Department of Population Health Sciences, Virginia Tech, Blacksburg, VA, USA.
Translational Biology, Medicine and Health (TBMH), Virginia Tech, Blacksburg, VA, USA.
J Expo Sci Environ Epidemiol. 2021 Jul;31(4):641-653. doi: 10.1038/s41370-021-00303-x. Epub 2021 Feb 18.
Heatwave warning systems rely on forecasts made for fixed-point weather stations (WS), which do not reflect variation in temperature and humidity experienced by individuals moving through indoor and outdoor locations. We examined whether neighborhood measurement improved the prediction of individually experienced heat index in addition to nearest WS in an urban and rural location. Participants (residents of Birmingham, Alabama [N = 89] and Wilcox County, Alabama [N = 88]) wore thermometers clipped to their shoe for 7 days. Shielded thermometers/hygrometers were placed outdoors within participant's neighborhoods (N = 43). Nearest WS and neighborhood thermometers were matched to participant's home address. Heat index (HI) was estimated from participant thermometer temperature and WS humidity per person-hour (HI[individual]), or WS temperature and humidity, or neighborhood temperature and humidity. We found that neighborhood HI improved the prediction of individually experienced HI in addition to WS HI in the rural location, and neighborhood heat index alone served as a better predictor in the urban location, after accounting for individual-level factors. Overall, a 1 °C increase in HI[neighborhood] was associated with 0.20 °C [95% CI (0.19, 0.21)] increase in HI[individual]. After adjusting for ambient condition differences, we found higher HI[individual] in the rural location, and increased HI[individual] during non-rest time (5 a.m. to midnight) and on weekdays.
热浪预警系统依赖于针对定点气象站（WS）进行的预测，而这些预测无法反映个人在室内和室外位置移动时所经历的温度和湿度变化。我们研究了在城市和农村地区，除了最近的 WS 之外，邻里测量是否可以改善个体经历的热指数预测。参与者（阿拉巴马州伯明翰的居民[N=89]和阿拉巴马州威尔科克斯县[N=88]）在 7 天内将温度计夹在鞋上。在参与者的邻里内（N=43）放置了带有屏蔽的温度计/湿度计。将最近的 WS 和邻里温度计与参与者的家庭住址相匹配。根据参与者温度计的温度和 WS 湿度（每人每小时的热指数[个体]），或 WS 温度和湿度，或邻里温度和湿度来估算热指数（HI）。我们发现，除了 WS HI 之外，邻里 HI 还可以改善农村地区个体经历的 HI 预测，并且在考虑到个体因素后，邻里热指数本身在城市地区是更好的预测因素。总的来说，HI[邻里]每增加 1°C，HI[个体]就会增加 0.20°C[95%CI（0.19, 0.21）]。在调整环境条件差异后，我们发现农村地区的 HI[个体]更高，并且在非休息时间（凌晨 5 点至午夜）和工作日期间，HI[个体]增加。
