Epidemiologic studies have used aerosol optical depth (AOD)-PM_2.5 as a proxy for ambient PM_2.5 in urban and rural areas, even though its validation with air monitors has only occurred in urban areas. The contribution of elevated AOD-PM_2.5 on respiratory-cardiovascular true positive (TP) cases, exposed to high PM_2.5, and true negative (TN) controls, not exposed to elevated PM_2.5, was evaluated in 72 Community Multiscale Air Quality (CMAQ) grids with (urban) and without (rural) air monitors. The odds ratio (OR) algorithm and the newly developed beta (ẞ) algorithm were used to evaluate the reliability and validity of TP cases, and TN controls in grids with and without air monitors. Four experimental AOD-PM_2.5 fused surfaces and four health outcomes were evaluated. Only the linear predictor (ẞ) algorithm reliably and correctly identified TP cases and TN controls, with probabilities ~1.00. The OR algorithm only identified TN controls, with probabilities ~1.00, and significantly overestimated the percentage of TP cases. Regression analyses demonstrated that the OR algorithm's accuracy could be improved if the number of cases for all health outcomes was increased 50.8% in all grids and 73.9% in grids without monitors. Since the number and percentage of TP cases and TN controls were similar in grids with and without air monitors, this outcome suggests that the AOD-PM_2.5 and health outcome concentration-response function evaluated in grids with monitors also holds in grids without air monitors. The possible use of AOD-PM_2.5 fused surfaces, as another epidemiologic tool, to assess elevated ambient PM_2.5 concentration levels to respiratory-cardiovascular hospital events in rural areas is discussed.

流行病学研究已使用气溶胶光学深度 (AOD)-PM _2.5作为城市和农村地区环境 PM _2.5的替代指标，尽管其通过空气监测器的验证仅发生在城市地区。72 社区多尺度空气质量 (CMAQ) 评估了AOD-PM _2.5升高对暴露于高 PM _{2.5 的}呼吸心血管真阳性 (TP) 病例和未暴露于升高的 PM _{2.5 的}真阴性 (TN) 对照的贡献) 带（城市）和不带（农村）空气监测器的网格。优势比 (OR) 算法和新开发的 beta (ẞ) 算法用于评估 TP 案例的可靠性和有效性，以及有和没有空气监测器的网格中的 TN 控制。四实验AOD-PM_2.5融合表面和四个健康结果进行了评估。只有线性预测器 (ẞ) 算法可靠且正确地识别 TP 案例和 TN 控制，概率约为 1.00。OR 算法仅识别 TN 对照，概率约为 1.00，并且显着高估了 TP 病例的百分比。回归分析表明，如果所有健康结果的病例数在所有网格中增加 50.8%，在没有监视器的网格中增加 73.9%，则可以提高 OR 算法的准确性。由于在有和没有空气监测器的网格中 TP 病例和 TN 控制的数量和百分比相似，该结果表明 AOD-PM _2.5在有监测器的网格中评估的健康结果浓度-反应函数在没有空气监测器的网格中也成立。讨论了使用 AOD-PM _2.5融合表面作为另一种流行病学工具来评估农村地区呼吸心血管医院事件中环境 PM _2.5浓度升高的可能用途。