Sánchez-Balseca Joseph, Pérez-Foguet Agustí
Research Group on Engineering Sciences and Global Development (EScGD), Civil and Environmental Engineering Department, Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Spain.
Heliyon. 2020 Sep 14;6(9):e04794. doi: 10.1016/j.heliyon.2020.e04794. eCollection 2020 Sep.
Air pollutant data are compositional in character because they describe quantitatively the parts of a whole (atmospheric composition). However, it is common to use air pollutant concentrations in statistical models without considering this characteristic of the data and, therefore, without control of common statistical problems, such as spurious correlations and subcompositional incoherence. This paper now proposes a daily multivariate spatio-temporal model with a compositional approach. The air pollution spatio-temporal model is based on a dynamic linear modelling framework with Bayesian inference. The novel modelling methodology was applied in an urban area for carbon monoxide (CO, mg·m), sulfur dioxide (SO, μg·m), ozone (O, μg·m), nitrogen dioxide (NO, μg·m), and particulate matter less than 2.5 μm in aerodynamic diameter (PM, μg·m). The proposal complemented and improved the conventional approach in air pollution modelling. The main improvements come from a fast multivariate data description, high spatial-correlation, and adequate modelling of air pollutants with high variability.
空气污染物数据具有成分特征，因为它们定量描述了整体的各个部分（大气成分）。然而，在统计模型中使用空气污染物浓度时，通常不考虑数据的这一特征，因此也不控制常见的统计问题，如虚假相关性和子成分不连贯性。本文现提出一种采用成分方法的每日多变量时空模型。空气污染时空模型基于具有贝叶斯推断的动态线性建模框架。这种新颖的建模方法应用于一个城市地区的一氧化碳（CO，毫克·立方米）、二氧化硫（SO₂，微克·立方米）、臭氧（O₃，微克·立方米）、二氧化氮（NO₂，微克·立方米）以及空气动力学直径小于2.5微米的颗粒物（PM₂.₅，微克·立方米）。该提议补充并改进了空气污染建模中的传统方法。主要改进来自快速的多变量数据描述、高空间相关性以及对高变异性空气污染物的充分建模。
