School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China; National Institute of Health Data Science of China, Shandong University, Jinan, Shandong 250012, China.
School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China; National Institute of Health Data Science of China, Shandong University, Jinan, Shandong 250012, China.
Sci Total Environ. 2023 Jul 10;881:163401. doi: 10.1016/j.scitotenv.2023.163401. Epub 2023 Apr 11.
Health risk resulting from non-optimal temperature exposure, referred to as "systematic risk", has been a sustainable-development challenge in the context of global warming. Previous studies have recognized interactions between and among system components while assessing the vulnerability to climate change, but have left open the question of indicator directional interactions. The question is important, not least because indicator directional association analysis provides guidance to address climate risks by revealing the key nodes and pathways. The purpose of this work was to assess health vulnerability to short-term summer heat exposure based on a directional interaction network. Bayesian network model and network analysis were used to conduct a directional interaction network. Using indicator directional associations as weights, a weighted technique for the order of preference by similarity to ideal solution method was then proposed to assess heat-related health vulnerability. Finally, hotspots and coping strategies were explored based on the directional interaction network and health vulnerability assessments. The results showed that (1) indicator directional interactions were revealed in the health vulnerability framework, and the interactions differed between northern and southern China; (2) there was a dramatic spatial imbalance of health vulnerability in China, with the Beijing-Tianjin-Hebei Region and the Yangtze River Basin identified as hotspots; (3) particulate matter and ozone were recognized as priority indicators in the most vulnerable cities of northern China, while summer heat exposure level and variation were priority indicators in southern China; and (4) adaptive capacity could alter the extent of risk; thus, mitigation and adaptation should be implemented in an integrated way. Our study has important implications for strengthening the theoretical basis for the vulnerability assessment framework by providing indicator directional associations and for guiding policy design in dealing with heat-related health vulnerability in China.
健康风险源于非最佳温度暴露，即“系统性风险”，在全球变暖背景下，已成为可持续发展的挑战。先前的研究在评估气候变化脆弱性时，已经认识到系统组成部分之间的相互作用，但尚未解决指标方向相互作用的问题。这个问题很重要，尤其是因为指标方向关联分析通过揭示关键节点和途径，为应对气候风险提供了指导。本研究旨在基于方向相互作用网络评估短期夏季热暴露对健康的脆弱性。采用贝叶斯网络模型和网络分析进行方向相互作用网络分析。使用指标方向关联作为权重，提出了一种基于理想解相似性的偏好顺序加权技术，以评估与热相关的健康脆弱性。最后，根据方向相互作用网络和健康脆弱性评估，探讨了热点和应对策略。结果表明：（1）在健康脆弱性框架中揭示了指标方向相互作用，且北方和南方的相互作用存在差异；（2）中国健康脆弱性存在显著的空间不平衡，京津冀地区和长江流域被确定为热点地区；（3）在中国北方最脆弱的城市中，颗粒物和臭氧被认为是优先指标，而在南方，夏季热暴露水平和变化是优先指标；（4）适应能力可以改变风险程度；因此，应综合实施缓解和适应措施。本研究通过提供指标方向关联，为加强脆弱性评估框架的理论基础提供了重要依据，并为中国应对与热相关的健康脆弱性提供了政策设计指导。
