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E.Wikstr om, S.Marklund / Chemosphere 43 (2001) 227±234
used to characterize the objects, e.g. the unique PCDD/F
homologue level in the ¯ue gas for each sampling period.
The results of the PCA are visualized by two ®gures, a
so-called score plot and a loading plot. Samples with
similar PCDD/F emissions and pattern will be located
close to each other in the score plot, while those which
have divergent emission pattern will be located further
apart. The loading plot shows how the measured vari-
ables relate to each other. Variables close to each other
in the loading plot correlate, and variables diagonally
across from each other have a negative correlation. The
most important variables for the PCA model are located
far from the origin and the insigni®cant variables are
positioned close to the origin in the loading plot. Since
the variables characterize the objects, it is possible to
establish which variables -here: a speci®c homologue
concentration) that are responsible for the separation of
the samples in the score plot by examining the loading
plot. Prior to the PCA, the data was scaled to unit
variance and mean-centered. Cross-validation was used
to calculate the number of principal components -PC) to
be included to provide a signi®cant valid model. The
total fraction of the systematic variation in the data
explained by the model is expressed as an R2 value and
the prediction ability of the model is expressed as a Q2
value. The validity of a model is decided by the R2 and
Q2 value -<1.0).
vided into three groups: taken during bad, fair and good
combustion conditions -Table 3). The samples were di-
vided according to their emissions of CO during the
sampling period and number of CO-peaks over 2000
ppm. The data from the samples taken during fair and
good combustion conditions were further evaluated to-
gether as one group. The average PCDD and PCDF
pro®le of the 17 samples taken during good and fair
combustion are shown in Fig. 2. The average pro®le
agrees with an average PCDD/F pro®le found in full-
scale MSW combustors, which shows that the formation
conditions -e.g., catalytical reactions, temperatures) are
similar in the labscale and full-scale systems.
A plot of the total chlorine load in the fuels versus the
emissions of I-TEQs during good and fair combustion
conditions are shown in Fig. 3. A larger variation within
the same chlorine level is discerned than between the
dierent chlorine levels, consequently no correlation
between the chlorine content and the emissions appears
to exist with in the data set -R2 0:07).
Further evaluation of the data was done with PCA.
This model consists of 17 observations -all samples
taken at fair and good combustion conditions) and 25
variables: mono- to octa-PCDD/F homologues, the
three pPCBs the O2, CO2, CO, HCl, particulate matter
and Total Cl in the fuel. The two ®rst components of the
model are shown in Figs. 4 and 5. Component 1 de-
scribes most of the important information relating to
dierences between the samples -R2 0:50), which is
mainly the dierence in PCDD/F and pPCB emission
levels. The second component -R2 0:28) mainly de-
scribes the variation in the homologue pattern between
the dierent samples. The loading plot -Fig. 4) show as
well a distinct correlation between the formation of the
chlorinated homologues of the PCDDs/Fs and pPCBs.
For example, the PeCDD, PeCDF and PeCB -#126) are
placed close to each other in the loading plot, the same
can be seen for the tetra- and hexa-chlorinated homo-
logues -Fig. 4). This similarity indicates that they are
formed through a similar formation mechanism. A
correlation between the Total Cl in the pellets and for-
mation of the higher chlorinated PCDD/F; namely the
hepta- and octa-chlorinated congeners can be noticed as
well in the loadingplot. A separate PCA evaluation -not
shown) of the ten samples taken during good combus-
tion conditions showed as well a signi®cant positive
correlation between the Total Cl level in the fuel, HCl,
hepta- and octa-chlorinated PCDDs/Fs in the ¯ue gas
-Validity of the model: R2 0:91, Q2 0:60). A study
performed by Vehlow et al. -1996) in the TAMARA
reactor found a similar correlation between the higher
chlorinated PCDDs and the chlorine load in the fuel.
Our study shows that the amount of chlorine in the
waste is not the limiting parameter for formation of
chlorinated compounds. The amount of chlorine is
presented in percentage levels in the waste, which is
3. Results and discussion
Table 3 lists the measured levels of PCDDs, PCDFs,
pPCBs, O2, CO2, CO, HCl, particulate matter and
number of CO-peaks in the ¯ue gas during each sam-
pling. The O2-, CO2- and CO-levels are all calculated as
average values during the PCDD/F sampling period
-ꢀ50 min). The O2 level in the 13 experiments varies
between 10.3% and 13.3% while the corresponding
variation in CO2 emission is between 8.0% and 11.0% of
CO2. One of the largest dierences between the experi-
ments is the variation in CO emission -9±727 ppm). The
higher CO emissions was caused by uncontrolled dis-
turbances of the fuel feeding system of the reactor. A
PCA evaluation -not shown) of the complete data set
showed that the eciency of the combustion conditions
is a parameter that in¯uences the formation of PCDDs/
Fs and pPCBs more than the total chlorine content in
the fuel -validity of the model: R2 0:86, Q2 0:51). A
positive correlation between PCDDs/Fs, pPCBs forma-
tion and CO concentration was noticed. A conclusion
that can be seen in Table 3 as well. Even, a negative
correlation between the Total Cl and formation of
PCDDs/Fs and pPCBs was found within the complete
data set.
To evaluate the data with minimal in¯uence from
dierent combustion conditions, the samples were di-