Mercer Laina D, Szpiro Adam A, Sheppard Lianne, Lindström Johan, Adar Sara D, Allen Ryan W, Avol Edward L, Oron Assaf P, Larson Timothy, Liu L-J Sally, Kaufman Joel D
Department of Biostatistics, University of Washington.
Atmos Environ (1994). 2011 Aug 1;45(26):4412-4420. doi: 10.1016/j.atmosenv.2011.05.043.
Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. METHODS: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R(2) and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. RESULTS: UK models consistently performed as well as or better than the analogous LUR models. The best CV R(2) values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R(2) values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R(2) values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. CONCLUSION: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK.
评估长期暴露于环境空气污染对健康影响的流行病学研究被用于为公共政策提供信息。这些研究依赖于暴露模型，该模型使用从污染监测站点收集的数据来预测个体所在位置的暴露情况。土地利用回归（LUR）和通用克里金法（UK）已被提议作为潜在的预测方法。我们在一个包含加利福尼亚州洛杉矶三个季节测量数据的数据集上评估了这些方法。
本研究中使用的气态氮氧化物（NOx）测量数据来自“快照”采样活动，该活动是动脉粥样硬化与空气污染多民族研究（MESA Air）的一部分。洛杉矶的测量数据是在夏季、秋季和冬季的三个为期两周的时间段内收集的，每个时间段约有150个站点。该设计包括在繁忙道路两侧设置监测器集群，以捕捉与交通相关污染的近场梯度。使用基于地理信息系统（GIS）的协变量创建了LUR和UK预测模型。协变量的选择基于10折交叉验证（CV）的R²和均方根误差（RMSE）。由于UK需要专门的软件，还采用了一种计算更简单的两步程序，使用现成的回归和GIS软件来近似拟合UK模型。
UK模型的表现始终与类似的LUR模型相当或更好。预测log(NOx)的特定季节UK模型的最佳CV R²值，夏季、秋季和冬季分别为0.75、0.72和0.74（CV RMSE为0.20、0.17和0.15）。预测log(NOx)的特定季节LUR模型的最佳CV R²值分别为0.74、0.60和0.67（CV RMSE为0.20、0.20和0.17）。UK的两阶段近似法也比LUR表现更好，并且几乎与完整的UK模型一样好，夏季、秋季和冬季的CV R²值分别为0.75、0.70和0.70（CV RMSE为0.20、0.17和0.17）。
基于为MESA Air收集的数据，针对洛杉矶的三个季节开发了高质量的LUR和UK NOx预测模型。在我们的研究中，UK始终优于LUR。同样，两步法比LUR模型更有效，其性能与UK相当或略差。
