Associations between particulate matter (PM)

total mass for PM10 (PM with an aerodynamic diameter < 10 μm) and PM2.5 (PM

with an aerodynamic diameter < 2.5 μm)

have been demonstrated for mortality and

numerous morbidity outcomes including

hospital admissions and pulmonary function

[Dominici et al. 2006; Pope and Dockery

2006; U.S. Environmental Protection Agency

(EPA) 2004]. However, the effect estimates

vary by location and season, as shown by

recent national studies of PM2.5 and hospital

admissions (Dominici et al. 2006) and PM10

and mortality (Peng et al. 2005). The chemical composition of the particulate mixture

also exhibits substantial regional and seasonal

variation (Bell et al. 2007); this variation may

contribute to the heterogeneity in PM health

effect estimates.

Lippmann and colleagues (2006) recently

conducted an investigation on chemical components of ambient PM2.5 and mortality risk.

In addition to reporting findings of an inhalation exposure study in mice, the authors examined whether previously calculated effect

estimates for PM10 on mortality from the

National Morbidity, Mortality, and Air

Pollution Study (NMMAPS) (Dominici et al.

2003; Samet et al. 2000a, 2000b) were

associated with the communities’ long-term

levels of various PM2.5 chemical components.

They estimated the association between the

short-term effects of PM10 on all-cause mortality (β

ˆc

) and average concentrations of 16

PM2.5 chemical components (xc

) across 60

U.S. communities. Separately for each chemical component, they fitted a weighted linear

regression having β

ˆc as the dependent variable

and xc as the independent variable with

“weights based on standard errors of the β

ˆc

,” as

reported by the authors (Lippman et al. 2006).

The β

ˆc and their standard errors were obtained

from the NMMAPS data base [Internet-based

Health & Air Pollution Surveillance System

(iHAPSS) 2007; Zeger et al. 2006] for the

period 1987–1994, and the xc values were

obtained from the PM2.5 speciation network

for the period 2000–2003 (U.S. EPA 2003).

Lippman et al. (2006) found that average concentrations of nickel or vanadium PM2.5 positively modified the association between the

previous day’s (lag 1) PM10 and all-cause mortality. Based on this result, the authors concluded that “the PM10 mortality risk estimates

were high for Ni and V in the communities

where Ni and V were significantly high (95th

percentile), compared with the communities

where Ni and V were low (5th percentile)”

(Lippmann et al. 2006). Earlier work identified

the previous day as the single-day lag with the

strongest effect, and this lag was applied in the

work by Lippmann et al. (2006). 

We report an analysis similar to that of

Lippmann et al. (2006), but using the

NMMAPS extended data base for 1987–2000.

Our goal was to test whether there is still evidence to indicate that average concentrations

of Ni or V PM2.5 positively modified the association between the previous day’s (lag 1)

PM10 and all-cause mortality and whether this

evidence is robust to transformation of the

data and exclusion of outliers. 

Materials and Methods

As we continue to update NMMAPS data,

we have reestimated the short-term effects of

PM10 on all-cause and cause-specific mortality based on data for 1987–2000 for 90 U.S.

urban communities (Dominici et al. 2007;

iHAPPS 2007). Further, we have developed a

database of PM2.5 chemical composition for

2000–2005 for 187 U.S. counties generated

by the U.S. EPA (Bell et al. 2007). References

to Ni or V in this article reflect the concentrations of those components in PM2.5.

Address correspondence to F. Dominici, 615 N.

Wolfe St., Rm. E3634, Johns Hopkins Bloomberg

School of Public Health, Baltimore, MD 21215

USA. Telephone: (410) 614-5107. Fax: (410) 955-

0958. E-mail: fdominic@jhsph.edu

Funding for F.D., M.L.B., and R.D.P. was provided by the U.S. Environmental Protection Agency

(RD-83241701). Funding for F.D. and R.D.P. was

also provided by the National Institute for

Environmental Health Sciences (NIEHS)

(ES012054-03) and by the NIEHS Center in Urban

Environmental Health (P30 ES 03819). Funding for

M.L.B. was provided by the Health Effects Institute

through the Walter A. Rosenblith New Investigator

Award (4720-RFA04-2/04-16) and the NIEHS

Outstanding New Environmental Scientist (ONES)

Award (RO1-ES015028).

Although the research described in this article has

been funded wholly or in part by the U.S.

Environmental Protection Agency through grant

agreement RD-83241701 to Johns Hopkins

University, it has not been subjected to the agency’s

required peer and policy review and therefore does not

necessarily reflect the views of the agency and no official endorsement should be inferred.

The authors declare they have no competing

financial interests.

Received 6 August 2007; accepted 24 September

2007.

Does the Effect of PM10 on Mortality Depend on PM Nickel and Vanadium

Content? A Reanalysis of the NMMAPS Data

Francesca Dominici,1 Roger D. Peng,1 Keita Ebisu,2 Scott L. Zeger,1 Jonathan M. Samet,3 and Michelle L. Bell2

1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA; 2School of Forestry and

Environmental Studies, Yale University, New Haven, Connecticut, USA; 3Department of Epidemiology, Johns Hopkins Bloomberg

School of Public Health, Baltimore, Maryland, USA 

BACKGROUND: Lack of knowledge regarding particulate matter (PM) characteristics associated with

toxicity is a crucial research gap. Short-term effects of PM can vary by location, possibly reflecting

regional differences in mixtures. A report by Lippmann et al. [Lippmann et al., Environ Health

Perspect 114:1662–1669 (2006)] analyzed mortality effect estimates from the National Morbidity,

Mortality, and Air Pollution Study (NMMAPS) for 1987–1994. They found that average concentrations of nickel or vanadium in PM2.5 (PM with aerodynamic diameter < 2.5 μm) positively modified

the lag-1 day association between PM10 and all-cause mortality. 

OBJECTIVE: We reestimated the relationship between county-specific lag-1 PM10 (PM with aerodynamic

diameter < 10 μm) effects on mortality and county-specific nickel or vanadium PM2.5 average concentrations using 1987–2000 effect estimates. We explored whether such modification is sensitive to outliers.

METHODS: We estimated long-term average county-level nickel and vanadium PM2.5 concentrations

for 2000–2005 for 72 U.S. counties representing 69 communities. We fitted Bayesian hierarchical

regression models to investigate whether county-specific short-term effects of PM10 on mortality are

modified by long-term county-specific nickel or vanadium PM2.5 concentrations. We conducted sensitivity analyses by excluding individual communities and considering log-transformed data.

RESULTS: Our results were consistent with those of Lippmann et al. However, we found that when

counties included in the NMMAPS New York community were excluded from the sensitivity analysis,

the evidence of effect modification of nickel or vanadium on the short-term effects of PM10 mortality

was much weaker and no longer statistically significant. 

CONCLUSIONS: Our analysis does not contradict the hypothesis that nickel or vanadium may increase

the risk of PM to human health, but it highlights the sensitivity of findings to particularly influential

observations.

KEY WORDS: effect modification, mortality, Ni, particulate matter, PM2.5, PM10, V. Environ Health

Perspect 115:1701–1703 (2007). doi:10.1289/ehp.10737 available via http://dx.doi.org/ [Online

25 September 2007]

Environmental Health Perspectives • VOLUME 115 | NUMBER 12 | December 2007 1701

Research
Each NMMAPS community is based on a

single county or a set of contiguous counties.

We have identified 72 U.S. counties that have

both an NMMAPS PM10 mortality effect

estimate and data on PM2.5 chemical composition data. These 72 counties are included in

69 NMMAPS communities. More specifically,

all NMMAPS communities included in this

analysis were based on single counties except

two communities. The first is the New York,

New York, NMMAPS community, which

includes six counties (Bronx, Kings, New York,

Richmond, Queens, and Westchester). These

counties represent the New York metropolitan

area, not the official designation of New York

City. PM2.5 chemical composition data were

available for three counties in the New York

community (Queens, New York, and Bronx

counties). The second is the NMMAPS

Minneapolis community, which includes two

counties (Ramsey and Hennepin). PM2.5

chemical composition data were available for

both counties in the NMMAPS Minneapolis

community. For the three counties within

the NMMAPS New York community and for

the two counties within the NMMAPS

Minneapolis community, we used the same

value of the NMMAPS effect estimates,

respectively.

We estimated the association between the

true lag-1 day PM10 mortality effect (βc ) and

county-level averages of Ni and V (xc ) using

the following Bayesian hierarchical regression

model:

β

ˆc | βc

, v c ∼ N(βc, vc) [1]

βc | α0, α1, τ2 ∼ N[α0 + α1(xc – x

–), τ2], [2]

where β

ˆc is the NMMAPS community-specific

estimate of the effect of lag-1 PM10 on mortality, and vc its statistical variance (Peng 2007;

Peng et al. 2005). The parameter α0 denotes

the true lag-1 effect of PM10 on mortality for a

county with xc = x

–. The parameter α1 quantifies the effect modification—that is, the

change in the true PM10 effect estimate associated with a unit change in county-level averages of Ni or V PM2.5 (x c

) with respect to

their averages across the counties x

–. The parameter τ2 denotes the between-county variability of the true lag-1 day effects of PM10 on

mortality (βc

), unexplained by xc. 

We fitted the above Bayesian hierarchical

regression model using two-level normal independent sampling estimation (TLNise)

(Everson and Morris 2000) with noninformative priors. We also performed a weighted

regression with weights based on the inverse

of the variance, 1/v c

. A weighted regression

approach was used in the statistical analysis

conducted by Lippmann et al. (2006).

Results

As in Lippmann et al. (2006), we also found

strong evidence of effect modification:

Counties with high Ni or V average concentrations have higher effects of PM10 on mortality at lag 1. We then conducted sensitivity

analyses to investigate whether one or a few

counties were contributing more than others

toward the strength of the evidence of effect

modification.

Figure 1 shows the county-specific average

concentrations of Ni PM2.5 (x-axis) plotted

against the county-specific maximum likelihood estimates of the lag-1 effects of PM10 on

mortality (y-axis). The size of the circle corresponds to the inverse of the standard error of

the county’s maximum likelihood estimate. 

The red and blue lines denote the fitted

linear regression lines of the second-stage

regression βc = α0 + α1(x c – x

–) + N(0, τ2)

with the three counties that belong to the

NMMAPS New York community included

in the analysis (red) and excluded from the

analysis (blue). Figure 2 shows an analogous

figure with county-specific average concentrations of V used as independent variables.

When all 72 counties were included in

the analyses, we found strong evidence of

effect modification by either Ni or V. The

posterior probability that the parameter α1 is

positive is 0.99 for Ni and 1.0 for V. The

p-values corresponding to the statistical significance of α1 obtained from the weighted

regression are equal to 0.004 and 0.002 for

Ni and V, respectively.

Dominici et al.

1702 VOLUME 115 | NUMBER 12 | December 2007 • Environmental Health Perspectives

Figure 1. Maximum likelihood estimates of PM10

effect on total mortality and county-specific average concentrations of Ni. The size of the circle corresponds to the inverse of the standard error of the

community’s maximum likelihood estimate. The red

and blue lines denote the fitted linear regression

lines with the three counties that belong to the

New York community included (red) and excluded

from the analysis (blue).

2

1

0

–1

–2

Percent change in mortality risk

per 10 μg/m3 in lag 1 PM10

0.000 0.005 0.010 0.015 0.020

Nickel (μg/m3)

With New York, p = 0.01

Without New York, p = 0.48

Figure 2. Counties’ maximum likelihood estimates

and county-specific average concentrations of V.

The size of the circle corresponds to the inverse of

the standard error of the community’s maximum

likelihood estimate. The red and blue lines denote

the fitted linear regression lines with the three counties that belong to the New York community

included (red) and excluded from the analysis (blue).

2

1

0

–1

–2

Percent change in mortality risk

per 10 μg/m3 in lag 1 PM10

0.001

With New York, p = 0.01

Without New York, p = 0.23

0.002 0.003 0.004 0.005 0.006 0.007

Figure 3. Point estimates and 95% confidence intervals of the percent increase in PM10 risk estimates

associated with an interquartile range (IQR) increase

in Ni PM2.5. The top estimate (in blue) is achieved by

including data for all the 69 communities. The other

estimates are calculated by excluding one of the

69 communities at a time. The last estimate (in red) is

obtained when the New York community is excluded. Other communities removed

–20

Percent increase in PM10 risk estimates per IQR Ni

New York removed

–10 0 10 20 30

No communities removed

Figure 4. Point estimates and 95% confidence intervals of the percent increase in PM10 risk estimates

associated with an interquartile range (IQR) increase

in V PM2.5. The top estimate (in blue) is achieved by

including data for all the 69 communities. The other

estimates are calculated by excluding one of the

69 communities at a time. The last estimate (in red) is

obtained when the New York community is excluded. Other communities removed

–20

Percent increase in PM10 risk estimates per IQR V

New York removed

0 20 30 40

No communities removed
Effect of PM2.5 Ni and V on PM10 mortality effect estimates

Environmental Health Perspectives • VOLUME 115 | NUMBER 12 | December 2007 1703

When the three counties that belong to

the NMMAPS New York community were

excluded, evidence of effect modification

became much weaker, with loose statistical

significance. The posterior probability that the

effect modification parameter α1 is positive is

0.76 for Ni and 0.89 for V. The p-values corresponding to the statistical significance of α1

obtained from the weighted regression are

0.38 for Ni and 0.14 for V.

To further investigate the sensitivity of the

estimated effect modification parameter to outliers, we reestimated α1 by excluding a single

county at a time, for each of the 72 counties.

Figures 3 and 4 show the posterior means and

95% posterior intervals of the effect modification parameter α1 obtained by excluding one

county at a time for Ni and for V as independent variables, respectively. Again, when the

three counties corresponding to the New York

community were omitted (shown in red in

Figures 3 and 4), we found no evidence of

effect modification. However, when any other

single county was excluded, strong evidence of

effect modification remained. 

We reached the same conclusions when

we a) used the lag-1 NMMAPS estimates of

the effects of PM10 on mortality for

1987–1994 [the same data used by Lippmann

et al. (2006)]; b) estimated the effect modification parameter using a weighted linear

regression instead of Bayesian hierarchical

models; and c) used log-transformed data for

the independent variables. 

Conclusions

This analysis demonstrates that when the

three counties in the NMMAPS New York

community are excluded from the analysis,

the evidence of effect modification of Ni or V

PM2.5 on the short-term effects of PM10 mortality is much weaker. Setting aside the three

counties that belong to the New York community, the between-community variance of

Ni is reduced by 68%. Therefore, the statistical power for estimating the slope of the

regression line also diminishes substantially.

The New York community has particularly high levels of Ni and V. The three New

York counties have the highest Ni concentrations across all the 72 counties. The Ni and V

concentrations in the three New York counties were 8.9 and 3.4 times higher than the

other counties, respectively,

Elevated levels of Ni and V PM2.5 chemical components in New York are likely attributed to oil-fired power plants and emissions

from ships using oil, as noted by Lippmann

et al. (2006). Ni and V can result from oil

combustion and are often used as tracer components for these sources (Chen et al. 2004;

Galbreath et al. 2000; Juichang et al. 1995;

Thurston et al. 2005; U.S. EPA 2007).

Analysis of the sources of PM2.5 in New York

City identified Ni and V as indicators of oil

combustion (Li et al. 2004; Zheng et al.

2004), including ships burning oil as a source

(Qin et al. 2006).

Although scientific evidence on the human

health impact of PM2.5 chemical constituents

is limited, several studies have investigated the

impacts of Ni or V on health, including an animal study in which V was recovered from lung

tissues of rats exposed to concentrated air particles (Morishita et al. 2004). In addition to the

analysis of NMMAPS data, Lippmann et al.

(2006) found a significant association between

exposure to Ni and acute cardiac function

changes in mice. Ni and V were associated

with urinary 8-OHdG levels, a marker of

oxidative DNA damage and repair, and with

lower fractional concentration of expired nitric

oxide (FENO), a marker of airway responses,

in studies of boilermaker workers (Kim et al.

2003, 2004). 

Although the original work of Lippmann

et al. (2006) indicates strong evidence that the

short-term effects of PM10 on mortality are

modified by long-term averages of Ni and V,

our subsequent analysis reveals that this evidence is driven largely by the influence of a

few data points (the three New York counties). Our analysis does not contradict the

hypothesis that Ni or V may in fact be harmful to human health, but it highlights the sensitivity of findings on effect modification to

particularly influential observations. 

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