Mapping heat-related health risks of elderly citizens in mountainous area: A case study of Chongqing, China

Zhang, Wei; Zheng, Caigui; Chen, Feng

doi:10.1016/j.scitotenv.2019.01.240

Cited by 71 publications

(42 citation statements)

References 99 publications

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“…Our overall findings on heat health risk show that cities located in the national capital region (Metro Manila) are more at risk from heat than cities outside of the country's prime urban center. Other studies have also found that heat health risk is generally higher in urban areas than in rural areas 41,43,53,54 . Although the correlation between the derived HHRI and the NDAH was not very high (r = 0.436), the relationship was statistically significant (p < 0.0005) ( Supplementary Fig.…”

Section: Discussionmentioning

confidence: 91%

“…Furthermore, remotely sensed thermal data support spatially explicit heat-related risk assessments at the pixel or grid level 41,43,53 . However, this study could not take full advantage of this potential of the remote sensing data that were used, owing to the lack of spatially explicit data for most of the vulnerability indicators ( Supplementary Table 1)-a limitation that has been pointed out by other scholars 42 .…”

Section: Discussionmentioning

confidence: 99%

“…In addition, this study has attempted to integrate some public and environmental health concepts (e.g., the concepts of RR and MMT) into the indicator-based assessment of heat health risk [39][40][41][42][43]53,54 . Such integration was vital to our study, because the derived RR curves and MMT thresholds provided the needed critical values (temperatures) for heat hazard index determination.…”

Section: Discussionmentioning

confidence: 99%

“…Advances in remote sensing technology (e.g., remotely sensed thermal data) have been helpful, not only in the study of the UHI phenomenon 12,21,38 , but also in the assessment of heat health risk and vulnerability [39][40][41][42][43][44][45] . Here, drawing upon the concept of risk as a function of hazard, exposure, and vulnerability, and the latter as a function of sensitivity and capacity 30,46 , we assessed the current heat health risk in 139 (out of 145) Philippine cities during the hot dry season by using remote sensing data and social-ecological indicators.…”

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confidence: 99%

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Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators

et al. 2020

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More than half of the world's population currently live in urban areas and are particularly at risk from the combined effects of the urban heat island phenomenon and heat increases due to climate change. Here, by using remotely sensed surface temperature data and socialecological indicators, focusing on the hot dry season, and applying the risk framework of the Intergovernmental Panel on Climate Change, we assessed the current heat health risk in 139 Philippine cities, which account for about 40% of the country's total population. The cities at high or very high risk are found in Metro Manila, where levels of heat hazard and exposure are high. The most vulnerable cities are, however, found mainly outside the national capital region, where sensitivity is higher and capacity to cope and adapt is lower. Cities with high levels of heat vulnerability and exposure must be prioritized for adaptation. Our results will contribute to risk profiling in the Philippines and to the understanding of city-level heat health risks in developing regions of the Asia-Pacific.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators

et al. 2020

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“…Second, the prediction of urban heat risk is based on the relationship between LST and LUCC in the UHERM model. Most of the previous models predicted the risk of urban heat environment based on population density, socioeconomic status, and achieved good results [6,43]. However, most of the data in these models, calculated by empirical formula or anthropogenic statistics, cannot represent all the factors that lead to the change of urban heat environment.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

De-chao

Sun

et al. 2019

Sustainability

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For urban climatic environments, the urban heat island (UHI) effect resulting from land use and land cover change (LUCC) caused by human activities is rapidly becoming one of the most notable characteristics of urban climate change due to urban expansion. UHI effects have become a significant barrier to the process of urbanization and sustainable development of the urban ecological environment. Predicting the spatial and temporal patterns of the urban heat environment from the spatial relationship between land use and land surface temperature (LST) is key to predicting urban heat environment risk. This study established an Urban Heat Environment Risk Model (UHERM) as follows. First, the urban LST was normalized and classified during three different periods. Second, a Markov model was constructed based on spatio-temporal change in the urban heat environment between the initial year (2005) and middle year (2010), and then a cellular automata (CA) model was used to reveal spatial relationships between the urban heat environments of the two periods and land use in the initial year. The spatio-temporal pattern in a future year (2015) was predicted and the accuracy of the simulation was verified. Finally, the spatio-temporal pattern of urban heat environment risk was quantitatively forecasted based on the decision rule for the urban heat environment risk considering both the present and future status of the spatial characteristics of the urban heat environment. The MODIS LST product and LUCC dataset retrieved from remote sensing images were used to verify the accuracy of UHERM and to forecast the spatio-temporal pattern of urban heat environment risk during the period of 2015–2020. The results showed that the risk of urban heat environment is increasing in the Chongqing metropolitan area. This method for quantitatively evaluating the spatio-temporal pattern of urban heat environment risk could guide sustainable growth and provide effective theoretical and technical support for the regulation of urban spatial structure to minimize urban heat environment risk.

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Combining socio-economic and climate projections to assess heat risk

et al. 2021

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The assessments of future climate risks are common; however, usually, they focus on climate projections without considering social changes. We project heat risks for Finland to evaluate (1) what kind of differences there are in heat vulnerability projections with different scenarios and scales, and (2) how the use of socio-economic scenarios influences heat risk assessments. We project a vulnerability index with seven indicators downscaled to the postal code area scale for 2050. Three different scenario sets for vulnerability are tested: one with five global Shared Socioeconomic Pathways (SSPs) scenarios; the second with three European SSPs (EUSSPs) with data at the sub-national scale (NUTS2); and the last with the EUSSPs but aggregated data at the national scale. We construct projections of heat risk utilizing climatic heat hazard data for three different Representative Concentration Pathways (RCPs) and vulnerability and exposure data for five global SSPs up to 2100. In the vulnerability projections, each scenario in each dataset shows a decrease in vulnerability compared to current values, and the differences between the three scenario sets are small. There are evident differences both in the spatial patterns and in the temporal trends when comparing the risk projections with constant vulnerability to the projections with dynamic vulnerability. Heat hazard increases notably in RCP4.5 and RCP8.5, but a decrease of vulnerability especially in SSP1 and SSP5 alleviates risks. We show that projections of vulnerability have a considerable impact on future heat-related risk and emphasize that future risk assessments should include the combination of long-term climatic and socio-economic projections.

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Mapping heat-related health risks of elderly citizens in mountainous area: A case study of Chongqing, China

Cited by 71 publications

References 99 publications

Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators

Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

Combining socio-economic and climate projections to assess heat risk

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