### Associations between particulate matter (PM) total mass for PM 10 

### (PM with an aerodynamic diameter < 10 μm) and PM 2.5 

### (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 PM 2.5 

### and hospital admissions (Dominici et al. 2006) and PM 10

### 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) recentlyconducted an investigation on chemical components of ambient PM 2.5 

### and mortality risk.In addition to reporting findings of an inhalation exposure study in mice, the authors examined whether previously calculated effect estimates for PM 10 

### 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 PM 2.5 

### chemical components. They estimated the association between the short-term effects of PM 10 

### on all-cause mortality ( β ˆ c

### ) and average concentrations of 16
PM 2.5 

### chemical components ( xc 

### ) across 60U.S. communities. Separately for each chemical component, they fitted a weighted linear regression having β ˆ c 

### as the dependent variable and x c 

### as the independent variable with “weights based on standard errors of the β ˆ c



### ,” as reported by the authors (Lippman et al. 2006). The β ˆ c 

### 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 PM 2.5 

### 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) PM 10 

### and all-cause mortality. Based on this result, the authors concluded that “the PM 10 

### 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 PM 2.5 

### positively modified the association between the previous day’s (lag 1)
PM 10 

### 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
PM 10 

### 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 PM 2.5 

### chemical composition for 2000–2005 for 187 U.S. counties generated by the U.S. EPA (Bell et al. 2007). Referencesto Ni or V in this article reflect the concentrations of those components in PM 2.5 

### .

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 notnecessarily 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 PM 10 

# 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

**1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA;**  **2 School of Forestry and**  **Environmental Studies, Yale University, New Haven, Connecticut, USA; 3**  **Department 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 PM 10 and all-cause mortality.

O BJECTIVE: We reestimated the relationship between county-specific lag-1 PM 10 (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 PM 2.5 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 PM 10 on mortality are modified by long-term county-specific nickel or vanadium PM 2.5 concentrations. We conducted sensitivity analyses by excluding individual communities and considering log-transformed data.

R

ESULTS : 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 PM 10 mortality was much weaker and no longer statistically significant.

C ONCLUSIONS : 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.

K

EY WORDS : effect modification, mortality, Ni, particulate matter, PM 2.5 , PM 10 , 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 PM 10 

### 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. PM 2.5 

### 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). PM 2.5 

### 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 PM 10 

### mortality effect ( β c 

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

### ) using the following Bayesian hierarchical regression model:

β ˆ c 

### | β c



### , v c



### ∼ N (β c



### , v c 

### ) [1]

β c 

### | α 0 

### , α 1 

### , τ 2



### ∼ N [α 0 

### + α 1 

### ( x c


### – x –)

, τ2 

### ] , [2]

where β ˆ c 

### is the NMMAPS community-specific estimate of the effect of lag-1 PM 10 

### on mortality, and v c 

### its statistical variance (Peng 2007; Peng et al. 2005). The parameter α 0 

### denotes the true lag-1 effect of PM 10 

### on mortality for a county with x c 

### = x – . The parameter α 1 

### quantifies the effect modification—that is, the change in the true PM 10 

### effect estimate associated with a unit change in county-level averages of Ni or V PM 2.5 

### ( 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 PM 10 

### on mortality ( β c 

### ), unexplained by x c



### . We fitted the above Bayesian hierarchicalregression 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 PM 10 

### 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 PM 2.5 

### ( x -axis) plottedagainst the county-specific maximum likelihood estimates of the lag-1 effects of PM 10 

### 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 β c 

### = α 0 

### + α 1 

### ( x c


### – x – ) + N (0, τ 2 

### ) 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 analogousfigure 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 α 1 

### is positive is 0.99 for Ni and 1.0 for V. The
p-values corresponding to the statistical significance of α 1 

### 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 PM 10effect 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

–2Percent change in mortality riskper 10μg/m3in lag 1 PM10

0.000 0.005 0.010 0.015 0.020 Nickel ( μ g/m 3 ) 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 denotethe 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

–2Percent change in mortality riskper 10μg/m3in 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 PM 10 risk estimates associated with an interquartile range (IQR) increase in Ni PM 2.5 . 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 PM 10 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 PM 10 risk estimates associated with an interquartile range (IQR) increase in V PM 2.5 . 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 PM 10risk estimates per IQR V New York removed

0 20 30 40 No communities removedEffect of PM 2.5 Ni and V on PM 10 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 α 1 

### is positive is 0.76 for Ni and 0.89 for V. The p-values corresponding to the statistical significance of α 1 

### obtained from the weighted regression are 0.38 for Ni and 0.14 for V. To further investigate the sensitivity of theestimated effect modification parameter to outliers, we reestimated α 1 

### by excluding a single county at a time, for each of the 72 counties. Figures 3 and 4 show the posterior means and95% posterior intervals of the effect modification parameter α 1 

### obtained by excluding onecounty 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 PM 10 

### 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
PM 2.5 

### on the short-term effects of PM 10 

### mortality is much weaker. Setting aside the threecounties that belong to the New York community, the between-community variance ofNi 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 NewYork counties have the highest Ni concentrations across all the 72 counties. The Ni and Vconcentrations 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 PM 2.5 

### 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 oilcombustion 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 PM 2.5 

### 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 PM 2.5 

### chemical constituents is limited, several studies have investigated theimpacts of Ni or V on health, including an animal study in which V was recovered from lungtissues 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 (F E

### NO), 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 PM 10 

### 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 afew data points (the three New York counties). Our analysis does not contradict thehypothesis 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.

R EFERENCES

Bell ML, Dominici F, Ebisu K, Zeger SL, Samet JM. 2007. Spatial and temporal variation in PM 2.5 chemical composition in the United States for health effects studies. Environ Health Perspect 115:989–995. Chen Y, Shah N, Huggins FE, Huffman GP. 2004. Investigation of
the microcharacteristics of PM 2.5 in residual oil fly ash by analytical transmission electron microscopy. Environ Sci
Tech 38:6553–6560. Dominici F, McDermott A, Daniels M, Zeger SL, Samet JM. 2003. Mortality among residents of 90 cities. In: Revised Analyses of Time-Series Studies of Air Pollution and Health. Cambridge, MA:Health Effects Institute, 9–23. Dominici F, Peng RD, Bell ML, Pham L, McDermott A, Zeger SL,
et al . 2006. Fine particulate matter and hospital admission for cardiovascular and respiratory diseases. JAMA
295:1127–1134. Dominici F, Peng RD, Zeger SL, White RH, Samet JM. 2007. Particulate air pollution and mortality in the United States: did the risks change from 1987 to 2000? Am J Epidemiol 166(8):880–888; doi:10.1093/aje/kwm222 [Online 28 August 2007]. Everson P, Morris C. 2000. Inference for multivariate normal
hierarchical models. J R Stat Soc Series B 62:399–412. Galbreath KC, Tolman DL, Zygarlicke CJ, Huggins FE, Huffman GP, Wong JL. 2000. Ni speciation of residual oil fly ash andambient particulate matter using X-ray absorption spectroscopy. J Air Waste Manag Assoc 50:1876–1886. iHAPPS (Internet-based Health & Air Pollution Surveillance System). 2007. Home page. Available: http://www.ihapss. jhsph.edu/ [accessed 31 August 2007]. Juichang R, Freedman B, Coles C, Zwicker B, Holzbecker J, Chatt Z. 1995. V contamination of lichens and tree foliage in the vicinity of three oil-fired power plants in eastern Canada. J Air Waste Manag Assoc 45:461–464. Kim JY, Hauser R, Wand MP, Herrick RF, Amarsiriwardena CJ, Christiani DC. 2003. The association of expired nitric oxide with occupational particulate metal exposure. Environ Res
93:158–166. Kim JY, Mukherjee S, Ngo LC, Christiani DC. 2004. Urinary 8-hydroxy-2’-deoxyguanosine as a biomarker of oxidative DNA damage in workers exposed to fine particulates. Environ Health Perspect 112:666–671. Li Z, Hopke PK, Husain L, Qureshi S, Dutkiewicz VA, Schwab JJ, et al . 2004. Sources of fine particle composition in New York City. Atmos Environ 38:6521–6529. Lippmann M, Ito K, Hwang JS, Maciejczyk P, Chen LC. 2006.
Cardiovascular effects of Ni in ambient air. Environ Health Perspect 114:1662–1669. Morishita M, Keeler G, Wagner J, Marsik F, Timm E, Dvonch J,
et al . 2004. Pulmonary retention of particulate matter is associated with airway inflammation in allergic rats exposed to air pollution in urban Detroit. Inhal Toxicol 16:663–674.Peng RD. 2007. Compendium for “Seasonal analyses of air pollution and mortality in 100 U.S. cities” by Peng et al . (2005). Available: http://www.biostat.jhsph.edu/~rpeng/RR/ seasonal/ [accessed 21 July 2007]. Peng RD, Dominici F, Pastor-Barriuso R, Zeger SL, Samet SM. 2005. Seasonal analyses of air pollution and mortality in 100 US cities. Am J Epidemiol 161:585–594.Pope CA III, Dockery DW. 2006. Health effects of fine particulate air pollution: lines that connect. J Air Waste Manag
Assoc 56:709–742. Qin Y, Kim E, Hopke PK. 2006. The concentrations and sources
of PM 2.5 in the metropolitan New York City. Atmos Environ 40(suppl):312–332. Samet JM, Dominici F, Zeger SL, Schwartz J, Dockery DW. 2000a. The National Morbidity, Mortality, and Air Pollution Study, Part I: Methods and Methodologic Issues. Research Report 94. Cambridge, MA:Health Effects Institute. Samet JM, Zeger SL, Dominici F, Curriero F, Coursac I, Dockery DW, et al . 2000b. The National Morbidity, Mortality, and Air Pollution Study, Part II: Morbidity and Mortality from Air Pollution in the United States. Research Report 94. Cambridge, MA:Health Effects Institute. Thurston GD, Ito K, Mar T, Christensen WF, Eatough DJ, Henry RC, et al . 2005. Workshop report: workshop on sourceapportionment of particulate matter health effects—intercomparison of results and implications. Environ Health Perspect 113:1768–1774. U.S. EPA (U.S. Environmental Protection Agency). 2003. Technology Transfer Network Air Quality System. Download Detailed AQS Data. Available: http://www.epa. gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm [accessed 10 June 2004 by Lippmann et al . 2006]. U.S. EPA 2004. Air Quality Criteria for Particulate Matter. EPA/600/P-99/002aF-bF. Research Triangle Park, NC:U.S. Environmental Protection Agency Office of Research and Development.U.S. EPA (U.S. Environmental Protection Agency). 2007. SPECIATE Version 4.0. Available: http://www.epa.gov/ttn/chief/ software/speciate/index.html [accessed 1 March 2007]. Zeger SL, McDermott A, Dominici F, Peng D, Samet J. 2006.
Internet-Based Health and Air Pollution Surveillance System. Cambridge, MA:Health Effects Institute. Zheng L, Hopke PK, Husain L, Qureshi S, Dutkiewicz VA, Schwab JJ, et al . 2004. Sources of fine composition in New York City. Atmos Environ 38:6521–6529.