Research Article
Neural Models for Imputation of Missing Ozone Data in
Air-Quality Datasets
Ángel Arroyo ,
1
Álvaro Herrero,
1
Verónica Tricio,
2
Emilio Corchado,
3
and MichaB Wofniak
4
1
Department of Civil Engineering, University of Burgos, Burgos, Spain
2
Department of Physics, University of Burgos, Burgos, Spain
3
Departamento de Inform´ atica y Autom´ atica, University of Salamanca, Salamanca, Spain
4
Department of Systems and Computer Networks, Wrocław University of Science and Technology, Wrocław, Poland
Correspondence should be addressed to
´
Angel Arroyo; aarroyop@ubu.es
Received 5 December 2017; Accepted 31 January 2018; Published 8 March 2018
Academic Editor: Eloy Irigoyen
Copyright © 2018
´
Angel Arroyo et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Ozone is one of the pollutants with most negative effects on human health and in general on the biosphere. Many data-acquisition
networks collect data about ozone values in both urban and background areas. Usually, these data are incomplete or corrupt and the
imputation of the missing values is a priority in order to obtain complete datasets, solving the uncertainty and vagueness of existing
problems to manage complexity. In the present paper, multiple-regression techniques and Artificial Neural Network models are
applied to approximate the absent ozone values from five explanatory variables containing air-quality information. To compare the
different imputation methods, real-life data from six data-acquisition stations from the region of Castilla y Le´ on (Spain) are gathered
in different ways and then analyzed. e results obtained in the estimation of the missing values by applying these techniques and
models are compared, analyzing the possible causes of the given response.
1. Introduction and Related Work
e ozone (O
3
) is an odorless, colorless, and highly reactive
gas composed of three oxygen atoms. It is formed both
in the Earth’s upper atmosphere (stratospheric ozone) and
at ground level (tropospheric ozone). It can be “good” or
“bad” for people’s health and for the environment, depending
on its concentration levels and location in the atmosphere
[1].
Stratospheric O
3
is formed naturally through the inter-
action of solar UltraViolet (UV) radiation with molecular
oxygen (O
2
). Ground-level or “bad” ozone is not emitted
directly into the air. In the 1950s, hydrocarbons and nitro-
gen oxides (NO
) were identified as the two key chemi-
cal precursors of photochemical smog and its concomitant
high concentrations of O
3
and other photochemical oxidant
[2]. e majority of ground-level O
3
is formed from the
photochemical oxidation of Volatile Organic Compounds
(VOCs) in the presence of NO and other NO
. Significant
sources of VOCs are chemical plants, gasoline pumps, oil-
based paints, autobody shops, and print shops. NO
result
primarily from high temperature combustion, and its most
significant sources are power plants, industrial furnaces and
boilers, and motor vehicles [3].
1.1. Importance of Ozone. e O
3
exposition can cause dam-
age in different ways. In the stratosphere, reduced O
3
levels
as a result of O
3
layer depletion mean less protection from
the sun’s rays and more exposure to UltraViolet B (shortwave)
rays (UVB) radiation at the Earth’s surface [4]. e effects
on human health of the O
3
layer depletion have been much
analyzed, increasing the amount of UVB that reaches the
Earth’s surface. UVB causes nonmelanoma skin cancer and
plays a major role in malignant melanoma development.
In addition, UVB has been linked to the development of
certain cataracts, negative effects in patients with asthma, and
Hindawi
Complexity
Volume 2018, Article ID 7238015, 14 pages
https://doi.org/10.1155/2018/7238015