Comments to the CASAC Ozone Review Panel from Mark Nicolich, 

Independent Consultant, Sponsored by the American Petroleum Institute 

I thank the Panel for reading my submission for this meeting. 

BACKGROUND 

I am asking CASAC to consider in its deliberations the journal article “Cumulative effects and 

threshold levels in air pollution mortality: data analysis of nine large US cities using the 

NMMAPS dataset,” by my co-author Mario Stylianou and me that was published in 

Environmental Pollution in 2009 (Stylianou and Nicolich, 2009). This paper relates to several 

points used to develop the current and proposed ozone standards, and points referred to in the 

EPA charge questions. The specific points are the assumption of a linear no threshold 

concentration-response (CR) model for ozone exposure, the assumption that cities can be 

combined because they have similar CR functions, and the modeling of ozone without 

consideration of other pollutants. I’ll provide a brief overview of the paper, and then discuss the 

points of the paper in some detail to support the conclusions. 

ABSTRACT OF JOURNAL PAPER 

Briefly, in the journal article we examined the existence of thresholds, cumulative effects and the 

homogeneity of five air pollutants (simultaneously) on the relative risk of three mortality 

outcomes using data from nine major US cities using data from NMMAPS. Overall, PM10

(usually 200 day accumulation) and ozone (3 day accumulation) were the two important 

predictors of outcome but their effect was not the same across the nine cities. Many of the 

models for different cities and endpoints exhibited thresholds (25-45 μg/m3 for PM10, and 10-45 

ppb for O3). Our analyses suggested that the use of a linear, no threshold model for pollution 

studies is not consistent with the observed data. Also, the heterogeneity in the risk estimates 

across the nine cities suggested that the practice of combining individual city or location risk 

estimates to obtain a national risk estimate may not be justifiable and the resulting estimate is 

likely to be confounded. 

DETAILS OF JOURNAL PAPER 

Although most of the air pollution literature deals with the short-term effects of air pollutants on 

mortality, it seems reasonable to assume that pollutants may increase the risk for adverse events 

over longer duration of exposure because a narrow moving window may not adequately capture 

larger effect variations of pollutants under study. Several researchers have considered longerterm effects; Schwartz (2000) studied the effect of PM2.5 and weather variables using moving 

averages of up to 60 days. Janes et al. (2007) considered mean monthly exposures over the 

previous 12 months with an approximate 30-day window. More recently, Schwartz (2008) 

explored the effect of PM2.5 exposure to mortality for up to the previous five years. However, 

there is no consensus in the literature regarding the lag or cumulative time needed to evaluate the 

pollutants effect. 

Another consideration in air pollution models is the combined effect of several pollutants. Bell et 

al. (2004) estimated same day non-accidental mortality using cumulative ozone exposure during 

the previous week considering PM10 and the usual confounders of weather, day of the week, and 

calendar time. They report that ozone is associated with daily non-accidental mortality (and the 

subsets of cardiovascular and respiratory mortality) and these results are independent of 

adjustment for PM10, weather, seasonality and long-term trends. However, PM10 was considered 

only with a 1-day lag which is different from most published models and may explain why PM10

1
had no effect in the model with ozone. Bell et al. (2005) replicated their results in a subsequent 

meta-analysis of ozone mortality studies, again with short lag times for exposures to ozone and 

PM10 or PM2.5. It is possible that a PM effect on the death count may be more pronounced when 

it is cumulated over longer times. 

Many of the models assessing the effect of several pollutants on death rates produce relatively 

poor fitting or biased estimates of exposure-response association (Zeka and Schwartz, 2004). 

One explanation is that these pollutants are usually correlated in the short term and the correlation 

leads to unstable estimates of the pollution effects. One approach to the problem of correlated 

short-term data is to use long-term average or cumulative pollution data so the effect of 

correlation among long-term cumulative pollutants in the modeling process would be minimized. 

We have based our models on the work of Xia and Tong (2006) who suggested a method to 

account for the cumulative effects of longer-term exposures to air pollution by using a weighted 

cumulative sum for each pollutant without the restriction of a linear dose-response function. The 

weights are derived by a restricted minimization method where the weights are non-increasing (or 

decreasing) the further we go into the past. Their analysis of data from Hong Kong, El Paso, 

Chicago, and Pittsburgh suggests that cardiovascular disease (CVD) mortality is affected by a 

cumulative effect of PM10 for about 200 days, with roughly uniform weights, and that thresholds 

exist for all pollutants, noticeably for PM10 and ozone. Importantly, their study suggests that 

different pollutants may require different cumulative periods. 

The objective of our paper was to investigate the simultaneous cumulative effects of O3 and PM 

on non-accidental mortality and the existence of thresholds for these pollutants. We considerd the 

assumption of monotonicity between the risk function and pollutant levels. We thought that a 

monotonic (but not necessarily linear) assumption for a pollutant effect was reasonable in the 

absence of plausible mechanisms for beneficial effects and their likelihood that these effects at 

low concentrations are possibly due to chance. In addition, we explored the cumulative effect of 

other pollutants, such as NO2, SO2, and CO on death outcomes. Finally we questioned the 

usefulness of combining the city specific risk estimates to form a national average. 

This investigation was based on daily time-series data extracted from NMMAPS for 1987 to 2000 

from nine major U.S. cities; Baltimore, Chicago, Dallas/Fort Worth, Los Angeles, Miami, New 

York, Philadelphia, Pittsburgh, and Seattle, which have a combined population of more than 35 

million people. The response variables considered were total non-injury related mortality, 

cardiovascular disease (CVD) mortality and respiratory disease mortality. 

The following is a brief outline of the statistical models used and the full statistical details are in 

the journal article. The models we used were based on the research of Xia and Tong (2006). For 

each cause of death and for each city we first fit a model that considers calendar variables (day, 

time) and meteorological variables (temperature, dewpoint). Only those variables that are 

significantly associated with the outcome (at p≤ 0.05) remained in the model to form the “basic 

model”. We added each pollutant individually to assess its importance in predicting the number 

of events. Each pollutant associated with the response in the basic model would be further 

considered for a multivariate model. We modeled the effects of pollutants on each of the daily 

causes of death using the generalized additive model. For example, a “full” model in a 

multivariate setting with a basic model plus all five pollutants could be: 

2
6 29 29

, 1 1, 2 2, 3

10 0

199 2 2

4 4, 5 5, 6 6,

0 00

2 2

7 7, 8 8,

0 0

(log( )) ( )

2

2

t k k t t t

k

tt t

t t

E Y Dg T g DP g t

g PM g O g NO

g SO g CO

τ τ τ τ

τ τ

τ τ τ τ τ τ

τ ττ

τ τ τ τ

τ τ

μβ θ θ

θ θθ

θ θ

− −

== =

−− −

= ==

− −

= =

⎛ ⎞⎛ ⎞

=+ + + + ⎜ ⎟⎜ ⎟ ⎝ ⎠⎝ ⎠

⎛ ⎞⎛ ⎞⎛ ⎞

+ ++ ⎜ ⎟⎜ ⎟⎜ ⎟ ⎝ ⎠⎝ ⎠⎝ ⎠

⎛ ⎞⎛ ⎞

+ + ⎜ ⎟⎜ ⎟ ⎝ ⎠⎝ ⎠

∑∑ ∑

∑ ∑∑

∑ ∑

Time

 (1) 

where μ is the intercept term, the subscript t represents study time, the summations are over the 

cumulative period of interest (τ), Yt is the daily number of deaths at time t, Dk,t is the indicator 

function for day of the week, βk is the mortality adjustment for the kth day of the week, Tt is the 

average daily temperature, DPt is the average daily dew point, and Timet is calendar time. PMt

and Ot are the PM10 and ozone levels at time t, while, NO2t, SO2t and COt are the corresponding

pollutants at time t. are unknown smooth functions, 1 8 gx gx ( ), , ( ) " θ k ,τ is the weight for the kth

term at τ days prior to the current day, , 0

1 L

k τ τ θ= ∑ = and , , k L 1 0 ≤θ k L ≤ ≤ θ θ − "≤ k ,0 . The 

model fit was assessed both quantitatively and graphically using residual and partial residuals 

plots. Autocorrelation and partial autocorrelation plots were used to assess the existence of any 

autocorrelation remaining in the residuals. 

Estimates of the RR were derived using the final model. The RR at exposure x is the risk from 

being exposed at level x relative to the risk at the base level. When the effect-function is linear 

any base can be used because the effect change is the same at all points along the line. We have 

chosen to report risk relative to the lowest observed exposure as do Leitenstorfer and Tutz (2007): 

( | ( )) exp( ( ( )) ( ) ( | (min)) exp( ( (min))E event P t g Pt RR t

E event P g P = = (2) 

where P(t) is the pollutant level at time t, P(min) is the minimum pollutant level observed, and g

is a smooth function. 

In order to compare and contrast the individual city models with a model that is the same across 

cities, we have used a unified model that incorporated the same predictors for all cities. These 

predictors were time and meteorological variables and pollutants that are associated with death 

events across many cities. 

Of the three types of deaths assessed (total, CVD, and respiratory), respiratory deaths have the 

weakest pollutant associations and total deaths outcome has the strongest associations. We found 

that 3-day cumulative ozone is the most likely pollutant to have a threshold with an average value 

of 32 ppb using the non restricted models for the six cities that appear to have a threshold with a 

total death outcome. Using a monotonic assumption we obtain an average threshold level at 21 

ppb. Using CVD death as an outcome, the average threshold with a non-restricted and monotonic 

models is at 28 and 31ppb. With the 200-day cumulative PM10, a threshold is suggested only for 

Chicago using the unconstrained model, 29 and 30 μg/m3

 for total and CVD deaths. Under 

monotonic model the average threshold is 31 and 30 μg/m3

 for total and CVD deaths. 

The basic model analyses suggest that PM10 and ozone are the most likely pollutants predictive of 

different accountings of mortality. We evaluated the consistency of the pollutant effect across the 

different cities using a model that adjusts for the same variables, that is, the basic model plus 

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PM10 and ozone. Figure 1 graphically displays the statistical significance levels of PM10 and 

ozone in the above model. Neither PM10 nor ozone is a predictor of total deaths in Miami; CVD 

deaths in Pittsburgh, Baltimore and Miami; or respiratory deaths in Pittsburgh, Miami and 

Dallas/Fort Worth. However, both PM10 and ozone are associated with the total deaths in 

Chicago, NY, Pittsburgh, and LA; CVD deaths in Chicago; and respiratory deaths in NY and LA. 

Please note that we use p-values not as a strict decision value, but in the sense of Weinberg 

(2001) and Poole (2001) to provide a useful scale to evaluate the relative usefulness of terms in 

the models of different cities. 

The risk functions of PM10 on total deaths as shown in the journal article are mostly flat near a 

RR of 1, except Chicago where the RR for PM10 increases and flattens around 35 μg/m3

. Some 

risk estimates are not increasing with the PM10 level, such as NY or LA. Some even inexplicably 

decrease, as in Pittsburgh or Baltimore. This behavior could justify the monotonic (nondecreasing) assumption for the effect or risk function. The association and shape of the adjusted 

PM10 RR function (adjusted for ozone, day, time, temperature, and dp) are inconsistent across the 

nine cities; the only risk function significantly larger than one is for Chicago at 29 μg/m3

. Over 

half the functions appear linear but are either flat or declining. Chicago clearly appears to be 

nonlinear. The observed range of PM10 average cumulative levels and variability differ 

considerably between cities. Likewise, the shape of the ozone risk function is also inconsistent; 

thresholds appear to occur in the range of 20 to 40 ppb for all cities but Miami and LA. The 

estimated ozone risk function is not always a non-decreasing function (LA, Philadelphia, 

Pittsburgh, and Seattle). 

Overall, our analyses suggest the effect of these pollutants across the nine cities studied is 

inconsistent. Associations are more likely for CVD or total death in older large industrialized 

cities such as Chicago and NY. A possible reason as to why PM10 and ozone were not as 

strongly associated with respiratory deaths could be the low daily events. While high levels of 

ozone and PM10 are associated with increased non-accidental mortality, our analyses suggest that 

these effects are not uniform across the U.S., that dose-response relationships are not necessarily 

linear, and that some relationship shapes are suggestive of no-effect thresholds. We believe that a 

monotonic (but not necessarily linear) assumption for a pollutant effect is reasonable in the 

absence of plausible mechanisms for beneficial effects and the likelihood that effects at low 

concentrations are likely due to chance. 

The RR is often assumed or reported to be linear (Bell et a. 2004; Schwartz et al. 2001), implying 

that a specific increase of a given pollutant has a fixed risk. In contrast, our cumulative pollutant 

community-specific analyses indicate that the pollutant RR is often non-linear and the rate of risk 

increase is not constant. Bell et al.(2006) have explored the possibility of non-linearity or 

thresholds but they only reported the combined national average, leaving the reader wondering 

about the shape of the community-specific risk estimates. Even with the combined risk estimate 

they did find the possibility of thresholds at low levels of ozone. However, they dismissed the 

threshold models because the improvement in the model AIC was minimal (<1%). We have 

demonstrated that the inclusion of statistically significant PM10 or ozone in the basic model does 

not provide a considerable model improvement either (<0.4%). 

Our analyses suggest that thresholds exist for some of the cities. We found that PM10 riskfunction for Chicago tends to increase and then flatten at about a 200-day mean of 30 μg/m3

 in 

contrast to ozone risk-functions for most cities where the risk is flat at low levels and then starts 

to increase at about a 3-day mean of 20 to 30 ppb. These conclusions remain the same, even 

when we run the same basic model plus both PM10 and ozone. In another sensitivity analysis, we 

have compared the model R2

adj and AIC with an ozone term as either linear or with a threshold at 

4
30ppb. For most cities, the threshold model performs slightly better than the model using the 

linear ozone term. 

Many authors attempted to combine city-specific RRs into a single national estimate (Bell et al 

2004 and 2006; Dominici et al. 2002). This approach can increase the statistical power and give 

an overall RR perspective. However, several underlying assumptions must be true for the 

combined results to be valid. Since RR estimates are conditional on other predictors in the 

model, it is necessary to include the same variables in order to have the same risk interpretation. 

Our analyses suggest that even with the same variables in the model, the shapes of the riskfunctions from such models are inconsistent across the different cities. This indicates that a 

rigorous examination of the results from each city is necessary before including specific cities 

into combined estimates of the RR. Similar conclusions regarding associations between 12-

month PM2.5 exposure and mortality were recently reached by Janes et al. (2007), that is, the 

association between trends in PM2.5 and mortality at the national scale is more likely to be 

confounded. On the other hand our conclusions about linearity and thresholds are in contrast to 

those of Schwartz et al. (2008) using long term PM2.5 exposure and mortality from the Harvard 

Six Cities Study. 

CONCLUSION 

EPA’s ozone acute mortality risk assessment for the entire U.S. uses a fixed value based on a 

uniform linear no threshold model developed from 12 cities. Our work clearly shows that the 

assumption of a uniform linear no-threshold model across cities is inconsistent with the observed 

data. We have shown that each city has a different model form with different thresholds, and the 

threshold can change depending on the inclusion of other atmospheric pollutants in the model. In 

light of our results, ignoring location differences and combining the results into a single nationwide standard result appears to be unjustified. We believe that continuing to estimate risks, 

specifically at lower exposures, based on a single pollutant linear no threshold model is not 

technically supported and will add greatly to the uncertainty of the resulting estimated risks.

. 

Mark Nicolich, PhD 

Statistician 

24 Lakeview Rd 

Lambertville, NJ 0850 

mark.nicolich@gmail.com 

609.397.4089 

7 February 2011

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mortality and the adequacy of current ozone regulations. Environ Health Perspect 

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Bell ML, Dominici F, Samet JM ., 2005. A meta-analysis of time-series studies of ozone and 

mortality with comparison to the national morbidity, mortality, and air pollution study. 

Epidemiology 16(4), 436-45. 

Bell ML, McDermott A, Zeger SL, et al. 2004. Ozone and short-term mortality in 95 US 

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Dominici F, McDermott A, Zeger SL, et al., 2002. On the use of generalized additive models 

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