Geography of asbestos contamination near the World Trade Center site

ORIGINAL PAPER

Geography of asbestos contamination nearthe World Trade Center site

William C. Thayer Æ Daniel A. Griffith ÆGary L. Diamond

Published online: 6 June 2007

� Springer-Verlag 2007

Abstract Despite the dust cleanup and indoor air testing

program led by the U.S. Environmental Protection Agency

(EPA) and offered to all residents of Lower Manhattan

(south of Canal Street), concern remains about local

chemical residues from the collapse of the World Trade

Center (WTC) buildings. Data on post-cleanup indoor

airborne asbestos concentration, available from EPA

Region 2, were analyzed to assess the possibility that the

WTC site is the source of geographically concentrated rare

post-cleanup exceedances of the health-based standard for

asbestos. Recognizing that these rare exceedances may be

attributable to sources other than the WTC disaster, and

that these sources are very likely to exhibit geographic

patterns, the data were analyzed using a spatial filter

specification of the auto-Poisson probability model. Our

analysis shows that ignoring geographic patterns latent in

these exceedances affects the empirical probability of

exceeding the health-based standards for airborne asbestos.

We did not find any statistically-significant geographic

pattern in the exceedance events that would indicate the

WTC site as the source of the post-cleanup exceedances.

Apparent geographic patterns may be due to the geographic

variability in sampling intensity. Our analysis indicates the

Residential Dust Cleanup Program lead by EPA Region 2

has been effective at reducing the concentration of air-

borne asbestos in indoor air to below the health-based

benchmark.

Keywords Spatial autocorrelation � Spatial filter �Auto-Poisson distribution � Asbestos � World Trade Center

1 Introduction

Following the collapse of the World Trade Center (WTC)

buildings, residents of Lower Manhattan (south of Canal

Street, Fig. 1) expressed concern about the possible long-

term health problems associated with dust from the WTC

that had been deposited within their homes. In response to

this concern, EPA organized a team of federal, state and

local government agencies (the Indoor Air Task Force,

IATF) in February, 2002. Under EPA’s lead, the IATF

developed and implemented the Indoor Residential Dust

Cleanup Program (IRDCP). The goal of the IRDCP has

been to protect residents of Lower Manhattan from

potential exposures to residual dust from the WTC col-

lapse. The IRDCP comprises four components: the Con-

firmation Cleaning Building Study (CCBS) (USEPA

2003a); the Background Study (USEPA 2003b); the World

Trade Center Contaminants of Potential Concern (COPC)

report (USEPA 2003c); and, the Indoor Air Residential

Assistance Program—WTC Dust Cleanup (USEPA 2004).

The CCBS (USEPA 2003a) assessed the effectiveness of

various cleaning methods to reduce the concentration of

asbestos and other contaminants in indoor air and settled

dust. The purpose of the Background Study (USEPA

2003b) was to provide data about indoor air concentrations

for asbestos and other contaminants in residences located

outside of the area that was affected by the WTC collapse.

The COPC report (USEPA 2003c) describes the methods

W. C. Thayer (&) � G. L. Diamond

Syracuse Research Corporation, 301 Plainfield Road,

Suite 350, Syracuse, NY 13212, USA

e-mail: [email protected]

D. A. Griffith (&)

School of Economic, Political and Policy Sciences,

University of Texas at Dallas, P.O. Box 830688, GR31,

Richardson, TX 75083-0688, USA

e-mail: [email protected]

123

Stoch Environ Res Risk Assess (2007) 21:461–471

DOI 10.1007/s00477-007-0129-y

that were used to derive the health-based benchmarks for

asbestos and other contaminants in indoor air and settled

dust.

The fourth component of the IRDCP, cleaning and

sampling of residences, was open to all residents of Lower

Manhattan from June 5 through December 28, 2002.

Cleanup and sampling were performed in 3,387 residences

and 785 common areas (e.g., stairways, lobbies) that were

located in 408 buildings; sampling only (i.e., no cleaning)

was performed in 754 residences located in 219 buildings

(USEPA 2004). Residents could choose to have their

homes cleaned and tested, or just tested. Residents who

initially chose to have their homes tested only had the

option of having their homes cleaned and re-tested fol-

lowing disclosure of their initial test results. In addition to

residential areas, common areas also were cleaned if re-

quested by building owners, building managers, or coop

boards. Cleanup and testing were performed by contractors

certified by New York State and New York City (NYC).

Residences were cleaned using standard asbestos cleanup

methods: HEPA-filtered vacuums and wet wiping (USEPA

2003a, 2004).

Despite the comprehensive nature of the IRDCP, resi-

dents of Lower Manhattan and other NYC boroughs remain

concerned about the potential for adverse health effects

from asbestos and other contaminants dispersed by the

collapse of the WTC buildings (Cannaughton 2003;

DePalma 2006; McVay Hughes 2004). We describe an

analysis of the geographic distribution of asbestos

concentration in the indoor air of the residences whose

occupants participated in the IRDCP. We focus our dis-

cussion on results of a spatial analysis of the phase contrast

microscopy equivalent (PCMe) data because asbestos was

used by EPA as a surrogate for other potentially WTC-

related contaminants (USEPA 2003b), and because it was

sampled in all residences that participated in the IRDCP;

results of an analysis of lead and dioxin concentrations in

settled dust are described elsewhere (USEPA 2004).

The objectives of our analysis were to assess the

effectiveness of the IRDCP to reduce airborne concentra-

tions of asbestos to below the health-based benchmark, and

to assess the effect of ignoring spatial autocorrelation in the

PCMe data on empirical probabilities of exceeding the

health-based standard for airborne asbestos.

2 Materials and methods

2.1 Sampling and analytical methods

Three-to-five post-cleanup air samples per residence were

collected and analyzed for asbestos using transmission

electron microscopy (TEM). A modified version of the

sampling procedure described in the Asbestos Hazard

Emergency Response Act (AHERA) (modified aggressive)

was used in most of the apartments (USEPA 2003a). Air

samples were collected by drawing air through a

0.45 micrometer (lm) pore size mixed cellulose ester

membrane filter cassette at a rate of approximately 10 l/min

over 480 min, while reciprocating fans were run to promote

the resuspension of any settled dust (USEPA 2003a). To

facilitate comparison to AHERA standards, the analytical

method reported PCMe concentrations of airborne asbestos

(USEPA 2003a).

2.2 Statistical approach and methods

2.2.1 Modeling PCMe exceedance events

The PCMe data were divided into two groups: test only

data were collected from residences where only testing was

done; clean and test data were collected from residences

after these units were professionally cleaned. Challenges to

the analysis of the PCMe data included the very high rate

Fig. 1 Site location map. The WTC study area consists of Lower

Manhattan, south of Canal Street. Statistical summary areas (SSAs)

are indicated by heavy outlines. Cross-hatching indicates areas where

data were not collected

462 Stoch Environ Res Risk Assess (2007) 21:461–471

123

of results that were reported as ‘below detection level’ (i.e.,

non-detects), the lack of data to compare the post-cleanup

data to, and the presence of spatial autocorrelation in the

data. The very high rate of non-detects (96%) was effec-

tively managed by converting the continuous PCMe data to

binary indicator data (PCMe exceedance events): PCMe

concentrations above the health-based benchmark of

0.0009 fibers/cm3 of air (i.e., events) were assigned a value

of 1; concentrations below the benchmark (non-events)

were assigned a value of 0. The rare nature of the excee-

dance events suggest a Poisson probability model would be

an appropriate statistical model for describing these data.

Meanwhile, recognizing that the exceedances are con-

strained by the number of samples collected suggests that

these data may be described by a binomial model.

Pre-cleanup data were not collected, and comparable

data on background levels of indoor airborne asbestos

concentration were not available (USEPA 2003b; Tang

et al. 2004). Therefore, a direct statistical comparison of

post-cleanup asbestos levels to a pre-cleanup level, or to a

background level expected in the absence of a specific

source of contamination (e.g., WTC building collapse)

could not be performed. Given the lack of comparative

data, methods from spatial statistics were used to test the

hypothesis that the geographical locations of PCMe ex-

ceedances exhibit a spatial pattern. The reasoning behind

this approach was that we would expect a spatial pattern in

the location of PCMe exceedances if the WTC site was the

source of the exceedances (i.e., the cleanup methods em-

ployed were not effective). If a spatial pattern can be de-

tected, further analysis would be warranted to evaluate the

likelihood that the WTC site was the source of the excee-

dances. Under the hypothesis that the WTC site was the

source of the asbestos exceedances, we would expect to

find high exceedance rates close to the WTC site, and

diminishing exceedance rates with distance from the site.

This hypothesized trend assumes that there were no other

sources of asbestos of sufficient magnitude to obscure any

pattern that would be attributable to the WTC site.

The presence of spatial autocorrelation in the PCMe data

was addressed by using a spatial filter specification of the

auto-Poisson and auto-binomial probability models (Griffith

2002, 2004). The spatial filter approach can be thought of as

a method of variable transformation that converts the ori-

ginal variable into two synthetic variables: one that captures

latent spatial autocorrelation (the mean response term), and

one that is correlation-free (the error term) (Griffith 2006).

Our motivation for using the spatial filter autoregression

models with the asbestos exceedance data was to improve

parameter estimation (i.e., the rate of PCMe exceedances

across Lower Manhattan), and to improve statistical infer-

ence that is based on the estimated statistical models.

In terms of estimating the parameters of a regression

model, spatial autocorrelation can be viewed as an indi-

cator of a missing variable and, also, as a variance inflation

factor (VIF; Griffith and Layne 1999). Important sources of

airborne asbestos in urban areas include automobile brakes

and clutches, and building materials (ATSDR 2001). These

sources of airborne asbestos are very likely to exhibit

particular geographic patterns. If the location and strength

of these sources can explain the location and amount of

exceedances, and they are not accounted for in a statistical

model, they will increase the variance of the model error

term and induce spatial autocorrelation in the error term.

Filtering spatial autocorrelation from the error term by

transferring it to the mean response term tends to increase

precision in estimates of the rate of PCMe exceedance

events by increasing the total variance that is explained by

a model (i.e., improving model fit statistics such as the

pseudo-R2 statistic) and reduce bias from the estimate of

the PCMe exceedance rate due to model misspecification

(i.e., omitting important explanatory variables from a

model, such as non-WTC related sources of asbestos). The

improvement in estimation is analogous to the improve-

ment that would be gained in an ordinary regression model

by adding an explanatory variable to a model that explains

a significant amount of variance in the data. Filtering

spatial autocorrelation from the error term also tends to

result in more accurate estimates of the standard error of

the mean PCMe exceedance rate (i.e., decrease the VIF),

thereby improving inference that is based on the estimated

model.

One feature of a Poisson random variable is that its

mean, l, and its variance are equal (equidispersion), a

property frequently violated by real world data. Violation

of the equidispersion assumption has qualitative conse-

quences similar to failure of the assumption of hom-

oskedasticity associated with the Gaussian distribution

(Cameron and Trivedi 1998, p. 77); e.g., actual Type I

error probabilities will be larger than intended. The

standard way of accommodating overdispersion (the

presence of more variation than is expected for a Poisson

random variable) is by replacing a Poisson random vari-

able with a negative binomial random variable—which

can be viewed as a gamma mixture of Poisson random

variables. In doing so, the distribution of counts is viewed

as either (1) having missing variables for the mean

specification, or (2) being dependent (i.e., the occurrence

of an event increases the probability of further events

occurring). The most popular implementation of the

negative binomial probability model specifies the variance

as being quadratic in the mean:

lþ gl2 ¼ ð1þ glÞl ð1Þ

Stoch Environ Res Risk Assess (2007) 21:461–471 463

123

with the dispersion parameter, g, to be estimated. The

magnitude of g may be interpreted as follows (after

Cameron and Trivedi 1998, p. 79):

g = 0 implies no overdispersion;

g � 1l implies a modest degree of overdispersion; and,

g � 2l implies considerable overdispersion.

In other words, if 0 � g\ 0:5l ; a spatial analyst may

consider overdispersion detected in georeferenced data to

be inconsequential, with little to be gained by replacing a

Poisson with a negative binomial model specification.

2.2.2 Comparing exceedance rates between SSAs

An analysis of the pairwise differences between the SSA

exceedance rates provides a quantitative assessment of the

presence of a geographic pattern in the PCMe exceedance

rates across lower Manhattan. SSAs with exceedance rates

based upon a sample size of 30 or more were compared to

each other to assess whether or not statistically significant

differences exist. Aggregate sample sizes less than 30 were

considered too small to include in these comparisons. The

sample size restriction left 22 SSAs for the test only and 32

for the clean and test data. Comparisons were based on the

estimated spatial-filter auto-Poisson models. These com-

parisons essentially consisted of calculating the difference

between the spatial-filter Poisson model-estimated rates for

two SSAs, and determining if the absolute value of the dif-

ference statistically differs from zero. The expected value of

the difference of means for two random variables equals the

difference between their expected values, l1 – l2. If

the random variables are independent (e.g., not spatially

autocorrelated), the sampling variance of their difference

isl1

n1þ l2

n2(Skellam 1946). As the magnitudes of the two

means, l1 and l2, increase, the distribution of the differ-

ence of the two independent Poisson variables rapidly

converges to normality. Convergence on a normal prob-

ability distribution is quick, with a very good approxi-

mation attained once l1 > 4 and l2 > 4. For smaller

values of l1 and/or l2, the sampling distribution of the

difference of two Poisson random variables still tends to

conform to a Poisson distribution. The low number of

exceedances in most SSAs indicates that the normal

approximation would furnish poor results here; this con-

tention was confirmed by a simulation experiment

involving 50,000 difference of means replications

(USEPA 2004). Consequently, the pairwise difference of

rates assessments are based upon a Hope-type nonpara-

metric simulation test (Hope 1968), involving 99,999

replications coupled with each observed difference. The

simulated distribution is based on a pair of Poisson ran-

dom variables, each with the same mean ofn1l2þn2l1

2n1n2;

which yields a null hypothesis difference of 0 and the

correct theoretical variance ofl1

n1þ l2

n2:

When performing multiple statistical tests, the proba-

bility of rejecting the null hypothesis when it is true (Type I

error, a) increases. In the present context, the overall

probability (global, aglobal) of incorrectly concluding that a

difference exists between the exceedance rates for two

SSAs would be greater than intended, unless the compar-

ison-wise Type I error probability (acomp) is adjusted

downward to compensate for the number of multiple tests.

For example, setting aglobal = 0.05 means that there would

be a very good chance of finding at least one in twenty of

the tests between SSAs to be statistically significant solely

due to sampling variability, incorrectly concluding that a

difference exists in the population. There were 231 and 496

tests between SSAs for the test only and clean and test

data, respectively, which means as many as 12 and 25

significant differences could be found for the test only and

clean and test data, respectively, due solely to sampling

variability. The Bonferroni correction/adjustment is the

most basic procedure for modifying acomp to compensate

for this increase in aglobal. When the variables are inde-

pendent, the comparison-wise Type I error probability is

calculated according to the following equation:

acomp ¼aglobal

# of testsð2Þ

where # of tests = n(n–1)/2 = 231 for test only data and 496

for clean and test data.

For example, for the test only data, and a two-tailed test,

acomp ¼ 0:01231ffi 0:005 for aglobal ffi 0:01; acomp ffi 0:025

231for

aglobal ffi 0:05; and acomp ffi 0:05231

for aglobal ffi 0:10: As cor-

relation between the samples increases, the denominator of

this adjustment effectively decreases toward 1. As tests

were performed with the uncorrelated spatial-filtered auto-

Poisson data, the full Bonferroni adjustment was used.

Because a two-tailed test was employed here, an observed

rank of 1–2 or 99,999–100,000 resulted in a rejection of the

null hypothesis for aglobal = 0.01, an observed rank of 3–12

or 99,990–99,998 resulted in a rejection of the null

hypothesis for aglobal = 0.05, and an observed rank of 12–

22 or 99,979–99,989 resulted in a rejection of the null

hypothesis for aglobal = 0.10.

For descriptions of cluster detection methods the reader

may consult Bailey and Gatrell (1995), Alexander and

Boyle (1996), Kulldorff (1997) and ClusterSeer (see

http://www.terraseer.com/products/clusterseer.html). Ans-

elin (2004) provides a review of publicly available soft-

ware for cluster detection. The objective of our analysis

was to detect local clusters of high PCMe exceedances

rather than the presence of global clustering of PCMe

exceedance rates (e.g., Anselin 2004). This approach


123

differs in a fundamental way from other methods that are

widely employed, which are based on distances between

events (when the events are analyzed individually as

points) or on the number of events within an area of the

region of interest (e.g., Kulldorff’s spatial scan statistic;

Kulldorff 1997).

2.2.3 Computing empirical probabilities

of PCMe exceedance

The probability of one or more PCMe exceedance events

was computed using the Poisson density function

Pr PCMe � 1ð Þ ¼ 1� e�l̂ l̂x=x!� �

ð3Þ

where

x = 0 (i.e., 0 PCMe exceedance events), and

l̂ ¼ estimate of the mean exceedance rate computed

with either the constant mean Poisson or the

spatial-filter auto-Poisson model.

3 Results and discussion

3.1 Geographic distribution of PCMe exceedance

The test only data set contains 4,316 observations, 21 of

which are exceedances, for an exceedance rate of

0.004866. The clean and test data set contains 24,358

observations, of which 102 are exceedances, for an

exceedance rate of 0.004188. The geographic distributions

of the PCMe exceedance rates (test only, clean and test),

aggregated at the SSA level, are shown in Figs. 2a and

3a. These figures illustrate the rareness of the exceedance

events: 80% (test only) and 66% (clean and test) of the

SSAs have no exceedance events. The highest rate of

exceedance (at the SSA level) for both data sets is

approximately 6%. Test only SSAs that fall within the 4th

quartile contain 1–9 exceedance events. Clean and test

SSAs that fall within the 4th quartile contain 2–32

exceedance events. Clean and test SSAs with similar

exceedance rates tend to be located near each other (i.e.,

exhibit positive spatial autocorrelation). Comparing

Fig. 2a and b (the spatial distributions of sample sizes)

indicates a relationship between SSAs with larger sample

sizes and positive exceedance rates (the Law of Large

Numbers at work); a similar relationship is indicated for

the clean and test data (Fig. 3a, b). Overall, SSAs located

near the WTC site have larger sample sizes than SSAs

located further from the WTC site. Therefore, any

apparent geographic pattern in the exceedance rates may

be due in part to the variability and geographic pattern in

sample sizes, which is at least partially controlled for in

the modeling with an offset variable.

3.2 Spatial-filter auto-Poisson model estimation

3.2.1 Test only data

Estimation results for the auto-binomial and auto-Poisson

models are presented in Table 1.

One important implication from the tabulated results is

that the Poisson model description of the exceedance rates

may suffer from a marked violation of the equidispersion

assumption:

2

l̂¼ 2

0:5833� 3:4286\4:6066

ðl̂ ¼ 21 exceedance events/36 SSAs ¼ 0:5833Þ:

Estimation results for the spatial-filter models also are

shown in Table 1. Now the assumption of equidispersion

appears to be reasonable:

0\0:5232\0:5

0:5833� 0:8571:

Overdispersion accompanying the simple Poisson model

description, principally, is attributable to latent spatial

autocorrelation in the PCMe data. The spatial-filter auto-

Poisson model specification accounts for nearly 30% of the

variation in the geographic distribution of rates.

3.2.2 Clean and test data

Estimation results for the auto-binomial and auto-Poisson

models for the clean and test data are presented in Table 2.

The results indicate that the Poisson model description of

the exceedance rates may suffer from a dramatic violation

of the equidispersion assumption:

2

l̂¼ 2

2:6842� 0:7451\2:8797

ðl̂ ¼ 102 exceedance events/38 SSAs ¼ 2:6842Þ:

The negative binomial model was not estimable with the

clean and test data; however, the deviance measure for

the estimated Poisson model (1.36) suggests a lack of

serious overdispersion. Again, the overdispersion accom-

panying the simple Poisson model description, princi-

pally, is attributable to latent spatial autocorrelation. The

spatial-filter auto-Poisson and auto-binomial models

account for approximately 50% of the variation in the

geographic distribution of rates. However, the auto-Pois-

son model was used for the rate comparisons and for

computing the empirical probabilities of PCMe excee-


123

dance as it provides a more theoretically sound basis for

the comparisons.

3.3 Comparisons between SSAs

Comparisons of the test only exceedance rates between

SSAs reveal three SSAs with exceedance rates that are

statistically significantly greater (at a = 0.01) than the ex-

ceedance rates observed in approximately one-half of the

other SSAs (Fig. 4). The numbers of exceedance events for

these three SSAs range from 2 to 9; their exceedance rates

range from 0.021 to 0.060. Results of the comparison of the

clean and test exceedance rates between SSAs are por-

trayed in Fig. 5. The number of significant pairwise com-

parisons at a = 0.01 are shown for SSAs that have one or

more exceedances. Three SSAs with exceedance rates

greater than the majority of the other SSAs are located east

of the WTC site. The number of exceedance events for

these three SSAs range from 17 to 32; the exceedance rates

range from 0.006 to 0.059.

There is no obvious geographic pattern in the SSAs that

are identified as having exceedance rates that are statisti-

cally greater than the rates observed in other SSAs. As

discussed above (in Geographic distribution of PCMe

exceedance), Figs. 4 and 5 should be interpreted with

caution, as the apparent geographic pattern in the excee-

dance rates may be due in part to the geographic variability

in sample sizes (Figs. 2b, 3b).

The geographic distributions of exceedance rates and

rate comparisons that are shown in Figs. 2a, 3a, 4 and 5 do

not implicate the WTC site as the source of the excee-

dances. While there is a tendency for SSAs with similar

exceedance rates to be located near each other, there is no

pattern of decreasing exceedance rates with distance from

the WTC site. For example, SSAs adjacent to the WTC site

on the north and south have no exceedances. Our results

should be interpreted with caution; it is likely that the

variation in sample size and the rare nature of the excee-

dance events result in a low power to detect spatial clusters.

3.4 Probability of exceeding the health-based standard

for asbestos

Figures 6, 7, 8 and 9 illustrate the effect that filtering spatial

autocorrelation from the PCMe exceedance residuals has on

the predicted probability of exceeding the health-based

standard for airborne asbestos. The probabilities were

computed using the mean exceedance rate for each SSA

predicted with the unfiltered (Figs. 6, 8) and spatial-filter

(Figs. 7, 9) Poisson models, using the log of the sample size

Fig. 2 a Spatial distribution of PCMe exceedance rates for the testonly data, by statistical summary areas (SSAs). Test only data refer to

samples collected at residences where residents requested to have

their indoor air tested for asbestos but declined to have their

residences cleaned. The exceedance rate for each SSA equals the

number of PCMe results for a SSA that exceeded the health-based

benchmark, divided by the number of samples collected within that

SSA. SSAs where data were not collected are indicated by diagonal

hatching. SSAs with one or more PCMe exceedances fall in the upper

quartile of the exceedance rate, which indicates the rareness of the

exceedance events. Six of the seven SSAs that had one or more

exceedance are located east and north of the WTC site; the seventh

SSA, which is located southwest of the WTC, had one exceedance.

The apparent geographic pattern of exceedance rates is likely to be

due, at least in part, to the geographic distribution of sample sizes

(Fig. 2b). b The spatial distribution of sample sizes for the test onlydata, by statistical summary areas (SSAs). Quartiles of the distribution

of sample sizes are shown. More samples were collected within SSAs

that were located nearby than in SSAs that were located further from

the WTC site. The geographic distribution of sample sizes should be

considered when interpreting the geographic distribution of excee-

dance events (a)


123

as an offset variable to account for the unequal sample sizes

between SSAs. Despite the inclusion of this offset variable,

the apparent geographic pattern in the probability of

exceeding the health-based standard is most likely, at least

partly due to the geographic pattern in sample size

(Figs. 2b, 3b). Of note is that the probabilities illustrated in

Figs. 6, 7, and 8, 9 apply to each SSA as a whole, and not to

each residence within an SSA (e.g., a probability of 0.5

Fig. 3 a The spatial distribution of PCMe exceedance rates for the

clean and test data, by statistical summary areas (SSAs). Clean andtest data refer to samples collected from residences where the

residents had requested EPA to clean their residences and test their

indoor air for asbestos. Quartiles of the distribution of PCMe

exceedance rates are shown. SSAs with one or more exceedances fall

in the upper two quartiles, indicating the rareness of the exceedance

events. Statistical summary areas with exceedance rates in the upper

quartile of the distribution of PCMe exceedances are located north

and east of the WTC site. Modest positive spatial autocorrelation in

the exceedance rates is indicated by the tendency for SSAs with

similar rates to be located near each other. The apparent geographic

pattern of exceedance rates is likely to be due, at least in part, to the

geographic distribution of sample sizes (b). b The spatial distribution

of sample sizes for the clean and test data, by statistical summary

areas (SSAs). Quartiles of the distribution of sample sizes are shown.

More samples were collected within SSAs that were located nearby

than in SSAs that were located further from the WTC site; therefore,

the geographic distribution of sample sizes should be considered

when interpreting the geographic distribution of exceedance events

(a)

Table 1 Selected model estimation results for test only PCMe exceedance rate data

Modela Without spatial-filter With spatial-filter

Expected

mean rate

Dispersion

parameter

Pseudo-R2 Expected

mean rate

Dispersion

parameter

Pseudo-R2

Poisson 0.004877 NA 0 0.002625 NA 0.290

Negative binomial 0.006119 4.6066 0 0.002407 0.5232 0.286

Binomial 0.004901 NA 0 0.002643 NA 0.291

a Models were estimated with SAS PROC GENMOD using ln(# observations) as an offset variable

Table 2 Selected model estimation results for clean and test PCMe exceedance rate data

Modela Without spatial-filter With spatial-filter

Expected

mean rate

Dispersion

parameter

Pseudo-R2 Expected

mean rate

Dispersion

parameter

Pseudo-R2

Poisson 0.004188 NA 0 0.002638 NA 0.523

Negative binomial 0.005487 2.8797 0 –b –b –b

Binomial 0.004207 NA 0 0.002647 NA 0.519

a Models were estimated with SAS PROC GENMOD using ln(# observations) as an offset variableb Model was not estimable


123

means there is a 50% chance that at least one residence

within the associated SSA has an indoor air concentration of

asbestos that exceeds the health-based standard).

The effect of spatial filtering is to reduce the estimated

probabilities of exceeding the health-based standard for

asbestos. After filtering spatial autocorrelation from the test

only PCMe residuals, the number of SSAs with estimated

probabilities of exceeding the health-based standard for

asbestos of 0.50 or greater decreased from nine to two.

Similarly, the spatial filter Poisson model estimates seven

fewer SSAs having probabilities of 0.50 or more of

exceeding the health-based standard than is estimated with

the unfiltered Poisson model. The average reduction in the

empirical probabilities is 0.10 and 0.12 for the clean and

test, and test only data, respectively.

Decreases in the estimated probabilities are due to a

reduction in the estimate of the mean exceedance rate for

the spatial-filter Poisson models, compared to the unfiltered

Poisson models (Table 1). The spatial-filter Poisson mod-

els satisfy the assumption of equidispersion while the

unfiltered models do not; therefore, the former provide

more reliable estimates of the mean exceedance rates for

the two data sets.

4 Conclusions

We describe an approach for analyzing the effectiveness of

the WTC Residential Cleanup Program with data that pose

Fig. 4 Significant differences between estimated exceedance rates

for test only data. Estimates are based on the spatially-filtered Poisson

model. The number of significant pairwise comparisons at a global

a = 0.01 (with a Bonferroni adjustment) are shown for statistical

summary areas (SSAs) that had one or more exceedances. Compar-

isons with SSAs having sample sizes less than 30 (indicated by cross-

hatching) were deemed unreliable and, therefore, were not included in

the analysis. The three SSAs that were found to have the largest

number of significant comparisons are located east of the WTC site.

The numbers of exceedances for these three SSAs range from 2 to 9;

their exceedance rates range from 0.021 to 0.060. The spatial pattern

exhibited here is similar to the pattern of exceedance rates that is

shown in Fig. 2; however, four of the seven SSAs with exceedance

rates in the fourth quartile (Fig. 2) were found to be significantly

different from five or fewer of the other SSAs

Fig. 5 Significant differences between estimated exceedance rates

for clean and test data. Estimates are based on the spatially-filtered

Poisson model. The number of significant pairwise comparisons at a

global a = 0.01 (with a Bonferroni adjustment) are shown for

statistical summary areas (SSAs) that had one or more exceedances.

Comparisons with SSAs with sample sizes less than 30 (indicated by

cross-hatching) were deemed unreliable and, therefore, were not

included in the analysis. Three of the SSAs that were found to have

the largest number of significant comparisons are located east of the

WTC site. The numbers of exceedances for these three SSAs range

from 17 to 32; their exceedance rates range from 0.006 to 0.059. The

spatial pattern exhibited here is similar to the pattern of exceedance

rates that is shown in Fig. 3; however, three of the 9 SSAs with

exceedance rates in the fourth quartile (Fig. 3) were found to be

significantly different from four or fewer of the other SSAs


123

several challenges, including: a lack of pre-cleanup data, a

high rate of non-detects, a large variability in sample size

between SSAs, and the presence of spatial autocorrelation.

Lacking pre-clean-up data with which to compare post-

clean-up data, we employed spatial statistics to test for the

presence of geographic patterns in PCMe exceedance

events. Employing spatial statistical methods that focus on

the geographic location of the PCMe exceedance events

also effectively addresses the challenge posed by the high

rate of non-detects. The geographic pattern in sample sizes

for the SSAs is partially addressed by using the log of the

sample size as an offset variable in the model specifica-

tions. However, a useful exercise for addressing the vari-

ability in sample sizes would be to assess the effectiveness

of other statistical approaches for modeling the PCMe data.

This analysis illustrates the importance of employing

statistical methods that account for the presence of spatial

autocorrelation. Filtering spatial autocorrelation from the

PCMe data residuals bolsters the reliability of the

assumptions of a nonconstant mean and equidispersion,

thereby increasing confidence in the estimated rates of

PCMe exceedance and the empirical probabilities that are

computed with the models. The spatial filter approach also

produces an equivalent sample of independent SSA ex-

ceedance rates that facilitates a statistical test for geo-

graphic patterns using a non-parametric Hope-type

simulation for the inferential basis.

This paper reports the analysis of the PCMe data

aggregated at the SSA level. The pairwise-comparisons

between SSAs provide a quantitative test for geographic

patterns that considers the extreme rareness of the excee-

dance events. No obvious geographic pattern in asbestos

contamination is indicated for either data set. The lack of

geographic pattern at the SSA-level agrees with the results

we obtained from an analysis of the PCMe data at the

individual building level, using methods from point pattern

analysis (USEPA 2004).

While a lack of a comparison data set precludes

assessment of cleanup effectiveness by direct comparison

of pre-cleanup (or pre-WTC collapse) and post-cleanup

exceedence rates, our results suggest that post cleanup

Fig. 6 The probability of an exceedance of the health-based standard

of 0.009 fibers/cm3 for airborne asbestos by statistical summary area

(SSA) for the test only data, without spatial filtering. The probabilities

were computed using the mean exceedance rate for each SSA

predicted with the estimated unfiltered Poisson model, with the log of

the sample size as an offset variable to account for the unequal sample

sizes. Despite the inclusion of an offset variable, it is likely that the

apparent geographic pattern in the probability of exceeding the

health-based standard is partly due to the geographic pattern in

sample size (Fig. 2b)



(SSA), for the test only data, with spatial filtering. The probabilities

were computed using the mean exceedance rate for each SSA

predicted with the estimated spatial-filter Poisson model, with the log

of the sample size as an offset variable to account for the unequal

sample sizes. After filtering spatial autocorrelation from the PCMe

data, all except one SSA are predicted to have a probability of 0.20 or

less of exceeding the health-based standard for asbestos. Despite the

inclusion of an offset variable, it is likely that the apparent geographic

pattern in the probability of exceeding the health-based standard is

partly due to the geographic pattern in sample size (Fig. 3b)


123

exceedence events were extremely rare: the exceedence

rate is <0.005, based on estimates unfiltered for spatial

autocorrelation, and substantially lower (<0.003) when

filtered for spatial autocorrelation. The low exceedance

rates, coupled with the absence of a geographic pattern in

the exceedance rates that pinpoints the WTC site as a po-

tential source, suggest that the Residential Dust Cleanup

Program was effective in reducing any WTC-related indoor

air asbestos contamination to below the health-based

benchmarks established for the program.

Our conclusions are tempered by the realization that the

variation in sample size and the extreme rareness of the

PCMe exceedance events likely result in low statistical

power to detect spatial clusters for this data set. As such,

we do not suggest the results of our analysis provide suf-

ficient evidence to conclude that further investigation of the

PCMe exceedance events is unnecessary. And, in the ab-

sence of pre-cleanup data, the best basis for establishing a

benchmark probability is from the naturally occurring

typical background levels of asbestos: 0.000002 fibers/ml

outdoors, and 0.000003 fibers/ml indoors (Jenkins 2003).

The similarity in the exceedance rates between the test

only and clean and test data sets warrants further investi-

gation. The test only population included a large number of

people who cleaned their own apartments or retained

cleaning services directly. Thus, testing may have been

confounded by self-cleaning. These types of ‘‘behavioral’’

factors were not included in the analysis. Additional re-

search to identify attributes of the participants of the

cleanup program, including their environment (e.g.,

dwelling and neighborhood), may be useful for explaining

some aspects of the geographic pattern in the exceedance

rates.

Acknowledgments Daniel A. Griffith holds an Ashbel Smith chair

in Geospatial Information Sciences. This work was supported, in part,

by U.S. EPA grant R-83034501-0.

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Documents

Geography of asbestos contamination near the World Trade Center site