A Comparison of Spatial and Nonspatial Methods in Statistical Modeling of NO2: Prediction Accuracy,Uncertainty Quantification,and Model Interpretation

${\text{NO}}_2$ is a traffic-related air pollutant. Ground ${\text{NO}}_2$ monitoring stations measure ${\text{NO}}_2$ concentrations at certain locations and statistical predictive methods have been developed to predict ${\text{NO}}_2$ as a continuous surface. Among them, ensemble tree-based methods have shown to be powerful in capturing nonlinear relationships between ${\text{NO}}_2$ measurements and geospatial predictors but it is unclear if the spatial structure of ${\text{NO}}_2$ is also captured in the response-covariates relationships. We dive into the comparison between spatial and nonspatial data models accounting for prediction accuracy, model interpretation and uncertainty quantification. Moreover, we implement two new spatial and a nonspatial methods that have not been applied to air pollution mapping. We implemented our study using national ground station measurements of ${\text{NO}}_2$ in Germany and the Netherlands of 2017. Our results indicate heterogeneous levels of importance of modeling the spatial process in different areas. The prediction intervals predicted with ensemble tree-based methods are more satisfactory than the geostatistical methods. The two new methods implemented each obtained better prediction accuracy compared to the original ensemble tree-based and stacking methods. The probabilistic distribution of the spatial random field estimated by the geostatistical methods could provide useful information for analyzing emission sources and the spatial process of observations.     

古建筑振动标准分级探讨

为了对城市古建进行微振动控制,尤其是对交通振动的控制,本工作总结了国内外古建筑振动控制标准,提出了古建筑微振动控制的基本前提和考虑因素.分析了《古建筑防工业振动技术规范》中存在的问题.基于既有国标研究成果,提出了一种考虑古建筑维修历史、保护现状的振动控制标准降级方法,并汇总形成了适用于城市古建筑的微振动控制矩阵.