Carter Anabel W, Zaitchik Benjamin F, Gohlke Julia M, Wang Suwei, Richardson Molly B
Department of Earth and Planetary Sciences Johns Hopkins University Baltimore MD USA.
Department of Population Health Sciences Virginia Polytechnic Institute and State University Blacksburg VA USA.
Geohealth. 2020 May 21;4(5):e2019GH000231. doi: 10.1029/2019GH000231. eCollection 2020 May.
Heat stress is a significant health concern that can lead to illness, injury, and mortality. The wet bulb globe temperature (WBGT) index is one method for monitoring environmental heat risk. Generally, WBGT is estimated using a heat stress monitor that includes sensors capable of measuring ambient, wet bulb, and black globe temperature, and these measurements are combined to calculate WBGT. However, this method can be expensive, time consuming, and requires careful attention to ensure accurate and repeatable data. Therefore, researchers have attempted to use standard meteorological measurements, using single data sources as an input (e.g., weather stations) to calculate WBGT. Building on these efforts, we apply data from a variety of sources to calculate WBGT, understand the accuracy of our estimated equation, and compare the performance of different sources of input data. To do this, WBGT measurements were collected from Kestrel 5400 Heat Stress Trackers installed in three locations in Alabama. Data were also drawn from local weather stations, North American Land Data Assimilation System (NLDAS), and low cost iButton hygrometers. We applied previously published equations for estimating natural wet bulb temperature, globe temperature, and WBGT to these diverse data sources. Correlation results showed that WBGT estimates derived from all proxy data sources-weather station, weather station/iButton, NLDAS, NLDAS/iButton-were statistically indistinguishable from each other, or from the Kestrel measurements, at two of the three sites. However, at the same two sites, the addition of iButtons significantly reduced root mean square error and bias compared to other methods.
热应激是一个重大的健康问题,可能导致疾病、伤害和死亡。湿球黑球温度(WBGT)指数是监测环境热风险的一种方法。一般来说,WBGT是使用热应激监测仪估算的,该监测仪包括能够测量环境温度、湿球温度和黑球温度的传感器,并将这些测量值结合起来计算WBGT。然而,这种方法可能成本高昂、耗时,并且需要仔细关注以确保数据的准确和可重复。因此,研究人员试图使用标准气象测量数据,将单一数据源作为输入(例如气象站)来计算WBGT。在这些努力的基础上,我们应用来自各种来源的数据来计算WBGT,了解我们估算方程的准确性,并比较不同输入数据源的性能。为此,我们从安装在阿拉巴马州三个地点的Kestrel 5400热应激追踪仪收集了WBGT测量数据。数据还取自当地气象站、北美陆地数据同化系统(NLDAS)和低成本的iButton湿度计。我们将先前发表的用于估算自然湿球温度、黑球温度和WBGT的方程应用于这些不同的数据源。相关性结果表明,在三个地点中的两个地点,从所有替代数据源(气象站、气象站/iButton组合、NLDAS、NLDAS/iButton组合)得出的WBGT估算值在统计学上与Kestrel测量值之间没有差异,或者彼此之间没有差异。然而,在相同的两个地点,与其他方法相比,添加iButton显著降低了均方根误差和偏差。