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Models and Forecasts on the Future Heat-Related Mortality Under Climate Change

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Urban Overheating: Heat Mitigation and the Impact on Health

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Abstract

This chapter summarizes a review of selected literature on models and forecasts of heat-related mortality in the context of climate change. Climate change is a global health threat, with urbanization, the urban heat island effect, and heatwaves exacerbating the problem. Heatwaves are consecutive days with unusually high temperatures above certain thresholds. Temperature and humidity measures are combined to formulate various heatwave definitions. The apparent and wet bulb temperature as well as the Humidex index have been used. Heat-related health consequences include increased morbidity and mortality, particularly among people over the age of 65. There is a typically nonlinear relationship between temperature and mortality. Population acclimatization may help to mitigate some of the effects of heat mortality. Missing data are a common concern in heat mortality research with shorter or longer time series and panel data, and they are occasionally hindcasted. The majority of studies have a geographic focus and make use of weather and socioeconomic data. Atmospheric and general circulation models are frequently used, with meteorological simulations and synoptic classification downscaled to regional scale on occasion. Additionally, multiple models are averaged and Monte Carlo simulations are used. Agent-based models have been used in individual-based approaches. Descriptive, case study, time series, spatial, and synoptic weather analyses have been used, with confounders such as trends, seasonal cycles, humidity, and air pollution considered. In the published literature, t-tests, smoothing spline regressions, moving averages, trend analyses, multiple linear regression, quantile regression, distributed lags, autoregressive models, logistic and Poisson regression, Poisson and negative binomial count regression, Cox proportional hazard models, meta-regressions, generalized estimating equations, and cluster analysis have been used. Effect modifiers, dummy variables, and variable normalization are frequently employed. To analyze and develop heat mortality projections, multiple futures with shared socioeconomic pathways and representative concentration pathways have been used. A gene expression algorithm, random forest regression, and the Boruta method for automatic identification have been used, along with consideration of artificial neural networks. Quantitative and qualitative data are used to inform scenarios. As global warming worsens, much heat-related mortality will be concentrated in capital cities with larger heat islands. Building information may not be required, but air-conditioning does influence the outcome. Social isolation is a contributing factor, and long-term inpatients in social care facilities are particularly vulnerable. Green roofs and vegetation are examples of published nature-based solutions. The impact of deforestation is a contributing factor. Uncertainties include regional disparities and future demographic shifts. Because research has shown that mitigation efforts are effective, it has been proposed to develop city-specific heatwave definitions and targeted public health interventions.

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Acknowledgements

The author wishes to express his gratitude to Ms. Nicoletta Kontoulis, doctoral candidate, and Mr. Vasileios Papakostas, research associate, both of the Department of International and European Studies of the University of Piraeus, for their assistance in collating published literature. Additionally, the author wishes to convey appreciation to Drs. T. Nadasdi and S. Sinclair for their online Spell Check Plus (https://spellcheckplus.com/) service, which was used to proofread this chapter.

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Paravantis, J.A. (2022). Models and Forecasts on the Future Heat-Related Mortality Under Climate Change. In: Aghamohammadi, N., Santamouris, M. (eds) Urban Overheating: Heat Mitigation and the Impact on Health. Advances in Sustainability Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-4707-0_6

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