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是否需要将人体热模型与天气预报相结合以预测热应激?

Is There a Need to Integrate Human Thermal Models with Weather Forecasts to Predict Thermal Stress?

机构信息

Thermal Environment Laboratory, Division of Ergonomics and Aerosol Technology, Department of Design Sciences, Faculty of Engineering, Lund University, 22362 Lund, Sweden.

Institute for Safety (IFV), 2701 AC Zoetermeer, The Netherlands.

出版信息

Int J Environ Res Public Health. 2019 Nov 19;16(22):4586. doi: 10.3390/ijerph16224586.

Abstract

More and more people will experience thermal stress in the future as the global temperature is increasing at an alarming rate and the risk for extreme weather events is growing. The increased exposure to extreme weather events poses a challenge for societies around the world. This literature review investigates the feasibility of making advanced human thermal models in connection with meteorological data publicly available for more versatile practices and a wider population. By providing society and individuals with personalized heat and cold stress warnings, coping advice and educational purposes, the risks of thermal stress can effectively be reduced. One interesting approach is to use weather station data as input for the wet bulb globe temperature heat stress index, human heat balance models, and wind chill index to assess heat and cold stress. This review explores the advantages and challenges of this approach for the ongoing EU project ClimApp where more advanced models may provide society with warnings on an individual basis for different thermal environments such as tropical heat or polar cold. The biggest challenges identified are properly assessing mean radiant temperature, microclimate weather data availability, integration and continuity of different thermal models, and further model validation for vulnerable groups.

摘要

随着全球气温以惊人的速度上升,极端天气事件的风险不断增加,未来会有越来越多的人经历热应激。人们越来越多地暴露在极端天气事件中,这对全世界的社会构成了挑战。本文献综述探讨了使与气象数据相关的先进人体热模型可供更广泛的实践和更广泛的人群使用的可行性。通过为社会和个人提供个性化的热和冷应激警告、应对建议和教育目的,可以有效降低热应激的风险。一种有趣的方法是使用气象站数据作为输入,用于湿球 globe 温度热应激指数、人体热平衡模型和风寒指数,以评估热应激和冷应激。本综述探讨了这种方法在正在进行的欧盟项目 ClimApp 中的优势和挑战,在该项目中,更先进的模型可以为不同的热环境(如热带热或极地冷)的个体提供社会预警。确定的最大挑战是正确评估平均辐射温度、微气候天气数据的可用性、不同热模型的集成和连续性,以及对弱势群体的进一步模型验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cd4/6888075/88f5795b34d2/ijerph-16-04586-g001.jpg

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