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Prediction of particle pollution through spatio-temporal multivariate geostatistical analysis: spatial special issue

机译：通过时空多元地统计分析预测颗粒物污染：空间特刊

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Vehicular traffic, industrial activity and street dust are important sources of atmospheric particles, which cause pollution and serious health problems, including respiratory illness. Hence, techniques for analyzing and modeling the spatio-temporal behavior of particulate matter (PM), in the recent statistical literature, represent an essential support for environmental and human health protection. In this paper, air pollution from particles with diameters smaller than 10 μm and related meteorological variables, such as temperature and wind speed, measured during November 2009 in the south of Apulian region (Lecce, Brindisi, and Taranto districts) are studied. A thorough multivariate geostatistical analysis is proposed, where different tools for testing the symmetry assumption of the spatio-temporal linear coregionalization model (ST-LCM) are considered, as well as a recent fitting procedure of the ST-LCM, based on the simultaneous diagonalization of symmetric real-valued matrix variograms, is adopted and two non-separable classes of variogram models, the product-sum and Gneiting classes, are fitted to the basic components. The most significant aspects of this study are (a) the quantitative assessment of the assumption of symmetry of the ST-LCM, (b) the use of different non-separable spatio-temporal models for fitting the basic components of a ST-LCM and, more importantly, (c) the application of the spatio-temporal multivariate geostatistical analysis to predict particle pollution in one of the most polluted geographical area. Prediction maps for particle pollution levels with the corresponding validation results are given.
机译：车辆交通，工业活动和街道灰尘是大气颗粒物的重要来源，会造成污染和严重的健康问题，包括呼吸系统疾病。因此，在最近的统计文献中，用于分析和建模颗粒物（PM）时空行为的技术代表了对环境保护和人类健康的重要支持。本文研究了2009年11月在阿普利亚南部（莱切，布林迪西和塔兰托地区）测量的直径小于10μm的颗粒以及相关的气象变量（如温度和风速）造成的空气污染。提出了全面的多元地统计分析方法，其中考虑了用于测试时空线性共区域化模型（ST-LCM）的对称性假设的不同工具，以及基于同时对角化的ST-LCM的最近拟合过程采用对称实值矩阵变异函数的形式，并将两个不可分的变异函数模型乘积和和Gneiting类拟合到基本组件。这项研究的最重要方面是（a）对ST-LCM对称性假设的定量评估，（b）使用不同的不可分离的时空模型拟合ST-LCM的基本组成部分，以及更重要的是，（c）应用时空多元地统计分析来预测污染最严重的地理区域之一中的颗粒污染。给出了颗粒污染水平的预测图以及相应的验证结果。

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