Urban heat island effect-related mortality under extreme heat and non-extreme heat scenarios: A 2010-2019 case study in Hong Kong

How urban heat island magnifies hot day exposure: Global unevenness derived from differences in built landscape

Urban heat-islands reportedly expose densely populated areas to higher temperatures. However, the magnitude of the impact of extra hot-day exposure (EHDE) and its association with the effects of urbanization on a global scale remain unclear. As local climate zones (LCZs) refine the impact of differences in urban built-type on heat-island effects, this study aimed to quantify the global EHDE caused by the urban heat-island effect based on LCZs and explored the joint impacts of low gross-domestic product and an increasing vulnerable-age population on EHDE. The results showed that EHDE accounted for 48.01 % of overall hot-day exposure. Additionally, despite a significant geographic differentiation among LCZ types with the highest EHDE intensity, they are almost typically building-intensive LCZs. Furthermore, our study revealed regional differences in the structure of the EHDE share in LCZs, which support the adoption of targeted EHDE mitigation strategies.

Optimal decision-making in relieving global high temperature-related disease burden by data-driven simulation

The rapid acceleration of global warming has led to an increased burden of high temperature-related diseases (HTDs), highlighting the need for advanced evidence-based management strategies. We have developed a conceptual framework aimed at alleviating the global burden of HTDs, grounded in the One Health concept. This framework refines the impact pathway and establishes systematic data-driven models to inform the adoption of evidence-based decision-making, tailored to distinct contexts. We collected extensive national-level data from authoritative public databases for the years 2010-2019. The burdens of five categories of disease causes - cardiovascular diseases, infectious respiratory diseases, injuries, metabolic diseases, and non-infectious respiratory diseases - were designated as intermediate outcome variables. The cumulative burden of these five categories, referred to as the total HTD burden, was the final outcome variable. We evaluated the predictive performance of eight models and subsequently introduced twelve intervention measures, allowing us to explore optimal decision-making strategies and assess their corresponding contributions. Our model selection results demonstrated the superior performance of the Graph Neural Network (GNN) model across various metrics. Utilizing simulations driven by the GNN model, we identified a set of optimal intervention strategies for reducing disease burden, specifically tailored to the seven major regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. Sectoral mitigation and adaptation measures, acting upon our categories of Infrastructure & Community, Ecosystem Resilience, and Health System Capacity, exhibited particularly strong performance for various regions and diseases. Seven out of twelve interventions were included in the optimal intervention package for each region, including raising low-carbon energy use, increasing energy intensity, improving livestock feed, expanding basic health care delivery coverage, enhancing health financing, addressing air pollution, and improving road infrastructure. The outcome of this study is a global decision-making tool, offering a systematic methodology for policymakers to develop targeted intervention strategies to address the increasingly severe challenge of HTDs in the context of global warming.

The influence of climate and human factors on a regional heat island in the Zhengzhou metropolitan area, China

The development of urbanization and the establishment of metropolitan areas causes the urban heat island to cross the original single-city scale and form a regional heat island (RHI) with a larger influence range. Due to the decreasing distance between cities, there is an urgent need to reevaluate RHI for urban agglomerations, considering all cities instead of a conventional single-city perspective. The impact of climatic conditions and human factors on heat islands still lacks a general method and framework for systematic evaluation. Therefore, we used land and night light data as background conditions to study the diurnal and seasonal changes of heat islands in the Zhengzhou metropolitan area, China. Pearson correlation analysis and random forest regression analysis were then used to explore the influence of climatic conditions and human factors on RHI and its internal relationship. We found that the daytime RHI had strong spatial heterogeneity and seasonal differences from 2001 to 2020. The daytime RHI was stronger than nighttime in spring, summer, and autumn, and the nighttime RHI was stronger than daytime in winter. From spring to winter, RHI increased first and then decreased during the daytime, while the opposite was observed at night. In this study, temperature has a greater effect on daytime RHI; CO2 and NL have a greater effect on nighttime RHI. There was strong spatial heterogeneity in the effects of climatic conditions and human factors on the RHI, with climatic conditions contributing more to the daytime RHI in the northern mountainous areas, while human factors had a greater impact on the nighttime RHI in the main urban areas of each location. The results of this study highlight more targeted and informed strategies for RHI mitigation in the Zhengzhou metropolitan area and provide helpful insights into RHI evaluation in other urban agglomerations.

Socio-environmental modifiers of heat-related mortality in eight Swiss cities: A case time series analysis

In the light of growing urbanization and projected temperature increases due to climate change, heat-related mortality in urban areas is a pressing public health concern. Heat exposure and vulnerability to heat may vary within cities depending on structural features and socioeconomic factors. This study examined the effect modification of the temperature-mortality association of three socio-environmental factors in eight Swiss cities and population subgroups (<75 and ≥ 75 years, males, females): urban heat islands (UHI) based on within-city temperature contrasts, residential greenness measured as normalized difference vegetation index (NDVI) and neighborhood socioeconomic position (SEP). We used individual death records from the Swiss National Cohort occurring during the warm season (May to September) in the years 2003-2016. We performed a case time series analysis using conditional quasi-Poisson and distributed lag non-linear models with a lag of 0-3 days. As exposure variables, we used daily maximum temperatures (Tmax) and a binary indicator for warm nights (Tmin ≥20 °C). In total, 53,593 deaths occurred during the study period. Overall across the eight cities, the mortality risk increased by 31% (1.31 relative risk (95% confidence interval: 1.20-1.42)) between 22.5 °C (the minimum mortality temperature) and 35 °C (the 99th percentile) for warm-season Tmax. Stratified analysis suggested that the heat-related risk at 35 °C is 26% (95%CI: -4%, 67%) higher in UHI compared to non-UHI areas. Indications of smaller risk differences were observed between the low vs. high greenness strata (Relative risk difference = 13% (95%CI: -11%; 44%)). Living in low SEP neighborhoods was associated with an increased heat related risk in the non-elderly population (<75 years). Our results indicate that UHI are associated with increased heat-related mortality risk within Swiss cities, and that features beyond greenness are responsible for such spatial risk differences.

Using individual approach to examine the association between urban heat island and preterm birth: A nationwide cohort study in China

BACKGROUND

Evidence suggests that maternal exposure to heat might increase the risk of preterm birth (PTB), but no study has investigated the effect from urban heat island (UHI) at individual level.

AIMS

Our study aimed to investigate the association between individual UHI exposure and PTB.

METHODS

We utilized data from the ongoing China Birth Cohort Study (CBCS), encompassing 103,040 birth records up to December 2020. UHI exposure was estimated for each participant using a novel individual assessment method based on temperature data and satellite-derived land cover data. We used generalized linear mixed-effects models to estimate the association between UHI exposure and PTB, adjusting for potential confounders including maternal characteristics and environmental factors.

RESULTS

Consistent and statistically significant associations between UHI exposure and PTB were observed up to 21 days before birth. A 5 °C increment in UHI exposure was associated with 27 % higher risk (OR = 1.27, 95 % confident interval: 1.20, 1.34) of preterm birth in lagged day 1. Stratified analysis indicated that the associations were more pronounced in participants who were older, had higher pre-pregnancy body mass index level, of higher socioeconomic status and living in greener areas.

CONCLUSION

Maternal exposure to UHI was associated with increased risk of PTB. These findings have implications for developing targeted interventions for susceptible subgroups of pregnant women. More research is needed to validate our findings of increased risk of preterm birth due to UHI exposure among pregnant women.

Using a novel strategy to identify the clustered regions of associations between short-term exposure to temperature and mortality and evaluate the inequality of heat- and cold-attributable burdens: A case study in the Sichuan Basin, China

BACKGROUND

Few studies have focused on the spatially clustered regions in the association between short-term exposure to temperature and mortality, which is important for identifying high-susceptibility population and enhancing the prevention of high/low temperatures. Previous studies have explored the association inequality, but no study has evaluated the inequalities of temperature-attributable burdens, which may be more meaningful for reducing temperature-related regional inequality.

METHODS

Taking the Sichuan Basin (SCB), an economically imbalanced area with high humidity and four distinctive seasons, as an example, we used a novel multi-stage strategy to investigate the two issues. First, distributed lag nonlinear models were independently constructed to obtain the county-level associations between daily temperature and cardiorespiratory mortality. Then, an estimation-error-based spatial scan statistic was used to detect the association-clustered regions. Third, multivariate meta-regression incorporating the identified clustered regions and socioeconomic and natural factors was used to obtain stable county-specific associations, based on which the heat- and cold-attributable deaths were mapped and their inequalities were evaluated using concentration indices and Lorenz curves.

RESULTS

On average, a U-shaped temperature-mortality association was examined. A significantly association-clustered region was detected (P = 0.017), in which heat and cold temperatures presented significantly stronger associations than those in the non-clustered region, particularly for heat temperatures. The cold-attributable deaths (3.5%) were substantially more than the heat-attributable deaths (0.5%). Both presented severe inequalities over counties. Significant temperature-attributable inequalities were also found over per-capital public budget, urbanization rate, employment rate and per-capital GDP. The directions of inequalities over GDP and urbanization rate were opposite between heat and cold temperatures.

CONCLUSIONS

Our analysis provided the first evidence about the clustering of temperature-mortality associations and the inequality of cold- and heat-attributable burdens. Significantly association-clustered regions and heavy temperature-attributable inequalities were found in the SCB. Rural people bore heavier cold-attributable but less heat-attributable mortality risk than urban people, suggesting that different policies should be designed to reduce the temperature-attributable inequalities for heat and cold temperatures and different regions. This novel strategy can provide an interesting new perspective in the association between environmental exposure and human health.

Water-energy-vegetation nexus explain global geographical variation in surface urban heat island intensity

Surface urban heat island (SUHI) is a key climate risk associated with urbanization. Previous case studies have suggested that precipitation (water), radiation (energy), and vegetation have important effects on urban warming, but there is a lack of research that combines these factors to explain the global geographic variation in SUHI intensity (SUHII). Here, we utilize remotely sensed and gridded datasets to propose a new water-energy-vegetation nexus concept that explains the global geographic variation of SUHII across four climate zones and seven major regions. We found that SUHII and its frequency increase from arid zones (0.36 ± 0.15 °C) to humid zones (2.28 ± 0.10 °C), but become weaker in the extreme humid zones (2.18 ± 0.15 °C). We revealed that from semi-arid/humid to humid zones, high precipitation is often coupled with high incoming solar radiation. The increased solar radiation can directly enhance the energy in the area, leading to higher SUHII and its frequency. Although solar radiation is high in arid zones (mainly in West, Central, and South Asia), water limitation leads to sparse natural vegetation, suppressing the cooling effect in rural areas and resulting in lower SUHII. In extreme humid regions (mainly in tropical areas), incoming solar radiation tends to flatten out, which, coupled with increased vegetation as hydrothermal conditions become more favorable, leads to more latent heat and reduces the intensity of SUHI. Overall, this study offers empirical evidence that the water-energy-vegetation nexus highly explains the global geographic variation of SUHII. The results can be used by urban planners seeking optimal SUHI mitigation strategies and for climate change modeling work.

Urban heat island impacts on heat-related cardiovascular morbidity: A time series analysis of older adults in US metropolitan areas

Many United States (US) cities are experiencing urban heat islands (UHIs) and climate change-driven temperature increases. Extreme heat increases cardiovascular disease (CVD) risk, yet little is known about how this association varies with UHI intensity (UHII) within and between cities. We aimed to identify the urban populations most at-risk of and burdened by heat-related CVD morbidity in UHI-affected areas compared to unaffected areas. ZIP code-level daily counts of CVD hospitalizations among Medicare enrollees, aged 65-114, were obtained for 120 US metropolitan statistical areas (MSAs) between 2000 and 2017. Mean ambient temperature exposure was estimated by interpolating daily weather station observations. ZIP codes were classified as low and high UHII using the first and fourth quartiles of an existing surface UHII metric, weighted to each have 25% of all CVD hospitalizations. MSA-specific associations between ambient temperature and CVD hospitalization were estimated using quasi-Poisson regression with distributed lag non-linear models and pooled via multivariate meta-analyses. Across the US, extreme heat (MSA-specific 99th percentile, on average 28.6 °C) increased the risk of CVD hospitalization by 1.5% (95% CI: 0.4%, 2.6%), with considerable variation among MSAs. Extreme heat-related CVD hospitalization risk in high UHII areas (2.4% [95% CI: 0.4%, 4.3%]) exceeded that in low UHII areas (1.0% [95% CI: -0.8%, 2.8%]), with upwards of a 10% difference in some MSAs. During the 18-year study period, there were an estimated 37,028 (95% CI: 35,741, 37,988) heat-attributable CVD admissions. High UHII areas accounted for 35% of the total heat-related CVD burden, while low UHII areas accounted for 4%. High UHII disproportionately impacted already heat-vulnerable populations; females, individuals aged 75-114, and those with chronic conditions living in high UHII areas experienced the largest heat-related CVD impacts. Overall, extreme heat increased cardiovascular morbidity risk and burden in older urban populations, with UHIs exacerbating these impacts among those with existing vulnerabilities.