Assessing historical exposure to air pollution in epidemiological studies is often problematic because of limited spatial and temporal measurement coverage. Several methods for modelling historical exposures have been described, including land-use regression (LUR). Satellite-based LUR is a recent technique that seeks to improve predictive ability and spatial coverage of traditional LUR models by using satellite observations of pollutants as inputs to LUR. Few studies have explored its validity for assessing historical exposures, reflecting the absence of historical observations from popular satellite platforms like Aura (launched mid-2004). We investigated whether contemporary satellite-based LUR models for Australia, developed longitudinally for 2006–2011, could capture nitrogen dioxide (NO₂) concentrations during 1990–2005 at 89 sites around the country. We assessed three methods to back-extrapolate year-2006 NO₂ predictions: (1) ‘do nothing’ (i.e., use the year-2006 estimates directly, for prior years); (2) change the independent variable ‘year’ in our LUR models to match the years of interest (i.e., assume a linear trend prior to year-2006, following national average patterns in 2006–2011), and; (3) adjust year-2006 predictions using selected historical measurements. We evaluated prediction error and bias, and the correlation and absolute agreement of measurements and predictions using R² and mean-square error R² (MSE-R²), respectively. We found that changing the year variable led to best performance; predictions captured between 41% (1991; MSE-R² = 31%) and 80% (2003; MSE-R² = 78%) of spatial variability in NO₂ in a given year, and 76% (MSE-R² = 72%) averaged over 1990–2005. We conclude that simple methods for back-extrapolating prior to year-2006 yield valid historical NO₂ estimates for Australia during 1990–2005. These results suggest that for the time scales considered here, satellite-based LUR has a potential role to play in long-term exposure assessment, even in the absence of historical predictor data.

在流行病学研究中评估空气污染的历史暴露通常是有问题的，因为时空测量范围有限。已经描述了几种对历史暴露进行建模的方法，包括土地利用回归（LUR）。基于卫星的LUR是一项最新技术，旨在通过使用卫星对污染物的观测作为LUR的输入来提高传统LUR模型的预测能力和空间覆盖率。很少有研究探索其用于评估历史暴露的有效性，这反映出缺乏流行的卫星平台（如Aura）（2004年中期推出）的历史观测结果。我们调查了在2006–2011年间纵向开发的当代澳大利亚基于卫星的LUR模型是否可以捕获二氧化氮（NO ₂）在1990-2005年期间在全国89个地点集中。我们评估了三种反推2006年NO ₂预测的方法：（1）“不做任何事”（即，对前几年直接使用2006年NO2预测）；（2）在我们的LUR模型中更改自变量“年”以匹配感兴趣的年（即，假设2006年之前呈线性趋势，遵循2006-2011年全国平均水平），并且；（3）使用选定的历史测量值来调整2006年的预测。我们使用R ²和均方误差R ²（MSE-R ²）， 分别。我们发现，更改年份变量可以带来最佳效果；在给定年份中，NO ₂空间变异的预测介于41％（1991; MSE-R ² = 31％）和80％（2003; MSE-R ² = 78％）之间，而76％（MSE-R ² = 1990年至2005年的平均值为72％）。我们得出的结论是，在2006年之前进行反外推的简单方法可以得出1990-2005年间澳大利亚的有效历史NO ₂估计值。这些结果表明，在此处考虑的时间范围内，即使没有历史预测数据，基于卫星的LUR在长期暴露评估中也可能发挥作用。