Emory University, Department of Biostatistics and Bioinformatics, Atlanta, GA, 30322, USA.
University of Nevada, Reno, Department of Physics, Reno, NV, 89557, USA.
Environ Res. 2019 Nov;178:108601. doi: 10.1016/j.envres.2019.108601. Epub 2019 Jul 25.
Ambient fine particulate matter less than 2.5 μm in aerodynamic diameter (PM) has been linked to various adverse health outcomes. PM arises from both natural and anthropogenic sources, and PM concentrations can vary over space and time. However, the sparsity of existing air quality monitors greatly restricts the spatial-temporal coverage of PM measurements, potentially limiting the accuracy of PM-related health studies. Various methods exist to address these limitations by supplementing air quality monitoring measurements with additional data. We develop a method to combine PM estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous ensemble approaches in its ability to not only incorporate uncertainties in PM estimates from individual models but also to provide uncertainties for the resulting ensemble estimates. In an application of estimating daily PM in the Southeastern US, the ensemble approach outperforms previously developed spatial-temporal statistical models that use either AOD or bias-corrected CTM simulations in cross-validation (CV) analyses. More specifically, in spatially clustered CV experiments, the ensemble approach reduced the AOD-only and CTM-only model's root mean squared error (RMSE) by at least 13%. Similar improvements were seen in R. The enhanced prediction performance that the ensemble technique provides at fine-scale spatial resolution, as well as the availability of prediction uncertainty, can be further used in health effect analyses of air pollution exposure.
大气中空气动力学直径小于 2.5μm 的细颗粒物（PM）与各种不良健康后果有关。PM 既来自自然源也来自人为源，且 PM 浓度在空间和时间上存在差异。然而，现有的空气质量监测器稀疏极大地限制了 PM 测量的时空覆盖范围，可能限制了与 PM 相关的健康研究的准确性。通过用其他数据补充空气质量监测测量，存在多种方法可以解决这些限制。我们开发了一种方法，使用统计模型结合卫星检索的气溶胶光学深度（AOD）和化学传输模型（CTM）模拟来估算 PM。虽然大多数先前的方法分别使用 AOD 或 CTM，但我们旨在利用这两个数据源在分辨率和覆盖范围方面的优势，通过贝叶斯集成平均来实现。我们的方法与以前的集成方法不同，不仅可以合并来自单个模型的 PM 估算的不确定性，还可以为得出的集成估算提供不确定性。在对美国东南部的每日 PM 进行估算的应用中，与使用 AOD 或经偏差校正的 CTM 模拟的先前开发的时空统计模型相比，集成方法在交叉验证（CV）分析中表现更好。更具体地说，在空间聚类 CV 实验中，集成方法至少将 AOD 仅和 CTM 仅模型的均方根误差（RMSE）降低了 13%。在 R 中也观察到了类似的改进。集成技术在精细空间分辨率下提供的增强预测性能以及预测不确定性的可用性，可以进一步用于空气污染暴露的健康影响分析。
