A global coupled climate model shows that there is a distinct geographic pattern to future changes in heat waves. Model results for areas of Europe and North America, associated with the severe heat waves in Chicago in 1995 and Paris in 2003, show that future heat waves in these areas will become more intense, more frequent, and longer lasting in the second half of the 21st century. Observations and the model show that present-day heat waves over Europe and North America coincide with a specific atmospheric circulation pattern that is intensified by ongoing increases in greenhouse gases, indicating that it will produce more severe heat waves in those regions in the future.

Devastating health effects from recent heat waves, and projected increases in frequency, duration, and severity of heat waves from climate change, highlight the importance of understanding health consequences of heat waves.We analyzed mortality risk for heat waves in 43 U.S. cities (1987-2005) and investigated how effects relate to heat waves' intensity, duration, or timing in season.Heat waves were defined as ≥ 2 days with temperature ≥ 95th percentile for the community for 1 May through 30 September. Heat waves were characterized by their intensity, duration, and timing in season. Within each community, we estimated mortality risk during each heat wave compared with non-heat wave days, controlling for potential confounders. We combined individual heat wave effect estimates using Bayesian hierarchical modeling to generate overall effects at the community, regional, and national levels. We estimated how heat wave mortality effects were modified by heat wave characteristics (intensity, duration, timing in season).Nationally, mortality increased 3.74% [95% posterior interval (PI), 2.29-5.22%] during heat waves compared with non-heat wave days. Heat wave mortality risk increased 2.49% for every 1°F increase in heat wave intensity and 0.38% for every 1-day increase in heat wave duration. Mortality increased 5.04% (95% PI, 3.06-7.06%) during the first heat wave of the summer versus 2.65% (95% PI, 1.14-4.18%) during later heat waves, compared with non-heat wave days. Heat wave mortality impacts and effect modification by heat wave characteristics were more pronounced in the Northeast and Midwest compared with the South.We found higher mortality risk from heat waves that were more intense or longer, or those occurring earlier in summer. These findings have implications for decision makers and researchers estimating health effects from climate change.

The Urban Heat Island (UHI) is a well-studied phenomenon, whereby urban areas are generally warmer than surrounding suburban and rural areas. The most direct effect on health from the UHI is due to heat risk, which is exacerbated in urban areas, particularly during heat waves. However, there may be health benefits from warming during colder months. This review highlights recent attempts to quantitatively estimate the health impacts of the UHI and estimations of the health benefits of UHI mitigation measures.Climate change, increasing urbanisation and an ageing population in much of the world, is likely to increase the risks to health from the UHI, particularly from heat exposure. Studies have shown increased health risks in urban populations compared with rural or suburban populations in hot weather and a disproportionate impact on more vulnerable social groups. Estimations of the impacts of various mitigation techniques suggest that a range of measures could reduce health impacts from heat and bring other benefits to health and wellbeing. The impact of the UHI on heat-related health is significant, although often overlooked, particularly when considering future impacts associated with climate change. Multiple factors should be considered when designing mitigation measures in urban environments in order to maximise health benefits and avoid unintended negative effects.

热浪作为城市化特征灾害之一,严重影响着城市居民的生命健康。目前针对热浪的研究主要聚焦基于静态数据的时空模式、风险管理和脆弱性评价分析方向,对动态人口暴露度的研究尚少。论文基于手机定位数据,首先融合深圳市逐时人口与气温时空分布模型,揭示热浪动态人口暴露度水平;其次,构建基于7类城市兴趣点(point of interest,POI)与不同时段人口分布的地理加权回归模型,初步分析了热浪环境下POI对人群行为模式的影响机制。结果显示：① 相比于基准时段(2018年7月28日12:00~18:00),2018年7月26日至8月1日热浪平均辐射范围在7月29日以8.66倍速增长,至7月30日则以18.93倍速跃至峰值,覆盖区域整体呈现西部高于东部、南部低于北部的特征;② 人口在不同时段均表现为明显的带状聚集分布态势,且人口暴露度与气温和人口的动态演变紧密关联,其暴露度同热浪扩散幅度相似,总体呈2.29倍等比增长,辐射范围包括南山区、福田区、罗湖区等城市商业、工业、住宅中心人口密集区域;③ 同类POI在不同时刻、不同POI在相同时刻对人群减少热浪暴露的移动交互行为具有明显的时空驱动机制差异及选择偏好特征。在持续性城市化背景下,该研究方法可为同类的城市灾害人口暴露度分析提供一定的科学参考。

As one of the characteristic disasters of urbanization, heatwaves seriously affect the life and health of urban residents. Existing research on heatwaves mainly focuses on the spatial and temporal pattern based on static data, risk management, and vulnerability assessment, and studies on dynamic population exposure are relatively few. This study first integrated spatial and temporal distribution models of population and temperature hourly in Shenzhen to reveal the dynamic population exposure to heatwaves based on mobile phone location data. Then a set of geographically weighted regression models in different time were built based on seven types of points of interest (POIs) and population distribution to explore the influencing mechanisms of POIs on crowd behavior patterns during the heatwaves. The results show that: 1) Compared with the baseline (12:00 to 18:00 on 28 July 2018), the average radiation range of the heatwaves increases by 8.66 times on 29 July, and jumped to the peak of 18.93 times on 30 July from 26 July to 1 August 2018. The overall coverage shows that temperature in the west was higher than the east and temperature in the south was lower than the north. 2) Population distribution exhibited an obvious zonal distribution of aggregates in different time periods, and population exposure was closely related to the dynamic evolution of temperature and population. The population exposure was similar to that of heatwaves, showing 2.29 times proportional growth. The coverage included densely populated urban commercial, industrial, and residential centers such as Nanshan District, Futian District, and Luohu District. 3) The same type of POIs at different times and the different types of POIs at the same time showed obvious spatial-temporal differences as driving mechanisms and selection preferences in the interactive mobility behavior of reducing population exposure. Under the background of sustainable urbanization, this research can provide a scientific reference for the analysis of population exposure to similar urban hazards and disasters.

The frequency and intensity of extreme heat wave events have increased in the past several decades and are likely to continue to increase in the future under the influence of human-induced climate change. Exposure refers to people, property, systems, or other elements present in hazard zones that are thereby subject to potential losses. Exposure to extreme heat and changes therein are not just determined by climate changes but also population changes. Here we analyze output for three scenarios of greenhouse gas emissions and socio-economic growth to estimate future exposure change taking account of both climate and population factors. We find that for the higher emission scenario (RCP8.5-SSP3), the global exposure increases nearly 30-fold by 2100. The average exposure for Africa is over 118 times greater than it has been historically, while the exposure for Europe increases by only a factor of four. Importantly, in the absence of climate change, exposure is reduced by 75-95% globally and across all geographic regions, as compared with exposure under the high emission scenario. Under lower emission scenarios RCP4.5-SSP2 and RCP2.6-SSP1, the global exposure is reduced by 65% and 85% respectively, highlighting the efficacy of mitigation efforts in reducing exposure to extreme heat.

极端高温对人类健康与城市社会发展影响显著,如何衡量城市高温特征及其社会后果是地理学人地关系研究的新命题。将社会脆弱性分析工具应用到该命题研究中,以中国296个城市为研究对象,采集各城市日最高气温数据、社会经济统计数据和人口普查数据,建立了高温日数、高温强度、热浪频次、热浪持续时间和热浪强度等表征城市高温特征的量化指标,对1960—2016年中国主要城市高温特征进行了系统分析;基于城市高温社会脆弱性分析框架,从暴露度、敏感性和适应能力3个维度构建中国城市高温社会脆弱性评价指标体系,并开展社会脆弱性评价,划分社会脆弱性等级,分析城市社会脆弱性致脆原因。结果表明：① 中国极端高温和热浪事件的分布主要集中在南方城市,尤其是在华东和华中地区,尽管北方城市高温热浪事件较少,但高温强度较为突出。② 中国城市高温暴露度指数具有明显的空间集聚特征,而各城市的敏感性指数和适应能力指数分布较为分散。③ 中国高社会脆弱性指数城市主要集中在华东和华中大部分地区,以及西南和华北少部分地区;高、中、低社会脆弱性指数城市比例分别为25.3%、46.3%和28.4%。④ 社会敏感致脆型城市数量最多（46.9%）,其次为高温暴露致脆型（44.3%）,适应能力不足致脆型城市数量最少（8.8%）,在高社会脆弱性城市中,暴露度指数贡献度最大,中、低社会脆弱性城市中敏感性指数贡献度最大,适应能力指数对城市高温社会脆弱性的贡献度较小。本研究可在灾害-社会关系研究、城市高温特征的量化表达与高温社会脆弱性评价等方面提供借鉴和启示。

Human health and social development are significantly affected by urban extreme heat. It is a new proposition for human-land relationship in the field of geography to measure the characteristics and social consequences of urban extreme heat. Applying the tool of social vulnerability to studies of urban extreme heat, this paper takes 296 cities in China as research objects and establishes quantitative indicators of urban extreme heat such as high temperature days, high temperature intensity, heat wave frequency, heat wave duration and heat wave intensity. By using daily maximum temperature data, urban statistics and census data, we systematically analyze the characteristics of urban extreme heat. Meanwhile, we construct a framework for urban social vulnerability to extreme heat and based on this framework, we developed a common evaluation index system of social vulnerability according to the three dimensions of exposure, sensitivity and adaptive capacity. Finally, we conduct social vulnerability assessments for the 296 cities, classify social vulnerability levels, and analyze the causes of urban social vulnerability. The results are shown as follows. (1) The extreme heat events are mainly concentrated in southern cities, especially in the eastern and central parts of the country. Although there are fewer extreme heat events in northern cities, the intensity of high temperature is more prominent. (2) The urban exposure index to extreme heat in China has obvious spatial agglomeration characteristics, while the sensitivity index and adaptability index are scattered. (3) Cities with high social vulnerability index are mainly concentrated in most areas of East and Central China, and in a few areas of Southwest and North China. The proportion of cities with high, middle and low social vulnerability index was 25.3%, 46.3% and 28.4%, respectively. (4) The number of social sensitive cities is the largest, followed by high temperature exposure cities, and the number of insufficient adaptability cities is the smallest. In addition, exposure index contributes the most in cities with high social vulnerability index, and sensitivity index contributes the most in cities with middle and low social vulnerability indexes. This study can provide reference and enlightenment for relationship research between disaster and society, quantitative expression of characteristics of urban extreme heat and assessment of social vulnerability to extreme heat.

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“暴露—敏感—适应能力”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。

With global climate change and urbanization, nature hazards such as extreme heat, rainstorm, water-logging, and fog and haze pollution have become much more frequent in urban areas during the past decades. As a part of the urban hazards, heat wave has been a leading cause of weather related human mortality in many countries and given rise to dramatic negative effects on the health of urban residents and the economy. Heat wave events are attracting widespread attention of the academic research community. In China, existing studies on heat waves mainly focused on the intensity, frequency, duration as well as the spatial patterns of the events and seldom explored the impacts of the heat waves on the health of vulnerable urban residents. Adverse health effects of heat waves can be avoided by identifying populations that are vulnerable to heat waves and providing targeted assistance, thus the improvement of the assessment framework and methodology for health related urban heat wave vulnerability is of great significance. This article reviews the development in research themes, assessment framework, and quantitative methods about health related urban heat wave vulnerability in China and abroad during the past decades and presents a new research framework. Most of the conventional vulnerability assessments contain the elements of exposure and sensitivity. However, in this research, adaptive capacity at both individual and community scales has been taken into account in addition to exposure and sensitivity. An index system combining environmental indicators (such as air temperature, land surface temperature, and land use), demographic indicators (such as age, gender, and education level), and socioeconomic indicators (such as household income, employment, and neighborhood stability) has been built for quantitative vulnerability assessment. Meanwhile, qualitative data from questionnaire survey and in-depth interviews are recommended to be added to the framework for first hand information of the residents and local governments. This research provides theoretical support for health related heat wave vulnerability assessment, as well as help focus attention and resources on more targeted health interventions, heat hazards mitigation, and climate change adaptation strategies.

对城市人口空间分布的动态把握是了解人口活动规律、认识城市空间结构、配置城市基础设施和公共服务设施及制订城市公共安全应急保障方案的重要依据。由于目前国内缺少系统的人口动态变化统计数据,城市内部层面的人口空间分布和活动的动态特征方面的相关研究难以开展,研究成果较为有限。移动电话是目前普及率最高的通讯终端设备,其用户的动态分布信息可以准确地反应整个城市人口的空间分布与活动的动态特征。利用手机信令数据,以上海市为例,构建“人口—时间—行为”关系的人口空间动态分析框架,分析上海市人口分布和活动的动态特征。结果表明：上海整体人口密度呈单中心的圈层空间分布结构,昼夜空间分布经历“白天向中心集聚、夜晚向郊区分散”的流动过程;人的各类活动（如通勤、消费休闲）会产生人口空间分布的动态变化,职住关系的不匹配和活动对中心的高度依赖使得人口的空间分布不均,形成向心流动模式。消费休闲行为对中心城区的依赖度明显高于就业活动,且集中体现在紧邻中心城区的外围近郊呈圈层分布。

Analysis of the dynamic characteristics of Shanghai's population distribution is an important basis for recognizing people's behaviors, allocating urban infrastructures, and making safety emergency plans. Be short of statistical data of temporal and spatial dynamic distribution of population, research on this topic is limited in China. Due to cell phone is the most popular communication terminal equipment, the distribution of cell phone users is able to reflect the distribution of population accurately. Using datasets of cell phone signaling records from Shanghai, this study builds a framework based on the relationship among population, time, and behavior to analyze dynamic characteristics of Shanghai's population distribution. The results show that: (1) there is a single center where the density of population at daytime and nighttime is the highest; (2) people gather in the center at daytime and flow to the suburbs at nighttime; (3) different types of people's behavior result in dynamic changes of population distribution; (4) spatial mismatch between employment and place of residence, and the dependence on city center cause a large number of people flow to city center; (5) the degree of dependence of leisure consumption behavior on city center is obviously higher than that of employment, especially in the ring areas adjacent to city center.

精细时空尺度下城市人口分布的近实时预测可为优化公共资源配置、协助城市交通诱导、制定公共安全应急预案、探索城市居民活动规律等提供重要科学依据。本文采用城市手机定位数据,基于时间序列分析方法,分别建立参数预测模型和非参数预测模型,对精细尺度下的城市人口空间分布开展近实时预测。预测结果表明,基于时间序列分析方法的预测模型可为精细尺度下的城市人口分布近实时预测提供方法支持;在本文实验条件下,从人口规模、时空分布、多时间尺度、特殊事件等多个角度评估模型精度,非参数预测模型其预测误差均小于参数预测模型,且预测结果更为稳定。

在全球极端气象、气候事件频发的背景下,获取高分辨的灾害风险信息对于区域防灾减灾决策具有重要的参考价值。而在当前的灾害风险评估中,基于行政单元的人口和社会经济等承灾体信息与栅格水平上的致灾因子普遍存在空间不匹配的现象。本文通过融合多源遥感数据和人口、社会经济统计数据,在利用人居指数对高温人口暴露进行空间化的基础上,获取了250 m分辨率的长江三角洲地区高温热浪人群健康风险空间格局。结果表明,风险等级较高的地区集中在上海、常州、杭州、宁波、无锡、嘉兴、泰州等城市的中心城区,主要是较高的人口暴露度和城市高温共同作用的结果;而大城市近郊以及各规模较小的城区的风险等级次之;相对欠发达地区虽然人口暴露程度较低,但较高的高温危险性和社会经济脆弱性指数使得这些地区的高温人群健康风险也不容忽视。识别高风险地区的风险主导因子对于提高人群高温适应能力以及减轻高温健康风险具有重要意义。

作为气候变化风险问题之一,高温热浪已引起各国政府与学术界的普遍关注。基于VSD （The Vulnerability Scoping Diagram）模型,文章构建了高温热浪风险评价指标体系,同时引入空间关联指数,探讨了福建省高温热浪风险时空分异特征、热点区演化以及类型划分。结果表明：① 2000-2015年,福建省高温热浪风险水平整体呈下降趋势,且不同城市间风险等级转化明显;② 高温热浪风险空间为跳跃变化的“圈层”结构,风险指数呈由中心向外围 “低-高-低”变化;③ 高温热浪风险的空间集聚程度整体趋于减小,热点区呈收缩态势并由“多核心”演变为“双核心”结构,冷点区则稳定分布于闽东北的宁德地区;④ 福建省高温热浪风险划分为5大类型,即省会-副省级高风险区、市辖区次高风险区、河谷中风险区、沿海平原次低风险区以及闽东-内陆山区低风险区。研究结果能够为未来福建省高温热浪风险格局演变提供预判依据,并为风险管理和合理布局公共服务设施提供决策参考。

近年来,气候变化尤其是气候变暖已成为一个全球性问题。下垫面作为大气的直接热源、水源,对气候的形成及维持具有重要意义。因此,研究地温变化特征及其与气温变化之间的关系,对探究气候变化机理具有重要意义。本研究利用中国581个气象站1961-2010年0cm地温、气温数据,将全国分为8个区域,采用相关分析、M-K非参数检验等方法,对0cm地温的时空变化、区域分异以及季节分异特征进行了研究,并分析了0cm地温变化与气温变化的关系。研究表明：中国0cm地温呈先降低后升高的趋势,2000年突变后升温趋势更为明显;中国大部分地区0cm地温都表现为升温趋势,且北方地区较南方升温更加明显,在过去50年中,南北温差呈现缩小的趋势;就各季节而言,冬季升温最明显,夏季升温最弱;相关分析表明,气温与0cm地温变化趋势及程度在大部分地区很相近,但在20世纪70年代之前及20世纪后期,0cm地温与气温变化存在较大差异。相对于气温的变化,0cm地温的升温幅度更大,突变时间较晚。

In recent years,climate warming has become a global problem. As the underlying surface is the direct heat and water source of the atmosphere,it has a strong effect on forming and maintaining local climates. The study of ground temperature variation characteristics helps to understand climate change more clearly and researching the relationship between ground temperature and air temperature variation is of great significance to understanding the mechanism of climate change. Based on daily mean air temperature and ground temperature at a depth of 0 cm from 581 stations during 1961 to 2010,correlation analysis and Mann-Kendall nonparametric tests,spatial and temporal variation characteristics,regional differentiation characteristics and seasonal differentiation characteristics of ground temperature at the depth of 0 cm and air temperature were analyzed. The results indicate that ground temperature at a depth of 0 cm in China decreased and then increased with an abruption around 2000,after the abruption the rising trend of ground temperature tended to be more obvious. In most regions,ground temperature at 0 cm rose and the trend in the north was more obvious than the south. The rise in ground temperature at 0 cm in winter was more apparent than in summer. Correlation analysis showed that variation air temperature and ground temperature at 0 cm were similar,but before the1970s and after the 20th century,large differences were apparent. Compared to variation in air temperature,ground temperature at 0 cm from 1961 to 2010 tended to have a larger range and later abruption.

探究影响城市热岛空间格局的因子,及科学分析各因子的作用机制,对揭示城市热岛效应的机理有着重要意义。本文以粤港澳大湾区为研究区,综合利用数据空间化表达、空间叠置、地理探测器等方法,对影响城市热岛空间格局的因子开展研究,并构建地表温度与影响因子间的多元关系模型。结果表明,粤港澳大湾区的城市热岛强度等级呈现中间高四周低的空间分布格局,并在珠江入海口两岸形成半环状城市热岛带。本文选取的5种影响因子对城市热岛的空间格局皆具有较高的解释力,平均解释力排序为：单元人口密度（0.668）>建设用地面积占比（0.577）>单元路网密度（0.573）>植被面积占比（0.538）>水体面积占比（0.428）。所构建的多元关系模型,能较准确地反映城市热岛区域地表温度的分布状况,所拟合的地表温度结果与实际地表温度平均值的误差为0.34℃。

To explore the influence of various factors on the spatial differentiation of urban heat island intensity, and to reveal the impact mechanism of the factors, spatial expression, spatial overlay, and geographical detector methods were used in this study. The impact of five influencing factors on the spatial differentiation of urban heat island intensity in the study area was examined, and a multivariate relationship model was constructed. The results show that the intensity of urban heat island in the Guangdong-Hong Kong-Macao Greater Bay area is high in the central part and low in the surroundings, which has formed a semicircular urban heat island belt on both sides of the estuary of the Pearl River. According to the results of the geographical detector analysis, the five selected factors have a high explanatory power on the spatial differentiation of the urban heat island intensity at the 1 km×1 km grid scale, in the order of population density (0.668) > proportion of construction land area (0.577) > length of roads (0.573) > proportion of vegetation cover (0.538) > proportion of surface water area (0.428). The constructed multivariate relationship model can accurately reflect the distribution of land surface temperature in urban heat island area, and the error between the modeling result and the observed average land surface temperature is 0.34℃.

针对城市人居热环境效应研究缺乏从自然—人文多维因子组合综合评价分析的现状,本文基于多源空间数据（Landsat 5、Landsat 8卫星影像数据、POI空间大数据、数字高程模型等）反演长沙市2000年、2009年和2016年城市地表温度格局,并快速获取与城市热环境密切相关的自然和人文因素共12个影响因子。应用标准差椭圆、空间主成分分析（PCA）等方法多角度分析了城市人居热环境效应及影响因素的联动关系。结果表明：① 2000—2016年共16年间热岛面积共增加547 km²、地表最高温度差达到10.1 ℃。城市热岛区的空间分布主要集中在城市建成区,如工商业集中和人口密集的城市中心地区,并呈现出地表温度从城市中心向郊区逐渐降低的热岛的空间分布格局,出现多个高温中心点,如五一广场商圈、长沙县星沙经济技术开发区、望城工业区、岳麓工业集中区、天心工业区等;② 2000—2016年热岛空间发展主轴保持在东北—西南方向,2000—2009年热岛重心向西南偏移了2.7 km,偏转角度为54.9°,2009—2016年热岛重心向东北偏移了4.8 km,偏转角度为60.9°。整体上,长沙市热环境空间格局的变化和城市的建设强度的变化存在一定的关联;③ 通过主成分分析得出影响长沙城市热环境格局的因子为景观格局、城市建设强度、地形地貌3个主因子;④ 人文因素对于当前加剧热岛效应形成产生的促进作用明显大于自然因素的抑制作用,综合作用下地区将升温0.293 ℃。影响城市人居热环境的因素众多,多源数据有助于揭示城市热环境空间格局及演变规律,深化对城市热岛效应成因分析认知,明确人文和自然影响因素间相关关系和相关程度,以此为改善城市人居环境质量提供科学依据。

In view of the lack of comprehensive evaluation and analysis from the combination of natural and human multi-dimensional factors, the urban surface temperature patterns of Changsha in 2000, 2009 and 2016 are retrieved based on multi-source spatial data (Landsat 5, Landsat 8 satellite image data, POI spatial big data, digital elevation model, etc.), and 12 natural and human factors closely related to urban thermal environment are quickly obtained. The standard deviation ellipse method and spatial principal component analysis (PCA) method are used to analyze the effect of urban human residential thermal environment and the linkage of its influencing factors. The results show that: (1) During the 16 years from 2000 to 2016, the heat island area increased by 547 km2, and the maximum surface temperature difference reached 10.1 ℃. The spatial distribution of urban heat island was mainly concentrated in urban built-up areas, such as industrial and commercial areas and densely populated urban centers. The spatial distribution pattern of heat island is gradually decreasing from the urban center to the suburbs. There were many high-temperature centers, such as Wuyi square business circle, Xingsha economic and technological development zone in Changsha County, Wangcheng industrial zone, Yuelu industrial concentration zone, and Tianxin industrial zone. (2) From 2000 to 2016, the main axis of spatial development of heat island maintained in the northeast-southwest direction. From 2000 to 2009, the center of gravity of heat island shifted 2.7 km to the southwest, and the deflection angle was 54.9°; the center of gravity of heat island shifted to the northeast by 4.8 km, and the deflection angle was 60.9°. On the whole, the change of spatial pattern of thermal environment in Changsha was related to the change of urban construction intensity. (3) Through the principal component analysis method, it was concluded that the factors affecting the urban thermal environment pattern of Changsha were landscape pattern, urban construction intensity and landform. (4) The promotion effect of human factors on the formation of heat island effect was obviously greater than that of natural factors. Under the comprehensive effect, the temperature would rise by 0.293 units. There are many factors influencing the urban human settlements thermal environment. Multi-source data could help to reveal the spatial pattern and evolution law of urban thermal environment, deepen the understanding of the causes of urban heat island effect, and clarify the correlation and degree between human and natural factors, so as to provide scientific supports for the improvement of the quality of urban human settlements.

近年来,中国空气污染及其对人口健康危害的时空分布呈现出新的特征。论文使用5 a(2015—2019年)间逐小时的空气质量监测数据,利用变化率计算、热点分析、趋势分析和超标频数统计等方法,分析了中国332个城市的空气质量及人口空气污染暴露风险的时空分布特征,结论如下：① 中国城市近年来空气质量有好转趋势,环境空气质量指数(AQI)下降的城市占所研究城市总数的91.3%;PM_2.5、PM₁₀、SO₂和CO等4种污染物浓度均有所下降,而NO₂和O₃浓度有所上升;② PM_2.5、PM₁₀、SO₂和CO浓度变化率的热点分布于新疆地区和云南—华南地区,NO₂浓度变化率的热点为新疆地区和河套平原,O₃浓度变化率的热点为华北平原至长江中下游流域;西北地区和华南地区空气质量变化幅度较小;③ 9个城市在PM_2.5、PM₁₀、SO₂、NO₂、O₃和CO等6种污染物中均有暴露,分布于山西、河北与山东。暴露风险均为0级的低风险城市共有12个,分别位于新疆、云南、贵州、四川、广东、福建和黑龙江。研究结论对于跨区域空气污染的协同治理以及制定差异化的空间人口流动管理政策具有重要参考价值。

In recent years, the spatiotemporal distribution and its hazards to republic health of air pollution in China have shown new characteristics. Using hourly air quality monitoring data for five years (2015-2019) in 332 Chinese cities, this study analyzed the spatiotemporal distribution characteristics of air quality and urban population exposure risks by different methods. The results suggest that: 1) Air quality in Chinese urban areas has improved in recent years. Ambient Air Quality Index (AQI) decreased in 303 cities (91.3%). The concentrations of PM2.5, PM10, SO2, and CO declined while the concentrations of NO2 and O3 increased. 2) The hotspots of PM2.5, PM10, SO2, and CO concentration change rates were distributed in Xinjiang and Yunnan-South China. The hotspots of NO2 concentration change rate were in the Xinjiang area and the Hetao Plain. The hotspots of O3 concentration change rate were from the North China Plain to the middle and lower reaches of the Yangtze River. The trends of air quality change in the Northwest and South China were relatively slow. 3) Nine cities were exposed to PM2.5, PM10, SO2, NO2, O3, and CO pollution, which were located in Shanxi, Hebei, and Shandong provinces; 12 cities had no exposure risks to these six pollutants, which were distributed in Xinjiang, Yunnan, Guizhou, Sichuan, Guangdong, Fujian, and Heilongjiang provinces. These conclusions are of important reference value for collaborative treatment of cross-regional air pollution and formulating spatially diffenrentiated population flow management policies in China.

高温给城市人口健康和社会发展带来的脆弱性后果愈发严重,如何科学量化与评估城市高温人口脆弱性,为制定更具针对性的高温适应对策提供科学依据成为当前国际研究热点。在城市高温人口脆弱性分析框架基础上,以西安市为例,整合遥感影像、手机信令、POI、社会经济等多源数据,从高温暴露、敏感性、适应能力3个维度构建高温影响下人口脆弱性评估指标体系和脆弱性测度模型,揭示高温人口脆弱性等级分布特征和空间异质性,识别人口脆弱性空间地域及其致脆因子类型。结果表明：① 西安市高温暴露、敏感性和适应能力都表现出显著的空间集聚特征,且总体上均呈现出“中心-边缘”结构,即城市中心地区形成“高暴露、高敏感、高适应”,城市边缘表现为“低暴露、低敏感、低适应”。② 西安市人口脆弱性以低值和较低水平为主,脆弱性也具有显著的空间集聚性,脆弱性较高地区主要集中在城市三环以内,形成断续的“岛状”分布形态,脆弱性低值和较低值地区分布在城市边缘。③ 不同致脆类型的面积占比依次为综合主导型（37.3%）>高温暴露主导型（33.3%）>适应能力不足主导型（23.6%）>人口敏感主导型（5.8%）;高温暴露主导型广泛分布在城市中心、北部和西部等地,人口敏感主导型相对集中在城市中心偏南地区,适应能力不足主导型主要分布在城市边缘,综合主导型主要集中在城市南部,北部也有大量分散式分布。本研究可在城市高温人口脆弱性评估方法,城市高温人口脆弱性的减缓与治理等方面提供借鉴和启示。

Due to extreme heat events and urban heat island effects, urban population health and socio-economic development have become increasingly vulnerable. How to accurately quantify and assess the urban population vulnerability to heat, and provide a scientific basis for formulating more targeted heat adaptation strategies has become an international research hotspot. This study aimed to assess the population vulnerability to heat stress in Xi'an, and obtain a fine-scale urban vulnerability map. First, we integrated remote sensing, cellular signaling data, POI and other socioeconomic data, and constructed an assessment index system of population vulnerability based on three dimensions including exposure, sensitivity and adaptive capacity. Second, we developed a population vulnerability model to measure exposure index, sensitivity index, adaptive capacity index, and population vulnerability index at grid scale in Xi'an. Finally, this study revealed the spatial distribution and heterogeneity of population vulnerability in the study area, and identified vulnerability types and its spatial pattern. The results are shown in the following aspects. (1) The exposure index, sensitivity index and adaptability index not only present significant spatial agglomeration characteristics, but also show a clear center-periphery structure. The central areas of the city are dominated by the high exposure index, high sensitivity index, and high adaptive capacity index, while the outer urban areas are dominated by low exposure index, low sensitivity index, and low adaptive capacity index. (2) The proportion of the area with the lowest and lower levels of population vulnerability index is larger in Xi'an. The population vulnerability index also shows significant spatial agglomeration. The areas with high population vulnerability index are mainly concentrated within the third ring road of the city, while the areas with low vulnerability index are concentrated in the urban fringe areas. (3) According to different dominant factors, vulnerability is grouped into four types including exposure-dominated, sensitivity-dominated, low adaptability, and comprehensive type, and their proportions to the total urban area are 33.3%, 5.8%, 23.6%, and 37.3%, respectively. The spatial distribution of the four types is as follows: the exposure-dominant areas are concentrated in the center, north and west of the city; the sensitivity-dominant areas are in the center and south of the city; the low adaptability areas are concentrated in the urban fringe areas; the comprehensive type areas are mainly distributed in the south and north of the city. This study can provide a new approach for assessing urban population vulnerability to heat stress at fine-scale, and give enlightenment for mitigation and governance of urban heat vulnerability.