Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA.
Environ Res. 2013 Feb;121:1-10. doi: 10.1016/j.envres.2012.11.003. Epub 2012 Dec 6.
Most of currently reported models for predicting PM(2.5) concentrations from satellite retrievals of aerosol optical depth are global methods without considering local variations, which might introduce significant biases into prediction results. In this paper, a geographically weighted regression model was developed to examine the relationship among PM(2.5), aerosol optical depth, meteorological parameters, and land use information. Additionally, two meteorological datasets, North American Regional Reanalysis and North American Land Data Assimilation System, were fitted into the model separately to compare their performances. The study area is centered at the Atlanta Metro area, and data were collected from various sources for the year 2003. The results showed that the mean local R(2) of the models using North American Regional Reanalysis was 0.60 and those using North American Land Data Assimilation System reached 0.61. The root mean squared prediction error showed that the prediction accuracy was 82.7% and 83.0% for North American Regional Reanalysis and North American Land Data Assimilation System in model fitting, respectively, and 69.7% and 72.1% in cross validation. The results indicated that geographically weighted regression combined with aerosol optical depth, meteorological parameters, and land use information as the predictor variables could generate a better fit and achieve high accuracy in PM(2.5) exposure estimation, and North American Land Data Assimilation System could be used as an alternative of North American Regional Reanalysis to provide some of the meteorological fields.
目前大多数利用卫星反演气溶胶光学厚度来预测 PM(2.5)浓度的模型都是全球方法，没有考虑到局部变化，这可能会给预测结果带来显著偏差。本文利用地理加权回归模型来研究 PM(2.5)、气溶胶光学厚度、气象参数和土地利用信息之间的关系。此外，还分别利用两个气象数据集（北美区域再分析和北美陆面数据同化系统）来拟合模型，以比较它们的性能。研究区域以亚特兰大都会区为中心，数据采集自 2003 年的多个来源。结果表明，使用北美区域再分析的模型的平均局部 R(2)为 0.60，使用北美陆面数据同化系统的模型达到 0.61。均方根预测误差表明，模型拟合的预测精度分别为 82.7%和 83.0%，交叉验证的预测精度分别为 69.7%和 72.1%。结果表明，地理加权回归结合气溶胶光学厚度、气象参数和土地利用信息作为预测变量，可以更好地拟合，并实现 PM(2.5)暴露估计的高精度，而且北美陆面数据同化系统可以作为北美区域再分析的替代方法，提供一些气象场。
