2幅影像分别标准化至[0,1],利用ArcGIS栅格计算工具将影像进行等权累加,最终再标准化至[0,1]从而获取研究区的高温危险性指数。
3.2.3 人口暴露度评价
基于DMSP/OLS夜间灯光数据进行人口空间化是当前获取人口空间分布信息的重要方法之一。然而,受空间分辨率及像元饱和或溢出等现象的影响[40-41],该方法的应用受到一定限制。本文根据Yang等[42]提出的方法,通过融合DMSP/OLS夜间灯光、增强型植被指数以及DEM高程数据,获取了250 m分辨率的经过海拔校正的人居指数（EAHSI）,以此来表征研究区高温热浪人口暴露程度。具体方法如下：
首先,根据式（2）计算长三角地区的EAHSI：
EAHSI = （ 1 - EV I max ） + OL S nor 1 - OL S nor + EV I max + OL S nor × EV I max × e - 0.003 DEM （2）
式中：EVImax为2013年MODIS 16 d最大值合成EVI数据;DEM为高程数据;OLSnor是经过标准化的2013年夜间灯光数据。然后,将EAHSI标准化至[0, 1],并利用自然断点法划分为5个等级。
3.2.4 人口社会经济脆弱性评价
社会经济脆弱性评价需要根据不同灾害类型的特点选取具有代表性的承灾体脆弱性指标。就高温热浪而言,婴幼儿和老人由于其自身的生理能力及对疾病和高温的抵抗、免疫能力相对较低,被认为是高温敏感人群[43-44],而独居老人由于在居住空间上处于相对隔离的状态,在高温灾害来临时难以得到快速有效的帮助与救护[11],脆弱性水平也较高。与此同时,较高的个体或区域社会经济水平对高温人口脆弱性起到一定的削减作用,如空调等降温设备的可得性被认为是缓解高温不利影响的重要因子[6,11],人口的受教育水平影响着个体对高温灾害的认知能力和灾害规避行为[12],区域尺度的经济水平及医疗资源、设施也自上而下地约束着人群抵抗和适应高温的平均能力[45]。
因此,根据高温灾害的特征,考虑数据的可得性及统计口径一致性等方面,选取年龄（包括5岁及以下人口和65岁及以上人口比例）、居住隔离（60岁及以上独居人口比例）及社会经济状况（包括文盲占15岁及以上人口比例、有洗澡设施住房比例、每百户居民空调拥有量、卫生机构床位数和人均GDP）3个综合指标对长江三角洲地区人口社会经济脆弱性进行综合评价。首先,对以上数据进行标准化并与行政边界矢量数据进行空间链接,年龄、居住隔离和社会经济状况指标由其包含的变量累加并标准化至[0,1],人口的社会经济脆弱性指数由以上3个综合指标等权累加并标准化而成,最终利用自然断点法将其划分为5个等级。

4 结果分析

4.1 长江三角洲地区高温危险性分析

根据2013年8月7日长江三角洲地区地表温度空间分布图可知,白天（10:30 am左右）研究区LST分布空间差异较大（图2（a））,主要高温区沿常州-无锡-苏州-上海-杭州-绍兴-宁波的城市建成区呈“Z”字形分布,LST在40 ℃以上,浙江的杭州和宁波市区的高温强度尤为突出（≥45 ℃）,最高可达48 ℃;次高温区主要分布在郊区,林地和水体（如太湖）是相对低温的主要分布区（≤30 ℃）。夜间（1:30 am左右）LST较白天有所降低、空间差异也缩小（图2（b））,然而相对高温面积相比白天有所扩大,宁绍平原及太湖平原是高温的主要分布区,城市建成区温度显著高于周边像元,普遍在30 ℃以上,城市热岛效应明显;与白天相比,夜间水体成为次高温像元,低温区主要分布在植被覆盖高的南部林地。
Fig. 2 Land surface temperature (LST) of daytime and nighttime in the Yangtze River Delta region

图2 长江三角洲地区2013年8月7日白天和夜间地表温度

图3为融合白天和夜间LST而成的研究区高温危险性等级空间分布图。受城市热岛效应的影响,大部分市县的中心城区均处于高温的胁迫下。高温危险性“高”和“较高”等级区主要集中于研究区中部平原地区,其中“高”等级主要分布于各地级市市辖区的中心城区,“较高”等级区以“高”等级区为中心向外围圈层式发散;高温危险性程度“低”和“较低”的区域主要位于长江以北平原及西南部山地、丘陵区。
Fig. 3 Map of the heat hazard index of the Yangtze River Delta region

图3 长江三角洲地区高温危险性指数空间分布图

4.2 长江三角洲地区人口暴露度分析

基于各市县行政边界范围内人居指数累计值与统计人口数据的相关分析表明（图4）,融合了夜间灯光、植被指数和高程数据的人居指数与统计人口数据之间存在较强的线性相关（R2=0.8725）,表明人居指数能够较好地反映长江三角洲地区人口暴露度的空间分布特征。
Fig. 4 Scatterplots of the accumulated EAHSI value and population for counties in the Yangtze River Delta region

图4 长江三角洲地区各市县人居指数累计值与统计人口数据相关性分析

从研究区人口暴露度指数空间分布来看（图5）,人口暴露度等级“较高”和“高”的区域主要分布于南京、常州、苏州、无锡、上海、杭州、宁波市等市辖区的中心城区范围内。“中等”等级区以“高”和“较高”等级区为中心向外围发散,各个城市规模相对较小的市县是人口暴露度“中等”级别的主要分布区;“低”和“较低”的区域主要位于研究区北部及西南部两端的耕地、林地、水体及其周边地区。
Fig. 5 Map of the heat exposure index in the Yangtze River Delta region

图5 长江三角洲地区人口暴露度指数空间分布图

4.3 长江三角洲地区人口社会经济脆弱性分析

根据研究区各社会经济脆弱性因子的统计信息可知（表1）,研究区各市县社会经济状况差异较大,其中“人均GDP”的差异最为突出（26 772~189 352元）,“卫生机构床位数”次之（326~61 703个）,标准差分别为33 972（元）和14 187（个）,均大于其他因子。
由研究区的人口社会经济脆弱性指数分布图可知（图6）,长江三角洲地区人口脆弱性“高”和“较高”等级区主要分布在研究区北部的宝应县和兴化市、南部的山区及江苏的东南沿海县市,其中浙江台州市是人口脆弱性指数高值较为集中的地级市,该市的仙居县、天台县和三门县人口脆弱性指数均较高,以上地区人口脆弱性指数较高主要是由于社会经济水平和受教育水平较低、婴幼儿比例大所致。此外,一些经济水平比较发达的地区也存在脆弱性指数较高的现象,如上海和嘉兴市的郊区,这主要是由于这些地区老龄人口尤其是独居老人比例较高。人口脆弱性指数的低值区则主要集中在江苏苏州、无锡、南京、扬州、杭州、宁波以及上海的中心城区,这些区域整体经济发展水平较高、基础设施完善、高温敏感人群（如婴幼儿、老人、独居老人）比例相对较低。
Tab. 1 Descriptive statistics of the vulnerability variables

表1 社会经济脆弱性因子统计性描述

综合指标 变量 平均值 标准差 最小值 最大值 性质
年龄 ≤5岁人口比例 0.0383 0.0077 0.0184 0.0634 +
≥65岁人口比例 0.1088 0.0285 0.0413 0.1900 +
居住隔离 ≥60岁独居人口比例 0.0350 0.0359 0.0074 0.2241 +
社会经济状况 文盲占15岁及以上人口比例 0.0451 0.0241 0.0121 0.1180 +
有洗澡设施住房比例 0.7610 0.1279 0.4022 0.9394 -
卫生机构床位数/个 9408.08 14 187.12 326 61703 -
每百户居民空调拥有量/台 159.20 27.13 82 214.30 -
人均GDP/元 81 487.39 33 971.60 26 771.92 189 351.91 -

注：性质字段为各变量对人口社会经济脆弱性指数的作用,+表示正向作用,-表示负向作用

Fig. 6 Map of the heat vulnerability index in the Yangtze River Delta region

图6 长江三角洲地区高温热浪人口社会经济脆弱性等级分布

4.4 长江三角洲地区高温热浪人群健康风险空间分布

根据“高温危险性-人口暴露-社会经济脆弱性”的高温热浪风险评估体系,将各因子进行空间叠置得到研究区高温热浪人群健康风险空间分布（图7）。由图7可知,研究区的风险高值区主要分布于上海市中心城区及近郊、江苏常州、泰州及浙江无锡和嘉兴、宁波慈溪和余姚的中心城区。表2列出了研究区高温风险指数最高的20个行政单元（约占总数的20%）,上海市的中心城区、宝山区及闵行区的高温风险指数位列所有行政单元的前三位。就省域而言,上海市和江苏省的高温风险等级较高,风险指数排名靠前的20个行政单元中上海和浙江分别占据9个和6个。
值得注意的是,在研究区北部市县（如兴化市、宝应县）、江苏东部沿海（如海安县）以及研究区西南各市县的乡村地区,虽然高温危险性指数较低、人口密度小、高温人口暴露相对低于大城市,但由于经济发展水平及自然条件的制约,再加上高温敏感人群（如受教育程度低人群和婴幼儿）聚集,高温人群健康风险等级也较高。
Fig. 7 Map of the heat health risk index in the Yangtze River Delta region

图7 长江三角洲地区高温热浪人群健康风险区划

Tab. 2 The top 20 counties with heat health risks in the Yangtze River Delta region

表2 研究区高温热浪人群健康风险最高的20个行政单元

排名 统计单元 所处省市 排名 统计单元 所处省市
1 上海中心城区 上海市 11 常州市市辖区 江苏常州
2 宝山区 上海市 12 镇江市市辖区 江苏镇江
3 闵行区 上海市 13 桐乡市 浙江台州
4 嘉定区 上海市 14 杭州市市辖区 浙江杭州
5 泰州市市辖区 江苏泰州 15 嘉兴市市辖区 浙江嘉兴
6 浦东新区 上海市 16 江阴市 江苏无锡
7 奉贤区 上海市 17 金山区 上海市
8 青浦区 上海市 18 泰兴市 江苏泰州
9 松江区 上海市 19 海宁市 浙江嘉兴
10 慈溪市 浙江宁波 20 无锡市市辖区 江苏无锡

4.5 长江三角洲地区高温风险主导因子分区

为在空间上认识各高温风险因子之间的内在作用机制,将研究区的高温危险性、人口暴露及社会经济脆弱性指数按照自然断点法分为“高”和“低”两个等级,根据各指数之间的组合关系将长三角地区“中等”及以上高温热浪风险区域划分为7个主导因子分区（如将高温危险性等级高而人口暴露度和脆弱性等级低的地区划分为高温危险性因子主导区,将高温危险性等级低而人口暴露度和脆弱性等级高的地区划分为人口暴露度与脆弱性主导区）,其中将三者等级都较高的地区定为综合主导区。
Fig. 8 Driving factors of heat health risks in the Yangtze River Delta region

图8 研究区高温热浪人群健康风险主导因子

由分析结果可知（图8）,研究区受单一因子主导的区域较少,约占“中等”及以上高温风险区域的12.73%;受到高温危险性和人口暴露度因子协同作用的地区主要位于各地级市的城区范围内,约占61.01%;而在大城市的近郊及城市规模相对较小的市、县城区范围内则是各个风险因子的综合主导区,各评价指标的等级均较高;研究区南北两端的经济相对欠发达市县以及海岛县,则主要受到高温危险性和社会经济脆弱性因子的双重影响。
根据研究区的高温风险主导因子分区,可以有针对性地制定防灾减灾策略,如在高温危险性和人口暴露度因子主导区以及综合主导区,尤其是大城市的中心城区,通过增加植被和绿地面积,能够在一定程度上缓解局地高温对人群健康的胁迫。同时及时传达高温预警信息,减少高温期间不必要的外出,能够减轻这些区域的人群健康风险。而对于社会经济脆弱性指数较高的地区,则需有效的防灾减灾宣传以及降温设备等的普及。

5 结论和讨论

本文基于“高温危险性-人口暴露-社会经济脆弱性”的高温热浪风险评估框架,以2013年的一次高温热浪事件为例,通过融合夜间灯光、植被指数及高程数据构建能够代表人口空间分布的人居指数来表征人口暴露度,利用遥感地表温度数据和相关社会经济数据分别计算高温危险性和人口社会经济脆弱性指数,获取了高分辨率的长江三角洲地区高温热浪人群健康风险格局和主导因子分区,获得的主要结论如下：
（1）人群健康风险等级较高的地区集中在上海、江苏常州、浙江杭州、无锡、嘉兴、宁波等大城市的中心城区,主要是较高的人口暴露度和城市高温共同作用的结果,体现了在快速城市化背景下,城市热岛效应加剧及人口暴露度加大导致的城市地区人群健康风险增加。而在上海、嘉兴等大城市的近郊以及城市规模相对较小的地区,则是各个风险因子的综合主导区。
（2）相对欠发达地区如研究区南部市县的人群健康风险也不容忽视,这些地区虽然高温危险性等级和人口暴露程度相对较低,但人口的社会经济脆弱性决定了其也存在较高的高温风险。
在当前国内的高温热浪风险评估研究中,从评价指标选取来看,多数研究关注高温热浪本身的强度、频次、持续时间等特征[46],对与死亡率密切相关的夜间气温相对关注较少。而研究表明城市热岛效应的存在使得城市地区的夜间最低气温显著高于郊区及乡村[47-48],因而加剧了高温热浪对城市居民健康的威胁[27]。夜间LST由于不受太阳辐射的影响被认为比白天LST能更准确地代表城市热岛的空间分布[49]。本文综合白天和夜间LST的高温危险性分析表明,城市地区白天和夜间LST均普遍高于郊区及乡村地区,城市居民在高温热浪期间更容易受到持续的高温威胁,这也与以往国外类似研究的结果保持一致[25-26,48]。
此外,人群作为高温热浪灾害的主要承灾体,人口密度、人口自身特点以及区域社会经济发展水平存在着显著的空间差异,获取人口密度的空间分布信息至关重要[19,29,50]。相比以往基于统计人口数据的研究,本文基于夜间灯光、植被指数及高程数据获取能够代表人口空间分布的人居指数,以此来表征人口暴露度,从而实现与其它风险因子的空间匹配。该方法采用的数据容易获取、适用于大范围的风险评估,最终获取的栅格水平上的人群健康风险信息为理解高温危险性、人口暴露度和社会经济脆弱性因子之间内在作用机制及其空间差异提供了参考,有助于有针对性地制定区域高温灾害风险防范策略。
然而,本文还存在一定的局限性。首先,在高温危险性分析中本文目前仅考虑了LST的影响,而事实上高温对人体舒适度及健康的影响是温度、湿度、风速等气象和环境因素共同作用的结果[19,24],如城市热岛效应导致的空气污染加剧将对人体健康构成更严重的威胁,而城市化导致的“城市干岛效应”则可能在一定程度上缓解对人群的热胁迫[51],这些因子之间的协同作用有待未来进一步的研究。其次,从社会经济脆弱性来看,人群本身的身体健康状况是影响其脆弱性的重要指标[27,37],然而目前此类数据还难以获取,这也是评估结果存在不确定性的可能因素之一。

The authors have declared that no competing interests exist.

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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.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。

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[ Zhao Y C, Zhao X F, Liu L L.Analysis on spatial pattern of human health risk of heatwave in Xiamen City[J]. Journal of Geo-infomation Science, 2016,18(8):1094-1102. ]

[18]
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.Background Climate change poses unprecedented challenges, ranging from global and local policy challenges to personal and social action. Heat-related deaths are largely preventable, but interventions for the most vulnerable populations need improvement. Therefore, the prior identification of high risk areas at the community level is required to better inform planning and prevention. We aimed to demonstrate a simple and flexible conceptual framework relying upon satellite thermal data and other digital data with the goal of easily reproducing this framework in a variety of urban configurations. Results The study area encompasses Rennes, a medium-sized French city. A Landsat ETM???+???image (60 m resolution) acquired during a localized heatwave (June 2001) was used to estimate land surface temperature (LST) and derive a hazard index. A land-use regression model was performed to predict the LST. Vulnerability was assessed through census data describing four dimensions (socio-economic status, extreme age, population density and building obsolescence). Then, hazard and vulnerability indices were combined to deliver a heatwave health risk index. The LST patterns were quite heterogeneous, reflecting the land cover mosaic inside the city boundary, with hotspots of elevated temperature mainly observed in the city center. A spatial error regression model was highly predictive of the spatial variation in the LST (R2???=???0.87) and was parsimonious. Three land cover descriptors (NDVI, vegetation and water fractions) were negatively linked with the LST. A sensitivity analysis (based on an image acquired on July 2000) yielded similar results. Southern areas exhibited the most vulnerability, although some pockets of higher vulnerability were observed northeast and west of the city. The heatwave health risk map showed evidence of infra-city spatial clustering, with the highest risks observed in a north???south central band. Another sensitivity analysis gave a very high correlation between 2000 and 2001 risk indices (r???=???0.98, p???<???10-12). Conclusions Building on previous work, we developed a reproducible method that can provide guidance for local planners in developing more efficient climate impact adaptations. We recommend, however, using the health risk index together with hazard and vulnerability indices to implement tailored programs because exposure to heat and vulnerability do not require the same prevention strategies.

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[19]
Johnson D P, Wilson J S, Luber G C.Socioeconomic indicators of heat-related health risk supplemented with remotely sensed data[J]. International Journal of Health Geographics, 2009,8(1):57.pAbstract/p pBackground/p pExtreme heat events are the number one cause of weather-related fatalities in the United States. The current system of alert for extreme heat events does not take into account intra-urban spatial variation in risk. The purpose of this study is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature derived from thermal remote sensing data./p pResults/p pComparison of logistic regression models indicates that supplementing known sociodemographic risk factors with remote sensing estimates of land surface temperature improves the delineation of intra-urban variations in risk from extreme heat events./p pConclusion/p pThermal remote sensing data can be utilized to improve understanding of intra-urban variations in risk from extreme heat. The refinement of current risk assessment systems could increase the likelihood of survival during extreme heat events and assist emergency personnel in the delivery of vital resources during such disasters./p

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[20]
Fouillet A, Rey G, Laurent F, et al.Excess mortality related to the August 2003 heat wave in France[J]. International Archives of Occupational and Environmental Health, 2006,80(1):16-24.From August 1st to 20th, 2003, the mean maximum temperature in France exceeded the seasonal norm by 11-12 degrees C on nine consecutive days. A major increase in mortality was then observed, which main epidemiological features are described herein. The number of deaths observed from August to November 2003 in France was compared to those expected on the basis of the mortality rates observed from 2000 to 2002 and the 2003 population estimates. From August 1st to 20th, 2003, 15,000 excess deaths were observed. From 35 years age, the excess mortality was marked and increased with age. It was 15% higher in women than in men of comparable age as of age 45 years. Excess mortality at home and in retirement institutions was greater than that in hospitals. The mortality of widowed, single and divorced subjects was greater than that of married people. Deaths directly related to heat, heatstroke, hyperthermia and dehydration increased massively. Cardiovascular diseases, ill-defined morbid disorders, respiratory diseases and nervous system diseases also markedly contributed to the excess mortality. The geographic variations in mortality showed a clear age-dependent relationship with the number of very hot days. No harvesting effect was observed. Heat waves must be considered as a threat to European populations living in climates that are currently temperate. While the elderly and people living alone are particularly vulnerable to heat waves, no segment of the population may be considered protected from the risks associated with heat waves.

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[21]
Tan J G, Zheng Y F, Song G, et al.Heat wave impacts on mortality in Shanghai, 1998 and 2003[J]. International Journal of Biometeorology, 2007,51(3):193-200.

[22]
郑山,王敏珍,尚可政,等.高温热浪对北京3所医院循环系统疾病日急诊人数影响的病例-交叉研究[J].卫生研究,2016,45(2):246-251.

[ Zheng S, Wang M Z, Shang K Z, et al.A case-crossover analysis of heat wave and hospital emergency department visits for cardiovascular diseases in 3 hospitals in Beijing[J]. Journal of Hygiene Research, 2016,45(2):246-251. ]

[23]
Smargiassi A, Goldberg M S, Plante C, et al.Variation of daily warm season mortality as a function of micro-urban heat islands[J]. Journal of Epidemiology & Community Health, 2009,63(8):659-664.Background: Little attention has been paid to how heat-related health effects vary with the micro-urban variation of outdoor temperatures. This study explored whether people located in micro-urban heat islands are at higher risk of mortality during hot summer days. Methods: Data used included (1) daily mortality for Montreal (Canada) for June鈥擜ugust 1990-2003, (2) daily mean ambient outdoor temperatures at the local international airport and (3) two thermal surface images (Landsat satellites, infrared wavelengths). A city-wide temperature versus daily mortality function was established on the basis of a case-crossover design; this function was stratified according to the surface temperature at decedents' place of death. Results: The risk of death on warm summer days in areas with higher surface temperatures was greater than in areas with lower surface temperatures. Conclusions: This study suggests that measures aimed at reducing the temperature in micro-urban heat islands (eg, urban greening activities) may reduce the health impact of hot temperatures. Further studies are needed to document the variation of heat-related risks within cities and to evaluate the health benefits of measures aimed at reducing the temperature in micro-urban heat islands.

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[24]
Romero-Lankao P, Qin H, Dickinson K.Urban vulnerability to temperature-related hazards: A meta-analysis and meta-knowledge approach[J]. Global Environmental Change, 2012,22(3):670-683.Research on urban vulnerability has grown considerably during recent years, yet consists primarily of case studies based on conflicting theories and paradigms. Assessing urban vulnerability is also generally considered to be context-dependent. We argue, however, that it is possible to identify some common patterns of vulnerability across urban centers and research paradigms and these commonalities hold potential for the development of a common set of tools to enhance response capacity within multiple contexts. To test this idea we conduct an analysis of 54 papers on urban vulnerability to temperature-related hazards, covering 222 urban areas in all regions of the world. The originality of this effort is in the combination of a standard metaanalysis with a meta-knowledge approach that allows us not only to integrate and summarize results across many studies, but also to identify trends in the literature and examine differences in methodology, theoretical frameworks and causation narratives and thereby to compare “apples to oranges.” We find that the vast majority of papers examining urban vulnerability to temperature-related hazards come from an urban vulnerability as impact approach, and cities from middle and low income countries are understudied. One of the challenges facing scholarship on urban vulnerability is to supplement the emphasis on disciplinary boxes (e.g., temperature–mortality relationships) with an interdisciplinary and integrated approach to adaptive capacity and structural drivers of differences in vulnerability.

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[25]
Dousset B, Gourmelon F, Laaidi K, et al.Satellite monitoring of summer heat waves in the Paris metropolitan area[J]. International Journal of Climatology, 2011,31(2):313-323.Summer warming trends in Western Europe are increasing the incidence, intensity and duration of heat waves. They are especially deadly in large cities owing to population density, physical surface properties, anthropogenic heat and pollutants. In August 2003, for 9 consecutive days, the Paris metropolitan area experienced an extreme heat wave that caused 4867 estimated heat-related deaths. A set of 61 NOAA-AVHRR (advanced very high-resolution radiometer) images and one SPOT-high resolution visible (HRV) image were used to analyse the spatial variations of land surface temperature (LST) over the diurnal cycle during the heat wave. The LST patterns were markedly different between daytime and night-time. A heat island was centred downtown at night, whereas multiple temperature anomalies were scattered in the industrial suburbs during the day. The heat wave corresponded to elevated nocturnal LST compared to normal summers. The highest mortality ratios matched the spatial distribution of the highest night-time LSTs, but were not related to the highest daytime LSTs. LSTs were sampled from images at the addresses of 482 elderly people (half were deceased persons and half were control ones) to produce daily and cumulative minimal, maximal and mean thermal indicators, over various periods of time. These indicators were integrated into a conditional logistic regression model to test their use as heat exposure indicators, based on risk factors. Over the period 1鈥13 August, thermal indicators taking into account minimum nocturnal temperatures averaged over 7 days or over the whole period were significantly linked to mortality. These results show the extent of the spatial variability in urban climate variables and the impact of night-time temperatures on excess mortality. These results should be used to inform policy and contingency planning in relation to heat waves, and highlight the role that satellite remote sensing can play in documenting and preventing heat-related mortality. Copyright 漏 2010 Royal Meteorological Society

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[26]
Hu K J, Yang X C, Zhong J M, et al.Spatially explicit mapping of heat health risk utilizing environmental and socioeconomic data[J]. Environmental Science & Technology, 2017,51(3):1498-1507.Abstract Extreme heat events, a leading cause of weather-related fatality worldwide, are expected to intensify, last longer, and occur more frequently in the near future. In heat health risk assessments, a spatiotemporal mismatch usually exists between hazard (heat stress) data and exposure (population distribution) data. Such mismatch is present because demographic data are generally updated every couple of years and unavailable at the subcensus unit level, which hinders the ability to diagnose human risks. In the present work, a human settlement index based on multisensor remote sensing data, including nighttime light, vegetation index, and digital elevation model data, was used for heat exposure assessment on a per-pixel basis. Moreover, the nighttime urban heat island effect was considered in heat hazard assessment. The heat-related health risk was spatially explicitly assessed and mapped at the 250 m 脳 250 m pixel level across Zhejiang Province in eastern China. The results showed that the accumulated heat risk estimates and the heat-related deaths were significantly correlated at the county level (Spearman's correlation coefficient = 0.76, P 鈮 0.01). Our analysis introduced a spatially specific methodology for the risk mapping of heat-related health outcomes, which is useful for decision support in preparation and mitigation of heat-related risk and potential adaptation.

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[27]
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(42):2187-2198.pAbstract/p pBackground/p pHeatwaves 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./p pResults/p pWhen 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./p pConclusions/p pThe 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./p

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[28]
谢盼,王仰麟,刘焱序,等.基于社会脆弱性的中国高温灾害人群健康风险评价[J].地理学报,2015,70(7):1041-1051.本研究通过综合考虑高温胁迫、社会脆弱性和人口暴露,提出基于社会脆弱性的高温灾害风险评价框架,结合气象数据、遥感数据、社会经济数据构建多元数据融合的评价指标体系,开展全国分县高温灾害风险评价。研究结果表明,高温灾害脆弱性热点区域主要集中在中国新疆西部、豫西皖北交界处、四川盆地、洞庭湖流域、广西境内珠江流域;而华中地区湖北江汉平原和湖南洞庭湖流域、西南地区四川省和重庆市交界处的四川盆地、华东地区江浙沪一带、华南珠江流域,则是中国突出的高温灾害风险热点区。高温灾害脆弱性热点区和高温灾害风险热点区的分布出现比较明显的差异,高温灾害脆弱性热点区主要分布于高温胁迫较高或社会经济较差的不发达地区,区域人群由于经济上的适应能力较差而受到高温威胁的概率较大;而高温灾害风险则强调灾害一旦发生时的可能损失,其热点区域主要分布于人口聚集、经济较为发达的大城市区域。就主导因子分区来说,高温胁迫主导区域主要为平原、盆地以及大江大河流域,社会脆弱性主导区域主要位于经济欠发达地区以及脆弱性人群聚集区;人口暴露主导区域则主要集中在人口密集的中心城市和沿海地区。

DOI

[ Xie P, Wang Y L, Liu Y X, et al.Incorporating social vulnerability to assess population health risk due to heat stress in China[J]. Acta Geographica Sinica. 2015,70(7):1041-1051. ]

[29]
Johnson D, Lulla V, Stanforth A, et al.Remote sensing of heat-related health risks: The trend toward coupling socioeconomic and remotely sensed data[J]. Geography Compass, 2011,5(10):767-780.Heat-related death is considered the number one weather-related cause of mortality throughout the world. There is growing concern that, heat waves, the primary meteorological phenomena responsible, will become more intense and numerous in the near future. Provided with this growing hazard the responsibility for mitigation, early detection and warning rests with emergency response agencies as well as academic researchers. Numerous tools exist in the present time to model very complex relationships that truly define vulnerability to such impending disasters. However, compared to other disasters (i.e. flooding, hurricanes, tornadoes, earthquakes, etc.) heat-related effects have not been thoroughly investigated in a geospatial framework. It seems likely that such approaches will provide significant benefit to the vulnerable communities and to policy makers responsible for planning. These approaches involve the usage of multiple sensor data (multi-sensor data fusion) coupled with socioeconomic characteristics to truly capture the fabric of social vulnerability. Evidence is growing that these approaches are beginning to have an impact in forecasting and planning for heat-related health disasters.

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[30]
罗晓玲,杜尧东,郑璟.广东高温热浪致人体健康风险区划[J].气候变化研究进展,2016,12(2):139-146.本文采用国际上通用的自然灾害风险概念和分析方法，从高温热浪致人体健康风险的构成出发，分别构建了广东省高温热浪危险性指数、暴露度指数和承灾体脆弱性指数（含敏感性指数和适应性指数），在定量化的风险指数基础上尝试进行风险区划研究。结果表明：高危险性区域位于广东东北部、西北部以及中部偏西地区，低危险性区域主要分布在沿海地区；人体健康高敏感性区域主要位于粤北和粤西，珠三角及粤东南沿海地区敏感性相对较低；适应性较高区域主要分布在珠三角地区，其他地区适应性较低，粤东和西南部分地区适应性最低；风险较高区域主要集中在粤东、粤西北和中部偏西及雷州半岛南部地区，风险较低区域主要在珠江三角洲及其以西沿海地区。该风险指数能较好地反映广东省高温热浪致人体健康风险的分布状况。

DOI

[ Luo X L, Du Y D, Zheng J.Risk regionalization of human health caused by high temperature & heat wave in Guangdong Province[J]. Climate Change Research, 2016,12(2):139-146. ]

[31]
Gong D Y, Pan Y Z, Wang J A.Changes in extreme daily mean temperatures in summer in eastern China during 1955-2000[J]. Theoretical and Applied Climatology, 2004,77(1):25-37.Statistical characteristics of extremely low and high daily mean temperatures in summer (June, July and August) in eastern China have been investigated. The extremely low temperatures are defined as those days with temperatures not exceeding the 10th percentile with respect to the reference period of 1961–90; similarly the extremely high temperatures are defined as those exceeding the 90th percentile. There are well-defined spatial structures in trends of the frequency of extremely low temperatures as well as of high temperature extremes. In the north region (i.e. northern and northeastern China) the linear trends of frequency of low and high temperature extremes are 611.09 and +1.23 days/1065yr, respectively. For the southern portion of the study area, the trends are 611.32 and 612.32 days/1065yr. Taking the study area as a whole, the linear trends are 610.76 days/1065yr and +1.08 days/1065yr, respectively. The changes of frequency of extreme temperatures are mainly related to the shift in the temperature means. There is a dominant anticyclonic pattern in the lower- to middle troposphere over East Asia in association with warmer conditions in the north region. For the south region there is a jump-like change in the summer mean temperature and the extreme temperature events in around 1976. The large-scale northwestern Pacific subtropical high plays an important role in the jump-like changes of the temperature extremes.

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[32]
张尚印,王守荣,张永山,等.我国东部主要城市夏季高温气候特征及预测[J].热带气象学报,2004,20(6):750-760.With monthly data of high temperature for the summer (June 锝 August) of East China in 1961锝2002, climatological characteristics of high temperature is discussed for cites in the region and a fairly complete time series is set up for the regional processes high temperature and unusually high temperature. The climatological characteristics of high temperature for 12 cities (Shijiazhuang, Nanjing, Fuzhou, etc.) in the region are studied. As shown in the observations, hot weather doesn't last long whether it be with high or low humidity, in Shijiazhuang, which is also marked with high extreme temperature, small diurnal mean wind speed and small diurnal mean relative humidity; hot and humid weather last relatively longer during unusually hot temperature periods in Nanjing and Fuzhou, which is marked with high extreme temperature, small diurnal mean wind speed and large diurnal mean relative humidity. Monthly mean insolation hours have been decreasing in summer in recent years. The subtropical high and continental transforming high in East Asia are two governing systems for high summer temperature in the Chinese cities. Strong and persistent, they are the main factors responsible for relatively more days of high temperature and more frequent in the east of China. On its basis, a monthly climate prediction model is set up using the generalized function and optimized subset regression. It is shown to predict monthly high temperature with miner errors. The model is stable and useful.

DOI

[ Zhang S Y, Wang S R, Zhang Y S, et al.The climate characteristics of high temperature and the prediction in the large cities of East China[J]. Journal of Tropical Meteorology, 2004,20(6):750-760. ]

[33]
Zhou B, Rybski D, Kropp J P.On the statistics of urban heat island intensity[J]. Geophysical Research Letters, 2013,40(20):5486-5491.We perform a systematic study of all cities in Europe to assess the Urban Heat Island (UHI) intensity by means of remotely sensed land surface temperature data. Defining cities as spatial clusters of urban land cover, we investigate the relationships of the UHI intensity, with the cluster size and the temperature of the surroundings. Our results show that in Europe, the UHI intensity in summer has a strong correlation with the cluster size, which can be well fitted by an empirical sigmoid model. Furthermore, we find a novel seasonality of the UHI intensity for individual clusters in the form of hysteresis-like curves. We characterize the shape and identify apparent regional patterns.

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[34]
Yang X C, Hou Y L, Chen B D.Observed surface warming induced by urbanization in east China[J]. Journal of Geophysical Research: Atmospheres, 2011,116(D14):263-294.Monthly mean surface air temperature data from 463 meteorological stations, including those from the 1981-2007 ordinary and national basic reference surface stations in east China and from the National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) Reanalysis, are used to investigate the effect of rapid urbanization on temperature change. These stations are dynamically classified into six categories, namely, metropolis, large city, medium-sized city, small city, suburban, and rural, using satellite-measured nighttime light imagery and population census data. Both observation minus reanalysis (OMR) and urban minus rural (UMR) methods are utilized to detect surface air temperature change induced by urbanization. With objective and dynamic station classification, the observed and reanalyzed temperature changes over rural areas show good agreement, indicating that the reanalysis can effectively capture regional rural temperature trends. The trends of urban heat island (UHI) effects, determined using OMR and UMR approaches, are generally consistent and indicate that rapid urbanization has a significant influence on surface warming over east China. Overall, UHI effects contribute 24.2% to regional average warming trends. The strongest effect of urbanization on annual mean surface air temperature trends occurs over the metropolis and large city stations, with corresponding contributions of about 44% and 35% to total warming, respectively. The UHI trends are 0.398掳C and 0.26掳C decade. The most substantial UHI effect occurred after the early 2000s, implying a significant effect of rapid urbanization on surface air temperature change during this period.

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[35]
韩冬锐,徐新良,李静,等.长江三角洲城市群热环境安全格局及土地利用变化影响研究[J].地球信息科学学报,2017,19(1): 39-49.城市化进程的加快对区域热环境具有重要影响，热环境的改变会引发一系列生态环境问题，科学地评价城市群热环境安全对于城市发展的规划布局和建设舒适的人居环境具有重要意义。本文利用多期MODIS地表温度数据产品，在构建热环境安全等级分级标准的基础上，对长江三角洲城市群热环境安全格局时空变化特征和土地利用变化的影响进行了探讨。结果表明：①2015年长江三角洲城市群热环境不安全区域多分布于城市建成区及建成区周围，以南京、上海、杭州和宁波等城市形成的“Z”型区域最明显，临界安全区域多分布于郊区，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，太湖大部分区域以及长江三角洲城市群北部区域；②2005—2015年长江三角洲城市群热环境不安全区域、临界安全区域、较安全区域和安全区域分别呈现扩张、小幅增长、缩减和先缩减后扩张趋势；③土地利用结构中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，其次，建设用地侵占大量耕地也是导致热环境不安全区域扩张的主要原因。

DOI

[ Han D R, Xu X L, Li J, et al.A 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. ]

[36]
Yang X C, Leung L R, Zhao N Z, et al.Contribution of urbanization to the increase of extreme heat events in an urban agglomeration in east China[J]. Geophysical Research Letters, 2017,44(13):6940-6950.react-text: 69 To measure the urban sprawl in china; to reveal the features of urban sprawl in China from west countries; to explain the driving forces of urban sprawl in China. /react-text react-text: 70 /react-text

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[37]
Reid C E, O'neill M S, Gronlund C J, et al. Mapping community determinants of heat vulnerability[J]. Environmental Health Perspectives, 2009,117(11):1730-1736.

[38]
Heaton M J, Sain S R, Greasby T A, et al.Characterizing urban vulnerability to heat stress using a spatially varying coefficient model[J]. Spatial and Spatio-temporal Epidemiology, 2014,8:23-33.Identifying and characterizing urban vulnerability to heat is a key step in designing intervention strategies to combat negative consequences of extreme heat on human health. This study combines excess non-accidental mortality counts, numerical weather simulations, US Census and parcel data into an assessment of vulnerability to heat in Houston, Texas. Specifically, a hierarchical model with spatially varying coefficients is used to account for differences in vulnerability among census block groups. Socio-economic and demographic variables from census and parcel data are selected via a forward selection algorithm where at each step the remaining variables are orthogonalized with respect to the chosen variables to account for collinearity. Daily minimum temperatures and composite heat indices (e.g. discomfort index) provide a better model fit than other ambient temperature measurements (e.g. maximum temperature, relative humidity). Positive interactions between elderly populations and heat exposure were found suggesting these populations are more responsive to increases in heat.

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[39]
Vancutsem C, Ceccato P, Dinku T, et al.Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa[J]. Remote Sensing of Environment, 2010,114(2):449-465.The estimation of near surface air temperature (Ta) is useful for a wide range of applications such as agriculture, climate related diseases and climate change studies. Air temperature is commonly obtained from synoptic measurements in weather stations. In Africa, the spatial distribution of weather stations is often limited and the dissemination of temperature data is variable, therefore limiting their use for real-time applications. Compensation for this paucity of information may be obtained by using satellite-based methods. However, the derivation of near surface air temperature (Ta), from the land surface temperature (Ts) derived from satellite is far from straight forward. Some studies have tried to derive maximum Ta from satellites through regression analysis but the accuracy obtained is quite variable according to the study. The main objective of this study was to explore the possibility of retrieving high-resolution Ta data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Ts products over different ecosystems in Africa. First, comparisons between night MODIS Ts data with minimum Ta showed that MODIS nighttime products provide a good estimation of minimum Ta over different ecosystems (with (ΔTs 61 Ta) centered at 0 °C, a mean absolute error (MAE) = 1.73 °C and a standard deviation = 2.4 °C). Secondly, comparisons between day MODIS Ts data with maximum Ta showed that (ΔTs 61 Ta) strongly varies according to the seasonality, the ecosystems, the solar radiation, and cloud-cover. Two factors proposed in the literature to retrieve maximum Ta from Ts, i.e. the Normalized Difference Vegetation Index (NDVI) and the Solar Zenith Angle (SZA), were analyzed. No strong relationship between (ΔTs 61 Ta) and (i) NDVI and (ii) SZA was observed, therefore requiring further research on robust methods to retrieve maximum Ta.

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[40]
Letu H, Hara M, Yagi H, et al.Estimating energy consumption from night-time DMPS/OLS imagery after correcting for saturation effects[J]. International Journal of Remote Sensing, 2010,31(16):4443-4458.A methodology is presented to accurately estimate electric power consumption from saturated night-time Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) imagery using a stable light correction. An area correction for the stable light image of DMSP/OLS for the year 1999 was performed and the build-up area rate data were used to clarify the intensity distribution characteristics of the stable light. Based on the spatial distribution characteristics of the stable light, the saturation light of the electric power supply area of Japan was corrected using a cubic regression equation. The regression between the correction calculations by the cubic regression equation and the statistical electric power consumption data was applied in Japan and also in China, India and 10 other Asian countries. The correction method was then evaluated. This study confirms that electric power consumption can be estimated with high precision from the stable light.

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[41]
Townsend A C, Bruce D A.The use of night-time lights satellite imagery as a measure of Australia's regional electricity consumption and population distribution[J]. International Journal of Remote Sensing, 2010,31(16):4459-4480.

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[42]
Yang X C, Yue W, Gao D W.Spatial improvement of human population distribution based on multi-sensor remote-sensing data: An input for exposure assessment[J]. International Journal of Remote Sensing, 2013,34(15):5569-5583.A spatial mismatch of hazard data and exposure data (e.g. population) exists in risk analysis. This article provides an integrated approach for a rapid and accurate estimation of population distribution on a per-pixel basis, through the combined use of medium and coarse spatial resolution remote-sensing data, namely the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night-time imagery, enhanced vegetation index (EVI), and digital elevation model (DEM) data. The DMSP/OLS night-time light data have been widely used for the estimation of population distribution because of their free availability, global coverage, and high temporal resolution. However, given its low-radiometric resolution as well as the overglow effects, population distribution cannot be estimated accurately. In the present study, the DMSP/OLS data were combined with EVI and DEM data to develop an elevation-adjusted human settlement index (EAHSI) image. The model for population density estimation, developed based on the significant linear correlation between population and EAHSI, was implemented in Zhejiang Province in southeast China, and a spatialized population density map was generated at a resolution of 250 mx250 m. Compared with the results from raw human settlement index (59.69%) and single night-time lights (35.89%), the mean relative error of estimated population by EAHSI has been greatly reduced (17.74%), mainly due to the incorporation of elevation information. The accurate estimation of population density can be used as an input for exposure assessment in risk analysis on a regional scale and on a per-pixel basis.

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[43]
Yu W W, Vaneckova P, Mengersen K, et al.Is the association between temperature and mortality modified by age, gender and socio-economic status?[J]. Science of the Total Environment, 2010,408(17):3513-3518.

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[44]
Cutter S L, Finch C.Temporal and spatial changes in social vulnerability to natural hazards[J]. Proceedings of the National Acadamy of Sciences of the United States of America, 2008,105(7):2301-2306.During the past four decades (1960-2000), the United States experienced major transformations in population size, development patterns, economic conditions, and social characteristics. These social, economic, and built-environment changes altered the American hazardscape in profound ways, with more people living in high-hazard areas than ever before. To improve emergency management, it is important to recognize the variability in the vulnerable populations exposed to hazards and to develop place-based emergency plans accordingly. The concept of social vulnerability identifies sensitive populations that may be less likely to respond to, cope with, and recover from a natural disaster. Social vulnerability is complex and dynamic, changing over space and through time. This paper presents empirical evidence on the spatial and temporal patterns in social vulnerability in the United States from 1960 to the present. Using counties as our study unit, we found that those components that consistently increased social vulnerability for all time periods were density (urban), race/ethnicity, and socioeconomic status. The spatial patterning of social vulnerability, although initially concentrated in certain geographic regions, has become more dispersed over time. The national trend shows a steady reduction in social vulnerability, but there is considerable regional variability, with many counties increasing in social vulnerability during the past five decades.

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[45]
谢盼,王仰麟,彭建,等.基于居民健康的城市高温热浪灾害脆弱性评价-研究进展与框架[J].地理科学进展,2015,34(2):165-174.随着全球气候变化和城市热岛效应增强,近年来城市高温热浪灾害在世界各地频繁发生,给城市居民健康和社会经济带来了极大的负面影响。目前,国内已有的高温热浪灾害研究大多关注热浪强度、发生频率、持续时间等灾害特征,以城市居民健康作为承灾体的城市高温热浪灾害脆弱性研究尚不多见,相关的评价框架和方法亟待梳理和完善。本文从高温热浪灾害脆弱性的研究主题、脆弱性框架和定量化方法三个方面系统梳理了高温热浪灾害脆弱性国内外研究进展;在广义脆弱性概念框架的基础上完善了基于&#x0201c;暴露&#x02014;敏感&#x02014;适应能力&#x0201d;的高温热浪灾害脆弱性评价概念框架,并梳理了相应的指标体系;强调通过自然环境、社会经济、居民感知等多角度的定性、定量数据综合表征城市居民高温热浪灾害脆弱性,以期为高温热浪灾害脆弱性评价提供理论与方法支持,并为规避高温热浪灾害风险、响应高温热浪紧急事件及适应气候变化等提供科学指引。

DOI

[ Xie P, Wang Y L, Peng J, et al.Health related urban heat wave vulnerability assessment: research progress and framework[J]. Progress in Geography, 2015,34(2):165-174. ]

[46]
陈见,李艳兰,高安宁,等.广西高温灾害评估[J].灾害学,2007,22(3):24-27.

[ Chen J, Li Y L, Gao A N, et al.Evaluation of high temperature disaster in Guangxi Province[J]. Journal of Catastrophology, 2007,22(3):24-27. ]

[47]
Tan J G, Zheng Y F, Tang X, et al.The urban heat island and its impact on heat waves and human health in Shanghai[J]. Internaltional of Biometeorology, 2010,54(1):75-84.With global warming forecast to continue into the foreseeable future, heat waves are very likely to increase in both frequency and intensity. In urban regions, these future heat waves will be exacerbated by the urban heat island effect, and will have the potential to negatively influence the health and welfare of urban residents. In order to investigate the health effects of the urban heat island (UHI) in Shanghai, China, 3002years of meteorological records (1975–2004) were examined for 11 first- and second-order weather stations in and around Shanghai. Additionally, automatic weather observation data recorded in recent years as well as daily all-cause summer mortality counts in 11 urban, suburban, and exurban regions (1998–2004) in Shanghai have been used. The results show that different sites (city center or surroundings) have experienced different degrees of warming as a result of increasing urbanization. In turn, this has resulted in a more extensive urban heat island effect, causing additional hot days and heat waves in urban regions compared to rural locales. An examination of summer mortality rates in and around Shanghai yields heightened heat-related mortality in urban regions, and we conclude that the UHI is directly responsible, acting to worsen the adverse health effects from exposure to extreme thermal conditions.

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[48]
Antics A, Pascal M, Laaidi K, et al.A simple indicator to rapidly assess the short-term impact of heat waves on mortality within the French heat warning system[J]. International Journal of Biometeorology, 2013,57(1):75-81.We propose a simple method to provide a rapid and robust estimate of the short-term impacts of heat waves on mortality, to be used for communication within a heat warning system. The excess mortality during a heat wave is defined as the difference between the observed mortality over the period and the observed mortality over the same period during the N preceding years. This method was tested on 19 French cities between 1973 and 2007. In six cities, we compared the excess mortality to that obtained using a modelling of the temperature-mortality relationship. There was a good agreement between the excess mortalities estimated by the simple indicator and by the models. Major differences were observed during the most extreme heat waves, in 1983 and 2003, and after the implementation of the heat prevention plan in 2006. Excluding these events, the mean difference between the estimates obtained by the two methods was of 13 deaths [1:45]. A comparison of mortality with the previous years provides a simple estimate of the mortality impact of heat waves. It can be used to provide early and reliable information to stakeholders of the heat prevention plan, and to select heat waves that should be further investigated.

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[49]
Nichol J.Remote sensing of urban heat islands by day and night[J]. Photogrammetric Engineering & Remote Sensing, 2005,71(5):613-621.Urban heat island is a term used for the elevation of urban air temperatures over those in surrounding rural areas; the difference is generally greater at night than during the day. This article reports on a study in which a night-time thermal image from the ASTER satellite sensor, of the western New territories of Hong Kong is compared with a daytime Landsat Enhanced Thematic Mapper Plus (ETM+) thermal image obtained nineteen days earlier. Densely built high rise areas which appear cool on daytime images are conversely, relatively warm on nighttime images, though the temperature differences are not well developed at night. At night, proximity to extensive cool surfaces such as forested mountain slopes appears to be influential in maintaining cooler building temperatures. The relevance of satellite-derived surface temperatures for studies of urban microclimate is supported by field data of surface and air temperatures collected in the study area. The author concludes that thermal images from both the ETM+ and ASTER sensors are of adequate spatial and radiometric resolution for the study of urban microclimatic patterns. However, a major problem still occurs in that urban heat islands are essentially a nighttime phenomenon and satellite-based studies are less able to make nighttime images.

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[50]
Golden J S, Hartz D, Brazel A, et al.A biometeorology study of climate and heat-related morbidity in Phoenix from 2001 to 2006[J]. International Journal of Biometeorology, 2008,52(6):471-480.Heat waves kill more people in the United States than hurricanes, tornadoes, earthquakes, and floods combined. Recently, international attention focused on the linkages and impacts of health vulnerability to urban climate when Western Europe experienced over 30,000 excess during the heat waves of the summer of 2003-surpassing the 1995 heat wave in Chicago, Illinois, that killed 739. While Europe dealt with heat waves, in the United States, , , established a new all-time high minimum temperature for the region on July 15, 2003. The low temperature of 35.5 degrees C (96 degrees F) was recorded, breaking the previous all-time high minimum temperature record of 33.8 degrees C (93 degrees F). While an extensive literature on heat-related mortality exists, greater understanding of influences of heat-related morbidity is required due to climate change and rapid urbanization influences. We undertook an analysis of 6 years (2001-2006) of heat-related dispatches through the Fire Department regional dispatch center to examine temporal, climatic and other non-spatial influences contributing to high-heat-related medical dispatch events. The findings identified that there were no significant variations in day-of-week dispatch events. The greatest incidence of heat-related medical dispatches occurred between the times of peak solar irradiance and maximum diurnal temperature, and during times of elevated comfort indices (combined temperature and relative humidity).

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[51]
Wang J, Feng J M, Yan Z, et al.Nested high-resolution modeling of the impact of urbanization on regional climate in three vast urban agglomerations in China[J]. Journal of Geophysical Research: Atmospheres, 2012,117(D21):21103.In this paper, the Weather Research and Forecasting Model, coupled to the Urban Canopy Model, is employed to simulate the impact of urbanization on the regional climate over three vast city agglomerations in China. Based on high-resolution land use and land cover data, two scenarios are designed to represent the nonurban and current urban land use distributions. By comparing the results of two nested, high-resolution numerical experiments, the spatial and temporal changes on surface air temperature, heat stress index, surface energy budget, and precipitation due to urbanization are analyzed and quantified. Urban expansion increases the surface air temperature in urban areas by about 1掳C, and this climatic forcing of urbanization on temperature is more pronounced in summer and nighttime than other seasons and daytime. The heat stress intensity, which reflects the combined effects of temperature and humidity, is enhanced by about 0.5 units in urban areas. The regional incoming solar radiation increases after urban expansion, which may be caused by the reduction of cloud fraction. The increased temperature and roughness of the urban surface lead to enhanced convergence. Meanwhile, the planetary boundary layer is deepened, and water vapor is mixed more evenly in the lower atmosphere. The deficit of water vapor leads to less convective available potential energy and more convective inhibition energy. Finally, these combined effects may reduce the rainfall amount over urban areas, mainly in summer, and change the regional precipitation pattern to a certain extent.

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