Notice

The information in this document has been funded in part by the United States 

Environmental Protection Agency under Contract EP-D-10-070 to Alion, Inc. 

It has been subjected to the Agency’s peer and administrative review and has 

been approved for publication as an EPA document. Mention of trade names or 

commercial products does not constitute endorsement or recommendation for use.
EPA/600/R-12/051| September 30, 2012

Polychlorinated Biphenyls 

(PCBs) in School Buildings: 

Sources, Environmental 

Levels, and Exposures

Kent Thomas, Jianping Xue, Ronald Williams, 

Paul Jones, Donald Whitaker

Office of Research and Development

National Exposure Research Laboratory
ii

Acknowledgements

We would like to acknowledge and thank Dennis Santella 

and James Haklar, U.S. EPA Region 2, and Kimberly Tisa, 

U.S. EPA Region 1, for their assistance and their insight into 

schools with PCBs. We would also like to acknowledge 

the New York City School Construction Authority and TRC 

Engineers, Inc. for designing and conducting the remedial 

pilot school investigation program and for providing 

measurement results and school information. We thank 

Tamira Cousett, Carlton Witherspoon, Keith Kronmiller, 

Paulette Yongue, and Hunter Daughtry of Alion Inc. for their 

project management, field sampling, and data coordination 

efforts. We thank the staff of NEA Pace Analytical for 

the PCB Aroclor and congener sample analysis. We 

appreciate the assistance and consultation regarding PCB 

emissions, transport, and indoor modeling by Zhishi Guo 

in the U.S. EPA National Risk Management Research 

Laboratory. Finally, we would like to thank Peter Egeghy, 

U.S. EPA National Exposure Research Laboratory, for his 

efforts in collecting and organizing extant data from other 

measurement efforts that were used in the SHEDS model 

comparison. 
iii

Executive Summary

E.1 Background

Caulk containing polychlorinated biphenyls (PCBs) was 

used in some buildings, including schools, in the 1950s 

through the 1970s. PCBs were used as a plasticizer in 

caulk, added either during manufacture or mixed on site 

prior to installation. Other potential sources of PCBs, such 

as fluorescent light ballast capacitors, were also used in 

school buildings during that era. PCBs were used in other 

types of capacitors, transformers, plasticizers, coatings, inks, 

adhesives, and carbonless copy paper but the extent of these 

uses in school buildings is not well known. Materials and 

components containing PCBs are still present today in many 

of these older buildings. PCBs are semi-volatile organic 

chemicals and can be transported in and around buildings 

through vaporization into the air and through absorption into 

dust and materials. PCBs may be present in the air, dust, soil 

and on surfaces in and around school buildings leading to the 

potential for occupant exposure through multiple routes. In 

September 2009 the U.S. EPA announced new guidance for 

school administrators and building managers for managing 

PCBs in caulk and to help minimize possible exposure. 

However, there was limited information on PCBs in school 

buildings in the United States. Neither the PCB sources, 

nor the routes of exposure, have been well-characterized 

in school buildings. As such, there remained considerable 

uncertainty regarding the extent to which children and staff 

members may be exposed to PCBs in school environments. 

The EPA also announced in 2009 that additional research 

would be performed by the Office of Research and 

Development’s (ORD) National Exposure Research 

Laboratory (NERL) and the National Risk Management 

Research Laboratory (NRMRL) to further study this issue. 

The research was intended to help fill information gaps and 

improve our understanding of PCBs in school buildings and 

approaches for mitigating exposures. 

E.2 Objectives

Information on sources of PCBs and levels in school 

environments is needed to improve risk management 

decision-making. ORD’s NERL planned research to better 

understand and characterize PCB sources, emissions, 

environmental concentrations, and exposures in school 

environments. Research was also planned by ORD’s 

NRMRL to perform laboratory studies of PCB sources and 

transport and to evaluate selected mitigation approaches. 

In order to better understand the significance of PCBcontaining caulk and other building materials and 

components as a source of PCB exposures to children, 

teachers, and staff in school buildings, the ORD’s NERL 

planned research to utilize a limited set of real-world 

measurements to: 

1. characterize PCB-contaminated caulk and other 

potential primary and secondary sources of PCBs in 

school buildings, 

2. characterize levels of PCBs in school air, dust, soil, and 

on surfaces and to investigate relationships between 

potential PCB sources and environmental levels, 

3. apply an exposure model for estimating children’s 

exposure to PCBs in schools with PCB sources, 

4. evaluate which routes of exposure (e.g., inhalation, 

contact with surfaces or dust) are likely to be most 

important, and

5. provide information to assist in developing risk 

management practices for reducing exposure to PCBs 

in schools. 

E.3 Approach

NERL exposure scientists and their contractor collected 

multiple air, dust, soil, and surface wipe samples from a 

school building scheduled for demolition. Scientists also 

collected samples from building materials like caulk, tiles, 

paints, mastics, and others. These environmental and 

material samples were analyzed for PCB Aroclor mixtures 

to determine total PCB concentrations. A subset of these 

samples was then analyzed for PCB congeners (individual 

PCB compounds) to provide information regarding the 

presence and relative amounts of individual PCBs and 

to evaluate relationships between different sources and 

environmental media. 

NERL scientists also used environmental and building 

material PCB Aroclor measurement data from five 

schools that were generated by the New York City School 

Construction Authority under a pilot remedial investigation 

plan developed under an agreement with U.S. EPA Region 2. 

Measurement results and information from these six school 

buildings provided a limited data set for characterizing 

real-world PCB sources and environmental levels in schools 

that were built from the late 1950s to the early 1970s. 

Environmental measurement results were used to generate 

distributions of estimated PCB exposures for several different 

child age groups using the Stochastic Human Exposure and 

Dose Simulation (SHEDS) model. The SHEDS model was 

also used to estimate the relative contribution of inhalation, 

dermal contact, and non-dietary ingestion of dust and soil 

components of the total exposure. 
iv

E.4 Results

Primary PCB Source Assessment

Primary sources of PCBs are defined here as those that 

were manufactured containing PCBs or had PCBs added 

during construction. Primary sources of PCBs that might be 

currently found in buildings include caulk or other sealants, 

window glazing, fluorescent light ballast capacitors, ceiling 

tile coatings, and possibly other materials such as paints or 

floor finishes. Numerous samples of caulk, window glazing, 

and joint sealants were collected at the six schools. When 

considering measurement results for these samples it is 

important to remember that multiple samples of the same 

type of material may have been collected from several places 

in a school building, and that these schools were selected 

because they had suspected or known PCB sources. Key 

results for caulk and light ballast characterization at the six 

schools are reported below.

Caulk

• All six schools contained some interior or exterior 

caulk with PCB levels greater than 10,000 ppm. 

8.6 % of the 427 samples of interior caulk, window 

glazing, and building joint material from six 

schools had total PCB levels > 10,000 ppm, while 

82% had levels < 50 ppm. The maximum concentration 

was 440,000 ppm (which is 44% PCBs by weight).

• 41% of the 73 samples of exterior caulk, window 

glazing, and building joint material from three schools 

had total PCB concentrations > 10,000 ppm, while 

37% had levels < 50 ppm. The maximum concentration 

was 328,000 ppm. No exterior caulk measurement results 

were available for the other three schools. 

• Aroclor 1254 was the Aroclor mixture reported for most 

caulk samples, although the analytical laboratory often 

reported an altered PCB pattern with Aroclor 1254 as 

the best match. Caulk with an Aroclor 1260 pattern was 

found at one school.

• Caulk with high PCB concentrations was generally found 

to be intact and still somewhat flexible. Other flexible 

caulks and sealants without high PCB levels were also 

found, while dry and brittle sealants did not have high 

levels of PCBs.

• Total PCB emission rates were estimated for caulks 

with > 50,000 ppm PCBs collected in several rooms, 

around the exterior of a window, and for an exterior joint. 

Estimated emission rates ranged from 53 to 3100 μg/hr 

and depended on the PCB concentration in the caulk and 

the total surface area of the caulk in the specific room or 

location. The effect of different temperatures was not 

evaluated. 

• Screening-level estimates of the range of indoor air 

concentrations that might result from PCB emissions 

from caulk inside or outside several rooms gave results 

ranging from 10 to 1900 ng/m3, and depended on the 

estimated emission rate, air exchange rate, and for the 

exterior window caulk the percentage of emitted PCBs 

estimated to enter the classroom. Measured indoor air 

concentrations were higher than the screening-level 

estimates in two of four rooms. 

Light Ballasts

• Light ballast survey results were available for five 

schools. The percentage of ballasts found likely to be 

PCB-containing ranged from 24% to 95% across the 

schools. 

• Estimated total PCB emission rates for intact ballasts 

in three rooms ranged from 1.2 to 290 μg/hr (based on 

laboratory emissions tests of four ballasts at 45°C, near 

the operating temperature of ballasts when the lights 

are on) and from 0.08 to 18 μg/hr (based on laboratory 

emissions tests of four ballasts at 23°C, near the 

temperature of ballasts when light are off). The estimates 

depended on the number of PCB-containing ballasts in the 

room and the lowest and highest emitting ballasts in the 

chamber testing, which saw a nearly 80-fold difference in 

emission rates.

• Screening-level estimates of the range of air 

concentrations that might result from PCB emissions from 

intact PCB-containing ballasts in three rooms gave results 

ranging from 1.6 to 2400 ng/m3 (at a ballast temperature 

of 45°C). At the median estimated emission rate the 

screening-level air concentration estimates ranged from 

2.3 to 44 ng/m3 and were lower than the measured air 

concentrations that ranged from 690 to 1460 ng/m3. 

• There was evidence of previous ballast failure in and 

around some light fixtures in these schools and some 

ballasts had been replaced with non-PCB containing 

ballasts. Emissions of PCBs from light fixtures that 

have been previously contaminated by leaking or failed 

ballast capacitors have not been measured, but could be 

substantial if PCB oil residues are present. Emission rates 

have not been estimated for ballasts with capacitors that 

are leaking or have burst, but based on the bursting of a 

ballast capacitor during laboratory testing it is anticipated 

that emissions would be substantially higher than those 

measured for intact ballast capacitors and would have a 

large impact on indoor air PCB concentrations. 

Secondary PCB Source Assessment

Secondary sources of PCBs are defined here as those 

materials that become contaminated due to absorption from 

direct contact with primary PCB sources such as caulk, or 

through absorption of PCBs in the indoor air that have been 

emitted by primary sources such as caulk and light ballasts. 

Materials such as paints, dust, masonry, floor and ceiling 

tiles, and mastics may become secondary sources after years 

of exposure to PCBs emitted from primary sources. These 

materials should be considered sinks as well as sources, due 

to their ability to absorb PCBs from direct contact or from 

the air. When considering measurement results for these 

samples it is important to remember that multiple samples 

of the same type of material may have been collected from 
v

several places in a school building. Key results for secondary 

source characterization at the three schools with material 

measurements are reported below.

• 93% of the 411 building material samples collected at 

three schools had measureable levels of PCBs. The 

overall median total PCB concentration was 16 ppm 

(interquartile range 6.1 - 39.6 ppm).

• Paint had the highest total PCB concentrations with a 

median of 39 ppm (interquartile range 26 – 72 ppm). 

Fiberboard had a median level of 31 ppm (interquartile 

range 13 – 39 ppm), while lower levels were found in 

some other materials that often have high surface areas 

in buildings such as ceiling tile with a median of 7.6 ppm 

(interquartile range 2.7 – 12 ppm) and floor tile with a 

median of 4.4 ppm (interquartile range 1.4 – 8.7 ppm).

• It is impossible to be certain that these materials did not 

contain some PCBs when originally installed. Measured 

concentrations and relative levels among different 

materials are consistent with these materials being “sinks” 

that have absorbed PCBs emitted from primary sources.

• Screening-level estimates of emission rates for 

multiple materials in several rooms were calculated 

to evaluate their relative potential as secondary 

sources of PCB emissions following removal of 

primary sources. Estimated emission rates for 

different materials in classrooms ranged from 

< 1 to 100 μg/hr and cumulative totals for 20 materials 

in a room ranged up to 270 μg/hr. Estimated emission 

rates for different materials in gymnasiums ranged 

from < 1 to 1100 μg/hr and cumulative totals for 

16 materials ranged up to 2700 μg/hr. Estimated 

emission rates depended on the surface area of the 

material and concentration of PCBs in the material. 

Paints and varnishes generally had the highest relative 

potential emissions due to the combination of higher 

PCB concentrations and high surface areas. (There are 

considerable uncertainties in these estimates, which are 

based on emission parameters derived from laboratory 

emissions testing of caulks. Emission parameters for 

the many different types of other materials could be 

substantially different than those for caulk). 

• It is difficult to estimate indoor air concentrations of 

PCBs that might result from secondary sources following 

removal of primary sources because of the large number 

of different types of PCB-containing material in a room, 

and because the source – sink dynamics for multiple 

different materials are difficult to characterize. However, 

the cumulative PCB emissions from secondary sources 

could potentially result in indoor air PCB levels above 

background in school rooms following mitigation of 

primary sources, depending on relative emission rates, 

sink rates, and ventilation rates by outdoor air. 

• Surface dust and dust in ventilation systems may be 

another secondary source for potential exposures to PCBs. 

PCB Concentrations in the School Environment

PCBs may be transported from primary and secondary 

sources to environmental media in and around buildings 

including indoor air, dusts, and soils. PCBs may also be 

found on surfaces inside buildings. Key results for PCB 

concentration measurements in environmental media at six 

schools are reported below.

• The median indoor air total PCB concentration 

based on 64 measurements across six schools was 

318 ng/m3 (interquartile range 59 - 732 ng/m3). There 

was considerable variability between schools with 

median air levels at individual schools ranging from 

<50 to 807 ng/m3. There was considerable variability 

within schools; for example, indoor air levels ranged from 

236 to 2920 ng/m3 in different rooms at one school.

• Surface wipe samples were collected from highcontact (desks, tables) and low-contact (walls, floors, 

window sills) surfaces at six schools. Median total PCB 

concentrations were 0.147 μg/100cm2 (interquartile 

range <0.100 – 0.330 μg/100cm2) for 72 high-contact 

surfaces and 0.201 μg/100cm2

 (interquartile range 

0.128 – 0.419 μg/100cm2) for 78 low-contact surfaces. 

Concentrations ranged from <0.100 to 2.84 μg/100cm2 for 

high contact surfaces and <0.100 to 2.30 μg/100cm2 for 

low-contact surfaces. Median high-contact wipe levels 

ranged from <0.100 to 0.380 μg/100cm2 at individual 

schools. There was no consistent difference between 

median high-contact and low-contact concentrations 

across the six schools.

• There was a modest but significant correlation 

between indoor air total PCB concentrations and total 

PCB levels measured in high-contact surface wipes 

(Spearman r = 0.531, p-value <0.001) with a lower 

but still significant correlation between PCBs in air 

and low-contact surface wipes (Spearman r = 0.247, 

p-value = 0.050). 

• Soil samples were collected at six schools at distances 

of 0.15, 0.91, and 2.44 m (0.5, 3, and 8 feet) from 

the building. Only 33% of the samples had PCB 

concentrations above the quantifiable limit. The median 

total PCB concentration across all 309 soil samples 

was less than the quantifiable limit. The 75th percentile 

concentration was 0.98 ppm and the maximum value was 

211 ppm. There was considerable variability between 

schools; for example one school had only 10% of the 

PCB levels above the quantifiable limit while another 

school had 100%. The 75th percentiles of total PCB 

concentrations across six schools at 0.15, 0.91, and 

2.44 m from the building were 2.13, 0.55, and <0.5 ppm 

respectively, supporting the idea that higher soil PCB 

levels are likely to be found in closer proximity to 

building sources including exterior window caulk and 

building joints with PCBs. 
vi

• Indoor dust samples were collected at only one school. 

The median total PCB concentration was 22 ppm 

(interquartile range 17 – 53 ppm) across samples collected 

in seven rooms.

PCB Congener Analysis Results

Individual PCB congeners were measured in all of the air 

samples and in a subset of surface wipe, indoor dust, soil, 

caulk, and other building material samples at one school. 

Examining patterns of individual congeners can provide 

insight regarding relationships between PCBs sources and 

environmental media, and can also provide information 

useful in exposure and risk assessment. Key results for PCB 

congener measurements at one school are reported below.

• Analysis of sample extracts for congeners resulted in total 

PCB concentrations that were approximately 20% lower 

than the analysis of the same sample extracts using an 

Aroclor method. 

• Air samples collected both vapor and particle-bound 

PCBs, but these fractions were not analyzed separately. 

The pattern of congeners in indoor air was more heavily 

weighted towards more volatile congeners as compared 

to non-aged Aroclor 1254 and as compared to the 

PCB-containing caulk at the building. The pattern of 

congeners in air was not as heavily weighted towards 

more volatile congeners as would be predicted if they 

were from vapor emissions from caulk alone, suggesting 

that a portion of the PCBs in air may be associated with 

airborne particles. 

• The congener pattern in the PCB-containing caulk was 

somewhat more heavily weighted towards less volatile 

congeners as compared to non-aged Aroclor 1254. It 

is possible that the more volatile congeners have been 

depleted from sources such as the exterior caulk over a 

period of 43 years. 

• Many PCB-containing fluorescent light ballasts have been 

shown to contain Aroclor 1242. However, Aroclor 1254 

was found to be the capacitor oil in one NYC ballast and 

was reportedly used in light ballast capacitors prior to 

1952. The congener pattern in indoor air at the school 

with congener measurements did not resemble a pattern 

that would be expected if the predominant source of PCBs 

was Aroclor 1242 from light ballasts, but the capacitor oil 

was not analyzed to determine if it was, in fact, Aroclor 

1242. 

• Congener patterns in surface wipe, indoor dust, and other 

building materials were generally similar to Aroclor 1254 

and to the PCB-containing caulk. Because only four dust 

samples were analyzed for congeners we did not assess 

air – dust congener correlations, but we anticipate that 

such a relationship is likely to exist.

• Soil samples had a congener pattern weighted towards 

less volatile congeners as compared to either Aroclor 

1254 and even compared to the PCB-containing exterior 

caulk. The higher proportion of less volatile congeners 

as compared to the likely source is possibly a result 

of weathering and favored partitioning of less volatile 

congeners into soil rather than air.

• Congener-specific analysis provides information that 

may be useful for risk assessment. For example, between 

5% (indoor air) and 14% (soil) of the total amount of 

PCBs were comprised of the sum of the 12 dioxin-like 

congeners. As additional information on PCB toxicity 

accumulates, having congener-specific information 

becomes more important.

Modeled Exposure and Dose Estimates

The PCB concentrations in the air, surface wipe, soil, and 

estimates for dust were used in the SHEDS model to generate 

distributions of estimated exposures to PCBs for children 

in four age groups (4-5, 6-10, 11-13, and 14-18 years old). 

The model incorporated environmental concentration 

distributions based on the range of air, surface wipe, and soil 

measurements across the six schools. Dust measurements 

were not made at most schools and dust concentrations were 

estimated based on air concentrations and estimated dust/air 

partition coefficients. The model incorporated distributions 

of activity levels appropriate for each age group in estimating 

exposures. The model then generated distributions of 

estimated absorbed doses of PCBs resulting from the 

estimated exposures in the school environment for the four 

age groups. The model provided information on the potential 

relative contribution to total absorbed dose from inhalation, 

dermal contact, and dust and soil ingestion. Various remedial 

approaches were examined by the NYC School Construction 

Authority at the five New York City schools, and pre-and 

post-remediation absorbed dose estimates were prepared 

using environmental measurements from these schools. 

Absorbed dose estimation included only those exposures to 

PCBs that would occur to children while at school and do not 

include background exposure through the diet or inhalation 

from indoor and outdoor air while away from the school 

and at home. Sensitivity testing was performed to examine 

the impact of uncertainty in the pulmonary PCB absorption 

fraction and for different PCB levels in dust and soil. Key 

results for estimated exposures and exposure pathways are 

reported for the 6 – 10 year-old age group below.

• Estimated absorbed doses for 6–10 year-olds at the 

pre-remediation time point were 0.022 μg/kg/day at the 

50th percentile and 0.041 μg/kg/day at the 95th percentile 

of the distribution. Estimated absorbed doses for 4 – 5, 

11 – 13, and 14 – 18 year old age groups were somewhat 

lower. 

• After incorporating indoor air and surface wipe 

measurements obtained following different remedial 

activities in five schools, estimated absorbed doses 

for 6–10 year-olds were 0.007 μg/kg/day at the 50th 

percentile and 0.012 μg/kg/day at the 95th percentile. 

These levels were approximately 64 - 69% lower than 

those at the pre-remediation time point. 
vii

• Up to the 90th percentile of the estimated absorbed 

dose distribution, the predominant route of exposure 

for all age groups at all time points in schools with the 

measured environmental levels would be via inhalation. 

It was estimated that, on average, 74% of the absorbed 

dose would result from inhalation at the pre- and postremediation time points (assuming a 70% pulmonary 

absorption fraction). However, above the 90th percentile 

of the estimated absorbed dose distribution, intake 

from dust was predicted to be greater than intake from 

inhalation. 

• Sensitivity analysis showed that the estimated absorbed 

doses were sensitive to assumptions regarding the 

pulmonary absorption fraction. When the pulmonary 

absorption fraction was changed from the baseline 

of 70% to 30% in the model, the median estimated 

exposure for the 6 – 10 year old group decreased 

49% to 0.011 μg/kg/day. When the pulmonary absorption 

fraction was increased to 100%, the median estimated 

exposure for the 6 – 10 year old group increased 

35% to 0.028 μg/kg/day. 

• As described in more detail in subsequent sections, there 

are uncertainties and limitations in modeled estimates 

of exposure distributions and contributions of relative 

exposure pathways. There are currently insufficient data 

to perform a full uncertainty analysis for the SHEDS PCB 

exposure estimation. 

• Exposure estimation was not performed for adults, 

including teachers and staff, as part of this effort due to 

the lack of personal activity data at school suitable for 

SHEDS modeling, such as those available for children 

in the Consolidated Human Activity Database. It is 

anticipated that adults would spend more time in school 

buildings, which would tend to lead to higher doses, but 

less ingestion of dust and soil and higher relative body 

masses would lead to lower doses. 

E.5 Conclusions

Sources of PCBs in School Buildings

• PCBs-containing caulk is a primary source of PCBs in 

and around school buildings. PCBs from exterior caulks 

around windows and mechanical ventilation system air 

intakes can lead to elevated concentrations in indoor 

spaces. PCBs in exterior caulk are likely to enter the 

soil near school buildings with the highest soil PCB 

levels found in closest proximity to the building. Caulk 

containing PCBs was found to be mostly intact and still 

somewhat flexible, but visual examination alone may 

not be adequate for determining if PCBs are present and 

testing is needed to determine if caulk or other sealants in 

a building contain PCBs.

• PCB-containing fluorescent light ballasts remain in use 

in some older school buildings and are a primary source 

of PCBs. Emissions from intact ballast capacitors can 

lead to the presence of PCBs in school environments. 

PCB residues from previously failed ballast capacitors 

may remain in fixtures even if the ballast is replaced. 

Leaking or bursting capacitors are likely to substantially 

elevate PCB levels in indoor environments when they 

fail. Because these ballasts have exceeded their expected 

operational lifetimes, failure and possible leakage will 

continue and is likely to increase for ballasts remaining in 

place. 

• Several paint samples had total PCB concentrations above 

100 ppm, up to 718 ppm. PCBs were used as plasticizers 

or flame retardants in some paints, so it is possible that 

these paints may have incorporated PCB when they were 

originally applied. Thus, it is possible that paints could 

be primary sources of PCBs in buildings based on our 

definition. Although they were not encountered in this 

study, window glazing and ceiling tile surface coatings 

containing PCBs have been reported in school buildings 

and would be considered primary sources.

• Other primary sources of PCBs may have been used in 

school buildings but are no longer present today. For 

example, carbonless copy paper and PCB-containing 

capacitors in early computer video display terminals may 

have been used in school buildings. The potential impact 

of previously removed sources on current PCB levels in 

building environments cannot be easily determined. 

• Many of the building and furnishing materials in 

schools were found to contain PCB levels in the 

4 to 100 ppm range. It appears likely that these materials 

have absorbed PCBs that have been emitted from 

primary sources. While primary sources remain in 

buildings these other materials are likely to be in quasidynamic equilibrium, with PCB emission and absorption 

roughly balancing. However, when primary sources 

are removed, these materials may serve as secondary 

sources for emissions of PCBs into the air in the building. 

Paints may be the most significant secondary sources 

given their large surface areas and relatively high PCB 

concentrations, but other materials may be important as 

well. Following mitigation of primary sources it may, in 

some cases, be necessary to consider mitigation actions 

for secondary sources. 

School Environment PCB Levels and Exposures

• PCBs are present in indoor air, dust, and on surfaces in 

school buildings with PCB-containing source materials, 

and are likely to be present in the soil near buildings 

with exterior PCB-containing caulk. Building occupants 

would be exposed to PCBs through expected normal 

contacts with these environmental media. 

• Estimated average total absorbed doses that could 

occur from the PCBs in school buildings with 

environmental levels that were found in these six 

schools were near the reference oral dose levels for 

Aroclor 1254 (0.020 μg/kg/day), and the reference dose 

adjusted for absorption (0.017 μg/kg/day) which is a more 

direct comparison with modeled dose estimates from 

SHEDS, which estimated absorbed dose. The reference 

dose level was exceeded by 75% or more of the estimated 
viii

distributions of absorbed doses for the two younger age 

groups. These estimates do not include the additional 

background exposures to PCBs that occur outside of the 

school environment, including exposures from dietary 

intake and inhalation of PCBs in outdoor and indoor air at 

non-school locations. 

• PCB concentrations in indoor air were found to exceed 

EPA’s 2009 public health guidance levels (ranging from 

70 to 600 ng/m3 depending on age) in many school 

locations. Inhalation was estimated to be responsible for 

over 70% of the exposure that could occur in buildings 

with the environmental levels of PCBs that were found 

in these six schools. Mitigation efforts that focus on 

reducing indoor air PCB concentrations are likely to 

have the greatest impact on reducing exposures, although 

cleaning to reduce dust will contribute to reducing 

exposures, particularly for children on the upper end of 

the dust exposure distribution. Based on information 

from the five New York City schools, it appears that 

mitigation efforts can be successful in substantially 

reducing indoor air concentrations and exposures to 

PCBs. 

Complexity of PCBs in School Buildings

PCBs in school buildings present a complex problem from 

exposure assessment, risk assessment, and mitigation 

decision-making perspectives. Different aspects of this 

complexity are summarized below.

• There may be multiple primary sources of PCBs in 

school buildings. Numerous different kinds of caulks 

and sealants may be present throughout many building 

locations and they must be sampled to determine 

whether they contain PCBs. Fluorescent light ballasts 

containing PCBs may be present and light fixtures may 

be contaminated with residues from ballasts that have 

previously failed. 

• PCBs are semi-volatile organic chemicals with a wide 

range of vapor pressures that will vaporize from primary 

sources and will be transported through indoor and into 

outdoor environments. They are absorbed by dust and 

soil which can result in additional transport and exposure. 

• PCBs absorb into numerous materials in a building 

resulting in a reservoir that remains even after primary 

sources are removed. These secondary sources may result 

in continuing exposures following removal or remediation 

of primary sources.

• Over 120 different PCB congeners were measured 

in indoor air. These different congeners have a wide 

range of physical properties. In addition to adding 

exposure and toxicity complexity, the large numbers 

of congeners raises a challenge of selecting the most 

appropriate measurement methods for assessing PCBs in 

buildings. Aroclor measurements are the simplest and 

least expensive, but they may suffer in accuracy since 

the congener patterns in school environmental media do 

not exactly match those in the Aroclors. Homolog and 

congener-specific analyses allow for improved accuracy 

and better understanding and are recommended for use 

where possible.

• Ventilation of building spaces with outdoor air is an 

important factor in the indoor air PCB concentrations that 

will result from source emissions. However, ventilation 

in some older school buildings has been found to be poor, 

and ventilation in older buildings is often difficult to 

assess and to improve. 

E.6 Limitations

There are important limitations and uncertainties in the 

information included in this report. Key limitations and 

uncertainties are summarized below.

• PCB measurement results were available from only six 

schools. These schools were selected because they had 

known or suspected PCB sources. It is not known if these 

results are representative of older schools nationwide, 

both in terms of the presence of PCB-containing materials 

and components and the environmental concentrations 

measured in and around the school buildings. 

• Materials and components containing PCBs were likely 

to have been used in buildings other than schools. This 

report does not address whether and to what extent PCBs 

may be a potential problem in other types of buildings, 

and if so, whether environmental concentrations and 

exposures are likely to be similar.

• PCB emissions from materials and light ballasts were not 

directly measured at the six schools. Modeled emission 

estimates and the resulting predictions of indoor air and 

dust concentrations have considerable uncertainties. 

Emission parameters are likely to vary across different 

materials and for different temperature and ventilation 

conditions. Two different types of chambers were used to 

test caulk and light ballasts emissions, possibly impacting 

comparability. Emissions from light ballasts are likely 

to vary depending on the lighting fixture design and the 

condition of the ballast and capacitor. Emissions from 

light fixtures potentially contaminated from previously 

leaking or failed ballasts were not evaluated. 

• Attributing the relative impact of PCB emissions from 

caulk and light ballasts on PCB levels in the schools 

was difficult because both sources were present in most 

buildings, and the Aroclor mixture used in all light 

ballasts was not identified. Several paint samples were 

found to have several hundred ppm of PCBs, and it is not 

clear whether these contained PCBs when installed and 

might be considered primary sources. 

• There is uncertainty in modeled estimates of PCB 

exposures due to uncertainties in key exposure model 

parameters. In particular, there is limited information for 

pulmonary absorption fraction from the range of PCB 

congeners in vapor and particle-bound forms. There is 

also uncertainty in total PCB exposures because of the 

lack of robust data for background exposures from dietary 

and other non-school sources. 
ix

Table of Contents

1.0 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Need for Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3 Research Objectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4 About this Report. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.0 Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1 Generation of School Measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 Sample Collection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3 Sample Extraction and Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4 Caulk Emissions Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.5 Light Ballast Emissions Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.6 Building Material Emissions Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.7 SHEDS Exposure Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.0 Quality Assurance and Quality Control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.1 Quality Control Results for Aroclor Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.2 Quality Control Results for Congener Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.3 Quality Assurance Assessments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.0 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.1 School Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.2 PCB Source Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.3 PCBs in Environmental Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.4 Congener and Homolog Measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.5 SHEDS Exposure Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.0 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5.1 Sources of PCBs in School Buildings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5.2 School Environment PCB Levels and Exposures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5.3 Complexity of PCBs in School Buildings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

5.4 Study Limitations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

6.0 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Appendix A.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

Appendix B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

Appendix C.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

Appendix D.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1

Appendix E. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1
x

List of Tables

Table 1-1. Several commonly used Aroclor mixtures, their chlorine weight percent values, and their highest five 

congeners by weight percent. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Table 1-2. PCB vapor pressure ranges: examples from three reports. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Table 2-1. Summary of sample collection and analysis methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Table 2-2. Sample collection information (numbers of samples by type and location). . . . . . . . . . . . . . . . . . . . . . . . . 9

Table 2-3. Vapor pressures for selected target congeners in Aroclor 1254. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Table 2-4. Predictive error for the P-N correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Table 2-5. Estimated total PCB emission rates for four intact light ballasts in chamber testing. . . . . . . . . . . . . . . . . . 15

Table 2-6. Ratio of PCB emissions at different temperatures from four light ballasts tested in a laboratory chamber. . 16

Table 2-7. Input variables for the SHEDS-multimedia model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Table 2-8. Key input variables for the SHEDS-multimedia model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Table 2-9. Key input variables for the SHEDS-multimedia model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Table 2-10. Average inhalation rate (m3/day). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Table 3-1. Aroclor analysis: recovery of Aroclor 1254 from field and laboratory controls. . . . . . . . . . . . . . . . . . . . . . 23

Table 3-2. Aroclor analysis: recovery of Aroclor 1254 from laboratory matrix spikes. . . . . . . . . . . . . . . . . . . . . . . . . . 24

Table 3-3. Aroclor analysis: recovery of surrogate analytes from samples and duplicate samples. . . . . . . . . . . . . . . . 24

Table 3-4. Aroclor analysis: total PCBs measured on field and laboratory blanks. . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Table 3-5. Aroclor analysis: total PCBs measured in laboratory method blanks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Table 3-6. Aroclor analysis: precision results for duplicate sample measurements. . . . . . . . . . . . . . . . . . . . . . . . . . 26

Table 3-7. Congener analysis: total recovery of congeners in Aroclor 1254 from field controls. . . . . . . . . . . . . . . . . . 26

Table 3-8. Congener analysis: total recovery of congeners in Aroclor 1254 from laboratory matrix spikes. . . . . . . . . . 27

Table 3-9. Congener analysis: recovery of surrogate analytes from samples and duplicate samples. . . . . . . . . . . . . . 27

Table 3-10. Congener analysis: total PCBs measured on field blanks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Table 3-11. Congener analysis: total PCBs measured in laboratory method blanks. . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Table 3-12. Congener analysis: precision results for duplicate sample total PCB measurements. . . . . . . . . . . . . . . . . 28

Table 4-1. School building information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Table 4-2. School building remediation activity and environmental sampling summary. . . . . . . . . . . . . . . . . . . . . . . 31

Table 4-3. School building information at sampling time points used in exposure modeling. . . . . . . . . . . . . . . . . . . . 32

Table 4-4. Caulk total PCB measurement results for schools with available data. . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Table 4-5. Caulk total PCB measurement results by school. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Table 4-6. Interior and exterior caulk and window glaze total PCB measurement results by concentration category. . 35

Table 4-7. Estimates of total PCB emission rates for several examples of PCB-containing caulk. . . . . . . . . . . . . . . . . 36

Table 4-8. Screening-level comparison of predicted air concentrations resulting from PCB emissions from caulk to 

measured concentrations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Table 4-9. PCB-containing fluorescent light ballast survey results from five schools. . . . . . . . . . . . . . . . . . . . . . . . . . 40

Table 4-10. PCB-containing fluorescent light ballast survey results at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Table 4-11. Screening-level comparison of predicted air concentrations resulting from PCB emissions from fluorescent 

light ballasts to measured concentrations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Table 4-12. Total PCB measurement results for materials at three schools with available data. . . . . . . . . . . . . . . . . . . 43
xi

Table 4-13. Total PCB measurement results for subsets of paint uses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Table 4-14. Screening-level estimates of total PCB emission rates for selected interior materials in three locations at 

School 2 for relative comparisons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Table 4-15. Screening-level estimates of total PCB emission rates for selected interior materials in three rooms 

at School 3 for relative comparisons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Table 4-16. Screening-level estimates of total PCB emission rates for selected interior materials in three rooms 

at School 6 for relative comparisons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Table 4-17. Summary of environmental media total PCB measurement results for six schools. . . . . . . . . . . . . . . . . . . 53

Table 4-18. Indoor air total PCB measurement results at six schools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Table 4-19. Outdoor air total PCB measurement results at six schools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Table 4-20. Surface wipe total PCB measurement results at six schools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Table 4-21. Indoor dust total PCB measurement results at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Table 4-22. Soil total PCB measurement results at six schools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Table 4-23. Soil total PCB measurement results for two soil depths at School 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Table 4-24. Pre- and post-remediation indoor air total PCB measurement results at five schools. . . . . . . . . . . . . . . . . 60

Table 4-25. Pre- and post-remediation surface wipe total PCB measurement results at five schools. . . . . . . . . . . . . . 63

Table 4-26. Correlations between total PCB concentrations in selected school environment samples. . . . . . . . . . . . . 64

Table 4-27. Comparison of Aroclor and congener measurement results for total PCBs at School 6. . . . . . . . . . . . . . . 65

Table 4-28. Summary of average PCB congener concentrations at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Table 4-29. Summary of average PCB congener weight percents at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Table 4-30. Summary of average PCB homolog weight percents at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Table 4-31. Differences in PCB congener emission estimates from exterior caulk assuming an un-weathered Aroclor 

1254 pattern vs. measured congener concentrations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Table 4-32. Distributions of total absorbed PCB dose estimated by SHEDS based on measurement data from six 

schools (units: μg/kg/day). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Table 4-33. Distributions of total absorbed PCB dose estimated by SHEDS based on measurement data from five 

schoolsa at pre-remediation and post-remediation time points (units: μg/kg/day). . . . . . . . . . . . . . . . . . . 83

Table 4-34. Post-remediation/pre-remediation ratios of total absorbed PCB dose estimates based on measurement 

data from five schools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Table 4-35. Distributions of total absorbed PCB dose estimated by SHEDS based on measurement data from three 

schoolsa at three time points (units: μg/kg/day). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Table 4-36. Distributions of total absorbed PCB dose estimated by SHEDS based on measurement data from 

six schools in this report and from extant measurement dataa (units: μg/kg/day). . . . . . . . . . . . . . . . . . . 87

Table 4-37. Proportion of mean estimated total absorbed PCB dose for 6 – 10 year olds for 

inhalation, non-dietary ingestion, and dermal absorption routes of exposure based on measurements 

from five schools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Table 4-38. Sensitivity test results of different pulmonary absorption rates on the distributions of estimated 

total absorbed PCB dose for 6 – 10 year olds based on pre- and post-remediation measurements 

from five schools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Table 4-39. Sensitivity test results for post-remediation decreases in dust and soil PCB concentrations on the 

distributions of estimated total absorbed PCB dose for 6 – 10 year olds based on measurements from 

five schools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Table A-1. Information for the 209 PCB congeners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

Table B-1. Interior caulk and window glaze total PCB measurement results by concentration category. . . . . . . . . . B-1

Table B-2. Exterior caulk and window glaze total PCB measurement results by concentration category. . . . . . . . . . B-2

Table B-3. Total PCB measurement results for selected materials by school. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-3
xii

Table C-1. Air sample PCB congener concentrations at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

Table C-2. Surface wipe PCB congener concentrations at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-3

Table C-3. Dust sample PCB congener concentrations at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-5

Table C-4. Soil sample PCB congener concentrations at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-6

Table C-5. Caulk sample PCB congener concentrations at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-8

Table C-6. Other building material sample PCB congener concentrations at School 6. . . . . . . . . . . . . . . . . . . . . . . C-10

Table C-7. Air sample PCB homolog weight percents at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-12

Table C-8. Surface wipe sample PCB homolog weight percents at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-12

Table C-9. Dust sample PCB homolog weight percents at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-13

Table C-10. Soil sample PCB homolog weight percents at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-13

Table C-11. Caulk sample PCB homolog weight percents at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-14

Table C-12. Building material PCB homolog weight percents at School 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-14

Table E-1. Distributions of total PCB concentrations on dust estimated from 

solid/air partition coefficient estimation.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-2

Table E-2. Distributions of SHEDS estimates of total PCB absorbed doses using two approaches for estimating 

indoor dust levels at the six schools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-3
xiii

List of Figures

Figure 1-1. Three of the 209 different PCB congeners showing chlorine substitution patterns and schematic

examples of non-planar (congeners 1 and 209) and co-planar (congener 77) orientations. 

Source: PubChem Compound - http://www.ncbi.nlm.nih.gov/pccompound.. . . . . . . . . . . . . . . . . . . . . . . 1

Figure 2-1. Hypothetical exposure profile for an individual. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Figure 2-2. Overview of SHEDS residential methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Figure 2-3. General overview of SHEDS multimedia exposure model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Figure 4-1. Estimated total PCB emission rates from caulk in several building locations . . . . . . . . . . . . . . . . . . . . . . 37

Figure 4-2. Example of the estimated range of total PCB emission rates from fluorescent light ballasts in a school 

classroom using the lowest, median, and highest rates from chamber tests of four ballasts. . . . . . . . . . . 41

Figure 4-3. Screening-level estimates of total PCB emission rates from materials in the gymnasium in School 2. . . . . 49

Figure 4-4. Screening-level estimates of total PCB emission rates from materials in a classroom in School 3 . . . . . . . 50

Figure 4-5. Screening-level estimates of total PCB emission rates from materials in a classroom in School 6 . . . . . . . 50

Figure 4-6. Distributions of indoor air total PCB concentrations across all six schools and at 

each individual school. The box plots show the median, 25th, and 75th percentiles. 

The whiskers show the 10th and 90th percentiles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Figure 4-7. Public health levels of PCBs in school indoor air developed in 2009 by the U.S. EPA 

(http://www.epa.gov/pcbsincaulk/maxconcentrations.htm) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Figure 4-8. Distributions of surface wipe total PCB concentrations collected from high-contact and low-contact 

surfaces across all six schools and at each individual school. The box plots show the median, 25th, 

and 75th percentiles. The whiskers show the 10th and 90th percentiles.. . . . . . . . . . . . . . . . . . . . . . . . 55

Figure 4-9. Distributions of outdoor soil total PCB concentrations across all six schools and at each individual school. 

The box plots show the median, 25th, and 75th percentiles. The whiskers show the 10th and 90th 

percentiles.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Figure 4-10. Pre- and post-remediation indoor air total PCB concentrations across five schools and at each individual 

school. The box plots show the median, 25th, and 75th percentiles. The whiskers show the 10th 

and 90th percentiles.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Figure 4-11. Pre- and post-remediation indoor air total PCB concentrations across three schools with several 

different remedial activities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Figure 4-12. Patterns of congeners in Aroclor 1254 (top) and exterior caulk collected at School 6 . . . . . . . . . . . . . . . 70

Figure 4-13. Patterns of congeners in indoor air (top) and indoor dust collected at School 6 . . . . . . . . . . . . . . . . . . . 72

Figure 4-14. Patterns of relative weight percent for selected congeners in Aroclor 1254 (top) and Aroclor 1242 (bottom) 

compared to averages for exterior caulk and indoor air at School 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Figure 4-15. Patterns of relative weight percent for selected congeners in Aroclors 1254 compared to average 

values for indoor air, paint, and exterior caulk (top), and indoor air, surface wipes, dust, and 

soil (bottom) at School 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 4-16. Relative weight percents of PCB chlorine-number homologs for Aroclors 1242 and 1254 compared to the 

averages of the exterior PCB-containing caulk, the indoor building materials, and indoor air. . . . . . . . . . 76

Figure 4-17. Relative weight percents of PCB chlorine-number homologs for Aroclors 1242 and 1254 compared to the 

averages of the environmental media. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Figure 4-18. Patterns of estimated emission rates from exterior caulk collected at School 6 assuming the 

caulk contains congeners in an Aroclor 1254 proportions (top) and using congener values measured 

in the caulk (bottom). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Figure 4-19. Patterns of estimated emission rates from exterior caulk collected at School 6 (top) versus the congeners 

measured in indoor air (bottom) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
xiv

Figure 4-20. Distributions of estimated absorbed total PCB doses from exposures at school for four age groups at 

five schools for pre- and post-remediation time points. The box plots show the median, 25th, and 75th 

percentiles. The whiskers show the 10th and 90th percentiles.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Figure 4-21. Distributions of estimated absorbed total PCB doses from exposures at school for the 6 – 10 year old 

age group at five schools for pre- and post-remediation time points. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Figure 4-22. Distributions of estimated absorbed total PCB doses from exposures at three schools for the 

6 – 10 year old age group at three pre- and post-remediation time points across two years. . . . . . . . . . . 86

Figure 4-23. Contributions of different exposure routes towards total estimated absorbed PCB doses for the 6 – 10 year 

old age group at different percentiles of the total dose estimate based on measurements at six schools 

(assuming 70% pulmonary absorption). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Figure 4-24. Contributions of different exposure routes towards total estimated absorbed PCB doses for the 6 – 10 year 

old age group at different percentiles of the total dose estimate based on pre-remediation (top) and 

post-remediation (bottom) measurements at five schools (assuming 70% pulmonary absorption). . . . . . 89

Figure 5-1. Illustration of the complexity of PCBs in school buildings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Figure A-1. Relative congener weight percents for Aroclors 1242 and 1254 (based on Frame et al. 1996). . . . . . . . . A-7

Figure D-1. Distributions of total PCB concentrations in several environmental media collected at school and 

college buildings (GM is the geometric mean) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-2
xv

Acronyms and Abbreviations

AC Air conditioning

AER Air exchange rate

ANSI American National Standard Institute

ASHRAE American Society of Heating, Refrigerating, 

and Air Conditioning Engineers

ASTM American Society for Testing and Materials 

(now ASTM International)

BMR Basal metabolic rate

BVA Basal alveolar ventilation

CAFO Consent agreement and final order

CHAD Comprehensive Human Activity Database

CQCS Comprehensive quantitative congener specific 

analysis

DCBP Decachlorobiphenyl

GC/ECD Gas chromatography/electron capture 

detector

GM Geometric mean

EPA United States Environmental Protection 

Agency

FDA Food and Drug Administration

HID High intensity discharge lamp ballast

H/V Heating and ventilation

HVAC Heating, ventilation, and air conditioning

ND Not detected

NELAP National Environmental Laboratory 

Accreditation Program

NERL National Exposure Research Laboratory

NIOSH National Institute for Occupational Safety and 

Health

NRMRL National Risk Management Research 

Laboratory

NYC SCA New York City School Construction Authority

ORD Office of Research and Development

PCB Polychlorinated biphenyl

PPM Parts per million (equivalent to mg/kg)

PBPK Physiologically-based pharmacokinetic

PK Pharmacokinetic

PUF Polyurethane foam

QA Quality assurance

QAPP Quality Assurance Project Plan

QC Quality control

QL Quantitation limit

QSAR Quantitative structure-activity relationship

RfD Reference dose

RPD Relative percent difference

RSD Relative standard deviation

SD Standard deviation

SHEDS Stochastic Human Exposure and Dose 

Simulation model

SVOC Semi-volatile organic compound

TCDD 2,3,7,8-Tetrachlorodibenzodioxin

TCMX Tetrachloro-m-xylene 

TEQ Toxic equivalence (as compared to TCDD)

TSCA Toxic Substances Control Act

1

1.0

Introduction

1.1 Background

1.1.1 About PCBs

Polychlorinated biphenyls (PCBs) are synthetic chemicals 

that were manufactured in the United States between about 

1930 and 1977 for use in various industrial and commercial 

applications because of their non-flammability, chemical 

stability, plasticizer, and electrical insulation properties 

(ATSDR, 2000). PCBs were used in numerous products and 

processes, including electrical, heat transfer, and hydraulic 

equipment; as plasticizers in various products; in paints and 

finishes; in pigments, dyes, and carbonless copy paper; and 

in other industrial and commercial applications (Erickson and 

Kaley, 2011; U.S. EPA, 1976). There are no known natural 

sources of PCBs. PCBs that were produced commercially 

were mixtures of individual chlorinated biphenyl compounds 

known as congeners (see Figure 1-1). There are a total 

of 209 different possible PCB congeners (Appendix A), 

although many congeners did not appear in commercial 

mixtures.

Figure 1-1. Three of the 209 different PCB congeners showing 

chlorine substitution patterns and schematic examples of non-planar 

(congeners 1 and 209) and co-planar (congener 77) orientations. 

Source: PubChem Compound - http://www.ncbi.nlm.nih.gov/

pccompound.

Most of the PCB mixtures manufactured for commercial use 

in the United States are known by the trade name Aroclor. 

Each commercially produced Aroclor contained mixtures 

of some of the 209 congeners, with chlorine contents of 

the different Aroclors ranging from 21% to 68% (Table 

1-1). Between 1957 and 1971, 12 types of Aroclors were 

produced (ATSDR, 2000). During this time PCBs were 

used in completely closed systems (such as transformers and 

Common 

Aroclors 

Chlorine 

Weight %

Number of Congeners 

Measureda

Congeners 

Five Highest by Weight %

Aroclor 1221 21% 63 1, 3, 8, 4, 15

Aroclor 1232 32% 93 1, 8, 3, 4, 18

Aroclor 1016 41% 71 18, 31, 28, 8, 33

Aroclor 1242 42% 95b 18, 31, 28, 8, 33

Aroclor 1248 48% 95+ 70, 66, 52, 44, 31

Aroclor 1254 54% 95+b 118, 110, 105, 138, 70

Aroclor 1260 60% 93 180, 153, 149, 138, 187

Aroclor 1262 62% 95 180, 187, 153, 149, 174

Table 1-1. Several commonly used Aroclor mixtures, their chlorine weight percent values, and their highest five 

congeners by weight percent

a

 Based on Frame et al., 1996.

b

 See Appendix A for more information about Aroclor 1242 and Aroclor 1254 congeners.

capacitors), nominally closed systems (such as hydraulic 

systems and vacuum pumps), and open systems (such as 

plasticizers and paints) (Erickson and Kaley, 2011). In 1970, 

the primary U.S. manufacturer decided to discontinue use of 

Aroclors in open products and uses that could lead to direct 

transfer into the environment (Erickson, 1997). Information 

about commercial PCB mixtures, PCB production, and PCB 

nomenclature is provided by Erickson and Kaley (2011).
2

1.1.2 PCB Exposure and Effects

Manufacture of PCBs was banned in the United States 

with final rules published by the U.S. EPA in May, 1979. 

Their use was phased out, except for certain limited uses, 

because of evidence they are persistent in the environment 

and can cause harmful health effects. PCBs can accumulate 

in people over time, and PCB exposure over a long period 

of time may be harmful to health. Short term exposure to 

large amounts of PCBs can lead to skin conditions such as 

acne and rashes and may be associated with decreased liver 

function, neurological effects, and gastrointestinal effects 

(ATSDR 2000). These types of acute toxic effects due to 

high levels of exposure are generally rare in the general 

population. Chronic exposure to lower levels of PCBs may 

also cause health effects (ATSDR 2000; ATSDR 2011). In 

animal studies, PCBs have been shown to cause effects on 

the immune, reproductive, nervous, hepatic, and endocrine 

systems. PCBs have also been shown to cause cancer 

in animals. Some studies in humans provide supportive 

evidence for some of these health effects. Studies also show 

that PCBs in pregnant women can have an impact on their 

children’s birth weight, short-term memory, and learning. 

Because of potential neurotoxic and endocrine effects, there 

is concern regarding children’s exposures to PCBs (ATSDR 

2000; ATSDR 2011). 

PCBs are highly persistent in the environment. As such, they 

are still present in soils and sediments in some locations 

today and may be found at low levels in ambient air and 

water. PCBs can be released into the environment from 

hazardous waste sites, illegal or improper disposal of 

industrial wastes and consumer products, leaks from old 

electrical transformers and capacitors containing PCBs, 

and burning of some wastes in incinerators. PCBs undergo 

bioaccumulation and may eventually enter foods that people 

consume. Foods with the highest PCB levels are typically 

fish, meat, and dairy products. Dietary consumption of 

contaminated foods is believed to be an important route of 

background exposure to PCBs for people in the United States 

(ATSDR, 2000). 

1.1.3 PCB Uses in Buildings

Additional exposure to PCBs may occur for people who 

spend time in buildings where PCB-containing materials and 

equipment are present. A number of products manufactured 

before the late-1970s contained PCBs (Erickson and Kaley, 

2011; U.S. EPA, 1976). Products and equipment that may 

contain PCBs include: 

• dielectric fluid in transformers and capacitors,

• other electrical equipment, including voltage regulators, 

switches, circuit breakers, reclosers, bushings, and 

electromagnets,

• oil used in motors and hydraulic systems, 

• microscope oil,

• old electrical devices or appliances containing capacitors 

with PCBs,

• fluorescent light ballast capacitors,

• cable insulation,

• thermal insulation material, including fiberglass, felt, 

foam, and cork,

• adhesives and tapes,

• oil-based paints,

• caulk, window glazing, and other sealants,

• plastics,

• carbonless copy paper, 

• ceiling tile coatings, and

• floor finish.

Some of these materials can still be found in buildings, 

particularly those constructed or renovated between about 

1950 and 1978. 

Production of PCBs used as plasticizers in the United States 

ranged from approximately 1.4 million kg in 1957 to 8.6 

million kg in 1969, decreasing to zero by 1971 (NIOSH, 

1975). Caulk and other sealant materials that incorporated 

PCBs as plasticizers have been examined as a potential 

source of exposure to building occupants. Kohler et al. 

(2005) reported on concentrations of PCBs in more than 

1300 samples of joint sealants collected from buildings in 

Switzerland built between 1950 and 1980. Nearly half (48%) 

contained PCBs, and levels exceeding 50 ppm were found in 

42% of the samples. Concentrations exceeding 10,000 ppm 

were found in 21% of the samples, whereas concentrations 

exceeding 100,000 ppm were found in 9.6% of the samples. 

PCB content of 100,000 ppm in caulk means that the caulk 

contains 10% PCBs by weight. Chlorine content was 

examined in a subset of Swiss samples, and more than 90% 

had results consistent with mixtures of Aroclors 1248, 1254, 

1260, and 1262.

PCB mixtures were used as the dielectric fluid in fluorescent 

light ballast capacitors used in standard fluorescent light 

fixtures and in high-intensity discharge (HID) ballasts 

manufactured in North America from the 1960s through the 

late 1970s (Environment Canada, 1991). Staiff et al. (1974) 

reported uses of PCBs in ballasts before 1952. The earliest 

date of use is not clear, but PCB use in ballast capacitors 

continued to 1978, a year of transition in which alternative 

capacitor dielectric fluids began to be used. By 1979, no 

ballasts capacitors were manufactured with PCBs. The 

most commonly used Aroclor mixtures were Aroclor 1242 

and Aroclor 1016 (which has a similar chlorine content and 

congener mixture as Aroclor 1242) (Erickson and Kaley, 

2011) but the use of Aroclors 1221 and 1254 in capacitors 

has also been reported (U.S. EPA, 1976). According to a 

citation in Staiff et al. (1974) Aroclor 1254 was primarily 

used in ballasts prior to 1952 with Aroclor 1242 thereafter. 

Capacitors used in ballasts for fluorescent light fixtures 

containing two 4-foot lamps contained approximately 10 – 

24 g of PCBs, while HID ballast capacitors could contain 

between 90 and 390 g (Environment Canada, 1991; General 

Electric, 2004). 
3

Most fluorescent light ballasts are designed for about 50,000 

hours of operation under standard conditions (Natural 

Resources Canada, 2009) although an earlier report suggested 

approximate lifetimes ranging from 10 to 15 years of normal 

service at operating temperatures of 85 - 90oC (U.S. EPA, 

1976). Ballast lifetime calculations from one manufacturer 

indicate that at an operating temperature of 65°C ballasts 

can be expected to operate for 50,000 hours with only 

10% failure, but that the failure rate is likely to increase 

substantially after this period due to drying up of liquid 

electrolytic fluid in the capacitor and degradation of soldered 

contacts (Philips Lighting, undated). Staiff et al. (1974) gave 

a normal operating ballast surface temperature of 67 - 72°C 

and cited a 1966 lighting handbook stating that ballast life 

was normally estimated at around 12 years. If lights are 

operated in a building for 10 hours per day, 250 days per 

year, then 50,000 hours would cover approximately 20 years 

of use. PCB-containing light ballasts have been present in 

school buildings in many cases for over 40 years. 

As previously noted, PCBs were used in many types of 

materials and components. It is not clear to what extent 

other types of PCB-containing materials remain in buildings 

today. Ceiling tiles with surface coatings containing PCBs 

have been reported (personal communication, U.S. EPA 

Region 1). It is possible that PCB-containing microscope oil 

remains in some buildings, and other electrical or hydraulic 

system components with PCBs could be present. PCBs 

were sometimes used as a plasticizer in paints and other 

types of coatings. While these coatings were used in marine, 

military, and grain silo applications it not clear whether 

PCB-containing paints and coatings were used extensively 

in buildings. There is one report of PCBs in floor finish in a 

residential application (Rudel et al., 2008). 

1.1.4 PCBs in Building Environments

PCBs from caulk and other materials containing PCBs may 

move into the air and dust indoors and into the soil around 

older buildings, leading to the potential for exposures to 

building occupants (Currado and Harrad, 1998; Kohler 

et al., 2002, 2005; Priha et al., 2005; Hazrati and Harrad, 

2006; Heinzow et al., 2007; Harrad et al., 2010; Herrick et 

al., 2004, 2007; Zhang et al., 2011; MacIntosh et al., 2012). 

The different vapor pressures of the 209 PCB congeners and 

the effects of weathering over 40 or more years may affect 

which congeners are present and available for exposure from 

different environmental media (Heinzow et al., 2007; Harrad 

et al., 2009). The extent of exposure to PCBs in indoor 

environments may depend on their vaporization into indoor 

air in combination with degradation of materials resulting 

in contaminated particles available for contact. Many 

researchers have measured or estimated PCB congeners 

vapor pressures, and results vary considerably depending on 

the method (Erickson, 1997). In general, congener vapor 

pressures decrease with increasing levels of chlorination 

(Table 1-2, Appendix A). Within chlorine-number homolog 

groups, vapor pressures increase with increasing levels of 

chlorination in ortho positions (Falconer and Bidleman, 

1994). Multimedia fugacity modeling has been applied to 

PCBs in residential and office environments to examine 

emissions and fate in indoor environments 

(Zhang et al., 2011). 

Table 1-2. PCB vapor pressure ranges: examples from three reports

a Delle Site, 1997: Compiled vapor pressures for selected congeners from multiple references using direct, indirect, and 

prediction methods.

b Falconer and Bidleman, 1994: Liquid saturation vapor pressures; range across average vapor pressures of different levels of 

othro-substituted chlorines within homolog (estimated from Figure 2).

c Holmes et al., 1993: Originally from Buckhard et al., 1985, ES&T, 22:503-509. Based on liquid or sub-cooled liquid values 

rather than from solid phase. 

Homolog Series

Delle Site, 1997a

Pa at 25o C

(Number of Congeners)

Falconer and 

Bidleman, 1994b

Pa at 20o C or 25o C

Holmes et al., 1993c

Pa at 25o C

(Number of Congeners)

 Monochlorobiphenyls 7.9E-2 to 2.1E-1 (3) Not reported 3.2E-1 to 9.3E-1 (3)

 Dichlorobiphenyls 7.4E-4 to 3.2E-1 (5) 1.0E-1 to 2.5E-1 5.1E-2 to 4.2E-1 (12)

 Trichlorobiphenyls 4.8E-3 to 7.6E-2 (5) 6.3E-3 to 4.0E-2 8.4E-3 to 1.7E-1 (24)

 Tetrachlorobiphenyls 1.8 E-5 to 2.2E-2 (5) 3.2E-3 to 1.6E-2 1.4E-3 to 6.6E-2 (42)

Pentachlorobiphenyls 4.0E-4 to 2.2E-3 (1) 5.0E-4 to 2.5E-3 2.7E-4 to 1.7E-2 (46)

 Hexachlorobiphenyls 2.9E-6 to 1.6E-3 (3) 2.5E-4 to 7.9E-4 5.4E-5 to 6.4E-3 (35)

Heptachlorobiphenyls Not reported 5.0E-5 to 2.5E-4 1.4E-4 to 1.6E-3 (24)

 Octachlorobiphenyls 2.4E-6 to 3.0E-5 (1) Not reported 3.8E-5 to 6.2E-4 (12)

 Nonachlorobiphenyls Not reported Not reported 1.0E-4 to 1.3E-4 (3)

 Decachlorobiphenyl 2.9E-9 to 1.4E-5 (1) Not reported 2.8E-5 (1)
4

Based on vapor pressure characteristics, it might be 

anticipated that inhalation exposures to PCBs will be 

weighted to congeners with higher vapor pressures and lower 

chlorine numbers. However, PCBs in indoor environments 

may also be bound to particles that can become suspended in 

the indoor air (Zhang et al., 2011). PCB congeners measured 

in indoor air may not match congener patterns found in the 

Aroclor mixtures used in materials in a building because 

of the differences in congener vapor pressures and because 

congeners are absorbed onto particles differentially based on 

their vapor pressures and solid/air partition coefficients. 

Harrad et al. (2009) noted that the combination of residential 

indoor air inhalation and dust ingestion could exceed dietary 

intake of PCBs in some scenarios. Exposures to PCBs in 

air and dust from contaminated nonresidential buildings 

could substantially increase exposures above those seen in 

residential environments. For example, levels of PCBs in the 

indoor air of some office buildings with PCB-contaminated 

sealants (up to 6000 ng/m3 [Kohler et al., 2005]) exceed 

the levels reported in residential indoor air (maximum of 

14 ng/m3

 total PCBs [Harrad et al., 2009] and 35 ng/m3 for 

congeners 52, 105, and 153 in a contaminated home [Rudel 

et al., 2008]). 

Of particular concern is the potential for school children’s 

exposures to PCBs in older school buildings. Schools 

constructed or renovated between the 1950s and 1970s may 

contain both lighting fixtures with PCB capacitors and caulk 

or other sealants that incorporated PCBs as a plasticizer. 

Caulk containing PCBs may have been used around exterior 

windows and doors, exterior building joints, and in interior 

locations. PCBs may vaporize from the caulk and become 

airborne. PCBs may be absorbed onto (or into) other 

building surfaces, materials, or dust (Herrick et al., 2004). 

Caulk may degrade or suffer abrasion wear that can create 

PCB-containing dust that is then available for transport in 

indoor areas. PCBs from exterior caulk may be transported 

into soils near the building (Herrick et al., 2007). 

1.1.5 PCBs in School Buildings

There have been few systematic efforts to characterize 

PCB sources and environmental levels at schools across 

the United States. Measurements of PCBs in caulk have 

been made for several college buildings and a number of 

primary and secondary schools. Environmental samples, 

including air, dust, wipe, and soil samples, also have been 

collected at a number of school buildings. Measurement 

results for PCBs in schools in the United States have not 

been widely published in the scientific literature, although 

Herrick et al. (2007) reported on PCBs in soil collected near 

buildings with PCB-contaminated caulk or joint material 

and MacIntosh et al. (2012) published on environmental 

levels and remediation results for a primary school building. 

Measurement results have been reported in presentation 

and report formats (e.g., Coghlan et al., 2002; Sullivan, 

2008; TRC, 2006, 2008, 2009). From available extant data 

examined through 2009 (Appendix D), total PCBs in caulk in 

U.S. college and school buildings ranged from not detected 

to over 200,000 ppm. Concentrations in school indoor air 

ranged up to approximately 1000 ng/m3

. Total PCB levels 

in dust ranged up to approximately 80 ppm. Wipe samples 

ranged up to approximately 1 μg/100cm2 total PCBs. Soil 

concentrations in samples collected next to buildings ranged 

up to approximately 80 ppm of total PCBs. 

Although caulk is believed to be a primary source of PCBs 

in some older schools, there is still considerable uncertainty 

regarding the extent to which PCBs in other materials used in 

schools might contribute to exposures (Coghlan et al., 2002). 

Other primary sources (materials manufactured with PCBs, 

or materials to which PCBs were added during construction) 

or secondary sources (sources that have absorbed PCBs 

emitted from primary sources) may be present in some 

schools. For example, window glazing has been found in 

several locations to contain levels of PCBs greater than 

50 ppm. Fluorescent light ballasts with PCB-containing 

capacitors remain in some older buildings. Secondary 

sources might include surfaces, materials, and dust that 

have been contaminated through transport and absorption 

of PCBs from caulk or other primary sources. An in-depth 

investigation in a high school found PCBs in numerous 

materials, including but not limited to laminate adhesive, 

mastics, paint, gasket, carpet, foam padding, and bulk dust 

(TRC, 2008; Sullivan, 2008; TRC, 2009). Some of these 

materials had concentrations exceeding 10 ppm, ranging up 

to more than 250 ppm. The potential for secondary PCB 

sources to contribute to exposures to children and other 

building occupants is not well understood. To make sound 

decisions regarding mitigation of exposures to PCBs in 

schools, it is important to understand the range of potential 

sources of PCBs in schools; their contributions to PCBs in 

air, dust, soil and on surfaces; and the magnitude of potential 

exposures to children and staff in school environments. 

1.2 Need for Research

In September 2009 the U.S. EPA announced new guidance 

for school administrators and building managers for 

managing PCBs in caulk and to help minimize possible 

exposure. However, there was limited information on 

PCBs in school buildings in the United States. Neither the 

sources, including PCB-contaminated caulk, light ballasts, 

and secondary sources; nor the potential magnitude and 

routes of exposure have been systematically characterized in 

schools. There remains considerable uncertainty regarding 

the extent to which children and staff members may 

exposed to PCBs in school environments. Research was 

needed to help fill these information gaps to improve our 

understanding of exposure to PCBs in schools. Therefore, 
5

the EPA also announced in 2009 that research would be 

performed by the Office of Research and Development’s 

(ORD) National Exposure Research Laboratory (NERL) 

and the National Risk Management Research Laboratory 

(NRMRL) to further study this issue. The research was 

intended to help fill information gaps and improve our 

understanding of PCBs in school buildings and approaches 

for mitigating exposures. Information on sources of PCBs 

and levels in school environments is needed to improve risk 

management decision-making. NERL developed a research 

plan to better understand and characterize PCB sources, 

emissions, environmental concentrations, and exposures in 

school environments (U.S. EPA NERL, 2010). A research 

plan was also developed by NRMRL to perform laboratory 

studies of PCB sources and transport and to evaluate selected 

mitigation approaches (U.S. EPA NRMRL, 2010). 

1.3 Research Objectives

In order to better understand the significance of PCBcontaining caulk and other building materials and 

components as a source of PCB exposures to children, 

teachers, and staff in school buildings, NERL planned 

research to utilize a limited set of real-world measurements 

to: 

1) characterize PCB-contaminated caulk and other 

potential primary and secondary sources of PCBs in 

school buildings, 

2) characterize levels of PCBs in school air, dust, soil, 

and on surfaces and to investigate relationships 

between potential PCB sources and environmental 

levels, 

3) apply an exposure model for estimating children’s 

exposure to PCBs in schools with PCB sources, 

4) evaluate which routes of exposure (e.g., inhalation, 

contact with surfaces or dust) are likely to be most 

important, and,

5) provide information to assist in developing risk 

management practices for reducing exposure to PCBs 

in schools. 

1.4 About this Report

A limited set of measurement data and information 

collected using a systematic approach at six schools was 

used to characterize primary sources of PCBs, to evaluate 

whether secondary sources were present and their relative 

importance, to describe PCB concentrations in school 

environmental media, and to prepare modeled estimates 

of exposure and characterize the relative importance of 

different routes of exposure. Section 2 describes the 

methods for sample collection, sample analysis, emissions 

modeling, and exposure modeling. Section 3 provides 

information regarding quality control and quality assurance. 

Section 4 describes the results for source characterization, 

environmental media PCB concentrations, and exposure 

modeling for several child age-groups. Conclusions are 

presented in Section 5. 

7

2.0 

Methods

2.1 Generation of School Measurements

NERL’s objective was to recruit up to nine schools to 

participate in the research by allowing NERL and contractor 

scientists to collect air, surface wipe, dust, soil, caulk, 

and other building material samples in multiple indoor 

and outdoor locations (U.S. EPA NERL, 2010). With the 

assistance of EPA’s Office of Science Policy and several EPA 

Regional Offices, contacts were made with numerous state 

and local educational agencies and school districts. Although 

there was often interest in understanding the problem of 

PCBs in school buildings at state and local levels, most 

organizations decided not to participate in the research study 

due to a variety of potential concerns and issues. NERL 

reached preliminary agreement with two school districts to 

participate in the study. But, due to factors outside of their 

control, the NERL researchers ultimately were not able to 

enroll schools from these districts into the study. NERL was 

able to implement the research plan and collect samples at 

one school building scheduled for demolition.

At the same time that NERL was planning research on PCBs 

in schools, the New York City School Construction Authority 

(NYC SCA) reached an agreement regarding the assessment 

and remediation of caulk containing PCBs in public school 

buildings with the United States Environmental Protection 

Agency Region 2, under a Consent Agreement and Final 

Order (CAFO, Docket Number TSCA-02-2010-9201). The 

goal of the CAFO was to develop a city-wide approach to 

assessing and managing caulk containing PCBs in schools 

built between 1950 and 1978. As a result of the agreement, 

New York City initiated a comprehensive pilot study during 

the summer of 2010, when students were absent, to evaluate 

the possible presence of PCB-containing caulk and preferred 

remedial actions in three schools, with evaluations for two 

additional schools conducted in 2011. 

A remedial investigation plan was developed by the SCA 

and TRC Engineers Inc. describing the selection of the pilot 

schools, the approach for measuring PCBs in and around 

school buildings, and the caulk remediation approaches to be 

investigated (NYC SCA, 2010). The sampling and analysis 

approach was similar in many respects to the NERL research 

plan and sampling strategy but added remedial investigation 

steps. Pre-remediation samples of caulk, indoor and 

outdoor air, indoor surface wipes, and soils were collected 

in and around three NYC elementary schools during 2010. 

Different remedial approaches were then instituted at each 

school including caulk patch and repair, caulk removal and 

replacement, and caulk encapsulation. Post-remediation 

indoor and outdoor air and indoor surface wipe samples 

were collected to evaluate the remedial effect on PCB levels 

in the school environment. Analysis of both the pre- and 

post-remediation air samples showed levels of PCBs at 

some indoor locations were greater than Public Health 

Levels recommended by the U.S. EPA (http://www.epa.gov/

pcbsincaulk/maxconcentrations.pdf). Several actions were 

subsequently taken at the three schools to investigate and 

reduce the elevated PCB concentrations in air. Examination 

of materials that could potentially serve as additional 

sources of PCBs was conducted. As part of this effort it was 

determined that PCB-containing fluorescent light ballasts 

were present throughout the schools. An additional set of air 

samples was collected at each school following removal of 

the PCB-containing light fixtures and a period of ventilation 

with outdoor air. The NYC SCA remedial investigation 

continued with two additional pilot schools in 2011, and 

additional work was also performed in the three previous 

schools. Investigation reports and measurement results are 

available at the NYC PCB Program web site maintained by 

the NYC SCA (http://www.nycsca.org/Community/Programs/

EPA-NYC-PCB/Pages/default.aspx). 

U.S. EPA Region 2 requested the assistance of EPA’s 

NERL in characterizing potential exposures associated with 

environmental levels of PCBs measured at the New York 

City Schools. Region 2 specifically requested application of 

NERL’s Stochastic Human Exposure and Dose Simulation 

(SHEDS) model for estimating multi-pathway exposure 

distributions. Region 2 also requested assistance in 

understanding and characterization of primary and potential 

secondary sources of PCBs. Because the NYC SCA data 

were collected in a systematic fashion using strategies 

and methods similar to the NERL research plan, it was 

determined that the measurement results could be used to 

address the Region 2 interests and to meet many of the NERL 

objectives for source and environmental characterization and 

exposure modeling. Measurement results from the five NYC 

schools and one school sampled by NERL are used in this 

report.

2.2 Sample Collection

The sample collection strategy and methods used by the NYC 

SCA in the pilot remedial investigation efforts are described 

elsewhere (NYC SCA, 2010). Both the approach and the 

sampling methods used by the SCA were generally similar to 

those used by NERL. 

Methods for sample collection and analysis used by NERL 

are summarized in Table 2-1; these methods are described in 

more detail in the following sections. The samples collected 

by NERL at one school are summarized in Table 2-2. 

NERL’s contractor, Alion Inc., was responsible for preparing 
8

for and conducting field sampling operations. They were 

joined at the field site during sample collection activities by 

NERL scientists. 

2.2.1 Indoor and Outdoor Air Sample Collection

U.S. EPA Method TO10A was used to collect total PCBs in 

air, using a low-volume sampling approach to minimize the 

size and noise of the pumps to be used in school buildings. 

Sample filters were pre-cleaned open-cell polyurethane foam 

(PUF) in 30-mm × 70-mm tubes. Total suspended particle 

quartz filters were used as part of the sample filter assembly; 

however, the filter and the PUF were extracted and analyzed 

together as a single sample. Separate particle- and gas-phase 

air concentrations were not obtained. Collection of inline 

backup filters to assess potential break-through were not 

used based on results from the NYC SCA sampling, which 

showed no breakthrough in almost all cases.

Sample collection tubes were situated with the inlet in a 

downward facing position at a height of 1 m from ground 

or floor level. A sampling stand was used to secure the 

sample filter. An active air-flow pump, capable of unattended 

24-hr operation was used to provide a flow of 3.5 to 5.0 liters 

per minute (lpm) through the PUF. Flow measurements 

were performed and recorded at the initiation of sampling 

and then again at the completion of the nominal 24-hr 

sampling period. Start and stop times were recorded, so 

that a cumulative amount of time at an average flow rate 

could be calculated to yield the volume of air sampled. 

Total air volumes sampled through the filters ranged from 

5.3 to 6.5 m3

. Indoor and outdoor air temperatures were 

measured periodically through the sample collection period. 

2.2.2 Surface Wipe Sample Collection

Two types of indoor surface wipe samples were collected. 

The first type was individual samples collected from two 

different surfaces that might be contacted routinely by a 

student. These “high-contact” surface sampling locations 

included the tops of desks or tables. The second type was 

individual samples collected from building surfaces. These 

“low-contact” surface sampling locations included floors, 

walls, and window sills (where present). Several wipe 

samples were also collected from light fixtures including 

from the outside cover, diffuser, exterior of the ballast cover, 

and interior of the ballast cover. 

Wipe samples were collected based on the wipe sample 

collection procedure described in ASTM Method 

D6661-01 [2006], “Standard Practice for Field Collection of 

Organic Compounds from Surfaces Using Wipe Sampling.” 

PCB-free gauze wipes were wetted with 5 mL of hexane 

to collect surface wipe samples. Each wipe sample was 

collected in a 100-cm2

 area as defined by a template secured 

to the surface. Transfer of PCBs from the surface occurred 

through physical wiping of the defined surface area while 

applying moderate pressure in a serpentine pattern. Wipes 

were handled using appropriate chemical-resistant gloves, 

followed by storage in a clean amber glass jar with a Teflonlined cap.

2.2.3 Dust Sample Collection

There is the potential for loose (accumulated and visible) 

dust to be present on surfaces in school rooms. A sample 

size of 2 g for each sample was preferred, whereas 1 g was 

considered to be the minimum sample size. To obtain 

Sample 

Type

Sample Collection 

Method

Sample 

Extraction 

Method

Aroclor Sample 

Analysis Method

Congener Sample

Analysis Method

Air EPA Method TO-10A

(Sampling duration 

approx. 24-hr)

EPA Method 3540C EPA Method10A/EPA 8082 NEA CQCS Method

(Modified EPA Method 8082)

Surface wipe ASTM D6661-01 EPA Method 3540C EPA SW-846 8082 NEA CQCS Method

(Modified EPA Method 8082)

Dust (indoor) Research Operating 

Protocola

: Procedure for 

Collecting Loose Dust 

for PCB Analysis

EPA Method 3540C EPA SW-846 8082 NEA CQCS Method

(Modified EPA Method 8082)

Soil (outdoor) Research Operating 

Protocol: Procedure for 

Collecting Soils for PCB 

Analysis

EPA Method 3540C EPA SW-846 8082 NEA CQCS Method

(Modified EPA Method 8082)

Table 2-1. Summary of sample collection and analysis methods
9

Sample 

Type

Sample Collection 

Method

Sample 

Extraction 

Method

Aroclor Sample 

Analysis Method

Congener Sample

Analysis Method

Caulk and 

window glaze

Research Operating 

Protocol: Procedure for 

Screening Collection 

of Caulk and Window 

Glazing From Buildings 

for PCB Analysis 

EPA Method 3540C EPA SW-846 8082 NEA CQCS Method

(Modified EPA Method 8082)

Bulk 

materialsb

Research Operating 

Protocol: Procedure 

for Collecting Select 

Materials from Buildings, 

Fixtures, and Associated 

Items for PCB Analysis

EPA Method 3540C EPA SW-846 8082 NEA CQCS Method

(Modified EPA Method 8082)

a Research operating protocols were developed for the U.S. EPA/ORD research study on PCBs in schools.

b

 Bulk materials include paints, floor tiles, ceiling tiles, foam, cove molding, etc.

Collection

Location Air

Surface 

Wipe

Interior 

Dust

Exterior SoilInterior Caulk 

and Glazing

Exterior 

Caulk

Bulk 

Materialsc

Indoor Locations

Classroom 1 1 6 1 3b

Lab Classroom 2 1 7b 1 3

Classroom 3 1 7 2b 6b 20

Classroom 4 1 6b 2b 4b 12

Shop Classroom 5 2b 6 1 2

Cafeteria 2b 6 1 5b

Gymnasium 2b 5b 1 2 15

Outdoor Locations

Rear side of building 1 5b

Front side of building 8b 7b

50’ from front side 1

Total Number of Samples 11 42 9 9 25 12 47

Table 2-2. Sample collection information (numbers of samples by type and location)a

a

 Quality control field blank and field control samples are not included in this table.

b

 Includes duplicate samples.

c

 Bulk materials include samples of building materials and furnishings.

Table 2-1. Summary of sample collection and analysis methods (continued)
10

suitable sample sizes in most rooms, multiple surfaces had 

to be sampled including surfaces not ordinarily contacted, 

including cabinet tops and the tops of door molding. Where 

sufficient quantities of dust were available, the dust was 

collected using a cartridge filter assembly (pre-cleaned dust 

collection cassettes, Forensic Source part # 4-3109) and 

specialized vacuum system (3M Trace Evidence Vacuum 

System, Forensic Source part # 4-3005). Following 

collection, the dust was transferred from the filter into a 

clean amber vial with Teflon-lined cap. Dust samples were 

collected following the completion of air sample collection.

2.2.4 Outdoor Soil Sample Collection

Soil samples were collected at two locations adjacent to 

the building, below one joint containing caulk and below a 

window assembly with exterior caulk. At each of the two 

locations, soil samples were collected at three distances from 

the building: 0.15, 0.91, and 2.44 m (0.5 ft, 3 ft, and 8 ft). 

Each soil sample was collected to a 5-cm (2-inch) depth after 

carefully removing any vegetation. A total sample size of 

approximately 100 g was collected at each sampling location. 

A pre-cleaned stainless scoop was used to collect the soil. 

The soil was placed into a clean amber glass container with a 

Teflon-lined cap. 

Another soil sample was collected from an area further from 

the school at a location that could be contacted by students. 

In this case, the sample came from an island in the parking 

area in front of the school. This sample was not intended 

to fully characterize a school yard but is intended to assess 

whether there is a potential for soil exposures in areas that 

would need to be considered in exposure modeling efforts. 

An area 3.05 m × 3.05 m (10 ft × 10 ft) was identified. Soil 

samples were collected from the 0 to 5-cm depth at five 

locations (corners and center) within the designated area. 

Soil samples were combined into a single container and 

mixed. A total sample size of approximately 100 g was 

collected.

2.2.5 Caulk Sample Collection

Caulk samples (including caulk, window glaze, and joint 

sealant) were collected from interior locations in which 

environmental samples were collected and from selected 

exterior locations including from around window frames, 

building joints, and an entranceway joint. Sample collection 

generally followed the standard operating procedure, 

“Sampling Porous Surfaces for Polychlorinated Biphenyls 

(PCBs)” (U.S. EPA, 2008). However, only one sample of 

each selected type of caulk was collected, rather than three 

to form a composite. Duplicate samples were collected at 

designated locations to examine variability. Also, a sample 

size of 2 to 4 g was collected, rather than 10 g. Caulk 

samples were collected by physically removing sections of 

caulk using clean knives, scalpels, and tweezers (or other 

clean implements, as needed). Pieces of caulk were removed 

from the site of interest and placed in a single pre-cleaned 

amber glass jar with a Teflon-lined cap. Caulk samples were 

collected as they existed in the selected rooms; no emphasis 

was made on collecting samples of deteriorating caulk 

because intact caulk potentially may contain higher levels of 

PCBs. Attempts were made to collect a sample of each type 

of caulk, window glazing, and joint sealant in each room. 

Additional descriptive information was recorded including 

its location, use, current condition, and the presence of 

any paints or coatings. The length, width, and depth of the 

sampled caulk was also measured and recorded. Caulk 

samples were collected after air sampling was completed.

2.2.6 Other Material Sample Collection

Selected materials other than caulk were collected from 

three rooms in the school building (two classrooms and the 

gymnasium), when they were available and accessible, as 

potential primary or secondary sources of PCBs in school 

buildings. Where possible, 10 g of material was the preferred 

material sample size, with a minimum sample size of 2 g. 

Samples were collected using pre-cleaned instruments such 

as a scalpel, razor, spatula, utility knife, paint scraper, putty 

knife, or other hand tool, as needed. Wall concrete block 

was collected from three distances from an interior joint 

caulk, with the material collected up to 0.5-inch depth using 

an impact drill. Materials collected for PCB analysis were 

stored in clean amber glass containers with Teflon-lined lids. 

Material samples were collected following the completion of 

air sample collection.

2.2.7 Light Ballast Survey

Fluorescent light ballasts were surveyed in the rooms in 

which samples were collected to determine if PCB-containing 

ballasts might be present. A visual survey of a subset of 

fixtures was conducted by removing the light fixture lamps, 

removing the ballast cover, and examining the ballast label. 

Information from the ballast label was recorded. Many of 

the ballast labels stated “No PCBs” and were considered 

to be PCB-free. A smaller number of ballast labels did not 

state “No PCBs” and these ballasts were considered likely to 

contain PCBs. Ballasts were not surveyed in the gymnasium. 

2.2.8 Sample Transport and Storage

All samples were transported and stored for analysis under 

conditions appropriate for minimizing contamination by 

PCBs or losses of PCBs. At the field collection site all 

samples were stored in coolers with ice packs sufficient to 

maintain a temperature of approximately 4°C. Samples 

were transported to the laboratory with ice packs sufficient 

to maintain a temperature of approximately 4° C. Storage 

of samples and sample extracts at the analytical laboratory 

was at general freezer temperatures of approximately –20°C. 

Samples with potentially high levels of PCBs (such as caulks 

or other primary PCB source materials) were stored and 

transported in separate coolers to minimize any potential 

cross-contamination of samples with low levels of PCBs 

(such as air and wipe samples). 
11

2.3 Sample Extraction and Analysis

All samples were analyzed by NEA, a Division of Pace 

Analytical Services, Inc. NEA is a National Environmental 

Laboratory Accreditation Program (NELAP) accredited 

laboratory and maintains certifications for New York 

(EPA: NY00906, ELAP: 11078), New Jersey (NY026), 

Connecticut (PH-0337), Massachusetts (M-NY906), and 

North Carolina (668). 

2.3.1 Sample Extraction

All samples were extracted following EPA Method 3540C. 

Method 3540C is a Soxhlet procedure for extracting 

non-volatile and semi-volatile organic compounds from 

solid materials. The surrogate recovery compounds 

tetrachloro-meta-xlyene (TCMX) and decachlorobiphenyl 

(DBCP – a PCB congener) were added to the sample media 

prior to extraction. Sulfuric acid, Florisil, and mercury shake 

clean-up steps were used to prepare extracts for analysis. 

Extracts were concentrated to different final volumes 

depending on the sample type and detection limit goals. 

2.3.2 Sample Analysis for Aroclors

All sample extracts were analyzed for Aroclor mixtures 

following EPA Method 8082 using dual column gas 

chromatography with electron capture detection. Multilevel calibrations were performed for Aroclors 1016, 1221, 

1232, 1242, 1248, 1254, 1260, 1262, and 1268. Calibration 

levels of 20, 100, 250, 500, and 1000 parts per billion 

were used for each Aroclor. Continuing calibration check 

standards were analyzed periodically to verify calibration 

stability. Sample extracts were injected on two different 

gas chromatography columns, Phenomenex, Zebron ZB-1, 

30 m, 0.25 mm ID, 0.25 μm and Phenomenex, Zebron 

ZB-5, 30 m, 0.25 mm ID, 0.25 μm. Some sample extracts 

required dilution to bring the extract concentration into 

the calibration range. Aroclor quantitation was based on 

relative response factors for five chromatographic peaks 

following electron capture detection. A 40% relative percent 

difference acceptability limit was used for comparing the 

results obtained for the two chromatography columns. The 

highest value from the two columns was used for reporting 

when the acceptability criterion was met. In some cases 

sample results were reported as exhibiting an altered Aroclor 

pattern. In some cases, particularly for indoor air samples, 

more than one Aroclor (typically Aroclors 1248 and 1254 

for air samples) was used to report an altered PCB pattern 

exhibited by the sample. Actual Aroclor 1248 was not present 

in the samples, but was reported to more accurately quantify 

PCB present in sample that had undergone environmental 

alteration. When multiple Aroclor values were reported, they 

were summed to generate total PCB concentrations in the 

samples. The laboratory used procedures to estimate relative 

contributions from different Aroclors for shared congeners 

so as not to overestimate the concentration attributed to each 

Aroclor mixture. In a few cases for samples with very high 

concentrations (exterior caulks for example) the dilution was 

so large that it was not possible to determine the surrogate 

recoveries. A final concentration was calculated using the 

extract concentration and applying air volume, surface area, 

or solid material mass and dilution factors as appropriate. 

2.3.3 Sample Analysis for PCB Congeners

All of the air samples and a subset of other samples types 

were analyzed for PCB congeners using an NEA adaptation 

of EPA Method 8082 titled “Comprehensive Quantitative 

Congener Specific” (CQCS) analysis. The same sample 

extracts that had been previously analyzed for Aroclors were 

also analyzed using the CQCS method. The method utilizes 

high resolution gas chromatography with electron capture 

detection. Multi-level calibrations were performed using 

calibration mixtures containing all 209 PCB congeners. 

Calibration levels of 0.1, 0.5, 5.0, 25, and 50 ng/mL were 

used for each congener. 1,2,3,4,5,6,7,8-octachloronaphtalene 

was added to all calibration solutions and sample extracts 

as the internal standard. Continuing calibration check 

standards were analyzed periodically to verify calibration 

stability. Calibration and sample extracts were injected 

onto a Chrompack CP-Sil5/C18, 50 m length, 0.25 mm ID, 

and 0.10 micron phase thickness gas chromatography 

column. Of the 209 congeners, approximately 62 do not 

occur in Aroclor mixtures or are present at trace level 

(<0.05% by weight percentage). The chromatography 

column resolves 146 chromatographic peaks, providing 

complete separation for 99 congeners and co-elution of 

congeners for the remaining 47 chromatographic peaks. 

Of those 47 peaks with co-eluting congeners, 20 include a 

congener that is not found in Aroclor mixtures. Congener 

quantitation was based on relative response factors for each 

chromatographic peaks following electron capture detection. 

Peaks which are known to represent co-eluting congeners 

were mathematically quantitated as individual congeners 

using established ratios from Frame et al. (1996). A final 

concentration of each congener was calculated using the 

extract concentration and applying air volume, surface area, 

or solid material mass and dilution factors as appropriate. 

Chlorine-number homolog concentrations were calculated by 

summing the individual congener concentrations belonging 

to each homolog group. 

2.4 Caulk Emissions Modeling

Caulk containing high concentrations of PCBs has been 

found in older buildings, including school buildings. It 

is of interest to understand the potential impact that PCBcontaining caulk may have on indoor air levels of PCBs in 

school buildings. Total emission rates of PCBs from PCBcontaining caulk collected at six locations at two schools 

were estimated using emission parameters generated from 

laboratory chamber emission rate test results for caulk 

described by Guo et al. (2011). The laboratory testing was 

performed using microchambers to minimize loss of PCBs to 

chamber surfaces. There were a limited number of locations 

that had both PCB-containing caulk and the information 

available to calculate parameters used in the estimation, 

including the surface area of the caulk that was present and 

the room volume. These six locations were used as examples 

to highlight a range of emissions that might be encountered. 

Emission rates of chemicals from solid materials are 

primarily controlled by the chemical’s solid/air partition 

coefficient and the diffusion coefficient for the chemical 
12

in the solid material. Guo et al. (2011) described the 

relationship between a normalized emission factor and a 

chemical’s vapor pressure:

ln NEi = b1 + b2 ln Pi (Eq. 2.1.1)

where 

NEi = emission factor for congener i (μg/m2/hr) 

normalized to a constant (1000 μg/g) concentration of a 

PCB congener in a material. 

Pi = vapor pressure for congener i (torr)

b1, b2 = constants

Guo et al. (2011) measured the emission rates for ten PCB 

congeners (17, 52, 66, 77, 101, 105, 110, 118, 154, 187) from 

12 samples of caulk, each tested from 4 to 7 times at 23°C, to 

generate information for calculating the emission constants 

(b1 and b2 in Equation 2.1.1). Both constants (b1 and b2) were 

observed to be consistent among different caulk samples, 

which indicated that a single correlation could be applied 

to all caulk samples. The average values for b1 and b2 were 

14.02 and 0.976, respectively (Guo et al., 2011), resulting in 

the following relationship: 

ln NEi = 14.02 + 0.976 ln Pi (Eq. 2.1.2)

Equation 2.1.2 can be used to estimate the emission rate for 

a PCB congener from caulk when its concentration in the 

caulk is known and the vapor pressure is known or can be 

estimated. 

PCB Aroclor concentrations were measured in the caulks 

from six locations for which the surface area of the caulk 

at that location could also be calculated. Aroclor 1254 was 

the reported Aroclor in each caulk. The concentration of 

each congener in the caulk was estimated by multiplying the 

weight fraction of each congener as reported by Frame et al. 

(1996) for the G4 Aroclor 1254 standard (see Appendix A) by 

the total measured Aroclor concentration in the material. 

Several sets of vapor pressure data for PCBs congeners 

are available in the literature for many of the 209 PCB 

congeners. Two publications were used to obtain vapor 

pressures for as many congeners as possible and for all 

of the congeners reported by Frame et al. in Aroclor 1254 

(Fischer et al. 1992, Methods A and B; Foreman and 

Bidleman 1985, Methods A and B). For some congeners, 

vapor pressure data have not been reported in the literature 

but have been estimated using physical/chemical properties 

approaches [U.S. EPA Estimation Programs Interface SuiteTM

(MPBWINTM model as accessed via ChemSpider)]. Vapor 

pressures were selected for emission rate estimation in the 

following descending priority scheme, where all of the 

congener vapor pressures available from one source were 

used prior to moving to the next source:

1. Fischer et al. Method B,

2. Fischer et al. Method A, 

3. Foreman and Bidleman Method A

4. Forman and Bidleman Method B, and

5. EPI SuiteTM.

Examples of PCB congener vapor pressures are described in 

Table 2-3 (for a more complete list see Appendix A). The 

congeners in Table 2-3 are those that were measured during 

laboratory chamber emissions measurement testing (Guo et 

al, 2011). There is a large range in vapor pressures among 

the congeners, with congener 17 having a vapor pressure 

approximately 200-fold higher than congener 187. 

The calculation to estimate the total emission rate of PCBs 

from a material in a room includes multiple steps. The steps 

are described below along with an example calculation: 

PCB Congener Number of Chlorines P (torr)

17 3 5.82×10-4

52 4 1.50×10-4

66 4 4.42×10-5

77 4 1.43×10-5

101 5 2.99×10-5

105 5 5.82×10-6

110 5 1.68×10-5

118 5 8.42×10-6

154 6 1.36×10-5

187 7 2.79×10-6

Table 2-3. Vapor pressures for selected target congeners in Aroclor 1254
13

Step 1: Calculate the concentration of each congener in a 

material [Xi in (μg/g)]:

X1 = Weight percent of congener in 

Aroclor 1254 × Aroclor 1254 concentration

Congener 52 example in a gymnasium door 

frame caulk: 

Xi = 5.38% × 117,000 μg/g = 6290 μg/g

Step 2: Obtain the vapor pressure for the congener

Congener 52 example: 1.5 ×10-4 torr 

(Fisher et al. 1992, Method B)

Step 3: Calculate the normalized emission factor (NEi) from 

Equation 2.1.2:

Congener 52 example:

ln NEi = 14.02 + 0.976 ln 1.5 × 10-4 = 5.42

NEi = 226 (μg/m2/hr)

Step 4: Convert the normalized emission factor to the 

emission factor (Ei):

Ei = (NEi ÷ 1000 μg/g) × Xi

Congener 52 example:

Ei = (226 μg/m2/hr ÷ 1000 μg/g) × 

 6290 μg/g = 1420 μg/m2/hr

Step 5: Multiply the emission factor by the surface area 

of the material in the room to obtain the total estimated 

congener emission rate from the material in that room:

Ri (μg/hr) = Ei (μg/m2/hr) × S (m2)

Congener 52 example:

Ri = 1420 μg/m2/hr × 0.143 m2 = 203 μg/hr

Step 6: Sum the estimated emission rates across all of the 

congeners expected to be present in Aroclor 1254 (based on 

Frame et al., 1996):

 

 

 

(Eq. 2.1.3)

Step 7: Adjust the total estimated Aroclor 1254 emission 

rate. Equation 2.1.3 estimates the total Aroclor 1254 

emission rate assuming that the congener proportions in the 

caulk are equivalent to those in unaged Aroclor 1254. As 

shown in Section 4.5, caulk that has been in a building for 

over 40 years has an altered congener pattern. Based on 

congener measurements for caulk in School 6, Table 4-31 

shows that the total emission rate was overestimated by a 

factor of 1.8 assuming unaged Aroclor 1254 as compared 

to the actual congener proportions measured in the caulk. 

Another factor to consider is that the caulk chamber testing 

might have overestimated emission rates due the presence 

of freshly cut surfaces. Three tests used to examine this 

(Guo et al., 2011) showed an average of 19% higher 

emissions for freshly cut surfaces in chamber testing. The 

two factors, when combined (0.55 x 0.81 = 0.45), yield 

an adjustment factor of 0.45 that can be applied to total 

emissions estimates:

Rtotadj = RA1254 × 0.45 

RA1254= ∑  Ri

n

i=1

This approach assumes that the emission factors generated 

for the emissions of PCBs from caulk in the laboratory test 

chamber experiments are applicable to the caulks sampled 

in the building. It also assumes that the temperature and air 

flow conditions used in chamber testing are applicable to the 

conditions in the rooms. 

Guo et al. (2011) reported the predictive errors for the P-N 

calculation method for the ten congeners measured in their 

chamber emission rates studies. The error was calculated 

by using Equation 2.1.4, and the results are presented in 

Table 2-4. It is anticipated that the average error would be 

applicable across estimated total PCB emission rates using 

the P-N calculation method. 

 

 

(Eq. 2.1.4)

where 

ε = predictive error (%)

Ep = predicted emission factor (μg/m2/hr)

Em = measured emission factor (μg/m2/hr)

Table 2-4. Predictive error for the P-N correlationsa

Congener ID P-N Correlation Error 

#52 30.0%

#66 40.8%

#101 32.3%

#105 21.6%

#110 31.2%

#118 29.7%

#154 59.4%

Average 35.0%

a (Guo et al. 2011 Eq. 4.6)

Screening-level estimates of the concentration of PCBs in 

indoor air that might result from emission from caulk were 

generated for four school building locations. The following 

equation, including the adjustment factor described in Step 

7 above, was used to estimate steady-state indoor air PCB 

concentrations resulting from emissions from caulk, if there 

was only one type of caulk with PCBs in the location:

(Eq. 2.1.5)

where: 

C = total PCB concentration in room air (μg/m3)

Ri = emission rate from caulk from congener i (μg/hr)

n = number of congeners with emission rate estimates

Q = ventilation rate for the room with outdoor air (m3/hr)

(based on room volume × ACH)

×100% − =

m

mp

E

EE

ε

C = x 0.45

n

 Ri i =1

Q
14

If there were multiple kinds of caulk with PCBs in a location, 

the following equation was used:

 

Σ

m 

Σ

n 

R

i= 1 j= 1 C = x 0.45 

Q 

ij

 

(Eq. 2.1.6)

where 

Rij = emission rate for congener i from source j

m = number of congeners with emission rate estimates

n = number of caulk types in the room

The estimates are based on numerous assumptions, and 

therefore should be considered only screening level. 

Assumptions include:

• well mixed air in room

• constant temperature

• temperature equivalent to chamber conditions that 

generated caulk emission parameters (23°C)

• constant ventilation rate

• steady-state emission

• steady state and approximately equal absoption/

desorption of PCBs in other materials in the room

• no chemical reactions of PCBs

• PCBs from other school spaces are not impacting the 

levels in air for the room of interest

• emission parameters for caulk in the room are the same as 

for the caulk tested in lab chambers

2.5 Light Ballast Emissions Modeling

PCB-containing fluorescent light ballasts remain in operation 

in some older buildings, including school buildings. The 

capacitors in these ballasts are not perfectly sealed and small 

amounts of PCBs can be emitted from intact capacitors (Guo 

et al., 2011). Measured emission rates from apparently intact 

light ballasts are highly dependent on the ballast temperature, 

with very low emissions at room temperature and much 

greater emissions, relatively, at temperatures of 45 - 50°C, 

approaching normal ballast operating temperatures 

(Guo et al., 2011; Hosomi, 2005). There is interest in 

understanding the impact that light ballasts may have on PCB 

concentrations in indoor spaces. 

Laboratory chamber emissions tests were performed for 

four intact PCB-containing ballasts at temperatures from 

23 to 45°C (Guo et al., 2011). These emission tests were 

performed using 55-L stainless steel chambers, as compared 

to the microchambers used for caulk emission testing. 

Emission measurement results were used here to perform 

screening-level estimates of a range of total emission rates 

that might be encountered in school classrooms and the 

resulting air concentrations. 

Light ballasts capacitors tested in the chambers were found 

to contain Aroclor 1242 (Guo et al., 2011). Guo et al. 

measured congeners 13, 15, 17, 18, 22, 44, 49, 52 in chamber 

testing as important components of the Aroclor 1242 mixture. 

Measured emission rates for each congener are presented 

in Table 2-5 for the four ballasts tested at 45°C. In order to 

use the congener emission results for estimating total PCB 

emissions (assuming Aroclor 1242) several steps were taken.

First, the congeners were grouped by chlorine-number 

homolog and the emission rates were summed. Estimates 

for the total emission for all congeners in the homolog 

group were generated by multiplying summed emission 

rate for measured congeners at 45°C by the ratio of total 

weight percent in the homolog group to the weight percent 

represented by the measured congeners (Table 2-5). Next, 

the sum of the total emissions for the 2, 3, and 4-homolog 

groups was calculated to estimate a value approaching the 

total PCB emission rate for Aroclor 1242. The combined 

weight percent of congeners in those three homolog groups 

represents over 95% of the total weight percent for Aroclor 

1242 (see Appendix A) based on Frame et al. (1996). Finally, 

the estimated total PCB emission rate for each chambertested ballast was multiplied by the number of PCBcontaining ballasts in three building rooms as an estimate 

of total PCB emission rates that might be encountered in 

rooms where intact PCB-containing ballasts are present 

and are in operation (lights are on). Because there was an 

almost 80-fold difference in the highest and lowest estimated 

emission rate across the four chamber-tested ballasts, the 

lowest, median, and highest total emission rates were used to 

generate a range of results. 

It is also of interest to estimate ballast emissions for the 

condition when the lights are off and the ballasts are at room 

temperature. The four ballasts were tested in the laboratory 

chamber at several temperatures, including 23°C which is 

similar to room temperature. Because the emission rates 

were much lower at 23°C as compared to 45°C during 

chamber tests, many of the congener analyses had results 

below the detection limit (Table 2-6). Congeners 17 and 

18 had 50% and 100% measurable results at 23°C. Ratios 

of emission rates at 45°C vs. 23°C were calculated. The 

average of the ratios for congeners 17 and 18 were used to 

estimate the overall total PCB emission rates at 23°C by 

dividing the emission rates previously estimated at 45°C by 

the average ratio of 16.2 (Table 2-6). The estimated total 

PCB emission rate for each chamber-tested ballast at 23°C 

was multiplied by the number of PCB-containing ballasts in 

three building rooms as an estimate of total PCB emission 

rates that might be encountered in rooms where intact PCBcontaining ballasts are present and are not in operation (lights 

are off). 

Screening-level estimates of the ranges of concentrations 

of PCBs in indoor air that might result from emission from 

intact light ballasts were generated for three school building 

locations. These three school rooms were selected only as 

examples to highlight the ranges of total emissions that might 

be found based simply on the number of ballasts present 

in the room. The following equation was used to estimate 
15

steady-state indoor air PCB concentrations resulting from 

emissions from ballasts using the lowest, median, and highest 

rates across the four chamber-tested ballast emission rates:

 

Q

R

C

n

b

∑ b

= = 1

 

(Eq. 2.1.7)

where:

C = total PCB concentration in room air (μg/m3)

Rb = emission rate from ballast b (μg/hr)

n = number of PCB-containing ballasts in the room

Q = ventilation rate for the room with outdoor air (m3/hr) 

 (based on room volume × ACH)

The estimates are based on numerous assumptions, and 

therefore should be considered only screening level. 

Assumptions include:

• well mixed air in room

• constant temperature

• temperature equivalent to chamber conditions that 

generated emission rates

• constant ventilation rate

• steady-state emission

• steady state and approximately equal absoption/

desorption of PCBs in other materials in the room

• no chemical reactions of PCBs

Ballasts Tested in Emission Chamber at 45°C

Ballast 1 Ballast 2 Ballast 3 Ballast 4

Congener 13 (μg/hr) NDa 0.0029 0.0049 0.224

Congener 15 (μg/hr) 0.0072 0.0120 0.0210 0.953

 Σ Congeners (μg/hr) 0.0072 0.0149 0.0259 1.18

Weight Percent of Measured Congeners in Aroclor 1242 2.12 2.12 2.12 2.12

Total 2-Chlorine Homolog Weight Percent in Aroclor 1242 13.4 13.4 13.4 13.4

Estimated Emission Rate for 2-Chlorine Homologs (μg/hr) 0.0459 0.0941 0.164 7.45

Congener 17 (μg/hr) 0.0256 0.038 0.0313 1.72

Congener 18 (μg/hr) 0.0832 0.114 0.0856 5.42

Congener 22 (μg/hr) 0.0050 0.0113 0.0058 0.364

 Σ Congeners (μg/hr) 0.114 0.162 0.123 7.51

Weight Percent of Measured Congeners in Aroclor 1242 15.5 15.5 15.5 15.5

Total 3-Chlorine Homolog Weight Percent in Aroclor 1242 48.0 48.0 48.0 48.0

Estimated Emission Rate for 3-Chlorine Homologs (μg/hr) 0.352 0.504 0.380 23.3

Congener 44 (μg/hr) ND 0.0059 0.0024 0.161

Congener 49 (μg/hr) ND 0.0045 ND 0.154

Congener 52 (μg/hr 0.0055 0.0091 0.0036 0.254

Σ Congeners 0.0055 0.0195 0.0060 0.569

Weight Percent of Measured Congeners in Aroclor 1242 9.7 9.7 9.7 9.7

Total 4-Chlorine Homolog Weight Percent in Aroclor 1242 32.7 32.7 32.7 32.7

Estimated Emission Rate for 4-Chlorine Homologs (μg/hr) 0.0184 0.0658 0.0203 1.92

Σ 2, 3, and 4-Chlorine Homologs (μg/hr) 0.416 0.664 0.564 32.7

Median of Total (μg/hr) 0.614

Mean ± Standard Deviation of Total (μg/hr) 8.57 ± 16.0

Table 2-5. Estimated total PCB emission rates for four intact light ballasts in chamber testing

a

 ND = Not detected.
16

• PCBs from other school spaces are not impacting the 

levels in air for the room of interest

• ballasts contains congeners equivalent to Aroclor 1242

• emission rates for ballasts in the rooms are the same as 

for the ballasts tested in lab chambers

Because emission testing for caulk and light ballasts were 

performed in two different types of chambers, it is possible 

that surface velocity conditions were different, possibly 

affecting the emission rates due to different boundary layer 

conditions. Losses to the chamber walls were also possible 

for the larger chambers used for light ballast emissions, 

potentially resulting in underestimation of total emission 

rates. Thus, some caution is warranted in making direct 

comparisons between estimated caulk and light ballast 

emission rates. 

2.6 Building Material Emissions Modeling

Most of the building materials collected in three school 

buildings had measurable levels of PCBs. There is interest 

in understanding whether these materials might be important 

as secondary sources of PCB emissions, particularly once 

primary sources have been removed or otherwise mitigated. 

Screening-level emission rates were calculated to provide a 

relative sense of potential emissions from different building 

materials and to better understand whether these materials 

are potentially important sources for exposures to PCBs in 

school buildings.

Screening-level emission estimates for materials in nine 

building rooms were generated using the material PCB 

concentration and the measured surface area of the material 

in the room. These rooms were selected as examples 

Table 2-6. Ratio of PCB emissions at different temperatures from four light ballasts tested in a laboratory chamber

PCB

Congener

% Results > Detection Limita

Average Ratio of

Emission Rates

Average Ratio of

Emission Rates

23°C 35°C 45°C 45/23°C 45/35°C

13 0 0 75 NCb NC

15 0 75 100 NC 4.0

17 50 100 100 15.9 3.0

18 100 100 100 16.4 2.8

22 0 50 100 NC 2.2

44 0 25 75 NC NC

49 0 25 50 NC NC

52 0 25 100 NC NC

Average of Ratios 16.2 3.0

a

 For four intact light ballasts with no visible leaks that were tested for emissions at several different temperatures in a 

laboratory test chamber. 

b

 NC = Not Calculated. Ratio calculations were used only when at least 50% of the measurements were greater than the 

detection limit. 

based on the availability of information regarding the air 

concentration, the surface areas of multiple materials in 

those rooms, and PCB concentration measurements for the 

materials. The approach described in Section 2.4 for caulk 

was applied to the materials. The estimated emission rates 

should only be considered screening-level estimates because 

emission parameters generated for caulk in laboratory 

chamber tests were applied to all of the other materials. No 

emission parameter data are available for PCB congeners for 

the many different materials that were sampled in the school 

rooms. It is not clear whether, and how well, the caulk 

emission parameters apply to the other materials. It is likely 

that are considerable differences in emission parameters 

for materials that have different physical and chemical 

properties, different thicknesses, and different surface areas. 

The screening-level estimates generated for the building 

materials were not used to generate estimated room air PCB 

concentrations for two reasons. First, as noted above, the 

estimated emission rates have considerable uncertainties. 

Second, the relative dynamics of absorption and desorption 

(the materials acting both as sources and sinks) have not been 

well characterized for school room environments, particularly 

when there are multiple materials serving as sources and 

sinks at the same time. This makes it difficult to accurately 

predict the air concentration that will result from PCBs in 

these materials. 

2.7 SHEDS Exposure Modeling

2.7.1 SHEDS Background Information

SHEDS-Residential is one of modules of the SHEDSMultimedia human exposure/dose model (http://

www.epa.gov/heasd/products/sheds_multimedia/files/
17

SHEDSResidential_TechManual_2012.pdf; Zartarian et 

al., 2012; Glen et al., 2012). The primary function of the 

SHEDS-residential model is to estimate the exposure of 

a population to one or more specified chemicals from 

inhalation, ingestion (by mouthing of hands or objects), or 

dermal contact in a residential setting. SHEDS uses the 

Monte Carlo statistical method to simulate a population of 

individuals based on time-location-activity diaries in EPA’s 

Consolidated Human Activity Database (CHAD; www.epa.

gov/chadnet1) and weights from the U.S. Census. These 

individuals are not specific persons, but are stochastically 

created synthetic persons whose collective properties 

reflect the simulated population and input distributions for 

exposure-related variables. For each individual, SHEDS 

constructs a sequence of activities, media concentrations, and 

the resulting exposures over the selected simulation period, 

which may range from one day to a year or more (although 

simulation time steps can range from 1 minute to 1 hour 

within a day). These individual exposure time series may be 

stored or exported, or aggregated over time to give timeintegrated or time-averaged exposures (Figure 2-1). They 

may also be input to a dose model, either internal or external 

to SHEDS, to follow the fate of the chemical after it enters 

the human body. Exposure is defined in this model as the 

contact between a chemical agent and a simulated human 

target at the external body surface, either the skin surface 

or the oral/nasal boundary. Dose is defined in this model as 

the amount of chemical that enters the target after crossing 

the exposure surfaces. Details regarding the pathways, 

distributional functions, and exposure/dose equations are 

provided in the SHEDS Technical Manual (Glen et al., 2012).

SHEDS can be used for various purposes, including 

estimating population distributions of exposure and dose; 

understanding intensity, duration, frequency, and timing of 

exposures; identifying critical media, exposure routes, and 

factors; considering how to identify and address greatest 

uncertainties; and comparing modeled estimates against 

real-world data. Figures 2-2 and 2-3 illustrate the SHEDS 

methodology. The model estimates the exposure and/or 

dose of individuals in a user-specified population cohort 

Figure 2-2. Overview of SHEDS residential methodology

Figure 2-1. Hypothetical exposure profile for an individual
18

to a particular chemical via three primary exposure routes: 

inhalation, non-dietary ingestion (i.e., via soil/dust ingestion, 

hand mouthing, or object mouthing pathways), and dermal 

contact in a residential setting. To do this, it simulates the 

daily activities and locations of individuals using sequential 

time/location/activity diaries from EPA’s Consolidated 

Human Activity Database (CHAD) (McCurdy et al. 

2000). SHEDS utilizes the Xue et al. (2004) approach for 

longitudinal diary assembly. 

For each individual in a SHEDS-Residential run, the 

following general steps are applied (see the SHEDS Technical 

Manual for more detail: Glen et al., 2012):

1. Randomly select the age, gender, and other 

demographic properties of interest, given the 

distribution of the target population.

2. Generate a longitudinal activity diary, which indicates 

the sequence and duration of activities and locations 

for that person. For the residential module, these 

are based on sequential time-location-activity diaries 

from EPA’s CHAD database. 

3. Generate concentration time series for each potential 

contact medium (e.g., indoor air, indoor smooth 

surfaces, indoor textured surfaces, outdoor lawn). 

4. Simulate the contacts between the individual and the 

affected media. These depend on the diary activity/

location information and contact probabilities derived 

from user-specified inputs.

5. Calculate pathway-specific exposure time series for 

the individual, using the results of the prior two steps 

and user-specified distributions for exposure factors.

6. Generate an approximation for the components of the 

intake or absorbed dose time series and export these 

for use in a simple pharmacokinetic (PK) or more 

complex physiologically-based pharmacokinetic 

(PBPK) model. 

7. Time-aggregate to daily totals of absorbed dose.

The SHEDS-Residential model was applied to the dose 

modeling estimation for school PCB environmental 

measurement data because the school environment is, 

in many ways, similar to the residential environment, 

particularly with regard to multiple exposure pathways. 

Rather than using residential activity data, school activity 

data from CHAD were used in this PCBs assessment. Dose 

estimation was not performed for adults, including teachers 

and staff, as part of this effort due to the lack of school 

activity data such as those available for children in the 

Consolidated Human Exposure Database. 

2.7.2 Input Data for SHEDS School PCB Modeling

Key inputs for PCBs exposure simulation are concentrations 

of PCBs in various media. PCB environmental measurement 

data from the schools were pooled and fitted to lognormal 

distributions for indoor air, soil and wipe sample 

concentrations for total PCBs (Table 2-7). The hypothesis for 

a normal distribution of log-transformed measurement results 

for air was not rejected based on the Shapio-Wilk test (0.05 

level). Significance levels were slightly exceeded for surface 

wipe and dust measurements but this was likely due to the 

higher number of non-detect values; the log distribution 

appeared to best represent the data for SHEDS modeling 

purposes. 

Only those soil results for samples collected from the 

0 – 5 cm (0 – 2”) depth were used as inputs to the model. 

Dust measurements were not made at most schools 

and dust concentrations were estimated based on air 

concentrations measured in each room and estimated dust/

air partition coefficients (see Appendix E). Also, outdoor air 

concentrations from other studies (Appendix D) were used 

for PCB exposure simulation because there were insufficient 

measurements to fit distributions and the quantitation limit 

for the school measurements was high relative to typical 

outdoor air levels. Outdoor air measurement results used in 

this analysis had a median of 0.4 ng/m3 (mean of 18 ± 25 ng/

m3 total PCBs). Outdoor air concentrations were applied to 

the fraction of estimated time spent outdoors while at school.

Absorption parameters of PCBs by humans are another set 

of important inputs (Table 2-8, information from ATSDR, 

2000). Absorption information and their application in 

SHEDS is described below.

Inhalation Absorption

PCBs, when administered orally, are well absorbed by 

experimental animals and at generally high fractions by 

humans (ATSDR, 2000). There is very limited information 

available for PCB pulmonary absorption. A recent study of 

inhalation of vapor phase PCB congeners by rats estimated 

that 33 μg of the 40 μg inhalation exposure was present 

across multiple tissues, suggesting at least 82% of the PCBs 

were absorbed (Hu et al., 2010). Another study examined 

pulmonary absorption of a chemical similar to PCBs, 2,3,7,8-

TCDD, following instillation in rats and estimated 95% 

absorption of the administered dose (Dilberto et al., 1996). 

A portion of the PCBs in air are likely absorbed to dust and/

or soil particles. A study examined the relative absorption 

of 2,3,7,8-TCDD on soil as compared to a different substrate 

following pulmonary instillation and found 100% relative 

absorption (Nessel et al, 1992). These studies suggest a 

relatively high pulmonary absorption rate, but this is still 

uncertain for humans across a range of congeners and 

different vapor/particle phase fractions. Volckens and Leith 

examined the deposition of inhaled SVOCs and found for 

up to 7-chlorine PCB congeners that deposition to the lung 

as vapor dominated (2002). A value of 70% pulmonary 

absorption was used for this PCBs exposure simulation, and 

sensitivity analyses were conducted ranging from 35% to 

100% to examine the impact on modeled dose estimates. 
19

Figure 2-3. General overview of SHEDS multimedia exposure model

Gastrointestinal Absorption

As with fats and other fat-soluble chemicals, PCBs are 

most likely absorbed from the gut via lymphatic circulation 

and consequently avoid first-pass metabolism in the liver 

(Hansen 1999). Price et al. (1972) found that 88% of 

the ingested PCBs were not excreted, and were therefore 

assumed to be retained in the body (7–9 year old girls). This 

estimate of PCB absorption in young girls is supported by 

the more comprehensive, congener specific mass balance 

study of Schlummer et al. (1998). Retention was estimated 

to be >90% and 85.4% of the administered dose in monkeys 

(Allen et al. 1974b) and ferrets (Bleavins et al. 1984), 

respectively. An absorption value of 85% was used for the 

simulation.

Dermal Absorption

Experimental data on the percutaneous absorption of PCBs in 

humans is limited to in-vitro studies that used human cadaver 

skin (Wester et al. 1990, 1993) with 14C-labeled Aroclor 1242 

and 1254. Over a 24-hour period, 2.6, 10, and 43% of the 

dose was retained in human skin when the Aroclor 1242 was 

formulated in soil, mineral oil, or water, respectively. Similar 

results were observed with Aroclor 1254, with 1.6, 6.4, and 

44.3% of the dose retained in human skin, following PCB 

exposure in a soil, mineral oil, or water vehicle, respectively. 

The in-vitro data indicate that PCBs readily enter human skin 

and are available for systemic absorption, and that the dosing 

vehicle has a major role in regulating the relative retention of 

PCBs in human skin.

In a related study, Wester et al. (1990, 1993) assessed the 

in-vivo percutaneous absorption of PCBs in adult female 

Rhesus monkeys. Topical administration of Aroclor 

1242 resulted in 14, 20, 18, and 21% absorption of the 

administered dose when formulated in soil, mineral oil, 

trichlorobenzene, or acetone, respectively. In contrast to the 

above in-vitro results with human skin, the vehicle had little 

effect on the systemic absorption of the PCBs applied to the 

skin of monkeys. This may be due to the uncertain viability 

of the human skin used in the in-vitro studies and the fact 

that the in-vitro study primarily assessed retention of PCBs 

in human skin and could not estimate systemic absorption. 

Absorption efficiency ranged from 0.15 to 34% of the 

applied radioactivity in the monkeys and averaged 33% (42% 

chlorine) and 56% (54% chlorine) of the applied radioactivity 

in the guinea pigs. 

For this simulation, 2% was used for dermal absorption rate 

per day for dust or soil using a uniform distribution, with 

10% and 40% for the daily dermal absorption rate for the 

residues.

Other default inputs are listed in Table 2-9 (from Appendix 

G - default values for non-chemical specific variables from 

the SHEDS-Multimedia version 4 Technical Manual; Glen, 

2012). The U.S. EPA Child-specific Exposure Factors 

Handbook was consulted in selecting input values, but 

relevant data for fitting distributions for soil and dust contact 

and ingestion were available from Kissel et al. (1996), 

Holmes et al. (1999), and Ozkaynak et al. (2011) and were 

used in this analysis. The object mouthing rates shown in 

Table 2-9 were used in conjunction with the residue data 

from the dermal wipe samples. 

Inhalation rate

Short-term inhalation rates are generated for SHEDS based 

on several factors (Glen et al., 2012). The basal metabolic 

rate (bmr) is calculated from a regression equation using 

body weight as the independent variable. The units for bmr 

are megajoules per day. The slope, intercept, and standard 
20

Table 2-7. Input variables for the SHEDS-multimedia model

PCBs Environmental Concentration Inputs Units Distribution Geo. Mean Geo. SD

Distributions Used for Modeling All 6 Schools (pre-remediation)

Indoor air PCBs μg/m3 lognormal 2.29E-01 4.26

Outdoor air PCBs μg/m3 lognormal 2.08E-03 12.94

Wipe PCBs in high contact area μg/cm2 lognormal 1.51E-03 2.85

Soil PCBs μg/kg lognormal 524.6 3.71

Estimated dust PCBs μg/kg lognormal 7032 4.26

Distributions Used for Modeling 5 Schools (pre- and post-remediation)

Indoor air PCBs for 5 schools (pre-remediation) μg/m3 lognormal 2.03E-01 4.44

Outdoor air PCBs (pre- and post-remediation) μg/m3 lognormal 2.08E-03 12.94

Wipe PCBs in high contact area (pre-remediation) μg/cm2 lognormal 1.18E-03 2.45

Soil PCBs for 5 schools (pre- and post-remediation) μg/kg lognormal 535.6 3.65

Estimated dust PCBs (pre-remediation) μg/kg lognormal 6236 4.44

Indoor air PCBs (post-remediation) μg/m3 lognormal 7.45E-02 2.87

Wipe PCBs in high contact area (post-remediation) μg/cm2 lognormal 1.08E-03 2.47

Estimated dust PCBs (post-remediation) μg/kg lognormal 2294 2.87

Distributions Used for Modeling 3 Schools (Year 1 pre- and post-remediation and Year 2 pre-remediaiton)

Year 1 Indoor air PCBs (pre-remediation) μg/m3 lognormal 3.19E-01 3.86

Year 1 Wipe PCBs in high contact area (pre-remediation) μg/cm2 lognormal 1.29E-03 2.31

Soil PCBs μg/kg lognormal 524.6 3.71

Year 1 Estimated dust PCBs (pre-remediation) μg/kg lognormal 9820 3.86

Year 1 Air PCBs (post-remediation) μg/m3 lognormal 7.63E-02 2.87

Year 1 Wipe PCBs in high contact area (post-remediation) μg/cm2 lognormal 1.03E-03 2.38

Soil PCBs μg/kg lognormal 524.6 3.71

Year 1 Estimated dust PCBs (post-remediation) μg/kg lognormal 2347 2.87

Year 2 Indoor air PCBs (pre-remediation) μg/m3 lognormal 9.96E-02 3.07

Year 2 Wipe PCBs in high contact area (pre-remediation) μg/cm2 lognormal 8.46E-04 2.43

Soil PCBs μg/kg lognormal 524.6 3.71

Year 2 dust PCBs (pre-remediation) μg/kg lognormal 3065 3.07

deviation of the residual are taken from the body weight and 

surface area files by age and gender. A minimum value of 

0.1 megajoules per day is permitted. The basal inhalation 

rate is the rate in effect for activities with a METS of one and 

has units of cubic meters of air per hour. The basal alveolar 

ventilation rate, bva, is related to the basal metabolic rate: 

 bva = bmr × 0.166 × 0.01963 × (0.20 + 0.01× u) × 60 

The factor 0.166 converts from megajoules per day to 

kilocalories per minute. The factor 0.01963 converts from 

liters of oxygen consumed to cubic meters of air inhaled. 

The variable “u” is uniformly distributed between zero and 

one, and then term (0.20 + 0.01 × u) represents the metabolic 

efficiency (liters of oxygen consumed per kilocalorie 

expended). The final factor of 60 converts the per minute 

rate to the per hour rate. Multiplication of metabolic ratio of 

energy expenditure for an activity to the resting rate (METS) 

and bva leads to the inhalation rate for SHEDS. In this way, 

we link age, body weight and activity levels with inhalation 

rate. SHEDS is using macro activity, therefore, we only use 

short-term inhalation rates. Table 2-10 displays summary 

statistics of average inhalation rates by age groups. 
21

Table 2-8. Key input variables for the SHEDS-multimedia model 

Distribution Distribution Parametersa

Concentration or Process Inputs Units Form v1 v2

Absorption fraction for lungs [-] point 0.7

Dermal absorption rate per day for dust or soil 1/day point 0.02

Dermal absorption rate per day for surface residues 1/day uniform 0.1 0.43

GI tract absorption rate per day for dust or soil 1/day point 0.85

GI tract absorption rate per day for surface residues 1/day point 0.85

Bioavailability fraction for dust/soil [-] point 1

Bioavailability fraction for surface residues [-] point 1

Residue-skin transfer efficiency [-] normal 0.051 0.022

Soil-skin adherence factor mg/cm2 lognormal 0.11 2

Body-surface fractional contact rate 1/20min beta 42 166

Hand-surface fractional contact rate 1/20min Weibull 10 2.5

Fraction of body unclothed [-] beta 3 6.7

Surface-skin transfer coefficient for body (unclothed) cm2/hr lognormal 3070 1.68

Surface-skin transfer coefficient for hand cm2/hr lognormal 3070 1.68

Dust ingestion rate (indoor, direct only, 4=age =5) mg/hr lognormal 0.706 4.009

Dust ingestion rate (indoor, direct only, 6<=age <=10) mg/hr lognormal 0.446 8.011

Dust ingestion rate (indoor, direct only, age >=11) mg/hr point 0

Soil ingestion rate (outdoor, direct only, 4=age=5) mg/hr lognormal 0.722 6.293

Soil ingestion rate (outdoor, direct only, 6<=age <=10) mg/hr lognormal 0.276 9.774

Soil ingestion rate (outdoor, direct only, age >=11) mg/hr point 0

a

 Distributional parameters (v1, v2): lognormal (geometric mean, geometric standard deviation); normal (mean, standard 

deviation); uniform (minimum, maximum); Weibull (shape, scale); beta (α, β).

Time and activity in school

The simulated population of 6-18 year-old children was 

generated using ~35,000 person-days from the new CHAD 

database; time-location-activity diaries were selected 

according to age and school attendance information.

Longitudinal activity diaries of the simulated schoolchildren 

were generated using a published method to optimize interand intra- individual variability (that uses 8 CHAD persondays by season and weekday/weekend for each age/gender 

cohort; Xue et al., 2004). Applying this method generated 

an average 6.34 hours indoor and 0.2 hours outdoor during 

school time. Higher ventilation rates were applied for the 

outdoor time due to an expected higher level of physical 

activity. The longitudinal activity patterns for each individual 

were then combined with available PCB concentration data 

and exposure factors and inserted into exposure pathway 

equations as described in the SHEDS-Multimedia technical 

manual.

Only PCB exposures incurred during school hours (in/

around the school) were modeled; neither dietary intake nor 

intake away from school was considered. Routes considered 

were inhalation, dermal contact, and soil ingestion. For 

dermal contact, wipe data were used; these likely include 
22

Table 2-9. Key input variables for the SHEDS-multimedia model

Distribution Distribution Parametersa

Concentration or Process Inputs Units Form v1 v2

Hand mouthing events per hr (indoor, 4=age=5) events/hr Weibull 0.75 12.59

Hand mouthing events per hr (outdoor, 4=age=5) events/hr Weibull 0.55 5.53

Hand mouthing events per hr (indoor, 6<=age <=10) events/hr Weibull 1.36 7.34

Hand mouthing events per hr (outdoor, 6<=age <=10) events/hr Weibull 0.49 1.47

Hand mouthing events per hr (age >=10) events/hr point 0

Fraction of surface of one hand that enters mouth [-] beta 3.7 25

Object mouthing events per hr (indoor, 4=age=5) events/hr Weibull 0.58 6.9

Object mouthing events per hr (outdoor, 4=age =5) events/hr Weibull 0.55 5.38

Object mouthing events per hr (indoor, 6<=age <=10) events/hr Weibull 0.84 1.2

Object mouthing events per hr (outdoor, 6<=age <=10) events/hr Weibull 0.55 1.1

Object mouthing events per hr (age >=11) events/hr point 0

Object-surface concentration ratio [-] uniform 0 0.2

Object-mouth contact area cm2 exponential 1 10

Object-mouth transfer efficiency [-] beta 2 8

Transfer coefficient for object mouthing (age >=4) cm2/hr point 0

Removal efficiency during bath/shower [-] uniform 0.9 1

Removal efficiency during events without water 1/hr point 0

Removal efficiency during mouthing [-] beta 2 8

Removal efficiency during hand washing [-] uniform 0.3 0.9

Mean # hand washes/day per person 1/day lognormal 3.74 2.63

Maximum dermal loading for body μg/cm2 triangle 0.1 0.6

Maximum dermal loading for hands μg/cm2 triangle 0.1 1

a

 Distributional parameters (v1, v2): lognormal (geometric mean, geometric standard deviation); normal (mean, standard 

deviation); uniform (minimum, maximum) ; triangle (minimum, mode, maximum); Weibull (shape, scale); beta (α, β).

Table 2-10. Average inhalation rate (m3/day)

Age group 

(yr) Mean SD

Percentiles

p5 p25 p50 p75 p95 p99

06-10 8.20 1.85 5.82 6.96 7.91 9.01 11.95 14.46

11-13 10.98 2.50 7.56 9.16 10.68 12.34 15.72 18.21

14-18 12.86 3.01 8.60 10.78 12.50 14.60 18.24 21.06

both PCB residues and PCBs bound to dust. We assumed 

children 11 years and older had no soil/dust ingestion due to 

lack of data, however, this is likely to result in only a very 

small underestimation in the total exposure for children 11-

18 years old. Direct dermal contact with and ingestion of 

caulk was also not included due to an absence of information 

on relevant contact rates and how much PCBs would be 

available for dermal transfer from caulk-bound PCBs.

Handling of Values Below the Quantifiable Limit

Quantifiable limits (QLs) for air were usually about 50 ng/

m3. The QL for soil was 0.5 mg/kg for most samples and 

0.1 μg/100 cm2 for all wipe samples. A value of one-half of 

the QL was substituted for samples with values <QL. The 

models were re-run using a substitution of zero for values 

<QL; overall model results were similar with those using 

one-half QL substitution. Only the model results using 

substitution of one-half of the QL are reported here.
23

3.0 

Quality Assurance and Quality Control

Quality assurance (QA) and quality control (QC) 

procedures were implemented for the NERL PCBs in 

schools measurement study by following the guidelines 

and procedures detailed in the project’s Quality Assurance 

Project Plan (QAPP), “PCBs in Schools - Field and 

Laboratory Data Collections.” Quality control samples 

were used where appropriate and available for assessing 

potential contamination of field sampling materials, and 

spiked samples were used to assess recovery of PCB from 

sampling materials. Duplicate samples were collected where 

appropriate to assess precision. Laboratory quality control 

and quality assurance procedures and analyses were used by 

NEA-Pace Analytical to ensure data of known quality were 

produced. Data quality reviews were conducted by EPA’s 

research contractor (Alion Inc.) and by EPA staff. The QA 

and QC results described below apply to the measurements 

made at one school by NERL and NERL contractors. 

Quality assurance and quality control procedures and results 

for the New York City School Construction Authority 

remedial investigation pilot study for the five New York City 

schools are documented in the remedial investigation plan 

that was developed by the SCA and TRC Engineers Inc. 

(NYC SCA, 2010) and in the remedial investigation reports 

(NYC SCA, 2011; NYC SCA 2012). Overall, the NYC 

SCA reported that a very high percentage of measurements 

(>99%) were found to be acceptable for use, and in almost 

all cases < 2% of the measurement data were reported to be 

qualified across multiple QA/QC criteria. The NYC SCA 

quality assurance and quality control results are not included 

in this report, the reader is referred to the original primary 

documentation (NYC SCA, 2011; NYC SCA 2012).

3.1 Quality Control Results for Aroclor Analysis

Field and laboratory quality control samples were prepared 

and analyzed to assess recovery of target chemicals and 

the recovery of surrogate compounds added to all samples 

prior to analysis, to evaluate the potential for contamination 

of sampling and analysis materials by target analytes, 

and to examine precision in sampling and analysis. The 

completeness of scheduled sample collection and analysis 

was also examined. This section describes results for 

analysis of PCB Aroclors. 

All of the air, surface wipe, dust, soil, and caulk samples 

scheduled for collection were successfully collected and 

analyzed for Aroclors (see Table 2-2). Three of the other 

building material samples scheduled for collection were 

not collected. These included samples of blackboard, 

whiteboard, and spray insulation. Over 98% of the scheduled 

samples were successfully collected and analyzed. 

Field controls were prepared for air and surface wipe samples 

by adding known amounts of Aroclor 1254 to sampling filters 

and wipes. Field controls were transported to the sampling 

site and then were transported and stored with the samples 

until extraction and analysis. Recovery of Aroclor 1254 from 

field controls is reported in Table 3-1. Average recoveries 

were 85.3 ± 11.0% for air filter media and 86.0 ± 8.9% for 

wipe media. Laboratory controls were prepared for air and 

surface wipes by adding known amounts of Aroclor 1254 

to sampling filters and wipes. Laboratory controls were 

prepared at the same time as the field controls, but then 

were stored at the analytical laboratory until extraction and 

analysis along with the samples and field controls. Recovery 

of Aroclor 1254 from laboratory controls is reported in Table 

Table 3-1. Aroclor analysis: recovery of Aroclor 1254 from field and laboratory controls

Spiking Field Controlsa Laboratory Controlsb

Level % Recovery % Recovery

Media N ng/sample Mean SD Mean SD

Air 3 1000 85.3 11.0 92.7 3.9

Surface Wipe 3 1000 86.0 8.9 112 0.6 

a Sample media were spiked, transported to and from the field site, and stored with samples until analysis. 

b

 Sample media were spiked at the same time as field controls and stored in the laboratory until analysis. 
24

3-1. Average recoveries were 92.7 ± 3.9% for air filter media 

and 112 ± 0.6% for wipe media. Laboratory matrix spikes 

were used to assess recovery of Aroclor 1254 from all media 

at the laboratory. Unused sampling media or surrogates of 

each type of sample matrix (PUF filter, wipe material, dust, 

soil, and caulk) was fortified with known amounts of Aroclor 

1254 and the matrix spike samples were extracted and 

analyzed along with the samples. Recovery of Aroclor 1254 

are reported in Table 3-2 and ranged from 98.5 ± 3.5% for 

air filter media to 108 ± 5.6% for caulk media. All recovery 

results met the 80 – 120% QAPP data quality objective.

The two surrogate standard compounds tetrachloro-metaxylene (TCMX) and decachlorobiphenyl (PCB Congener 

#209; DCBP) were added to every sample prior to analysis 

Table 3-2. Aroclor analysis: recovery of Aroclor 1254 from laboratory matrix spikesa

Spiking Spiking % Recovery

Media N Level Units Mean SD

Air 4 1000 ng/sample 98.5 3.5

Surface Wipe 5 12.5 μg/sample 106 13.0

Dust 2 0.245 - 1.25 μg/g 97.2 1.8

Soil 2 1.21 – 1.24 μg/g 96.4 6.3

Caulk 4 1.20 – 1.25 μg/g 108 5.6

Other Materials 4 1.24 – 1.25 μg/g 99.8 5.6

a Sample media were spiked in the laboratory immediately prior to extraction. 

Table 3-3. Aroclor analysis: recovery of surrogate analytes from samples and duplicate samplesa

Spiking Level TCMX DCBP

μg/sample % Recovery % Recovery

Media N TCMXb DCBPc Mean SD Mean SD

Air 11 0.025 0.25 82.1 6.8 87.6 5.8

Surface Wipe 44 0.25 2.5 94.2 6.5 99.3 13.0

Dustd 8 0.05 0.5 79.4 22.8 89.4 15.1

Soil 9 0.25 2.5 96.2 6.0 106 6.2

Caulkd 26 0.25 2.5 100 6.3 102 16.2

Other Materials 47 0.25 2.5 98.3 12.5 101 15.5

a

 Two surrogate analytes were spiked on every sample and duplicate sample prior to extraction.

b TCMX = Tetrachloro-meta-xylene. 

c

 DCBP = Decachlorobiphenyl (PCB Congener #209). 

d One dust sample and 12 caulk samples were diluted significantly for analysis; surrogate recoveries not available for these 

samples and are not included in the recovery statistics.

to assess recovery through the laboratory extraction and 

analysis process. Surrogate analyte recovery results are 

reported in Table 3-3. Average recoveries ranged from 82.1 

± 6.8% for TCMX in air samples to 106 ± 6.2% for DCBP 

in soil samples. Surrogate samples in one dust sample 

and in 12 caulk samples were diluted significantly and the 

surrogate standard recoveries could not be measured. One 

wipe sample, one caulk sample, and two materials samples 

had recoveries below the 60% acceptance level for DBCP, 

and one dust sample had a TCMX recovery lower than the 

acceptance level. In those samples, the analytical result for 

the other surrogate analyte met the acceptance level and the 

sample analysis results were accepted.
25

Field blanks were used to assess potential contamination by 

PCBs. Air filters and wipe sampling media were used along 

with PCB-free caulk and soil materials. The media were 

placed in the same type of containers used for samples. Field 

blanks were transported to the sampling site and then were 

transported and stored with samples until extraction and 

analysis. Field blank results are shown in Table 3-4. Mean 

total PCB concentrations on all types of field blanks were 

lower than the quantifiable limits (QLs). Laboratory blanks 

were prepared for air filters and surface wipes by storing 

unused media at the laboratory until extraction and analysis 

along with samples. Laboratory blank results are shown in 

Table 3-4. Mean total PCB concentrations were lower than 

the QL. Laboratory method blanks were prepared to assess 

potential PCB contamination in the extraction materials 

and methods through the instrumental analysis procedure. 

Unfortified extraction solvent was carried through the 

extraction and analysis procedures along with the samples. 

Laboratory method blank results are shown for all media in 

Table 3-5. Except for caulk, all laboratory blank results were 

lower than the QLs. For caulk the average measured total 

PCB values on the method blanks was 0.063 ± 0.092 μg/g 

which was slightly higher than the 0.05 μg/g QL. Overall, 

the background contamination levels as measured in field 

blanks, laboratory blanks, and laboratory method blanks 

were judged to have no impact on the use and interpretation 

of measurement results. Although the laboratory method 

blank result for caulk was slightly greater than the QL value, 

almost all caulk samples had concentrations well above the 

QL, and values of the samples of most interest had levels 

hundreds of times higher than that measured in the method 

blanks. Sample measurement results were not adjusted for 

background concentrations.

Table 3-4. Aroclor analysis: total PCBs measured on field and laboratory blanks

Field Blanksa Laboratory Blanksb

Media N QLc Units Mean SD Mean SD

Aird 3 50 ng/m3 3.1 0.58 10.5 12.1

Surface Wipe 3 0.1 μg/100 cm2 NDe ND 0.001 0.002

Dust 3 0.047 ppm 0.024 0.022 NPf NP

Soil 2 0.050 ppm 0.012 0.002 NP NP 

a Unfortified sample media, transported to and from the field site, and stored with samples until analysis. 

b

 Unfortified sample media, prepared at the same time as field blanks and stored in the laboratory until analysis. 

c

 QL = quantitation limit.

d

 Based on an assumed nominal air volume of 5.5 m3.

e

ND = not detected.

f

 NP = none prepared.

Table 3-5. Aroclor analysis: total PCBs measured in laboratory method blanks

Lab Method Blanksa

Media N QLb Units Mean SD

Airc 2 50 ng/m3 10.3 10.0

Surface Wipe 5 0.1 μg/100 cm2 0.027 0.028

Dust 2 0.047 μg/g 0.010 0.011

Soil 2 0.050 μg/g 0.003 0.004

Caulk 4 0.050 μg/g 0.063 0.092

Other Materials 4 0.050 μg/g 0.008 0.008 

a Unfortified solvent taken through extraction and analysis. 

b

 QL = quantitation limit.

c

 Based on an assumed nominal air volume of 5.5 m3.
26

Precision was examined using duplicate sample collection 

and analysis as well as the precision in measurements for 

various quality control samples and analyses. Precision 

results for duplicate sample collection and analysis are 

reported in Table 3-6. Results are reported as the relative 

percent difference (RPD). Duplicate air samples collected 

in close proximity provide a good measure of sampling and 

analysis precision because pollutants in indoor air collected 

side-by-side within a room are typically homogeneous. 

The mean RPD for duplicate air samples was 13.6 ± 

6.0% which met the precision QAPP objective of ± 20%. 

Duplicate sample collection for surface wipe, dust, soil, 

and caulk materials includes a sample non-homogeneity 

component in addition to sampling and analysis method 

precision. One of the purposes of this work was to provide 

information regarding variability of PCBs in these media 

in school buildings. The mean RPD ranged from 26.0 ± 

12.0% for surface wipes to 70.8 ± 31.1% for dust. The 

dust collected for duplicate samples was collected in the 

same room, but obtaining sufficient sample sizes required 

vacuuming multiple surfaces in the room. The results 

suggest the potential for considerable variability in dust PCB 

concentrations for different locations within the same school 

room. 

Table 3-6. Aroclor analysis: precision results for 

duplicate sample measurementsa

Media N

Relative % Differenceb

Mean SD

Air 3 13.6 6.0

Surface 

Wipe 3 26.0 12.0

Dust 2 70.8 31.1

Soil 2 43.0 38.5

Caulk 9 29.8 18.8

Other 

Materialsc 0 --- ---

a Duplicate sample results serve as an indicator of 

measurement precision for relatively uniform media 

such as air; for other media the duplicate sample results 

include elements of both measurement precision and nonhomogeneity of analytes in the environment. 

b

 Relative % difference calculated as

 2 × (|X1 – X2|)/(X1 + X2) × 100.

c

 No duplicate samples collected.

3.2 Quality Control Results for 

 Congener Analysis

All of the indoor air samples, the outdoor air sample, and a 

subset of the remaining sample extracts previously analyzed 

for Aroclors were scheduled for congener-specific analysis 

and were successfully analyzed. 

Recovery of the sum of the congeners in Aroclor 1254 in 

the air filter media field controls is reported in Table 3-7. 

Average recoveries were 85.9 ± 12.3%. Laboratory matrix 

spike recovery results are reported in Table 3-8. Recovery 

of the sum of the congener concentrations in Aroclor 1254 

congeners ranged from 91.1% for caulk to 112% for dust. 

Recoveries of the surrogate compounds TCMX and DCBP 

are shown in Table 3-9 and ranged from 72.6 ± 28.6% for 

caulk TCMX to 153 ± 13.2% for DCBP in dust samples. The 

remaining average recovery results ranged from 81 to 117%.

Air media field blank results are reported in Table 3-10. No 

congeners were measured in the air field blanks at detectable 

levels (the congener QL was 0.5 ng/sample). Laboratory 

method blank results are shown in Table 3-11. Results were 

below detectable levels for all congeners for the air, surface 

wipe, dust, soil, and other materials blanks. The method 

blank value for the sum of measured congeners in the single 

caulk method blank was 0.03 μg/g which was far below the 

total PCB concentrations measured in most caulk samples. 

Precision was examined using duplicate samples collected 

for air and caulk. Precision results for duplicate sample 

collection and analysis are reported in Table 3-12. The 

average RPD for three air sample duplicates was 17.9 ± 4.5% 

while the RPD for the single caulk duplicate sample analyzed 

for congeners was 41.7%.

Table 3-7. Congener analysis: total recovery of 

congeners in Aroclor 1254 from field controls

Spiking 

Level

ng/

sample

Field Controlsa

% Recovery

Media N Mean SD

Air 3 1000 85.9 12.3

a Sample media were spiked, transported to and from the 

field site, and stored with samples until analysis. 
27

Table 3-8. Congener analysis: total recovery of congeners in Aroclor 1254 from laboratory matrix spikesa

Spiking Spiking % Recovery

Media N Level Units Mean SD

Air 4 1000 ng/sample 96.1 2.6

Surface Wipe 2 12.5 μg/sample 94.2 24.1

Dust 1 0.245 μg/g 112 ---

Soil 2 1.21 μg/g 95.0 7.0

Caulk 1 1.23 μg/g 91.1 ---

Other Materials 1 1.25 μg/g 95.2 ---

a Sample media were spiked in the laboratory immediately prior to extraction. 

Table 3-9. Congener analysis: recovery of surrogate analytes from samples and duplicate samplesa

Spiking Level

μg/sample

TCMX

% Recovery

DCBP

% Recovery

Media N TCMXb DCBPc Mean SD Mean SD

Air 11 0.025 0.25 93.1 6.9 104 10.3

Surface Wipe 10 0.25 2.5 81.0 5.1 85.0 4.2

Dust 4 0.05 0.5 103 3.2 153 13.2

Soil 3 0.25 2.5 94.9 1.0 109 12.7

Caulkd 5 0.25 2.5 72.6 28.6 88.2 27.5

Other Materials 18 0.25 2.5 93.3 8.7 117 20.1 

a

 Two surrogate analytes were spiked on every sample and duplicate sample prior to extraction.

b TCMX = Tetrachloro-meta-xylene. 

c

 DCBP = Decachlorobiphenyl (PCB Congener #209). 

d Four caulk samples were diluted significantly for analysis; surrogate recoveries not available for these samples and are not 

included in the recovery statistics.

Table 3-10. Congener analysis: total PCBs measured on field blanks

Field Blanksa

Media N QLb Units Mean SD

Air 3 0.5 ng/m3 NDc ---

a Unfortified sample media, transported to and from the field site, and stored with samples until analysis. 

b

 QL = quantitation limit.

c

 ND = not detected.
28

Table 3-11. Congener analysis: total PCBs measured in laboratory method blanks

Lab Method Blanksa

Media N QLb Units Mean SD

Air 2 0.5 ng/m3 NDc ---

Surface Wipe 2 0.0025 μg/100 cm2 ND ---

Dust 1 0.00025 ppm ND ---

Soil 2 0.00125 ppm ND ---

Caulk 1 0.00125 ppm 0.030 ---

Other Materials 4 0.00125 ppm ND ---

a Unfortified solvent taken through extraction and analysis. 

b

 QL = quantitation limit.

c

 ND = not detected.

Table 3-12. Congener analysis: precision results for duplicate sample total PCB measurementsa

Relative % Differenceb

Media N Mean SD

Air 3 17.9 4.5

Caulk 1 41.7 ---

a Duplicate sample results serve as an indicator of measurement precision for relatively uniform media such as air; for other 

media the duplicate sample results include elements of both measurement precision and non-homogeneity of analytes in the 

environment. 

b

 Relative % difference calculated as 2 × (|X1 – X2|)/(X1 + X2) × 100 

3.3 Quality Assurance Assessments

Quality assurance assessments of field and laboratory data 

collection and analyses were performed at multiple levels. A 

summary of reviews and outcomes is provided below.

• On-site QA assessment of field sampling procedures and 

adherence to protocols was performed. Air sampling flow 

rate measurements were verified using audit flow devices. 

Corrective action was taken when it was determined that 

air sampling pumps were not being started at targeted 

flow rates. 

• All field sampling data were QA reviewed to ensure 

accuracy and completeness. Corrective action was taken 

for three air samples that did not have the correct total 

sampled air volume calculated correctly. The correct 

air volume data were applied to Aroclor and congener 

analysis results.

• The analytical laboratory performed ongoing review 

of calibration, continuing calibration checks, QC 

recovery and background assessments, and instrumental 

performance parameters. Two groups of sample extracts 

were reanalyzed based on data quality review. 

• Analytical laboratory results were first QA reviewed by 

NERL’s contractor, and then again by NERL scientists 

to ensure that the results were complete and accurate. 

Quality control results were summarized and examined to 

ensure overall data quality objectives were met.

• NRMRL experts reviewed the caulk/materials emission 

model and model calculations. 

• A NERL QA review was performed to ensure that the 

measurement data were accurately transcribed into 

data analysis files and that calculations were correctly 

performed. Transcription accuracy into the report tables 

was also assessed. 
29

4.0

Results

4.1 School Information 

Basic information regarding the six schools with PCB 

measurement results used in this report is shown in Table 4-1. 

Five of the schools were primary schools and two of those 

contained pre-kindergarten classes. The sixth school was a 

secondary school. The school buildings were constructed 

between 1959 and 1972 and all but one had 3 floors. The 

secondary school had multiple wings with 2 to 3 floors per 

wing. Only one school (School 1) had heating, ventilation, 

and air conditioning (HVAC) forced air units serving the 

entire building. For the other five schools, different types of 

heating and ventilation systems were present in the schools 

including exhaust ventilation ducts only, single room unit 

ventilators, and zoned heating/ventilation systems for specific 

spaces such as gymnasiums. These schools did not have 

building-wide air conditioning systems and in many cases 

relied on natural ventilation, including opening windows, 

in warmer months. Several schools had window air 

conditioners in some rooms.

Table 4-1. School building information

School

Grade

Levels

Year

Constructed Floors Ventilation System(s)

1 K-5/PK-12a 1972 3 - 6 HVAC units serving different building zones

- Window glazing previously replaced

2 K-5 1962 3

- Classroom/bathroom exhaust systems with 19 roof exhaust fans

- Separate H/V systems for gymnasium and auditorium

- Window-mounted AC units in most classrooms

- Windows/frames previously replaced

3 PK-5/K-1 1963 3

- Classroom/bathroom exhaust systems with 15 roof exhaust fans

- Basement fans provide H/V for gymnasium and auditorium

- Window-mounted AC units in most classrooms

- Windows/frames previously replaced

4 PK-5

1959

Additions

1968,2005

1

- Exhaust systems vented to roof in older building areas

- Office window-mounted AC units

- 3 HVAC units in new construction

5 PK-8 1961 3

- Four HVAC systems - not operable

- 11 roof exhaust fans

- 10 window and 10 portable AC units

6 9-12 1968 2 to 3 

- Unit ventilators in classrooms and cafeteria

- H/V systems in auditorium and gymnasium

- Several window AC units

a

 K = kindergarten; PK = pre-kindergarten
30

Five schools were part of the NYC remedial pilot 

investigation while the sixth school was scheduled for 

demolition and had no measurements associated with 

remedial activities. Information regarding the sampling 

time points and different remedial and sampling activities is 

summarized in Table 4-2. Much more detailed information 

regarding remedial activities and outcomes at the five NYC 

schools is provided in the NYC SCA remedial investigation 

reports (NYC SCA 2011; NYC SCA 2012). The remedial 

investigation is ongoing and further information will be 

available in the future (http://www.nycsca.org/Community/

Programs/EPA-NYC-PCB/Pages/default.aspx). The 

measurement time points that were used in SHEDS exposure/

dose modeling are shown in Table 4-2. It is important to 

recognize that different types of remedial actions were taken 

at the different school buildings, and that caulk remediation 

completed during the first year at Schools 1, 2, and 3 only 

occurred in the rooms or transitory areas to be sampled. 

The conditions at each of the schools at the measurement 

time points used in the SHEDS exposure/dose modeling 

are shown in Table 4-3. All of the measurements at these 

time points occurred between late spring and early fall, with 

outdoor temperatures ranging from 69 to 94°F and indoor 

temperatures ranging from 70 to 84°F. The status of the 

operation of ventilation systems and whether doors and 

windows were open or closed in the measurement rooms 

is also reported in Table 4-3. At School 1, it was learned 

following the post-remedial sampling time points that the 

HVAC system controllers were not operating correctly on 

all units and that exterior air had not been incorporated 

at the designed ventilation rates. This may have been a 

factor in indoor air PCB concentrations at that school. The 

gymnasium heating/ventilation system at School 6 was not 

operating during the indoor air sample collection period 

and the lack of forced ventilation with outdoor air may have 

impacted indoor air concentrations in the gym. 
31

Table 4-2. School building remediation activity and environmental sampling summary

School Activity Additional Information Date Samplesa

SHEDSb

Modeling

1 Pre-remediation July 2010 A, W, S Yes

Caulk patch and repair Sampled spaces only July 2010 A, W

Ventilation with outdoor air 24-hr high vol., filtered Aug 2010 A

Soil cover and access restriction >1 ppm PCB Aug 2010

Cleaning & light fixture removal Pre-K, K only Aug 2010 A Yes

Supplemental cleaning Aug 2010 A

HVAC evaluation and repair Sept 2010 A

Pre-remediation June 2011 A,W,S Yes

Encapsulate exterior caulk July 2011

Soil removal/replacement >1 ppm PCB Aug 2011

Caulk patch and repair Whole building Aug 2011 A,W

2 Pre-remediation July 2010 A,W,S Yes

Caulk removal Sampled spaces only Aug 2010 A,W

Ventilation with outdoor air 24-hr high vol., filtered Aug 2010 A

Soil cover and access restriction >1 ppm PCB Aug 2010

Heating/ventilation cleaning Whole building Aug 2010

Cleaning & light fixture removal Whole building Aug 2010 A Yes

Caulk encapsulation Whole building Sept 2010 A

Exterior caulk removal Apr 2011

Pre-remediation June 2011 A,W Yes

Soil removal/replacement >1 ppm PCB Aug 2011

Caulk removal Whole building Aug 2011 A,W

3 Pre-remediation July 2010 A,W,S Yes

Caulk encapsulation Sampled spaces only Aug 2010 A,W

Ventilation with outdoor air 24-hr high vol., filtered Aug 2010 A

Soil cover and access restriction >1 ppm PCB Aug 2010

Heating/ventilation cleaning Whole building Aug 2010

Cleaning & light fixture removal Whole building Aug 2010 A Yes

Pre-remediation June 2011 A,W Yes

Soil removal/replacement >1 ppm PCB July 2011

Caulk encapsulation Whole building Aug 2011 A,W

Re-Cleaning Selected rooms Aug 2011 A

4 Pre-remediation May 2011 A,W,S Yes

Soil cover and access restriction >1 ppm PCB 2011

Light fixture removal Whole building Aug 2011 A,W

Caulk encapsulation One stairwell Sept 2011 A Yes

5 Pre-remediation June 2011 A,W,S Yes

Soil cover and access restriction >1 ppm PCB 2011

Remove/replace windows Selected areas w old windows Aug 2011 A, W

Re-cleaning Sampled spaces Sept 2011 A,W Yes

6 Pre-demolition July 2011 A,W,S,D Yes

a

 A = air samples, W = surface wipe samples, S = soil samples, D = dust samples.

b

 SHEDS exposure modeling using data at this time point.
32

Table 4-3. School building information at sampling time points used in exposure modeling

School

Sample Time 

Point

Sample

Date

Average

Indoor

Temp. °F

Outdoor

High

Temp. °Fa Conditions

1 Pre-Remediation July 2010 74 87 - HVAC systems operational

- Windows and doors closed

Post-Remediation

(Caulk patch/repair & 

light removal)

Aug 2010 70 69

- HVAC systems operational

- Outdoor air dampers found closed in some units

- Windows and doors closed

 Pre-Remediation June 2011 74 72 - HVAC systems operational

- Doors closed

2 Pre-Remediation July 2010 83 94 - Exhaust systems operational; window ACs off

- Windows and doors closed

Post-Remediation

(Caulk removal & light 

removal) 

Aug 2010 76 79 - Exhaust systems operational; window ACs off

- Windows opened; doors closed

 Pre-Remediation June 2011 77 83 - Exhaust systems operational

- Doors closed, windows opened slightly

3 Pre-Remediation July 2010 77 83 - Exhaust systems operational; window ACs off

- Windows and doors closed

 Post-Remediation

(Caulk encapsulation & 

light removal)

Aug 2010 74 82 - Exhaust systems operational; window ACs off

- Windows opened; doors closed

 Pre-Remediation June 2011 78 80 - Exhaust systems operational

- Doors closed, windows opened slightly

4

 Pre-Remediation

(Light removal) May 2011 83 84

- Exhaust systems operational; window AC units on

- Windows opened slightly where no AC units

- Doors closed

 Post-Remediation Aug/Sept 

2011 72 80/78

- Exhaust systems operational; window AC units on

- Windows opened slightly where no AC units

- Doors closed

5 Pre-Remediation May 2011 84 79 - Exhaust systems operational

- Doors closed, windows opened slightly

 Post-Remediation

(Window replacement)

Aug/Sept 

2011 79 84/83

- Exhaust systems operational; window AC units on

- Windows opened slightly where no AC units

- Doors closed

6 Pre-Demolition July 2011 81 86/91

- Room unit ventilators operating

- Gymnasium H/V not operating

- Doors and windows closed; lights on

a

 Outdoor air high temperature on sampling day(s) from a nearby National Weather Service reporting station.
33

4.2 PCB Source Characterization 

4.2.1 Caulk

Between the 1950s and early 1970s PCBs were sometimes 

added to caulk and other sealants as a plasticizer. In some 

cases PCBs were added to caulk at the construction site 

to improve its application properties. PCB-containing 

sealants have been shown to be present in buildings, and 

buildings with PCB-containing sealants have higher indoor 

air PCB concentrations than other buildings. A total of 

427 samples of interior caulk and other sealants such as 

window glazing and building joint material were collected 

at five of the six schools. A total of 73 samples of exterior 

caulks and sealants were collected at three of the schools. 

A summary of total PCB measurement results is reported 

in Table 4-4. Measurement results reported in Table 4-4 

were divided into samples with values greater than or less 

than 100 ppm because only a few samples had measurement 

results between 100 and 1000 ppm, and very few had values 

between 100 and 200 ppm. The analysis laboratory reported 

that Aroclor 1254 was the PCB mixture best matched in most 

of the caulk samples, with Aroclor 1260 reported for some 

caulk samples at School 1. The median concentration for 

interior caulks with values <100 ppm was 6.9 ppm, while 

the median level for caulks with >100 ppm was 102,000 

ppm (or about 10% PCBs by weight). The highest measured 

concentration in indoor caulk was 440,000 ppm (44% PCBs 

by weight) from caulk around a school display case. In 

exterior caulks the median for <100 ppm caulks was 5.9 ppm 

and for >100 ppm caulks the median was 130,000 ppm. 

The highest concentration measured in exterior caulk was 

328,000 ppm. 

Caulk total PCB measurement results are shown separately 

by school for interior and exterior caulks in Table 4-5. 

When considering caulk measurement results from these 

schools it is important to note that multiple samples of the 

same kind of caulk may have been collected from different 

locations in and around the building. Schools 1 and 6 had 

lower levels of PCBs in interior caulks than the other three 

schools. The median interior caulk value for School 1 was 

309 ppm for samples with >100 ppm, while School 6 did 

not have any interior caulks with >100 ppm. At the other 

three schools the median interior caulk values for caulks 

with >100 ppm ranged from 127,000 to 232,000 ppm. The 

maximum value measured at School 1 was 90,700, which 

was at least 60% lower than the maximum levels at Schools 

2, 3, and 4. For three schools with exterior caulks, median 

total PCB concentrations for caulks with <100 ppm ranged 

from 3.5 to 19 ppm. Median values ranged from 77,500 to 

193,000 for caulks with >100 ppm. 

Another way to show the range of caulk PCB concentrations 

is by concentration category (Table 4-6). Over 82% 

of the 427 interior caulk samples had concentrations 

<50 ppm while 6% had concentrations greater than 

100,000 ppm. Only 37% of the exterior caulk samples had 

concentrations <50 ppm while 41% had concentrations 

Table 4-4. Caulk total PCB measurement results for schools with available data

Total PCB Levels in Caulka,b

N

Schools

N

Samples

%

> QLc

Median

ppm

Inter-Quartile 

Range

ppm

Overall 

Range

ppm

Interior Caulks

 < 100 ppm 5d 375 86 6.91 2.90 - 17.4 <QL - 88.9

 > 100 ppm 5 52 100 102,000 2,110 - 233,000 103 - 440,000

Exterior Caulks

 < 100 ppm 3e 27 96 5.88 2.16 - 9.94 <QL - 45.0

 > 100 ppm 3 46 100 130,000 3,870 - 248,000 126 - 328,000 

a Reported as total PCBs from Aroclor measurements. 

b When duplicate samples were collected, the average of the duplicates was used.

c QL = quantitation limit; sample size dependent, typically ≤ 1 ppm (range 0.3 – 79 ppm). 

d

 Schools 1,2,3,5,6.

e

 Schools 4,5,6.
34

>100,000 ppm. Caulk concentrations by category are 

shown for individual schools in Appendix B. Insufficient 

information was available for most of the schools to 

determine how many discrete types of caulk were present at 

each school. Approximately six different kinds of interior 

caulk, window glazing, and joint material were found at 

School 6 across the seven rooms that were sampled, with 

most of those appearing in multiple rooms. Two types of 

exterior caulk were found at School 6 where 8 samples 

collected from around windows and in building joints 

contained >80,000 ppm of PCBs and were likely the same 

material while a ninth exterior sample from an entranceway 

brick/masonry seam contained 19 ppm. 

None of the interior caulks at School 6 (which included 

caulk, window glazing, and joint material) contained high 

levels of PCBs. However, no sealants were found in the 

hallways and stairwells. In the seven rooms that were 

sampled, at least one sample of every type of sealant present 

in the room was collected, and it appeared that the materials 

that were collected were common in rooms throughout the 

building. Given the number of sealants present inside large 

school buildings it is possible that a interior sealant with 

high PCB levels was not collected, but it is unlikely that 

the sampling failed to identify one that was widely used in 

accessible areas throughout the building. 

All of the caulk samples with high PCB levels collected 

in NERL studies were still at least somewhat flexible and 

largely intact. All of the brittle or dry caulk and other sealant 

materials had levels of PCBs <50 ppm. However, intact and 

flexible caulks and other sealants were found that also had 

<50 ppm PCBs. It is difficult to determine whether a caulk 

or other sealant is likely to contain high levels of PCBs, 

Table 4-5. Caulk total PCB measurement results by school

Total PCB Levels in Caulka,b

N % > QLc

Median 

ppm

Inter-Quartile 

Range ppm

Overall Range 

ppm

Interior Caulks <100 ppm

 School 1 97 62 1.78 <QL - 6.20 <QL - 64.6

 School 2 101 95 10.9 5.33 - 34.6 <QL - 88.9

 School 3 106 93 9.33 5.34 - 15.7 <QL - 43.6

 School 5 51 92 5.84 2.55 - 16.8 <QL - 83.1

 School 6 20 100 11.5 4.95 - 23.5 1.99 - 67.7

Interior Caulks >100 ppm

 School 1 14 100 309 204 - 19,300 114 - 90,700

 School 2 12 100 127,000 83,000 - 151,000 103 - 243,000

 School 3 14 100 217,000 82,600 - 284,000 1,430 - 440,000

 School 5 12 100 232,000 138,000 - 264,000 1,600 - 306,000

 School 6 0 -- -- -- --

Exterior Caulks <100 ppm

 School 4 8 100 3.50 2.07 - 5.51 1.68 - 29.6

 School 5 18 94 6.62 2.34 - 10.7 <QL - 45.0

 School 6 1 100 19.1 -- --

Exterior Caulks >100 ppm

 School 4 7 100 77,500 5,690 - 91,100 126 - 226,000

 School 5 31 100 193,000 2,560 - 288,000 319 - 328,000

 School 6 8 100 138,000 118,000 - 144,000 84,400 - 152,000 

a Reported as total PCBs from Aroclor measurements. 

b When duplicate samples were collected, the average of the duplicates was used.

c QL = quantitation limit; sample size dependent, typically ≤ 1 ppm (range 0.3 – 79 ppm). 
35

Table 4-6. Interior and exterior caulk and window glaze total PCB measurement results by concentration category

Concentration 

Category

Interior Caulk and Window 

Glaze

Five Schoolsa

Exterior Caulk and Window Glaze

Three Schoolsb

Number of Samples

All samples 427 73

< 50 ppm 351 27

50 – 999 ppm 33 5

1,000 – 9,999 ppm 6 11

10,000 – 99,000 ppm 11 5

100,000 – 199,999 ppm 10 9

200,000 – 299,999 ppm 14 11

300,000 – 399,999 ppm 1 5

400,000 – 499,999 ppm 1 0

Percentage of Samples

< 50 ppm 82.2 37.0

50 – 999 ppm 7.7 6.8

1,000 – 9,999 ppm 1.4 15.1

10,000 – 99,000 ppm 2.6 6.8

100,000 – 199,999 ppm 2.3 12.3

200,000 – 299,999 ppm 3.3 15.1

300,000 – 399,999 ppm 0.2 6.8

400,000 – 499,999 ppm 0.2 0

a

 Schools 1,2,3,5,6.

b

 Schools 4,5,6.

particularly when that material is still somewhat flexible. 

Sampling and analysis in a laboratory is the only sure way to 

know at this time. Development of field-portable screening 

methods would allow more rapid characterization of sealants 

in buildings. For example, hand-held x-ray fluorescence 

(XRF) devices could be evaluated for their ability to detect 

PCBs in materials such as caulk in-situ, but issues regarding 

caulk dimensions and interferences or materials that would 

give false positives would need to be examined.

A total PCB emission modeling approach was used to 

estimate PCB emissions from caulk collected at several 

locations at two schools. The total concentration of PCBs 

in caulk was measured as Aroclors and information was 

available for the caulks at these locations for calculation of 

their total surface area. The method used to estimate the 

total PCB emission rate was described in Section 2, and 

assumes that the PCB content in the caulk is equivalent to 

the congener mixture in Aroclor 1254, which was the Aroclor 

reported by the analysis laboratory. 

Emission rate estimates shown in Table 4-7 are based on the 

emission parameters derived from chamber tests of different 

caulk samples at 23°C. Estimates of total PCB emission 

rates from several caulks collected in the gymnasium and 

cafeteria ranged from 140 to 600 μg/hr. Estimates from three 

types of caulk collected in a third floor corridor at School 

2 ranged from 53 to 3100 μg/hr. Estimates of emissions 

from caulk at exterior window locations at two classrooms 

at School 6 ranged from 830 to 940 μg/hr. These window 

locations included caulk around the window frame, around 

the concrete/brick seams below the window, and from around 

the unit ventilator air intake grill. Finally, a total PCB 

emission rate of 320 μg/hr was estimated for caulk collected 

from a single 2-story building joint at School 6. Graphical 

representations of the relative emissions for several of the 

sets of caulk are shown in Figure 4-1. There are uncertainties 

in these estimates because it is not known if the emission 

parameters for the measured caulks match those tested in the 

chamber (although emission parameters for the 12 caulks 

that were tested were consistent). Also, the actual emission 
36

rate is likely to depend on the temperature. Guo et al. (2011) 

found in chamber testing that emission rates increased by 

approximately 6-fold with a 10°C increase in temperature. 

The temperature of caulk on the exterior of a building may 

also become higher than ambient temperature due to radiant 

heating from the sun and/or, in the winter because of heat 

from the building.

The emission rates estimated for caulks at four of the building 

locations (Table 4-7) were used to generate screening level 

estimates of the indoor air PCB concentrations that could 

potentially result under different assumed conditions of 

ventilation with outdoor air. The approach for calculating 

screening-level PCB indoor air concentration estimates was 

described in Section 2, using Equation 2.1.6. Numerous 

assumptions apply to this estimation approach, and results 

reported in Table 4-8 are considered to be only screeninglevel estimates. One important factor is the room air 

exchange rate. 

While recognized as important for indoor pollutant 

modeling, accurate measurement of ventilation conditions 

including both indoor/outdoor air exchange and interzonal flows in large old buildings – and for specific rooms 

within those buildings - is difficult. Information on this 

approach and the conditions that must be met to do this 

have been described (Persily, 1997) and an ASTM method 

has been developed, Standard Guide for Using Indoor 

Carbon Dioxide Concentrations to Evaluate Indoor Air 

Quality and Ventilation, ASTM D6245-07. A central tenet 

of the procedure for producing accurate ventilation rate 

information, whether using decay or equilibrium procedures, 

is that the space to be evaluated is a single zone wherein the 

tracer concentration is uniform and that only exchanges air 

with the outdoors. By definition, the tracer concentration 

must not differ by more than 10% from the average across all 

locations in the building or building zone. For spaces that do 

not meet the 10% criterion, it must be demonstrated that there 

is no significant airflow from other building spaces, such as 

hallways or other rooms, into the test space. It is important 

that the indoor pollutant of interest be measured at the same 

time that AER is being measured since ventilation rates in a 

room can change rapidly under different ventilation system 

and door/window use conditions. 

Other researchers have measured air exchange rates 

(AERs) in school building rooms and a wide range of AERs 

have been reported, ranging from less than 0.5 to 12 hr-1

(MacIntosh et al., 2012; Nazaroff et al., 2010; Godwin and 

Batterman, 2007; Bartlett et al., 2004; Scheff et al., 2000). 

AER measurements have been based on measurements 

of ventilator airflow rates, equilibrium or decaying CO2, 

or SF6 decay. These methods allow an assessment of the 

Table 4-7. Estimates of total PCB emission rates for several examples of PCB-containing caulk

School Room Material

Linear 

Length of 

Caulk

m (ft)

Surface 

Area

m2

Total PCB

ppm

Estimated 

Emission Ratea

μg/hr

2 Gymnasium Interior Door Frame Caulk 33.5 (110) 0.1431 117,000 460

2 Gymnasium Interior Bay Door Caulk 5.2 (17) 0.0442 137,000 160

2 Cafeteria Door Frame Caulk 25.6 (84) 0.219 57,100 340

2 Cafeteria Metal Door Frame Caulk 23.2 (76) 0.198 112,000 600

2 Cafeteria Bay Door Frame Caulk 4.3 (14) 0.036 146,000 140

2 Corridor Interior Door Caulk 110 (360) 0.468 243,000 3,100

2 Corridor Interior Wall Panel Caulk 3.7 (12) 0.0468 217,000 280

2 Corridor Interior Metal Panel Caulk 2.7 (9) 0.0117 165,000 53

6 Classroom 3 Exterior Window Caulkb 28.3 (93) 0.271 112,000 830

6 Classroom 4 Exterior Window Caulkb 29.9 (98) 0.286 120,000 940

6 N/A Exterior Joint Caulkc 6.4 (21) 0.0813 142,000 320

a

 Based on PCB emission parameters derived from chamber measurements of other caulks at 23°C.

b

 Calculated using entire length of caulk around both window frame units, the single unit ventilator inlet, and concrete-brick seams below 

both windows. 

c Calculated for one joint that was 2 stories high. There were multiple joints around the building exterior.
37

Figure 4-1. Estimated total PCB emission rates from caulk in several building locations 
38

Table 4-8. Screening-level comparison of predicted air concentrations resulting from PCB emissions from caulk to 

measured concentrations

Rooma

Room

Volume

m3

Estimated 

Emission 

Rateb

μg/hr

% 

Emissions

Entering

Roomc

Predicted Total PCB Air 

Concentration in Rooms at Different 

Air Exchange Ratesd

ng/m3

Measured

Total 

PCBs

In Room 

Aire

ng/m3

AER = 

0.5

AER = 

1.0

AER = 

2.0

AER = 

4.0

Gymnasium 2666 620 100 460 230 120 58 1360

Cafeteria 1134 1080 100 1900 950 480 240 600

Classroom 3 201 830 1 83 41 21 10 950

5 410 210 100 52

10 830 410 210 100

Classroom 4 182 940 1 100 52 26 13 690

5 520 260 130 65

10 1000 520 260 130

a

 Gymnasium and cafeteria at School 2; Classroom 3 and Classrom 4 at School 6.

b

 Estimated total PCB emission rates from caulk based on PCB emission parameters derived from chamber measurements of 

other caulks at 23°C.

c

 Percent of the total PCB emissions entering the room air. For the two classrooms, the PCB-containing caulk was around the 

two exterior windows, one exterior unit ventilator intake grill, and exterior concrete/brick joints below the windows. Values 

of 1%, 5%, and 10% of the PCB emissions entering the classrooms from the exterior caulk were used. For the gymnasium 

and cafeteria, all caulk was located inside the rooms and a value of 100% was used. 

d Predicted air concentrations at several possible air exchange rates for the room. Calculations based on the estimated 

PCB emission rate and ventilation rate (see Eq. 2.1.6). Assumes 23°C, steady state emission and ventilation conditions, 

steady-state absorption/desorption from other room materials, complete room air mixing, the concentration of PCBs in the 

ventilation air is zero, caulk is the only source of PCBs, and that the PCB mixture in the caulk is equivalent to Aroclor 1254. 

e

 Air samples were collected under different temperature conditions; gym and cafeteria @ 28°C; classrooms 3 and 4 @ 

27°C. The exterior temperature for classrooms 1 and 2 ranged from an overnight low of 19°C and a daytime high of 33°C. 

The estimated emission rates and predicted room air concentrations may be underestimates relative to actual conditions 

because they are based on emission parameters generated at 23°C.
39

overall ventilation rate for a room, but do not always provide 

accurate information regarding how much of the total 

ventilation air flow is occurring between the room being 

assessed and other building spaces. This is important for 

indoor pollutants such as PCBs, which can be generated 

in other building spaces and transported between building 

spaces – it can’t be assumed that the ventilation air coming 

into a room from other interior building spaces is PCB-free. 

It is also important to recognize that AERs for a room 

can change rapidly depending on changes in operation of 

mechanical ventilation systems and opening and closing of 

doors and windows. 

Actual ventilation rates were not measured in the schools 

included in this report at the time of sample collection, and 

ventilation rates may change substantially under different 

conditions of mechanical ventilation system operation, 

window and door opening, human activity, and temperature. 

Screening-level estimates were prepared using air exchange 

rates (AER) of 0.5, 1.0, 2.0, and 4.0 air changes per hour 

(assuming that all of the ventilation air was from outdoors 

with low/no PCBs). For comparison, current ANSI/

ASHRAE standards call for 7.4 L/s (15 cubic feet per minute) 

per person for outdoor air ventilation rates in classrooms with 

5 – 8 year old children; giving an overall AER with outdoor 

air of 3.3 ach for 25 people in a 200 m3

 classroom. Forty- to 

fifty-year-old school buildings may not meet current ANSI/

ASHRAE standards. The gymnasium described in Table 

4-8 had a ventilation system design AER of 4.33 and had 

measured AERs of 1.75 and 1.52 using ventilator airflow 

measurement and tracer decay measurement, respectively 

(NYC SCA, 2012). Measurement-based values are not 

available for the other three rooms used in this screeninglevel example, but it is anticipated that the range of 0.5 – 4.0 

AER will cover the range of likely ventilation rates under 

most use conditions. Thus, screening level estimates were 

generated for a range of possible AER. 

Exterior caulk with high PCB levels was present at two 

windows for each of two of the classrooms in Table 4-8. 

These classrooms had no interior caulk with high PCB 

levels. It is not known what fraction of the PCBs emitted 

from outdoor installations will enter indoor spaces. The 

amount is likely quite variable depending on the location and 

extent of the PCB-containing caulk, the temperature, wind 

speed, whether windows are open or closed, whether caulk 

is present at ventilation system intake openings, and the 

operation of the ventilation system. In this screening-level 

estimation exercise, indoor air concentrations were estimated 

using a range of penetration rates including 1, 5, and 10% of 

total PCB emissions. 

Screening-level estimates of indoor air total PCB 

concentrations under different AER conditions are shown in 

Table 4-8 for four rooms. A wide range of possible indoor 

air PCB concentrations resulting from emissions from PCBcontaining caulk were found under the range of assumed 

AER and penetration conditions. Indoor air concentrations 

measured at one time point are shown for comparison. In 

general, measured concentrations were consistent with 

indoor air levels that might be present from caulk emissions 

at the Cafeteria and Classroom 4. Measured concentrations 

were higher than the largest estimated concentrations for the 

Gymnasium and Classroom 3. There are many assumptions 

in these estimates and the results can be considered only 

screening-level. Reasons for the differences could include 

higher room temperatures as compared to chamber testing 

temperatures, higher penetration rates from exterior caulk 

emissions, the presence of other sources (such as emissions 

from light ballasts or fixtures), or ventilation from more 

highly contaminated spaces within the building. 

4.2.2 Light Ballasts

Fluorescent light fixtures installed in buildings prior to 

the late 1970s often used ballasts with PCB-containing 

capacitors. Some of those fixtures and ballasts may remain in 

school buildings today. PCB emissions from intact ballasts 

have been demonstrated in laboratory chamber studies and 

have been shown to be highly temperature dependent (Guo et 

al., 2011; Hosomi et al., 2005). PCB-containing ballasts are 

likely to have exceeded their expected operational lifetime, 

and capacitors in ballasts can fail with PCBs rapidly released 

into the building environment. One ballast capacitor burst 

during laboratory chamber emissions testing, resulting in 

very high levels of PCBs in the air and on chamber surfaces 

(Guo et al., 2011). Fluorescent light ballasts may be 

important sources of PCBs in building environments. 

Light ballast capacitors for schools of this age are most 

likely to contain Aroclor 1242 or Aroclor 1016, which means 

that emissions of more volatile, lower chlorine-number 

congeners would be expected to be released into the school 

environment. However, use of Aroclors 1221 and 1254 in 

capacitors was also reported (U.S. EPA, 1976). Capacitor 

oil was analyzed in three ballasts tested by Guo et al. (2011) 

and all were identified as Aroclor 1242. NYC SCA analysis 

of school light ballast capacitor oil was analyzed showed 

that Aroclor 1242 was present in the single ballasts tested 

from Schools 1, 3 and 4, while Aroclor 1254 was present in 

a ballast from School 2. Ballast capacitor oil measurements 

were not available for Schools 5 and 6.

Survey results are available for fluorescent light fixtures 

at five schools (Table 4-9). Between 24 and 95% of the 

surveyed ballasts were likely to be PCB-containing. The 

total number of ballasts that were likely to be PCBcontaining ranged as high as 879 at one school. A breakdown 

of the ballast survey result by room at School 6 is shown in 

Table 4-10. Temperatures at the surfaces of several operating 

ballasts (with lights on) were measured at School 6 and 

ranged from 48°C to 54°C. 

No emission estimates have been made for two conditions 

that might be present in buildings. First, some ballast 

capacitors may have previously failed and leaked contents 

into light fixtures. Residues may serve as a source of PCB 

emissions. Second, some ballasts may be leaking and have 

emission rates higher than those measured in chamber 

testing. At this time there are no data suitable for estimating 

emission rates for these two conditions. It is not clear 
40

Table 4-9. PCB-containing fluorescent light ballast survey results from five schools

School

Ballasts

Not Containing

PCBs

Ballasts

Likely Containing

PCBs

Total

Ballasts in 

School

% Ballasts

Likely Containing

PCBs

1 310 417 727 57%

2 114 373 487 77%

3 344 275 619 44%

4 48 879 927 95%

6a 25 8 33 24%

a

 Only a subset of ballasts in School 6 were surveyed.

Table 4-10. PCB-containing fluorescent light ballast survey results at School 6

Location

Number of Ballasts 

Visually Examineda

Number of Ballasts 

Not Containing PCBsb

Number of Ballasts 

Likely Containing 

PCBsc

Classroom 1 1 1 0

Lab Classroom 2 5 4 1

Classroom 3 5 5 0

Classroom 4 16 13 3

Shop Classroom 5 2 0 2

Cafeteria 4 2 2

Gymnasium 0 -- --

Total across locations 33 25 8

a

 In-place visual examination of ballasts to examine ballast label.

b

 Ballast labels explicitly state “No PCBs”. Ballasts included Sylvania Quicktronic QT 2X32/120 IS; Advance REL-2P32-SC; Advance 

R-2S40-1-TP; Advance RQM-2S40-3-TP; Phillips Advance ICN-2P32-N; Universal 446-L-SLH-TC-P.

c

 Ballast labels did not state “No PCBs”. All ballasts were General Electric 7G1020B. 
41

whether the one ballast tested in laboratory chambers by 

Guo et al. (2011) that had a much higher emission rate than 

the other three ballasts that were tested represents a leaking 

ballast condition. 

Total PCB emission rates were estimated from four intact 

ballasts that were tested at 23°C and 45°C in a laboratory 

chamber (Section 2). Those emission rates were used 

to generate a range of estimated total emission rates for 

likely PCB-containing ballasts in three classrooms, with 

an assumption that the emission rates from the ballasts in 

the classroom would fall within the range of tested ballasts. 

Total estimated emission rates at 45°C ranged from 1.2 μg/hr 

from three ballasts at the lowest emission rate, up to 290 μg/

hr from nine ballasts at the highest emission rate. The range 

of potential emission rates is shown in Figure 4-2. Total 

estimated emission rates at 23°C ranged from 0.08 μg/hr 

from three ballasts at the lowest emission rate, and up to 18 

μg/hr from nine ballasts at the highest emission rate. 

The estimated emission rates for intact light ballasts in the 

three classrooms were used to generate screening level 

estimates of the resulting indoor air PCB concentrations 

that might occur under different assumed conditions of 

ventilation with outdoor air. The approach for calculating 

screening-level PCB indoor air concentration estimates was 

described in Section 2, using Equation 2.1.7. Numerous 

assumptions apply to this estimation approach, and results 

reported in Table 4-11 are considered to be only screeninglevel because information is not available to understand 

whether all of the assumptions are correct for the light 

ballasts and conditions in the school rooms. Screening-level 

estimates of total PCB indoor air concentrations ranged from 

1.6 to 2400 ng/m3

 for ballasts at 45°C, with median values 

ranging from 2.3 to 44 ng/m3. Screening-level estimates 

of total PCB indoor air concentrations ranged from 0.1 to 

150 ng/m3 for ballasts at 23°C, with median values ranging 

from 0.14 to 2.8 ng/m3. The lowest and median screeninglevel estimates of total PCB indoor air concentration are 

substantially lower than concentrations measured at one 

point in time, which ranged from 690 to 1460 ng/m3. If all 

of the ballasts in a room had emission rates similar to the 

highest-emitting ballast in the chamber tests, it is possible 

that emissions could result in indoor air concentrations 

approaching those that were measured. 

4.2.3 Secondary Source Characterization

Secondary sources of PCBs are defined here as materials 

that have become contaminated due to absorption of PCBs, 

either from direct contact with primary sources such as 

caulk, or through absorption of PCBs in the indoor air that 

have been emitted by caulk, light ballasts, or other primary 

sources. There are numerous materials and furnishings in 

buildings that have the potential to absorb semi-volatile 

organic chemical like PCBs. These materials can include 

paints, dust, foam, masonry, floor and ceiling tiles, mastics, 

wood, cork and pin board, and many others. After years 

of exposure to PCBs from primary sources, enough PCBs 

may have become absorbed to be become emission sources 

once the primary sources have been removed or otherwise 

remediated. These materials are also likely to continue to 

serve sinks as well as sources, due to their continued ability 

to absorb PCBs from the air and their often large surface 

areas. The materials’ net effect as secondary sources depends 

on source-sink dynamics, which is affected by a number of 

factors including PCB concentration in materials and indoor 

air, diffusion and partition coefficient parameters, ventilation 

and room air flow rates, and temperature. 

A wide range of building material samples was collected 

at Schools 2, 3, and 6. Materials were analyzed for total 

PCBs as Aroclors; Aroclor 1254 was reported for most 

materials, sometimes with an altered Aroclor pattern. Total 

PCB measurement results for 411 materials are shown in 

Table 4-12. When considering measurement results for these 

samples it is important to remember that multiple samples 

of the same type of material might have been collected from 

several places in a school building. Across all 411 materials, 

93% had PCB levels higher than the quantitation limit, with 

a median concentration of 16.1 ppm (interquartile range 6.1 

– 39.6 ppm) and a maximum value of 718 ppm. Paint had 

the highest total PCB concentrations with a median of 39 

ppm (interquartile range 25.9 – 71.7 ppm). Fiberboard had 

a median level of 30.9 ppm (interquartile range 13.0 – 38.7 

ppm), while lower levels were found in some other materials 

that often have high surface areas in buildings such as ceiling 

tile with a median 7.6 ppm (interquartile range 2.7 – 11.8 

ppm) and floor tile with a median of 4.4 ppm (interquartile 

range of 1.4 – 8.7 ppm). Results for several material types 

Figure 4-2. Example of the estimated range of total PCB emission 

rates from fluorescent light ballasts in a school classroom using the 

lowest, median, and highest rates from chamber tests of four ballasts
42

Table 4-11. Screening-level comparison of predicted air concentrations resulting from PCB emissions from fluorescent light ballasts to measured concentrations

Room and 

Balllast 

Conditiona

Room

Volume

m3

Number 

of PCB

Containing 

Ballasts

Estimated 

Emission 

Rateb 

μg/hr

Predicted Total PCB Air 

Concentration in Rooms at 

Different Air Exchange Ratesc 

ng/m3

Measured

Total PCBs

In Room 

Aird

ng/m3

AER = 

0.5

AER = 

1.0

AER = 

2.0

AER = 

4.0

Using Emission Estimates at 45°C (near the temperature of light ballasts when lights are on)

Classroom 1 231 5 1460

 Lowest 0.416 18 9.0 4.5 2.2

 Median 0.614 27 13 6.6 3.3

 Highest 32.7 1400 710 350 180

Classroom 2 249 9 859

 Lowest 0.416 30 15 7.5 3.8

 Median 0.614 44 22 11 5.6

 Highest 32.7 2400 1200 590 300

Classroom 4 182 3 690

 Lowest 0.416 12 6.2 3.1 1.6

 Median 0.614 18 9.2 4.6 2.3

 Highest 32.7 980 490 240 120

Using Emission Estimates at 23°C (near the temperature of light ballasts when lights are off)

Classroom 1 231 5 1460

 Lowest 0.026 1.1 0.56 0.28 0.14

 Median 0.038 1.7 0.83 0.42 0.21

 Highest 2.04 88 44 22 11

Classroom 2 249 9 859

 Lowest 0.026 1.9 0.94 0.47 0.23

 Median 0.038 2.8 1.4 0.69 0.35

 Highest 2.04 150 74 37 18

Classroom 4 182 3 690

 Lowest 0.026 0.78 0.39 0.19 0.10

 Median 0.038 1.1 0.57 0.29 0.14

 Highest 2.04 61 31 15 7.6

a Classrooms 1 and 2 at School 2; Classroom 4 at School 6. Several estimates are made using the lowest, highest, and median 

emission rates from four ballasts tested in a chamber. Many light fixtures in schools show evidence of leaking or ballasts 

that had previously failed. There is insufficient information to estimate emissions when these conditions are present.

b

Estimated total PCB emission rates from light ballasts based on PCB emission rates measured for several congeners from 

chamber measurements of four intact ballasts at several temperatures. 

c Predicted air concentrations at several possible air exchange rates for the room. Calculations based on the estimated PCB 

emission rate and ventilation rate (see Eq 2.1.7). Assumes steady state emission and ventilation conditions, steady-state 

absorption/desorption from other room materials, complete room air mixing, the concentration of PCBs in the ventilation 

air is zero, the only source of PCBs is the ballasts, and that the PCB mixture in the caulk is equivalent to Aroclor 1242. No 

caulk with high PCB levels was found inside these classrooms. Caulk with PCBs was in hallways outside Classrooms 1 and 

2 and on the exterior window and unit ventilator intake grill for Classroom 4.

d Air samples were collected under different temperature conditions; Classrooms 1 and 2 @ 28°C; classroom 4 @ 27°C. 
43

Table 4-12. Total PCB measurement results for materials at three schools with available data

Material Category N

%

> 

QLb

Total PCB Levels in Other Materialsa

Median

ppm

Inter-Quartile 

Range

ppm

Overall Range

ppm

All Material Samples 411 93 16.1 6.10 - 39.6 <QL - 718

Paint 143 100 39.1 25.9 - 71.7 3.31 - 718

Fiberboard 28 100 30.9 13.0 - 38.7 2.85 - 54.8

Chair Rail/Radiator Cover 6 100 30.5 23.9 - 47.6 20.0 - 68.5

Baseboard Cove Molding 5 100 28.0 9.88 - 40.9 2.44 - 42.3

Foams/Pinboard/Corkboard 8 100 15.5 10.0 - 37.4 6.68 - 48.6

Particle Board 19 100 13.5 11.4 - 17.4 6.88 - 28.8

Varnish 30 97 11.4 7.01 - 17.8 <QL - 61.5

Mastics (Tile and Molding) 65 92 7.83 2.30 - 13.9 <QL - 230

Ceiling Tile 8 100 7.59 2.67 - 11.8 2.06 - 14.0

Wood 6 100 7.46 3.70 - 13.5 0.13 - 32.2

Laminate 29 76 5.35 1.47 - 56.9 <QL - 199

Floor Tile 56 82 4.43 1.41 - 8.68 <QL - 57.1

Ventilator Insulation 2 100 1.75 -- 1.28 - 2.23

Oils and Cleaner (liquids) 3 0 <QL <QL <QL

Wall Concrete Block 3 0 <QL <QL <QL

a Reported as total PCBs from Aroclor measurements for materials at Schools 2,3, and 6. 

b QL = quantitation limit; sample size dependent, typically ≤ 1 ppm. 

are shown separately for each school in Appendix B. It is 

impossible to be certain that these materials did not contain 

some PCBs when originally installed. However, measured 

concentrations and relative levels among different materials 

are consistent with these materials being “sinks” that have 

absorbed PCBs emitted from primary sources (Guo et al., 

2011).

A majority of the interior caulk samples had total PCB 

concentrations < 100 ppm (Table 4-4). The median 

concentration for caulks with < 100 ppm was 6.9 ppm. This 

appears to be consistent with many of the other materials in 

the building. While it can’t be ruled out that some of these 

caulks contained PCBs when originally installed, the results 

suggest that these caulks may have absorbed PCBs from the 

air that were emitted by primary sources.

Because of their relatively high concentration and high 

surface areas, paints may be among the most important 

secondary sources to consider. A large number of paint 

samples were collected and analyzed, allowing an assessment 

of different types of paints (Table 4-13). Wall and ceiling 

paints had median total PCB levels of 29.1 and 30.5 ppm, 

with a maximum value of 227 ppm for a ceiling paint sample. 

These paints are very often flat latex paints. Paints used on 

metals, floors, and door and trim paints had higher median 

values, ranging from 36.3 to 55.0 ppm with a maximum 

value of 718 ppm. Several handrail paints were collected at 

one school, with a median total PCB concentration of 121 

ppm. These types of paints are more often oil-based and 

glossy paint, and in some cases have thicker film thicknesses 

than flat latex paint. It is important to recognize that many, 

if not most surfaces, had multiple coats of paint. It was not 

possible to determine whether there were gradients in PCB 

levels across different coats. Other materials with relatively 

high surface areas in many schools, including ceiling tile and 

floor tile had relatively low median PCB levels of 7.6 and 

4.4 ppm, respectively. Maximum PCB concentrations were 

much lower in these materials than they were in paints. 

PCBs were historically formulated as plasticizers or 

flame retardants in some paints. Although most of the 

concentrations measured in paint samples were below 100 

ppm, several were above 200 ppm, raning up to 718 ppm. It 

is possible that these paints, or portions of the underlying 

coats for some of these paints may have contained PCBs. 

Polyurethane foam (PUF) has been shown to be an important 

reservoir and potential secondary source of PBDEs and 

PCBs in some buildings (Zhang et al., 2011). However, the 
44

Table 4-13. Total PCB measurement results for subsets of paint uses

Material Category N % > QLb

Total PCB Levels in Paintsa

Median

ppm

Inter-Quartile 

Range ppm

Overall Range

ppm

All Paints 143 100 39.1 25.9 - 71.7 3.31 - 718

Wall Paint 36 100 29.1 21.8 - 41.8 3.31 - 129

Ceiling Paint 28 100 30.5 14.5 - 46.6 3.43 - 227

Metal Paint (radiator, locker, etc) 31 100 36.3 27.6 - 78.0 7.00 - 382

Floor Paint 4 100 54.9 34.6 - 83.3 32.6 - 110

Door and Door Trim Paint 33 100 55.0 37.5 - 87.0 5.54 - 718

Basketball Backboard Paint 2 100 63.3 -- 50.8 - 75.8

Handrail Paint 9 100 121 57.2 - 132 49.1 - 172

a Reported as total PCBs from Aroclor measurements for materials at Schools 2,3, and 6. 

b QL = quantitation limit; sample size dependent, typically ≤ 1 ppm. 

amount of PUF in most older school buildings is relatively 

small, particularly when compared to residential settings. 

Interestingly, the the concentration of PCBs in PUF for the 

few samples that were collected were generally lower than 

the concentrations in paints in the same buildings. 

Understanding the relative potential of different secondary 

sources to emit PCBs is of interest for informing remedial 

action, should it be needed. Information about materials 

in nine interior spaces across three school buildings was 

organized to better understand both the relative potential 

source impact as well as the cumulative potential impact from 

PCB emissions from secondary sources. Screening-level 

estimates of emission rates for multiple materials in these 

rooms were calculated following the approach described for 

caulk in Section 2. Material descriptions, surface areas, total 

measured PCB concentrations, and screening-level estimates 

of emission rates are shown in Tables 4-14, 4-15, and 4-16. 

Figures 4-3, 4-4, and 4-5 show the relative screeninglevel emission rates for materials in three of the rooms. It 

is important to note that the emission rates estimated for 

secondary sources are applicable only after primary sources 

have been removed or otherwise mitigated. There are 

considerable uncertainties in these estimates, in part because 

they assume that the emission parameters derived for caulk 

in chamber testing apply to the wide range of different 

materials. In practice, the emission parameters for PCBs 

may be considerably different for many materials. Therefore, 

these are only screening level estimates for the purposes of 

relative comparisons.

Screening-level estimated emission rates for different 

materials in classrooms ranged from < 1 up to 100 μg/

hr and cumulative totals for 20 materials in a room ranged 

up to 270 μg/hr. Estimated emission rates for different 

materials in gymnasiums ranged from < 1 up to 1100 μg/

hr and cumulative totals for 16 materials ranged up to 2700 

μg/hr. Estimated emission rates depended on the surface 

area of the material and concentration of PCBs in the 

material. Paints and varnishes generally had the highest 

relative potential emissions due to the combination of higher 

PCB concentrations and high surface areas. There are 

considerable uncertainties in these estimates, which are based 

on emission parameters derived from laboratory emissions 

testing of caulk. Emission parameters for the many different 

types of other materials could be substantially different 

than those for caulk. It is difficult to estimate indoor air 

concentrations of PCBs that might result from secondary 

sources following removal of primary sources because 

of the large number of different types of PCB-containing 

material in a room, and because the cumulative source – 

sink dynamics for multiple different materials is difficult 

to characterize. However, the cumulative emission rates 

from secondary sources could potentially result in indoor air 

PCB levels above ambient air background levels in school 

rooms following mitigation of primary sources, depending 

on relative emission rates, sink rates, and rates of ventilation 

from indoor and outdoor air. 

4.2.4 Source Assessment Uncertainties and Limitations

Characterizing the source(s) of PCBs in and around school 

buildings is important because it will inform remediation 

approaches for cases where exposure reduction decisions 

need to be made. PCB source assessment for buildings can 

be difficult because there may be multiple primary sources, 

and transport of the semi-volatile congeners through air 

can contaminate dust and soil and create secondary sources 

of other materials in a building. Information from six 

school buildings examined in this work was used to try 

to characterize and to understand the relative potential of 

various PCB sources. There remain important uncertainties 

and limitations in this information and the emission rate 

estimates as discussed below. 

Attribution of primary sources - The school buildings 

examined in this work had both caulk with high PCB 

concentrations and PCB-containing light ballasts. It would 

be helpful to understand whether one of the primary sources 
45

Table 4-14. Screening-level estimates of total PCB emission rates for selected interior materials in three locations at 

School 2 for relative comparisonsa,b,c

School Room Material

Surface Area

m2

Total PCB

ppm

Estimated

Emission Rate

μg/hr

2 Classroom Wall Paint - Blue 102 36.7 100

2 Classroom Door Paint - Blue 3.90 444 47

2 Classroom Wall Paint - Cream 19.5 51.5 27

2 Classroom Floor Tile - Grey 59.5 16.5 27

2 Classroom Radiator Paint - Blue 9.66 52.0 14

2 Classroom Door Varnish 7.80 48.2 10

2 Classroom Door Frame Paint - Blue 3.34 102 9.3

2 Classroom Closet Door Varnish 8.36 39.5 9.0

2 Classroom Fiberboard 12.4 26.5 9.0

2 Classroom Ceiling Tile 52.0 4.48 6.4

2 Classroom Ceiling Paint - White 4.65 32.0 4.1

2 Classroom Cove Molding 3.21 42.3 3.7

2 Classroom Ceiling Paint - Blue 2.79 29.6 2.3

2 Classroom Sink Door Varnish 1.86 13.4 0.68

2 Classroom Door Frame Varnish 0.743 28.9 0.59

2 Classroom Particle Board 0.929 10.3 0.26

2 Classroom Wormhole Ceiling Tile 1.21 2.11 0.070

2 Classroom Transom Glaze 0.0064 51.0 0.009

2 Classroom Electrical Penetration Caulk 0.0052 5.30 0.001

2 Classroom Toilet Caulk 0.0006 9.50 0.0002

2 Gymnasium Wall Paint - Cream 358 51.5 500

2 Gymnasium Wood Floor Varnish 401 32.2 350

2 Gymnasium Fiber Ceiling Tile 402 10.7 120

2 Gymnasium Ceiling Beam Paint - Cream 191 20.4 110

2 Gymnasium Backboard Paint - White 25.6 50.8 36

2 Gymnasium Radiator Paint - Blue 20.0 52.0 28

2 Gymnasium Baseboard Paint - Black 5.88 115 18

2 Gymnasium Duct Paint - Cream 46.5 12.1 15

2 Gymnasium Bench - Varnish 10.0 34.8 9.5

2 Gymnasium Bay Door Paint - Gray 4.37 59.1 7.0

2 Gymnasium Vent Paint - Blue 3.90 52.0 5.5

2 Gymnasium Door Paint - Blue 5.20 37.5 5.3

2 Gymnasium Door Frame Paint - Blue 1.89 102 5.3

2 Gymnasium Door Varnish 9.29 12.9 3.3

2 Gymnasium Bay Door Soffit Paint - Gray 0.929 59.1 1.5

2 Gymnasium Door Window Glaze 0.0520 15.0 0.021

2 Gymnasium Electrical Penetration Caulk 0.0020 68.0 0.004

2 Gymnasium Sink Caulk 0.0117 7.40 0.002

2 Corridor Door Paint - Blue 23.8 116 75

2 Corridor Radiator Paint - Blue 20.1 92.0 50

2 Corridor Floor Tile - White 121 12.1 40
46

School Room Material

Surface Area

m2

Total PCB

ppm

Estimated

Emission Rate

μg/hr

2 Corridor Ceiling Tile 133 4.48 16

2 Corridor Door Frame Paint - Blue 5.79 102 16

2 Corridor Door Varnish 18.6 16.5 8.4

2 Corridor Door Louver Paint - Blue 0.465 444 5.6

2 Corridor Fiberboard -Gray 7.43 20.7 4.2

2 Corridor Fiberboard - Brown 5.57 26.5 4.0

2 Corridor Floor Tile - Cream 1 9.29 4.60 1.2

2 Corridor Interior Door Window Glaze 0.0988 88.0 0.24

2 Corridor Floor Tile - Cream 2 0.929 6.79 0.17

a Not all materials in each room were sampled. Materials such as mastics are not included here since they are not directly 

exposed to room air.

b Based on chamber-derived emission parameters for caulk – these may not apply well to all materials.

c These materials also act as sinks, absorbing PCBs from the room air. The estimated emission rates shown here cannot be 

simply used to estimate total indoor air concentrations. 

Table 4-14. Screening-level estimates of total PCB emission rates for selected interior materials in three locations at 

School 2 for relative comparisonsa,b,c (continued)

is much more important than the other with regard to 

increasing environmental levels and potential exposures to 

PCBs. There appears to be evidence that both caulk with 

high PCB levels and PCB-containing light ballasts are likely 

to be important sources of PCBs in older school buildings. 

However, given the limitations of this study, it is difficult to 

determine whether one source is likely to be more important 

than another in school buildings. 

One approach might be to examine congener patterns to 

try to determine if a source signature can be elucidated. A 

limitation was that most of the samples were analyzed for 

Aroclors and not specific congeners. Some of the caulk 

samples collected at the six buildings contained high 

concentrations of PCBs (in the range of 1% to 44% by 

weight). Aroclor analysis of these high-PCB caulks showed 

that most contained a pattern consistent with Aroclor 1254, or 

in the case of one school, a pattern resembling Aroclor 1260. 

The capacitor fluids in several light ballasts were analyzed; 

three of these contained Aroclor 1242 and one contained 

Aroclor 1254. Because these schools were constructed 

after 1952, when capacitor fluids reportedly transitioned 

from Aroclor 1254 to Aroclor 1242, one might expect that 

most ballasts contained Aroclor 1242. But with hundreds 

of unmeasured light ballasts in each school and the finding 

of a ballast with Aroclor 1254, it is not clear to what extent 

different Aroclor capacitor fluids may have been present 

in each building. Most of the air samples were reported 

to contain an altered Aroclor pattern with characteristics 

of Aroclors 1248 and 1254. Given that Aroclor 1242 is 

primarily composed of 2-, 3-, and 4-chlorine homologs, and 

Aroclor 1254 is primarily composed of 4-, 5-, and 6- chlorine 

homologs, it would be surprising – based on the air PCB 

composition - if sources with Aroclor 1242 (i.e. light ballasts) 

were contributing a much higher amount of PCBs into the 

indoor air than sources with Aroclor 1254 or 1260 (caulk 

and possibly some light ballasts). This is particularly true 

because the 2-, and 3- chlorine congeners have much greater 

vapor pressures that the 5-chlorine congeners, and Aroclor 

1242 sources would contribute an even higher fraction of 

their PCBs to air than Aroclor 1254 sources. Congenerspecific measurements were obtained for one school (see 

Section 4.4) and provide information for more directly 

examining source and environmental relationships. 

Estimates of total PCB emission rates for several examples 

of school building caulk were made. As discussed later, 

there are limitations and uncertainties in the estimates. But, 

if those estimates are reasonable, then emission rates of 

hundreds to thousands of micrograms of PCBs per hour at 

specific school locations would appear to be possible. For 

interior caulks, these levels of emissions would contribute 

to increased indoor air PCB concentrations, and could serve 

as a source for partitioning into dust and other materials. 

Even assuming low penetration rates for emissions from 

exterior caulks around windows and ventilation intakes, 

exterior caulk could also contribute to increased PCB levels 

in indoor air, but this may be highly dependent on ambient 

temperature and wind conditions, as well as specific locations 

and uses of windows for ventilation. Given the estimates of 

emission rates from caulk, a question arises as to whether 

all of the available PCBs might have been depleted over 

the course of 40 – 50 years in a building, and whether these 

rates have been overestimated. Guo et al. (2011) used a 

modeling approach to show that over 50% of congener 52 

would remain after 50 years in a building, and even higher 

proportions of less volatile congeners would remain. It is 

possible that some of the more volatile congeners, such as 8 

and 18 might have been largely depleted in that time frame, 

but these two comprise < 1% of all congeners, by weight, in 
47

Table 4-15. Screening-level estimates of total PCB emission rates for selected interior materials in three rooms at School 

3 for relative comparisonsa,b,c

School Room Material

Surface Area

m2

Total PCB

ppm

Estimated Emission 

Rate

μg/hr

3 Classroom 1 Wall Paint - Cream 53.9 29.4 43

3 Classroom 1 Ceiling Paint - Gray 85.5 10.7 25

3 Classroom 1 Door Paint - Orange 5.57 108 16

3 Classroom 1 Cove Base - Black 6.04 40.9 6.7

3 Classroom 1 Floor Tile - Green 89.2 1.73 4.2

3 Classroom 1 Fiber Board 11.1 13.0 3.9

3 Classroom 1 Radiator Paint - Light Orange 4.83 27.5 3.6

3 Classroom 1 Fiber Board - Green 3.34 21 1.9

3 Classroom 2 Floor Tile - Gray 82.9 9.69 22

3 Classroom 2 Wall Paint - Yellow 59.5 13.5 22

3 Classroom 2 Ceiling Paint - White 62.3 10.5 18

3 Classroom 2 Cove Base Paint - Black 6.69 40.9 7.5

3 Classroom 2 Radiator Paint - Yellow 14.4 9.71 3.8

3 Classroom 2 Fiber Board - Brown 9.29 13.0 3.3

3 Classroom 2 Door Paint - Yellow 1.86 29.6 1.5

3 Classroom 3 Wall Paint - Yellow/Green 51.1 27.1 38

3 Classroom 3 Ceiling Paint - White 60.4 9.47 16

3 Classroom 3 Cove Base Paint - Light Gray 3.53 35.9 3.5

3 Classroom 3 Fiber Board - Brown 10.2 10.5 2.9

3 Classroom 3 Door Paint - Light Gray 1.86 29.7 1.5

3 Classroom 3 Particle Board - Brown 0.93 12.9 0.33

3 Classroom 3 Floor Tile - Beige 7.43 <QLd --

a

 Not all materials in each room were sampled. Materials such as mastics are not included here since they are not directly 

exposed to room air.

b

 Based on chamber-derived emission parameters for caulk – these may not apply well to all materials.

c These materials also act as sinks, absorbing PCBs from the room air. The estimated emission rates shown here cannot be 

simply used to estimate total indoor air concentrations. 

d

 QL = quantifiable limit.
48

School Room Material

Surface Area 

m2

Total PCB 

ppm

Estimated 

Emission Rate 

μg/hr

6 Classroom 3 Locker Paint - Tan 17.9 79.7 39

6 Classroom 3 Wall Paint - Beige 29.2 38.3 31

6 Classroom 3 Wall Paint - White 11.8 64.9 21

6 Classroom 3 Ventilator Paint - Beige 5.71 71.9 11

6 Classroom 3 Pin Board 7.32 48.6 9.7

6 Classroom 3 Door Paint - Brown 4.08 71.4 7.9

6 Classroom 3 Ceiling Tile 68.5 2.06 3.8

6 Classroom 3 Floor Tile - Grey 58.1 2.07 3.3

6 Classroom 3 Floor Tile - Beige 19.4 5.73 3.0

6 Classroom 3 Chair PUF Foam 0.534 42.9 0.62

6 Classroom 3 Cove Molding 2.36 2.44 0.16

6 Classroom 3 Interior Window Glaze 1 0.109 9.22 0.027

6 Classroom 3 Ventilator Gasket Foam 0.0277 35.6 0.027

6 Classroom 3 Interior Window Glaze 2 0.0176 40.4 0.019

6 Classroom 3 Interior Joint Caulk 0.197 2.40 0.013

6 Classroom 3 Ventilator Insulation 0.0987 2.23 0.006

6 Classroom 3 Door Window Glaze 0.0064 11.7 0.002

6 Classroom 3 Wall Concrete Block 41.0 <QLd --

6 Classroom 4 Wall Paint - White 40.1 41.4 45

6 Classroom 4 Locker Paint - Tan 18.9 35.2 18

6 Classroom 4 Door Paint - Brown 6.19 72.6 12

6 Classroom 4 Ventilator Paint - Brown 5.68 52.7 8.2

6 Classroom 4 Ceiling Tile 62.2 2.85 4.8

6 Classroom 4 Floor Tile - Grey 70.1 0.959 1.8

6 Classroom 4 Pin Board (2 boards) 4.94 10.4 1.4

6 Classroom 4 Cove Molding 2.38 9.88 0.64

6 Classroom 4 Cork Board 1.11 11.4 0.35

6 Classroom 4 Interior Window Glaze 2 0.0697 23.01 0.044

6 Classroom 4 Interior Window Glaze 1 0.0998 11.79 0.032

6 Classroom 4 Interior Joint Caulk 0.0955 4.15 0.011

6 Classroom 4 Ventilator Insulation 0.0987 1.28 0.003

6 Gymnasium Wood Floor Varnish 668 61.5 1100

6 Gymnasium Wall Paint 1 524 39.1 560

6 Gymnasium Wall Paint 2 189 92.2 480

6 Gymnasium Floor Underlayment 668 19.7 360

6 Gymnasium Gym Floor Wood 668 5.39 98

6 Gymnasium Backboard Paint 18.9 75.8 39

6 Gymnasium Door Paint - Black 25.6 35.1 24

6 Gymnasium Bleacher Seat Wood 194 3.13 17

6 Gymnasium Cove Molding 10.3 28.0 7.9

6 Gymnasium Exercise Mat PUF Foam 32.4 6.68 5.9

Table 4-16. Screening-level estimates of total PCB emission rates for selected interior materials in three rooms at School 

6 for relative comparisonsa,b,c
49

School Room Material

Surface Area 

m2

Total PCB 

ppm

Estimated 

Emission Rate 

μg/hr

6 Gymnasium Backboard Wood 18.9 9.53 4.9

6 Gymnasium Bleacher End Wood 8.43 14.8 3.4

6 Gymnasium Plywood Subfloor 668 0.125 2.3

6 Gymnasium Interior Joint Caulk 0.627 21.7 0.37

6 Gymnasium Pin Board 1.11 8.79 0.27

6 Gymnasium Interior Caulk - Fountain 0.0108 5.21 0.002

Table 4-16. Screening-level estimates of total PCB emission rates for selected interior materials in three rooms at 

School 6 for relative comparisonsa,b,c (continued)

a

 Not all materials in each room were sampled. Materials such as mastics are not included here since they are not directly 

exposed to room air. The gym sub-floor and underlayment are included because it is not clear whether and to what extent 

their emissions might impact room air.

b

 Based on chamber-derived emission parameters for caulk – these may not apply well to all materials.

c

 These materials also act as sinks, absorbing PCBs from the room air. The estimated emission rates shown here cannot be 

simply used to estimate total indoor air concentrations. 

d QL = quantifiable limit.

Figure 4-3. Screening-level estimates of total PCB emission rates from materials in the gymnasium in School 2
50

Figure 4-4. Screening-level estimates of total PCB emission rates from materials in a classroom in School 3 

Figure 4-5. Screening-level estimates of total PCB emission rates from materials in a classroom in School 6 
51

Aroclor 1254. We have performed additional calculations 

(not shown) based on our estimated emission rates, and 

for reasonable initial total PCB concentrations that are 

within the range that have been measured in caulks now, 

substantial fractions of PCBs would still be present in the 

caulk after more than 40 years. Other research has shown 

that sealants with PCBs are an important contributor to 

PCB levels in indoor spaces. For example, the average 

total PCB concentration in indoor air in apartments 

with PCB-containing sealants was 1030 ng/m3 (range 

168 – 3843 ng/m3) (Frederiksen et al., 2012). MacIntosh 

et al. (2012) reported an average indoor air total PCB 

concentration of 533 ng/m3 (range 299 – 1800 ng/m3) in 

an elementary school with PCB-containing sealants but 

no PCB-containing light ballasts. These results show that 

emissions of PCBs from caulk, in the absence of light 

ballast sources, are sufficient to create indoor air total PCB 

concentrations two orders of magnitude or more higher 

than outdoor ambient concentrations. 

Estimates of PCB emissions from light ballasts were also 

made. These estimates are limited and some caution is 

recommended regarding their interpretation. Estimates 

were based on the testing of four intact light ballasts at 

multiple temperatures by Guo et al. (2011). One of the 

four ballasts showed substantially higher emissions than 

the other three, approximately 80-fold greater than the 

ballast with the lowest emissions. Thus, it is not certain 

what ballast-related emissions are likely to occur across the 

large number of ballasts that may be present in a building. 

If a larger percentage of ballasts emit at the highest rate, 

then emissions from intact ballasts alone could have an 

important impact on indoor air PCB concentrations. If 

most of the intact ballasts emit at the lower levels, then the 

impact on levels in air would be more modest. Perhaps 

most importantly, this work was not able to provide 

information on emissions resulting from failing ballasts 

and for light fixtures and other building components 

that may have been contaminated from previously failed 

ballasts. Ballast capacitors that burst or suddenly fail and 

leak their PCB contents will clearly have a substantial 

impact on PCB levels in indoor spaces. The impact of 

contaminated fixtures and other components is less certain 

but could be important. The New York City remedial 

investigation showed that remedial measures could 

substantially reduce indoor air PCB concentrations for 

schools with initially elevated levels, and a considerable 

part of that decrease occurred after a school cleaning 

step and removal of PCB-containing light fixtures (see 

Figure 4-11). The Agency has prepared a guide for school 

administrators and maintenance personnel titled “Proper 

Maintenance, Removal, and Disposal of PCB-Containing 

Fluorescent Light Ballasts” (http://www.epa.gov/epawaste/

hazard/tsd/pcbs/pubs/ballasts.htm) that recommends 

removal.

Another uncertainty regarding sources of PCBs in school 

buildings is that other primary sources of PCBs may 

have been used in school buildings that are no longer 

present today. For example, carbonless copy paper and 

PCB-containing capacitors in early computer video display 

terminals may have been present in school buildings during 

a period of the school’s history. The potential impact of 

previously removed sources on current PCB levels in 

building environments cannot be easily determined. 

Importance of secondary sources for exposure – PCBs were 

found to be widespread throughout many materials collected 

in three buildings, mostly in the range of 4 to 100 ppm. 

Given the indoor air PCB concentrations and the chamber 

sink test results from Guo et al. (2012) these levels would 

appear to be consistent with the materials being sinks that 

have absorbed PCBs from the air over many years. A few 

of the paint samples had higher levels of PCBs, up to 718 

ppm, and it can’t be ruled out that some of the materials 

contained PCBs when they were installed in the building. 

Regardless of their provenance, these materials represent a 

reservoir of PCBs in a building that might, in some cases, 

need to be considered as part of building mitigation efforts 

to reduce environmental levels of PCBs. While screeninglevel emission estimation approaches were used to assess 

the relative potential importance of various materials, 

the report is limited about what it can say quantitatively 

with regard to the impact these secondary sources will 

have on environmental levels of PCBs following removal 

or mitigation of primary sources. This is because the 

cumulative source-sink dynamics are difficult to predict 

for multiple widespread materials with various PCB 

concentrations, particularly given the lack of important 

diffusion and partition parameters for most of the materials 

and the range of PCB congeners. 

Emission estimation uncertainties – Emission estimates in 

this report are largely based on the work of Guo et al. (2011) 

and their laboratory chamber testing of PCB-containing 

caulk and light ballasts, combined with measurements and 

characterization of PCBs in materials and components in the 

schools. Caulk emission rate parameters were determined by 

Guo et al. in the laboratory using micro-chambers because 

it was determined that large losses of PCB congeners to 

chamber walls were occurring for caulk emissions testing 

in larger chambers. This problem did not occur with the 

micro-chambers. Numerous samples of caulk obtained from 

older buildings were tested for emissions using the microchambers, and consistent congener emission parameters were 

obtained from the different caulks. Several light ballasts 

were obtained from older buildings and were tested in larger 

55-L chambers. Four of these ballasts were tested at multiple 

temperatures. Congener emission rates were found to be 

variable across the different ballasts. Because these ballasts 

contained Aroclor 1242, with proportionatly more volatile 

congeners, loss to chamber walls was less significant than for 

the less-volatile congeners from Aroclor 1254 in the caulks, 

but the emissions estimates from light ballasts in chamber 

tests may still be somewhat underestimated due to losses. 

Because the caulk and light ballasts were tested in two 

different chamber systems, there could be some limitations 

in making direct comparisons using emission factors derived 

from the testing. However, the micro-chambers have been 
52

shown to produce emissions estimates comparable to those 

from larger chambers for VOCs and SVOCs (Scheff et al., 

2000). Material/air boundary conditions are affected by 

surface air flow velocities and these could be different across 

the two types of chambers and between the chambers and the 

surfaces in school rooms (which are typically in the range 

of 2 to 25 cm/s). The micro-chamber manufacturer reports 

that air velocity is in a range of 0.5 cm/s at 50 mL/min and 

5 cm/s at 350 mL/min. At caulk test flow rate of 449 mL/

min, the estimated velocity would be about 6.4 mL/min. A 

published paper (Scheff et al., 2000) showed an empty micro 

chamber likely had velocities less than 10 cm/s. However, 

it is possible that the caulk testing configuration could have 

resulted in somewhat higher velocities. 

Even if the boundary air flow conditions during caulk 

emissions testing in micro-chambers was substantially 

different than the surface air velocities across caulk in school 

spaces, the impact on estimated emissions likely would 

not be large. The rate determining step for evaporation of 

a chemical from a pure liquid into air is governed largely 

by mass transfer in the boundary layer immediately above 

the liquid. The depth of the boundary layer depends in part 

on the air velocity across the surface, and this can be an 

important factor in measuring and estimating emissions. 

For solid materials, the emission rate is controlled by 

two factors, the boundary layer conditions and the rate of 

chemical diffusion from inside the material to the surface of 

the material. The boundary layer affects the emission rate at 

early times of the emissions and the effect diminishes over 

time. For long-term emissions, particularly for SVOCs, 

the internal diffusion becomes more important as the ratedetermining step and the boundary layer has more limited 

effect on the emission rate (Qian et al., 2007). 

Efforts were made to better understand whether the predicted 

caulk emission rates were realistic as compared to estimates 

of PCB emissions from evaporation from pure PCB liquids. 

Emission estimates from caulks in school buildings in this 

report would appear to higher than would be expected 

based on an early QSAR-based estimate of the evaporation 

rate of pure Aroclor 1254 (U.S. EPA, 1976) but would not 

overestimate evaporation rates derived from more recent 

modeling approaches (Hummel et al., 1996; Guo, 2000) (data 

not shown). 

There are still considerable limitations and uncertainties 

regarding the predicted emission rates and air concentration 

estimates presented in this report. The actual emission rates 

were not directly measured and the estimations were based 

on a number of assumptions. For caulk, it was assumed that 

the PCB emission parameters for the school caulks were 

the same as those in the chamber testing and estimates were 

based on an assumed temperature equivalent to that used 

in the chamber testing. The chamber testing showed that 

PCB emissions from caulk were sensitive to temperature, 

with an approximate six-fold increase in emissions for 

a 10°C increase in temperature. In estimating room air 

concentrations resulting from caulk, many other assumptions 

were made that might not be true or might change from time 

to time in a school building. Assumptions include:

• well mixed air in room

• constant temperature

• temperature equivalent to chamber conditions that 

generated caulk emission parameters (23°C)

• constant ventilation rate

• steady-state emission

• steady state and approximately equal absoption/

desorption of PCBs in other materials in the room

• no chemical reactions of PCBs

• PCBs from other school spaces are not impacting the 

levels in air for the room of interest

• emission parameters for caulk in the room are the same as 

for the caulk tested in lab chambers

Because congener-specific measurements were not performed 

for most of the caulk samples, we estimated total PCB 

emission rates by first assuming that the caulk contained an 

un-aged Aroclor 1254 mixture. It is likely that over time 

the congener composition has changed and current emission 

rates may not match those made assuming an un-aged 

Aroclor 1254. We examined this where we had congener 

specific data and found that the emission rate estimated 

using actual congener composition was 45% lower than 

using an assumption of un-aged Aroclor 1254. Also, in the 

emissions testing by Guo et al., three caulks were tested to 

assess the effects of having freshly cut surfaces. Emissions 

from freshly cut surfaces were, on average, about 19% higher 

than those from the original surface. To limit potential 

overestimation of PCB emissions from caulk, we elected to 

adjust (reduce) the total PCB emission rates by 55% based on 

the available information. However, this adjustment factor 

was based on limited data. 

As noted earlier, the information available for estimating 

PCB emissions from light ballasts was limited, and it is not 

clear how well those emission rates apply to the hundreds 

of ballasts in the schools. Likewise, the emissions of PCBs 

from contaminated light fixtures and other components have 

not been characterized, so it is possible that screening-level 

estimates of PCB concentrations school room air could 

be underestimated. Most of the assumptions listed for air 

concentrations estimated from caulk emissions also apply to 

estimates from light ballasts. 
53

4.3 PCBs in Environmental Media 

PCBs are semi-volatile organic chemicals and may 

experience transport from sources into and throughout the 

environment in and around school buildings where they 

can become available for human contact and exposure. In 

order to understand the potential for exposure and ranges 

of possible exposures, it is important to characterize 

PCB concentrations in environmental media. Indoor air, 

surface wipe, and outdoor soil samples were collected from 

multiple locations at the six schools. Indoor dust samples 

were collected from multiple locations in one school 

building. Outdoor air samples were collected at all school 

buildings. At the five NYC schools, indoor air and surface 

wipe samples were collected before and following different 

remedial actions. Measurement results are summarized 

here to characterize the magnitude, range, and within- and 

between-school variability in PCB concentrations in school 

environments. A summary of total PCB concentrations for 

indoor and outdoor air, surface wipes, soil, and dust is shown 

in Table 4-17.

4.3.1 Indoor Air

Indoor air samples were collected in several locations in each 

school. Collection locations included classrooms, cafeterias, 

gymnasiums, and transitory spaces. The median indoor air 

total PCB concentration based on 64 measurements across 

six schools was 318 ng/m3

 (interquartile range of 59.4 – 732 

ng/m3

) with a maximum concentration of 2920 ng/m3 (Table 

4-18). These samples were collected prior to any of the 

reported remedial activities as shown in Table 4-2. There 

was considerable variability between schools with median 

air levels at individual schools ranging from <50 ng/m3 at 

School 4 to 807 ng/m3 at School 2. The distribution of indoor 

air PCB concentrations is shown in Figure 4-6. There was 

considerable variability within schools; for example, indoor 

air levels ranged from 236 to 2920 ng/m3 in different rooms 

at School 3. 

Indoor air concentrations at these schools can be compared 

to measurements at other buildings. MacIntosh et al. (2012) 

reported a median pre-remediation value of 429 ng/m3 at 

a U.S. primary school for samples collected in nine indoor 

locations. Concentrations ranging from 2 to 310 ng/m3 were 

measured in a U.S. secondary school (TRC, 2006). Coughlan 

et al., (2002) reported indoor concentrations ranging from 

111 to 393 ng/m3 in a U.S. university building. Indoor PCB 

concentrations measured at three of the six schools in this 

report had higher concentrations than those reported by 

MacIntosh et al., TRC, and Herrick et al., while the indoor 

levels for the other three schools in this report were similar to 

or below those previously reported. 

The U.S. EPA developed information in 2009 on public 

health levels of PCBs in school indoor air (Figure 4-7). If 

school indoor air levels are kept below these concentrations, 

Table 4-17. Summary of environmental media total PCB measurement results for six schoolsa

Total PCB Levelsb,c

% Mean Inter-Quartile Overall

Environmental Medium (units) Nd > QLe QL Median Range Range

Indoor Air (ng/m3

) 64 77 47 318 59.4 - 732 <QL - 2920

Indoor Surface Wipes (μg/100cm2

)

 High-contact surfaces 72 62 0.100 0.147 <QL - 0.330 <QL – 2.84

 Low-contact surfaces 78 80 0.100 0.201 0.128 - 0.419 <QL – 2.30

Indoor dust (ppm) 7 100 3.0 22.0 16.6 - 53.4 11.6 – 86.8

Outdoor Soil (ppm)

 0.5’ from building; 0 – 2” soil depth 99 48 0.5 <QL <QL - 2.13 <QL - 211

 3’ from building; 0 – 2” soil depth 102 28 0.5 <QL <QL - 0.548 <QL – 20.6

 8’ from building; 0 – 2” soil depth 105 21 0.5 <QL <QL - <QL <QL – 5.28

Outdoor Air (ng/m3

) 6 0 47 <QL <QL <QL 

a Air, wipe, and soil measurements at six schools; dust measurements from one school.

b Reported as total PCBs from Aroclor measurements.

c When duplicate samples were collected, the average of the duplicates was used.

d

 Number of samples.

e Quantitation limit. 
54

Table 4-18. Indoor air total PCB measurement results at six schools

School/

Condition Nc

Mean

QLd

ng/m3

%

> QL

Total PCB Levels in Aira,b

Median

ng/m3

Inter-Quartile 

Range

ng/m3

Overall Range

ng/m3

All Six Schools 64 47 77 318 59.4 - 732 <QL - 2920

School 1 11 49 54 58 <QL - 121 <QL - 194

School 2 12 50 100 807 674 - 874 414 - 1460

School 3e 14 51 100 504 271 - 960 236 - 2920

School 4 9 50 44 <QL <QL - 154 <QL - 674

School 5 11 50 54 154 <QL - 425 <QL - 867

School 6 7 24 100 679 520 - 700 359 - 953 

a Reported as total PCBs from Aroclor measurements in indoor air.

b When duplicate samples were collected, the average of the duplicates was used.

c

 Number of samples and number of rooms sampled.

d QL = Quantitation limit.

e Does not include a second day of pre-remediation air results for School 3.

Figure 4-6. Distributions of indoor air total PCB concentrations across 

all six schools and at each individual school. The box plots show the 

median, 25th, and 75th percentiles. The whiskers show the 10th and 

90th percentiles

and if background exposures to PCBs are typical, then 

the total exposure would be expected to remain below the 

Aroclor 1254 reference dose of 0.020 μg/kg/day. Median 

indoor air concentrations exceeded the 300 ng/m3 public 

health level for 6 – 11 year-old children at several schools. 

PCB concentrations in many individual classrooms were 

higher than the public health levels for their age group. The 

effectiveness of remedial actions in reducing indoor air 

concentrations is shown later in Section 4.4.6. 

4.3.2. Outdoor Air

Outdoor air measurement results are shown in Table 4-19. 

All outdoor air PCB concentrations were below the QL; 

however, the QL was relatively high (approximately 

50 ng/m3) at five schools and 17 ng/m3 at School 6. Outdoor 

air total PCB concentrations are typically much lower 

than these QL values. Based on the congener analysis 

measurement result, the outdoor air PCB concentration was 

7.7 ng/m3 at School 6 (see Section 4.5). 

4.3.3 Surface Wipes

Surface wipe samples were collected from high-contact 

(desks, tables) and low-contact (walls, floors, window 

sills) surfaces at six schools. Measurement results 

are shown in Table 4-20. Measurement concentration 

distributions are shown in Figure 4-8. Median total PCB 

concentrations were 0.147 μg/100cm2 (interquartile range 

<0.100 – 0.330 μg/100cm2) for 72 high-contact surfaces 

and 0.201 μg/100cm2 (interquartile range 0.128 – 0.419 

μg/100cm2) for 78 low-contact surfaces. Concentrations 

ranged from not detected to 2.84 μg/100cm2

 for high contact 

surfaces and not detected to 2.30 μg/100cm2 for low-contact 

surfaces. Median high-contact wipe levels ranged from 

<0.001 to 0.380 μg/100cm2 at individual schools. School 1 

had the lowest overall surface PCB concentrations and this 

school also had the lowest indoor air levels. There was no 

consistent difference between median high-contact and lowcontact concentrations across the six schools. 

Wipe samples collected on building surfaces may provide 

information regarding the availability of PCBs in those 

materials at the material surface. High contact surface 

measurements can provide information regarding PCB levels 

most relevant for potential exposure. Since the high-contact 

wipe samples are collected on horizontal surfaces they may 

collect both settled dust and PCB residue at the material 

surface. Wipe sampling can extract PCBs from within 

materials near the surface because of the use of hexane as the 

wipe wetting agent. 
55

Figure 4-7. Public health levels of PCBs in school indoor air developed in 2009 by the U.S. EPA 

(http://www.epa.gov/pcbsincaulk/maxconcentrations.htm) 

PCB concentrations measured in this study were somewhat 

lower than the 0.49 μg/100 cm2 geometric mean calculated 

from extant data (Appendix D). Several of the extant 

measurement results were greater than 10 μg/100 cm2 while 

the maximum value for the six schools in Table 4-20 was 

2.84 μg/100 cm2. 

4.3.4 Indoor Dust

Indoor dust samples were collected only at School 6. 

Measurement results are shown in Table 4-21 for the 

25th, 50th, and 75th percentiles. The median total PCB 

concentration was 22 ppm (interquartile range 16.6 – 53.4 

ppm) across samples collected in seven rooms. At the time 

of sample collection, School 6 had not been cleaned for 

five weeks. It is possible that PCB concentrations in the 

dust were higher than they would have been under routine 

Figure 4-8. Distributions of surface wipe total PCB concentrations 

collected from high-contact and low-contact surfaces across all six 

schools and at each individual school. The box plots show the median, 

25th, and 75th percentiles. The whiskers show the 10th and 90th

percentiles.

cleaning because the dust had more time for contact with 

PCBs in the indoor air. School and college building dust 

measurement results available from extant sources (Appendix 

D) had a geometric mean level of 4.4 ppm ranging up to 

about 80 ppm. The median concentration at School 6 was 

larger, at 22 ppm, but the maximum value of 86.8 ppm is 

similar to the maximum in the extant data. Estimates of 

dust total PCB concentrations based on the measured air 

concentrations and estimated solid/air partition coefficients 

are shown in Appendix E.

4.3.5 Soil

Soil samples were collected at six schools at a depth 

0 to 5 cm (0 to 2 inches) and at distances of 0.15, 0.91, and 

2.44 m (0.5, 3, and 8 feet) from the building. Only 33% of 

the samples had PCB concentrations above the quantifiable 

limit (Table 4-22). The median total PCB concentration 

across all 309 soil samples was less than the quantifiable 

limit. The 75th percentile concentration was 0.98 ppm and 

the maximum value was 211 ppm. The 75th percentiles of 

Table 4-19. Outdoor air total PCB measurement results 

at six schoolsa

School/

Condition N

Mean

QLb

ng/m3

%

>QL

Total PCB 

Levels in 

Air

ng/m3

School 1 1 49 0 <QL

School 2 1 50 0 <QL

School 3 1 51 0 <QL

School 4 1 50 0 <QL

School 5 1 49 0 <QL

School 6 1 17 0 <QLc

a Reported as total PCBs from Aroclor measurements in 

outdoor air.

b QL = quantitation limit.

c The result for the congener-specific analysis was 

7.7 ng/m3.
56

Table 4-20. Surface wipe total PCB measurement results at six schools

School/Condition Nc

%

> 

QLd

Total PCB Levels in Surface Wipesa,b

Median

μg/100cm2

Inter-Quartile 

Range

μg/100cm2

Overall Range

μg/100cm2

All Six Schools

 High-contact surfaces 72 62 0.147 <QL - 0.330 <QL – 2.84

 Low-contact surfaces 78 80 0.201 0.128 - 0.419 <QL – 2.30

School 1

 High-contact surfaces 11 54 0.106 <QL - 0.128 <QL – 0.177

 Low-contact surfaces 11 46 <QL <QL - 0.135 <QL – 0.716

School 2

 High-contact surfaces 12 83 0.207 0.141 - 0.328 <QL – 0.748

 Low-contact surfaces 12 83 0.165 0.139 - 0.369 <QL – 0.570

School 3

 High-contact surfaces 14 57 0.154 <QL - 0.214 <QL – 0.396

 Low-contact surfaces 14 86 0.208 0.175 - 0.305 <QL – 0.864

School 4

 High-contact surfaces 9 44 <QL <QL - 0.288 <QL – 0.454

 Low-contact surfaces 9 100 0.328 0.213 - 0.407 0.140 – 2.10

School 5

 High-contact surfaces 12 33 <QL <QL - 0.182 <QL – 1.10

 Low-contact surfaces 12 83 0.388 0.166 - 0.749 <QL – 1.51

School 6

 High-contact surfaces 14 93 0.380 0.303 - 0.723 <QL – 2.84

 Low-contact surfaces 20 80 0.206 0.127 - 0.431 <QL – 2.30

a Reported as total PCBs from Aroclor measurements in surface wipes.

b When duplicate samples were collected, the average of the duplicates was used.

c

 Number samples and number of rooms sampled, except for School 6 where two high-contact and three low contact 

samples were collected in most rooms. 

d QL = Quantitation limit; 0.100 μg/100 cm2

 for all samples.
57

Table 4-21. Indoor dust total PCB measurement results at School 6

School Nc

%

> 

QLd

Total PCB Levels in Indoor Dust a,b

Median

ppm

Inter-Quartile 

Range

ppm

Overall Range

ppm

School 6e 7 100 22.0 16.6 - 53.4 11.6 – 86.8

a Reported as total PCBs from Aroclor measurements in dust.

 b When duplicate samples were collected, the average of the duplicates was used.

c

 Number of samples and number of rooms sampled.

d QL = Quantitation limit; ranged from 0.54 to 14.2 ppm depending on sample size

e This school building had not been cleaned in the approximately 5 weeks prior to sample collection; the residence time for 

PCB absorption may have been longer than for most school situations.
58

Table 4-22. Soil total PCB measurement results at six schools

School/Condition Nc

%

 > 

QLd

Total PCB Levels in Soila,b

Median

ppm

Inter-Quartile 

Range 

ppm

Overall Range 

ppm

All Six Schools

 All soil samples 309 33 <QL <QL - 0.975 <QL – 211

 0.15m from building; 0–5cm depth 99 48 <QL <QL - 2.13 <QL - 211

 0.91m from building; 0–5cm depth 102 28 <QL <QL - 0.548 <QL – 20.6

 2.44m from building; 0–5cm depth 105 21 <QL <QL - <QL <QL – 5.28

School 1

 0.15m from building; 0–5cm depth 12 50 0.910 <QL - 4.27 <QL - 211

 0.91m from building; 0–5cm depth 12 8 <QL <QL - <QL <QL – 4.74

 2.44m from building; 0–5cm depth 26 8 <QL <QL - <QL <QL – 0.795

School 2

 0.15m from building; 0–5cm depth 30 13 <QL <QL - <QL <QL – 1.03

 0.91m from building; 0–5cm depth 29 7 <QL <QL - <QL <QL – 1.34

 2.44m from building; 0–5cm depth 27 11 <QL <QL - <QL <QL – 2.48

School 3

 0.15m from building; 0–5cm depth 8 100 9.60 6.10 - 15.4 5.70 – 32.7

 0.91m from building; 0–5cm depth 9 100 5.84 4.89 - 9.43 2.31 – 20.6

 2.44m from building; 0–5cm depth 14 100 2.14 1.66 - 2.57 0.797 – 5.28

School 4

 0.15m from building; 0–5cm depth 35 49 <QL <QL - 1.38 <QL – 7.67

 0.91m from building; 0–5cm depth 38 18 <QL <QL - <QL <QL – 3.17

 2.44m from building; 0–5cm depth 31 0 <QL <QL - <QL <QL

School 5

 0.15m from building; 0–5cm depth 12 92 3.68 2.16 - 16.0 <QL – 36.0

 0.91m from building; 0–5cm depth 12 67 1.75 <QL - 5.46 <QL – 13.4

 2.44m from building; 0–5cm depth 5 40 <QL <QL - 0.72 <QL – 1.62

 3.7/4.9m from building; 0–5cm depth 2 100 1.21 1.21 0.674 – 1.75

School 6

 0.15m from building; 0–5cm depth 2 100 1.87 -- 1.69 – 2.05

 0.91m from building; 0–5cm depth 2 100 0.267 -- 0.186 – 0.348

 2.44m from building; 0–5cm depth 2 50 <QL -- <QL – 0.132

 15.2m from building; 0–5cm depth 1 0 <QL -- --

a Reported as total PCBs from Aroclor measurements in soil.

b When duplicate samples were collected, the average of the duplicates was used.

c

 Number of samples.

d QL = Quantitation limit; 0.5 ppm for Schools 1 – 5 and from 0.05 to 0.58 ppm for School 6. 
59

Table 4-23. Soil total PCB measurement results for two soil depths at School 1

School/Condition Nc

%

>QLd

Total PCB Levels in Soil a,b

Median

ppm

Inter-Quartile 

Range

ppm

Overall Range

ppm

School 1

 0.15m from building; 0–5cm depth 12 50 0.910 <QL - 4.27 <QL - 211

 0.15m from building; 5–10cm depth 12 67 1.43 <QL - 4.78 <QL – 19.4

 0.91m from building; 0–5cm depth 12 8 <QL <QL - <QL <QL – 4.74

 0.91m from building; 5–10cm depth 12 58 0.780 <QL - 1.48 <QL – 7.00

 2.44m from building; 0–5cm depth 26 8 <QL <QL - <QL <QL – 0.795

 2.44m from building; 5–10cm depth 26 12 <QL <QL - <QL <QL – 0.662

 a Reported as total PCBs from Aroclor measurements in soil.

 b When duplicate samples were collected, the average of the duplicates was used.

c

 Number of samples.

 d QL = Quantitation limit; 0.5 ppm for School 1.

total PCB concentrations across six schools at 0.15, 0.91, 

and 2.44 m from the building were 2.13, 0.55, and <0.5 ppm 

respectively. Soil PCB concentrations decreased with 

increasing distance from the school building. There was 

considerable variability between schools; for example one 

school had only 10% of the PCB levels above the quantifiable 

limit while another school had 100%. Distributions of the 

soil concentrations are shown by school in Figure 4-9 where 

there were sufficient measurable results. 

Additional soil samples were collected from the 5 to 10 

cm (2 to 4 inch) depth at School 1. A comparison of the 

results from the 0 - 5 and 5 – 10 cm samples are shown in 

Table 4-23. The results are difficult to interpret. At both the 

0.15 and 0.91 m distances, the median concentration at the 

5 – 10 cm depth was greater than the median concentration 

at the 0 – 5 cm depth. On the other hand, the maximum 

concentrations at the 0 – 5 cm depth were greater than the 

maximum values at the 5 – 10 cm depth for samples collected 

0.15 and 2.44 m from the building. Concentrations at the 

0 – 5 cm depth are likely to be more relevant for assessing 

exposure potential. 

The geometric mean total PCB concentration for school soils 

from extant data (Appendix D) was 0.98 ppm with a range 

up to about 80 ppm. The median value for the six schools 

in this report was less than the QL (a value of 0.5 ppm for 

most samples), lower than the extant data geometric mean. 

On the other hand, the maximum value from these schools 

(211 ppm) was greater than the extant data maximum. There 

are few reports of PCB levels in urban soils in the United 

States. One recent report from Cedar Rapids, IA showed a 

mean total PCB level of 0.056 ± 0.16 ppm with a maximum 

value of 1.2 ppm (Martinez et al., 2012). The authors report 

that the mean is about 10 times higher than the world-wide 

background levels based on the sum of 29 congeners. The 

75th percentile level of PCBs in soils adjacent to school 

buildings in this study is considerably higher than the 0.045 

ppm 75th percentile value for urban soils in Cedar Rapids, and 

much greater than background non-urban levels. 

4.3.6 Pre- and Post-Remediation Air and Wipes

As part of the NYC SCA remedial pilot investigation, 

various remedial actions for reducing potential exposures 

to PCBs were examined in five NYC schools. These 

Figure 4-9. Distributions of outdoor soil total PCB concentrations 

across all six schools and at each individual school. The box plots 

show the median, 25th, and 75th percentiles. The whiskers show the 

10th and 90th percentiles.
60

remedial activities are briefly summarized in Table 4-2. 

It is not within the scope of this report to examine and 

explain the remedial activities and results in detail. That 

information has been well described in other reports 

(NYC SCA 2011; NYC SCA 2012). However, it is of 

interest to understand how the changes in school environment 

PCB concentrations resulting from remedial actions may 

impact the potential exposures to building occupants. Preremediation and post-remediation air and surface wipe 

measurement results at selected remedial time points were 

assembled for use in the SHEDS modeling effort. 

Summaries of the indoor air PCB concentrations at pre- and 

post-remediation time points for five schools are shown in 

Table 4-24. Distributions of indoor air PCB levels at pre- and 

post-remediation time points are shown in Figure 4-10. (The 

Table 4-24. Pre- and post-remediation indoor air total PCB measurement results at five schools

School/Condition Nc

Mean

QLd

ng/m3

%

>QL

Total PCB Levels in Aira,b

Median

ng/m3

Inter-Quartile 

Range 

ng/m3

Overall Range

ng/m3

All Five Schools

 Pre-Remediation 57 50 74 257 <QL - 763 <QL - 2920

 Post-Remediation 163 48 58 73 <QL - 209 <QL - 480

School 1

 Pre-Remediation 11 49 54 58 <QL - 121 <QL - 194

 Post-Remediation 16 48 50 42 <QL - 120 <QL - 227

School 2

 Pre-Remediatione 12 50 100 807 674 - 874 414 - 1460

 Post-Remediation 59 48 97 224 192 - 262 <QL - 362

School 3

 Pre-Remediation 14 51 100 504 271 - 960 236 - 2920

 Post-Remediation 68 48 29 <QL <QL - 56 <QL - 331

School 4

 Pre-Remediation 9 50 44 <QL <QL - 154 <QL - 674

 Post-Remediation 9 49 44 <QL <QL - 138 <QL - 299

School 5

 Pre-Remediation 11 50 54 154 <QL - 425 <QL - 867

 Post-Remediation 11 49 54 76 <QL - 173 <QL - 480 

a Reported as total PCBs from Aroclor measurements in indoor air.

b When duplicate samples were collected, the average of the duplicates was used.

c

 Number of samples and number of rooms sampled.

d QL = Quantitation limit.

e Does not include a second day of pre-remediation air results for School 2.

post-remediation air values in Table 4-24 and in Figure 4-10 

are based on the time point following light fixture removal 

at Schools 1 – 3 and the final remediation time point for 

Schools 4 and 5; see Table 4-2 for details). Across all five 

schools, the median indoor air concentrations decreased 72% 

from the pre- to post-remediation time point and average 

levels decreased 74%. The magnitude of the decrease was 

different across the five schools. For example, at School 3 

the median levels decreased from 504 ng/m3 to < 50 ng/m3

while at School 5 the median value decreased from 154 to 

76 ng/m3. The pre-remediation indoor air levels at School 

1 were relatively low at 58 ng/m3 and showed only a small 

decrease. However, problems were found with the HVAC 

outdoor air intake controls at School 1and it is not clear 

whether ventilation conditions were the same at both the 

pre- and post-remediation time points. 
61

Figure 4-10. Pre- and post-remediation indoor air total PCB concentrations across five schools and at 

each individual school. The box plots show the median, 25th, and 75th percentiles. The whiskers show 

the 10th and 90th percentiles.

Figure 4-11. Pre- and post-remediation indoor air total PCB concentrations across three schools with several 

different remedial activities. 
62

Air measurements were conducted at several stages of 

remedial activities at at the three 2010 NYC pilot schools. 

Summaries of the indoor air total PCB levels in air are shown 

in Figure 4-11. For School 1, pre-remediation concentrations 

were relatively low as compared to Schools 2 and 3, but 

appeared to increase somewhat at two remedial time points. 

This may have been a result with problems subsequently 

found in the ventilation system outside air inlet. For Schools 

2 and 3, there appeared to be substantial reductions in indoor 

air levels at each stage of remedial action. Caulk remediation 

included removal at School 2 and encapsulation at School 3, 

but only in those spaces that had indoor air measurements. 

It appeared that cleaning/ventilation steps and light fixture 

removal also contributed to decreased indoor air levels at 

subsequent time points in these buildings. The reductions 

in air concentrations occurred based on measurements 

taken within days or weeks following the specific remedial 

activities.

Pre- and post-remediation results for surface wipe samples 

are shown in Table 4-25. (The post-remediation wipe 

samples were collected following the caulk remediation step, 

while the air results shown in Table 4-24 and Figure 4-11 

were collected following the additional ventilation, cleaning, 

and light removal steps). There was no clear pattern of preto post-remediation change in surface wipe measurement 

results. Median measurement values across the five schools 

showed virtually no change from pre- to post-remediation 

for the high-contact surfaces, while the median and 75th

percentile values at the post-remediation samples were 

approximately 40% lower than pre-remediation levels for 

low-contact surfaces. PCBs collected by the surface wipes 

may reflect PCB concentrations in the underlying materials 

which could serve as reservoirs for longer time periods 

that the few weeks between the pre- and post-remediation 

sample collection. PCB concentrations in many materials 

were reported in Section 4.3. Based on modeling estimates 

by Guo et al. (2012) it is likely that PCB concentrations in 

these secondary sources will persist for some time following 

removal of primary sources. This may be a factor in the lack 

of large differences in pre- and post-remediation surface wipe 

concentrations measured over this time frame. On the other 

hand, the surface wipe measurements were generally low 

even at the pre-remediation time point. 

4.3.7 Relationships Between School Environment 

PCB Concentrations

Relationships between total PCB concentrations in different 

school environmental media can be examined for those 

situations with sufficient numbers of samples collected from 

the same school building rooms at the same time. A total 

of 64 sets of collocated indoor air, high-contact surface 

wipe, and low-contact surface wipe samples were collected 

across the six schools. Modest but significant correlations 

were found between indoor air PCB concentrations and 

high-contact surface wipe levels for samples collected at the 

six schools (Table 4-26). Higher correlation values were 

found for the Spearman correlation approach, which uses 

rank orders and may be more appropriate for data exhibiting 

logarithmic distributions. While some association was 

shown to exist, it is difficult to interpret in a larger exposure 

context because the air levels are relatively large and the 

surface wipe levels relatively low. Most of the exposure 

comes from the indoor air, not contact with surfaces. It 

is also not certain whether higher indoor air levels lead 

to associated higher surface concentrations or vice-versa, 

and whether the relationship might suggest some level of 

dynamic equilibrium between PCBs in the air and at surface 

boundaries. 

A total of seven sets of collocated indoor air, surface wipe, 

and dust samples were collected at School 6. Pearson and 

Spearman correlation coefficients among these media are 

reported in Table 4-26. The correlations between indoor 

air and high-contact surface wipe PCB levels was similar 

to those for all six schools, but with only seven samples the 

correlation was not significant. High and significant Pearson 

correlations were found between indoor air and dust levels, 

and between high-contact and low-contact surface wipe 

values. On the other hand, Spearman correlations were lower 

and not significant for these media combinations. Pearson 

correlations for small sample sizes must be interpreted 

cautiously because they can be highly influenced by one or 

two measurements. 
63

Table 4-25. Pre- and post-remediation surface wipe total PCB measurement results at five schools

School/Condition Nc

%

> 

QLd

Total PCB Levels in Surface Wipesa,b

Median

μg/100cm2

Inter-Quartile Range

μg/100cm2

Overall Range

μg/100cm2

All Five Schools

 Pre-Remediation (H)e 58 55 0.121 <QL - 0.214 <QL – 1.10

 Pre-Remediation (L) 58 79 0.201 0.131 - 0.407 <QL – 2.10

 Post-Remediation (H) 58 50 <QL <QL - 0.213 <QL – 0.947

 Post-Remediation (L) 58 55 0.123 <QL - 0.218 <QL – 11.0f

School 1

 Pre-Remediation (H) 11 54 0.106 <QL - 0.128 <QL – 0.177

 Pre-Remediation (L) 11 46 <QL <QL - 0.135 <QL – 0.716

 Post-Caulk Remediation (H) 11 9 <QL <QL - <QL <QL – 0.180

 Post-Caulk Remediation (L) 11 54 0.125 <QL - 0.191 <QL – 0.538

School 2

 Pre-Remediation (H) 12 83 0.207 0.141 - 0.328 <QL – 0.748

 Pre-Remediation (L) 12 83 0.165 0.139 - 0.369 <QL – 0.570

 Post-Caulk Remediation (H) 12 75 0.130 0.100 - 0.177 <QL – 0.806

 Post-Caulk Remediation (L) 12 92 0.192 0.132 - 0.240 <QL – 11.0f

School 3

 Pre-Remediation (H) 14 57 0.154 <QL - 0.214 <QL – 0.396

 Pre-Remediation (L) 14 86 0.208 0.175 - 0.305 <QL – 0.864

 Post-Caulk Remediation (H) 14 57 0.128 <QL - 0.259 <QL – 0.947

 Post-Caulk Remediation (L) 14 43 <QL <QL - 0.274 <QL – 0.753

School 4

 Pre-Remediation (H) 9 44 <QL <QL - 0.288 <QL – 0.454

 Pre-Remediation (L) 9 100 0.328 0.213 - 0.407 0.140 – 2.10

 Post-Caulk & Light Remediation (H) 9 33 <QL <QL - 0.161 <QL – 0.788

 Post-Caulk & Light Remediation (L) 9 33 <QL <QL - 0.124 <QL – 0.928

School 5

 Pre-Remediation (H) 12 33 <QL <QL - 0.182 <QL – 1.10

 Pre-Remediation (L) 12 83 0.388 0.166 - 0.749 <QL – 1.51

 Post-Caulk & Light Remediation (H) 12 67 0.135 <QL - 0.271 <QL – 0.592

 Post-Caulk & Light Remediation (L) 12 50 <QL <QL - 0.158 <QL – 0.546

a Reported as total PCBs from Aroclor measurements in surface wipes.

b When duplicate samples were collected, the average of the duplicates was used.

c

 Number of samples and number of rooms sampled.

d QL = Quantitation limit; 0.100 μg/100 cm2

 for all samples. e

 (H) = high contact surfaces (desks/tables); (L) = low contact surfaces (walls, floors, windowsills). f

 A wipe sample with an elevated PCB level (97.6 μg/100 cm2) was collected from the gymnasium at School 2. Eight other 

low-contact samples were also collected from the gym with results ranging from not detected to 0.202 μg/100 cm2. An 

average of the nine results (10.97 μg/100 cm2) was used.
64

Table 4-26. Correlations between total PCB concentrations in selected school environment samplesa,b

Schools/Sample Media N

Pearson Correlation Spearman Correlation

r p-value r p-value

Schools 1 - 6

 Indoor Air 64 0.256 0.041 0.531 <0.001

 High-Contact Surface Wipe

 Indoor Air 64 0.104 0.415 0.247 0.050

 Low-Contact Surface Wipe

 High-Contact Surface Wipe 64 0.270 0.031 0.220 0.081

 Low-Contact Surface Wipe

School 6

 Indoor Air 7 0.258 0.577 0.500 0.253

 High-Contact Surface Wipe

 Indoor Air 7 0.270 0.558 0.500 0.253

 Low-Contact Surface Wipe

 Indoor Air 7 0.805 0.029 0.536 0.215

 Dust

 High-Contact Surface Wipe 7 0.840 0.018 0.357 0.432

 Low-Contact Surface Wipe

 High-Contact Surface Wipe 7 0.170 0.716 0.393 0.383

 Dust

 Low-Contact Surface Wipe 7 0.010 0.983 -0.179 0.702

 Dust

a Based on total PCBs from Aroclor measurements.

b When duplicate samples were collected, the average of the duplicates was used.
65

4.4 Congener and Homolog Measurements

Examining patterns of individual congeners can provide 

insight regarding relationships between PCBs sources and 

environmental media, and can also provide information 

useful in exposure and risk assessment. Individual PCB 

congeners were measured in all of the air samples and in a 

subset of surface wipe, indoor dust, soil, caulk, and other 

building material samples collected at School 6. 

Many of the building measurements that have been 

performed for PCB assessment have used an Aroclor analysis 

approach as a way to keep costs and complexity relatively 

low as compared to performing congener-specific analysis. 

There is concern, however, that building environmental 

media and materials may not contain PCB congeners in 

the mixture proportions found in Aroclors. Congener 

mixtures in indoor air may reflect changes associated with 

vapor pressure and the resulting emission differences from 

sources such as caulk. PCBs in both materials and soil may 

show characteristics of “weathering” where the congener 

mixture has changed over time due to losses or perhaps even 

depletion of more volatile components over the 40 – 60 

year residence time. It is possible that analyzing building 

environmental media and materials as Aroclors could provide 

an inaccurate result for total PCB concentrations because of 

the way analytical quantitation is done for Aroclor analyses. 

It is possible that Aroclor results could be biased high or low, 

depending on the specifics of which congeners are used for 

quantitation and how the quantitation is handled. Because of 

the improved information obtained from congener analysis, 

and because of the potential biases in applying Aroclor 

analysis to environmentally altered mixtures, congener 

specific analysis would be preferred in most situations. 

A comparison can be made for the total PCB concentration 

determined by analysis using both Aroclor and congener 

quantitation approaches for the samples collected at School 

6. Results are shown in Table 4-27 for the several types of 

samples that were collected. Analysis of sample extracts 

for congeners resulted in total PCB concentrations that were 

approximately 20% lower (range 14 to 46% lower) than 

the analysis of the same sample extracts using an Aroclor 

method. The indoor air analysis results are of greatest 

interest because it is likely, if emissions from PCBs in caulk 

Table 4-27. Comparison of Aroclor and congener measurement results for total PCBs at School 6

Measurement N Units

Aroclor

Analysis 

Mean ± SD

Congener

Analysis

Mean ± SD % Differencea

Indoor Air 7 ng/m3 633 ± 189 500 ± 154 21

Surface Wipe 10 μg/100 cm2 0.507 ± 0.404 0.407 ± 0.379 20

Indoor Dust 4 ppm 36.0 ± 24.6 30.9 ± 18.6 14

Outdoor Soil 3 ppm 1.50 ± 1.38 1.09 ± 0.95 27

Interior Caulk 5 ppm 21.2 ± 12.3 11.5 ± 5.9 46

Exterior Caulk 3 ppm 143000 ± 8180 114000 ± 2230 20

Other Materials 18 ppm 47.2 ± 25.2 36.9 ± 19.9 22

a

 Calculated as (Aroclor Mean – Congener Mean)/Aroclor Mean) × 100. 

with an un-weathered Aroclor 1254 pattern are the primary 

source, that the congener pattern in air would be substantially 

different than the Aroclor 1254 pattern. This effect was seen 

in chamber emissions testing of caulk (Guo et al., 2011) 

where the pattern in chamber air was shifted substantially 

towards higher weight percentages of more volatile 

congeners.

Averages of the congener-specific measurement results 

are shown for 45 selected congeners in Table 4-28 for all 

of the different types of samples that were analyzed. The 

45 congeners were selected to represent a wide range of 

chlorination and vapor pressures; because they are congeners 

that have been widely reported in other studies; and because 

the sum of the congeners accounted for 82.9% or more of the 

sum of all 209 congeners. Table 4-28 shows the individual 

congener concentrations, the sum of all 209 congeners, the 

sum and weight percent of the 45 congeners in the table, 

the sum and weight percent of the 12 dioxin-like congeners 

(which are often used as a comparative toxicity estimate), and 

calculated toxic equivalence concentration for the 12 dioxinlike congeners (U.S. EPA, 2010; WHO, 2006). Results for 

the congener measurements calculated as weight percents 

are shown in Table 4-29. The weight percent results allow 

an easier comparison of patterns across the different sample 

types. Congener measurement results are reported in more 

detail by sample type in Appendix C. 

A comparison of the measured congener concentration 

pattern in the PCB-containing exterior caulk collected at 

School 6 to the congener weight percent pattern in Aroclor 

1254 as reported by Frame et al. (1996) is shown in Figure 

4-12. The congener pattern in caulk appears to be shifted 

towards higher percentages of congeners with lower vapor 

pressure as compared to Aroclor 1254. It is not known with 

any certainty whether this is a result of weathering over 43 

years or possibly because the original congener mixture was 

different than Aroclor 1254. However, the pattern did not 

appear to be similar to 1260; for example the weight percent 

of the 7-chlorine congeners in the caulk was 4.8% whereas it 

is 38% in Aroclor 1260. The analysis laboratory reported the 

result of the Aroclor analysis as Aroclor 1254 with an altered 

Aroclor pattern, and that is consistent with the congener 

specific results shown in Figure 4-12. 
66

Table 4-28. Summary of average PCB congener concentrations at School 6

PCB 

Congener #

Indoor

Air

N = 7 

ng/m3

Outdoor

Air 

N = 1

ng/m3

Surface

Wipe

N = 10

μg/100 

cm2

Indoor

Dust

N = 4

ppm

Outdoor

Soil

N = 3

ppm

Interior

Caulk

N = 5

ppm

Exterior

Caulk

N = 3

ppm

Other

Materials

N = 18

ppm

4 0.34 <QL <QL <QL <QL <QL <QL 0.009

8 1.5 <QL <QL <QL <QL 0.007 <QL 0.018

17 2.1 <QL <QL 0.001 <QL 0.011 11 0.041

18 3.8 <QL <QL 0.003 <QL 0.029 11 0.080

28 3.8 <QL 0.0010 0.018 <QL 0.048 46 0.18

31 3.7 <QL 0.0007 0.018 <QL 0.046 52 0.16

44 19 0.24 0.0066 0.29 0.0027 0.309 1300 0.85

47 4.8 0.06 0.0015 0.073 0.0003 0.077 340 0.21

49 15 0.17 0.0043 0.15 0.0026 0.19 770 0.46

52 45 0.45 0.0133 0.59 0.0087 0.70 3100 1.5

56 2.6 0.08 0.0019 0.13 0.011 0.063 350 0.27

64 5.5 0.04 0.0015 0.087 0.0007 0.085 380 0.25

66 6.9 0.09 0.0037 0.23 0.0043 0.14 770 0.51

70 25 0.39 0.0108 0.73 0.011 0.42 2600 1.2

74 12 0.18 0.0051 0.34 0.0053 0.20 1200 0.58

82 3.9 0.11 0.0035 0.40 0.0102 0.12 1200 0.45

84 14 0.24 0.0072 0.51 0.0078 0.29 1800 0.85

85 5.4 0.11 0.0044 0.42 0.012 0.15 1300 0.46

87 17 0.34 0.0144 1.4 0.031 0.48 2900 1.5

91 5.8 0.15 0.0032 0.19 0.0033 0.11 770 0.30

92 10 0.15 0.0057 0.46 0.011 0.22 1800 0.57

95 50 0.72 0.0180 1.3 0.020 0.77 5200 2.0

97 11 0.22 0.0086 0.74 0.017 0.28 2500 0.85

99 15 0.24 0.0136 1.1 0.031 0.48 3900 1.3

101 54 0.86 0.0336 2.6 0.064 1.1 9600 3.0

105 5.5 0.11 0.0108 1.2 0.053 0.27 3900 0.95

110 39 0.49 0.0270 2.7 0.069 0.87 8900 2.8

118 19 0.52 0.0242 2.6 0.095 0.64 8300 2.1

128 1.4 <QL 0.0043 0.51 0.034 0.10 2200 0.35

130 0.83 <QL 0.0018 0.19 0.011 0.043 730 0.14

132 6.6 0.23 0.0112 1.0 0.038 0.25 3600 0.98

138 8.3 0.23 0.0226 2.1 0.11 0.45 8200 1.6

141 2.2 0.07 0.0057 0.44 0.022 0.099 1600 0.42

146 1.2 0.03 0.0023 0.26 0.012 0.059 860 0.21

149 13 0.24 0.0188 1.5 0.050 0.42 5100 1.5

151 5.8 0.12 0.0070 0.49 0.015 0.16 1700 0.55

153 9.6 0.23 0.0225 1.7 0.075 0.40 6200 1.4

156 0.49 <QL 0.0027 0.28 0.023 0.058 1500 0.17

158 1.2 0.03 0.0033 0.30 0.014 0.065 1200 0.23

163 1.4 0.04 0.0039 0.36 0.020 0.078 1400 0.28

170 0.24 <QL 0.0030 0.15 0.016 0.032 970 0.13

174 0.70 <QL 0.0044 0.18 0.011 0.037 720 0.24
67

PCB 

Congener #

Indoor

Air

N = 7 

ng/m3

Outdoor

Air 

N = 1

ng/m3

Surface

Wipe

N = 10

μg/100 

cm2

Indoor

Dust

N = 4

ppm

Outdoor

Soil

N = 3

ppm

Interior

Caulk

N = 5

ppm

Exterior

Caulk

N = 3

ppm

Other

Materials

N = 18

ppm

180 0.76 <QL 0.0089 0.28 0.020 0.057 1400 0.35

187 0.83 <QL 0.0052 0.14 0.012 0.033 500 0.25

206 0.19 <QL 0.0068 0.008 0.0024 0.008 <QL 0.051

Σ 209 Cong.a 500 7.7 0.407 30.9 1.09 11.5 114000 36.9

Σ 45 Cong.b 456 7.20 0.359 28.1 0.96 10.5 101000 32.5

Σ DLC Cong.c 26 0.64 0.039 4.3 0.18 1.0 14500 3.4

DLC TEQd 7.88E-04 1.91E-05 1.19E-06 1.29E-04 5.53E-06 3.08E-05 0.436 1.05E-04

% 45 Cong.e 91.0 93.2 90.4 89.5 82.9 91.7 89.2 87.6

% DLC Cong.f 5.0 8.2 9.8 12.2 13.7 8.7 12.8 8.6

a

 Sum of all 209 congeners, including those not shown in this table.

b

 Sum of the 45 congeners shown in this table.

c

 Sum of the 12 dioxin-like congeners (77, 81,105, 114, 118, 123, 126, 156, 157, 167, 169, 189).

d TCDD toxic equivalence for the sum of the 12 dioxin-like congeners. 

e

 Percent of the total concentration of all 209 congeners for the sum of the 45 congeners in this table. 

f

 Percent of the total concentration of all 209 congeners for the sum of the 12 dioxin-like congeners.

Table 4-28. Summary of average PCB congener concentrations at School 6 (continued)
68

Table 4-29. Summary of average PCB congener weight percents at School 6a

PCB 

Congener #

Indoor

Air

N = 7

Outdoor

Air

N = 1

Surface

Wipe

N = 10

Indoor

Dust

N = 4

Outdoor

Soil

N = 3

Interior

Caulk

N = 5

Exterior

Caulk

N = 3

Other

Materials

N = 18

4 0.07 <QL <QL <QL <QL <QL <QL 0.02

8 0.3 <QL <QL <QL <QL 0.06 <QL 0.05

17 0.4 <QL <QL <QL <QL 0.1 0.01 0.1

18 0.8 <QL <QL 0.01 <QL 0.2 0.01 0.2

28 0.8 <QL 0.2 0.06 <QL 0.4 0.04 0.5

31 0.7 <QL 0.2 0.06 <QL 0.4 0.05 0.4

44 3.8 3.1 1.6 1.0 0.2 2.7 1.2 2.3

47 1.0 0.8 0.4 0.2 0.02 0.7 0.3 0.6

49 3.0 2.2 1.1 0.5 0.2 1.6 0.7 1.2

52 8.9 5.9 3.3 1.9 0.8 6.0 2.8 4.2

56 0.5 1.0 0.5 0.4 1.0 0.6 0.3 0.7

64 1.1 0.6 0.4 0.3 0.06 0.7 0.3 0.7

66 1.4 1.2 0.9 0.8 0.4 1.3 0.7 1.4

70 5.1 5.1 2.7 2.4 1.0 3.6 2.3 3.3

74 2.4 2.4 1.3 1.1 0.5 1.7 1.1 1.6

82 0.8 1.4 0.9 1.3 0.9 1.1 1.1 1.2

84 2.8 3.2 1.8 1.6 0.7 2.6 1.6 2.3

85 1.1 1.4 1.1 1.4 1.1 1.3 1.2 1.2

87 3.5 4.4 3.5 4.4 2.8 4.2 2.6 4.1

91 1.2 2.0 0.8 0.6 0.3 1.0 0.7 0.8

92 2.1 1.9 1.4 1.5 1.0 1.9 1.5 1.6

95 10 9.3 4.4 4.1 1.9 6.7 4.5 5.5

97 2.2 2.8 2.1 2.4 1.6 2.4 2.2 2.3

99 3.0 3.2 3.4 3.6 2.9 4.2 3.5 3.5

101 11 11 8.3 8.4 5.9 10 8.4 8.2

105 1.1 1.5 2.7 3.9 4.8 2.3 3.4 2.6

110 7.8 6.4 6.6 8.7 6.3 7.6 7.8 7.5

118 3.7 6.8 5.9 8.3 8.7 5.6 7.3 5.8

128 0.3 <QL 1.1 1.6 3.1 0.9 1.9 1.0

130 0.2 <QL 0.4 0.6 1.0 0.4 0.6 0.4

132 1.3 3.0 2.8 3.4 3.5 2.2 3.2 2.6

138 1.6 3.0 5.6 6.8 10 3.9 7.2 4.3

141 0.4 0.9 1.4 1.4 2.0 0.9 1.4 1.1

146 0.2 0.3 0.6 0.9 1.1 0.5 0.8 0.6

149 2.5 3.1 4.6 4.8 4.6 3.7 4.5 4.0

151 1.2 1.6 1.7 1.6 1.4 1.4 1.5 1.5

153 1.9 3.0 5.5 5.5 6.9 3.4 5.4 3.9

156 0.1 <QL 0.7 0.9 2.1 0.5 1.3 0.5

158 0.2 0.4 0.8 1.0 1.3 0.6 1.1 0.6

163 0.3 0.5 1.0 1.2 1.8 0.7 1.3 0.8
69

PCB 

Congener #

Indoor

Air

N = 7

Outdoor

Air

N = 1

Surface

Wipe

N = 10

Indoor

Dust

N = 4

Outdoor

Soil

N = 3

Interior

Caulk

N = 5

Exterior

Caulk

N = 3

Other

Materials

N = 18

170 0.05 <QL 0.7 0.5 1.5 0.3 0.8 0.4

174 0.1 <QL 1.1 0.6 1.0 0.3 0.6 0.6

180 0.2 <QL 2.2 0.9 1.8 0.5 1.2 0.9

187 0.2 <QL 1.3 0.5 1.1 0.3 0.4 0.7

206 0.04 <QL 1.7 0.03 0.2 0.07 <QL 0.1

Σ 45 Cong. %b 91 93 90 90 83 92 89 88

ng/m3 ng/m3 μg/100 cm2 ppm ppm ppm ppm ppm

Σ 209 Cong.c 500 7.7 0.407 30.9 1.09 11.5 114000 36.9

a

 Each congener’s percent of the total concentration of all 209 congeners. 

b

 Percent of the total concentration of all 209 congeners for the sum of the 45 congeners in this table. 

c

 Sum of the measured concentrations for all 209 congeners, including those not shown in this table.

Table 4-29. Summary of average PCB congener weight percents at School 6a

 (continued)
70

Figure 4-12. Patterns of congeners in Aroclor 1254 (top) and exterior caulk collected at School 6 
71

Figure 4-13 shows congener concentration patterns for 

indoor air and dust samples collected in School 6. The 

indoor air pattern appears to be shifted toward higher 

percentages of congeners with higher vapor pressures as 

compared to the dust, caulk, and Aroclor 1254 congener 

patterns. This is consistent with vapor emission as a source 

of the PCBs in air. 

Comparisons of congener patterns between indoor air, the 

exterior caulk, and Aroclors 1242 and 1254 are shown in 

Figure 4-14 for selected congeners ordered by decreasing 

vapor pressure. The congeners are shown on a weight 

percent basis so direct comparisons can be made. Once 

again, the comparison shows congeners in indoor air shifted 

towards more volatile congeners and those in caulk shifted 

towards less volatile congeners as compared to the Aroclor 

1254 pattern. The indoor air pattern does not match the 

overall Aroclor 1242 pattern, but some contribution of 

the more volatile Aroclor 1242 congeners in air can’t be 

ruled out. Congener weight percent patterns are shown for 

a number of different sample media in Figure 4-15. The 

congener pattern in paint more closely matches the Aroclor 

1254 pattern than does the caulk. As Guo et al., (2012) 

demonstrated in chamber studies, this might be expected 

as a result of greater emission of more volatile PCBs from 

caulk, but preferential absorption of less volatile PCBs 

into the paint, resulting in a pattern that looks remarkably 

like Aroclor 1254. Surface wipe and dust samples had 

lower percentages of more volatile congeners as compared 

to indoor air and Aroclor 1254 but as the congener vapor 

pressures decreased their patterns became more similar to the 

Aroclor 1254 pattern. 

Another way to examine patterns and differences in patterns 

of PCBs in different types of environmental media and 

materials is by examining PCB homologs. Congener 

measurement results can be used to calculate PCB homolog 

concentrations by summing all of the results for congeners 

with the same number of chlorines. A summary of the 

average chlorine-number homolog measurement results 

is shown as weight percents in Table 4-30. Results for 

the individual sample types are shown in more detail in 

Appendix C. 

Homolog weight percents for the PCB-containing exterior 

caulk and the indoor building materials are compared to 

homolog patterns in Aroclors 1242 and 1254 and indoor air 

in Figure 4-16. The caulk contains higher fractions of 6- and 

7-chlorine homologs as compared to Aroclor 1254 and a 

smaller fraction of 4-chlorine homologs compared both to 

Aroclor 1254 and the other materials. Homolog patterns for 

the air, wipe, dust, and soil samples are compared to patterns 

for Aroclors 1242 and 1254 in Figure 4-17. The indoor 

air has substantially higher percentages of the 2-, 3-, and 

4-chlorine homologs and less of the 6-, 7-, and 8-chlorine 

homologs compared to wipes, dust, soil, and Aroclor 1254. 

Surface wipe and dust patterns look similar, which is not 

surprising since the wipes likely have a dust component. 

The higher fraction of 6-chlorine homolog in dust as 

compared to air shows, as expected, that higher molecular 

weight congeners are more likely to be particle-bound. The 

outdoor soil has higher fractions of the 6-, 7-, and 8-chlorine 

homologs than Aroclor 1254 and the other media, possibly 

as a result of weathering loss of the more volatile congeners 

over time. 

Estimates of PCB emissions from caulk were reported in 

Section 4.2.1 based on an assumption that the congener 

pattern in the caulk was the same as the congener pattern in 

Aroclor 1254. This was a requirement since only Aroclor 

measurement results were available for most of the caulk 

samples. However, the availability of congener specific 

measurements for caulk at School 6 allows examination 

of the assumption. Table 4-31 shows a comparison of the 

estimated emission rates for selected congeners and total 

PCBs using first using the assumption that an Aroclor 1254 

pattern is present, and then again using the actual measured 

congeners in the exterior caulk. 

Because the relative concentrations of the more volatile 

congeners such as congener 52 were lower in the caulk 

than in Aroclor 1254, the assumed approach leads to higher 

emission estimates for the more volatile congeners. The 

opposite is true for the less volatile congeners such as 

congener 153. Overall, the estimated total PCB emission 

rate assuming an Aroclor 1254 mixture was 80% higher 

than the rate using the actual measurements of congener 

concentrations in the caulk. The impact is illustrated in 

Figure 4-18, which shows the estimated congener emission 

rates using the assumption of an Aroclor 1254 mixture 

and the measured congener concentrations in the caulk. 

Figure 4-19 shows the congener emission rates based on the 

congeners measured in the exterior caulk at School 6 and 

the concentration of congeners measured in indoor air. The 

indoor air pattern more closely matches the pattern using 

the measured congener concentrations than for the estimated 

emission rates assuming an un-weathered Arcolor 1254 

mixture as shown in Figure 4-18. A caution is warranted in 

this assessment. It is not certain whether the most volatile 

congeners were not detected in the School 6 caulk because 

they were not present or because of the level of dilution that 

was used for these sample extracts (due to the very high 

PCB concentrations). If those more volatile congeners were 

actually present but unmeasured, the total PCB emission rate 

could be underestimated. 

A summary of the key findings from the assessment of 

congener and homolog data using samples collected at one 

school is provided below. 

• The pattern of congeners in indoor air was more heavily 

weighted towards more volatile congeners as compared to 

Aroclor 1254 and compared to the PCB-containing caulk 

at the building. The pattern of congeners in air was not 

as heavily weighted towards more volatile congeners as 

would be predicted if they were from vapor emissions 

from caulk alone, suggesting that a portion of the PCBs 

in air may be associated with airborne particles, but 

this could also reflect in part a higher proportion of less 

volatile congeners in the caulk. 
72

Figure 4-13. Patterns of congeners in indoor air (top) and indoor dust collected at School 6 
73

Figure 4-14. Patterns of relative weight percent for selected congeners in Aroclor 1254 (top) and Aroclor 1242 (bottom) compared to averages for 

exterior caulk and indoor air at School 6 
74

Figure 4-15. Patterns of relative weight percent for selected congeners in Aroclors 1254 compared to average values for indoor air, paint, and 

exterior caulk (top), and indoor air, surface wipes, dust, and soil (bottom) at School 6 
75

Table 4-30. Summary of average PCB homolog weight percents at School 6a

PCB 

Homolog

Indoor

Air

N = 7

Outdoor

Air

N = 1

Surface

Wipe

N = 10

Indoor

Dust

N = 4

Outdoor

Soil

N = 3

Interior

Caulk

N = 5

Exterior

Caulk

N = 3

Other

Materials

N = 18

1-Chlorine 0.02 <QL <QL <QL <QL <QL <QL <QL

2-Chlorine 0.6 <QL <QL <QL <QL 0.1 <QL 0.1

3-Chlorine 4.3 <QL 1.1 0.4 0.2 1.8 0.1 2.6

4-Chlorine 31.1 26.4 11.6 11.3 14.4 21.8 10.5 19.9

5-Chlorine 51.1 56.6 44.8 50.8 34.6 53.4 49.6 47.4

6-Chlorine 11.9 17.0 30.4 31.6 40.6 21.0 34.8 24.5

7-Chlorine 0.8 <QL 7.1 5.4 8.2 1.7 4.8 4.6

8-Chlorine 0.1 <QL 1.7 0.5 1.0 0.1 0.2 0.6

9-Chlorine 0.04 <QL 3.3 0.1 1.0 0.1 <QL 0.2

10-Chlorine <QL <QL <QL <QL <QL <QL <QL <QL

Σ 209 Cong.b ng/m3

500

ng/m3

7.7

μg/100 cm2

0.407

ppm

30.9

ppm

1.09

ppm

11.5

ppm

114000

ppm

36.9

a

 Each chlorine-number homolog’s percent of the total concentration of all 209 congeners. 

b

 Sum of the measured concentrations for all 209 congeners.
76

1-CL

0.5%

2-Cl

13.4%

3-Cl

48.0%

4-Cl

32.7%

5-Cl

5.2%

Aroclor 1242

3-Cl

1.2%

4-Cl

16.3%

5-Cl

52.7%

6-Cl

26.8%

7-Cl

2.7%

Aroclor 1254

3-Cl

2.6%

4-Cl

19.9%

5-Cl

47.4%

6-Cl

24.5%

7-Cl

4.6%

8-Cl

0.6%

Indoor Materials

3-Cl

0.1%

4-Cl

10.5%

5-Cl

49.6%

6-Cl

34.8%

7-Cl

4.8%

8-Cl

0.2%

ExteriorCaulk

2-Cl

0.6%

3-Cl

4.3%

4-Cl

31.1%

5-Cl

51.1%

6-Cl

11.9%

7-Cl

0.8%

IndoorAir

Figure 4-16. Relative weight percents of PCB chlorine-number homologs for Aroclors 1242 and 1254 compared to the averages of the exterior 

PCB-containing caulk, the indoor building materials, and indoor air
77

1-CL

0.5%

2-Cl

13.4%

3-Cl

48.0%

4-Cl

32.7%

5-Cl

5.2%

Aroclor 1242

3-Cl

1.2%

4-Cl

16.3%

5-Cl

52.7%

6-Cl

26.8%

7-Cl

2.7%

Aroclor 1254

2-Cl

0.6%

3-Cl

4.3%

4-Cl

31.1%

5-Cl

51.1%

6-Cl

11.9%

7-Cl

0.8%

IndoorAir

3-Cl

1.1%

4-Cl

11.6%

5-Cl

44.8%

6-Cl

30.4%

7-Cl

7.1%

8-Cl

1.7%

9-Cl

3.3%

Surface Wipes

3-Cl

0.4%

4-Cl

11.3%

5-Cl

50.8%

6-Cl

31.6%

7-Cl

5.4%

8-Cl

0.5%

IndoorDust

3-Cl

0.2%

4-Cl

14.4%

5-Cl

34.6% 6-Cl

40.6%

7-Cl

8.2%

8-Cl

1.0%

Outdoor Soil

Figure 4-17. Relative weight percents of PCB chlorine-number homologs for Aroclors 1242 and 1254 compared to the averages of the 

environmental media
78

Table 4-31. Differences in PCB congener emission estimates from exterior caulk assuming an un-weathered Aroclor 

1254 pattern vs. measured congener concentrations

Congener

Congener Weight

Percent

Concentration

In Caulk 

ppm

Estimated

Emission Rate 

μg/hr

Assuming Caulk Contains Aroclor 1254 Congener Mixture, Total PCBs 112,000 ppm, Surface Area 0.271 m2

8 0.13 146 67.6

18 0.25 280 70.9

28 0.19 213 21.0

44 2.31 2,590 121

52 5.38 6,030 370

70 3.49 3,910 78.1

110 9.29 10,400 75.6

153 3.77 4,220 10.2

180 0.67 750 0.31

Σ209 Cong. 100 112,000 1,841

Using Congeners Measured in Exterior Caulk with Total PCBs 112,000 ppm, Surface Area 0.271 m2

8 <QL <QL --

18 <QL <QL --

28 <QL <QL --

44 0.92 1,030 48.3

52 2.04 2,280 140

70 2.15 2,400 48.0

110 7.98 8,910 64.8

153 5.70 6,360 15.4

180 1.41 1,570 0.66

Σ209 Cong. 100 112,000 1,020

 Ratios of Assumed/Measured

8 -- -- --

18 -- -- --

28 -- -- --

44 2.51 2.51 2.51

52 2.64 2.64 2.64

70 1.62 1.63 1.63

110 1.16 1.17 1.17

153 0.66 0.66 0.66

180 0.48 0.48 0.47

Σ209 Cong. 1.00 1.00 1.80
79

Figure 4-18. Patterns of estimated emission rates from exterior caulk collected at School 6 assuming the caulk contains congeners in an Aroclor 

1254 proportions (top) and using congener values measured in the caulk (bottom)
80

Figure 4-19. Patterns of estimated emission rates from exterior caulk collected at School 6 (top) versus the congeners measured in indoor 

air (bottom) 
81

• The congener pattern in the PCB-containing caulk was 

somewhat more heavily weighted towards less volatile 

congeners as compared to Aroclor 1254. It is possible 

that the more volatile congeners have been depleted from 

sources such as the exterior caulk over a period of 43 

years. 

• The congener pattern in indoor air in this school did not 

resemble Aroclor 1242, the Aroclor most likely to be 

present in light ballasts. However, the oil content in the 

ballasts in this school was not measured and Aroclor 1254 

can’t be ruled out given that Aroclor 1254 was found in a 

ballast in one of the NYC schools. 

• Congener patterns in surface wipe, indoor dust, and other 

building materials were generally similar to Aroclor 1254 

and to the PCB-containing caulk.

• Soil samples had a congener pattern weighted towards less 

volatile congeners as compared to the PCB-containing 

caulk and Aroclor 1254, possibly as a result of weathering.

• The congener patterns in some environmental media, 

particularly indoor air, did not match typical congener 

patterns in the source materials. Total PCBs measured 

as Aroclors were approximately 20% higher than total 

PCBs based on congener analysis. A homolog analysis 

approach could provide more accurate total PCB 

measurements than Aroclor measurements, but at a 

somewhat higher cost. Homolog measurements are likely 

to be less costly than full congener-specific analyses. 

• Congener-specific analysis provides information that 

can be used in risk assessment. For example, between 

5% (indoor air) and 14% (soil) of the total amount of 

PCBs were comprised of the sum of the 12 dioxin-like 

congeners. However, the congener analysis was limited 

to samples from one school. This was not sufficient for 

probabilistic analysis in the SHEDS model. 
82

4.5 SHEDS Exposure Modeling 

4.5.1 Distributions of Exposure Estimates

The Stochastic Human Exposure and Dose Simulation 

(SHEDS) model was used to generate estimated PCB 

absorbed dose distributions resulting from exposures to 

environmental levels measured at the six schools. The 

model was run for four age groups (4-5, 6-10, 11-14, and 

14-18 years old). Mean estimates of absorbed dose, and the 

estimated absorbed doses across selected percentiles of the 

modeled distribution are shown in Table 4-32. For the 6-10 

year-old age group, the estimated absorbed dose was 0.022 

μg/kg/day at the 50th percentile of the distribution and 0.041 

μg/kg/day at the 95th percentile. Estimated absorbed dose 

levels were lower for the other three age groups relative to 

the 6-10 year old group, and were 55% and 56% lower for 

the 14 – 18 year old age group at the 50th and 95th percentiles, 

respectively. 

Estimates of absorbed PCB doses were generated using 

environmental measurements at five schools with available 

pre- and post-remediation measurements. Mean estimates 

of total absorbed dose, and the estimated absorbed doses 

across selected percentiles of the modeled distribution are 

shown for the four age groups at pre- and post-remediation 

time points in Table 4-33. The ratios of post-remediation 

to pre-remediation absorbed dose estimates are shown in 

Table 4-34. For the 6-10 year-old age group the 

post-remediation absorbed dose estimates were 64% lower 

than pre-remediation at the 50th percentile and 69% lower 

at the 95th percentile. Similar pre- to post-remediation 

decreases in estimated absorbed dose were found for all 

age groups. Box plots of the pre- and post-remediation 

estimated absorbed doses by age group are shown in 

Figure 4-20 and percentile distribution plots are shown for 

the 6-10 year-old group in Figure 4-21. 

These results indicate that remedial actions are likely to 

result in decreased exposures. At the pre-remediation time 

point, 25 to >75% of the distribution was lower than the 

adjusted RfD value of 17 ng/kg/day, depending on age group. 

At the post-remediation time point, >95 to >99% of the 

distribution was less than the adjusted RfD, for school-related 

exposures only. It is important to remember that different 

types and scales of remedial action were taken at each of 

the five schools (see Table 4-2) and that different relative 

decreases in indoor air levels were seen across the schools 

(see Table 4-24). It is possible that other factors, such as 

differences in temperature and ventilation conditions at the 

pre- and post-remediation time points could have affected the 

environmental concentrations and estimated exposures. 

Estimates of absorbed PCB doses were also generated using 

environmental measurements at three schools with available 

pre- and post-remediation measurements taken during the 

same year and with subsequent pre-remediation indoor 

air and surface wipe measurements taken the following 

year. The purpose of this assessment is to evaluate whether 

reductions in exposure may be expected to be sustained over 

time following remedial actions. SHEDS model estimates 

of absorbed doses using measurements from the second-year 

post-remediation time point were not performed because the 

indoor air and surface wipe pre- and post remediation results 

were not significantly different (NYC SCA, 2012). Mean 

estimates of absorbed dose, and the estimated absorbed doses 

across selected percentiles of the modeled distribution are 

shown for the four age groups at the three time points in 

Table 4-35. Percentile distribution plots are shown for the 

6-10 year-old group in Figure 4-22. Overall, the estimated 

absorbed dose levels for the pre-remediation time point in the 

second year were slightly higher than the post-remediation 

time point from the first year, but were still substantially 

lower than the first year pre-remediation time point. These 

results suggest reductions in exposure resulting from 

remedial actions may be retained over time. It is important to 

remember that different types and scales of remedial action 

were taken at these three schools and that some activities 

were performed between the first year post-remediation time 

point and the second year pre-remediation time point (see 

Table 4-2). It is also possible that differences in temperature 

and ventilation conditions at the different time points could 

have affected the environmental concentrations and estimated 

exposures, although temperatures were generally within 6°F 

or less across the time points (see Table 4-3). 

SHEDS model estimates of PCB total absorbed dose 

generated using the environmental measurement data from 

the six schools examined in this report were compared to 

estimates generated using extant PCB data not associated 

with the six schools measurements. These data were 

gleaned from several reports and internet sources in 2009 

and included measurements for indoor air, dust, surface 

wipes, and soil (see Appendix D for the data sources and 

environmental measurement distributions). The estimated 

absorbed doses using the other extant data are shown in 

Table 4-36 for comparison with results generated using 

results from the six schools in this report. Comparisons 

were made only for the 6-10, 11-13, and 14-18 year old age 

groups. In general, the estimated absorbed doses using data 

from the six schools are similar to estimates using other data. 

Overall, the mean and median estimated absorbed doses 

were similar for the two sets of data when comparisons are 

for the pre-remediation time point for the six school sets of 

measurements in this report. The fraction of absorbed dose 

resulting from inhalation was higher for the results used in 

this report than for the other school data at most percentile 

levels largely because the indoor air PCB concentrations 

in the six schools were about 3-fold higher at the median 

than the other extant school data. On the other hand, 

concentrations in wipe samples in the six schools were about 

one-third of the levels obtained from the other extant school 

data, with the dermal and non-dietary ingestion representing 

a lower proportion of the intake at the six schools used in this 

report. 

4.5.2 Estimated Exposures by Exposure Route

Information on the relative importance and contribution of 

different exposure pathways to the total exposure can help 

inform mitigation decision-making. The SHEDS model 

provides estimates of the school PCB exposure from each 

relevant route. Table 4-37 shows the apportionment of 
83

Table 4-32. Distributions of total absorbed PCB dose estimated by SHEDS based on measurement data from six schools 

(units: μg/kg/day)

Child Age 

Group Mean SD

Percentiles of the Distribution of Dose Estimates

p5 p25 p50 p75 p95 p99

4 – 5 year olds 0.023 0.012 0.012 0.017 0.021 0.026 0.036 0.061

6 – 10 year olds 0.027 0.032 0.015 0.019 0.022 0.027 0.041 0.125

11 – 13 year olds 0.015 0.005 0.009 0.012 0.015 0.018 0.022 0.028

14 – 18 year olds 0.011 0.005 0.004 0.007 0.010 0.014 0.018 0.023

Table 4-33. Distributions of total absorbed PCB dose estimated by SHEDS based on measurement data from five 

schoolsa at pre-remediation and post-remediation time points (units: μg/kg/day)

Child Age 

Group Mean SD

Percentiles of the Distribution of Dose Estimates

p5 p25 p50 p75 p95 p99

4 – 5 year olds

 Pre-remediation 0.019 0.006 0.010 0.016 0.019 0.022 0.029 0.032

 Post-remediation 0.007 0.002 0.004 0.006 0.007 0.008 0.010 0.014

6 – 10 year olds

 Pre-remediation 0.023 0.015 0.014 0.018 0.021 0.025 0.037 0.071

 Post-remediation 0.008 0.004 0.005 0.006 0.007 0.009 0.012 0.018

11 – 13 year olds

 Pre-remediation 0.014 0.004 0.008 0.011 0.013 0.016 0.021 0.023

 Post-remediation 0.005 0.001 0.003 0.004 0.005 0.006 0.007 0.009

14 – 18 year olds

 Pre-remediation 0.010 0.004 0.003 0.007 0.010 0.013 0.018 0.022

 Post-remediation 0.003 0.002 0.001 0.002 0.003 0.005 0.006 0.008

a

 Schools 1, 2, 3, 4, and 5.
84

Figure 4-20. Distributions of estimated absorbed total PCB doses from exposures at school for four age 

groups at five schools for pre- and post-remediation time points. The box plots show the median, 25th, and 

75th percentiles. The whiskers show the 10th and 90th percentiles.

Table 4-34. Post-remediation/pre-remediation ratios of total absorbed PCB dose estimates based on measurement data 

from five schools

Child Age 

Group Mean

Post/Pre-Remediation Ratio for the Distribution of Dose Estimates

p5 p25 p50 p75 p95 p99

4 – 5 year olds 0.37 0.37 0.37 0.37 0.38 0.35 0.43

6 – 10 year olds 0.34 0.37 0.36 0.36 0.35 0.31 0.26

11 – 13 year olds 0.37 0.37 0.36 0.37 0.37 0.36 0.38

14 – 18 year olds 0.35 0.34 0.34 0.35 0.36 0.36 0.35
85

Figure 4-21. Distributions of estimated absorbed total PCB doses from exposures at school 

for the 6 – 10 year old age group at five schools for pre- and post-remediation time points

inhalation, dermal absorption, and non-dietary ingestion 

routes for the estimated PCB absorbed doses for the 6-10 

year old age group based on pre- and post-remediation 

measurements at five schools. The results in Table 4-37 

were estimated using several estimates of PCB pulmonary 

absorption since the actual value is not well known. 

Figures 4-23 and 4-24 show contributions from different 

exposure routes at different percentiles of the distributions for 

the 6-10 year-old age group. 

Overall, the inhalation pathway would appear to be the 

predominant route of exposure based on data from these 

schools. Inhalation exposures would account for over 73% 

of the total absorbed dose for 6-10 year-olds at the preremediation time point (when PCB concentrations in air 

were greatest) if pulmonary absorption fractions are 70% or 

higher. If pulmonary absorption were as low as 30%, then 

inhalation would be esimtated to account for 47% of the 

total absorption, with dust/soil ingestion accounting for 41% 

and dermal absorption 12%. At the post-remediation time 

point, the fraction from each exposure route was similar, in 

most cases, to the pre-remedition time point, although the 

relative contribution for non-dietary ingestion decreased 

when 30% pulomonary absorption is assumed. Similar 

patterns were seen for the other age groups. The contribution 

of non-dietary ingestion was not modeled for the two older 

age groups due to the lack of hand-to-mouth data, but would 

be expected to be lower than for 6-10 year olds because of 

reduced hand-to-mouth activity. 

Assuming a 70% pulmonary absorption, the fraction of the 

overall absorbed dose from non-dietary ingestion of PCBs 

in dust an soil remained relatively small (≤ 12%) for 6 – 10 

year-olds for most of the percentiles of the distribution of 

estimated absorbed doses (Figures 4-23 and 4-24). However, 

in the highest 10% of the distribution, over 50% of the 

pre-remediation total absorbed dose is predicted to result 

from non-dietary ingestion, greater than the amount from 

inhalation. This result occurs because of a combination 

of relatively high dust ingestion at the upper end of the 

activity distribution for the 6 – 10 year-old age group and 

relatively high PCB concentrations in dust and soil in the 

upper ends of the distributions. This could represent a 

relatively highly exposed sub-group of children, as the total 

estimated absorbed dose is also higher at the upper end of 

the distribution. Some caution is needed in interpreting the 

upper ends of modeled exposure distributions, and more 

information would be needed to determine whether more 

highly exposed sub-groups of children occur. 

4.5.3 Sensitivity Testing

Model sensitivity analyses are used to assess the relative 

impact and importance of uncertainties in model parameters 

and input data. Limited sensitivity analysis was conducted 

for two important but uncertain parameters used in 

the SHEDS PCB model. These include the fraction of 

pulmonary absorption of PCBs following inhalation, and the 

concentration of PCBs in dust and soil in the schools. 

To the best of our knowledge, the pulmonary absorption 

fraction for PCBs following inhalation has not been 

determined for humans. Extant rat data for PCBs and dioxin 

in the vapor phase, and dioxin bound to soil, suggest that the 

pulmonary absorbed fraction is likely to be high (Hu et al., 

2010; Dilberto et al., 1996 Nessel et al, 1992 - all resulted 

in values >80% ). On the other hand, three biomonitoring 

studies suggest that people exposed to PCBs in contaminated 

buildings may show increases in blood levels of the more 

volatile congeners, but overall circulating blood levels of 

total PCBs had only small differences compared to those in 

unexposed groups (Herrick et al., 2011; Liebel et al., 2004; 

Gabio et al., 2000). A value of 70% pulmonary absorbed 

fraction was assumed for the SHEDS PCB analysis. 

To examine the modeled absorbed dose estimate sensitivity 

associated with uncertainty in pulmonary absorbed fraction, 

sensitivity testing was performed on the impact of using 

30%, 80%, or 100% values for pulmonary absorbed fraction 

for 6-10 year-olds at the pre- and post-remediation time 

points for five schools (Table 4-38). Using a pulmonary 
86

Figure 4-22. Distributions of estimated absorbed total PCB doses from exposures at three schools for the 6 – 10 year old 

age group at three pre- and post-remediation time points across two years

Table 4-35. Distributions of total absorbed PCB dose estimated by SHEDS based on measurement data from three 

schoolsa at three time points (units: μg/kg/day)

Child Age Group Mean SD

Percentiles of the Distribution of Dose Estimates

p5 p25 p50 p75 p95 p99

4 – 5 year olds

 Pre-first-year remediation 0.025 0.009 0.013 0.020 0.024 0.029 0.039 0.052

 Post-first-year remediation 0.007 0.002 0.004 0.006 0.007 0.008 0.011 0.012

 Pre-second-year remediation 0.009 0.003 0.005 0.008 0.009 0.011 0.015 0.019

6 – 10 year olds

 Pre-first-year remediation 0.031 0.031 0.017 0.022 0.026 0.031 0.051 0.141

 Post-first-year remediation 0.008 0.003 0.005 0.006 0.007 0.009 0.012 0.017

 Pre-second-year remediation 0.011 0.007 0.007 0.009 0.010 0.012 0.016 0.030

11 – 13 year olds

 Pre-first-year remediation 0.018 0.005 0.010 0.014 0.018 0.021 0.026 0.033

 Post-first-year remediation 0.005 0.002 0.003 0.004 0.005 0.006 0.008 0.010

 Pre-second-year remediation 0.007 0.002 0.004 0.005 0.007 0.008 0.011 0.012

14 – 18 year olds

 Pre-first-year remediation 0.012 0.006 0.004 0.008 0.012 0.016 0.023 0.026

 Post-first-year remediation 0.004 0.002 0.001 0.002 0.004 0.005 0.006 0.007

 Pre-second-year remediation 0.005 0.002 0.002 0.003 0.005 0.006 0.008 0.010

a

 Schools 1, 2, and 3.
87

Table 4-36. Distributions of total absorbed PCB dose estimated by SHEDS based on measurement data from six schools 

in this report and from extant measurement dataa (units: μg/kg/day)

Child Age Group Mean SD

Percentiles of the Distribution of Dose Estimates

p5 p25 p50 p75 p95 p99

6 – 10 year olds

 6 schools, this report 0.027 0.032 0.015 0.019 0.022 0.027 0.041 0.125

 Extant data (Appx. D) 0.021 0.009 0.012 0.016 0.019 0.024 0.037 0.054

11 – 13 year olds

 6 schools, this report 0.015 0.005 0.009 0.012 0.015 0.018 0.022 0.028

 Extant data (Appx. D) 0.016 0.006 0.009 0.012 0.015 0.019 0.027 0.034

14 – 18 year olds

 6 schools, this report 0.011 0.005 0.004 0.007 0.010 0.014 0.018 0.023

 Extant data (Appx. D) 0.011 0.005 0.004 0.008 0.011 0.014 0.020 0.028

a

 Extant data and data sources shown in Appendix D.
88

Table 4-37. Proportion of mean estimated total absorbed PCB dose for 6 – 10 year olds for inhalation, non-dietary 

ingestion, and dermal absorption routes of exposure based on measurements from five schools

Inhalation

%

Non-Dietary 

Ingestion

%

Dermal Absorption

%

Pre-remediation

30% inhalation absorption 47 41 12

70% inhalation absorption (baseline) 74 18 8

100% inhalation absorption 78 15 6

Post-remediation

30% inhalation absorption 52 30 19

70% inhalation absorption (baseline) 74 15 12

100% inhalation absorption 79 13 8

Figure 4-23. Contributions of different exposure routes towards total estimated absorbed PCB doses for the 

6 – 10 year old age group at different percentiles of the total dose estimate based on measurements at six 

schools (assuming 70% pulmonary absorption)
89

Figure 4-24. Contributions of different exposure routes towards total estimated absorbed PCB doses for the 6 – 10 year 

old age group at different percentiles of the total dose estimate based on pre-remediation (top) and post-remediation 

(bottom) measurements at five schools (assuming 70% pulmonary absorption)
90

Table 4-38. Sensitivity test results of different pulmonary absorption rates on the distributions of estimated total 

absorbed PCB dose for 6 – 10 year olds based on pre- and post-remediation measurements from five schoolsa

Model Paramter Mean SD

Percentiles of the Distribution

p5 p25 p50 p75 p95 p99

Estimated Absorbed Dose μg/kg/day

Pre remediation

30% absorption 0.016 0.040 0.007 0.009 0.011 0.013 0.028 0.122

70% absorption (baseline) 0.023 0.015 0.014 0.018 0.021 0.025 0.037 0.071

80% absorption 0.026 0.021 0.015 0.019 0.023 0.028 0.043 0.106

100% absorption 0.032 0.024 0.018 0.024 0.028 0.033 0.046 0.101

Post remediation

30% absorption 0.005 0.004 0.003 0.003 0.004 0.005 0.009 0.019

70% absorption (baseline) 0.008 0.004 0.005 0.006 0.007 0.009 0.012 0.018

80% absorption 0.009 0.005 0.006 0.007 0.008 0.010 0.013 0.033

100% absorption 0.011 0.004 0.007 0.008 0.010 0.012 0.016 0.029

Ratio of Estimated Doses at 30, 80, and 100% to Baseline of 70%

Pre remediation

30% absorption 0.66 0.49 0.49 0.51 0.52 0.75 1.71

80% absorption 1.13 1.10 1.09 1.09 1.09 1.16 1.50

100% absorption 1.35 1.31 1.34 1.35 1.31 1.23 1.43

Post remediation

30% absorption 0.60 0.52 0.53 0.55 0.56 0.77 1.05

80% absorption 1.15 1.09 1.12 1.13 1.12 1.13 1.82

100% absorption 1.31 1.30 1.32 1.32 1.29 1.35 1.58

a Schools 1, 2, 3, 4, and 5.

absorption fraction of 30% at the pre-remediation time 

point resulted in a 49% decrease in the estimated absorbed 

dose at the 50th percentile and a 25% decrease at the 95th 

percentile. At the post-remediation time point, using the 30% 

pulmonary absorption fraction resulted in a 45% decrease in 

the estimated absorbed dose at the 50th percentile and a 23% 

decrease at the 95th percentile. The changes were smaller at 

the post-remediation time point because the contribution of 

inhalation to total exposure was lower due to the decreases in 

indoor air concentrations. 

Using a pulmonary absorption fraction of 100% at the preremediation time point resulted in an increase in the estimated 

absorbed dose of 35% at the 50th percentile and a 23% 

increase at the 95th percentile. At the post-remediation time 

point using a pulmonary absorption fraction of 100% resulted 

in an increase in the estimated absorbed dose of 32% at the 

50th percentile and 35% at the 95th percentile. The Public 

Health Levels were derived using an assumption of 100% 

pulmonary absorption. 

Modeled estimates of total PCB absorbed dose resulting from 

exposure to PCBs in the school environment was sensitive 

to the value used for pulmonary absorption fraction based 

on environmental PCB levels for the schools used in this 

assessment. While the available literature suggests that 

the pulmonary absorbed fraction is likely to be >80%, the 

available biomonitoring data do not necessarily support 

the high fraction. This is further complicated because the 

assumption for SHEDS modeling is that all congeners 

in Aroclor 1254 will have the same absorption fraction. 

However, the different congeners have different physical 

properties that affect both their absorption in pulmonary 

tissues as well as the fractions that will be found in vapor and 

particle-phase in indoor air and in different human tissues. 

This complicates both the understanding of pulmonary 

exposure and absorption and interpretation in biomonitoring 

measurements with regard to distribution and relative storage 

in different body tissues versus the relative fractions in 
91

circulating blood across the range of congeners. Differences 

in congener metabolism are likely to further complicate the 

interpretation of blood-based biomonitoring. On the other 

hand, congener-specific information can provide information 

about the various environmental and dietary sources, as well 

as potential toxicity when such information is available. 

Dust can be an important source of exposure for children. 

Indoor dust samples were not collected from the five 

NYC schools. Dust samples were collected from the sixth 

school, but since the school had not been routinely cleaned 

in the preceding five weeks it is not known if the dust 

concentrations were typical for this school. Therefore, in 

the SHEDS model analyses estimates of dust concentrations 

in each room with an air measurement were made using 

partition estimates (see Appendix E). Using this approach, 

the estimated concentrations of PCBs in dust were lower 

at the post-remediation time point due to the lower air 

concentrations. However, there were reasons to assess the 

sensitivity of modeled PCB exposures for lower dust and 

soil ingestion. First, given that schools are often regularly 

cleaned, child exposures to dust in school may be lower 

than those in residences. Second, the soil at the five NYC 

schools was remediated first by restricted access followed by 

removal of soils with > 1ppm PCBs. So the post-remediation 

exposures to PCBs in soil were likely to be lower. 

Sensitivity tests were run by decreasing the dust and soil 

concentrations by 70% and by 90% to examine the impact 

on SHEDS model absorbed dose estimates. Sensitivity test 

results for decreased dust and soil levels are reported in 

Table 4-39 for 6-10 year-olds at the post-remediation time 

points using measurement information from five schools. For 

an assumed 70% reduction in dust and soil concentrations 

at the post-remediation time point, there was a 7% decrease 

in estimated absorbed dose at the 50th percentile and a 

15% decrease at the 95th percentile. For an assumed 90% 

reduction in dust and soil levels the decreases in estimated 

absorbed doses were 9% and 21% at the 50th and 95th

percentiles, respectively. Although the pre-remediation time 

point sensitivity was not examined, the impact would have 

been less due to the higher proportion of total exposure from 

inhalation at that time point. 

4.5.4 Exposure Modeling Uncertainties and Limitations

Models can be useful tools for estimating human exposure 

to chemicals in the environment, but it is important to 

understand the limitations and uncertainties associated with 

model inputs and outputs. Exposure models are designed 

to use information about concentrations of chemical in 

environmental media, and a person’s contact with chemicals 

in that environment to estimate the amount of exposure that 

may occur. Simple point-estimation models often do not 

incorporate variability in environmental levels and human 

contact and do not characterize the range of exposures likely 

to be encountered by a human population or sub-population. 

The SHEDS model incorporates variability in chemical 

concentrations and some aspects of human activity (e.g., time 

spent in different locations and activities, distributions of 

contact rates) in order to estimate distributions of exposure 

and absorbed dose. However, there are uncertainties in 

some of the assumptions and exposure pathways/scenarios 

modeled (e.g., ingestion of caulk was not modeled), the 

information available for input into the model, and in some 

of the underlying model parameters. Also, while SHEDS 

includes sophisticated exposure algorithms, the dose 

estimation module in SHEDS is a simple 1-compartment 

PK model based on daily absorption rates, and is intended 

for screening purposes; it can be linked to PBPK models for 

more sophisticated tissue dose modeling if sufficient data are 

available (but they are not available at this time for PCBs; 

thus, the SHEDS PK model was used in this study). 

Table 4-39. Sensitivity test results for post-remediation decreases in dust and soil PCB concentrations on the 

distributions of estimated total absorbed PCB dose for 6 – 10 year olds based on measurements from five schoolsa

Model Parameter Mean SD

Percentiles of the Distribution

p5 p25 p50 p75 p95 p99

Estimated Absorbed Dose μg/kg/day

Post remediation

Baseline 0.008 0.004 0.005 0.006 0.007 0.009 0.012 0.018

70% reduction in dust/soil 0.007 0.002 0.005 0.006 0.007 0.008 0.010 0.014

90% reduction in dust/soil 0.007 0.001 0.005 0.006 0.007 0.008 0.009 0.010

Ratio of Estimated Doses at 70 and 90% Reductions to Baseline Estimate

Post remediation

70% reduction in dust/soil 0.89 0.92 0.94 0.93 0.91 0.85 0.74

90% reduction in dust/soil 0.85 0.90 0.92 0.91 0.86 0.79 0.57

a

 Schools 1, 2, 3, 4, and 5.
92

it is not possible to quantitatively characterize the impact 

of ventilation on air concentrations from the available data. 

Exposures (and doses) might be substantially different under 

different ventilation conditions. Doubling the outdoor air 

ventilation rate to a room would result in an approximately 

50% decrease in indoor air PCB concentrations if all other 

factors were unchanged, while reducing the outdoor air 

ventilation rate by half would approximately double indoor 

air PCB levels. Uncertainties due to ventilation effects can 

be reduced by collection of baseline data on ventilation and, 

where successive measurements are performed, making 

those measurements under similar ventilation conditions. 

However, it will remain difficult to accurately assess air flows 

between a room and other adjacent spaces in older buildings 

that may also contain PCBs, limiting the ability to fully 

account for ventilation impacts on PCBs in indoor air. 

Temperature conditions – Both laboratory and building 

studies have demonstrated that PCB emissions increase 

with increasing temperature, and that temperature can affect 

indoor air PCB concentrations (Guo et al, 2011; MacIntosh 

et al., 2012). The measurements used in this study were 

based primarily on measurements made during the summer 

when ambient temperatures are highest. Indoor air levels 

and estimated exposures may be somewhat lower during 

colder months. There was some information to suggest a 

relationship between indoor air PCB concentrations and 

ambient temperature with one of the NYC schools (NYC 

SCA, 2012). However, Hazrati and Harrad (2006) showed 

no seasonal variations in indoor PCB concentrations as, in 

effect, any increased volatilization in summer may be offset 

by increased ventilation. 

Dermal contact and non-dietary ingestion rates – Dermal 

contact rates with potentially contaminated surfaces have not 

been directly assessed for children in school environments. 

Likewise, non-dietary ingestion rates of PCBs have not been 

directly characterized for children in school environments. 

Thus, values from the literature based on other studies were 

used as model inputs. These values are the best available 

information at this time. 

Pulmonary absorption of PCBs – Limited information is 

available to determine the pulmonary absorption of PCBs 

through the lungs; thus, a value of 70% absorption was 

assumed for this purpose and sensitivity tests were performed 

using values of 30%, 80%, and 100% absorption to examine 

the impact on estimates of absorbed dose. Because a 

majority of the modeled absorbed dose resulted from 

the inhalation pathway, the value selected for pulmonary 

absorption can have an important impact on absorbed dose 

estimates. Sensitivity analyses indicated that the median 

absorbed dose estimate for 6 – 10 year-old children would be 

49% lower assuming pulmonary absorption of 30%, and 35% 

higher assuming pulmonary absorption of 100%. 

Dermal absorption of PCBs – Some animal and human 

cadaver skin absorption data are available for selected 

Aroclors. However, dermal absorption may be affected by a 

While there are uncertainties in the SHEDS absorbed dose 

estimates, the probabilistic modeling approach provides 

estimates for the range of absorbed doses based on variability 

in concentrations and activity. Such information can inform 

risk assessments by characterizing not only the average 

absorbed dose, but also the upper end of exposures and 

absorbed doses in a population. It is also important to 

recognize that many of the uncertainties in parameters for 

the SHEDS model would also apply to other absorbed dose 

estimation approaches, including point-estimation models. 

Some of the limitations and uncertainties important for 

modeling PCB exposures in school environments are 

described below. In many of these areas, uncertainties can be 

reduced in the future through collection of additional data or 

information. 

Estimated Adult PCB Absorbed Dose - Exposure estimation 

was not performed for adults, including teachers and staff, 

as part of this effort due to the lack of personal activity 

data at schools such as those available for children in the 

Consolidated Human Activity Database. It is anticipated that 

adults would spend more time in school buildings, which 

would tend to lead to higher doses, while less contact with 

dust and soil and higher relative body masses would lead 

to lower doses. The absorbed doses experienced by adults 

in school environments with PCB levels found in the six 

schools may be similar to those estimated for the 14 – 18 

year old age group, but good adult activity information would 

need to be applied in the model to determine if that is the 

case. 

Levels of PCBs in school dust – Interior dust samples were 

not collected as part of the NYC remedial pilot investigation. 

Dust can be an important source of exposure through 

inhalation, non-dietary ingestion, and dermal contact. PCB 

concentration data were not available for dust. For the 

purposes of this modeling effort, the dust concentrations 

were estimated for each room with an indoor air PCB 

measurement using an estimated solid/air partition coefficient 

(see Appendix E). Wipe sample data were not used as the 

surrogate for dust because the wipes likely contained some 

distribution of dust-bound and surface-residue PCBs, but that 

distribution cannot be defined from the measurement. Also, 

the μg/100 cm2

 units for wipes cannot be simply translated to 

the μg/kg units for dust. The uncertainty in concentrations of 

PCBs in dust can be reduced by collection of dust samples; 

protocols for future sampling should including bulk dust 

sample collection and should include both concentration and 

loading where possible. 

Building ventilation conditions – Air samples were 

collected at multiple locations (including classrooms, 

gymnasiums, cafeterias, transitional spaces) at several time 

points at multiple schools under different conditions. Air 

concentrations of indoor pollutants are strongly impacted 

by ventilation rates in a building or in a room. Actual rates 

of ventilation with outdoor air and with air from adjoining 

spaces are difficult to measure in individual rooms in older 

buildings. While the air PCB measurements certainly 

incorporated some level of variability in ventilation effects, 
93

number of factors including skin conditions; dermal loading 

rates; and how much of the PCBs are bound to soil, dust, 

or caulk particles. There remains uncertainty in absorption 

rates in natural environments under different conditions. 

The default values from the literature are the best available 

information for estimating dermal absorption at this time. 

Estimation of dose as Aroclor 1254 – Most of the available 

absorption data is based on Aroclor 1254, and the 

environmental data generated at five of the schools was only 

available as Aroclor measurements (primarily reported as 

Aroclor 1254 with an altered Aroclor pattern). Thus, the 

SHEDS results are based on modeling total PCBs as Aroclor 

1254. This is appropriate since the RfD of 0.020 μg/kg/d is 

based on Aroclor 1254 as well. However, it is likely that the 

actual congener concentrations in environmental media did 

not exactly match an Aroclor 1254 pattern. The analytical 

results suggested the mixture in school air was similar 

to a modified Aroclor 1254 pattern. Congener-specific 

results showed that the pattern in air was weighted more 

towards the more volatile congeners, but not as much as 

would be predicted from vapor emissions alone, suggesting 

inhalation of dust with a congener pattern similar to Aroclor 

1254. The absorption rates for all congeners may not be 

well characterized when testing is done with Aroclor 1254 

mixtures. Norstrom et al. (2010) showed predictions of 

exposure to PCBs in ambient air and predicted inhalation 

leads to greater exposures of lower-chlorinated congeners 

as compared to dietary intake of more highly chlorinated 

congeners. 

Comparisons of dose estimates within and between schools - 

The strength of the SHEDS model is its ability to estimate the 

distribution of exposures in a population or sub-population 

that incorporates variability in chemical concentrations 

in multiple media and variability in human activities that 

contribute to exposure. Its ability to characterize exposure 

distributions is improved as more data become available 

for more locations and/or scenarios, and it relies on 

relatively large sample sizes to generate useful estimates for 

relevant populations. In this study, SHEDS modeling has 

been performed using measurements at up to six schools, 

providing some range of variability in concentrations that 

may, or may not represent well the larger universe of older 

school buildings with PCB sources. Due to the relatively 

small number of measurements at each school, SHEDS was 

not applied to estimate exposures on a school-by-school 

basis. It is clear from the air measurements of total PCBs 

that differences in exposures among individuals might be 

expected both between schools, and also within schools. 

SHEDS incorporates this variability in its distributional 

estimates, but is not well suited for comparison of exposure 

levels across different rooms in a school or between schools 

given the small number of schools and small number of 

measurements within each school. 

Dietary and residential exposure to PCBs – SHEDS 

modeling estimates in this report are limited to estimates of 

absorbed doses (and exposure pathway analysis) resulting 

from school environments. A more complete model 

assessment would include the contribution from dietary 

sources as well as contributions from residential and outdoor 

exposures away from school. The evaluation of dietary 

exposures is important because dietary intake is often 

characterized as the primary route of exposure to PCBs in the 

general population. Summaries of estimated children’s PCB 

dietary intake based on FDA Total Dietary Study data for the 

years 1991 through 1997 show considerable variability across 

quarters and an average intake of 0.008 μg/kg/day, with 

the most recent 1997 estimate of 0.003 μg/kg/day for 6 and 

10-year old children (ATSDR 2000). These values can be 

compared to the median estimates of 0.020 μg/kg/day (preremediation) and 0.008 μg/kg/day (post-remediation) time 

points for 6 – 10 year-old children based on the measurement 

results for five schools. It is possible that PCB levels in 

foods have continued to decrease since 1997. Given the 

small number of samples with measurable PCB results in the 

most recent Total Diet Study, it is not clear whether there are 

sufficient U.S. data to support development of distributional 

parameters for SHEDS modeling of total dietary exposure. 

In addition, there may be segments of the population with 

above average dietary intakes, particularly for high fish 

consumers. Residential indoor air levels have been found 

in a range of a few to about 14 ng/m3

, much lower than 

the levels found in schools with PCB sources. Outdoor air 

levels are typically lower than those in indoor residential 

air. Additional time and effort are needed to examine the 

extant PCB dietary data (including recent Total Diet Study 

data from the FDA) as well as residential and ambient air 

data to determine their suitability for incorporation into the 

SHEDS model. However, the current extant data available 

for assessing background exposures is limited and may not be 

sufficiently robust for probabilistic estimates in the general 

population. Initial work is focusing on dietary intake of 

PCBs from fish and seafood. 

The factors discussed above, as well as other model inputs, 

contribute to uncertainty in modeled exposure and absorbed 

dose estimates resulting from PCBs in school environments. 

Sensitivity testing for two important parameters helps 

define some of the range of uncertainty. Uncertainty around 

SHEDS absorbed dose estimation distributions could be 

better characterized given sufficient data. However, there 

is still insufficient school measurement data for a full 

uncertainty characterization for SHEDS PCB modeling 

of school exposures. Collection of additional data or 

information is likely to help reduce uncertainties and allow 

for better uncertainty characterizations in the future.

95

5.0 

Conclusions

5.1 Sources of PCBs in School Buildings

PCB-containing caulk is a primary source of PCBs in and 

around school buildings. PCB emissions from caulk can 

potentially result in concentrations from hundreds to over 

a thousand nanograms per cubic meter in indoor air. PCBs 

from exterior caulks around windows and ventilation intakes 

can lead to elevated concentrations in indoor spaces. PCBs 

in exterior caulk are likely to enter the soil near school 

buildings. Caulk containing PCBs was found to be mostly 

intact and still somewhat flexible, but visual examination 

alone may not be adequate for determining if PCBs are 

present and testing is needed to determine if caulk or other 

sealants in a building contain PCBs.

PCB-containing fluorescent light ballasts remain in use in 

some older school buildings and are a primary source of 

PCBs. Emissions from intact ballast capacitors can lead to 

the presence of PCBs in school environments. PCB residues 

from previously failed ballast capacitors may remain in 

fixtures even if the ballast is replaced. Leaking or bursting 

capacitors are likely to substantially elevate PCB levels in 

indoor environments when they fail. Because these ballasts 

have exceeded their expected operational lifetimes, failure 

and possible leakage will continue and is likely to increase 

for ballasts remaining in place. 

Several paint samples had total PCB concentrations above 

100 ppm, up to 718 ppm. PCBs were used as plasticizers 

or flame retardants in some paints, so it is possible that 

these paints may have incorporated PCB when they were 

originally applied. Thus, it is possible that paints could 

be primary sources of PCBs in buildings based on our 

definition. Although they were not encountered in our study, 

window glazing and ceiling tile surface coatings containing 

PCBs have been reported in school buildings and would be 

considered primary sources.

Other primary sources of PCBs may have been used in school 

buildings but are no longer present today. For example, 

carbonless copy paper and PCB-containing capacitors in 

early computer video display terminals may have been used 

in school buildings. The potential impact of previously 

removed sources on current PCB levels in building 

environments cannot be easily determined. 

Many of the building and furnishing materials in schools with 

apparent primary PCB sources contain PCB levels in the 4 

to 100 ppm range. It appears likely that these materials have 

absorbed PCBs that have been emitted from primary sources. 

While primary sources remain in buildings these other 

materials are likely to be in quasi-dynamic equilibrium when 

temperature and ventilation conditions remain relatively 

constant. However, when primary sources are removed 

these materials may serve as secondary sources for emissions 

of PCBs into air in the building. Paints may be the most 

significant secondary sources given their large surface areas 

and relatively high PCB concentrations, but other materials 

may be important as well. Following mitigation of primary 

sources it may, in some cases, be necessary to consider 

mitigation actions for secondary sources. 

5.2 School Environment PCB Levels and 

Exposures

PCBs are present in indoor air, dust, and on surfaces in 

school buildings with PCB-containing source materials, and 

are likely to be present in the soil near buildings with exterior 

PCB-containing caulk. Building occupants are exposed to 

PCBs through contact with these environmental media. 

Estimated average total absorbed doses that could occur 

from the PCBs in school buildings with environmental levels 

that were found in these six schools were near the reference 

dose levels for Aroclor 1254 (0.020 μg/kg/day). Because the 

Aroclor 1254 reference dose is based on an oral applied dose, 

a more direct comparison with the SHEDS absorbed dose 

might be the RfD adjusted by a gastrointestinal absorption 

factor. Using the 85% absorption factor applied in SHEDS, 

the adjusted RfD would be 0.017 μg/kg/d. Over 50% of 

the estimated distribution of absorbed doses exceeded the 

adjusted reference dose level for the two younger age groups. 

These estimates do not include the additional background 

exposures to PCBs that occur outside of the school 

environment, including exposures from dietary intake and 

inhalation of PCBs in outdoor and indoor air at non-school 

locations. 

Pre-remediation PCB concentrations in indoor air were found 

to exceed EPA’s 2009 public health guidance levels (ranging 

from 70 to 600 ng/m3

 depending on age) in many of the 

rooms at the six schools that were evaluated. Inhalation was 

estimated to be responsible for over 70% of the exposure 

that could occur in buildings with environmental levels of 

PCBs that were found in these six schools. Mitigation efforts 

that focus on reducing indoor air PCB concentrations are 

likely to have the greatest impact on reducing exposures, 

although cleaning to reduce dust levels will also have an 

impact. Based on information from the five New York City 

schools, it appears that mitigation efforts can be successful 

in substantially reducing indoor air concentrations and 

exposures to PCBs. 
96

5.3 Complexity of PCBs in School Buildings

PCBs in school buildings present a complex problem from 

exposure assessment, risk assessment, and mitigation 

decision-making perspectives. Different aspects of this 

complexity are summarized below.

There may be multiple primary sources of PCBs in school 

buildings. Numerous different kinds of caulks and sealants 

may be present and widespread across many building 

locations and they must be sampled to determine whether 

they contain PCBs. Fluorescent light ballasts containing 

PCBs may be present and light fixtures may be contaminated 

with residues from ballasts that have previously failed. 

PCBs are semi-volatile organic chemicals with a wide range 

of vapor pressures that will vaporize from primary sources 

and can be transported throughout indoor and outdoor 

environments. They are absorbed by dust and soil which can 

result in additional transport and exposure. 

PCBs absorb into numerous materials in a building resulting 

in a reservoir that remains even after primary sources are 

removed or otherwise mitigated. These secondary sources 

may result in continuing exposures following removal or 

remediation of primary sources.

Over 120 different PCB congeners have been measured in 

indoor air. These different congeners have a wide range of 

physical properties. 

Ventilation with outdoor air is an important factor in the 

indoor air PCB concentrations that will result from source 

emissions. However, ventilation in older school buildings is 

often poor, difficult to assess, and difficult to improve. 

An illustration of complexity of the situation that could be 

faced in a school classroom with different sources of PCBs 

is shown in Figure 5-1. Multiple primary sources of PCBs 

may be emitting PCBs into the air, onto surfaces, into dust 

and soil. Some of the PCBs are absorbed into other building 

materials that serve both as sources and sinks for PCBs. 

Ventilation occurs both from exchange with outdoor air and 

from exchange with air in other building spaces. PCBs can 

be carried between these school spaces. Finally, it is likely 

that there may be over 100 different PCB congeners present, 

with a range of vapor pressures and other physical and 

chemical properties that affect transport and absorption. 

5.4 Study Limitations

There are important limitations and uncertainties in the 

information included in this report. Key limitations and 

uncertainties are summarized below.

PCB measurement results were available from only six 

schools. It is not known if these results are representative 

of older schools nationwide, both in terms of the presence 

of PCB-containing materials and components and the 

environmental concentrations measured in and around the 

school buildings.

 Figure 5-1. Illustration of the complexity of PCBs in school buildings

Materials and components containing PCBs were likely to 

have been used in buildings other than schools. This report 

does not address whether and to what extent PCBs may be 

a potential problem in other types of buildings, and if so, 

whether environmental concentrations and exposures are 

likely to be similar.

PCB emissions from materials and light ballasts were not 

directly measured at the six schools. Modeled emission 

estimates and the resulting predictions of indoor air 

concentrations have considerable uncertainties. Emission 

parameters are likely to vary across different materials and 

for different temperature and ventilation conditions. Two 

different types of chambers were used to test caulk and 

light ballasts emissions, possibly impacting comparability. 

Emissions from light ballasts are likely to vary depending on 

the lighting fixture design and the condition of the ballast and 

capacitor. Emissions from light fixtures contaminated from 

previously leaking or failed ballasts could not be evaluated. 

Attributing the relative impact of PCB emissions from caulk 

and light ballasts on PCB levels in the schools was difficult 

because both sources were present in most buildings, and the 

Aroclor mixture used in every light ballast was not identified. 

Several paint samples were found to have several hundred 

ppm of PCBs, and it is not clear whether these contained 

PCBs when installed and might be considered primary 

sources. 

There is uncertainty in modeled estimates of PCB exposures 

due to uncertainties in key model parameters. In particular, 

there is limited information for pulmonary absorption 

fraction from the range of PCB congeners in vapor and 

particle-bound forms. There is also uncertainty in total PCB 

exposures because of the lack of robust data for background 

exposures from diet and other non-school sources. 
97

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A-1

Congener 

Number

Chlorine 

Positions

No. of 

Chlorines

DioxinLike 

Congener

Vapor 

Pressure 

(torr)

Vapor 

Pressure 

Ref.a-e

Weight 

% of 

Congener 

in Aroclor 

1242f

Weight 

% of 

Congener 

in Aroclor 

1254f

Weight 

% of 

Homolog 

in A1242

Weight 

% of 

Homolog 

in A1254

1 2 1 0.34 0.47 0

2 3 1 0.02

3 4 1 2.07E-03 a 0.11

4 2-2 2 2.72E-03 b 2.71 0.06 13.42 0.24

5 23 2 1.22E-03 b 0.11

6 2-3 2 1.33E-03 b 1.24 0.02

7 24 2 1.46E-03 b 0.18

8 2-4 2 1.19E-03 b 6.48 0.13

9 25 2 1.53E-03 b 0.4

10 26 2 2.60E-03 b 0.14

11 3-3 2 6.85E-04 c

12 34 2 5.56E-04 b 0.04

13 3-4 2 6.24E-04 c 0.17

14 35 2 9.44E-04 c

15 4-4 2 5.82E-04 b 1.95 0.03

16 23-2 3 4.96E-04 b 3.44 0.09 48.02 1.24

17 24-2 3 5.82E-04 b 3.29 0.08

18 25-2 3 6.38E-04 b 9.14 0.25

19 26-2 3 1.04E-03 b 0.84

20 23-3 3 2.27E-04 b 0.77

21 234 3 2.21E-04 b

22 23-4 3 1.97E-04 b 3.08 0.04

23 235 3 3.35E-04 b 0.01

24 236 3 5.31E-04 b 0.13

25 24-3 3 2.79E-04 b 0.61

26 25-3 3 2.92E-04 b 1.38 0.03

27 26-3 3 5.31E-04 b 0.44

28 24-4 3 2.43E-04 b 7.31 0.19

29 245 3 3.48E-04 d 0.08

30 246 3 6.38E-04 b

Table A-1. Information for the 209 PCB congeners 

Appendix A.

PCB Congener Information
A-2

Congener 

Number

Chlorine 

Positions

No. of 

Chlorines

DioxinLike 

Congener

Vapor 

Pressure 

(torr)

Vapor 

Pressure 

Ref.a-e

Weight 

% of 

Congener 

in Aroclor 

1242f

Weight 

% of 

Congener 

in Aroclor 

1254f

Weight 

% of 

Homolog 

in A1242

Weight 

% of 

Homolog 

in A1254

31 25-4 3 2.60E-04 b 7.82 0.28

32 26-4 3 4.42E-04 b 2.05 0.05

33 34-2 3 2.21E-04 b 5.35 0.16

34 35-2 3 3.35E-04 b 0.02

35 34-3 3 9.66E-05 b 0.07

36 35-3 3 1.43E-04 b

37 34-4 3 8.61E-05 b 2.19 0.07

38 345 3

39 35-4 3 1.52E-04 c

40 23-23 4 9.02E-05 b 0.79 0.12 32.71 16.4

41 234-2 4 9.02E-05 b 0.69 0.01

42 23-24 4 1.01E-04 b 1.25 0.15

43 235-2 4 1.43E-04 b 0.19

44 23-25 4 1.14E-04 b 3.63 2.31

45 236-2 4 2.21E-04 b 0.91 0.05

46 23-26 4 1.16E-04 a 0.38

47 24-24 4 1.22E-04 b 0.92 0.14

48 245-2 4 1.27E-04 b 1.17 0.12

49 24-25 4 1.36E-04 b 2.6 1.1

50 246-2 4

51 24-26 4 1.97E-04 b 0.23

52 25-25 4 1.50E-04 b 3.47 5.38

53 25-26 4 2.60E-04 b 0.71 0.12

54 26-26 4 3.88E-04 d

55 234-3 4 4.35E-05 c 0.11

56 23-34 4 3.51E-05 b 1.85 0.55

57 235-3 4

58 23-35 4

59 236-3 4 8.45E-06 e 0.32 0.02

60 234-4 4 3.43E-05 b 1.17 0.18

61 2345 4

62 2346 4

63 235-4 4 5.43E-05 b 0.11 0.02

64 236-4 4 1.06E-04 b 1.68 0.59

65 2356 4 1.14E-04 b

66 24-34 4 4.42E-05 b 3.38 1.01

67 245-3 4 5.31E-05 b 0.17

68 24-35 4

Table A-1. Information for the 209 PCB congeners (continued)
A-3

Congener 

Number

Chlorine 

Positions

No. of 

Chlorines

DioxinLike 

Congener

Vapor 

Pressure 

(torr)

Vapor 

Pressure 

Ref.a-e

Weight 

% of 

Congener 

in Aroclor 

1242f

Weight 

% of 

Congener 

in Aroclor 

1254f

Weight 

% of 

Homolog 

in A1242

Weight 

% of 

Homolog 

in A1254

69 246-3 4 1.43E-04 b

70 25-34 4 4.73E-05 b 3.7 3.49

71 26-34 4 8.61E-05 b 1.06 0.15

72 25-35 4 7.53E-05 c 0.01

73 26-35 4

74 245-4 4 4.73E-05 b 1.76 0.84

75 246-4 4 1.51E-04 d 0.03

76 345-2 4 4.84E-05 b 0.08 0.02

77 34-34 4 Yes 1.43E-05 b 0.33 0.03

78 345-2 4 2.39E-05 c

79 34-35 4 2.56E-05 c

80 35-35 4 4.10E-05 c

81 345-4 4 Yes 2.17E-05 c 0.01

82 234-23 5 2.22E-06 e 0.22 1.11 5.17 52.95

83 235-23 5 1.53E-05 a 0.09 0.48

84 236-23 5 4.12E-05 b 0.35 2.32

85 234-24 5 3.31E-05 d 0.24 1.28

86 2345-2 5 2.16E-05 b 0.06

87 234-25 5 2.07E-05 b 0.38 3.99

88 2346-2 5 4.84E-05 b

89 234-26 5 2.22E-06 e 0.07 0.09

90 235-24 5 2.60E-05 b

91 236-24 5 4.42E-05 b 0.17 0.93

92 235-25 5 3.35E-05 b 0.09 1.29

93 2356-2 5 5.31E-05 b

94 235-26 5 2.22E-06 e 0.02

95 236-25 5 5.56E-05 b 0.51 6.25

96 236-26 5 2.22E-06 e 0.02 0.04

97 245-23 5 2.16E-05 b 0.31 2.62

98 246-23 5 5.19E-05 b

99 245-24 5 2.60E-05 b 0.36 3.02

100 246-24 5

101 245-25 5 2.99E-05 b 0.57 8.02

102 245-26 5 5.00E-05 d 0.05 0.15

103 246-25 5 4.62E-05 a 0.03

104 246-26 5

105 234-34 5 Yes 5.82E-06 b 0.37 2.99

106 2345-3 5 9.89E-06 b

Table A-1. Information for the 209 PCB congeners (continued)
A-4

Congener 

Number

Chlorine 

Positions

No. of 

Chlorines

DioxinLike 

Congener

Vapor 

Pressure 

(torr)

Vapor 

Pressure 

Ref.a-e

Weight 

% of 

Congener 

in Aroclor 

1242f

Weight 

% of 

Congener 

in Aroclor 

1254f

Weight 

% of 

Homolog 

in A1242

Weight 

% of 

Homolog 

in A1254

107 234-35 5 7.85E-06 a

108 2346-3 5 1.06E-05 c

109 235-34 5 2.22E-06 e 0.04 0.37

110 236-34 5 1.68E-05 b 0.68 9.29

111 235-35 5

112 2356-3 5 2.37E-05 b

113 236-35 5 1.80E-05 b

114 2345-4 5 Yes 5.19E-06 a 0.03 0.18

115 2346-4 5 1.68E-05 b 0.03 0.2

116 23456 5 1.80E-05 b

117 2356-4 5 1.93E-05 b 0.02 0.23

118 245-34 5 Yes 8.42E-06 b 0.51 7.35

119 246-34 5 2.16E-05 b 0.08

120 245-35 5 1.52E-05 d

121 246-35 5 3.76E-05 b

122 345-23 5 7.45E-06 c 0.1

123 345-24 5 Yes 7.00E-06 a 0.02 0.15

124 345-25 5 4.62E-06 e 0.02 0.29

125 345-26 5 1.50E-05 b 0.02 0.02

126 345-34 5 Yes 2.32E-06 a

127 345-35 5 5.85E-06 c

128 234-234 6 2.48E-06 b 1.42 0.18 26.95

129 2345-23 6 3.51E-06 b 0.38

130 234-235 6 4.32E-06 b 0.6

131 2346-23 6 5.19E-06 a 0.19

132 234-236 6 7.50E-06 b 0.03 2.29

133 235-235 6 8.24E-06 d 0.11

134 2356-23 6 9.89E-06 b 0.37

135 235-236 6 1.36E-05 b 0.61

136 236-236 6 2.48E-05 b 0.7

137 2345-54 6 2.27E-06 b 0.42

138 234-245 6 3.67E-06 b 0.05 5.8

139 2346-24 6 9.44E-06 b 0.15

140 234-246 6

141 2345-25 6 4.84E-06 b 0.98

142 23456-2 6

143 2345-26 6 1.01E-05 b

144 2346-25 6 7.85E-06 b 0.24

Table A-1. Information for the 209 PCB congeners (continued)
A-5

Congener 

Number

Chlorine 

Positions

No. of 

Chlorines

DioxinLike 

Congener

Vapor 

Pressure 

(torr)

Vapor 

Pressure 

Ref.a-e

Weight 

% of 

Congener 

in Aroclor 

1242f

Weight 

% of 

Congener 

in Aroclor 

1254f

Weight 

% of 

Homolog 

in A1242

Weight 

% of 

Homolog 

in A1254

145 2346-26 6 2.32E-05 b

146 235-245 6 6.38E-06 b 0.67

147 2356-24 6 0.1

148 235-246 6 2.08E-05 d

149 236-245 6 1.11E-05 b 0.04 3.65

150 236-246 6

151 2356-25 6 1.43E-05 b 0.69

152 2359-26 6

153 245-245 6 5.43E-06 b 0.04 3.77

154 245-246 6 1.36E-05 b 0.04

155 246-246 6

156 2345-34 6 Yes 1.22E-06 b 0.82

157 234-245 6 Yes 1.19E-06 b 0.19

158 2346-34 6 3.43E-06 b 0.81

159 2345-35 6

160 23456-3 6

161 2346-35 6

162 235-345 6

163 2356-34 6 3.94E-06 b 0.02 1.03

164 236-345 6 5.81E-07 e 0.4

165 2356-35 6

166 23546-4 6 5.81E-07 e 0.05

167 245-345 6 Yes 1.76E-06 b 0.27

168 246-345 6 5.16E-06 c

169 345-345 6 Yes 5.07E-07 b

170 2345-234 7 5.82E-07 b 0.52 0 2.65

171 2346-234 7 1.57E-06 b 0.14

172 2345-235 7 1.06E-06 b 0.07

173 23456-23 7 1.76E-06 d

174 2345-236 7 2.07E-06 b 0.34

175 2346-235 7 2.99E-06 b

176 2346-236 7 5.43E-06 b 0.04

177 2356-234 7 1.88E-06 b 0.2

178 2356-235 7 3.43E-06 b 0.03

179 2356-236 7 6.53E-06 b 0.1

180 2345-245 7 9.02E-07 b 0.67

181 23456-24 7 3.16E-06 d

182 2345-246 7 2.79E-06 b

Table A-1. Information for the 209 PCB congeners (continued)
A-6

Congener 

Number

Chlorine 

Positions

No. of 

Chlorines

DioxinLike 

Congener

Vapor 

Pressure 

(torr)

Vapor 

Pressure 

Ref.a-e

Weight 

% of 

Congener 

in Aroclor 

1242f

Weight 

% of 

Congener 

in Aroclor 

1254f

Weight 

% of 

Homolog 

in A1242

Weight 

% of 

Homolog 

in A1254

183 2346-245 7 2.43E-06 b 0.18

184 2346-246 7

185 23456-25 7 2.21E-06 b

186 23456-26 7 5.66E-06 d

187 2356-245 7 2.79E-06 b 0.25

188 2356-345 7 1.12E-05 d

189 2345-345 7 Yes 2.02E-07 a 0.01

190 23456-34 7 5.31E-07 b 0.07

191 2346-345 7 7.85E-07 b

192 23456-35 7 1.06E-06 b

193 2356-345 7 9.02E-07 b 0.03

194 2345-2345 8 9.66E-08 a 0.01 0 0.04

195 23456-234 8 2.72E-07 b

196 2345-2346 8 4.42E-07 b

197 2346-2346 8 6.84E-07 a

198 23456-235 8 5.31E-07 b

199 2345-1256 8 1.06E-06 b 0.01

200 23456-236 8 1.53E-06 b

201 2346-2356 8 4.42E-07 b

202 2356-2356 8 1.80E-06 b

203 23456-245 8 4.22E-07 b 0.02

204 23456-246 8 1.04E-06 b

205 23456-345 8 2.18E-07 d

206 23456-2345 9 1.15E-07 d 0.03 0 0.04

207 23456-2346 9 3.74E-07 d

208 23456-2356 9 1.64E-07 a 0.01

209 23456-23456 10 9.93E-08 d 0 0

a

 Fischer RC, Wittlinger R, and Ballschmiter K. (1992) – Method A. 

b Fischer RC, Wittlinger R, and Ballschmiter K. (1992) – Method B.

c

 Foreman, W. T. and Bidleman, T. F. (1985) – Method A.

d Foreman, W. T. and Bidleman, T. F. (1985) – Method B.

e U.S. EPA Estimation Program Interface (EPI) SuiteTM. Accessed MPBPWINTM module though ChemSpider at 

http://www.chemspider.com/.

f

 Frame et al., 1996; based on analysis of G3 Aroclor 1242 and G4 Aroclor 1254 standards.

Table A-1. Information for the 209 PCB congeners (continued)
A-7

Figure A-1. Relative congener weight percents for Aroclors 1242 and 1254 (based on Frame et al. 1996)

B-1

Appendix B. 

Source Measurement Results for 

Individual Schools

Table B-1. Interior caulk and window glaze total PCB measurement results by concentration category

Concentration 

Category

5 Schools

Combined School 1 School 2 School 3 School 5 School 6

Number of Samples

All samples 427 111 113 120 63 20

< 50 ppm 351 93 87 106 46 19

50 – 999 ppm 33 12 15 0 5 1

1,000 – 9,999 ppm 6 2 0 2 2 0

10,000 – 99,000 ppm 11 4 3 3 1 0

100,000 – 199,999 ppm 10 0 6 2 2 0

200,000 – 299,999 ppm 14 0 2 6 6 0

300,000 – 399,999 ppm 1 0 0 0 1 0

400,000 – 499,999 ppm 1 0 0 1 0 0

Percentage of Samples

< 50 ppm 82.2 83.8 77.0 88.3 73.0 95.0

50 – 999 ppm 7.7 10.8 13.3 0 7.9 5.0

1,000 – 9,999 ppm 1.4 1.8 0 1.7 3.2 0

10,000 – 99,000 ppm 2.6 3.6 2.7 2.5 1.6 0

100,000 – 199,999 ppm 2.3 0 5.3 1.7 3.2 0

200,000 – 299,999 ppm 3.3 0 1.8 5.0 9.5 0

300,000 – 399,999 ppm 0.2 0 0 0 1.6 0

400,000 – 499,999 ppm 0.2 0 0 0.8 0 0
B-2

Table B-2. Exterior caulk and window glaze total PCB measurement results by concentration category

Concentration 

Category

3 Schools 

Combined School 4 School 5 School 6

Number of Samples

All samples 73 15 49 9

< 50 ppm 27 8 18 1

50 – 999 ppm 5 1 4 0

1,000 – 9,999 ppm 11 2 9 0

10,000 – 99,000 ppm 5 3 0 2

100,000 – 199,999 ppm 9 0 3 6

200,000 – 299,999 ppm 11 1 10 0

300,000 – 399,999 ppm 5 0 5 0

400,000 – 499,999 ppm 0 0 0 0

Percentage of Samples

< 50 ppm 37.0 53.3 36.7 11.1

50 – 999 ppm 6.8 6.7 8.2 0

1,000 – 9,999 ppm 15.1 13.3 18.4 0

10,000 – 99,000 ppm 6.8 20.0 0 22.2

100,000 – 199,999 ppm 12.3 0 6.1 66.7

200,000 – 299,999 ppm 15.1 6.7 20.4 0

300,000 – 399,999 ppm 6.8 0 10.2 0

400,000 – 499,999 ppm 0 0 0 0
B-3

Table B-3. Total PCB measurement results for selected materials by school

Material Category N

%

>

QLc

Total PCB Levels in Other Materialsa,b

Median

ppm

Mean ± SD

ppm

Range

ppm

Paint 

 School 2 93 100 46.5 75.8 ± 102 3.43 - 718

 School 3 37 100 27.5 31.7 ± 28.1 3.31 - 135

 School 6 13 100 64.9 59.3 ± 19.7 35.1 - 92.2

Fiberboard

 School 2 25 100 33.2 30.8 ± 15.2 2.85 - 54.8

 School 3 3 100 13.0 14.8 ± 5.48 10.5 - 21.0

 School 6 0 -- -- -- --

Particle Board

 School 2 18 100 14.8 15.2 ± 5.92 6.88 - 28.8

 School 3 1 100 12.9 -- --

 School 6 0 -- -- -- --

Varnish 

 School 2 29 97 11.0 15.1 ± 12.9 <QL - 48.2

 School 3 0 -- -- -- --

 School 6 (gym floor) 1 100 61.5 -- --

Mastics (Tile and Molding) 

 School 2 31 97 6.84 19.2 ± 36.6 <QL - 188

 School 3 29 86 8.71 19.3 ± 42.6 <QL - 230

 School 6 5 100 1.61 2.82 ± 3.09 0.98 - 8.33

Ceiling Tile

 School 2 6 100 11.3 9.17 ± 4.73 2.11 - 14.0

 School 3 0 -- -- -- --

 School 6 2 100 2.45 2.45 ± 0.56 2.06 - 2.85

Laminate

 School 2 29 76 5.35 41.1 ± 58.7 <QL - 199

 School 3 0 -- -- -- --

 School 6 0 -- -- -- --

Floor Tile

 School 2 27 89 4.60 5.04 ± 3.69 <QL - 14.6

 School 3 26 73 4.76 8.11 ± 11.6 <QL - 57.1

 School 6 3 100 2.07 2.92 ± 2.50 0.96 - 5.73

 a Reported as total PCBs from Aroclor measurements. 

 b One-half the quantitation limit was substituted for values less than the quantitation.

 c QL = quantitation limit; sample size dependent, typically ≤ 1 ppm. 

C-1

Appendix C. 

Congener Measurement Results by Sample Type

Table C-1. Air sample PCB congener concentrations at School 6

Indoor Air 

N = 7

ng/m3

Outddoor Air

N = 1

ng/m3

PCB 

Congener # Mean SD Mean SD

4 0.3 0.5 <QL ---

8 1.5 0.9 <QL ---

17 2.1 1.3 <QL ---

18 3.8 2.0 <QL ---

28 3.8 2.1 <QL ---

31 3.7 1.9 <QL ---

44 19.2 5.1 0.24 ---

47 4.8 1.3 0.06 ---

49 14.8 4.7 0.17 ---

52 44.7 14.6 0.45 ---

56 2.6 0.8 0.08 ---

64 5.5 1.7 0.04 ---

66 6.9 1.9 0.09 ---

70 25.3 7.2 0.39 ---

74 11.9 3.4 0.18 ---

82 3.9 1.6 0.11 ---

84 13.9 3.4 0.24 ---

85 5.3 2.0 0.11 ---

87 17.6 6.7 0.34 ---

91 5.8 2.0 0.15 ---

92 10.5 3.3 0.15 ---

95 50.0 14.6 0.72 ---

97 11.2 4.0 0.22 ---

99 15.1 4.7 0.24 ---

101 53.8 19.1 0.86 ---

105 5.5 3.1 0.11 ---

110 39.0 21.5 0.49 ---

118 18.6 9.3 0.52 ---

128 1.4 0.9 <QL ---

130 0.8 0.5 <QL ---

132 6.6 3.2 0.23 ---
C-2

Table C-1. Air sample PCB congener concentrations at School 6 (continued)

Indoor Air 

N = 7

ng/m3

Outddoor Air

N = 1

ng/m3

PCB 

Congener # Mean SD Mean SD

138 8.3 4.9 0.23 ---

141 2.2 1.1 0.07 ---

146 1.2 0.7 0.03 ---

149 12.6 5.6 0.24 ---

151 5.8 2.4 0.12 ---

153 9.6 5.0 0.23 ---

156 0.5 0.3 <QL ---

158 1.2 0.7 0.03 ---

163 1.4 0.9 0.04 ---

170 0.2 0.1 <QL ---

174 0.7 0.4 <QL ---

180 0.8 0.6 <QL ---

187 0.8 0.6 <QL ---

206 0.2 0.0 <QL ---

Σ 209 Cong.a 500 154 7.7 ---

Σ 45 Cong.b 456 143 7.20 ---

Σ DLC Cong.c 26 13 0.64 ---

DLC TEQd 7.88E-04 4.07E-04 1.91E-05 ---

% 45 Cong.e 91.0 1.5 93.2 ---

% DLC Cong.f 5.0 1.6 8.2 ---

a

 Sum of all 209 congeners, including those not shown in this table.

b

 Sum of the 45 congeners shown in this table.

c

 Sum of the 12 dioxin-like congeners (77, 81,105, 114, 118, 123, 126, 156, 157, 167, 169, 189).

d TCDD toxic equivalence for the sum of the12 dioxin-like congeners. 

e

 Percent of the total concentration of all 209 congeners for the sum of the 45 congeners in this table. 

f

 Percent of the total concentration of all 209 congeners for the sum of the 12 dioxin-like congeners.
C-3

Table C-2. Surface wipe PCB congener concentrations at School 6

Surface Wipe – High Contact 

Surfaces

N = 3

μg/100 cm2

Surface Wipe – Low Contact 

Surfaces

N = 7

μg/100 cm2

PCB 

Congener # Mean SD Mean SD

4 <QL --- <QL ---

8 <QL --- <QL ---

17 <QL --- <QL ---

18 <QL --- <QL ---

28 0.0014 0.0024 0.0008 0.0015

31 0.0010 0.0017 0.0006 0.0014

44 0.0083 0.0112 0.0060 0.0091

47 0.0020 0.0029 0.0013 0.0024

49 0.0055 0.0075 0.0038 0.0053

52 0.0179 0.0248 0.0114 0.0174

56 0.0024 0.0033 0.0017 0.0032

64 0.0019 0.0033 0.0013 0.0029

66 0.0042 0.0050 0.0035 0.0053

70 0.0126 0.0129 0.0101 0.0129

74 0.0059 0.0061 0.0047 0.0061

82 0.0047 0.0040 0.0030 0.0048

84 0.0080 0.0076 0.0069 0.0086

85 0.0052 0.0038 0.0041 0.0049

87 0.0170 0.0123 0.0134 0.0146

91 0.0040 0.0039 0.0028 0.0035

92 0.0062 0.0045 0.0055 0.0068

95 0.0206 0.0216 0.0168 0.0216

97 0.0098 0.0078 0.0080 0.0086

99 0.0154 0.0133 0.0129 0.0149

101 0.0377 0.0314 0.0319 0.0354

105 0.0165 0.0113 0.0084 0.0087

110 0.0334 0.0247 0.0243 0.0269

118 0.0351 0.0249 0.0194 0.0202

128 0.0076 0.0066 0.0029 0.0029
C-4

Table C-2. Surface wipe PCB congener concentrations at School 6 (continued)

Surface Wipe – High Contact 

Surfaces

N = 3

μg/100 cm2

Surface Wipe – Low Contact 

Surfaces

N = 7

μg/100 cm2

PCB 

Congener # Mean SD Mean SD

130 0.0028 0.0019 0.0014 0.0014

132 0.0171 0.0133 0.0087 0.0085

138 0.0330 0.0240 0.0182 0.0151

141 0.0074 0.0047 0.0050 0.0040

146 0.0036 0.0025 0.0018 0.0020

149 0.0221 0.0143 0.0174 0.0151

151 0.0076 0.0054 0.0067 0.0059

153 0.0282 0.0178 0.0201 0.0170

156 0.0050 0.0051 0.0018 0.0016

158 0.0051 0.0033 0.0025 0.0025

163 0.0058 0.0042 0.0031 0.0027

170 0.0031 0.0035 0.0030 0.0031

174 0.0028 0.0031 0.0050 0.0061

180 0.0061 0.0068 0.0102 0.0127

187 0.0039 0.0035 0.0057 0.0071

206 0.0064 0.0055 0.0070 0.0049

Σ 209 Cong.a 0.491 0.430 0.371 0.385

Σ 45 Cong.b 0.444 0.375 0.323 0.335

Σ DLC Cong.c 0.059 0.044 0.031 0.032

DLC TEQd 1.78E-06 1.33E-06 9.39E-07 9.86E-07

% 45 Cong.e 92.2 3.0 89.7 9.0

% DLC Cong.f 13.0 1.7 8.4 2.9

a

 Sum of all 209 congeners, including those not shown in this table.

b

 Sum of the 45 congeners shown in this table.

c

 Sum of the 12 dioxin-like congeners (77, 81,105, 114, 118, 123, 126, 156, 157, 167, 169, 189).

d TCDD toxic equivalence for the sum of the12 dioxin-like congeners

e

 Percent of the total concentration of all 209 congeners for the sum of the 45 congeners in this table. 

f

 Percent of the total concentration of all 209 congeners for the sum of the 12 dioxin-like congeners.
C-5

PCB Congener 

#

Indoor Dust

N = 4

ppm

Mean SD

158 0.30 0.238

163 0.36 0.290

170 0.15 0.091

174 0.18 0.093

180 0.28 0.131

187 0.14 0.050

206 0.008 0.009

Σ 209 Cong.a 30.9 18.6

Σ 45 Cong.b 28.1 17.5

Σ DLC Cong.c 4.3 3.5

DLC TEQd 1.29E-04 1.04E-04

% 45 Cong.e 89.5 3.7

% DLC Cong.f 12.2 3.9

a

 Sum of all 209 congeners, including those not shown in this table.

b

 Sum of the 45 congeners shown in this table.

c

 Sum of the 12 dioxin-like congeners (77, 81,105, 114, 118, 123, 126, 

156, 157, 167, 169, 189).

d TCDD toxic equivalence for the sum of the12 dioxin-like congeners. 

e

 Percent of the total concentration of all 209 congeners for the sum of the 

45 congeners in this table. 

f

 Percent of the total concentration of all 209 congeners for the sum of the 

12 dioxin-like congeners.

PCB Congener 

#

Indoor Dust

N = 4

ppm

Mean SD

4 <QL ---

8 <QL ---

17 0.001 0.002

18 0.003 0.005

28 0.018 0.014

31 0.018 0.013

44 0.29 0.112

47 0.073 0.028

49 0.15 0.054

52 0.59 0.246

56 0.13 0.059

64 0.087 0.029

66 0.23 0.088

70 0.73 0.338

74 0.34 0.159

82 0.40 0.275

84 0.51 0.236

85 0.42 0.276

87 1.4 0.875

91 0.19 0.084

92 0.46 0.239

95 1.3 0.568

97 0.74 0.462

99 1.1 0.653

101 2.6 1.408

105 1.2 1.026

110 2.7 1.872

118 2.6 2.056

128 0.51 0.431

130 0.19 0.150

132 1.0 0.771

138 2.1 1.661

141 0.44 0.305

146 0.26 0.225

149 1.5 0.940

151 0.49 0.292

153 1.7 1.224

156 0.28 0.232

Table C-3. Dust sample PCB congener concentrations at School 6
C-6

Table C-4. Soil sample PCB congener concentrations at School 6

PCB 

Congener #

Soil Collected 0.15 m 

From Building

N = 2

ppm

Soil Collected 2.44 m 

From Building

N = 1

ppm

Mean SD Mean SD

4 <QL --- <QL ---

8 <QL --- <QL ---

17 <QL --- <QL ---

18 <QL --- <QL ---

28 <QL --- <QL ---

31 <QL --- <QL ---

44 0.0040 0.0010 <QL ---

47 0.0004 0.0006 <QL ---

49 0.0037 0.0009 0.0003 ---

52 0.0130 0.0043 0.0003 ---

56 0.0125 0.0009 0.0077 ---

64 0.0007 0.0010 0.0008 ---

66 0.0064 0.0024 <QL ---

70 0.0168 0.0066 0.0004 ---

74 0.0079 0.0031 <QL ---

82 0.0151 0.0056 0.0006 ---

84 0.0117 0.0042 <QL ---

85 0.0183 0.0072 0.0005 ---

87 0.0458 0.0179 0.0010 ---

91 0.0049 0.0016 <QL ---

92 0.0164 0.0057 0.0004 ---

95 0.0301 0.0099 0.0008 ---

97 0.0258 0.0096 0.0007 ---

99 0.0464 0.0165 0.0013 ---

101 0.0952 0.0323 0.0022 ---

105 0.0787 0.0241 0.0013 ---

110 0.1024 0.0334 0.0022 ---

118 0.1415 0.0488 0.0026 ---

128 0.0502 0.0149 0.0010 ---

130 0.0158 0.0043 0.0005 ---

132 0.0553 0.0221 0.0027 ---

138 0.1645 0.0488 0.0049 ---

141 0.0302 0.0100 0.0046 ---

146 0.0173 0.0053 0.0003 ---

149 0.0733 0.0211 0.0021 ---

151 0.0228 0.0064 0.0007 ---
C-7

PCB 

Congener #

Soil Collected 0.15 m 

From Building

N = 2

ppm

Soil Collected 2.44 m 

From Building

N = 1

ppm

Mean SD Mean SD

153 0.1113 0.0293 0.0038 ---

156 0.0341 0.0098 0.0005 ---

158 0.0212 0.0062 0.0005 ---

163 0.0288 0.0086 0.0009 ---

170 0.0237 0.0064 0.0009 ---

174 0.0159 0.0044 0.0007 ---

180 0.0294 0.0074 0.0016 ---

187 0.0177 0.0027 0.0013 ---

206 0.0027 0.0011 0.0019 ---

Σ 209 Cong.a 1.60 0.49 0.071 ---

Σ 45 Cong.b 1.41 0.44 0.052 ---

Σ DLC Cong.c 0.27 0.09 0.005 ---

DLC TEQd 8.22E-06 2.58E-06 1.52E-07 ---

% 45 Cong.e 87.8 1.0 72.9 ---

% DLC Cong.f 17.0 0.3 7.1 ---

a

 Sum of all 209 congeners, including those not shown in this table.

b

 Sum of the 45 congeners shown in this table.

c

 Sum of the 12 dioxin-like congeners (77, 81,105, 114, 118, 123, 126, 156, 157, 167, 169, 189).

d TCDD toxic equivalence for the sum of the12 dioxin-like congeners. 

e

 Percent of the total concentration of all 209 congeners for the sum of the 45 congeners in this table. 

f

 Percent of the total concentration of all 209 congeners for the sum of the 12 dioxin-like congeners.

Table C-4. Soil sample PCB congener concentrations at School 6 (continued)
C-8

PCB 

Congener #

Interior Caulk Samples

N = 5

ppm

Exterior Caulk Samples

N = 3

ppm

Mean SD Mean SD

4 <QL --- <QL ---

8 0.01 0.01 <QL ---

17 0.01 0.01 11 19

18 0.03 0.04 11 19

28 0.05 0.04 46 80

31 0.05 0.04 52 89

44 0.31 0.15 1350 506

47 0.08 0.04 336 127

49 0.19 0.10 769 380

52 0.70 0.33 3140 1360

56 0.06 0.03 351 50

64 0.09 0.04 382 143

66 0.14 0.06 771 141

70 0.42 0.17 2650 379

74 0.20 0.08 1250 177

82 0.12 0.06 1240 34

84 0.29 0.12 1820 192

85 0.15 0.07 1310 45

87 0.48 0.21 2950 2550

91 0.11 0.05 773 85

92 0.22 0.10 1760 90

95 0.77 0.36 5160 673

97 0.28 0.13 2520 59

99 0.48 0.21 3940 194

101 1.15 0.53 9590 457

105 0.27 0.16 3920 274

110 0.87 0.40 8920 173

118 0.64 0.34 8300 511

128 0.10 0.09 2180 262

130 0.04 0.03 728 55

132 0.25 0.14 3610 413

138 0.45 0.32 8200 889

141 0.10 0.07 1620 133

146 0.06 0.03 861 60

149 0.42 0.24 5140 267

151 0.16 0.09 174 58

Table C-5. Caulk sample PCB congener concentrations at School 6
C-9

PCB 

Congener #

Interior Caulk Samples

N = 5

ppm

Exterior Caulk Samples

N = 3

ppm

Mean SD Mean SD

153 0.40 0.26 6150 556

156 0.06 0.06 1490 178

158 0.07 0.05 1250 128

163 0.08 0.06 1440 158

170 0.03 0.04 970 123

174 0.04 0.04 721 75

180 0.06 0.06 1410 177

187 0.03 0.03 504 28

206 0.01 0.01 <QL ---

Σ 209 Cong.a 11.5 5.88 114000 2250

Σ 45 Cong.b 10.5 5.28 101000 2070

Σ DLC Cong.c 1.02 0.59 14500 1000

DLC TEQd 3.08E-05 1.80E-05 0.436 0.029

% 45 Cong.e 91.7 1.9 89.2 3.2

% DLC Cong.f 8.7 1.2 12.8 1.1

a

 Sum of all 209 congeners, including those not shown in this table.

b

 Sum of the 45 congeners shown in this table.

c

 Sum of the 12 dioxin-like congeners (77, 81,105, 114, 118, 123, 126, 156, 157, 167, 169, 189).

d TCDD toxic equivalence for the sum of the12 dioxin-like congeners. 

e

 Percent of the total concentration of all 209 congeners for the sum of the 45 congeners in this table. 

f

 Percent of the total concentration of all 209 congeners for the sum of the 12 dioxin-like congeners.

Table C-5. Caulk sample PCB congener concentrations at School 6 (continued)
C-10

Table C-6. Other building material sample PCB congener concentrations at School 6

PCB 

Congener #

Paints and 

Floor Varnish

N = 12

ppm

Chair Cushion 

Foam and Pin 

Board

N = 2

ppm

Cove Molding and 

Floor Underlayment 

N = 2

ppm

Floor Tile and 

Ceiling Tile

N = 2

ppm

Mean SD Mean SD Mean SD Mean SD

4 <QL --- 0.07 0.05 0.01 0.02 <QL ---

8 <QL --- 0.09 0.10 0.06 0.06 <QL ---

17 0.02 0.03 0.10 0.06 0.15 0.15 <QL ---

18 0.05 0.06 0.17 0.07 0.24 0.22 <QL ---

28 0.13 0.09 0.37 0.20 0.53 0.52 0.002 0.002

31 0.11 0.09 0.31 0.15 0.43 0.40 0.000 0.000

44 0.92 0.45 1.30 0.13 0.79 0.09 0.008 0.012

47 0.23 0.11 0.33 0.03 0.20 0.02 0.002 0.003

49 0.45 0.31 0.78 0.06 0.59 0.08 0.01 0.002

52 1.72 0.86 2.23 0.10 1.29 0.007 0.04 0.015

56 0.29 0.14 0.40 0.16 0.25 0.17 0.01 0.001

64 0.27 0.14 0.40 0.07 0.28 0.06 0.01 0.001

66 0.56 0.28 0.73 0.32 0.49 0.26 0.01 0.003

70 1.47 0.53 1.41 0.17 0.76 0.07 0.04 0.002

74 0.69 0.25 0.66 0.08 0.36 0.03 0.02 0.001

82 0.59 0.21 0.31 0.05 0.15 0.07 0.02 0.007

84 1.01 0.35 1.01 0.23 0.53 0.04 0.02 0.000

85 0.59 0.20 0.41 0.06 0.18 0.12 0.02 0.006

87 1.97 0.67 1.31 0.14 0.53 0.37 0.05 0.024

91 0.37 0.11 0.37 0.03 0.17 0.10 0.01 0.002

92 0.70 0.22 0.65 0.07 0.29 0.20 0.02 0.002

95 2.50 0.81 2.32 0.01 0.86 0.26 0.03 0.047

97 1.08 0.35 0.81 0.11 0.34 0.23 0.03 0.013

99 1.59 0.52 1.46 0.20 0.65 0.44 0.05 0.012

101 3.80 1.27 3.02 0.36 1.32 0.80 0.12 0.028

105 1.29 0.60 0.50 0.02 0.25 0.14 0.04 0.034

110 3.62 1.31 2.18 0.20 0.81 0.49 0.10 0.057

118 2.87 1.25 1.33 0.11 0.59 0.41 0.07 0.095

128 0.49 0.26 0.15 0.03 0.08 0.06 0.02 0.014

130 0.20 0.09 0.06 0.01 0.04 0.02 0.01 0.003

132 1.33 0.51 0.54 0.01 0.26 0.13 0.04 0.059

138 2.21 1.01 0.73 0.08 0.39 0.27 0.07 0.073

141 0.54 0.22 0.41 0.22 0.12 0.06 0.02 0.015

146 0.28 0.13 0.11 0.00 0.05 0.03 0.01 0.008

149 1.97 0.69 1.02 0.05 0.50 0.32 0.06 0.049

151 0.71 0.24 0.45 0.04 0.24 0.09 0.02 0.016
C-11

PCB 

Congener #

Paints and 

Floor Varnish

N = 12

ppm

Chair Cushion 

Foam and Pin 

Board

N = 2

ppm

Cove Molding and 

Floor Underlayment 

N = 2

ppm

Floor Tile and 

Ceiling Tile

N = 2

ppm

Mean SD Mean SD Mean SD Mean SD

153 1.95 0.80 0.78 0.03 0.42 0.24 0.08 0.045

156 0.24 0.13 0.07 0.02 0.04 0.04 0.01 0.006

158 0.32 0.15 0.10 0.01 0.06 0.04 0.01 0.013

163 0.39 0.18 0.13 0.01 0.07 0.05 0.01 0.013

170 0.17 0.10 0.09 0.09 0.04 0.003 0.004 0.006

174 0.33 0.25 0.11 0.04 0.07 0.007 0.01 0.014

180 0.47 0.40 0.21 0.19 0.10 0.01 0.02 0.017

187 0.34 0.33 0.11 0.04 0.08 0.001 0.01 0.016

206 0.06 0.07 0.04 0.02 0.02 0.002 0.01 0.011

Σ 209 Cong.a 45.8 15.5 36.1 1.2 19.8 0.2 1.26 0.76

Σ 45 Cong.b 40.9 13.6 30.1 2.6 15.7 3.0 1.14 0.69

Σ DLC Cong.c 4.65 2.07 2.02 0.09 0.940 0.631 0.118 0.141

DLC TEQd 1.41E-04 6.25E-05 6.19E-05 2.89E-06 2.86E-05 1.96E-05 3.55E-06 4.24E06

% 45 Cong.e 89.2 2.5 83.4 4.6 78.9 14.4 90.2 0.9

% DLC Cong.f 10.0 2.7 5.6 0.1 4.7 3.1 7.3 6.8

a

 Sum of all 209 congeners, including those not shown in this table.

b

 Sum of the 45 congeners shown in this table.

c

 Sum of the 12 dioxin-like congeners (77, 81,105, 114, 118, 123, 126, 156, 157, 167, 169, 189).

d TCDD toxic equivalence for the sum of the12 dioxin-like congeners. 

e

 Percent of the total concentration of all 209 congeners for the sum of the 45 congeners in this table. 

f

 Percent of the total concentration of all 209 congeners for the sum of the 12 dioxin-like congeners.

Table C-6. Other building material sample PCB congener concentrations at School 6 (continued)
C-12

Table C-7. Air sample PCB homolog weight percents at School 6a

PCB Homolog

Indoor Air

N = 7

Outdoor

N = 1

Mean SD Mean SD

1-Chlorine 0.02 0.1 <QL ---

2-Chlorine 0.6 0.7 <QL ---

3-Chlorine 4.3 2.1 <QL ---

4-Chlorine 31.1 6.0 26.4 ---

5-Chlorine 51.1 5.4 56.6 ---

6-Chlorine 11.9 3.5 17.0 ---

7-Chlorine 0.8 0.5 <QL ---

8-Chlorine 0.1 0.1 <QL ---

9-Chlorine 0.04 0.0 <QL ---

10-Chlorine <QL --- <QL ---

ng/m3 ng/m3 ng/m3 ng/m3

Σ 209 Cong.b 500 154 7.7 ---

a

 Each chlorine-number homolog’s percent of the total concentration of all 209 congeners. 

b

 Sum of the measured concentrations for all 209 congeners.

Table C-8. Surface wipe sample PCB homolog weight percents at School 6a

PCB Homolog

Surface Wipe – High Contact 

Surfaces

N = 3

Surface Wipe – Low Contact 

Surfaces

N = 7

Mean SD Mean SD

1-Chlorine <QL --- <QL ---

2-Chlorine <QL --- <QL ---

3-Chlorine 0.4 0.7 1.4 1.0

4-Chlorine 10.5 6.8 12.1 5.1

5-Chlorine 46.6 3.3 44.0 14.4

6-Chlorine 36.4 5.1 27.8 5.5

7-Chlorine 4.4 0.8 8.3 11.6

8-Chlorine 0.2 0.3 2.3 4.4

9-Chlorine 1.4 1.6 4.1 7.2

10-Chlorine <QL --- <QL ---

μg/100 cm2 μg/100 cm2 μg/100 cm2 μg/100 cm2

Σ 209 Cong.b 0.491 0.430 0.371 0.385

a

 Each chlorine-number homolog’s percent of the total concentration of all 209 congeners. 

b

 Sum of the measured concentrations for all 209 congeners.
C-13

Table C-9. Dust sample PCB homolog weight percents at School 6a

PCB Homolog

Interior Dust

N = 4

Mean SD

1-Chlorine <QL ---

2-Chlorine <QL ---

3-Chlorine 0.4 0.5

4-Chlorine 11.3 5.3

5-Chlorine 50.8 3.1

6-Chlorine 31.6 7.1

7-Chlorine 5.4 3.4

8-Chlorine 0.5 0.6

9-Chlorine 0.1 0.1

10-Chlorine <QL ---

ppm ppm

Σ 209 Cong.b 30.9 18.6

a

 Each chlorine-number homolog’s percent of the total concentration of all 209 congeners. 

b

 Sum of the measured concentrations for all 209 congeners.

Table C-10. Soil sample PCB homolog weight percents at School 6a

PCB Homolog

Soil Collected 0.15 m From 

Building

N = 2

Soil Collected 2.44 m From 

Building

N = 1

Mean SD Mean SD

1-Chlorine <QL --- <QL ---

2-Chlorine <QL --- <QL ---

3-Chlorine <QL --- <QL ---

4-Chlorine 5.3 0.6 32.7 ---

5-Chlorine 40.8 1.7 22.1 ---

6-Chlorine 44.3 0.3 33.1 ---

7-Chlorine 8.6 0.8 7.5 ---

8-Chlorine 0.8 0.1 1.4 ---

9-Chlorine 0.2 0.02 2.7 ---

10-Chlorine <QL --- <QL ---

ppm ppm ppm ppm

Σ 209 Cong.b 1.60 0.49 0.071 ---

a

 Each chlorine-number homolog’s percent of the total concentration of all 209 congeners. 

b

 Sum of the measured concentrations for all 209 congeners.
C-14

Table C-11. Caulk sample PCB homolog weight percents at School 6a

PCB Homolog

Interior Caulk Samples

N = 5

Exterior Caulk Samples

N = 3

Mean SD Mean SD

1-Chlorine 0.0 0.0 0.0 0.0

2-Chlorine 0.1 0.1 0.0 0.0

3-Chlorine 1.8 1.2 0.1 0.2

4-Chlorine 21.8 2.9 10.5 2.8

5-Chlorine 53.4 4.1 49.6 1.3

6-Chlorine 21.0 3.4 34.8 3.7

7-Chlorine 1.7 1.2 4.8 0.6

8-Chlorine 0.1 0.1 0.2 0.1

9-Chlorine 0.1 0.1 0.0 0.0

10-Chlorine 0.0 0.0 0.0 0.0

ppm ppm ppm ppm

Σ 209 Cong.b 11.5 5.88 114000 2250

a

 Each chlorine-number homolog’s percent of the total concentration of all 209 congeners. 

b

 Sum of the measured concentrations for all 209 congeners.

Table C-12. Building material PCB homolog weight percents at School 6a

PCB 

Homolog

Paints and 

Floor Varnish

N = 12

Chair Cushion 

Foam and Pin 

Board

N = 2

Cove Molding 

and Floor 

Underlayment 

N = 2

Floor Tile and 

Ceiling Tile

N = 2

Mean SD Mean SD Mean SD Mean SD

1-Chlorine 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2-Chlorine 0.0 0.0 0.5 0.5 0.4 0.5 0.0 0.0

3-Chlorine 1.1 0.8 4.7 2.6 12.4 12.8 0.2 0.3

4-Chlorine 16.9 5.3 27.9 2.9 36.2 13.4 14.0 8.3

5-Chlorine 49.6 4.0 45.1 3.0 34.7 18.7 49.2 3.7

6-Chlorine 26.8 4.9 15.1 1.7 13.5 8.1 31.2 8.6

7-Chlorine 4.8 2.7 6.0 6.8 2.3 0.1 4.6 2.5

8-Chlorine 0.7 1.0 0.6 0.6 0.4 0.2 0.4 0.6

9-Chlorine 0.1 0.2 0.1 0.1 0.1 0.0 0.4 0.6

10-Chlorine 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

ppm ppm ppm ppm ppm ppm ppm ppm

Σ 209 Cong.b 45.8 15.5 36.1 1.2 19.8 0.2 1.26 0.76

a

 Each chlorine-number homolog’s percent of the total concentration of all 209 congeners. 

b

 Sum of the measured concentrations for all 209 congeners.
D-1

Appendix D. 

Other School Data for SHEDS PCB 

Exposure /Dose Modeling

Summary

In 2009, Dr. Peter Egeghy of the National Exposure Research 

Laboratory assembled PCB measurement data at schools and 

college buildings from various literature and internet sources 

(shown below). Distributions for PCB concentrations in 

air, surface wipe, dust, and soil were used to generate input 

distributions for SHEDS modeling (FigureD-1). All of the 

SHEDS absorption and activity model parameters and inputs 

that were used for modeling absorbed doses in this report 

were also used for these data. 

Data Sources

Indoor Air:

UMass Amherst (http://www.ehs.umass.edu/PCBinformation.htm)

University of Rhode Island (http://www.uri.edu/news/

chafeeclosedfinal.htm)

Coghlan et al., 2002 (http://www.pcbinschools.org/

Characterize%20pcb.pdf)

Sullivan et al., 2008 (http://www.trcsolutions.com/Files/

File/dioxin2008_Sullivan_Paper.pdf)

Harrad 2007 (http://www.rsc-aamg.org/Documents/Papers/

MAA2007/StuartHarrad.pdf)

[Survey of NYC Schools not included because only 1 of 

127 samples was above the rather high detection limit of 

0.56 μg/m3]

Outdoor Air:

UMass Amherst (http://www.ehs.umass.edu/PCBinformation.htm)

University of Rhode Island (http://www.uri.edu/news/

chafeeclosedfinal.htm)

Harrad 2007 (http://www.rsc-aamg.org/Documents/Papers/

MAA2007/StuartHarrad.pdf)

Dust:

UMass Amherst (http://www.ehs.umass.edu/PCBinformation.htm)

University of Rhode Island (http://www.uri.edu/news/

chafeeclosedfinal.htm)

Coghlan et al., 2002 (http://www.pcbinschools.org/

Characterize%20pcb.pdf)

Sullivan et al., 2008 (http://www.trcsolutions.com/Files/

File/dioxin2008_Sullivan_Paper.pdf)

Soil:

Massachusetts Schools (http://www.pcbinschools.org/

Sampling%20Reports.htm)

SUNY Oswego (http://www.pcbinschools.org/Sampling%20

Reports.htm)

MIT (http://westgate.mit.edu/node/10)

Herrick et al., 2004 (http://www.ehponline.org/

members/2004/6912/6912.pdf)

Wipe:

NYC Schools (http://www.pcbinschools.org/Caulking%20

Survey%20FINAL%204.5.08.xls)

French Hill (http://www.pcbinschools.org/Sampling%20

Reports.htm)

UMass Amherst (http://www.ehs.umass.edu/PCBinformation.htm)

Coghlan et al., 2002 (http://www.pcbinschools.org/

Characterize%20pcb.pdf)

PCB Concentration Distributions

Distributions of PCB measurement results for indoor and 

outdoor air, surface wipe, dust, and soil samples obtained 

from the source above were plotted and are shown in Figure 

D-1. Distributional parameters from these data sets were 

input into the SHEDS model to generate distributions of 

estimated absorbed doses for comparison with estimates 

derived from the 2010 NYC school measurement data.
D-2

Figure D-1. Distributions of total PCB concentrations in several environmental media collected at school and college buildings (GM is the 

geometric mean)
E-1

Appendix E. 

Estimation of PCB Concentrations in Dust

Background

The SHEDS model uses distributions of PCB concentrations 

measured in multiple environmental media to generate 

distributions of exposure estimates. Indoor air, surface 

wipe, and soil concentration measurement results were 

available to use in SHEDS modeling for the six schools 

considered for this report. However, interior dust samples 

were not collected as part of the NYC remedial pilot 

investigation. Dust samples were collected in several rooms 

at the other school, but this building had not been routinely 

cleaned over the previous five weeks, and it is not known 

if the PCB concentrations would be similar to those in the 

regularly cleaned building. Dust can be an important source 

of exposure to children through inhalation, non-dietary 

ingestion, and dermal contact. Therefore, several approaches 

for estimating PCB concentrations in dust were considered. 

Initial SHEDS modeling runs were performed using the 

distribution of PCBs in soil samples as a surrogate for 

the distribution of PCB levels in dust, followed by using 

5-times the soil concentrations as the dust surrogate. The 

latter approach was based on extant information shown 

in Appendix D, where the ratio between the geometric 

means of school dust PCB concentrations and school soil 

concentrations was 4400 μg/kg to 980 μg/kg, or a ratio of 

4.5. While track-in of outdoor soil may contribute to dust 

inside of school buildings, for SVOCs there is likely to be a 

stronger relationship between levels in indoor air and indoor 

dust, particularly when there are relatively strong sources 

of SVOCs in the building. Therefore, estimation based on 

relationships between indoor air and dust is likely to be 

more appropriate for estimating concentrations in dust. This 

approach also has the advantage that air measurements are 

available from multiple rooms in each school building at each 

of the time points used for SHEDS modeling. Thus, 

any change in air concentrations as a result of remedial 

actions would be reflected in both the air and the dust – a 

situation likely to occur in school buildings.

We considered two approaches for estimating PCB 

concentrations on dust using air measurement results, each 

with strengths and weaknesses. The first was a method 

published by Weschler and Nazaroff (2010) that uses an 

approach based on the octanol-air partition coefficient to 

estimate the partitioning between SVOCs in air and settled 

dust in indoor environments. Using published data for 

many SVOCs, the authors found a high correlation between 

measured and predicted concentrations in dust (R2

 = 0.76). 

But for a few PCB congeners that were included, the 

approach over-estimated the actual concentrations by a range 

of 2.6 to 9.9 fold, depending on the congener. This approach 

also required assumptions about the fraction of organic 

carbon in dust and for other factors. A second approach 

was based on a predictive model for material/air partition 

coefficients that was developed from chamber testing of 

multiple materials (Guo, 2002). 

Using the second approach the estimated geometric mean 

and geometric standard deviation based on pre-remediation 

air concentrations in six schools were 7.03 (4.26) ppm. 

This value and the resulting distribution were similar to 

those of the extant dust data shown in Appendix D. The 

geometric mean of 7.03 was somewhat greater than the 4.4 

ppm for the extant data, but the air levels in the six schools 

were also greater, on average, than those in the extant data 

set. We elected to use the second approach to generate dust 

PCB concentrations for SHEDS modeling, but there are 

clearly uncertainties in this estimate, particularly how well 

the model represents the actual partioning for the different 

PCB congeners for school dust. In general, in the absence 

of dust measurements there is no perfect approach for 

estimating the dust PCB concentration information needed 

for SHEDS modeling. The air/dust partitioning approach 

is more appropriate than estimates based on outdoor soil 

concentrations in this scenario. 
E-2

Condition

Geometric

Mean

Geometric

SD

Percentiles of the Distribution of 

Dust Estimates 

(ppm)

p5 p25 p50 p75 p95

6 Schools, Pre-remedition 7.03 4.26 0.75 1.82 9.77 22.8 41.9

5 Schools, Pre-remediation 6.24 4.44 0.75 0.78 7.91 23.5 44.9

5 Schools, Post-remediation 2.29 2.87 0.71 0.75 2.23 6.44 8.83

3 Schools, Year 1 Pre-remediation 9.82 3.86 0.75 4.90 15.3 25.8 47.8

3 Schools, Year 1 Post-remediation 2.35 2.87 0.71 0.74 2.33 6.54 8.71

3 Schools, Year 2 Pre-remediation 3.07 3.07 0.75 7.63 3.75 7.91 13.8

Table E-1. Distributions of total PCB concentrations on dust estimated from solid/air partition coefficient estimation.

Distributions of Dust Concentrations

Table E-1 shows selected parameters for the distribution of 

dust concentrations calculated using the room air total PCB 

concentrations at each SHEDS modeling time point. 

Comparision of SHEDS Results Using Differen Dust 

Estimation Approaches

Table E-2 shows distributions of estimated total PCB 

absorbed doses from the SHEDS model based on two 

different approaches for estimating total PCB concentrations 

in dust. The first approach is the one described here in 

Appendix E based on estimates of solid/air partition 

coefficients for the congeners. The second approach uses 

5-times the soil total PCB measured concentrations as a 

surrogate for dust concentrations. The geometric mean 

and geometric standard deviations for the solid/air partition 

approach were 7.03 (4.26) ppm and were 2.68 (3.65) for 

the 5-times soil concentration surrogate approach. Thus, 

the solid/air partition coefficient approach generates 

higher dust concentrations. Even though the estimated 

dust concentrations from the solid/air partition coefficient 

estimation approach are higher, SHEDS model estimates of 

total PCB absorbed dose are close to those estimated from 

5-times the soil concentrations from the 5th through about 

the 75th percentile of the distribution. For the two younger 

age groups, estimated absorbed doses using the solid/air 

partition coefficient approach become larger than those from 

the 5-times soil approach, particularly at the 99th percentile. 

This difference is not seen for the two older age groups 

because there are not suitable hand/mouth activity data for 

dust ingestion for these age groups, and as this component of 

exposure is considered much smaller for older children, was 

not included in the SHEDS model. 

Method

Step1: Estimate the congener concentrations in room air 

assuming an unaged Aroclor 1254 mixture. 

Step 2: For each congener, estimate the material/air partition 

coefficients for dust from Eq.1:

 ln Ki = 8.785 lnPi (1) (Guo et al., 2002)

where

Ki = solid/air partition coefficient (dimensionless)

Pi = vapor pressure (torr)

Step 3: Estimate the dust concentration from Eq. 2:

 Cdi = 10-3 (2)

where 

Cdi = estimated concentration if congener i in dust 

(μg/kg)

Cai = measured concentration of congener I in room air 

(μg/m3)

ρ = density of dust (g/cm3), assumed to be 1.5 g/cm3

Step 4: Estimate the total PCB concentration in dust by 

summing up all congeners for the room.

Step 5: Repeat for all school rooms with total PCB 

measurements.

This approach assumes that equilibrium conditions have been 

reached between air and dust PCB congener concentrations, 

that the density of school dust is 1.5 g/cm3, and that the data 

used to develop equation 1 applies reasonably well to PCB 

congeners and school dust. 

ρ

Cai x Ki
E-3

Table E-2. Distributions of SHEDS estimates of total PCB absorbed doses using two approaches for estimating indoor 

dust levels at the six schools

Percentiles of the Distribution of Dose Estimates

(μg/kg/day)

Child Age Group Mean SD p5 p25 p50 p75 p95 p99

4 – 5 year olds

 Dust from solid/air K 0.023 0.012 0.012 0.017 0.021 0.026 0.036 0.061

 Dust from 5-times soil 0.020 0.006 0.011 0.016 0.019 0.023 0.029 0.037

6 – 10 year olds

 Dust from solid/air K 0.027 0.032 0.015 0.019 0.022 0.027 0.041 0.125

 Dust from 5-times soil 0.022 0.008 0.014 0.018 0.021 0.025 0.032 0.050

11 – 13 year olds

 Dust from solid/air K 0.015 0.005 0.009 0.012 0.015 0.018 0.022 0.028

 Dust from 5-times soil 0.014 0.004 0.008 0.011 0.014 0.016 0.021 0.026

14 – 18 year olds

 Dust from solid/air K 0.011 0.005 0.004 0.007 0.010 0.014 0.018 0.023

 Dust from 5-times soil 0.010 0.004 0.003 0.007 0.010 0.013 0.017 0.020