A joint Bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA
A joint Bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA
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DOI：
10.1002/env.2644
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发表时间：
2020-07-29
期刊：
影响因子：
1.7
通讯作者：
Blangiardo, M.
中科院分区：
文献类型：
--
作者：
Forlani, C.;Bhatt, S.;Blangiardo, M.
在空气污染研究中，扩散模型提供了覆盖整个空间域的网格级浓度估计值，然后根据监测站的测量值进行校准。然而，这些不同的数据源在空间和时间上不一致。如果不考虑未对准，它可能会使预测产生偏差。我们的目标是展示如何结合多个数据源，如分散模型输出，地面观测和协变量，导致更准确的预测网格级的空气污染。我们考虑2007-2011年大伦敦及周边地区的二氧化氮浓度，并结合联合收割机两种不同的扩散模型。为了获得最佳的预测能力，还包括不同的空间和时间效应。我们提出的模型框架之间的校准和贝叶斯融合技术的数据融合。与其他例子不同，我们联合模型的响应（在监测站的浓度水平）和分散模型输出在不同的尺度上，占不确定性的不同来源。我们的时空模型使我们能够重建每个模型组件的潜在领域，并预测每天的污染浓度。我们将我们提出的模型的预测能力与其他已建立的方法进行了比较，以考虑未对准（例如，双线性插值），表明在我们的案例研究中，联合模型是一个更好的选择。
In air pollution studies, dispersion models provide estimates of concentration at grid level covering the entire spatial domain and are then calibrated against measurements from monitoring stations. However, these different data sources are misaligned in space and time. If misalignment is not considered, it can bias the predictions. We aim at demonstrating how the combination of multiple data sources, such as dispersion model outputs, ground observations, and covariates, leads to more accurate predictions of air pollution at grid level. We consider nitrogen dioxide (NO2) concentration in Greater London and surroundings for the years 2007-2011 and combine two different dispersion models. Different sets of spatial and temporal effects are included in order to obtain the best predictive capability. Our proposed model is framed in between calibration and Bayesian melding techniques for data fusion. Unlike other examples, we jointly model the response (concentration level at monitoring stations) and the dispersion model outputs on different scales, accounting for the different sources of uncertainty. Our spatiotemporal model allows us to reconstruct the latent fields of each model component, and to predict daily pollution concentrations. We compare the predictive capability of our proposed model with other established methods to account for misalignment (e.g., bilinear interpolation), showing that in our case study the joint model is a better alternative.