Ambient air pollution measurements from regulatory monitoring networks are routinely used to support epidemiologic studies and environmental policy decision making. However, regulatory monitors are spatially sparse and preferentially located in areas with large populations. Numerical air pollution model output can be leveraged into the inference and prediction of air pollution data combining with measurements from monitors. Nonstationary covariance functions allow the model to adapt to spatial surfaces whose variability changes with location like air pollution data. In the paper, we employ localized covariance parameters learned from the numerical output model to knit together into a global nonstationary covariance, to incorporate in a fully Bayesian model. We model the nonstationary structure in a computationally efficient way to make the Bayesian model scalable.

翻译：来自监管监测网络的环境空气污染测量数据常规用于支持流行病学研究和环境政策决策。然而，监管监测点在空间上分布稀疏，且优先设置在人口稠密区域。通过结合监测点测量数据，可将数值空气污染模型输出用于空气污染数据的推断与预测。非平稳协方差函数使模型能够适应像空气污染数据这样变异性随位置变化的空间场。本文中，我们利用从数值输出模型学习到的局部协方差参数，将其整合为全局非平稳协方差，并纳入一个完全贝叶斯模型中。我们以计算高效的方式对非平稳结构进行建模，从而使贝叶斯模型具备可扩展性。