EAGER: Predicting Electricity Demand and Indoor Air Quality During Heat Waves

热浪可以对人类健康产生重大影响，特别是对人口稠密地区的个人。该项目将收集城市家庭的空气质量和电力使用数据，并将这些数据与多层次气候模型相连接。这项工作的核心科学价值是将真实数据和气候模型结合起来预测某些人群的脆弱性。该研究小组将与新泽西州伊丽莎白市住房管理局合作，后者试图了解最贫困的城市家庭如何更好地应对越来越多的热浪。该奖项是美国国家科学基金会智能和互联社区项目的一部分。研究小组计划将建筑物及其居住者添加到以前的奖项中开发的多层次气候-人类模型框架中。建模框架将居住者对热应力的反应与室内污染物和电力消耗的变化联系起来。传感器将用于测量空气质量和电力使用情况。研究人员希望能够预测不同人群对热浪的适应能力，作为个人、建筑水平和位置特征的函数，从而确定与气候相关的压力超过阈值就会成为人类健康灾难。

Heat waves can have substantial impacts on human health, especially for individuals in densely populated locations. This project will collect air quality and power usage data from households in urban settings and connect this data to a multi-level climate model. The core scientific merit of the work is to integrate real data and climate models to predict the vulnerability of certain population segments. The research team will work with the Housing Authority of the City of Elizabeth, NJ, who seek to understand how the poorest urban households can better cope with an increasing number of heat waves. This award is part of the Smart and Connected Communities program at NSF. The research team plans to add buildings and their occupants to the multi-level climate-to-humans modeling framework developed under a previous award. The modeling framework will link occupant responses to heat stress to changes in indoor pollutants and electricity consumption. Sensors will be deployed to measure air quality and power usage. The researchers expect to be able to predict how adaptable different populations segments are to heat waves as a function of personal, building-level, and locational characteristics, thereby identifying thresholds beyond which climate-related stresses become human health disasters.