variable, and thus here to the residual variance of the Pu-FIDLER regression; hence the difference between FIDLER > 5,000 and FIDLER < 5,000 sills,
An enlargement of the variogram for FIDLER > 5,000 is also shown in

Figure 7.

We can fit it to a spherical model with a range "a" of 600

feet (the distance at which correlations vanish):

y(0) = 0

y(h) = 0.065 + 0.025 [3/2 (h/600) - 1/2 (h/600)3}

(12)

0 < |h] < 600 feet

y(h) = 0.09

In| < 600 feet

The "apparent nugget effect" of 0.065 is the sum of the FIDLER data

nugget effect aon = (1.287)2 x 0.0127 = 0.02 and the Pu data nugget

effect, which by Pu variogram extrapolation is found to be 0.045 (wariogram not shown here). Thus T(x)--the true value--has no nugget effect of

its own and its variogram is simply the spherical model with a sill at
0.025.
Though blurred by noise, T(x) does show a structure up to a
distance of 600 feet and it is worth exploiting it.

The variogram of T(x) is not well determined when FIDLER < 5,000, espe~
cially at short distances, as a consequence of much sparser sampling
(number of pairs typically less than 15).
For simplicity, we will assume
that the variogram in this case is the same as above, except for an

upward shift of 0.135 = 0.225 - 0.09 (Figure 7).

It is convenient to

think of this shift as due to a nonsystematic uncertainty of variance
0.135, attached to correction terms when FIDLER < 5,000.
Then, all data
may be processed by BLUEPACK in the same manner:
the uncertainty variance
is just added to the appropriate diagonal terms of the kriging system
matrix.

COMING BACK TO ARITHMETIC SCALE

We now have all the elements to carry out the estimation of Pov) --or
equivalently P(v), since these are equal~~that is, of the mean concentration in the log scale.
How can we come back to the arithmetic scale?
An Estimate of the Geometric Mean
*

P (v)
.
One way is to simply use the inverse transform 10
to estimate the
.

geometric mean 10
*

:

P(v)

By the unbiasedness property of kriging, we know

that the error P (v) - P(v) has mean zero.
symmetric distribution, then

If moreover this error has a

384

r-

:

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