Mapping heat-related health risks of elderly citizens in mountainous area: A case study of Chongqing, China.
Abstract
Heat wave becomes a leading cause of weather-related illness and death across the world under the background of climate change, urban heat island, and population ageing. Heat health risk assessment is an important starting point for heat-related morbidity and mortality reduction within the risk governance framework. Chongqing, a mountainous municipality with a fast rate of population ageing in China, was selected as a case study for mapping the heat health risk of the elderly population at a raster scale. The results indicated that the high heat hazard and human exposure areas were mainly distributed in the metropolitan areas, which largely resulted in high heat health risk in the urban areas. However, the high heat vulnerability pixels were mainly concentrated at the remote mountainous regions which have broken terrains and low socioeconomic statuses. Compared with traditional general heat risk indicator, this new model can provide more targeted spatial information to decision makers, and is helpful to improve the flexibility and comparability of heat risk assessment tool. Furthermore, this new model is particularly valuable for quantifying heat health risk in developing countries with limited open access data.
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In urban heat risk mapping, exposure is often represented by population density, pedestrian presence, residential concentration, or other indicators of human activity.
“In urban heat risk mapping, exposure is often represented by population density, pedestrian presence, residential concentration, or other indicators of human activity [30, 32].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [32]
Thermal indicators, including land surface temperature or related heat-stress measures, are used as the hazard component of the risk framework in earlier urban heat risk studies.
“Such an interpretation is consistent with earlier urban heat risk studies in which thermal indicators, including land surface temperature or related heat-stress measures, are used as the hazard component of the risk framework [30, 32].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [32]
In climate- and urban heat-risk assessment, the most critical areas are those in which a strong thermal hazard co-occurs with high exposure and increased vulnerability.
“This follows the general idea of climate- and urban heat-risk assessment, where the most critical areas are those in which a strong thermal hazard co-occurs with high exposure and increased vulnerability [32, 33].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [32]
Normalizing predicted fields to a common interval such as [0,1] is a standard step when combining heterogeneous indicators with different units and scales.
“For integration with the remaining components of the framework, the predicted field can be normalized to a common interval such as [0 , 1], which is a standard step when combining heterogeneous indicators with different units and scales [32, 33].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [32]
Multiplicative and similar composite formulations based on normalized indicators are often used in heat-vulnerability and heat-risk studies because they emphasize the joint effects of multiple factors, rather than relying solely on temperature.
“Multiplicative and similar composite formulations based on normalized indicators are often used in heatvulnerability and heat-risk studies because they emphasize the joint effects of multiple factors, rather than relying solely on temperature [32, 33].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [32]
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Hydro-meteorological risk assessment methods and management by nature-based solutions
Sahani, J., Kumar, P., Debele, S., Spyrou, C., Loupis, M., Aragão, L., Porcù, F., Shah, M. A. R., & Di Sabatino, S. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.133936 · Cited by 154 · see citing works
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GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment
Alihodžić, A. (2026) · arXiv · doi:10.48550/arxiv.2605.16435
“Its purpose is not to provide a fully operational urban risk system, but rather to illustrate how GPU-accelerated thermal prediction can support practical urban heat analysis and spatial screening of potentially critical urban zones [32].”
Influential
- Authors
- Zhang, W., Zheng, C., & Chen, F.
- Affiliations
- Southwest University (CN)
- Zhejiang Meteorological Bureau (CN)
- Corresponding author
- Zhang, Wei
- ORCID
- Venue
- Science of The Total Environment
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 663
- Pages
- 852-866
- ISSN
- 0048-9697 (ISSN-L)
- OpenAlex
- W2914674201
- Cited by
- 117
- References
- 5
- Citations by year
- 2026: 14 · 2025: 29 · 2024: 31 · 2023: 12 · 2022: 11 · 2021: 10 · 2020: 7 · 2019: 3
- FWCI
- 3.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Science of The Total Environment (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Social Science Planning Project of Chongqing, China (2018PY57)
- Fundamental Research Funds for the Central Universities (SWU115003)
- National Natural Science Foundation of China (41101039)
- Topics
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
- Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
- Health Sciences › Medicine › Physiology › Thermoregulation and physiological responses
- Keywords
- heat-related health risks, heat waves, older adults, mountainous terrain, urban heat island, population aging, socioeconomic status, Chongqing
- MeSH terms
- Aged, Aged, 80 and over, China, Hot Temperature, Humans, Middle Aged, Risk Factors, Risk Assessment, Extreme Heat, Climate Change
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