Cargando…
Spatially explicit assessment of heat health risk by using multi-sensor remote sensing images and socioeconomic data in Yangtze River Delta, China
BACKGROUND: The increase in the frequency and intensity of extreme heat events, which are potentially associated with climate change in the near future, highlights the importance of heat health risk assessment, a significant reference for heat-related death reduction and intervention. However, a spa...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5970500/ https://www.ncbi.nlm.nih.gov/pubmed/29801488 http://dx.doi.org/10.1186/s12942-018-0135-y |
_version_ | 1783326142759960576 |
---|---|
author | Chen, Qian Ding, Mingjun Yang, Xuchao Hu, Kejia Qi, Jiaguo |
author_facet | Chen, Qian Ding, Mingjun Yang, Xuchao Hu, Kejia Qi, Jiaguo |
author_sort | Chen, Qian |
collection | PubMed |
description | BACKGROUND: The increase in the frequency and intensity of extreme heat events, which are potentially associated with climate change in the near future, highlights the importance of heat health risk assessment, a significant reference for heat-related death reduction and intervention. However, a spatiotemporal mismatch exists between gridded heat hazard and human exposure in risk assessment, which hinders the identification of high-risk areas at finer scales. METHODS: A human settlement index integrated by nighttime light images, enhanced vegetation index, and digital elevation model data was utilized to assess the human exposure at high spatial resolution. Heat hazard and vulnerability index were generated by land surface temperature and demographic and socioeconomic census data, respectively. Spatially explicit assessment of heat health risk and its driving factors was conducted in the Yangtze River Delta (YRD), east China at 250 m pixel level. RESULTS: High-risk areas were mainly distributed in the urbanized areas of YRD, which were mostly driven by high human exposure and heat hazard index. In some less-urbanized cities and suburban and rural areas of mega-cities, the heat health risks are in second priority. The risks in some less-developed areas were high despite the low human exposure index because of high heat hazard and vulnerability index. CONCLUSIONS: This study illustrated a methodology for identifying high-risk areas by combining freely available multi-source data. Highly urbanized areas were considered hotspots of high heat health risks, which were largely driven by the increasing urban heat island effects and population density in urban areas. Repercussions of overheating were weakened due to the low social vulnerability in some central areas benefitting from the low proportion of sensitive population or the high level of socioeconomic development. By contrast, high social vulnerability intensifies heat health risks in some less-urbanized cities and suburban areas of mega-cities. |
format | Online Article Text |
id | pubmed-5970500 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59705002018-05-30 Spatially explicit assessment of heat health risk by using multi-sensor remote sensing images and socioeconomic data in Yangtze River Delta, China Chen, Qian Ding, Mingjun Yang, Xuchao Hu, Kejia Qi, Jiaguo Int J Health Geogr Research BACKGROUND: The increase in the frequency and intensity of extreme heat events, which are potentially associated with climate change in the near future, highlights the importance of heat health risk assessment, a significant reference for heat-related death reduction and intervention. However, a spatiotemporal mismatch exists between gridded heat hazard and human exposure in risk assessment, which hinders the identification of high-risk areas at finer scales. METHODS: A human settlement index integrated by nighttime light images, enhanced vegetation index, and digital elevation model data was utilized to assess the human exposure at high spatial resolution. Heat hazard and vulnerability index were generated by land surface temperature and demographic and socioeconomic census data, respectively. Spatially explicit assessment of heat health risk and its driving factors was conducted in the Yangtze River Delta (YRD), east China at 250 m pixel level. RESULTS: High-risk areas were mainly distributed in the urbanized areas of YRD, which were mostly driven by high human exposure and heat hazard index. In some less-urbanized cities and suburban and rural areas of mega-cities, the heat health risks are in second priority. The risks in some less-developed areas were high despite the low human exposure index because of high heat hazard and vulnerability index. CONCLUSIONS: This study illustrated a methodology for identifying high-risk areas by combining freely available multi-source data. Highly urbanized areas were considered hotspots of high heat health risks, which were largely driven by the increasing urban heat island effects and population density in urban areas. Repercussions of overheating were weakened due to the low social vulnerability in some central areas benefitting from the low proportion of sensitive population or the high level of socioeconomic development. By contrast, high social vulnerability intensifies heat health risks in some less-urbanized cities and suburban areas of mega-cities. BioMed Central 2018-05-25 /pmc/articles/PMC5970500/ /pubmed/29801488 http://dx.doi.org/10.1186/s12942-018-0135-y Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Chen, Qian Ding, Mingjun Yang, Xuchao Hu, Kejia Qi, Jiaguo Spatially explicit assessment of heat health risk by using multi-sensor remote sensing images and socioeconomic data in Yangtze River Delta, China |
title | Spatially explicit assessment of heat health risk by using multi-sensor remote sensing images and socioeconomic data in Yangtze River Delta, China |
title_full | Spatially explicit assessment of heat health risk by using multi-sensor remote sensing images and socioeconomic data in Yangtze River Delta, China |
title_fullStr | Spatially explicit assessment of heat health risk by using multi-sensor remote sensing images and socioeconomic data in Yangtze River Delta, China |
title_full_unstemmed | Spatially explicit assessment of heat health risk by using multi-sensor remote sensing images and socioeconomic data in Yangtze River Delta, China |
title_short | Spatially explicit assessment of heat health risk by using multi-sensor remote sensing images and socioeconomic data in Yangtze River Delta, China |
title_sort | spatially explicit assessment of heat health risk by using multi-sensor remote sensing images and socioeconomic data in yangtze river delta, china |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5970500/ https://www.ncbi.nlm.nih.gov/pubmed/29801488 http://dx.doi.org/10.1186/s12942-018-0135-y |
work_keys_str_mv | AT chenqian spatiallyexplicitassessmentofheathealthriskbyusingmultisensorremotesensingimagesandsocioeconomicdatainyangtzeriverdeltachina AT dingmingjun spatiallyexplicitassessmentofheathealthriskbyusingmultisensorremotesensingimagesandsocioeconomicdatainyangtzeriverdeltachina AT yangxuchao spatiallyexplicitassessmentofheathealthriskbyusingmultisensorremotesensingimagesandsocioeconomicdatainyangtzeriverdeltachina AT hukejia spatiallyexplicitassessmentofheathealthriskbyusingmultisensorremotesensingimagesandsocioeconomicdatainyangtzeriverdeltachina AT qijiaguo spatiallyexplicitassessmentofheathealthriskbyusingmultisensorremotesensingimagesandsocioeconomicdatainyangtzeriverdeltachina |