1\name{ambientNOxCH}
2\alias{ambientNOxCH}
3\docType{data}
4\encoding{utf8}
5\title{ Daily Means of NOx (mono-nitrogen oxides) in air}
6\description{
7  This dataset contains daily means (from midnight to midnight) of NOx,
8  i.e., mono-nitrogen oxides, in [ppb] at 13 sites in central
9  Switzerland and Aarau for the year 2004.
10}
11\usage{data(ambientNOxCH, package="robustbase")}
12\format{
13  A data frame with 366 observations on the following 14 variables.
14  \describe{
15    \item{\code{date}}{date of day, of class \code{"Date"}.}
16    \item{\code{ad}}{Site is located north of Altdorf 100 meters east of
17      motorway A2, on an open field at the beginning of a more than
18      2000m deep valley (690.175, 193.55; 438; inLuft)}
19    \item{\code{ba}}{Site is located in the centre of the little town of
20      Baden in a residential area.  Baden has 34'000
21      inhabitants and is situated on the swiss plateau (666.075,
22      257.972; 377; inLuft).}
23    \item{\code{ef}}{Site is located 6 km south of altdorf and 800 m
24      north of the village of Erstfeld. The motorway A2 passes 5 m west
25      of the measuring site. Over 8 million vehicles have passed
26      Erstfeld in 2004 where 13\% of the counts were
27      attributed to trucks (691.43, 187.69; 457; MFM-U).}
28    \item{\code{la}}{Site is located on a wooded hill in a rural area
29      called Laegern, about 190 m above Baden, which is about 5 km away
30      (669.8, 259; 690; NABEL).}
31    \item{\code{lu}}{Site is located in the center of town of Lucerne,
32      which has 57'000 inhabitants (666.19, 211.975; 460; inLuft).}
33    \item{\code{re}}{Site is located 1 km west of Reiden on the Swiss
34      plateau. The motorway A2 passes 5 m west of the measuring site
35      (639.56, 232.11; 462; MFM-U).}
36    \item{\code{ri}}{Site is located at Rigi Seebodenalp, 649 m above
37      the lake of Lucerne on an alp with half a dozen small houses
38      (677.9, 213.5; 1030; NABEL).}
39    \item{\code{se}}{Site is located in Sedel next to town of Lucerne
40      35m above and 250m south of motorway A14 from Zug to Lucerne on a
41      low hill with free 360° panorama (665.5, 213.41; 484; inLuft).}
42    \item{\code{si}}{Site is located at the border of a small industrial
43      area in Sisseln, 300 m east of a main road (640.725, 266.25; 305;
44      inLuft).}
45    \item{\code{st}}{Site is located at the south east border of Stans
46      with 7'000 inhabitants (670.85, 201.025; 438; inLuft).}
47    \item{\code{su}}{Site is located in the center of Suhr (8700
48      inhabitants), 10 m from the main road (648.49, 246.985; 403; inLuft).}
49    \item{\code{sz}}{Site is located in Schwyz (14'200 inhabitants) near
50      a shopping center (691.92, 208.03; 470; inLuft).}
51    \item{\code{zg}}{Site is located in the centre of Zug with 22'000
52      inhabitants, 24 m from the main road (681.625, 224.625; 420;
53      inLuft).}
54  }
55}
56\details{
57  The 13 sites are part of one of the three air quality monitoring networks:
58  inLuft (regional authorities of central Switzerland and canton Aargau)
59  \cr
60  NABEL (Swiss federal network)
61  \cr
62  MFM-U (Monitoring flankierende Massnahmen Umwelt), special Swiss
63  federal network along transit motorways A2 and A13 from Germany to
64  Italy through Switzerland
65  \cr
66  The information within the brackets means: Swiss
67  coordinates km east, km north; m above sea level; network
68
69 When the measuring sites are exposed to the same atmospheric condition
70 and when there is no singular emission event at any site,
71 \code{log(mean(NOx) of a specific day at each site)} is a linear
72 function of \code{log(yearly.mean(NOx) at the corresponding site)}. The
73 offset and the slope of the straight
74 line reflects the atmospheric conditions at this specific day. During
75 winter time, often an inversion prevents the emissions from being
76 diluted vertically, so that there evolve two separate atmospheric
77 compartements: One below the inversion boundary with polluted air and one
78 above with relatively clean air. In our example below, Rigi Seebodenalp
79 is above the inversion boundary between December 10th and 12th.
80}
81
82\source{
83  http://www.in-luft.ch/ \cr
84  http://www.empa.ch/plugin/template/empa/*/6794 \cr
85  http://www.bafu.admin.ch/umweltbeobachtung/02272/02280
86}
87\seealso{another NOx dataset, \code{\link{NOxEmissions}}.
88}
89\examples{
90data(ambientNOxCH)
91str (ambientNOxCH)
92
93yearly <- log(colMeans(ambientNOxCH[,-1], na.rm=TRUE))
94xlim <- range(yearly)
95lNOx <- log(ambientNOxCH[, -1])
96days <-     ambientNOxCH[, "date"]
97
98## Subset of 9 days starting at April 4:
99idays <- seq(which(ambientNOxCH$date=="2004-12-04"), length=9)
100ylim <- range(lNOx[idays,],na.rm=TRUE)
101op <- par(mfrow=c(3,3),mar=rep(1,4), oma = c(0,0,2,0))
102
103for (id in idays) {
104  daily <- unlist(lNOx[id,])
105  plot(NA, xlim=xlim,ylim=ylim, ann=FALSE, type = "n")
106  abline(0:1, col="light gray")
107  abline(lmrob(daily~yearly, na.action=na.exclude),
108         col="red", lwd=2)
109  text(yearly, daily, names(yearly), col="blue")
110  mtext(days[id], side=1, line=-1.2, cex=.75, adj=.98)
111}
112mtext("Daily ~ Yearly  log( NOx mean values ) at 13 Swiss locations",
113      outer=TRUE)
114par(op)
115
116## do all 366 regressions:  Least Squares and Robust:
117LS <- lapply(1:nrow(ambientNOxCH), function(id)
118             lm(unlist(lNOx[id,]) ~ yearly,
119                na.action = na.exclude))
120R <- lapply(1:nrow(ambientNOxCH),
121            function(id) lmrob(unlist(lNOx[id,]) ~ yearly,
122                               na.action = na.exclude))
123## currently 4 warnings about non-convergence;
124## which ones?
125days[notOk <- ! sapply(R, `[[`, "converged") ]
126## "2004-01-10" "2004-05-12" "2004-05-16" "2004-11-16"
127
128## first problematic case:
129daily <- unlist(lNOx[which(notOk)[1],])
130plot(daily ~ yearly,
131     main = paste("lmrob() non-convergent:",days[notOk[1]]))
132rr <- lmrob(daily ~ yearly, na.action = na.exclude,
133            control = lmrob.control(trace=3, max.it = 100))
134##-> 53 iter.
135
136## Look at all coefficients:
137R.cf <- t(sapply(R, coef))
138C.cf <- t(sapply(LS, coef))
139plot(C.cf, xlim=range(C.cf[,1],R.cf[,1]),
140           ylim=range(C.cf[,2],R.cf[,2]))
141mD1 <- rowMeans(abs(C.cf - R.cf))
142lrg <- mD1 > quantile(mD1, 0.80)
143arrows(C.cf[lrg,1], C.cf[lrg,2],
144       R.cf[lrg,1], R.cf[lrg,2], length=.1, col="light gray")
145points(R.cf, col=2)
146
147## All robustness weights
148aW <- t(sapply(R, weights, type="robustness"))
149colnames(aW) <- names(yearly)
150summary(aW)
151sort(colSums(aW < 0.05, na.rm = TRUE)) # how often "clear outlier":
152# lu st zg ba se sz su si re la ef ad ri
153#  0  0  0  1  1  1  2  3  4 10 14 17 48
154
155lattice::levelplot(aW, asp=1/2, main="Robustness weights",
156                   xlab= "day", ylab= "site")
157}
158\keyword{datasets}
159