Evaluating Data Product Exposure Metrics for Use in Epidemiologic Studies of Dust Storms.
Evaluating Data Product Exposure Metrics for Use in Epidemiologic Studies of Dust Storms.
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DOI：
10.1029/2023gh000824
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发表时间：
2023-08
期刊：
影响因子：
4.8
通讯作者：
Crooks, James
中科院分区：
文献类型：
--
作者：
Hohsfield, Kirk;Rowan, Claire;D'Souza, Rohan;Ebelt, Stefanie;Chang, Howard;Crooks, James
沙尘暴的频率正在增加，并与不利的健康结果相关，但在美国仍然研究不足，部分原因是当前数据集的时空覆盖范围、分辨率和准确性有限。在这项工作中，将来自四个公共区域数据产品的灰尘相关指标与基于监测器的“金标准”灰尘数据集进行了比较。数据产品包括国家气象局（NWS）风暴事件数据库，现代研究和应用回顾分析-第2版，EPA的空气质量时间系列（EQUATES）项目使用社区多尺度空气质量建模系统（CMAQ），以及哥白尼大气监测服务全球再分析产品。加州、内华达州、犹他州和亚利桑那州是美国报告的沙尘暴最多的地区，进行了检查。基于报告的沙尘暴、颗粒物浓度（PM10和PM2.5）和气溶胶类型变量的二分和连续指标是从数据产品中提取或导出的。通过准二项式回归估计了这些指标与利用受保护视觉环境监测器的机构间监测的经验证沙尘暴检测方法之间的关联。一般来说，CAMS的指标与“黄金标准”的关联最强，其次是NWS风暴数据库指标。沙尘气溶胶（0.9-20 μm）的混合比，垂直积分质量的沙尘气溶胶（9-20 μm），和沙尘气溶胶光学厚度在550 nm的CAMS产生最高的标准化优势比在所有指标。未来的工作将把机器学习方法应用于最佳性能指标，以创建一个适合长期流行病学研究的公共沙尘暴数据库。来自公共数据产品的粉尘指标的准确性（国家气象局风暴，哥白尼大气监测服务（CAMS），研究和应用的现代回顾分析-第2版，和CMAQ-EQUATES）与“金标准”数据集进行了比较来自CAMS的混合比度量（0.9-20 μm）与“金标准”的关联最强从CAMS得到的气溶胶混合比可能是比常用的沙尘表征方法更有用的流行病学研究的沙尘指标
Dust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio‐temporal coverage, resolution, and accuracy of current data sets. In this work, dust‐related metrics from four public areal data products were compared to a monitor‐based “gold standard” dust data set. The data products included the National Weather Service (NWS) storm event database, the Modern‐Era Retrospective analysis for Research and Applications—Version 2, the EPA's Air QUAlity TimE Series (EQUATES) Project using the Community Multiscale Air Quality Modeling System (CMAQ), and the Copernicus Atmosphere Monitoring Service global reanalysis product. California, Nevada, Utah, and Arizona, which account for most dust storms reported in the U.S., were examined. Dichotomous and continuous metrics based on reported dust storms, particulate matter concentrations (PM10 and PM2.5), and aerosol‐type variables were extracted or derived from the data products. Associations between these metrics and a validated dust storm detection method utilizing Interagency Monitoring of Protected Visual Environments monitors were estimated via quasi‐binomial regression. In general, metrics from CAMS yielded the strongest associations with the “gold standard,” followed by the NWS storm database metric. Dust aerosol (0.9–20 μm) mixing ratio, vertically integrated mass of dust aerosol (9–20 μm), and dust aerosol optical depth at 550 nm from CAMS generated the highest standardized odds ratios among all metrics. Future work will apply machine‐learning methods to the best‐performing metrics to create a public dust storm database suitable for long‐term epidemiologic studies. Accuracy of dust metrics from public data products (National Weather Service storms, Copernicus Atmosphere Monitoring Service (CAMS), Modern‐Era Retrospective analysis for Research and Applications—Version 2, and CMAQ‐EQUATES) were compared to a “gold standard” data set Maximum dust aerosol (0.9–20 μm) mixing ratio metric from CAMS had the strongest association with the “gold standard” dust event variable Aerosol mixing ratios from CAMS may be a more useful dust metric for epidemiologic studies than commonly used dust characterization methods