Spatially Explicit Mapping of Heat Health Risk ... - ACS Publications

Subscriber access provided by Fudan University

Article

Spatially explicit mapping of heat health risk utilizing environmental and socioeconomic data Kejia Hu, Xuchao Yang, Jieming Zhong, Fangrong Fei, and Jiaguo Qi Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b04355 • Publication Date (Web): 09 Jan 2017 Downloaded from http://pubs.acs.org on January 17, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 28

Environmental Science & Technology

Spatially explicit mapping of heat health risk utilizing environmental and socioeconomic data Kejia HU1, Xuchao YANG1*, Jieming ZHONG2*, Fangrong FEI2, and Jiaguo QI1 3 1. Institute of Island and Coastal Ecosystems, Ocean College, Zhejiang University, Zhoushan 316021, China 2. Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China 3. Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48823, USA Corresponding author:

Xuchao Yang, Email: [email protected], Address: No. 1 Zheda Road, Hui-min-qiao, Dinghai District, Zhoushan 316021, P. R. China Tel: +86 13735822563; Fax: +86 0580 2092891 Jieming Zhong, Email: [email protected], Tel: +86 13958109173

Resubmitted to ES & T Dec 2016

ACS Paragon Plus Environment


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 multi-sensor 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 × 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=0.72, P≤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. Keywords: extreme heat event, urban heat island, spatial risk assessment, heat health risk


Page 2 of 28

Page 3 of 28


TOC/Abstract Art Flowchart of the spatial heat risk assessment framework.



1. Introduction Extreme heat events (EHEs) pose an acute threat to human health. They increase human morbidity and mortality and are exacerbated in sustained heat waves1-3. Examples of notable EHEs that have occurred in the last two decades include the 2003 European heat wave, which resulted in about 70,000 deaths4. The initial death toll estimate for the 2010 Russian heat wave was around 55,0005. Moreover, there is growing evidence that the intensity, frequency, and duration of EHEs are likely to escalate in the future because of climate change6-8. Therefore, EHEs will continue to be the leading cause of weather-related deaths in countries across the world. Such potential scenarios emphasize the need to develop a potential methodology to improve spatial delineation of heat health risk and identify high-risk populations. Urbanized areas are more susceptible to EHEs than rural areas because of the preexisting urban heat island (UHI) effect, which slows down the cooling process at night and thus provides little relief from the heat stresses of the day9. Using observational and numerical modeling data, Li and Bou-Zeid

10

indicated that synergistic interactions between UHI and

EHEs will produce an added heat stress. A recent study in eastern China also suggested that heat wave augmented urban-related heat stress in urban areas during the summer of 201311. Moreover, previous studies have successively confirmed the impacts of the UHI effect on excess mortality during heat waves. Those studies showed how the UHI effect exacerbates the already pernicious effect of heat on human health by raising the minimum nocturnal temperatures12-14. During the 2003 European heat wave, the highest heat-related mortality ratios in Paris matched the spatial distribution of the highest nocturnal temperatures influenced by the UHI effect rather than that of the highest daytime temperatures15, 16. In Shanghai, China, the UHI effect resulted in additional hot days, heat waves, and heat-related mortality in urban regions compared to rural locales13. Therefore, the heat island coincides with a region of high mortality, called a “death island”17. Given a greater number of people projected to live in cities where heat stress is intensified by the nighttime UHI effect, the influence of EHEs would likely aggravate in the future18, 19. The increase in the frequency and intensity of EHEs in the recent decade, potentially associated with climate change in recent years, has prompted increased research into heat health risk assessment, which is an important starting point for heat-related mortality reduction


Page 4 of 28

Page 5 of 28


within the risk governance framework. Nevertheless, most studies on heat health risk (and vulnerability) assessment have been conducted in developed countries, such as the USA20-22, Canada23, UK24, Greece25, and France26, and mainly focused on cities. These case studies integrated sociodemographic vulnerability factors with temperature data, and mapped heat risks at the census tract level. The highest risk is usually shown to exist in the inner city areas21-25. Because of the limited availability of dense weather station data, remote-sensed land surface temperature data were increasingly used to measure hazard24,

26-28

. Additionally,

Geographical Information Systems (GIS) techniques were widely used for spatial risk assessment and to highlight potential heat health risk areas. It is worth noting that the geographical distribution of heat health risk in developing countries is not usually well known29, 30

. Therefore, an effective and low-cost method for identifying the high-risk hotspots is useful

to improve the ability to issue heat alerts and develop emergency interventions at finer scales for developing countries such as China. Effective disaster risk management and adaptation strategies, as well as proper risk assessment, depend on the full understanding of the key determinants of risk, namely, hazard, exposure, and vulnerability8. For heat-related health risk, the extent of the effect of the hazard to human health depends on the heat stress intensity. Although a growing number of studies have pointed out that the UHI effect results in an increase in the extent and intensity of extreme heat stress in cities31, 32, most spatial heat health risk studies only considered the daytime high temperature and generally did not consider the nighttime high temperature due to the nocturnal UHI effect in hazard assessment24,

33

, which may have significantly underestimated the

heat-related health risk in urban areas. As the world’s most populous country, China has experienced unprecedented rates of urban growth since the late 1970s. Combined with climate change, the increasing UHI effect in China has rendered urban residents more susceptible to EHEs. Risk assessment requires not only an analysis of the hazards but also information about the elements and values at risk, namely, exposure and vulnerability assessment8, 34. These two aspects are the major drivers of disaster risk changes. Understanding the multifaceted nature of both exposure and vulnerability is a prerequisite for damage modeling and risk assessment and for designing and conducting effective disaster risk management strategies8. Exposure analysis



identifies and maps the underlying elements at the risk of exposure, such as people, livelihoods, infrastructure, or economic, social, or cultural assets exposed to the hazard35. To achieve an effective heat-related health risk assessment, detailed and accurate information on population distribution is crucial in exposure analysis. Demographic and socioeconomic data is generally available from census data on the basis of demographics geographically counted by blocks, tracts, zip code areas, counties, or even states, which suffer from a number of important limitations in exposure measurement. Census data do not provide detailed information on the spatial distribution of population because the actual distribution is fairly heterogeneous within the border of these spatial units or administrative boundaries. Therefore, there exist differences in scale and resolution between the socioeconomic and environmental data, which hinder the integration of natural and social sciences36, 37. In risk analysis, Chen et al.35 noted a spatial mismatch between spatially explicit hazard data and spatially lumped exposure data, which makes the identification of most vulnerable populations challenging. Therefore, developing simpler methods for modeling population distribution at a finer spatial resolution is necessary for heat-related health risk assessment. Such strategy would greatly aid risk analysis at the regional scale under limited time, cost, and labor. Satellite-measured nighttime light (NTL) images provide a highly powerful tool for estimating population distribution across the globe38, 39

and can serve as a proxy measure for population exposure assessment in risk analysis on a

regional scale and on a per-pixel basis40. To date, studies on heat risk21, 24, 26, 28 or heat vulnerability assessment41-48 have been mainly conducted at the census tract level. Few attempts have been made on explicitly spatializing the comprehensive heat-related health risk and exhaustively identifying heat risk hotspots to provide useful information for adaptive planning and emergency management at a spatially explicit raster level25, 49, 50. The purpose of this interdisciplinary study is to improve spatial delineation of health risk from EHEs via a spatial risk assessment methodology that integrates three risk factors—heat hazard, human exposure, and vulnerability—derived from environmental and socioeconomic data8, 24, 51. Herein, we focus on Zhejiang Province in east China as a case study. This province is frequently threatened by EHEs due to the continuous influence of the west Pacific subtropical high during summer. As a representative of the developed provinces on the eastern coast of China, Zhejiang has experienced rapid economic


Page 6 of 28

Page 7 of 28


development and a tremendous growth in its urban population and urbanized areas since the late 1970s. With rapid urban sprawl, the UHI effect has also strongly enhanced52. The increasing threat of EHEs in Zhejiang Province are likely to be worsened by the combined effects of global warming and rapid urbanization in the near future. Therefore, the prior identification of heat risk hotspots in Zhejiang Province at the spatially explicit raster level is required to better inform planning and prevention. In hazard assessment, the nocturnal UHI effect was considered by integrating the daily minimum temperature (Tmin) with daily maximum temperature (Tmax) to create a heat hazard index. To bridge the gap between census population data and heat hazard data, a gridded human settlement index based on multi-sensor remote sensing data was used to estimate population exposure and meet the demands of large-scale risk assessments. For vulnerability assessment, four demographic and socioeconomic factors were adopted to determine the vulnerability. Finally, the resultant heat health risk map for Zhejiang Province was validated against the heat-related death data at the county level. The spatially specific methodology for heat health risk assessment in the current study was designed to be intelligible and to make use of readily available datasets so that it can be easily replicated in other regions, especially in developing countries. The method is also valuable for conducting spatially explicit high temperature health warning and future heat health risk projection.

2. Materials and Methods 2.1 Study Area Zhejiang Province (Figure 1a), with a total area of 10,552 km2, is a highly prosperous and populous region in east China. The resident population of the province reached 54.98 million by the end of 2013, and 64% of the population live in urban areas. Zhejiang constitutes a complex terrain, with hills and mountains accounting for 70.4% of the total area. With rapid urbanization during past two decades, three urban agglomerations initially formed, namely, around Hangzhou Bay, the Wenzhou-Taizhou coastal zone, and the Jinqu Basin (Figure 1b). Zhejiang is located in the central subtropics with a humid monsoon climate. It has typical hot and humid summers, with summer (June to August) mean temperature ranging from 24.7 °C to 28.0 °C. On average, there were 19 hot days (daily Tmax above 35 ℃) each year during 1961-2009 in Zhejiang Province. Hangzhou, the capital of Zhejiang Province, is known as one



of China’ s “four ovens” and has more than 27 hot days per year.

Figure 1. (a) Study area location, automatic weather station locations, and elevation. (b) Land cover type of water, forest, grassland, cropland, and impervious surface areas. Sources of elevation: ASTER GDEM (2009), ERSDAC

2.2. Data Collection and Pre-processing Heat-related death data. Daily death data during the summer (June–August) of 2008 to 2013


Page 8 of 28

Page 9 of 28


at the county level were obtained from Zhejiang Center for Disease Control and Prevention. The short-term effect of high temperature on cause-specific deaths, such as cardiovascular and respiratory, has been widely reported53, 54. In this study, we combined deaths caused by heat stroke (ICD10, X30), cardiovascular diseases (ICD-10 codes, I00–I99), respiratory diseases (ICD10, J00–J99), dehydration (ICD10, E86), and hyperpyrexia (ICD10, R50.9) into heat-related deaths. A total of 141,401 heat-related deaths were recorded during the study period.

Temperature data. The daily Tmax and Tmin data used in the study covered the period June– August of 2008 to 2013 and were obtained from a dense network of automatic weather stations (AWSs) across Zhejiang Province (Fig. 1a). The data from nearly 2000 AWSs were acquired from the Zhejiang Meteorological Bureau and underwent an extensive automated quality control to eliminate many random errors found in the original data.

Census data. Census total resident population data and aged population data at the subdistrict level for Zhejiang Province (1324 subdistricts in total) were obtained from China’s Sixth (2010) National Census. Other demographic and socioeconomic data for the year 2013 at the county level for Zhejiang Province (90 counties in total) were obtained from the Zhejiang Provincial Bureau of Statistics (Zhejiang Statistical Yearbook 2014) and some local Bureaus of Statistics (e.g., Hangzhou Statistical Yearbook 2014).

NTL imagery. NTL data were derived from the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) and can be downloaded for free from the official website of the National Geophysical Data Center of the National Oceanic and Atmospheric Administration. The pixels of the stable NTL data show brightness values in digital numbers (DNs) from 0 to 63 with a 30″ spatial resolution. High DN values in the DMSP/OLS NTL image generally indicate high fractional settlements. NTL data in the year 2010 for Zhejiang Province with a geographic (Lat/Lon) projection were reprojected to an Albers Conical Equal Area projection using the nearest-neighbor algorithm. The bilinear algorithm was used to resample the NTL image into a pixel size of 250 m × 250 m to match the spatial resolution of the vegetation index data.

Enhanced vegetation index (EVI). Moderate Resolution Imaging Spectroradiometer (MODIS) EVI data (MOD13Q1) at a 250 m resolution for the year 2010 were downloaded



from

the

National

Aeronautics

and

Space

Administration

Page 10 of 28

website

(http://reverb.echo.nasa.gov/reverb/). MODIS Reprojection Tools were used for image mosaic, format conversion, and reprojection. To remove the effect of cloud contamination, a new EVI composite (EVImax) was generated on the basis of multitemporal EVI data through the maximum algorithm, as expressed in Equation (1):

EVI max = MAX ( EVI1, EVI 2 ,L, EVI23 )

(1)

where EVI1, EVI2, …, EVI23 are 16-day MODIS EVI images taken in 2010. The image was then reprojected into Albers Conical Equal Area projection using the nearest-neighbor algorithm. The low values in the EVImax image generally correspond to the high DN values in the DMSP/OLS NTL image.

DEM data. The original DEM data used in the present study comprised the ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model) Version 2, with a 30 m resolution downloaded from the website of ERSDAC of Japan (http://www.gdem.aster.ersdac.or.jp/search.jsp). The 30 m DEM data were resampled using bilinear interpolation to generate a new dataset with a pixel size of 250 m to match the spatial resolution of MODIS data. The new DEM data were then reprojected to Albers Conical Equal Area projection.

Land cover. Land cover map for 2010 were derived from the 500m MODIS land cover product (MCD12Q1), downloaded from ftp://ladsweb.nascom.nasa. The non-artificial land types were combined into woodland, grassland, cropland, and waterbody based on the International Geosphere–Biosphere Program (IGBP) global vegetation classification scheme. The impervious surface areas at a resolution of 30 m were obtained from the 2010 Landsat 5 TM image using the linear spectral mixture analysis-based approach55.

2.3 Methodology Spatial Heat Risk Assessment Framework The heat health risk assessment was conducted on the basis of the Crichton’s Risk Triangle

51

concept by Tomlinson et al.24 and Buscail et al.26 The character and severity of

effects from climate extremes depend on not only the extremes themselves but also the


Page 11 of 28


exposure and vulnerability8. Therefore, risk is described as a function of hazard, exposure, and vulnerability24. For the EHEs, the hazard is the increase in temperature, which could be historical, measured, or predicted. In the present study, the increase in temperature from the nocturnal UHI was considered and measured from about 2000 AWSs across Zhejiang Province. A typical indicator of exposure to EHEs is population census data from the areas affected by the hazard that were generally spatially inconsistent with the hazard data. Herein, a human settlement index, based on multisensor remote sensing data and reflecting the inhomogeneity of population distribution, was used to represent the grid-level exposure, which was spatially coincident with the hazard. Heat vulnerability refers to the propensity to be adversely affected and is generally defined by the physical environment and the characteristics of a person or group and their socioeconomic statuses that influence their capacity to anticipate, cope with, resist, and recover from the EHEs. The multiplication of these three equally weighted risk components defines the score of the final heat risk index (HRI). Maps of hazard, exposure, vulnerability, and risk indices were created and analyzed with ArcGIS 10.1 software.

Hazard We employed a combination of daily Tmax and Tmin and temperature thresholds to estimate the hazard of EHEs. The UHI effect during EHEs was considered by integrating Tmin data because the urban warming is most pronounced at night56. According to the definition of the China Meteorological Administration, a hot day is defined as a day with Tmax above 35℃. The 90th percentile of all Tmin data during the summer of 2008-2013 was about 26℃ and was selected as the Tmin threshold57. Then we calculated the daytime hazard as the differences between Tmax and 35℃ (Hday) and the nighttime hazard as the differences between Tmin and 26℃ (Hnight) for all hot days during summer. If Tmin in a hot day is below 26℃, Hnight is set to 0 and the hazard equal to Hday. If Tmin is above 26℃ in a hot day, the hazard equals to (Hday + Hnight). Using the records from the dense network of AWSs, the accumulated temperatures of daily Hday and Hnight were calculated, during June–August of 2008–2013 and then were spatially interpolated to 250 m resolution using the inverse distance weighted method in ArcGIS software. Finally, the accumulated daily Hday and Hnight were aggregated and then normalized to form a heat hazard index (HHI) with a range between 0 and 1.



Page 12 of 28

Exposure By combining the NTL image, EVI data, and DEM data, Yang et al.40 developed an elevation-adjusted human settlement index (EAHSI) at 250 m resolution. This index is highly correlated with population distribution and was established using the following formula:

EAHSI =

ሺ1 − EVI୫ୟ୶ ሻ + OLS୬୭୰ × eି଴.଴଴ଷୈ୉୑ ሺ1 − OLS୬୭୰ ሻ + EVI୫ୟ୶ + OLS୬୭୰ × EVI୫ୟ୶

(2)

OLSnor = ( OLS − OLSmin )∕( OLSmax − OLSmin )

(3)

where

OLSnor is the normalized value of the DMSP/OLS DN image, whereas OLSmax and OLSmin are the maximum and minimum NTL values. Bilinear algorithm was used to resample the OLSnor images into a pixel size of 250 m × 250 m to match the spatial resolution of the MODIS EVI data. By combining the EVI and DMSP/OLS data, the EAHSI greatly reduced the saturation effect in DMSP/OLS data40, 58. The EAHSI was finally normalized to generate a heat exposure index (HEI) ranging from 0 to 1.

Vulnerability Vulnerability assessment requires the imputation of various datasets representative of the complex population characteristics59. Indicators reported to influence the vulnerability to harm from EHEs were identified a priori through a review of existing literature. The elderly are more vulnerable to EHEs60-62, particularly those living alone1, than other members of the general population. An inverse relationship has been recognized between low socio-economic status and heat-related mortality63-65. Air conditioners are deemed the strongest protective factor that mitigates the hazardous effect of heat and reduces the vulnerability66, 67. Given the above information and the data availability in China, a heat vulnerability index (HVI) was developed by combining four key demographic and socioeconomic indicators at the subdistrict or county level, including age (≥65), number of aged persons (≥60) living alone, socioeconomic status (per capita disposable income and illiteracy rates above 15 years), and air conditioners (per 100 household). The data were extracted from the Sixth National Population Census data (2010) and Statistical Yearbook 2014 of Zhejiang Province and 11 cities of Zhejiang (e.g. Hangzhou),


Page 13 of 28


respectively. The values of all five variables were normalized by Z-score transformation to scale each variable to a mean of 0 and a standard deviation of 1. The socioeconomic status indicator was measured by adding the normalized values of per capita disposable income and illiteracy rates above 15 years. We assumed that all the four vulnerability indicators were of equal importance and thus weighed them equally. The values of all four indicators were aggregated and again normalized to obtain the composite HVI at the subdistrict level with a range from 0 to 1.

3. Results 3.1 Heat Hazard Figure 2a shows the accumulated Hnight in hot days during the summer (sum for 2008–2013) in Zhejiang Province. Given the strong nocturnal UHI effect, three distinct warming centers exist in the three urban agglomerations (Figure 1b), namely, around the Hangzhou Bay, the coastal areas of Wenzhou-Taizhou, and the Jinqu Basin. The UHI effects in the accumulated Hday were significantly weaker than the accumulated Hnight (Figure 2b), and the daytime high temperature are mainly distributed in inland areas with low elevation, demonstrating the remarkable discrepancies in high-temperature distribution between the daytime and nighttime. By combining the accumulated Hday and Hnight, the HHI showed that hot spots during EHEs in Zhejiang are generally concentrated in highly urbanized areas of around the Hangzhou Bay region and the Jinqu Basin (Figure 2c).



Figure 2. (a) Accumulated Hnight, (b) accumulated Hday, (c) HHI during summer for 2008–2013 in the Zhejiang Province

3.2 Exposure By combining the NTL, EVI, and DEM data, we generated an EAHSI map for Zhejiang Province at a resolution of 250 m (figure not shown). The scatterplot between the accumulated EAHSI and total resident population in 2010 at the county level in Zhejiang Province is presented in Figure 3. The accumulated EAHSI values are closely linearly correlated with the total resident population at county level, with R2 equal to 0.92. Therefore, the normalized EAHSI is a superior proxy for population distribution and can serve as a HEI for human exposure estimation on a pixel-level basis (Figure 4).


Page 14 of 28

Page 15 of 28


Figure 3. Scatterplots of the accumulated EAHSI value and population for counties/cities of Zhejiang Province

Figure 4. Map of the HEI of Zhejiang Province

3.3 Vulnerability The mean value, standard deviation, and range of the five vulnerability variables are shown in Table 1. The variable “per capita disposable income” achieved by far the greatest variation



Page 16 of 28

among the counties (1490–189967), and the variable “air conditioners per 100 household” also recorded a large variation (72.6–198.0). These two variables displayed much greater standard deviations from the mean than the other variables. Conversely, the standard deviations of “percentage of elderly,” “percentage of elderly living alone,” and “population illiteracy rates” showed much less variation among different counties in Zhejiang Province. Table 1. Descriptive statistics of the vulnerability variables Indicators

Mean (Standard

Range

Variables deviation) Age

percentage of elderly (≥65 years) (2010)

0.1424 (0.0621)

0.0208–0.4047

Social isolation

percentage of elderly (≥60 years) living alone

0.0199 (0.0055)

0.0088–0.0327

(2013) Socioeconomic

per capita disposable income (RMB yuan) (2013)

68306 (42554)

14901–189967

status

illiteracy rates of population (≥15 years) (2013)

0.0820 (0.0377)

0.0139–0.1874

Air conditioners

air conditioners per 100 household (2013)

149.54 (28.99)

72.6–198.0

Following the data preparation and processing of the four individual vulnerability indicators, we derived the composite HVI by equally weighted aggregation and subsequent normalization (Figure 5). In general, hotspots of high HVI values were observed in the southwest and middle-east areas of Zhejiang Province, with some pockets of highly vulnerable regions in the coastal areas. The north Zhejiang plain and some developed coastal areas with high urbanization level were assigned low and very low HVI values.


Page 17 of 28


Figure 5. HVI distribution in Zhejiang Province (at subdistrict scale)

3.4 Health Risk A visual inspection suggests that the HRI patterns were seemingly driven by the hazard and exposure distribution and high risk areas were found distributed mainly in urbanized areas of Zhejiang province (Figure 6), including the downtown of Hangzhou, Shaoxing, Ningbo, Jinhua, Taizhou, and Wenzhou. Low-risk areas were mainly distributed in high-altitude areas. In some areas, the HRI patterns were in part driven by the geographic variations of HVI. For example, it is interesting to find a low HRI area in the western and eastern Hangzhou encircling the inner city regions, showing a clear break with the adjacent high-risk areas mainly because of its lowest HVI values.



Figure 6. Map of the HRI of Zhejiang Province

3.5 Validation Although a number of heat vulnerability or heat risk indices have been established in previous studies, they were validated mainly by qualitative assessments68. In present study, the heat-related deaths were available at the county level (n = 90); hence, the accumulated HRI of each county was calculated using the zonal statistic tool in ArcGIS. The significant Spearman’s correlation between HRI and heat-related deaths (ρ = 0.76) at the county level shows a good fit of the heat risk assessment model. We used power regression to predict heat-related deaths using the accumulated HRI as the independent variable, and the result (Figure 7) suggests that the estimated HRI correlated fairly well with the spatial variations in heat-related deaths (R2=0.61).


Page 18 of 28

Page 19 of 28


Figure 7. Scatterplots of the accumulated HRI values and heat-related deaths for counties of Zhejiang Province

4. Discussion Research focusing on extreme heat vulnerability and risk assessment has been conducted widely in developed countries, but few studies have been conducted to assess health risk to extreme heat in developing countries, such as China 29, 30. Under the general framework of the key determinants of risk (hazard, exposure, and vulnerability), we conducted a comprehensive and spatially explicit analysis to assess the heat health risk in Zhejiang Province, China. Temperature records, multisensor remote sensing data, demographic and socioeconomic data, and GIS techniques were used to develop an HRI, and a spatially explicit raster map of heat risk was generated at a regional scale. Heat health risk is greatly heterogeneous during the summer in Zhejiang Province, and the highest risk mainly concentrates in the inner city areas. This finding mainly reflects both the UHI effect and population exposure. Finally, heat-related health outcomes were used to quantitatively validate the HRI. The strong correlation between the accumulated HRI and heat-related deaths at the county level suggests that our simple methodology performs well for heat health risk assessment.



Page 20 of 28

Previous studies have suggested an urgent need for more spatial specificity in heat health risk assessment under a geospatial framework27, 28, 69, 70. Given the lack of spatially specific approaches for heat risk assessment, this study demonstrated a simple and flexible conceptual framework that may serve as a template for generating heat health risk maps at the pixel level in the future. Compared with previous studies at the census tract level, our pixel-level heat health risk map is more informative, more visual, and more useful in communicating and understanding specific human risk. To the best of our knowledge, the heat health risk map for Zhejiang Province is also the first of its kind at the pixel level and regional scale that does not focus only on quantifying risk in urban settings. The simple methodology for quantifying heat health risk can clearly be explained and understood and allows for greater replicability, especially in developing countries. This research is particularly valuable in guiding local policy makers to proactively develop mitigating interventions and climate impact adaptations strategies with limited cost, time, and labour71. Most previous studies on heat health risk assessments did not take a precise account of the potentially confounding UHI effect in hazard assessment. In the present study, observational temperature data from a dense network of AWSs across Zhejiang Province enabled precise delineation of nighttime versus daytime heat hazard. The inclusion of the nocturnal UHI in hazard assessment explicitly filled a specific research gap that exists in other heat risk studies24. The results support the findings of previous studies, which showed that increased nighttime temperatures were more severe in the urbanized areas and the urban inhabitants were more likely to experience sustained heat stress in both day and night during the EHEs14, 15. Spatially explicit heat risk modeling emphasized the application of geospatial technologies, including GIS and remote sensing, for improving the understanding of the exposure and vulnerability to EHEs37,

72

. Several researchers have applied satellite remote sensing

techniques to heat-related health studies by using land surface temperature24,

26, 28, 42

and

vegetation indices73. However, remote sensing technologies have been rarely used in heat exposure assessment. The strengths of this study also included a new application of the human settlement index based on multisensor remote sensing data for spatially explicit population exposure assessment. The method for exposure analysis offers a repeatable methodology that can be utilized in many countries. This progress was made possible by the flexibility of a


Page 21 of 28


GIS-based approach and the readily available worldwide satellite data, such as the DMSP/OLS NTL data, MODIS vegetation index, and DEM data. Most previous studies focused on quantifying heat vulnerability and heat health risk in urban settings 74. This study seeks to broaden the geographic context of earlier research and assesses heat health risk across Zhejiang Province, which offers diverse landscapes and populations with varying sociodemographic characteristics. The vulnerability map of Zhejiang Province (Figure 5) contradicts with the study by Aubrecht and Özceylan

21

, which showed

high HVI in the urbanized areas of the U.S. National Capital Region. However, our results agree with previous studies by Sheridan and Dolney 75, Wu et al 76, and Henderson et al. 77, which suggested that rural population may be more vulnerable to oppressive heat than urban ones. The explanation may be multifaceted. Urban areas can usually adapt better to EHEs than rural areas, plausibly because of their higher socioeconomic status and availability of better medical resources76. In addition, a population-based cross-sectional survey in Guangdong Province, south China, indicated that the rural populations had very low health risk perception to high temperature and seldom employed adaptation behaviors during EHEs78.

Our heat risk assessment has several uncertainties. First, risk assessment requires a considerable amount of detailed data, but our research was limited by the data available at the county level. Through an analysis of 54 papers on urban vulnerability to temperature-related hazard, Romero–Lankao et al.

29

suggested that people with pre-existing medical conditions

are particularly vulnerable to high temperature. This important indicator, pre-existing health conditions, was not available in our study but may become available in the future. Apart from excessive temperature anomalies, confounding health risk factors, such as humidity and low winds, may have played an important role. Humans respond physiologically not only to temperature but to a combination of temperature and humidity. Integration of additional recorded parameters in the hazard assessment, such as humidity, would more accurately describe heat stress. In our heat hazard assessment, only temperature was included because humidity was not recorded at most AWSs across Zhejiang Province. The absolute temperature thresholds of 35 °C for Tmax and 26 °C for Tmin might also introduce a degree of uncertainty. However, we utilize these cut-offs while considering that the final index particularly shows the “relative” heat hazard distribution in the study area. The absolute temperature thresholds



could be adjusted to achieve consistency with other studies. In addition, there may exist an “added effect” of the duration of EHEs lasting for several consecutive days79. The potential influence of sustained EHEs should be further considered in hazard analysis. Some studies have assessed the effect of air pollutants as confounders or effect modifiers of the temperature–mortality relationships, but their results remain mixed29. There has been increasing evidence on the synergistic effect between high temperatures and ozone concentrations on mortality80, 81. Our future research will investigate this effect. For indicator weighting, although literature shows that the contribution of environmental, demographic, and socioeconomic indicators differ, no standard weight of each indicator is currently acknowledged42, 82. Therefore, we assumed that all indicators are of equal importance and thus weighed them equally. Appropriate use of weightings requires considerable knowledge concerning all the indicators and techniques. Weightings can be easily modified according to new knowledge on heat-related health issues or specific user requirements26. Finally, the discrepancy among data collection dates (2010 census data, 2008–2013 temperature data, and 2008–2013 heat-related death data) inevitably created some temporal ambiguity in the index estimates. 200 The grid-level human settlement index is spatially compatible with other datasets from environmental and earth science fields, and it can serve as a good input for spatially explicit population exposure assessment. But it also has some limitations, such as attributing excessive population to industrial zones with sparkling nighttime light. The accuracy of the human settlement index for population estimation still need to be further improved, for instance, by integrating the OpenStreetMap data83, 84. In addition, the spatial distribution of the population, and hence, its exposure to EHEs, was time dependent, especially in the metropolitan areas. Given human activities and mobility, population density varies greatly in the diurnal cycle. Commonly available population census data do not capture the population dynamics as functions of space and time. Enhancing the temporal resolution of population distribution poses a greate challenge and is an important next step. The proliferation of mobile phones in recent years offers an unprecedented solution for recording time-specific population distribution information21, 85. A combination of mobile phone use and remote sensing methods, which has been proven to improve spatiotemporal resolutions and the accuracy of population


Page 22 of 28

Page 23 of 28


mapping85, would allow heat exposure and heat risk mapping at increased spatiotemporal resolutions.

Acknowledgments This study was supported by the National Natural Science Foundation of China (Grant 41371068 and 41611140116), and the Scientific and Technological Innovation and Development Project for Provincial Institutes of China Meteorological Administration.

REFERENCES 1. Semenza, J. C.; Rubin, C. H.; Falter, K. H.; Selanikio, J. D.; Flanders, W. D.; Howe, H. L.; Wilhelm, J. L., Heat-Related Deaths during the July 1995 Heat Wave in Chicago. New England Journal of Medicine 1996, 335, (2), 84-90. 2. Curriero, F. C.; Heiner, K. S.; Samet, J. M.; Zeger, S. L.; Strug, L.; Patz, J. A., Temperature and mortality in 11 cities of the Eastern United States. Am. J. Epidemiol. 2002, 155, (1), 80-87. 3. Browning, C. R.; Wallace, D.; Feinberg, S. L.; Cagney, K. A., Neighborhood Social Processes, Physical Conditions, and Disaster-Related Mortality: The Case of the 1995 Chicago Heat Wave. American Sociological Review 2006, 71, (4), 661-678. 4. Robine, J. M.; Cheung, S. L. K.; Le Roy, S.; Van Oyen, H.; Griffiths, C.; Michel, J. P.; Herrmann, F. R., Death toll exceeded 70,000 in Europe during the summer of 2003. C. R. Biologies. 2008, 331, 171-178. 5. Barriopedro, D.; Fischer, E. M.; Luterbacher, J.; Trigo, R. M.; Garcia-Herrera, R., The hot summer of 2010: redrawing the temperature record map of Europe. Science 2011, 332, 220-224. 6. Meehl, G. A.; Tebaldi, C., More intense, more frequent, and longer lasting heat waves in the 21st century. Science 2004, 305, 994-997. 7. Patz, J.; Campbell-Lendrum, D.; Holloway, T.; Foley, J., Impact of regional climate change on human health. Nature 2005, 438, 310-317. 8. IPCC Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, 582 pp; 2012. 9. Clarke, J. F., Some effects of the urban structure on heat mortality. Environmental Research 1972, 5, (1), 93-104. 10. Li, D.; Bou-Zeid, E., Synergistic Interactions between Urban Heat Islands and Heat Waves: The Impact in Cities Is Larger than the Sum of Its Parts. Journal of Applied Meteorology and Climatology 2013, 52, (9), 2051-2064. 11. Wang, J.; Yan, Z.; Quan, X.-W.; Feng, J., Urban warming in the 2013 summer heat wave in eastern China. Climate Dynamics 2016, 1-19. 12. Jones, T. S.; Liang, A. P.; Kilbourne, E. M.; Griffin, M. R.; Patriarca, P. A.; Wassilak, S. G.



F.; Mullan, R. J.; Herrick, R. F.; Donnell Jr, H. D.; Choi, K., Morbidity and mortality associated with the July 1980 heat wave in St Louis and Kansas City, Mo. JAMA: the Journal of the American Medical Association 1982, 247, (24), 3327-3331. 13. Tan, J.; Zheng, Y.; Tang, X.; Guo, C.; Li, L.; Song, G.; Zhen, X.; Yuan, D.; Kalkstein, A.; Li, F., The urban heat island and its impact on heat waves and human health in Shanghai. International Journal of Biometeorology 2010, 54, (1), 75-84. 14. Antics, A.; Pascal, M.; Laaidi, K.; Wagner, V.; Corso, M.; Declercq, C.; Beaudeau, P., A simple indicator to rapidly assess the short-term impact of heat waves on mortality within the French heat warning system. International Journal of Biometeorology 2013, 57, (1), 75-81. 15. Dousset, B.; Gourmelon, F.; Laaidi, K.; Zeghnoun, A.; Giraudet, E.; Bretin, P.; Mauri, E.; Vandentorren, S., Satellite monitoring of summer heat waves in the Paris metropolitan area. International Journal of Climatology 2011, 31, (2), 313-323. 16. Laaidi, K.; Zeghnoun, A.; Dousset, B.; Bretin, P.; Vandentorren, S.; Giraudet, E.; Beaudeau, P., The Impact of Heat Islands on Mortality in Paris during the August 2003 Heatwave. Environ Health Perspect 2012, 120, (2), 254-259. 17. Buechley, R.; Van Bruggen, J.; Truppi, L., Heat island= death island? Environmental Research 1972, 5, 85-92. 18. Hajat, S.; Kosatky, T., Heat-related mortality: a review and exploration of heterogeneity. Journal of Epidemiology and Community Health 2010, 64, (9), 753-760. 19. Woodward, A.; Smith, K. R.; Campbell-Lendrum, D.; Chadee, D. D.; Honda, Y.; Liu, Q.; Olwoch, J.; Revich, B.; Sauerborn, R.; Chafe, Z.; Confalonieri, U.; Haines, A., Climate change and health: on the latest IPCC report. The Lancet 2014, 383, 1185-1189. 20. Johnson, D. P.; Wilson, J. S.; Luber, G. C., Socioeconomic indicators of heat-related health risk supplemented with remotely sensed data. International Journal of Health Geographics 2009, 8, (1), 57. 21. Aubrecht, C.; Özceylan, D., Identification of heat risk patterns in the U.S. National Capital Region by integrating heat stress and related vulnerability. Environment International 2013, 56, 65-77. 22. Johnson, D. P.; Webber, J. J.; Urs Beerval Ravichandra, K.; Lulla, V.; Stanforth, A. C., Spatiotemporal variations in heat-related health risk in three Midwestern US cities between 1990 and 2010. Geocarto International 2014, 29, (1), 65-84. 23. Vescovi, L.; Rebetez, M.; Rong, F., Assessing public health risk due to extremely high temperature events: climate and social parameters. Climate Research 2005, 30, (1), 71-78. 24. Tomlinson, C. J.; Chapman, L.; Thornes, J. E.; Baker, C. J., Including the urban heat island in spatial heat health risk assessment strategies: a case study for Birmingham, UK. International Journal of Health Geographics 2011, 10, (1), 42. 25. Keramitsoglou, I.; Kiranoudis, C.; Maiheu, B.; De Ridder, K.; Daglis, I.; Manunta, P.; Paganini, M., Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic. Environ Monit Assess 2013, 185, (10), 8239-8258. 26. Buscail, C.; Upegui, E.; Viel, J.-F., Mapping heatwave health risk at the community level for public health action. International Journal of Health Geographics 2012, 11, (1), 38. 27. Johnson, D.; Lulla, V.; Stanforth, A.; Webber, J., Remote Sensing of Heat-Related Health Risks: The Trend Toward Coupling Socioeconomic and Remotely Sensed Data. Geography Compass 2011, 5, (10), 767-780.


Page 24 of 28

Page 25 of 28


28. Johnson, D. P.; Wilson, J. S.; Luber, G. C., Socioeconomic indicators of heat-related health risk supplemented with remotely sensed data. International Journal of Health Geographics 2009, 8, (1), 57. 29. Romero-Lankao, P.; Qin, H.; Dickinson, K., Urban vulnerability to temperature-related hazards: A meta-analysis and meta-knowledge approach. Global Environmental Change 2012, 22, (3), 670-683. 30. Zhu, Q.; Liu, T.; Lin, H.; Xiao, J.; Luo, Y.; Zeng, W.; Zeng, S.; Wei, Y.; Chu, C.; Baum, S.; Du, Y.; Ma, W., The spatial distribution of health vulnerability to heat waves in Guangdong Province, China. Global Health Action 2014, 7, 25051. 31. Stone, B.; Hess, J. J.; Frumkin, H., Urban form and extreme heat events: are sprawling cities more vulnerable to climate change than compact cities? Environmental Health Perspectives 2010, 118, (10), 1425-1428. 32. Habeeb, D.; Vargo, J.; Stone, B., Jr., Rising heat wave trends in large US cities. Natural Hazards 2015, 76, (3), 1651-1665. 33. Lindley, S. J.; Handley, J. F.; Theuray, N.; Peet, E.; McEvoy, D., Adaptation Strategies for Climate Change in the Urban Environment: Assessing Climate Change Related Risk in UK Urban Areas. Journal of Risk Research 2006, 9, (5), 543-568. 34. Seifert, I.; Thieken, A.; Merz, M.; Borst, D.; Werner, U., Estimation of industrial and commercial asset values for hazard risk assessment. Nat Hazards 2010, 52, (2), 453-479. 35. Chen, K.; McAneney, J.; Blong, R.; Leigh, R.; Hunter, L.; Magill, C., Defining area at risk and its effect in catastrophe loss estimation: a dasymetric mapping approach. Applied Geography 2004, 24, (2), 97-117. 36. Zandbergen, P. A.; Ignizio, D. A., Comparison of Dasymetric Mapping Techniques for Small-Area Population Estimates. Cartography and Geographic Information Science 2010, 37, (3), 199-214. 37. Wilhelmi, O.; Purvis, K.; Harriss, R., Designing a Geospatial Information Infrastructure for Mitigation of Heat Wave Hazards in Urban Areas. Natural Hazards Review 2004, 5, (3), 147-158. 38. Sutton, P.; Roberts, D.; Elvidge, C.; Baugh, K., Census from heaven: an estimate of the global human population using night-time satellite imagery. Int. J. Remote Sensing 2001, 22, (16), 3061-3076. 39. Elvidge, C. D.; Baugh, K. E.; Kihn, E. A.; Kroehl, H. W.; Davis, E. R.; Davis, C. W., Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. Int J Remote Sens 1997, 18, (6), 1373-1379. 40. Yang, X.; Yue, W.; Gao, D., Spatial improvement of human population distribution based on multi-sensor remote-sensing data: an input for exposure assessment. International Journal of Remote Sensing 2013, 34, (15), 5569-5583. 41. Bell, M. L.; O’Neill, M. S.; Ranjit, N.; Borja-Aburto, V. H.; Cifuentes, L. A.; Gouveia, N. C., Vulnerability to heat-related mortality in Latin America: a case-crossover study in Sao Paulo, Brazil, Santiago, Chile and Mexico City, Mexico. International Journal of Epidemiology 2008, 37, (4), 796-804. 42. Johnson, D. P.; Stanforth, A.; Lulla, V.; Luber, G., Developing an applied extreme heat vulnerability index utilizing socioeconomic and environmental data. Applied Geography 2012, 35, (1–2), 23-31.



43. Kershaw, S.; Millward, A., A spatio-temporal index for heat vulnerability assessment. Environ Monit Assess 2012, 184, (12), 7329-7342. 44. Wolf, T.; McGregor, G., The development of a heat wave vulnerability index for London, United Kingdom. Weather and Climate Extremes 2013, 1, 59-68. 45. Uejio, C. K.; Wilhelmi, O. V.; Golden, J. S.; Mills, D. M.; Gulino, S. P.; Samenow, J. P., Intra-urban societal vulnerability to extreme heat: the role of heat exposure and the built environment, socioeconomics, and neighborhood stability. Health & Place 2011, 17, (2), 498-507. 46. Yardley, J.; Sigal, R. J.; Kenny, G. P., Heat health planning: The importance of social and community factors. Global Environmental Change 2011, 21, (2), 670-679. 47. Heaton, M. J.; Sain, S. R.; Greasby, T. A.; Uejio, C. K.; Hayden, M. H.; Monaghan, A. J.; Boehnert, J.; Sampson, K.; Banerjee, D.; Nepal, V.; Wilhelmi, O. V., Characterizing urban vulnerability to heat stress using a spatially varying coefficient model. Spatial and Spatio-temporal Epidemiology 2014, 8, 23-33. 48. Klein Rosenthal, J.; Kinney, P. L.; Metzger, K. B., Intra-urban vulnerability to heat-related mortality in New York City, 1997–2006. Health & Place 2014, 30, (0), 45-60. 49. Vargo, J.; Stone, B.; Habeeb, D.; Liu, P.; Russell, A., The social and spatial distribution of temperature-related health impacts from urban heat island reduction policies. Environmental Science & Policy 2016, 66, 366-374. 50. Dong, W.; Liu, Z.; Zhang, L.; Tang, Q.; Liao, H.; Li, X. e., Assessing heat health risk for sustainability in Beijing’s urban heat island. Sustainability 2014, 6, (10), 7334-7357. 51. Crichton, D., The risk triangle. In Natural Disaster Management. Edited by Ingleton J. London: Tudor Rose; 1999:102–103. 52. Yang, X.; Yue, W.; Xu, H.; Wu, J.; He, Y., Environmental consequences of rapid urbanization in Zhejiang province, East china. International journal of environmental research and public health 2014, 11, (7), 7045-7059. 53. Basu, R.; Samet, J. M., Relation between elevated ambient temperature and mortality: A review of the epidemiologic evidence. Epidemiologic Reviews 2002, 24, (2), 190-202. 54. Gasparrini, A.; Guo, Y.; Hashizume, M.; Lavigne, E.; Zanobetti, A.; Schwartz, J.; Tobias, A.; Tong, S.; Rocklöv, J.; Forsberg, B., Mortality risk attributable to high and low ambient temperature: a multicountry observational study. The Lancet 2015, 386, (9991), 369-375. 55. Li, L.; Lu, D., Mapping population density distribution at multiple scales in Zhejiang Province using Landsat Thematic Mapper and census data. International Journal of Remote Sensing 2016, 37, (18), 4243-4260. 56. Yow, D. M., Urban heat islands: observations, impacts, and adaptation. Geogr Compass 2007, 1, (6), 1227-1251. 57. Zhang, X.; Alexander, L.; Hegerl, G. C.; Jones, P.; Tank, A. K.; Peterson, T. C.; Trewin, B.; Zwiers, F. W., Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Clim Change 2011, 2, (6), 851-870. 58. Lu, D.; Tian, H.; Zhou, G.; Ge, H., Regional mapping of human settlements in southeastern China with multisensor remotely sensed data. Remote Sens Environ 2008, 112, (9), 3668-3679. 59. Cutter, S. L.; Finch, C., Temporal and spatial changes in social vulnerability to natural hazards. Proc Natl Acad Sci U S A 2008, 105, (7), 2301-6.


Page 26 of 28

Page 27 of 28


60. Conti, S.; Meli, P.; Minelli, G.; Solimini, R.; Toccaceli, V.; Vichi, M.; Beltrano, C.; Perini, L., Epidemiologic study of mortality during the summer 2003 heat wave in Italy. Environmental Research 2005, 98, (3), 390-399. 61. Fouillet, A.; Rey, G.; Laurent, F.; Pavillon, G.; Bellec, S.; Guihenneuc-Jouyaux, C.; Clavel, J.; Jougla, E.; Hémon, D., Excess mortality related to the August 2003 heat wave in France. International Archives of Occupational and Environmental Health 2006, 80, (1), 16-24. 62. Yu, W.; Vaneckova, P.; Mengersen, K.; Pan, X.; Tong, S., Is the association between temperature and mortality modified by age, gender and socio-economic status? Science of The Total Environment 2010, 408, (17), 3513-3518. 63. Schuman, S. H., Paterns of urban heat-wave deaths and implications for prevention: data from New York and St. Louis during July, 1966. Environmental Research 1972, 5, 59-75. 64. Michelozzi, P.; De Donato, F.; Bisanti, L.; Russo, A.; Cadum, E.; DeMaria, M.; D'Ovidio, M.; Costa, G.; Perucci, C., The impact of the summer 2003 heat waves on mortality in four Italian cities. Euro surveillance: bulletin Europeen sur les maladies transmissibles= European communicable disease bulletin 2005, 10, (7), 161-165. 65. Chan, E. Y. Y.; Goggins, W. B.; Kim, J. J.; Griffiths, S. M., A study of intracity variation of temperature-related mortality and socioeconomic status among the Chinese population in Hong Kong. Journal of Epidemiology and Community Health 2012, 66, (4), 322-327. 66. Kaiser, R.; Rubin, C. H.; Henderson, A. K.; Wolfe, M. I.; Kieszak, S.; Parrott, C. L.; Adcock, M., Heat-Related Death and Mental Illness During the 1999 Cincinnati Heat Wave. The American Journal of Forensic Medicine and Pathology 2001, 22, (3), 303-307. 67. Naughton, M. P.; Henderson, A.; Mirabelli, M. C.; Kaiser, R.; Wilhelm, J. L.; Kieszak, S. M.; Rubin, C. H.; McGeehin, M. A., Heat-related mortality during a 1999 heat wave in Chicago1. American Journal of Preventive Medicine 2002, 22, (4), 221-227. 68. Wolf, T.; McGregor, G.; Analitis, A., Performance Assessment of a Heat Wave Vulnerability Index for Greater London, United Kingdom. Weather, Climate, and Society 2014, 6, (1), 32-46. 69. Smoyer, K. E., Putting risk in its place: methodological considerations for investigating extreme event health risk. Social Science & Medicine 1998, 47, (11), 1809-1824. 70. Golden, J.; Hartz, D.; Brazel, A.; Luber, G.; Phelan, P., A biometeorology study of climate and heat-related morbidity in Phoenix from 2001 to 2006. International Journal of Biometeorology 2008, 52, (6), 471-480. 71. Bradford, K.; Abrahams, L.; Hegglin, M.; Klima, K., A Heat Vulnerability Index and Adaptation Solutions for Pittsburgh, Pennsylvania. Environmental Science & Technology 2015, 49, (19), 11303-11311. 72. Wilhelmi, O. V.; Hayden, M. H., Connecting people and place: a new framework for reducing urban vulnerability to extreme heat. Environmental Research Letters 2010, 5, (1), 014021. 73. Harlan, S. L.; Brazel, A. J.; Prashad, L.; Stefanov, W. L.; Larsen, L., Neighborhood microclimates and vulnerability to heat stress. Social Science & Medicine 2006, 63, (11), 2847-2863. 74. Maier, G.; Grundstein, A.; Jang, W.; Li, C.; Naeher, L. P.; Shepherd, M., Assessing the Performance of a Vulnerability Index during Oppressive Heat across Georgia, United States. Weather, Climate, and Society 2013, 6, (2), 253-263.



75. Sheridan, S. C.; Dolney, T. J., Heat, mortality, and level of urbanization: measuring vulnerability across Ohio, USA. Climate Research 2003, 24, (3), 255-265. 76. Wu, P.-C.; Lin, C.-Y.; Lung, S.-C.; Guo, H.-R.; Chou, C.-H.; Su, H.-J., Cardiovascular mortality during heat and cold events: determinants of regional vulnerability in Taiwan. Occupational and Environmental Medicine 2011, 68, (7), 525-530. 77. Henderson, S. B.; Wan, V.; Kosatsky, T., Differences in heat-related mortality across four ecological regions with diverse urban, rural, and remote populations in British Columbia, Canada. Health & Place 2013, 23, 48-53. 78. Liu, T.; Xu, Y. J.; Zhang, Y. H.; Yan, Q. H.; Song, X. L.; Xie, H. Y.; Luo, Y.; Rutherford, S.; Chu, C.; Lin, H. L.; Ma, W. J., Associations between risk perception, spontaneous adaptation behavior to heat waves and heatstroke in Guangdong province, China. BMC Public Health 2013, 13, (1), 913. 79. Chen, K.; Bi, J.; Chen, J.; Chen, X.; Huang, L.; Zhou, L., Influence of heat wave definitions to the added effect of heat waves on daily mortality in Nanjing, China. Science of The Total Environment 2015, 506-507, 18-25. 80. Katsouyanni, D. K.; Pantazopoulou, A.; Touloumi, G.; Tselepidaki, I.; Moustris, K.; Asimakopoulos, D.; Poulopoulou, G.; Trichopoulos, D., Evidence for interaction between air pollution and high temperature in the causation of excess mortality. Archives of Environmental Health: An International Journal 1993, 48, (4), 235-242. 81. Filleul, L.; Cassadou, S.; Médina, S.; Fabres, P.; Lefranc, A.; Eilstein, D.; Le Tertre, A.; Pascal, L.; Chardon, B.; Blanchard, M., The relation between temperature, ozone, and mortality in nine French cities during the heat wave of 2003. Environmental Health Perspectives 2006, 114, (9), 1344. 82. Reid, C.; O'Neill, M.; Gronlund, C.; Brines, S.; Brown, D.; Diez-Roux, A.; Schwartz, J., Mapping Community Determinants of Heat Vulnerability. Environmental Health Perspectives 2009, 117, (11), 1730-1736. 83. Bakillah, M.; Liang, S.; Mobasheri, A.; Jokar Arsanjani, J.; Zipf, A., Fine-resolution population mapping using OpenStreetMap points-of-interest. Int J Geogr Inf Syst 2014, 28, (9), 1940-1963. 84. Rosina, K.; Hurbánek, P.; Cebecauer, M., Using OpenStreetMap to improve population grids in Europe. Cartography and Geographic Information Science 2016, 1-13. 85. Deville, P.; Linard, C.; Martin, S.; Gilbert, M.; Stevens, F. R.; Gaughan, A. E.; Blondel, V. D.; Tatem, A. J., Dynamic population mapping using mobile phone data. Proc Natl Acad Sci 2014, 111, (45), 15888-15893.


Page 28 of 28

Spatially Explicit Mapping of Heat Health Risk ... - ACS Publications

Recommend Documents