Abstract
The aim of this research work is to build a regression model of air quality by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (northern Spain) at a local scale. To accomplish the objective of this study, the experimental data set made up of nitrogen oxides (NO x ), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), and dust (PM10) was collected over 3 years (2006–2008). The US National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of these numerical calculations, using the MARS technique, conclusions of this research work are exposed.
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Acknowledgments
The authors wish to acknowledge the computational support provided by the Department of Mathematics at the University of Oviedo. Additionally, this paper has been funded by the Government of the Principality of Asturias through funds from the Programme of Science, Technology and Innovation (PCTI) of Asturias 2006–2009, cofinanced by 80 % within the priority Focus 1 of the Operational Programme FEDER of the Principality of Asturias 2007–2013 (research project FC–11–PC10–19). Finally, we would like to thank Anthony Ashworth for his revision of English grammar and spelling of the manuscript.
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Nieto, P.J.G., Antón, J.C.Á., Vilán, J.A.V. et al. Air quality modeling in the Oviedo urban area (NW Spain) by using multivariate adaptive regression splines. Environ Sci Pollut Res 22, 6642–6659 (2015). https://doi.org/10.1007/s11356-014-3800-0
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DOI: https://doi.org/10.1007/s11356-014-3800-0