of the standard with limited discussion of the
statistical form of the Standard. In doing so,
there has been a failure to recognize that the
stringency of the standard and the degree of health
protection provided depends on both the level and
statistical form of the standard for a particular
indicator and averaging time.
The challenges of selecting appropriate averaging times and statistical forms for the NAAQS are
substantial. The original epidemiological and toxicological studies that provide the scientific information that should inform the setting of the NAAQS do
not always report results with an averaging time that
is the same as that ultimately selected for the Standard. Hence, the need to make extrapolations from
results reported based on one metric, such as average daily exposure to a second metric such as an 8-h
averaging time. The setting of standards at extreme
values, the 98th percentile for NO 2 [86] or, even the
99th percentile form as done with the 1-h averaging
time standard for SO 2 [87], results in extremely
stringent standards that at best are only very loosely
related to the underlying data. In my view, selection
of the specific level and statistical form for a specific
averaging time is clearly policy judgments beyond
the dominion of the science. It is my opinion that
decisions on the selection of specific averaging
times are ultimately policy judgments reserved to
the Administrator by the language of the CAA.
In the 1990s, concurrent with the increasingly
widespread use of formal risk analysis procedures
across society [35, 40], EPA moved to quantify the
health benefits associated with setting the NAAQS
at various levels, with an associated statistical
form. I must admit to being an early advocate of
formal quantification of health benefits. I viewed
the approach then and I still do today, as providing
useful guidance for making policy decisions. I did
not envision that some enthusiastic advocates of
quantitative risk analysis would actually view the
results of the analyses as being highly accurate
projections of potential health benefits expressed
to two or more significant figures, sometimes without any indication of uncertainty.
The quantification of health effects potentially
associated with various levels and forms of the
Standards requires several kinds of input. Examples
are the risk assessments conducted by the USEPA
for particulate matter [69], ozone [77, 78, 79] and
sulfur dioxide [83]. Guidance has also been provided by the US National Research Council
[48]. First and foremost, conduct of a risk assessment requires some knowledge of the nature of the
ambient concentration-response relationships for
various temporal metrics for the pollutant in question. Typically, the response term is expressed as
excess risk per unit of increased concentration over
some range of ambient concentrations. The question
then becomes one of whether the relationship is
linear and whether there is a threshold level below
which the coefficient for excess risk holds or does
not hold. The issue of whether there are or are not
thresholds for noncancer health endpoints from
exposure to many agents is very contentious [56,
92]. It is well recognized that the use of linear
no-threshold exposure-response relationships for
cancer-causing agents is closely linked to experience
with radiation [47]. An additional issue becomes the
selection of suitable reference baseline statistics for
the particular health effects to use along with a given
ambient concentration-response coefficient. Yet
another question becomes the appropriate population to be evaluated, a single city, multiple cities, or
the population of the United States. It is obvious that
there are substantial uncertainties associated with
each component of the analysis of benefits of potential reductions in a given pollutant.
With the use of linear, no-threshold, ambient
concentration-response models, the EPA has, on
some occasions, calculated estimated excess morbidity and mortality effects attributable to the specific pollutant down to background concentrations.
The Health Risk Assessment [77] and the Regulatory Impact Analysis [82] for the 2008 Ozone
NAAQS [81] serve as examples. Further, dependent
on the assumptions made with regard to how ambient concentrations of the pollutant would change in
response to various levels and forms of the standard,
substantial health effects avoided, that is, health
benefits can be calculated. Expressing the benefits
in monetary units raises some critical issues. Typically, mortality effects drive the monetized benefits
calculations because of the substantial value placed
on deaths avoided. Sunstein [61] has addressed this
contentious issue and has argued that years of life
loss is a more appropriate metric than is the use of
24
Air Quality Guidelines and Standards
statistical form of the Standard. In doing so,
there has been a failure to recognize that the
stringency of the standard and the degree of health
protection provided depends on both the level and
statistical form of the standard for a particular
indicator and averaging time.
The challenges of selecting appropriate averaging times and statistical forms for the NAAQS are
substantial. The original epidemiological and toxicological studies that provide the scientific information that should inform the setting of the NAAQS do
not always report results with an averaging time that
is the same as that ultimately selected for the Standard. Hence, the need to make extrapolations from
results reported based on one metric, such as average daily exposure to a second metric such as an 8-h
averaging time. The setting of standards at extreme
values, the 98th percentile for NO 2 [86] or, even the
99th percentile form as done with the 1-h averaging
time standard for SO 2 [87], results in extremely
stringent standards that at best are only very loosely
related to the underlying data. In my view, selection
of the specific level and statistical form for a specific
averaging time is clearly policy judgments beyond
the dominion of the science. It is my opinion that
decisions on the selection of specific averaging
times are ultimately policy judgments reserved to
the Administrator by the language of the CAA.
In the 1990s, concurrent with the increasingly
widespread use of formal risk analysis procedures
across society [35, 40], EPA moved to quantify the
health benefits associated with setting the NAAQS
at various levels, with an associated statistical
form. I must admit to being an early advocate of
formal quantification of health benefits. I viewed
the approach then and I still do today, as providing
useful guidance for making policy decisions. I did
not envision that some enthusiastic advocates of
quantitative risk analysis would actually view the
results of the analyses as being highly accurate
projections of potential health benefits expressed
to two or more significant figures, sometimes without any indication of uncertainty.
The quantification of health effects potentially
associated with various levels and forms of the
Standards requires several kinds of input. Examples
are the risk assessments conducted by the USEPA
for particulate matter [69], ozone [77, 78, 79] and
sulfur dioxide [83]. Guidance has also been provided by the US National Research Council
[48]. First and foremost, conduct of a risk assessment requires some knowledge of the nature of the
ambient concentration-response relationships for
various temporal metrics for the pollutant in question. Typically, the response term is expressed as
excess risk per unit of increased concentration over
some range of ambient concentrations. The question
then becomes one of whether the relationship is
linear and whether there is a threshold level below
which the coefficient for excess risk holds or does
not hold. The issue of whether there are or are not
thresholds for noncancer health endpoints from
exposure to many agents is very contentious [56,
92]. It is well recognized that the use of linear
no-threshold exposure-response relationships for
cancer-causing agents is closely linked to experience
with radiation [47]. An additional issue becomes the
selection of suitable reference baseline statistics for
the particular health effects to use along with a given
ambient concentration-response coefficient. Yet
another question becomes the appropriate population to be evaluated, a single city, multiple cities, or
the population of the United States. It is obvious that
there are substantial uncertainties associated with
each component of the analysis of benefits of potential reductions in a given pollutant.
With the use of linear, no-threshold, ambient
concentration-response models, the EPA has, on
some occasions, calculated estimated excess morbidity and mortality effects attributable to the specific pollutant down to background concentrations.
The Health Risk Assessment [77] and the Regulatory Impact Analysis [82] for the 2008 Ozone
NAAQS [81] serve as examples. Further, dependent
on the assumptions made with regard to how ambient concentrations of the pollutant would change in
response to various levels and forms of the standard,
substantial health effects avoided, that is, health
benefits can be calculated. Expressing the benefits
in monetary units raises some critical issues. Typically, mortality effects drive the monetized benefits
calculations because of the substantial value placed
on deaths avoided. Sunstein [61] has addressed this
contentious issue and has argued that years of life
loss is a more appropriate metric than is the use of
24
Air Quality Guidelines and Standards