utm.maas {GRASS}R Documentation

Soil pollution data set

Description

The utm.maas data frame has 98 rows and 13 columns. It records contamination of the environment by selected metals along the Dutch bank of the River Maas just north of Maastricht and contains the following columns (topsoil data were collected as bulked samples in 1990 within a radius of 5m according to Burrough and McDonnell 1998: 102, 309):

Usage

data(utm.maas)

Format

This data frame contains the following columns:

east
a numeric vector - UTM zone 32 eastings coordinates m
north
a numeric vector - UTM zone 32 northings coordinates m
x
a numeric vector - local coordinates m
y
a numeric vector - local coordinates m
elev
a numeric vector - elevation above local reference level in meters
d.river
a numeric vector - distance from main River Maas channel in meters
Cd
a numeric vector - Cadmium in ppm
Cu
a numeric vector - Copper in ppm
Pb
a numeric vector - Lead in ppm
Zn
a numeric vector - Zinc in ppm
LOI
a numeric vector - percentage organic matter loss on ignition
Fldf
a numeric vector - flood frequency class, 1: annual, 2: 2-5 years, 3: every 5 years
Soil
a numeric vector - 3 unnamed soil types

Details

Maas river bank soil pollution data

The Maas river bank soil pollution data (Limburg, The Netherlands) are sampled along the Dutch bank of the river Maas (Meuse) north of Maastricht. This is a flood plain of the river Maas, not far from where the Maas enters the Netherlands (Borgharen, Itteren, about 3 to 5 km north of Maastricht).

The river Maas is at the north-west border of the project area, traversing the area in north-east direction. Burrough et al. 1998 use a subset of the same data set in their book (reduced area). The data have been re-projected for the GRASS/R interface from the Dutch standard coordinate system (TDN coordinates in stereographic projection) to UTM coordinate system zone 32, on WGS84 ellipsoid. A GRASS LOCATION has to be defined with following parameters: projection UTM, ellipsoid WGS84, zone 32, north 5652930, south 5650610, west 269870, east 272460, nsres 10, ewres 10, rows 232, cols 259. The pre-defined location including the data stored column-wise in sites lists can be downloaded from the GRASS web site, and is also contained in the "grassmeta" object maas.metadata.

Maas river bank dat GRASS LOCATION, http://grass.itc.it/statsgrass/maas_grass_location.tar.gz

Note

The functions in this package are intended to work with the GRASS geographical information system. The examples for wrapper functions will will work whether or not R is running in GRASS, and whether or not the current location is that of the data set used for the examples. Examples of interface functions will however (from version 0.2-2) only work outside GRASS, to avoid possible overwriting of GRASS database locations and/or files.

Source

Burrough, P. & McDonnell, R. (1998) Principles of geographical information systems, New York: Oxford, pp. 309–311; Rikken, M.G.J., Van Rijn, R.P.G., 1993. Soil pollution with heavy metals — An Inquiry into Spatial Variation, Cost of Mapping and the Risk evaluation of Copper, Cadmium, Lead and Zinc in the Floodplains of the Meuse West of Stein, The Netherlands. Doctoraalveldwerkverslag, Dept. of Physical Geography, Utrecht University.

Examples

data(utm.maas)
Zn.o <- as.ordered(cut(utm.maas$Zn, labels=c("insignificant", "low",
"medium", "high", "crisis"), breaks=c(100, 200, 400, 700, 1000, 2000), 
include.lowest=TRUE))
table(Zn.o)
plot(utm.maas$east, utm.maas$north, pch=unclass(Zn.o), xlab="", ylab="", asp=1)
legend(x=c(269900, 270500), y=c(5652000, 5652700), pch=c(1:5), 
legend=levels(Zn.o))
cat("Burrough & McDonnell p. 107\n")
round(tapply(utm.maas$Zn, as.factor(utm.maas$Fldf), mean), 2)
round(tapply(utm.maas$Zn, as.factor(utm.maas$Fldf), sd), 2)
round(tapply(log(utm.maas$Zn), as.factor(utm.maas$Fldf), mean), 3)
round(tapply(log(utm.maas$Zn), as.factor(utm.maas$Fldf), sd), 3)
round(exp(round(tapply(log(utm.maas$Zn), as.factor(utm.maas$Fldf), mean), 3)), 2)
hist(utm.maas$Zn, breaks=seq(0,2000,100), col="grey")
hist(log(utm.maas$Zn), breaks=seq(3.5,8.5,0.25), col="grey")
t.test(utm.maas$Zn[utm.maas$Fldf == 2], utm.maas$Zn[utm.maas$Fldf == 3])
cat("NB: B&McD p. 108, their relative variance is (1-(RSquared))\n")
anova(lm(Zn ~ as.factor(Fldf), data=utm.maas))
summary(lm(Zn ~ as.factor(Fldf), data=utm.maas))
anova(lm(log(Zn) ~ as.factor(Fldf), data=utm.maas))
summary(lm(log(Zn) ~ as.factor(Fldf), data=utm.maas))

[Package GRASS version 0.3-7 Index]