Research of PM_2.5 chronic health effects requires knowledge of large-scale and long-term exposure that is not supported by newly established monitoring networks due to their sparse spatial coverage and lack of historical measurements. Estimating PM_2.5 using satellite-derived aerosol optical depth (AOD) can be used to fill the data gap left by the ground monitors and extend the PM_2.5 data coverage to suburban and rural areas over long time periods. Two approaches have been applied in large-scale and long-term satellite remote sensing of PM_2.5, i.e., the scaling and statistical approaches. Compared to the scaling method, the statistical approach has greater prediction accuracy and has been widely used. There is a gap in the current literature and review papers on how the statistical methods work and specific considerations to best utilize them, especially for large-scale and long-term estimates. In this critical review, we summarize the evolution of large-scale and long-term PM_2.5 statistical models reported in the literature. We describe the framework and guidance of large-scale and long-term satellite-based PM_2.5 modeling in data preparation, model development, validation, and predictions. Sample computer codes are provided to expedite new model-building efforts. We also include useful considerations and recommendations in covariates selection, addressing the spatiotemporal heterogeneities of PM_2.5-AOD relationships, and the usage of cross validation, to aid the determination of the final model.

PM _2.5慢性健康影响的研究需要大规模和长期暴露的知识，而新建立的监测网络由于其空间覆盖稀少且缺乏历史测量数据而无法支持这些知识。使用卫星衍生的气溶胶光学深度 (AOD)估计 PM _2.5可用于填补地面监测器留下的数据空白，并将 PM _2.5数据覆盖范围扩展到郊区和农村地区。两种方法已应用于 PM _2.5的大规模和长期卫星遥感，即缩放和统计方法。与标度方法相比，统计方法具有更高的预测精度，得到了广泛的应用。当前的文献和评论论文中存在关于统计方法如何工作以及最佳利用它们的具体考虑因素的空白，特别是对于大规模和长期估计。在这篇批判性评论中，我们总结了文献中报道的大规模和长期 PM _2.5统计模型的演变。我们描述了大规模和长期基于卫星的 PM _2.5的框架和指导在数据准备、模型开发、验证和预测中建模。提供示例计算机代码以加快新的模型构建工作。我们还在协变量选择、解决 PM _2.5 -AOD 关系的时空异质性以及交叉验证的使用中包含有用的考虑和建议，以帮助确定最终模型。