% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{lisbon_air_quality.int}
\alias{lisbon_air_quality.int}
\title{Lisbon Air Quality Daily Interval Dataset}
\format{
A symbolic data frame (\code{symbolic_tbl}) with 1096 observations
(daily) and 8 interval-valued pollutant variables:
\itemize{
    \item \code{so2}: Sulphur dioxide (ug/m3).
    \item \code{pm10}: Particulate matter < 10 um (ug/m3).
    \item \code{o3}: Ozone (ug/m3).
    \item \code{no2}: Nitrogen dioxide (ug/m3).
    \item \code{co}: Carbon monoxide (ug/m3).
    \item \code{pm25}: Particulate matter < 2.5 um (ug/m3).
    \item \code{nox}: Nitrogen oxides (ug/m3).
    \item \code{no}: Nitric oxide (ug/m3).
}
}
\source{
QualAr, Entrecampos station, Lisbon, Portugal.
}
\usage{
data(lisbon_air_quality.int)
}
\description{
Interval-valued daily air quality data from the Entrecampos monitoring
station in Lisbon, Portugal, covering 2019--2021 (1096 days). Each day's
pollutant concentration is represented as a \eqn{[\min, \max]} interval
from hourly measurements. Missing days are imputed via linear
interpolation.
}
\section{Metadata}{

\tabular{ll}{
  \strong{Sample size (n)} \tab 1096 \cr
  \strong{Variables (p)} \tab 8 \cr
  \strong{Subject area} \tab Environment \cr
  \strong{Symbolic format} \tab Interval \cr
  \strong{Analytical tasks} \tab Regression, Time series
}
}

\examples{
data(lisbon_air_quality.int)
}
\references{
Dias, S. and Brito, P. (2017). Off the beaten track: A new linear model
for interval data. \emph{European Journal of Operational Research},
258(3), 1118--1130.

Data from the QualAr Portuguese air quality monitoring network
(\samp{https://qualar.apambiente.pt/}).
}
\keyword{datasets}
\keyword{interval}
