Wu Yunhan, Bi Jianzhao, Gassett Amanda J, Young Michael T, Szpiro Adam A, Kaufman Joel D
Department of Biostatistics, University of Washington, Seattle, WA, USA.
Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
Sci Total Environ. 2024 May 15;925:171652. doi: 10.1016/j.scitotenv.2024.171652. Epub 2024 Mar 13.
Accurately predicting ambient NO concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach incorporating traffic contribution to NO prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO exposure - especially near major roads in the U.S.
准确预测环境中的一氧化氮（NO）浓度对公众健康具有重要意义，因为与交通相关的空气污染是城市地区的主要关注点。在本研究中，我们提出了一种新方法，将交通贡献纳入精细尺度的时空模型中以进行NO预测。我们在可扩展的扩散模型——研究线源扩散模型（RLINE）中使用了全国可用的交通估计数据集。然后，RLINE估计值作为一个额外的输入，用于经过验证的时空污染建模方法。我们的分析使用了2006年至2009年在大洛杉矶地区进行的多民族动脉粥样硬化与空气污染研究收集的测量数据。我们通过具有最近邻高斯过程的国家级空间回归模型（spNNGP）预测路段上特定道路类型的年平均日交通量（AADT）；spNNGP模型是基于全国范围内超过50万个点级交通量测量数据进行训练的。所有高速公路上的AADT估计值与RLINE模型中的气象数据相结合。我们评估了两种将RLINE估计值整合到时空NO模型中的策略：1）将RLINE估计值作为仅空间的协变量纳入，以及2）作为时空协变量纳入。结果表明，将RLINE估计值作为仅空间的协变量纳入后，整体交叉验证R从0.83提高到0.84，均方根误差（RMSE）从3.58降至3.48 ppb。将估计值作为时空协变量纳入也带来了类似的模型改进。我们的时空模型在高速公路旁的路边监测器中的改进更为显著，R从0.56增加到0.66，RMSE从3.52降至3.11 ppb。观察到的改进表明，即使在考虑了包括到道路的距离在内的一系列综合地理协变量之后，RLINE估计值仍增强了模型对路边NO浓度梯度的预测能力。我们提出的建模框架可以推广，以改进NO暴露的高分辨率预测——特别是在美国主要道路附近。
