Spatial–temporal modellization of the $$\hbox {NO}_{2}$$ NO 2 concentration data through geostatistical tools

Raquel Menezes (), Helena Piairo, Pilar García-Soidán and Inês Sousa
Additional contact information
Raquel Menezes: University of Minho
Helena Piairo: University of Minho
Pilar García-Soidán: University of Vigo
Inês Sousa: University of Minho

Statistical Methods & Applications, 2016, vol. 25, issue 1, No 8, 107-124

Abstract: Abstract 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 $$\hbox {NO}_{2}$$ 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.

Keywords: $$\hbox {NO}_{2}$$ NO 2; Geostatistics; Time series analysis; Space–time analysis (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10260-015-0346-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:25:y:2016:i:1:d:10.1007_s10260-015-0346-3

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-015-0346-3

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().