Kloog Itai, Chudnovsky Alexandra A, Just Allan C, Nordio Francesco, Koutrakis Petros, Coull Brent A, Lyapustin Alexei, Wang Yujie, Schwartz Joel
Department of Geography and Environmental Development, Ben-Gurion University, Israel.
Department of Geography and Human Environment, Tel-Aviv University, Israel.
Atmos Environ (1994). 2014 Oct;95:581-590. doi: 10.1016/j.atmosenv.2014.07.014. Epub 2014 Jul 5.
The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data.
We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site.
Our model performance was excellent (mean out-of-sample R=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R=0.87, R=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99).
Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.
在过去几年中，利用基于卫星的气溶胶光学厚度（AOD）来估计用于流行病学研究的细颗粒物（PM）的情况大幅增加。这些近期研究常常报告预测能力一般，这可能在效应估计中产生向下偏差。此外，AOD测量的空间分辨率仅为中等，且存在大量缺失数据。
我们利用了中分辨率成像光谱仪（MODIS）卫星数据处理算法（大气校正多角度实现（MAIAC））的最新进展，这使我们能够使用1千米（而非目前可用的10千米）分辨率的AOD数据。我们开发并交叉验证了模型，以预测2003年至2011年美国东北部（新英格兰、纽约和新泽西）1×1千米分辨率下的每日PM，从而使我们能够更好地区分城市、郊区和农村地区的每日和长期暴露情况。此外，我们开发了一种方法，使我们能够生成每日高分辨率的200米局部预测，以表示与1×1千米区域网格预测的偏差。我们使用混合模型，将PM测量值与特定日期的随机截距以及固定和随机的AOD及温度斜率进行回归。然后，当AOD缺失时，我们使用具有空间平滑功能的广义相加混合模型来生成网格单元预测。最后，为了获得200米局部预测，我们将每个监测器最终模型的残差与每个监测站点的局部空间和时间变量进行回归。
我们的模型性能出色（样本外平均R = 0.88）。样本外结果的空间和时间成分对保留数据的拟合也非常好（R = 0.87，R = 0.87）。此外，我们的结果显示预测浓度几乎没有偏差（预测值与保留观测值的斜率 = 0.99）。
我们的每日模型结果在高空间分辨率下显示出高预测准确性，将有助于重建该地区流行病学研究的暴露史。
