The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of \(\text {NO}_{x}\) in the Los Angeles area during a 10 year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated \(R^2\) of approximately \(0.7\) at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy.

中文翻译：

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具有空间和时空协变量的空气污染的灵活时空模型。

在小空间尺度上提供准确的环境空气污染时空预测模型的开发对于评估空气污染的潜在健康影响非常重要。在这里，我们提出了一个时空框架，该框架通过将来自几个不同监测网络的数据和确定性空气污染模型与地理信息系统协变量相结合来预测环境空气污染。本文中介绍的模型已在 R 包SpatioTemporal 中实现，在 CRAN 上可用。EPA 资助的动脉粥样硬化和空气污染多种族研究 (MESA Air) 使用该模型来估计环境空气污染；MESA Air 使用这些估计值来调查长期暴露于空气污染与心血管疾病之间的关系。在本文中，我们使用该模型来预测\(\text {NO}_{x}\)在 10 年期间在洛杉矶地区。预测基于 EPA 空气质量系统的测量值、MESA Air 特定监测以及交通相关空气污染源扩散模型 (Caline3QHCR) 的输出。使用复杂的交叉验证设置评估预测长期平均浓度的准确性，该设置说明数据中的稀疏时空采样模式，并针对时间效应进行调整。模型的预测能力很好，交叉验证的\(R^2\)约为\(0.7\)在主题站点。用 Caline3QHCR 分散模型输出替换交通密度的四个地理协变量指标，从更简约和更易解释的模型中得到了非常相似的预测精度。将与交通相关的地理协变量添加到包含 Caline3QHCR 的模型中并没有进一步提高预测精度。