Spatial–temporal modellization of the \(\hbox {NO}_{2}\) concentration data

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• 作者：Raquel Menezes ; Helena Piairo ; Pilar García-Soidán…
• 关键词：\(\hbox {NO}_{2}\) ; ; Geostatistics ; Time series analysis ; Space–time analysis
• 刊名：Statistical Methods & Applications
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：25
• 期：1
• 页码：107-124
• 全文大小：1,936 KB
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• 作者单位：Raquel Menezes (1)
  Helena Piairo (1)
  Pilar García-Soidán (2)
  Inês Sousa (1)
  
  1. Centre of Mathematics, University of Minho, Campus of Azurem, 4800-058, Guimarães, Portugal
  2. Department of Statistics and O.R., University of Vigo, Campus of A Xunqueira, 36005, Pontevedra, Spain
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics
  Statistics for Business, Economics, Mathematical Finance and Insurance
  Economic Theory
  
• 出版者：Physica Verlag, An Imprint of Springer-Verlag GmbH
• ISSN：1613-981X

文摘

The nitrogen dioxide is a primary pollutant, regarded for the estimation of the air quality index, whose excessive presence may cause significant environmental and health problems. In the current work, we suggest characterizing the evolution of \(\hbox {NO}_{2}\) levels, by using geostatistical approaches that deal with both the space and time coordinates. To develop our proposal, a first exploratory analysis was carried out on daily values of the target variable, daily measured in Portugal from 2004 to 2012, which led to identify three influential covariates (type of site, environment and month of measurement). In a second step, appropriate geostatistical tools were applied to model the trend and the space–time variability, thus enabling us to use the kriging techniques for prediction, without requiring data from a dense monitoring network. This methodology has valuable applications, as it can provide accurate assessment of the nitrogen dioxide concentrations at sites where either data have been lost or there is no monitoring station nearby.