February,  1997
                  PEER REVIEW DRAFT
  DO NOT CITE,  QUOTE,  OR  DISTRIBUTE
   'COMPREHENSIVE ABATEMENT PERFORMANCE PILOT STUDY

       VOLUME II: MULTI-ELEMENT DATA ANALYSES
              Technical Programs Branch
            Chemical Management Division
Office of Prevention, Pesticides, and Toxic Substances
      Office of Pollution Prevention and Toxics
         U.S. Environmental  Protection Agency
                Washington,  DC  20460

-------
                       U.S. EPA DISCLAIMER

     This document is a preliminary draft.  It has not been
released formally by the Office of Pollution Prevention and
Toxics, U.S. Environmental Protection Agency.  It is being
circulated for comments on its technical merit and policy
implications.

     This report was prepared under contract to an agency of the
United States Government.  Neither the United States Government
nor any of its employees, contractors, subcontractors, or their
employees makes any warranty, expressed or implied,  or assumes
any legal liability or responsibility for any third party's use
of or the results of such use of any information, apparatus,
product, or process disclosed in this report, or represents that
its use by such third party would not infringe on privately owned
rights.

     Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
                       BATTELLE DISCLAIMER

     This is a report of research performed for the United States
Government by Battelle.  Because of the uncertainties inherent in
experimental or research work, Battelle assumes no responsibility
or liability for any consequences of use,  misuse,  inability to
use,  or reliance upon the information contained herein, beyond
any express obligations embodied in the governing written
agreement between Battelle and the United States Government.

-------
AUTHORS ND CONTRIBUTORS
The study that led to this report was funded and managed by
the U.S. Environmental Protection Agency. The study was
conducted collaboratively by two organizations under contract to
the Environmental Protection Agency, Battelle Memorial Institute
and Midwest Research Institute. Each organization’s
responsibilities and key staff are listed below.
Battelle Memorial Institute (Battelle)
Battelle was responsible for the design of the study, for
identifying the elements that were selected for analysis, for
producing the design documentation and the Quality Assurance
Project Plan, for developing training for the field teams, for
recruiting cooperators for the study, for providing team leaders
for the field teams, for auditing the field teams, for data
management of combined study data, for auditing the study data,
for conducting the statistical analysis of the data, and for
writing the final report. Key staff included: Bruce Buxton,
Steve Rust, Tamara Collins, Fred Todt, John Kinateder, Nancy
McMillan, Matt Palmgren, Nick Sasso, Robin Hertz, and Casey
Boudreau.
Midwest Research Institute (MRI)
Midwest Research Institute was responsible for participating
in the planning for the study, for identifying the elements that
were selected for analysis, for writing certain chapters and
appendices in the Quality Assurance Project Plan, for designing
and producing a vacuum device for collecting field samples, for
developing training for the field teams, for providing the
technicians who collected the field samples, for auditing the
field teams, for conducting the laboratory analysis of the field
samples, for managing the data associated with the field samples,
for auditing the laboratory results, and for producing the multi-
element data on which this report is based. Key staff included:
Gary Dewalt, Paul Constant, Jim McHugh, and Jack Balsinger.
U.S. Environmental Protection Agency (EPA)
The Environmental Protection Agency was responsible for
managing the study, for reviewing the design and the Quality
Assurance Project Plan, for assessing the performance of the
recruiters and the field teams, for reviewing audit reports, for
reviewing draft reports and for arranging the peer review of the
draft final report. The EPA Work Assignment Managers were Ben
Lim and John Schwemberger. The EPA Project Of f,icers were Gary
Grindstaff, Joe Breen, Jill Hacker, Phil Qbinson, and Sineta
Wooten.

-------
TABLE OF CONTENTS
EXECUTIVE SUMMARY v
1.0 INTRODUCTION 1
1.1 Study Design . . . . 1
1.2 Data 3
2.0 ANALYSIS
2.1 Characterization of Element Levels 6
2.2 Abatement and Renovation Effects 16
2.2.1 Abatement and Renovation Effects
By Element 16
2.2.2 Abatement and Renovation Effects
Across Elements 25
2.3 Relationships Among the Elements . . . 30
2.3.1 Bivariate Relationships
(Correlations) 30
2.3.2 Multivariate Relationships
(Principal Components) 36
3.0 RESULTS OF ANALYSIS 40
4.0 STUDY CONCLUSIONS 41
5.0 REFERENCES . . . 42
LIST OF TABLES
Table 1 Abatement and Renovation History by House 2
Table 2 Abbreviations for Sample Types Used in
Tables and Figures 4
Table 3 Results of Analysis of Variance to Test
for Significant Differences Among
Sample Types, by Element 14
Table 4 Geometric Mean Concentration and Log Standard
Deviation Across Houses by Sample Type . . . 15
Table 5 Model Estimates and Log Standard Errors
of Geometric Mean Concentrations in
tjnrenovated, Unabated Houses 18

-------
Peer Review Draft — Do Not Cite or Quote
TABLE OF CONTENTS
(continued)
Table 6 Estimates and Log Standard Errors
of Multiplicative Renovation Effects . . . 19
Table 7 Estimates and Log Standard Errors
of Multiplicative Abatement Effects . . . . 20
Table 8 Principal Components for Unit Mean
Log-Concentration by Sample Type 27
Table 9 Principal Components for Unrenovated,
Unabated Home Averages, Abatement Effects,
and Renovation Effects 38
LIST OF FIGURES
Figure la Lead Concentration vs. Sample Type
(Geometric House Mean) 7
Figure lb Aluminum Concentration vs. Sample Type
(Geometric House Mean) 7
Figure ic Barium Concentration vs. Sample Type
(Geometric House Mean) 8
Figure ld Cadmium Concentration vs. Sample Type
(Geometric House Mean) 8
Figure le Calcium Concentration vs. Sample Type
(Geometric House Mean) 9
Figure if Chromium Concentration vs. Sample Type
(Geometric House Mean) 9
Figure ig Magnesium Concentration vs. Sample Type
(Geometric House Mean) 10
Figure lh Nickel Concentration vs. Sample Type
(Geometric House Mean) 10
Figure ii Potassium Concentration vs. Sample Type
(Geometric House Mean) 11
Figure ij Titanium Concentration vs. Sample Type
(Geometric House Mean) ii
ii

-------
Peer Review Draft — Do Not Cite or Quote
TABLE OF CONTENTS
(continued)
Page
Figure 1k Zinc Concentration vs. Sample Type
(Geometric House Mean) 12
Figure 2a Block Chart of Estimated Average Log-
Concentrat ion in tjnrenovated, Unabated
Units for Window and Air Duct Dust Samples . . . 21
Figure 2b Block Chart of Estimated Average Log-
Concentration in Unrenovated, Unabated
Units for Floor and Bedcover/rug/upholstery Dust
Samples 22
Figure 2c Block Chart of Estimated Average Log-
Concentration in Unrenovated, Unabated
Units for Soil Samples . . . 23
Figure 3a Plot of First Two Principal Components
of Mean Log-Concentrations for Dust Samples . . 28
Figure 3b Plot of First Two Principal Components
of Mean Log-Concentrations for Soil Samples . . 29
Figure 4a Window Channel House Mean Correlation
Scatterplot 31
Figure 4b Window Stool House Mean Correlation
Scatterplot 31
Figure 4c Air Duct House Mean Correlation Scatterplot . . . . 32
Figure 4d Floor House Mean Correlation Scatterplot . 32
Figure 4e Bedcover/Rug/Upholstery House Mean
Correlation Scatterplot . . . 33
Figure 4f Entryway Dust House Mean Correlation
Scatterplot 33
Figure 4g Entryway Soil House Mean Correlation
Scatterplot 34
Figure 4h Foundation Soil House Mean Correlation
Scatterplot 34
Figure 4i Boundary Soil House Mean Correlation
Scatterplot 35
iii

-------
Peer Review Draft — Do Not Cite or Quote
TABLE OF CONTENTS
(continued)
Figure 5 Key to Relation Between Shape of Ellipse
and Observed Correlation in Figures
4a Through 4i 35
Figure 6 First Two Principal Components for Each
Building Component, Plotted Versus Each
Other for Unrenovated, Unabated Unit Mean
Log-Concentrations, Renovation Effects, and
Abatement Effects 39
LIST OF APPENDICES
Appendix A Summary of Multi-Element Data A-i
Appendix B Outlier Analysis for the CAPS
Pilot Multi-Element Data B-i
i.v

-------
Peer Review Draft — Do Not Cite or Quote
EXECUTIVE SW MARY
This report presents the results of the statistical analysis
of multi-element data collected during a pilot study that
preceded the Comprehensive Abatement Performance (CAP) Study.
The goal of the CAP Study was to assess the long-term impact of
lead-based paint abatement. The pilot study was conducted to
test the sampling and analysis protocols for the full study.
For the multi-element analysis, concentrations of lead, as
well as of aluminum, barium, cadmium, calcium, chromium,
magnesium, nickel, potassium, titanium, and zinc in dust and soil
samples were measured. Barium, cadmium, chromium, titanium, and
zinc concentrations were measured because they are often
components of paint. Aluminum, calcium, magnesium, nickel, and
potassium concentrations were measured because they are present
in soil.
The multi-element analysis was undertaken to determine
whether relationships among these elements could provide a
“tracer” for identifying the sources and pathways of lead in
households. Pilot study data were used to 1) characterize the
concentrations of lead, aluminum, barium, cadmium, calcium,
chromium, magnesium, nickel, potassium, titanium, and zinc
samples in household dust and soil; 2) determine the effect of
renovation and lead-based paint abatement on the concentrations
of these elements in household dust and soil; and 3) investigate
the relationship among the elements by sample type (i.e., samples
of different media taken from different locations).
Dust and soil samples from six houses in Denver, Colorado
were studied. Two houses were unabated (previously identified as
relatively free of lead-based paint). The remaining four houses
were abated using removal methods and/or encapsulation or
enclosure methods. One house was abated using primarily removal
methods on the interior and primarily encapsulation or enclosure
V

-------
Peer Review Draft— Do Not Cite or Quote
methods on the exterior. Another house was abated using
predominantly encapsulation or enclosure methods on the interior
and predominantly removal methods on the exterior. The other two
houses were abated by primarily the same method on the interior
as the exterior (one removal, the other encapsulation or
enclosure). Most of the lead levels in the paint in the houses
studied were less than 1.0 mg/cm 2 .
A total of 109 vacuum dust samples was collected. Between
16 and 22 dust samples were collected at each house from window
channels, window stools (often referred to as “sills”), air
ducts, floors, bedcover/rug/upholstery, and entryways. Forty-
eight (48) soil samples were collected. Eight samples were
collected from each house: from just outside the front and back
entryways, at different locations along the foundation, and at
different locations on the property boundary.
Analysis of the samples showed that the highest
concentrations of the elements analyzed were of calcium in the
indoor dust samples and of aluminum in the outdoor soil samples.
Lead concentrations were highest in air duct, window stool, and
window channel samples, and they were higher in foundation soil
samples than in boundary soil samples. Except for titanium and
aluminum, dust samples from floors and interior entryways had
similar concentrations of elements. After controlling for
abatement and renovation effects, relative concentrations of the
elements suggested grouping the following sample types: a) air
duct, window stool, and window channel dust; b) floor, interior
entryway, and bedcover/rug/upholstery dust; and c) foundation,
exterior entryway, and boundary soil.
Little difference, in general, was observed between levels
of the elements studied in abated and unabated units. Regardless
of the method of abatement, there were significantly higher lead
levels in interior entryway dust and exterior entryway soil in
abated houses. There were also significantly higher levels of
vi

-------
Peer Review Draft.- Do Not Cite or Quote
zinc in the soil outside the entryways of abated houses. There
were significantly lower levels of calcium in the dust on window
stools and significantly lower levels of chromium in the dust on
floors of abated houses. Lead concentrations in dust and soil
near the interior and exterior entryways of abated houses were
three to five times the levels in unabated houses. The
concentrations of lead in dust from floors and interior entryways
of renovated houses were about five times those in unrenovated
houses. Calcium concentrations in the dust of renovated houses
were significantly higher than in dust of unrenovated houses for
window stools, bedcover/rug/upholstery, floors, and interior
entryways. The difference was tenfold for bedcover/rug/
upholstery and interior entryways.
Zinc was the element most frequently correlated with lead.
Based on visual observation of correlation scatter plots, similar
bivariate relationships among the eleven elements were found in
a) floors, interior entryways, and exterior entryways; b)
boundary and foundation soil; and c) window channels and stools.
Multivariate principal component analysis showed similarities
among the concentrations of elements in a) exterior entryway,
foundation, and boundary soil samples and b) floor, interior
entryway, bedcover/rug/upholstery, window stool, and air duct
dust samples.
Study Conclusions
The data collected in this pilot study were analyzed to
determine sample sizes and test sampling protocols for the full
CAP study. This report focuses on a multi-element analysis of
the data collected in the pilot study. It was not possible to
determine definitively from the data collected in the pilot study
whether lead dust in the houses studied came primarily from paint
or soil. However, bivariate relationships among the elements in
soil outside entryways were more similar to those in interior
vii

-------
Peer Review Draft.- Do Not Cite or Quote
floor dust (including entryway dust) than they were to those in
soil samples taken near the foundation and boundary. This
suggests that soil near the entryways is transported indoors and
constitutes a portion of interior floor dust.
viii

-------
Peer Review Draft — Do Not Cite or Quote
COMPREHENSIVE ABATEMENT PERFORMANCE PILOT STUDY:
MULTI-ELEMENT DATA ANALYSES
2. .0 INTRODUCTION
This report presents the results of a multi-element analysis
of data obtained during a pilot study that preceded the
Comprehensive Abatement Performance (CAP) Study. This represents
Volume II of the CAP Pilot report. Volume I dealt exclusively
with the statistical analysis of observed levels of lead (US EPA,
1995). The goal of the CAP Study was to assess the long-term
impact of lead-based paint abatement. The pilot study was
conducted to test the sampling and analysis protocols that were
intended for the full study. These protocols called for
determining the levels of lead in dust and soil samples collected
at residential units.
1.1 STUDY DESIGN
In the CAP Pilot study, six houses of differing abatement
histories were sampled. These houses were located in Denver,
Colorado. Two houses were unabated (previously identified as
relatively free of lead-based paint). The remaining four houses
were abated using removal methods and/or encapsulation or
enclosure methods. One house was abated using primarily removal
methods on the interior and primarily encapsulation or enclosure
methods on the exterior. Another house was abated using
predominantly encapsulation or enclosure methods on the interior
and predominantly removal methods on the exterior. The other two
houses were abated by primarily the same method on the interior
as the exterior (one removal, the other encapsulation or
enclosure). Most of the lead levels in the paint in the houses
studied were less than 1.0 mg/cm 2 . For easy reference, Table 1
displays the abatement and renovation history of each of the six
houses sampled. (Renovation is described later.)

-------
Peer Review Draft — Do Not Cite or Quote
Table 1. Abatement and Renovation History by House
House
Inte ulor
Abatement
History
Extertor
Abatement
History
,
Renovation
17
19
33
43
51
80
Abated: Removal
Unabated
Unabated
Abated: Removal
Abated: E/E
Abated: E/E
Abated: E/E
Unabated
Unabated
Abated: Removal
Abated: Removal
Abated: EIE
None
Partial
None
None
Full
None
Along with the determinations of lead obtained in the study,
levels of ten other metals were measured within dust and soil
samples taken at these houses: aluminum, barium, cadmium,
calcium, chromium, magnesium, nickel, potassium, titanium, and
zinc. Five of these metals (barium, cadmium, chromium, titanium,
and zinc) have been used in the composition of paint. The other
five elements are present primarily in other sources such as soil
(Tisdale, Nelson, and Beaton, 1985). For example, magnesium is
found in clay, which is often observed in soil samples. The
purpose of measuring the levels of these other metals in the
samples was to identify groups of sample types that appear to
have come from similar sources, with the ultimate goal of
identifying prominent sources of lead found in household dust.
The major objectives addressed in the analysis of the multi-
element data from the pilot study were to:
(1) Characterize the concentration levels of lead,
aluminum, barium, cadmium, calcium, chromium,
magnesium, nickel, potassium, titanium, and zinc in
samples of household dust and soil,
(2) Determine the effect of renovation and abatement on the
concentration of these elements in household dust and
soil, and
(3) Investigate the relationships among these elements by
sample type (e.g., household dust, exterior soil, dust
2

-------
Peer Review Draft— Do Not Cite or Quote
from air ducts, and dust from bedcover/rug/
upholstery).
The intention of this examination was to identify analysis
methods for evaluating multi-element data and to apply these
methods to pilot study data to identify any strong relationships.
With data available for only six housing units, few relationships
were strongly detectable.
Subsection 1.2 describes the data and gives a summary of the
outlier analysis. Section 2 describes the analyses performed,
and the results are discussed in Section 3. Section 4 provides
conclusions. Section 5 lists the references cited in this
report. Appendix A is a summary of the multi-element data
collected, and Appendix B is the outlier analysis.
1.2 DATA
The study design required the collection of 25 vacuum dust
samples and 8 core soil samples from each of the six houses in
the study, for a total of 150 dust samples and 48 soil samples.
The vacuum dust samples were collected from six different
locations (window channels 1 , window stools 2 , air ducts, floors,
bedcover/rug/ upholstery, and entryways). Core soil samples were
taken from just outside the front and back entryways, at
different locations on the foundation, and at different locations
on the property boundary. Table 2 contains a description of the
acronyms used throughout this report in the tables and figures to
denote the building components from which samples were collected
(referred to hereafter as “sample types”).
1 Window channel: The surface below the window sash and inside the screen and/or
storm window.
2 Wjndow stool: The horizontal board inside the window that extends into the house
interior—often called the window sill.
3

-------
Peer Review Draft — Do Not Cite or Quote
Table 2. Abbreviations for Sample Types Used in Tables and
Figures
Media .
Mnemonic
ComponenUSample T pe
Vacuum Dust Samples
ARD
BRU
EWY (-I)
FLR
WCH
WST
Air ducts
Bedcover/rug/upholstery
Entryway (-Inside)
Floor
Window Channel
Window Stool
Soil Samples
BDY
EWY (-0)
FDN
Boundary
Entryway (-Outside)
Foundation
The number of dust samples actually collected from each
house varied from 16 to 22 for a total of 109 vacuum dust
samples. Fourteen of these 109 samples were side-by-side
duplicates. Eight soil samples were collected from each house
for a total of 48 soil samples. Twelve of the soil samples were
side-by-side duplicates.
The dust and soil samples collected during the pilot study
were analyzed to determine the amount of eleven different
elements present. Listings of the raw element concentration data
are displayed in Tables A-la through A-if of Appendix A. Each
table displays concentrations from given house for each of the
eleven elements by sample medium, sample type, location, and
sample ID. House number and sample ID uniquely identify each
sample. Only element concentrations ( g/g) were analyzed for
this report. Element loadings ( g/ft 2 ) were also measured for
dust samples, but were not considered in this analysis.
tjnivariate and multivariate outlier detection tests were
applied to the multi-element concentration data. Lists of
potential outliers were sent back to the laboratory for
verification. The results for all but one of the potential
4

-------
Peer Review Draft — Do NOL Cite or Quote
outliers were confirmed and included in the analysis as
originally reported. The sample for which an error was reported
was updated and the corrected value was used in the analysis.
This sample is documented in the footnotes to Table A-lb.
Details regarding the statistical approach to the outlier
analyses and their respective results are provided in Appendix B.
Twenty-three samples had zinc concentrations above the
calibration range of the measuring instrument. One sample had a
cadmium concentration above the calibration range. For the 23
samples with elevated zinc concentrations, the maximum detectable
concentration was corrected for the dilution factor 3 associated
with each sample. These adjusted values were used in the
statistical analysis and are identified by superscripts in the
appendix tables. Because only one sample had a cadmium
concentration above the calibration range, it was excluded from
the statistical analysis, rather than adjusted by its dilution
factor.
Results for seven dust samples were excluded from the
statistical analyses. No soil samples were excluded. One of the
seven dust samples omitted was the sample with the elevated
cadmium concentration described in the previous paragraph (sample
7 in house 19, see Appendix A-i for a data listing by house and
sample number) . Another sample (sample 12 in house 19) was
dropped in the laboratory. Four samples (samples 3, 9, and 17 in
house 19 and sample 19 in house 43) were eliminated because only
lead concentrations were available due to calcium interference.
Finally, sample 12 in house 51 was excluded due to sampling
problems; the cartridge filled with sawdust prior to completion
of the sample collection. Thus, 102 of the designed 150 vacuum
3 The maximum detectable concentration was 5 .tg/mL. The reported concentration
depended on the actual amount of dilution prior to chemical analysis.
5

-------
Peer Review Draft — Do Not Cite or Quote
dust samples and 48 of the designed 48 core soil samples were
available for the multi-element statistical analyses.
2.0 ANALYSIS
The analysis is divided into three parts corresponding to
the three major objectives introduced above. Section 2.1
contains a characterization of the concentration levels of the
different elements in the various sample types. Section 2.2
describes the estimated effects of abatement and renovation, and
Section 2.3 examines the relationships among the elements and
sample types.
2.1 CHARACTERIZATION OF ELEMENT LEVELS
Due to the general lack of room-level effects found in the
analysis of the CAP pilot lead data, the basic experimental unit
considered in the multi-element data analysis is the house.
House geometric mean concentrations of the eleven elements were
the basic quantities used in the statistical analyses. These are
tabulated in Table A-2 of Appendix A by sample type and house for
each of the eleven elements.
Levels of each of the eleven elements observed varied by
sample type. Figures la through 1k display geometric mean sample
concentrations by house and building component for lead,
aluminum, barium, cadmium, calcium, chromium, magnesium, nickel,
potassium, titanium, and zinc. These figures display all the
data considered in the analysis. Mean sample concentrations for
each house are plotted with different symbols. The grand means
over all houses are plotted with a circle and connected by a
solid line across sample types. Note that the last three sample
types in each plot represent soil samples, while the other sample
types represent dust samples.
6

-------
Peer Review Draft — Do Not Cite or Quote
1 99
*
S
I
I
10 ,
Samp’e Yy e
House Legend + 17 19 * * 33 0 00 43 51 2 2 2 80 0-0-0 99
Figure la. Lead Concentration vs. Sample Type (Geometric House Mean).
100099
C 0 * * +
1 _____________________________
p 4
Sample 1 jpe
House Legend 17 x X X 19 s S 5 33 0 0 0 43 Y Y V 51 2 Z 2 80 e -.. 99
Figure lb. Aluminum Concentration vs. Sample Type (Geometric House Mean).
7
V
1000
0 V
V
x
V
z
*
+ +

-------
Peer Review Draft — Do Not Cite or Quote
I
8
E
,, p 4 ’#
SamØe Type
KouseLagend 17 XXX19 u**33 00043 YYY51 ZZZ 5() e-e.e99
Figure ic. Barium Concentration vs. Sample Type (Geometric House Mean).
% 1000
C +
i +
10
,
Sam e Type
Hau eLagend 19 *** 00043 VYY5I ZZZIO O-OO
Figure id. Cadmium Concentration vs. Sample Type (Geometric House Mean).
10000
*
z
+
V
a
1000
ltv’
a
+
x
x + a
V
z
x
x
a
*
V
*
z
+
*
.4.
z z
x
8

-------
Peer Review Draft — Do Not Cite or Quote
13OC3 O
C 1X099
10
jim
E
. ,
1ooo
Samp’e Type
House Legend + 17 x X X 19 * 33 0 0 0 43 V Y V 51 Z Z ! 99 0-GO 99
Figure le. Calcium Concentration vs. Sample Type (Geometric House Mean).
1000
C
0
* *
x
100
,, I
Sa ipIe Type
I4 ij Legend + + + 17 X X 19 * * * 33 0 0 0 43 V V V 51 Z Z Z 99 0-0-0 99
Figure if. Chromium Concentration vs. Sample Type (Geometric House Mean).
V
x
+ +
V
9

-------
Peer Review Draft — Do Not Cite or Quote
10000
•i
1
C
8
E
Samp pe
HouseLegend 17 XXX19 s**33 00043 ZZZ8O O.0099
Figure 1g. Magnesium Concentration vs. Sample Type (Geometric House Mean).
x
x
100
1 , , — $
Sample e
HouseLegerxi +++ 7 XxX 1 9 •s*33 00043 ‘ 51 - - 99
Figure lh. Nickel Concentration vs. Sample Type (Geometric House Mean).
+
0
x
z
z
z
a
x o
10

-------
Peer Review Draft — Do Not Cite or Quote
C
8
E
I
1 aG,
1
+
o
+
+ *
z
x
z
#.
Sample Type
HoueeLegend X*X19 •SS 00043 YYY51 ZZZ8O ee.e99
Figure ii. Potassium Concentration vs. Sample Type (Geometric House Mean).
1
a
10
Sample Type
Hoi’seLagend 17 XX ( 9 **s33 00043 YYY51 ZZZ8D °.°°99
Figure lj. Titanium Concentration vs. Sample Type (Geometric House Mean).
a
x
*
0
*
0
+
x
+
11

-------
Peer Review Draft— Do Not Cite or Quote
i oco
* *
1
•1 0
io o
Swnple e
IIouseLagend 17 XXX19 •$s33 00043 ‘ 51 ZZZ 5 e-e.e99
Figure 1k. Zinc Concentration vs. Sample Type (Geometric House Mean).
As can be seen in the figures, the highest geometric mean
concentrations were observed for calcium in the indoor samples.
For the outdoor samples, the highest levels were observed for
aluminum. Of the different components sampled, lead
concentrations were highest in air duct, window stool, and window
channel samples (Figure la) - Levels of barium and zinc appear to
be similar to levels of lead across sample types. On average,
titanium was the least variable of the eleven elements within each
sample type. For each of the elements except titanium and
aluminum, dust samples taken from floors and entryways had similar
concentrations. However, the concentrations for these two
elements were higher for entryways on average than for floors.
The levels of aluminum and titanium observed in entryway dust
samples were more consistent with those observed in soil samples.
z
z
0
x
z
V
*
12

-------
Peer Review Draft — Do Not Cite or Quote
This could indicate that soil is being tracked into the homes
through the entryways, or it could be a reflection of the presence
of these elements in the construction of the entryways. Levels of
aluminum and titanium were also high in window channel samples.
The highest mean chromium concentration was observed in house
33 for regular floor dust samples and entryway dust samples. The
entryway soil samples from this house also had high chromium
concentrations. The individual dust and soil samples obtained
from the back side entrance to this home and a floor dust sample
collected from the adjoining room (kitchen) had the highest
chromium concentrations observed in the study (see Table A-lc),
suggesting a possible relationship between exterior and interior
chromium levels.
Two exceptionally high zinc concentrations were observed on
window channels and one high concentration on a window stool.
However, each of these three measures came from different houses.
When grouping the profiles in Figures la through 1k based on
similarity, three groups of elements are formed. Lead, barium,
and zinc seem to have similar contours and comprise one group.
Aluminum and titanium make up a second group, while cadmium,
calcium, and chromium make up the third group.
To quantify the degree of variation in the concentrations of
each element across sample types, an analysis of variance was
performed on the geometric means plotted in Figures la through 1k.
The results of this ANOVA are summarized in Table 3. For all
elements except potassium and chromium, the differences across
sample types were statistically significant. The strongest
differences were seen for magnesium and calcium, with lower levels
observed in soil than in dust.
Also included in Table A-2 are indicators of the primary
method of interior and exterior abatement for each house. A “U”
indicates that no abatement was performed in the house because no
significant lead-based paint was present, an “R” indicates that
13

-------
Peer Review Draft - Do Not Cite or Quote
the house was abated primarily by removal methods, and an “E”
indicates that the house was abated primarily by encapsulation!
enclosure methods. Table A-2 also contains the number of samples
for which concentrations were determined for all eleven elements.
Table 3. Results of Analysis of Variance to Test for Significant
Differences Among Sample Types, by Element
RootMeai
Squared E
.
F value
P value
.
Comment
1.14
4.47
.0006
0.48
6.55
.0001
0.95
3.83
.0019
1.01
5.54
.0001
0.67
9.71
.0001
Soil all lower than dust
0.78
1.59
.1570
Insignificant differences
0.48
31.27
.0001
Soil all lower than dust, EWY lower than FDN
0.77
4.83
.0003
0.74
0.55
.8096
Insignificant differences
0.38
8.44
.0001
0.76
16.40
.0001
ARD, WST, WSL higher than rest
Any sample in Tables A-la through A-if for which at least one
element had a missing value was not included in the Table A-2
summary.
Grand geometric mean concentrations for each element by
sample type are displayed in Table 4. These were obtained by
taking the geometric mean of the entries in Table A—2 across all
houses for each sample type and element. Thus, each house where a
sample was taken (for a particular sample type) is given equal
weight in these averages. These grand means are plotted in
Figures la through 1k by the circles connected by a solid line.
Each geometric mean is followed by its log standard deviation.
14

-------
I-I
U,
Table 4. Geometric Mean Concentration and Log Standard Deviation Across Houses by Sample Type
z
0
0
-I
0
C
0
No.of Dust
mple Sample Units Loading
dlum Typo Sampled (mgltt’)
Dust H 4 738
WST 6 468
ARD 5 352
FLR 6 583
BRU 5 416
EWY-I 6 718
Lead
Aluminum
Barium
Cadmium
Calcium
Chromium
Gee Mean Log SW.
(pglg) Day.
Gee Mean Log
(pqIg) Std. 0ev.
___________
Geo Mean Log
(pglg) Std. 0ev.
Gee Mean L g
(pglg) Std. 0ev.
Gee Mean Log
(pglg) SW. 0ev.
Gao Mean Log
(iaglg) Std. 0ev
2128 097
658 120
771 031
260 0 BI
152 072
314 091
12940 039
6266 036
7136 032
8331 030
624B 047
10761 037
1647 158
703 116
325 060
295 052
254 045
294 078
191 081
239 103
26 3 1 32
93 068
97 062
95 049
33730 023
53230 051
40465 081
25042 044
24598 051
32709 103
401 046
543 054
773 064
487 0 80
550 052
454 079
Soil
EWY.O
FDN
BDY
6
6
6
208 090
209 087
128 079
16058 033
14491 040
11373 042
278 021
257 031
166 031
56 085
4.0 041
28 051
9814 040
9812 031
8576 020
408 067
287 028
236 031
Sample
tadlum
Sample
Typo
No.of
UnIts
Sampled
Dust
LoadIng
(mglft 3 )
Magnesium
NIckel
PotassIum
Titanium
Zinc
Gao Mean Log
( sgig) Std. Day.
Gee Mean Log
( igIg) Std. 0ev.
Gee Mean Log
(pglg) SId. Day.
Gao Mean Log
(pglg) SW. D cv.
Gao Mean Log
(pglg) SW. Dcv.
Dust
WCH
WST
ARD
FLR
BRU
EWY-l
4
6
5
6
5
6
738
468
352
583
416
71.8
5553 032
4807 029
3877 042
3222 025
3094 029
4419 040
240 035
380 037
407 1 17
278 060
450 102
207 036
2651 0444
2818 0.87
4260 036
4311 070
4048 089
4045 067
496 027
376 013
262 038
199 029
191 057
351 033
3226 1 07
1939 066
4458 098
770 039
656 070
722 049
Soil
EWY-
FDN
BDY
6
6
6
574 0 16
1054 066
636 039
139 074
114 027
97 030
4069 0.26
3476 032
3504 033
482 023
421 024
372 026
296 037
372 035
178 046

-------
Peer Review Draft — Do Not Cite or Quote
This represents a measure of the between-house variation for that
response without controlling for abatement or renovation effects.
2.2 ABAT ’T AND RENOVATION EFFECTS
2.2.1 Abatement and Renovation Effects by Element
The impact of abatement and renovation on the multi-element
data was assessed by fitting a statistical model containing both
renovation and abatement effects to the data in Appendix A. The
model fitted to data for each element was
C 1 = nt + aI + rR 1 + E 1
where
c represents the observed (arithmetic) average log-
concentration in house j,
m represents the average log-concentration in
unrenovated unabated houses,
a represents the added effect of abatement,
I = 1 if house j was abated
0 if house j was an unabated house,
r represents the added effect of a full renovation,
R 1 is the degree of renovation house j was undergoing
at the time of sampling (see below), and
E represents house-to-house variation
House 51 was assigned an R) value of 3. indicating “full
renovation” and House 19 a value of 0.5 indicating “partial
renovation”. The other four houses were assigned R 1 values of
zero, indicating that no renovation was being performed.
In the analysis of the lead data, the method of abatement
(E/E or removal) was also considered as a factor in the
statistical model. No significant effect was found; and
16

-------
Peer Review Draft — Do Not Cite or Quote
therefore, this effect was not included in the above lead model
applied to all elements.
Estimates of the model parameters are reported in Tables 5,
6, and 7. Table 5 contains estimates and log-standard errors of
the geometric mean concentration of each element in unrenovated,
unabated houses, by sample type. Tables 6 and 7 contain estimates
and standard errors of the multiplicative effects of renovation
and abatement, respectively, by sample type. In Tables 6 and 7, a
multiplicative effect of 1.0 implies no effect. A multiplicative
effect less than 1.0 indicates that lower levels were observed in
renovated (abated) houses, while a multiplicative effect greater
than 1.0 indicates that higher concentrations were observed in
renovated (abated) houses. Those multiplicative effects that were
significantly different from 1.0 at the 0.05 significance level
are denoted by asterisks.
Figures 2a, 2b, and 2c display block charts of the estimates
in Table 5 (portrayed on a log scale). A distinction between
sample types was observed in the average levels displayed in these
figures. Therefore, the sample types were purposely presented in
three groups. Figure 2a displays the estimated average log-
concentration in unrenovated, unabated houses for air ducts,
window stools, and window channels. Figure 2b displays the
corresponding estimates for bedcover/rug/upholstery, entryway, and
floor samples. Figure 2c shows the estimates for soil samples
(boundary, entryway, and foundation). Air ducts, window stools,
and window channels typically had the highest baseline levels of
lead, calcium, and zinc. Soil samples had the lowest
concentrations of these elements. Notice the relatively similar
behavior of these estimates across the different elements within
each of the three sample groups. For example, the ratio of lead
to aluminum is smallest for soil samples, and largest for window
channels, window stools, and air ducts. Another distinction was
observed in the relationship between lead, titanium, and zinc.
17

-------
Table 5. Model Estimates and Log Standard Errors of Geometric Mean Concentrations in Unrenovated
Unabated Houses
CD
I
H
Sample
Medium
Sample
Type
#
Houses
Lead
Aluminum
Barium
Cadmium
- Calcium
Chromium
Gee Mean Log Std.
(iaglg) Err.
G ao Mean Log
(pglg) Std. Err.
Geo Mean Log
(pg g) SM. Err.
Geo Mean Log
(pglg) SM. Err.
Gee Mean Log
(pglg) Std. Err.
Gee Mean Log
(ligIg) Std Err
Oust
WCH
WST
ARD
FIR
BRU
EW ’-l
4
6
5
6
5
8
7238 064
226 117
875 041
102 033
117 045
96 019
13346 054
5808 039
5341 036
7687 030
11954 0.39
14146 034
7058 1 95
478 111
216 068
313 0.31
163 049
255 047
29.7 007
214 0.89
360 184
191 059
254 034
130 057
34866 037
57057 012
53114 068
20998 025
18230 018
25873 035
39 072
87 0.46
46 069
141 036
69 012
109 063
So
EWv’-O
FDN
BDY
6
6
8
63 043
102 089
- 53 081
22668 010
18568 033
11492 044
261 015
252 037
128 030
3.9 081
36 048
21 057
13126 0.36
13395 0.27
9977 025
80 068
32 0.18
21 0.33
Sample
Medium
Sample
Type
S
House.
Magnesium
Nickel
Potassium
Titanium
Zinc
Gao Mean Log
(iiglg) Bid. Err
GCO Mean Log
(oglg) Sit Err.
Gee Mean Log
(pglg) SW. Err,
Gao Mean Log
(jiglg) SW. Err
Gee Mean Log
(pglg) Bid. Err
Oust
WCI-I
WST
ARD
FLR
BRU
EWY-l
4
6
5
6
5
8
4237 045
4501 035
2719 050
3337 025
3558 039
4400 023
179 050
31 3 041
352 027
41.2 0.69
176 064
248 043
2563 072
2784 077
5553 041
4184 046
6723 092
5575 070
656 033
370 0 13
188 0.44
222 033
387 057
444 0.28
13783 035
1229 037
16504 067
5552 040
4478 094
439 1 040
Soil
EWY.O
FDN
BDY
6
6
6
535 0.11
1175 043
703 045
13.3 081
149 020
847 021
4955 012
4458 0.19
3500 033
601 008
443 026
338 022
1831 0.19
2695 029
1208 052

-------
I-
‘.0
Table 6. Estimates and Log Standard Errors of Multiplicative Renovation Effects
z
0
0
iO
Sample
Medium
Sample
Type
Lead
Aluminum
Barium
Cadmium
Calcium
ChromIum
Log Stil.
Effect Err.
Log
Effect Std. Err.
Log
Effect SW. Err.
Log
Effect SW. En.
Log
Effect SW. Em
Log
Effect SW. Err.
Dust
WCH
WST
ARD
FIR
BRU
EWY-I
045 062
1.34 157
051 132
467 012
1708 164
4 87 004
062 044
096 024
281 105
066 010
014 120
057 0 13
0 31 5.70
027 140
737 366
035 011
501 192
025 025
0 32 001
021 091
043 2717
059 039
017 092
084 037
084 021
283 002
172 366
255 007
1045 026
9 80 0 14
067 078
043 024
989 384
044 015
319 011
058 045
Soul
EWY.O
FDN
BOY
212 021
229 091
187 067
050 001
049 013
057 022
072 003
078 016
093 010
043 075
078 027
086 037
054 015
077 009
081 006
038 053
057 004
071 012
Sample
Medium
Sample Type
Magnesium
Nickel
PotassIum
Titanium
Zinc
Log
Effect SW. Err.
Log
Effect SW. Err.
Log
Effect SW. Err.
Log
Effect Ski. Em
Log
Effect SW. Err.
Dust
WCH
WST
ARD
FIR
BRU
EWY-l
114 030
127 014
352 1 98
138 007
088 122
221 006
110 038
159 019
79 23 058
084 054
14104 323
109 021
072 078
0.52 067
0 31 1 34
028 025
004 674
039 055
104 016
085 002
348 1 55
074 012
0.18 261
058 009
041 018
047 016
001 361
145 018
3.35 714
191 018
Soil
EWY-O
FDN
BDY
1 36 001
027 021
195 014
044 074
063 0.05
062 005
0.56 002
0 52 004
083 013
0 62 001
072 008
085 005
1 25 0 04
090 009
124 030
indicates effect was significant at p= 05 level

-------
Table 7. Estimates and Log Standard Errors of Multiplicative Abatement Effects
z
C
C,
0
Sample
Medium
Sample
Typo
Lead
Aluminum
Barium -
- Cadmium
Calcium
Chromium
Log S d
Effect Err.
Log
Effect Std. Err.
Log
Effect SW. Err.
- Log
Effect SW. Err.
Log
Effect SW. Err.
Log
Effect SW. Err.
Dust
WCH
WST
ARD
FLR
BRU
EWY-I
026 062
445 103
091 022
227 008
096 027
325* 003
113 044
109 016
136 017
087 007
047 020
082 009
021 570
293 092
142 061
136 007
161 032
208 0 16
082 001
211 060
068 453
0.41 026
027 015
067 024
102 021
061 001
058 061
092 005
111 004
080 009
119 0.78
068 016
160 064
027 010
056 002
033 0.30
Soul
EWY.O
FDN
BOY
451 014
213 060
242 044
077 0.01
090 008
114 0.14
122 002
113 010
149 007
241 049
1.30 018
1.58 025
081 010
069 006
092 004
oai o e
107 002
123 008
Sample
Medium.
Sample
Typo
Magnesium -
Nickel
- Potassium
Titanium
- Zinc
Log
Effect SW. Err.
Log
Effect SW. Err.
Log
Effect SW. Err.
Log
Effect SW. Err.
tog
Effect SW. Err.
Dust
WCH
WST
ARD
FIR
BRU
EWY.i
137 030
1 01 009
146 033
084 005
081 020
0.75 004
144 0.38
112 0.12
062 0.10
059 0.38
210 054
075 014
117 078
1 30 0.44
078 022
169 016
072 112
086 036
088 016
1 09 0.01
141 026
095 008
041 044
0.86 006
019 018
263 0 10
0.25 060
142 012
155 1.19
1 65 0 12
Soil
EWY.O
FDN
BDY
0.99 001
1 38 014
088 009
1 46 049
0.80 003
1.37 003
092 001
088 0.03
1 17 0.08
0.88 000
1.05 0.05
1.23 0.03
1 89 0.03
1 68 0 06
1 55 020
indicates effect was significant at p= 05 level

-------
COflW7 j I /111/li /111 1 J /
p ° °/ 10.5/ aeB aa / 2.88 7.85 6.401 ° /
iii I
5.42 8.69 6.17 3.07 110 4.46 6.41 3.44 7.88 5.91 7.11
M I)
6.71 8.58 5.31 / 3.58 10.9/ 3.84 7.01 / 3.56 8.62 5.24 9.71
Pb N Ba Cd Ca Mg N K 11 Zn
z
0
0
Figure 2a. Block Chart of Estimated Average Log-Concentration in Unrenovated, Unabated Units for
Window and Air Duct Dust Samples.

-------
Figure 2b. Block Chart of Estimated Average Log-Concentration in Unrenovated, Unabated Units for
Floor and Bedcover/ruglupholstery Dust Samples.
N)
N)
EWY
Pb Ba Cd Ca
cr
P1 K ii Zn
z
g
C
-I
0

-------
con1,/ it / 11 i/I iii j iI /
1110 ass/ t35J 9.48/ 4.0911828! 2 I&51 8.40 521 /
FON / If /IjI /1 ‘I /
4.63 9.83 5.53/ t39 9.50/ a s i.oi/ aio 8M too 5.60/
4.09 9.39 4.88 / 0.77 9.16 / 3.11 6.38/ 2.18 8.17 5.89 4.84/
Pb M Ba Cd Ca Cr Mg NI K 11 Zn
Figure 2c. Block Chart of Estimated Average Log-Concentration in Unrenovated, Unabated Units for Soil
Samples.

-------
Peer Review Draft — Do Not Cite or Quote
Figure 2a depicts a lower titanium level than lead or zinc for air
ducts and window channels. On the other hand, levels portray a
general rise as one moves from lead to titanium to zinc for floor,
entryway, and bedcover/rug/upholstery samples in Figure 2b.
Finally, in Figure 2c, titanium was the element with the highest
concentration among lead, titanium, and zinc in each of the soil
samples.
In trying to identify the source of dust on floors, the
relationship among levels of the different elements for window
stools appears more similar to those for floors, entryways, and
bedcover/rug/upholstery samples than to those for window channels
and air ducts. This is likely a reflection of the general
composition of these dust samples. Figure 2a indicates that the
window channel samples have especially high concentrations of
barium and lead relative to the concentrations of the other
elements. In this manner, window channel samples seem to differ
from the other types of samples.
Close attention should be given to the log standard errors of
the estimates in Tables 6 and 7. Most of these are very large in
comparison to the logarithm of the multiplicative estimates. Note
that a total of 198 statistical tests were performed in the
analysis supporting the results in Tables 6 and 7. Each test was
performed at the 51 level. Therefore, even if there were no
effects of abatement or renovation on any of these element
concentrations, we would still expect 9 or 10 sample type/element!
factor combinations to be significant. A total of 18 combinations
were found to be significant. Of these, calcium was involved in
five cases, lead was the element involved in four cases, potassium
was involved in two cases, while aluminum, cadmium, chromium,
magnesium, nickel, titanium, and zinc were each involved in one
case. Entryways were involved in 9 cases of significance (5 soil
and 4 dust), floors in 3 cases, and window stools in two cases.
24

-------
Peer Review Draft — Do Not Cite or Quote
One case of significance was observed for air duct, bedcover/rug/
upholstery, window channel, and foundation samples.
Thus, although more cases of significance were observed than
would be expected if there were no real effects, the number of
statistically significant results was small relative to the number
of tests performed. This, along with the limited data set
associated with the pilot study, makes it difficult and perhaps
inadvisable to draw general conclusions from the estimates
reported in Tables 6 and 7.
2.2.2 Abatement and Renovation Effects Across Elements
A principal components analysis was performed to determine
whether the relationships in element concentrations among the
houses (or houses with a similar abatement/renovation history)
were similar for the different sample types. This analysis is an
attempt to simplify the interpretation of the data by reducing the
number of elements characterized from eleven to perhaps two or
three “element classes”. These element classes represent weighted
averages of the eleven elements. Ultimately, this may provide
insight into the following source-assessment question: “Where
does the lead in household dust come from?” This analysis, was
performed on mean log-concentrations for each element and house by
sample type.
The purpose of this analysis was two-fold. A principal
components analysis provides a mathematical tool for estimating
the approximate dimensionality of the responses. Also, plotting
the higher-order principal components against each other affords
an objective means of identifying clusters of houses with similar
dust and soil element compositions. The ultimate goal is a
reduction in the complexity of the multivariate data analysis.
Principal component analyses can be performed based on either
correlations or covariances. Analyses based on correlations
standardize the range of each of the elements’ concentrations.
25

-------
Peer Review Draft — Do Not Cite or Quote
This prevents the most widely fluctuating elements from dominating
the analysis and gives equal attention to all variables regardless
of their range. Covariance-based analyses leave all element
concentrations in their original scale. Since the scales observed
varied substantially by element, and a priori there was no reason
to weight more heavily the elements with greater absolute
variation, the principal component analyses were performed based
on correlations.
Table 8 displays estimates of the first two principal
components (i.e., the two principal components explaining the most
variability in the data) by sample type, followed by the
cumulative proportion of total variation explained by these
components. Figures 3a and 3b display plots of the relationship
between the first and second principal components by sample type.
Figure 3a is for dust samples; Figure 3b is for soil samples.
Houses are distinguished by different plotting symbols in these
figures. Refer to Table 3 for a synopsis of abatement and
renovation history of these houses.
For many of the sample types, more than 70% of the total
variation is explained by the first two principal components. A
similar weighting pattern was applied to the elements for floor
and entryway dust and entryway soil samples. The weights are
assigned to the different elements to maximize the variation.
Therefore, if two elements are negatively correlated, then the
weights of the high-order principal components of the two elements
will likely be of opposite signs. For example, as will be seen in
Section 2.3, aluminum and calcium concentrations are negatively
correlated in entryway dust. Table 8 shows that their
coefficients in the first principal component are of an opposite
sign for entryway dust. Obviously, when considering so many
elements, it is impossible for this relationship between
correlation and principal component coefficients to hold for all
pairs of elements.
26

-------
Table 8. Principal Components for Unit Mean Log-Concentration by Sample Type
-J
(I
z
0
C)
0
0
Sample
Medium
Sample
Type
Principal
Component
Principal Component Coefficients
Cumulative
Explained
Variability
Pb
Al
Ba
Cd
Ca
Cr
Mg
Ni
K
TI
Zn
Dust
ARD
1
2
015
-0.14
0.29
041
0.11
-049
-0.23
0.43
0.37
-008
045
008
0.40
0.25
029
-0.10
-027
048
0.40
0.28
-0.14
0 11
044
071
BRU
1
2
040
022
-033
039
025
000
-0.17
037
040
026
0.33
037
-008
0.48
043
0.16
-031
013
-0.27
037
-0.05
021
044
070
EWV-l
1
2
-0.32
016
038
028
022
-047
0.28
034
-0 39
0.24
029
050
-0.31
0.35
-0 10
-016
0.31
-026
037
019
-023
-001
047
0.67
FLR
1
2
-0.44
0.12
032
038
030
-034
0.29
0 37
-0.43
0.24
0.29
0 37
-0.23
0.22
001
-026
0.26
-0.25
0.24
042
-0.27
0.20
0.36
068
WCH
1
2
029
0.36
-0.26
0.33
038
0 17
0.13
042
0.34
-0.17
0.30
-022
-040
-0.02
0.17
-047
-0.31
0.28
038
0 12
0.24
041
0.57
0.88
WST
1
2
041
002
-037
0.10
043
0.14
-019
0.41
-006
-0.53
033
012
-034
007
0.17
-021
-0.28
042
011
045
0.35
029
045
0.74
Soil
BDY
1
2
-0 10
046
043
-003
0.22
041
008
0.44
0 19
-036
042
-002
-0 12
-024
0.42
-002
0.42
000
042
-002
-0.01
048
048
084
EWY-O
1
2
-036
029
040
-0.13
0 15
019
022
058
029
-007
0.33
021
-0.25
014
0.24
0.52
037
0.04
036
-016
-0.25
0.40
052
070
FDN
1
2
-0 33
0 29
038
0 13
0 16
045
-0 17
034
0.32
-028
036
0 13
0.33
0.25
0 36
-0 18
0 39
0.00
027
0.37
-0 06
0 50
0.57
085

-------
Peer Review Draft.- Do Not Cite or Quote
wdH ’
N
0
vZ
____________ WST1
N +
0 *Z
ARM
N +
0
* x
I
_____________ FLR1
*
N Y
+
___________ BRU1
*0 X
+
z
___________ EWY41
N
—
>4 X
ZI’
HOUSE 17 XXX 19 ***33 00043 YYY 51 ZZZSO
Figure 3a. Plot of First Two Principal Components of Mean Log-Concentrations for Dust
Samples.
28

-------
Peer Review Draft— Do Not Cite or Quote
BDY1
z
V
+
x
EWY-Ol
+
‘4
V
z
x *
FDN1
HOUSE +++ 17 XXX 19 ***33 00043 YVY 51 ZZZ5
Figure 3b. Plot of First Two Principal Components of Mean Log-Concentrations for Soil
Samples.
29

-------
Peer Review Draft — Do Not Cite or Quote
If patterns in the relationships of these eleven elements
were affected by abatement or renovation history, then homes with
similar histories would be clustered in Figures 3a and 3b. The
figure allows inspection for such relationships separately by
sample type. However, comparing the proximity of the houses to
each other in Figures 3a and 3b, there do not seem to be any
consistent groupings of houses across sample types. Substantive
conclusions would require data from more than six houses.
2 • 3 RELATIONSHIPS AMONG THE ELEMENTS
2.3.1 Bivariate Relationships (Correlations)
Displays portraying the bivariate relationships among the
eleven elements are provided in Figures 4a through 4i. For each
sample type, average log-concentrations for each house are plotted
for each pair of elements. Ellipses are drawn on each plot that
represent 95% of the estimated bivariate distribution. Those
plots for which the ellipse is narrow represent pairs of elements
for which there was a strong observed correlation. Pairs of
elements which are negatively correlated have an ellipse with the
major axis running from upper left to lower right. The magnitude
of the correlation can be inferred from the shape of the ellipse
by comparing it to the key in Figure 5.
On the plots in Figures 4a-4i, each house is identified with
a different symbol. This permits determining whether certain
houses have similar characteristics with respect to the various
elements and/or sample types.
The strongest relationships among the elements across houses
were observed in foundation and boundary soil samples (Figures 4h
and 4i). These correlations were strongest among aluminum,
chromium, nickel, potassium, and titanium. Strong relationships
were also observed among lead, calcium, chromium, and nickel in
samples taken from bedcover/rug/upholstery (Figure 4e).
30

-------
Peer Review Draft — Do Not Cite or Quote
Pb
£9
c
Al
)
(
(
9
?
(
6’
Ba
( )
Q
c
p
‘
6’
Cd
6’
€
/
( )
c
ct
c
,
c
c
ç
c
ciV
cV
©
M
c
/
6’
K
c
c9
c
£9
6’
Figure 4a. Window Channel House Mean Correlation Scatterplot.
HouseLagend: * =17 0=19 +.33 X=43 Z=51 1=80
Pb
( \
j
, 7
Y
, )
y
/
( Y
f \

ç
N
(r\
\
(
\
x
&
Al
x
*d
*d
x
x
I
x

•
X
)C
ei
Ba
(
/
Y
?
3)
Cd
9
Y
(
/
c
,
g
\
c
6
\
/
K
F
c
/
4’
Figure 4b. Window Stool House Mean Correlation Scatterplot.
3].

-------
Peer Review Draft — Do Not Cite or Quote
ro
V
ç
ia
q

j
&
çjj
%
/

Cd
Q
( )
( ?
Y
(V
Y
g?
/
9
/
i
g
/
3
I
c
?
M
9
K
ç
‘
/
I
CV
Figure 4c. Air Duct House Mean Correlation Scatterplot.
HougaLegend: *17 0=19 +=33 X=43 Z=51 Y=80
Pb
Q
Q
,
Q:
(9
N
4 ’
( )
£9
c
I
Ba
( )
%
( )
)
F
Cd
c
/
9
6
%
Cd
c
c
£9
Q
9
c i,
@
Ma
( 3
(
g
P1
c
c
I
c
®
K
1/
Y
©
c9
a
!
Figure 4d. Floor House Mean Correlation Scatterplot.
32

-------
Peer Review Draft — Do Not Cite or Quote
Pb
/
6’
( )
6’
( )
M
£9
£ ?
/
3
3
c
Ba
9
c! 3
£
@
£9
Cd
9
,
Ca
6’
E
/
c)
;
6’
6’
( )
7
c ;
(9
6’
/
6’
( )
N
c
c
£
K
c
9
Figure 4e. BedcoverlRugfUpholstery House Mean Correlation
Scatterplot.
HouseLegend: *17 0=19 +=33 X=43 Z=51 7=80
Pb
£9
N
6’
(
‘
(
( )
(
Ba
( )
( )
( )
(
c
( )
9
Cd
6’
(
(;
(
9
c
Ca
( )
/
6’
£9
(
( ?
(
)
£9
P
/
D
3
c 7
N
£
c
K
Figure 4f. Entryway Dust House Mean Correlation Scatterplot.
33

-------
Peer Review Draft — Do Not Cite or Quote
Pb
%
c
c
M
3
Y
CV
/
c 9
D
1
Cd
c 7
/
&
?
3
ci?
(V
9
6
Q
@
9
4)
4
6)
c: )
( )
( )
Mg
( )
?
/
&
9
P1
cV
(V
K
?
,
3
Jy
7
Ø 9
c
D
m
Figure 4g. Entryway Soil House Mean Correlation Scatterplot.
HouBaLegend: *17 0=19 +=33 X=43 Z=51 Y=BO
Fe
\
\
M
6’
/
Y
c
©
D
©
‘
7
Cd
Q
\
?
Ca
if
)
5
‘
?
6’
( 9
Mg
if
9
\
(
P1
if
( 9
(
/
9
4
6’
6’
6’
K
( 9
(
©
!

c
V
c

@
42

9
Figure 4h. Foundation Soil House Mean Correlation Scatterplot.
34

-------
Peer Review Draft — Do Not Cite or Quote
House Legend: * = 17
0=19 +=33 X=43
Z.51 1=80
Figure 4i. Boundary Soil House Mean Correlation Scatterplot.
90%
60%
30%
0%
KEY
0
0
0
0
Figure 5. Key to Relation Between Shape of Ellipse and Observed
Correlation in Figures 4a Through 41.
Pb
J
Cd
c 3
Oa


P1
/3 7 6 7 g
K
4’
g,_
35

-------
Peer Review Draft — Do Not Cite or Quote
Lead concentrations were most frequently correlated with zinc
concentrations. High correlations were also observed on window
stools among lead, titanium, barium, and zinc (Table 4b).
A slightly different categorization of sample types could be
made based on the patterns observed in these scatter plots.
Relatively consistent sets of bivariate plots were observed for
the following groups of sample types: floor, interior entryway,
and exterior entryway; boundary soil and foundation soil; and
window stool and window well. Air ducts and bedcover/rug/
upholstery samples do not appear similar to any of the groups
mentioned nor to each other. These groupings of the sample types
do not appear particularly surprising although one might have
expected exterior entryway samples to be more like the other two
soil samples than like interior floor dust samples.
The floor, interior entryway, and exterior entryway group
displays consistent, strong bivariate relationships between
aluminum and titanium, cadmium and chromium, and barium and
potassium. As introduced above, the boundary and foundation soil
group displays the strongest bivariate relationships, suggesting
consistent correlations between lead and calcium; aluminum and
chromium; nickel, potassium, and titanium; chromium and nickel;
potassium and titanium; and nickel and potassium. The window
channel and window stool group has consistent bivariate
relationships among lead, barium, and zinc; chromium and
magnesium (negative correlation); and titanium and zinc.
2.3.2 Multivariate Relationships (Principal Components)
For the estimated model parameters displayed in Tables 5, 6,
and 7 (average log-concentrations in unrenovated unabated houses,
increments in log-concentration associated with renovation, and
increments in log-concentration associated with abatement), a
second principal components analysis was performed across the
nine sample types. The purpose of this analysis was not only to
36

-------
Peer Review Draft-- Do Not Cite or Quote
identify consistent patterns in the composition of dust across
different sample types (unrenovated, unabated house analysis),
but also to determine whether abatement or renovation impacts
different components in different ways.
The numerical results of the principal components analyses
and plots of the first two principal components are displayed in
Table 9 and Figure 6. Table 9 displays estimates of the
coefficients for the first two principal components followed by
the cumulative proportion of total variation explained by these
components. Figure 6 displays the relationship between the first
two principal components (the orthogonal directions in which the
greatest variability was observed).
The first two principal components generally accounted for
at least 68 of the total variability in the model parameter
estimates. This means that although eleven elements were
measured (lead, aluminum, barium, cadmium, calcium, chromium,
magnesium, nickel, potassium, titanium, and zinc), most of the
variation among the nine sample types occurred within a two-
dimensional space (i.e., two linear combinations of the eleven
element concentrations).
For averages in unrenovated, unabated houses it can be
argued that the three soil sample types are grouped into one
cluster; floor, entryway, window stool, bedcover/rug/upholstery,
and air duct dust sample types form another cluster; and window
channels stand alone. For the renovation effect, all samples are
grouped into one cluster except for air ducts and bedcover/rug/
upholstery, which stand alone. One must recognize that air ducts
and bedcover/rug/upholstery were not sampled in the fully
renovated house. Therefore, the estimated impact of renovation
on these sample types is less meaningful than on the other sample
types which were sampled in the fully renovated house.
37

-------
Table 9. Principal Components for Unrenovated, Unabated Home Averages, Abatement Effects, and
Renovation Effects
Response
Principal
Component
- Principal Component Coefficients
Cumulative
Explained
Vanabi lity
Pb
Al
Ba
Cd
Ca
Cr
Mg
Ni
K
TI
Zn
Unrenovated
Unabated Unit
Means
1
0.20
-0.37
0.17
0.43
0.41
0.15
0.37
0.36
-0.09
-0.17
0.32
0.40
2
0.48
0.20
0.48
0.04
-0.00
-0.32
-0.00
-0.28
-0.27
0.43
0.25
0.71
thatement Effect
1
0.34
-0.37
0.30
0.11
0.07
-0.43
-0.43
0.16
0.09
-0.23
0.42
0.36
2
0.35
0.29
0.31
0.31
-0.46
0.07
0.06
-0.34
0.10
0.44
0.22
0.68
Renovation Effect
1
0.02
0.40
0.43
-0.13
0.03
0.46
0.34
0.30
-0.22
0.40
-0.10
0.43
(A,
Coefficients are applied to the estimated parameters for each sample type to obtain maximum spread among sample types in two dimensions.
z
0
0
-I

-------
Peer Review Draft— Do Not Cite or Quote
c N n
+
a
V
4
ABA1EI
* + OA
Oaniporarit +++ H XX X WBT *R.D 000 p vvv BALi
zzz EWY—I uuaBNy_O 000 N £A BOY
Figure 6. First Two Principal Components for Each Building Component, Plotted
versus Each Other for Unrenovated, Unabated Unit Mean Log-
Concentrations, Renovation Effects, and Abatement Effects.
39

-------
Peer Review Draft.- Do Not Cite or Quote
For the abatement effects, there were no clear clusters or
outlying sample types, but the three soil sample types appear
close together in Figure 6, and interior entryway samples were
right on top of exterior entryway samples, with floor dust
samples nearby. This may be an indication that even after lead-
based paint abatement, the composition of the soil near the
foundation and entryway is similar to that of the soil at the
boundary.
3.0 RESULTS OF ANALYSIS
The following results were obtained from statistical
analyses of the multi-element data.
Characterization of multi-element concentration
1. Of those elements analyzed, calcium and aluminum had
the highest concentrations in indoor dust and outdoor
soil.
2. After controlling for abatement and renovation effects,
concentrations of the elements provide for the
following groupings of sample types:
- air duct, window stool, and window channel dust;
- floor, entryway, and bedcover/rug/upholstery dust;
and
- foundation, entryway, and boundary soil.
Effects of abatement and renovation on multi-element
concentrations
3. Lead concentrations in dust and soil near the entryways
of abated houses were three to five times the levels in
unabated houses. The concentrations of lead in dust
from floors and entryways of renovated houses were
about five times those in unrenovated houses.
4. Calcium concentrations in the dust of renovated houses
were significantly higher on window stools, floors,
bedcovers/rugs/upholstery, and interior entryways.
This difference was tenfold for bedcover/rug/upholstery
and interior entryways.
40

-------
Peer Review Draft— Do Not Cite or Quote
5. Several other statistically significant effects were
estimated for the remaining elements, but with little
consistency across elements or across sample types.
Relationships among element concentrations for sample types
6. Of the ten elements measured besides lead,
concentrations of zinc were most positively correlated
with lead, both within sample types and across sample
types.
7. The strongest bivariate relationships among the
elements were observed in boundary and foundation soil
samples; three groups of sample types were identified
as having similar bivariate relationships among many of
the elements; floor, interior entryway, and exterior
entryway; boundary and foundation soil; and window
channel and stool. The relationships among element
concentrations in entryway soil are more similar to
those in entryway dust and floor dust than to
relationships among element concentrations in boundary
and foundation soil.
8. A principal component analysis of estimated element
concentrations in unrenovated, unabated houses by
sample type suggested similarities in dust and soil
composition within the following groups: 1) exterior
entryway, foundation, and boundary soil, and 2) floor,
interior entryway, window stool, air duct, and
bedcover/rug/upholstery dust -
4.0 STUDY CONCLUSIONS
It was not possible to determine definitively from the data
collected in the pilot study whether lead dust in the houses
studied came primarily from paint or soil. However, bivariate
relationships among the elements in soil outside entryways were
more similar to those in interior floor dust (including entryway
dust) than they were to those in soil samples taken near the
foundation and boundary. This suggests that soil near the
entryways is transported indoors and constitutes a portion of
interior floor dust.
41

-------
Peer Review Draft — Do Not Cite or Quote
5.0 REFERENCES
US EPA, 1995, “Comprehensive Abatement Performance Pilot Study,
Volume I: Results of Lead Data Analyses”, EPA 747-R-93-007.
Morrison, D., 1976, Multivariate Statistical Methods , Second
Edition, McGraw-Hill.
Tisdale, S. L., Nelson, W. L., and Beaton, J. D., Soil Fertility
and Fertilizers, 4th edition, Macmillan Publishing Co., NY, 1985.
US EPA, 1996, “Comprehensive Abatement Performance Study, Volume
I: Summary Report,” EPA 230-R-94-013a.
US EPA, 1996, “Comprehensive Abatement Performance Study, Volume
II: Detailed Statistical Results,” EPA 230-R-94-013b.
42

-------
Peer Review Draft — Do Not Cite or Quote
APPENDIX A
SUMMARY OF MULTI - ELEMENT DATA
A-i. Multi-Element Data Listing
A-2. Geometric Mean Concentrations by Sample Type and Unit

-------
Table A-la. CAP Pilot Study Multi-Element Data, House 17
Sample Identification
ConcentratIons (pglg)
Medium
Type
Location
SarnplelD
Pb
Al
Ba
Ca
Cd
Cr
K
Mg
Ni
Ti
Z
Dust
ARD
BRU
EWY-I
FLR
ACH
WST
KIT
BD1
BD1
EWY
EWY
KIT
KIT
801
BD1
BD1
LVG
LVG
KIT
KIT
BD1
BD1
LVG
LVG
LVG
09
19
18
20
21
01
03
11
12
13
31
32
07
06
14
16
36
39
40
383
717
66.9
282
259
500
254
373
328
225
153
63.7
1140
221
727
336
506
270
337
8970
8660
5140
10200
10200
1690
6950
7290
9280
6090
5170
6460
268
6600
16300
12500
4480
12500
9770
187
173
434
367
1100
742
1840
742
875
698
442
165
915
440
627
725
377
1820
2170
16400
16900
19000
12300
16500
14200
23100
15400
8770
33700
13900
7060
22700
48000
39100
41700
29700
21200
27200
65.7
615
9.97
196
11.5
3.10
13.4
26.1
14.6
8.74
10.6
3.71
.‘
114
198
191
395
307
146
40 3
64 9
439
36.5
34.7
16.2
294
437
42 7
293
260
246
450
236
358
386
429
433
508
7740
5790
6100
6120
8420
14400
17200
10000
11500
14900
9870
4600
481
31900
3820
4990
8800
4920
6290
3180
3730
3210
2290
3090
2720
3950
2790
2240
4180
2490
1600
4870
8460
8040
7360
10900
3980
6380
22.7
19.6
76.6
27 3
27 9
13.0
16.3
120
45.5
339
222
166
205
159
23.1
22.2
188
168
271
245
296
64.9
285
332
55.1
104
207
188
243
159
209
957
323
552
368
243
627
505
517’
39900
572
426’
620’
502
1340
516’
284’
1750
486’
229’
14900
1730
10000
4220
2520
1310
1910’
Soil
BDY
EWY-O
FDN
LFT
BAG
1 ST
FRO
BAG
LFT
BAG
BAG
26
27
28
22
23
24
25
29
52.2
705
564
704
364
70.2
69.4
65.7
26700
20200
25100
20400
19600
20800
18000
18200
221
183
206
196
440
199
262
171
13100
8260
13300
12800
14200
12200
11300
11700
268
233
261
2.75
241
2.81
262
2.51
446
385
438
37 7
269
40 9
392
38.0
6400
5940
5870
5380
4570
5410
4420
4460
984
500
1030
540
614
668
2570
2960
171
15.5
16.4
15.1
238
15 7
139
14.3
692
454
643
486
582
422
391
385
116
177
108
181
499’
279
345
299
Analysis result was greater than upper calibration limit; reported value is an estimated lower bound on the true Zn concentration
b Analysis result was greater than upper calibration limit for cadmium, sample excluded from data analysis.
C
0
C
2
C
C
H
0
1
0
g
H

-------
Table A-lb. CAP Pilot Study Multi-Element Data, HouBe 19
Sample Identification
Concentrations (uglg)
Medium
Type
Location
Sample ID
Pb
Al
Ba
Ca
Cd
Cr
K
Mg
Ni
Ti
Zn
Dust
ARD
BRU
EWY-l
FLR
V H
WST
LVG
BD I
LVG
BDI
EWY
EWY
LVG
LVG
601
BD1
BD1
KIT
KIT
BD1
LVG
801
KIT
OP
19
08
18
20
21
01
03 b
11
12’
13
31
32
17 b
04
16
36
69.5
624
482
485
201
184
190
695
301
0.00
402
99.5
679
368
70.8
215
177
0.00
8950
6810
2900
8660
6740
4560
0.00
5500
0.00
5690
4250
4330
0.00
4130
7760
4190
0.00
585
695
190
275
58.8
179
0.00
598
0.00
831
103
53.1
0.00
74.1
281
209
0.00
69600
93800
37000
140000
94800
177000
0.00
20000
000
58500
9280
8140
0.00
149000
74200
92700
0.00
23.7
12.7
8.51
6.16
10 1
6.15
0.00
19.5
000
13.6
5.71
3.24
0.00
4.14
37.4
17.0
0.00
146
187
81.4
40.8
40.1
36.1
0.00
114
0.00
157
44.9
41.9
0.00
50.3
77.8
30.3
0.00
3100
1900
1020
5400
2050
2890
0.00
2470
0.00
2140
2290
2270
0.00
1200
2450
1690
0.00
5100
4600
2430
6690
5990
7940
0.00
3370
0.00
3990
2970
2900
0.00
12400
4050
2620
0.00
313
389
112
30.6
47.6
31.5
0.00
152
0.00
306
43.2
40.7
000
19.1
116
47.3
0.00
351
265
104
290
241
130
0.00
157
0.00
166
136
143
0.00
416
385
166
0.00
1470
1970
341
551
583
706
0.00
683
0.00
1520
316’
267’
0.00
231
2050
944
Soil
BDY
EWY-O
FDN
FRO
LFT
LFT
FRO
BAC
FRO
FRO
LFT
26
27
29
22
23
28
24
25
98.2
43.3
44.2
49.7
40.4
197
49.2
238
10900
8340
8030
12800
9280
31300
10200
10500
121
116
110
131
128
409
116
228
8320
11200
11700
12200
13400
15100
12600
12500
2.30
2.30
1.63
2.27
2.04
3.23
2.02
4.85
24.6
16.0
15.3
23.7
17.9
34.3
19.7
27.8
3430
3490
2950
3430
2840
6980
3010
3190
430
1510
1510
491
370
985
403
378
8.91
6.58
6.49
10.3
11.7
13.8
8.01
21.0
379 d
257
223
383
285
753
295
374
161
107
130
161
278
281
143
461
• Analysis result was greater than upper calibration limit; reported value is an estimated lower bound on the true Zn concentration.
ICP analysis hampered by calcium interference; no multi-element data reported.
Sample dropped in lab, therefore, no multi-element data reported.
d The titanium concentration was originally reported as 0.38 itglg This concentration was flagged in the outlier analysis, investigated, and revised to 379 pglg.
The outlier analysis is described in Appendix B.
0

-------
Table A-ic. CAP Pilot Study Multi-Element Data, House 33
(I
z
C
e
0
g
Sample Identification
Concentrations (pg!g)
Medium
Type
Location
Sample ID
Pb
Al
Ba
Ca
Cd
Cr
K
Mg
Ni
Ti
Zn
Dust
ARD
BRU
EWY-I
FLR
WST
WCH
BD2
LVG
LVG
EWY
EWY
BD2
6D2
LVG
LVG
LVG
KIT
KIT
BD2
LVG
LVG
bY
LVG
09
19
18
20
21
01
03
11
12
13
31
32
04
14
16
38
17
477
1610
117
128
88.4
135
183
189
128
107
116
88.2
575
175
562
581
7240
8030
3550
12000
21700
17900
4910
4880
13100
12400
13400
13600
13200
7040
9740
8050
3960
13300
206
225
163
226
298
357
139
300
453
167
288
301
488
594
1830
510
7060
76700
36800
18200
21000
15900
42300
41800
20800
21500
23900
19000
20200
37300
26900
55800
155000
34900
19.8
65.6
254
129
300
13 1
409
889
661
208
357
33.0
19.8
24.7
110
101
297
53.0
407
68.1
94.2
523
96.7
852
180
190
148
516
676
135
101
870
85.8
39.1
3670
8410
6720
5800
5830
1830
1210
5100
5710
5850
5990
5600
5960
3730
3350
1510
2560
3380
2190
3560
5180
3870
3250
2940
3170
2950
4060
3490
3670
4150
3220
4440
6780
4240
27.7
44.6
17.6
21.5
12.7
33.5
15.1
18.6
196
229
209
16.8
52.2
21.5
24.6
171
179
297
120
387
572
558
165
195
389
314
325
386
355
625
373
480
283
656
2620
104000
448
458
482
426
646
939
866
608
609
577
1180
1500
1610
1180
13800
Soil
BOY
EWY-O
FDN
IFT
FRO
FRO
BAC
FRO
LFT
FRO
LFT
26
27
22
23
28
24
25
29
44 1
168
63 2
136
57
167
108
176
10900
13200
22800
26200
21500
22000
22700
25500
121
161
252
401
280
356
309
369
12000
5270
8130
12500
8090
12400
12900
12300
2 18
201
2 52
144
1.75
3.51
3.27
417
27.0
199
294
952
26.9
31.3
284
36.8
2980
3060
4190
6240
3530
4960
3620
5540
474
497
495
849
494
3060
616
3350
9.59 321
758 443
10.8 730
131 687
6.78 575
15.9 423
11.9 601
133 498
165
112
140
243
122
258
263
285

-------
Table A-id. CAP Pilot Study Multi-Element Data, House 43
Sample k ’ntlflcatlon
Cone ntratlor (pqlq)
Medium
Type
Location
Sample ID
Pb
Al
Ba
Ca
Cd
Cr
K
M
Ni
Ti
Zn
Dust
ARD
DIN
BRU
EWY-I
FLR
WST
WCH
LVG
19 b
LVG
DIN
EWY
EWY
LVG
LVG
DIN
DIN
DIN
KIT
KIT
LVG
DIN
KIT
LVG
KIT
09
611
08
18
20
21
01
03
II
12
13
31
32
04
16
36
05
38
1140
.
102
195
263
589
147
205
234
256
149
308
309
964
378
397
963
1430
9150
.
6500
11500
13400
14300
6600
7830
6920
8630
7490
10400
13400
5170
10500
9170
13700
35400
243
.
209
304
331
2110
220
288
420
393
210
873
593
521
512
443
384
367
63500
.
28000
22100
18200
23300
15100
43300
30100
21900
15000
17800
25000
47400
20200
33800
56400
13100
11.0
.
6.00
6.15
5.26
6.91
4.71
7.26
7.73
8.12
4.59
8.23
8.79
18.2
20.6
221
8.93
72.3
165
.
40.0
37.6
35.3
26.2
33.8
30.2
51.5
42.0
44.1
47.0
45.6
82.7
28.6
44.3
23.8
32.5
4100
.
9200
7770
9790
6060
7020
31700
8610
6270
6800
7390
6910
4590
6630
3550
5340
4640
6720
.
3860
4100
4530
4460
2940
8090
3720
3450
2920
3150
4430
4450
4020
4210
14000
4540
28.7
.
25.6
25.8
18.9
16.5
23.7
26.1
44.9
21.5
15.0
20.4
61.5
25.3
17.5
226
17.3
18.8
408
.
198
344
486
467
198
257
231
237
262
291
422
440
312
353
509
244
7810
2990
1250
763
2070
1640
989
2870’
1160
1320
949
981
1340
6950
1160
2540
1720
Soil
BDY
EWY-O
FDN
FRO
BAC
FRO
BAC
BAC
FRO
BAC
FRO
26
27
22
23
28
24
25
29
290
60.8
623
205
304
337
181
245
12600
5340
13800
19400
15700
18500
21600
19400
203
83.2
374
374
284
460
339
337
12500
5790
12100
13100
13000
10000
15800
8240
4.53
094
6.58
283
2.45
5.39
3.80
4.29
28.3
13.8
28.7
32.1
25.9
41.8
36.4
34.6
4780
1740
3810
4550
3880
3800
4740
3800
491
301
494
508
493
3070
610
2410
12.1
9.57
11.8
11.7
10.8
12.6
14.1
12.0
473
314
326
741
497
601
723
577
221
68.7
492’
300
272
812
561
488’
Analysis result was greater than upper calibration limit; reported value is an estimated lower bound on the two Zn concentration.
b ICP analysis hampered by calcium interference; no multi-element data reported.
z
0
C,
e
0
0
g

-------
Table A-le. CAP Pilot Study Multi-Element Data, House 51
Sample fr ’intlflcatio
Conc tratlor (,iqlpl
Medium
Type
Location
Sample ID
Pb
Al
Ba
Ca
Cd
Cr
K
Mq
Ni
Ti
Zn
Dust
EWY-l
FLR
WST
WCH
EWY
EWY
BAT
BD3
BD3
8D3
BD1
BD1
BD3
BAT
BD3
BD3
BD1
BAT
BD3
BD3
20
21
01
11
12 b
13
31
32
44
06
14
16
40
07
15
17
640
4030
2450
966
467
712
1780
1760
646
6370
774
670
3580
2730
421
493
8490
7110
4410
6340
116
5060
5690
6090
3290
4020
7950
9160
6950
4830
13300
12500
234
755
930
432
862
135
1430
325
270
679
278
314
746
1190
288
300
130000
127000
134000
26400
14800
113000
91300
39300
17700
154000
92300
77300
77500
123000
13500
15800
6.98
116
878
7.50
172
530
7 19
644
4.37
199
473
608
700
132
671
521
22.5
37.8
20.7
25.8
559
16.8
287
229
141
31.1
22.9
30.3
26.1
261
336
338
2320
1630
1860
2080
815
1920
1690
2050
1760
905
2170
3110
2780
901
3280
3410
7220
7430
8590
3010
1020
5590
3690
3140
2010
9290
4730
4820
5120
14500
4390
4560
13.7
19.8
360
15.3
340
135
12.8
119
802
165
904
189
24.4
52.5
22.0
19.5
294
211
149
188
44.1
175
226
260
117
259
345
407
486
362
485
570
743
2760
3390
966’
304
782
1440
1470’
657’
4110
835
866
2170
3200
753
549
Soil
BDY
EWY-Q
FDN
FRO
SAC
SAC
FRO
BAC
FRO
BAC
BAC
26
27
29
22
23
24
25
28
346
329
300
899
505
938
539
426
7760
8190
7390
8710
9130
9170
9210
9320
207
177
178
232
269
258
262
257
5830
6560
7070
4100
5800
5450
7960
7520
3.86
2.55
2.40
4.51
374
4 13
3 81
3 16
246
192
169
224
231
15.9
22.5
202
2220
2600
2430
2290
2650
1610
2430
2310
304
1490
1690
1900
302
384
1520
295
11.2
614
583
6 90
770
7 51
7.10
6.90
306
305
271
342
324
378
343
346
314
235
217
433
376
533
377
340
• Analysis result was greater than upper calibration limit, reported value is an estimated lower bound on the true Zn concentration
b Dunng initial sampling attempt, cartndge filled with sawdust pnor to completion of sample collection. Sample was excluded from lead analysis and multi-
element analysis.
(1
z
0
0
-I
0

-------
Table A-if. CAP Pilot Study Multi-Element Data. Houae 80
Sampi. Ii”qntlflcatlor
Conc —ntratIor-- (p91pj
Medium
Type
Location
Sample ID
Pb
Al
Ba
Ca
Cd
Cr
I C
M
Ni
Ti
Zn
Dust
_____
ARD
BRU
EWY-i
FLR
WST
WCH
BAT
BD3
KIT
BAT
BD3
EWY
EWY
BAT
BAT
BD3
BD3
BD3
KIT
KIT
BAT
BD3
PAN
KIT
KIT
BD3
KIT
KIT
09
19
45
08
18
20
21
01
03
11
12
13
31
32
06
14
36
39
40
15
41
42
1700
965
389
344
66.3
342
222
1210
649
180
175
243
182
223
61600
680
535
7880
4660
938
4550
5790
5810
5270
3610
7780
2100
11800
7440
6870
8730
3720
4810
6430
4950
5510
610
6120
5200
3830
6260
11600
8140
11400
1640
366
470
263
101
303
257
1010
572
186
176
240
323
350
30300
1380
658
29400
6560
846
22500
10900
49700
32200
13400
41100
7620
25000
9620
51000
32800
13900
18000
9710
18200
15100
21300
38200
105000
29300
45900
51000
65400
29500
6.65
7.79
5.52
569
4.70
8.61
4.00
5.37
4.37
9.25
5.09
5.33
4.23
7.98
30.8
17.2
7.85
23.3
20.1
17.6
23.1
30.4
84.0
78.8
16.9
36.1
33.3
33.3
28.9
31.2
32.4
46.1
59.4
44.3
25.4
56.7
151
06.0
60.1
104
206
49.7
94.8
97.7
2210
3480
3420
2510
1140
4990
670
3850
4380
3520
5050
3840
2540
3840
1540
348
2470
745
3150
3340
959
1810
3760
2280
1820
2950
1180
3710
2350
2990
2860
1510
1940
1720
1890
2290
5080
3160
2740
2430
2710
5030
4060
3750
37.6
12.4
10.1
15.2
42.5
27.8
8.82
14.8
185
51.2
19 5
14.8
10.6
21.5
42.4
99.3
15.9
35.4
140
15.6
21 5
147
225
209
103
272
117
389
301
226
198
155
177
224
239
243
181
426
630
494
481
439
715
568
5900
1170’
1240
664
136
703
468
1640
1180
436
508
326’
436
514
35100
1630
2590
7560
3470
1850
4830’
4510’
Soil
BOY
EWYO
FDN
FRO
BAC
FRO
BAC
BAC
LFT
BAC
BAC
26
27
22
23
28
24
25
29
308
343
380
350
412
942
459
317
13000
13400
16400
15200
17600
17300
8810
8890
246
279
282
288
340
414
202
198
8320
7260
6960
10500
8230
6940
5160
7430
9.30
6.19
9.88
7.69
8.29
14.0
6.00
7.56
24.0
24.8
31.3
31.9
31.8
32.9
23.0
23.8
4220
4660
4970
4710
5220
4440
2470
2570
489
493
489
502
487
772
1510
1500
9.51
11.0
12.3
11.5
13.8
13.8
7.43
8.05
437
326
486
501
528
564
322
288
394
396
385
417
492’
973
345
377
‘Analysis result was greater than upper calibration lImit: reported value Is an estimated lower bound on the true Zn concentration.
z
0

-------
T le A-2
a m tric Mean Concentration by Sample Type and Unit
Sample
Type
House
Interior
Abatement
History
Extenor
Abatement
History
Renovation
Samples
Taken In
Unit
Geometnc Mean Concentrations (
qig)
Pb
Al
Ba
Ca
Cd
Cr
K
Mg
Ni
TI
Zn
WCH
33
43
51
80
U
R
E
E
U
R
R
E
None
None
Full
None
1
2
3
3
7238 3
11749
827.6
29136
13345.9
22025.3
9305.0
10248.9
70578
3755
469.3
5915.2
34866 1
27181.3
29601.8
46139.1
29.66
2540
7.73
23.09
39.05
27.82
3092
77.22
25634
4977.4
2158.1
1794.5
4237.1
7979.5
6625.4
4244.4
17.56
1802
28.21
36.69
655.7
352.5
464.2
5630
13782.9
20891
1097.5
34266
WST
17
19
33
43
51
80
R
U
U
R
E
E
E
U
U
R
R
E
None
Partial
None
None
Full
None
6
3
4
3
4
5
3683
139.2
4254
525.1
1854.4
3828 3
9505.7
5120.0
6836.3
7928.2
6718.3
34165
8176
163.3
721 8
4907
458.6
5556.1
33201.3
100782.1
543059
31861 3
96019.2
40917.9
140.20
13.80
15.22
43.54
796
18.10
38.12
49.10
100.43
47.14
2740
10518
7410.0
1708.4
32600
4764.2
2030.3
1254.4
71919
50904
4478.2
4222.3
5742.0
31059
43.74
47 15
2621
21 50
51.21
5058
414.4
298.3
422 0
364 5
3648
406 3
2781.5
765.2
1354.3
2212.6
1594.7
5223.5
ARD
17
19
33
43
80
R
U
U
R
E
E
U
U
R
E
None
Partial
None
None
None
2
1
2
1
3
5106
6244
8746
1137 7
861 2
88138
89480
5340.9
91524
48000
1799
585.1
215.5
243.1
8555
16615.1
69610.2
53114 2
63535.5
277952
201.09
23.72
38.02
11.03
6.59
51 14
14595
46.41
164.75
4820
6695.5
3097.1
5553.4
4100.3
2971 9
3759.1
5103.5
2719.4
6724 6
24974
21 10
312.90
35 15
2870
16.77
269.7
351.4
188.4
408.4
1696
4537.5
14659
18503.7
7806 0
2053 8
FLR
17
19
33
43
51
80
R
U
U
R
E
E
E
U
U
R
R
E
None
Partial
None
None
Full
None
7
5
7
7
6
7
1655
173 1
1307
2209
1227 1
304 8
5548.9
4830.6
9921 4
8504.5
50240
56884
642.6
217.4
287.2
380.0
137 8
3388
146863
274705
256223
22451 7
542046
19605 1
9.17
7.87
3589
656
6.43
571
2883
65.56
203.43
41.41
20.63
4040
109740
2398.4
3854.7
8828.0
1890.0
37896
2726.5
3911.9
3341.5
3844.8
3870.5
2109.9
39.95
7626
2042
2729
1444
19.03
151.1
145.9
290 5
264.2
179 6
206 6
5687
573 1
6476
13101
12352
6097
BRU
17
19
33
43
80
R
U
U
R
E
E
U
U
R
E
None
Partial
None
None
None
1
2
1
2
2
66.9
483.3
118.9
141.3
151.1
51393
4444.6
119543
8630.5
40403
4338
363.7
1625
252 1
1632
19032 9
589430
18229.9
248889
176923
9.97
1041
2544
607
522
4385
12327
6906
3878
34.67
80970
1395.4
6723.0
8456.8
1688.3
3210.0
3342.6
35583
3977.0
1867.1
7684
208.42
17.55
25.69
25.43
84 9
1663
3874
2609
178 7
572.3
819.4
4478
1931 2
300.7
EWY-l
17
19
33
43
51
80
R
U
U
R
E
E
E
U
U
R
R
E
None
Partial
None
None
Full
None
2
2
2
2
2
2
269.9
192.6
1064
3940
16054
2754
10232.5
7640.8
19721.0
13844.0
7773 1
9357.4
636.0
125.0
259.4
835.8
133.0
279.1
14240 0
114992.3
18226.2
20562.2
128563.4
15521.7
1499
7.88
1971
6.03
9.00
5.87
3556
4040
221.98
30.42
2912
3099
8268.5
3326.8
5812.2
7701.6
1944.0
18289
2659.3
6423.5
4477.7
44976
7325.5
29534
2759
38.19
16.55
17.63
1646
15.66
307.6
264.3
565 2
4765
249.1
341 8
513.8
566.6
469 9
12557
1432.1
5736
I
0
0
U = unabated, R = removal, and E = encapsulation/enclosure

-------
Table A-2.
(Continued)
Sample
Type
House
Intenor
Abatement
History
Exterior
Abatement
History
Renovation
Samples
Taken in
Unit
Geometric Mean Concentrations (
Pb Al Ba Ca Cd Cr K
gig)
Mg
Ni
Ti
Zn
EWY-O
17
19
33
43
51
80
R
U
U
R
E
E
E
U
U
R
R
E
None
Partial
None
None
Full
None
2
3
3
3
2
3
160.1
73.3
78.8
338.4
673.7
379.6
19994.7
15510.8
23437.0
16152.0
8916.5
16376.5
293.5
190 1
304.8
341.1
249.5
302.
13488.9
13527.8
9360.6
12687.1
4876.6
8453.0
25.75
2.48
3.99
3.57
4.11
8.57
100.69
24.38
90.97
28.76
22.75
31.08
4950.2
4083.4
4517.1
4069.0
2461.6
4962.2
575.9
563.6
591.9
497.6
757.7
492.6
59.90
11.83
9.85
11.39
7.29
12.51
532.3
434.5
654.3
493.4
333.0
504.5
300.2
232.7
160.7
342.3
403.5
429.4
FDN
17
19
33
43
51
80
R
U
U
R
E
E
E
U
U
R
R
E
None
Partial
None
None
Full
None
3
2
3
3
3
- 3
68.4
108.3
146.9
246.0
599.4
515.4
18939.6
10388.0
23354.5
19783.7
9231.2
11057.3
207.4
162.2
343.9
374.5
259.0
254.5
11734.0
12527.0
12542.9
10929.7
6884.4
6432.2
2.64
3.13
3.63
4.45
3.08
8.62
39.32
23.43
31.99
37.50
19.34
26.19
4740.2
3096.0
4632.8
4092.1
2081.4
3045.3
1718.7
390.2
1848.8
1652.2
856.2
1204.9
14.63
12.96
13.59
12.88
7.17
9.38
398.8
331.9
502.1
630.4
355.5
374.2
300.6
257.1
268.6
605.7
408.9
502.1
BDY
17
19
33
43
51
80
R
U
U
R
E
E
E
U
U
R
R
E
None
PartIal
None
None
Full
None
3
3
2
2
3
2
59.2
57.3
86.0
132.6
324.7
324.8
23827.4
9015.6
11982.1
8192.7
7773.8
13198.4
202.5
115.6
139.7
130.0
186.7
281.9
11296.0
10270.8
7981.7
8519.8
6504.0
7770.7
2.54
2.05
2.09
2.06
2.87
7.59
42.19
18.17
23.19
1974
19.98
2440
6063.7
3280.7
3019.1
2880.7
2413.1
4435.5
797.2
993.6
485.7
384.3
914.8
491.3
16.32
7.25
8.53
10.77
7.37
10.2t
587.0
278.8
377.2
385.3
293.8
377.5
130.4
131.0
1356
140.1
252.1
395.2
U = unabated, R = removal, and E =
z
0
C,
g
‘2
00

-------
Peer Review Draft— Do Not Cite or Quote
APPENDIX B
OUTLIER MTALTSIS FOR TEE CAPS
PILOT MULTI-ELE NT DATA

-------
Peer Review Draft — Do Not Cite or Quote
APPENDIX B
OUTLIER NALTSIS FOR THE CAPS
PILOT MULTI-ELE NT DATA
B-i INTRODUCTION
This appendix documents the statistical outlier analysis
performed on the CAPS Pilot multi-element data. The statistical
approach employed, the outliers identified, and the results of
the laboratory review of the outlier data are discussed.
Two outlier tests were applied to the multi-element data.
The first was a univariate outlier test, which evaluates one
element at a time. This is the same test that was previously
applied to the lead data. The test was applied to the natural
logarithms of the concentrations for lead, aluminum, barium,
cadmium, calcium, chromium, magnesium, nickel, potassium,
titanium, and zinc. The second test was a multivariate outlier
test, which evaluates measurements for all eleven elements
simultaneously. The multivariate test detects measurements which
for a single element may not be an outlier, but when viewed in
combination with the other elements is inconsistent with the
majority of the data. Before performing the outlier tests,
groupings of the data were defined.
B-2 DATA GROUPING
The following homogeneous groups of data were identified
for each indicated sample type:
• Vacuum Cassette Samples (7 groups) : air duct,
upholstery (including bed coverings and throw rugs),
B-i

-------
Peer Review Draft— Do Not Cite or Quote
interior entryway, floor (excluding entryway), window
stool, window channel, and floor (including entryway);
• Soil Samples (4 groups): boundary, foundation, exterior
entryway, and all exterior samples combined.
Initially, data for all six units in the Pilot Study were
combined before performing the univariate and multivariate
outlier tests on these groups. When there were sufficient data,
subsequent univariate outlier tests were also performed by
segregating the data in each group by abatement method and by
housing unit. Segregating by abatement method and unit was not
done for the multivariate test due to the need for larger sample
sizes with the increase in dimensionality.
B-3 ) THODS
The details of the univariate and multivariate outlier
tests are given in the following sections.
B-3-i. Univariate Outlier Teat
Formal statistical outlier tests were performed on the
natural logarithms of the concentrations for lead, aluminum,
barium, cadmium, calcium, chromium, magnesium, nickel, potassium,
titanium and zinc. Data were placed into groups of comparable
values, and a maximum absolute studentized residual procedure was
used to identify potential outliers. The SAS procedure GLM (SAS
PC, ver. 6.08) was used to compute the studentized residual for
each data value in a group by fitting a ‘ T constant” model (i.e.,
mean value plus error term) to the log-transformed data in each
group. The absolute values of the studentized residuals were
then compared to the upper .05/n quantile of a student-t
distribution with n—2 degrees of freedom, where n is the number
of data values in the group. If the maximum ab5olute studentized
B-2

-------
Peer Review Draft — Do Not Cite or Quote
residual was greater than or equal to the .05/n quantile, the
corresponding data value was flagged as a potential outlier.
When a potential outlier was identified, that value was excluded
from the group, and the outlier test was performed again. This
procedure was repeated until no more outliers were detected.
B-3-2 Multivariate Out].ier Test
The multivariate outlier test is based on the Hotelling T—
squared statistic, with one major difference. The Hotelling T—
squared statistic is discussed in most multivariate statistics
texts, such as Multivariate Statistical Methods. Second Edition ,
by Donald F. Morrison, copyright 1967, 1976 by McGraw—Hill, Inc.,
page 131. The difference in the statistic used here is that, in
computing the statistic for the th observation, that observation
is excluded from the computation of the mean vector and the
variance-covariance matrix. This yields estimates of location
and covariance that are unaffected by the observation in question
and lead to a more robust outlier test. This is a multivariate
extension of the univariate studentized residual used for the
univariate outlier test. Under assumptions of normality, the
resulting statistic has an F distribution, with numerator degrees
of freedom equal to p (the number of elements) and denominator
degrees of freedom equal to a function of p and the sample size,
N. In this case, p was equal to eleven.
The observation corresponding to the maximum value of the
statistic in a data group was declared a potential outlier if the
statistic exceeded the (1—.10/N) quantile of the F distribution
with appropriate degrees of freedom. When a potential outlier
was identified, that sample was excluded from the group, and the
outlier test was performed again. This procedure was repeated
until no more outliers were detected.
B-3

-------
Peer Review Draft — Do Not Cite or Quote
3-4 RESULTS OF OUTLIER ANALYSIS
The potential outliers identified by these two tests were
screened by a statistician to eliminate those that were merely
numerical anomalies due to very small sample sizes. The
remaining outliers identified by the univariate test are listed
in Table B—i, and those identified by the multivariate test are
listed in Table B-2. These lists of the remaining outliers were
sent back to the laboratory for verification. One outlier was
confirmed by the laboratory as an error and is documented in the
footnote to Table A—ib. All remaining outliers were verified and
declared by the laboratory to be correct as reported.
B-4

-------
Peer Review Draft— Do Not Cite or Quote
Table 3-1. Univariate Outliers Detected by Univariate Methods
Sample
Processing
Batch
House ID!
Sample ID
Concentration ( .iglg)
Al
Ba
Cd
Cr
Ni
Ti
Zn
CLS
33/20
9418
CRS
33/21
523.19
SSS
33/23
14.43
951.74
CSS
33/31
515.97
CSS
33/32
676.48
SSS
43/22
6.58
SSS
43126
4.53
SSS
43/27
_______
0.94
13.75
CSS
43/11
2866.97
CSS
43/32
422.12
CXC
43/38
220.60
CKC
17101
16.00
55.00
502.00
CLS
17/03
104.36
SSS
17/23
241.07
268.94
238.11
CLS
19104
231.35
CLS
19/08
186.60
CLS
19/13
1520.83
SSS
19125
4.85
SSS
19126
0.38
CLS
17/19
615.27
SKI
43/24
5.39
SSS
19/28
______
753.13
CLS
19/36
165.58
CRS
80/06
609.89
30315.04
181.30
35121.27
SSS
80/24
13.98
564.27
972.71
SSS
80/26
9.30
SSS
80/27
6.19
CLS
80/09
5963.48
CLS
80/45
16.92
CSS
80/39
29402.19
CSS
80/41
22466.22
CRS
51/12
1 72
5.59
44 14
CLS
51/20
22.45
SSS
51/24
533.06
SSS
51/26
386
CRS
33/19
99.07
CRS
43/16
306.14
B-5

-------
Table B-2. Outliers Detected by Multivariate Methods.
Sample
Processing
Batch
House
Sample
ID
Concentration (ljglg)
Pb
Al
Ba
Cd
Ca
Cr
Mg
Ni
K
Ti
Zn
CLS
17
03
253.91
6949.83
1841.07
1339
23113.58
29.35
3950.lla
16.27
17158.68
104.36a
1338.25
CKC
17
01
50
1694
742
31
14246
16.19
2724
13
14419
5507
502
CRS
80
06
61573.85
609.89
30315.04
30.83
21251.35
151.36
5080.89
42.43
1536.03
181.3
35121.27
SSS
51
26
345.81
7761.56
206.56
3.86
5934.11
24.57
303.99
11.18
2224.18
306.4
313.77
SSS
17
23
36388
19585.58
439.75
241.07
14160.18
268.94
614.15
238.11
4570.6
582.48
499.30
SSS
33
23
135.78
26178.44
401.46
14.43
12471.77
951.74
848.89
13.06
6241.22
667.37
243.15
z
0
C,
0
I

-------