Chapter 1. Introduction 

The Integrated Science Assessment (ISA) is a concise evaluation and synthesis of the most 

policy-relevant science for reviewing the national ambient air quality standards (NAAQS). Because 

the ISA communicates critical science judgments relevant to the NAAQS review, it forms the 

scientific foundation for the review of the NAAQS for carbon monoxide (CO). The existing primary 

CO standards include a 1-hour (h) average (avg) standard set at 35 parts per million (ppm), and an 

8-h avg standard set at 9 ppm, neither to be exceeded more than once per year. There is currently no 

secondary standard for CO. 

The ISA accurately reflects “the latest scientific knowledge useful in indicating the kind and 

extent of identifiable effects on public health which may be expected from the presence of [a] 

pollutant in ambient air” (42 U.S.C. 7408). Key information and judgments formerly contained in 

the Air Quality Criteria Document (AQCD) for CO are incorporated in this assessment. Additional 

details of the pertinent scientific literature published since the last review, as well as selected older 

studies of particular interest, are included in a series of annexes. This ISA thus serves to update and 

revise the evaluation of the scientific evidence available at the time of the previous review of the 

NAAQS for CO that was completed in 2000. 

The integrated Plan for Review of the National Ambient Air Quality Standards for Carbon 

Monoxide (U.S. EPA, 2008, 193995) identifies key policy-relevant questions that provide a 

framework for this assessment of the scientific evidence. These questions frame the entire review of 

the NAAQS for CO and thus are informed by both science and policy considerations. The ISA 

organizes, presents, and integrates the scientific evidence which is considered along with findings 

from risk analyses and policy considerations to help the U.S. Environmental Protection Agency 

(EPA) address these questions during the NAAQS review. In evaluating the health evidence, the 

focus of this assessment is on scientific evidence that is most relevant to the following questions 

taken directly from the Integrated Review Plan: 

 Has new information altered the scientific support for the occurrence of health effects 

following short- and/or long-term exposure to levels of CO found in the ambient air? 

 To what extent is key evidence becoming available that could inform our understanding 

of human subpopulations that are particularly sensitive to CO exposures? Is there new or 

emerging evidence on health effects beyond cardiovascular and respiratory endpoints 

(e.g., systemic effects, developmental effects, birth outcomes) that suggest additional 

sensitive subpopulations should be given increased focus in this review (e.g., neonates)? 

 What do recent studies focused on the near-roadway environment, including bus stops 

and intersections, tell us about high-exposure human subpopulations and the health 

effects of CO? What information is available on elevated exposures due to other 

transportation sources, such as shipping, port operations, and recreational vehicles? What 

is the effect of altitude on CO sources and health effects? 

 At what levels of CO exposure do health effects of concern occur? 

 To what extent is key scientific evidence becoming available to improve our 

understanding of the health effects associated with various time periods of CO exposures, 

including not only daily but also chronic (months to years) exposures? To what extent is 

critical research becoming available that could improve our understanding of the 

relationship between various health endpoints and different lag periods (e.g., single-day, 

multiday distributed lags)? 

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

January 2010 1-1 
 To what extent does the evidence suggest that alternate dose indicators other than 

carboxyhemoglobin (COHb) levels (e.g., tissue oxygenation) should be evaluated to 

characterize the biological effect? 

 Has new information altered conclusions from previous reviews regarding the 

plausibility of adverse health effects caused by CO exposure? 

 To what extent have important uncertainties identified in the last review been reduced 

and/or have new uncertainties emerged? 

 Have new information or scientific insights altered the scientific conclusions regarding 

the occurrence of direct (or indirect) welfare effects associated with levels of CO found 

in the ambient air? 

1.1. Legislative Requirements 

Two sections of the Clean Air Act (CAA, the Act) govern the establishment and revision of the 

NAAQS. Section 108 of the Act (42 U.S.C. 7408) directs the Administrator to identify and list “air 

pollutants” that “in [her] judgment, may reasonably be anticipated to endanger public health and 

welfare” and whose “presence ... in the ambient air results from numerous or diverse mobile or 

stationary sources” and to issue air quality criteria for those that are listed (42 U.S.C. 7408). Air 

quality criteria are intended to “accurately reflect the latest scientific knowledge useful in indicating 

the kind and extent of identifiable effects on public health or welfare which may be expected from 

the presence of [a] pollutant in ambient air...” 42 U.S.C. 7408(b). 

Section 109 of the Act (42 U.S.C. 7409) directs the EPA Administrator to propose and 

promulgate “primary” and “secondary” National Ambient Air Quality Standards (NAAQS) for 

pollutants listed under Section 108. Section 109(b)(1) defines a primary standard as one “the 

attainment and maintenance of which in the judgment of the Administrator, based on such criteria 

and allowing an adequate margin of safety, are requisite to protect the public health.”1

 A secondary 

standard, as defined in Section 109(b)(2), must “specify a level of air quality the attainment and 

maintenance of which, in the judgment of the U.S. EPA Administrator, based on such criteria, is 

required to protect the public welfare from any known or anticipated adverse effects associated with 

the presence of [the] pollutant in the ambient air.”2

 The requirement that primary standards include 

an adequate margin of safety was intended to address uncertainties associated with inconclusive 

scientific and technical information available at the time of standard setting. It was also intended to 

provide a reasonable degree of protection against hazards that research has not yet identified. See 

Lead Industries Association v. EPA, 647 F.2d 1130, 1154 (D.C. Cir 1980), cert. denied, 449 U.S. 

1042 (1980); American Petroleum Institute v. Costle, 665 F.2d 1176, 1186 (D.C. Cir. 1981) cert. 

denied, 455 U.S. 1034 (1982). The aforementioned uncertainties are components of the risk 

associated with pollution at levels below those at which human health effects can be said to occur 

with reasonable scientific certainty. Thus, in selecting primary standards that include an adequate 

margin of safety, the Administrator is seeking not only to prevent pollution levels that have been 

demonstrated to be harmful, but also to prevent lower pollutant levels that may pose an unacceptable 

risk of harm, even if the risk is not precisely identified as to nature or degree. 

In selecting a margin of safety, the EPA considers such factors as the nature and severity of the 

health effects involved, the size of susceptible population(s), and the kind and degree of the 

uncertainties that must be addressed. The selection of any particular approach to providing an 

 

1

 The legislative history of section 109 of the Clean Air Act indicates that a primary standard is to be set at “the maximum permissible 

ambient air level . . . which will protect the health of any [sensitive] group of the population,” and that for this purpose “reference should 

be made to a representative sample of persons comprising the sensitive group rather than to a single person in such a group” [S. Rep. No. 

91-1196, 91st Cong., 2d Sess. 10 (1970)]. 

2

 Welfare effects as defined in section 302(h) [42 U.S.C. 7602(h)] include, but are not limited to, “effects on soils, water, crops, vegetation, 

man-made materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and hazards to

transportation, as well as effects on economic values and on personal comfort and well-being.” 

January 2010 1-2 
adequate margin of safety is a policy choice left specifically to the Administrator’s judgment. See 

Lead Industries Association v. EPA, supra, 647 F.2d at 1161-62. 

In setting standards that are “requisite” to protect public health and welfare, as provided in 

Section 109(b), EPA’s task is to establish standards that are neither more nor less stringent than 

necessary for these purposes. In so doing, EPA may not consider the costs of implementing the 

standards. See Whitman v. American Trucking Associations, 531 U.S. 457, 465-472, 475-76 (D.C. 

Cir. 2001). 

Section 109(d)(1) requires that “not later than December 31, 1980, and at 5-year intervals 

thereafter, the Administrator shall complete a thorough review of the criteria published under Section 

108 and the national ambient air quality standards...and shall make such revisions in such criteria 

and standards and promulgate such new standards as may be appropriate...” Section 109(d)(2) 

requires that an independent scientific review committee “shall complete a review of the 

criteria...and the national primary and secondary ambient air quality standards...and shall 

recommend to the Administrator any new...standards and revisions of existing criteria and standards 

as may be appropriate...” Since the early 1980s, this independent review function has been 

performed by the Clean Air Scientific Advisory Committee (CASAC) of EPA’s Science Advisory 

Board (SAB). 

1.2. History of the NAAQS for CO 

On April 30, 1971, EPA promulgated identical primary and secondary NAAQS for CO, under 

Section 109 of the Clean Air Act, set at 9 ppm, 8-h avg and 35 ppm, 1-h avg, neither to be exceeded 

more than once per year (36 FR 8186). In 1979, EPA published the Air Quality Criteria Document 

for Carbon Monoxide (1979, 017687), which updated the scientific criteria upon which the initial 

CO standards were based. A Staff Paper (U.S. EPA, 1979, 194665) was prepared and, along with the 

AQCD (1979, 017687), served as the basis for development of proposed rulemaking (45 FR 55066) 

published on August 18, 1980. Delays due to uncertainties regarding the scientific basis for the final 

decision resulted in EPA announcing a second public comment period (47 FR 26407). Following 

substantial reexamination of the scientific data, EPA prepared an Addendum to the 1979 AQCD 

(1984, 012690) and an updated Staff Paper (1984, 012691). Following review by CASAC, EPA 

announced its final decision (50 FR 37484) not to revise the existing primary standard and to revoke 

the secondary standard for CO on September 13, 1985, due to a lack of evidence of direct effects on 

public welfare at ambient concentrations. 

In 1987, EPA initiated action to revise the criteria for CO and subsequently released a revised 

AQCD (U.S. EPA, 1991, 017643) for CASAC and public review. In a “closure letter” (McClellan, 

1991, 194666) sent to the Administrator, the CASAC concluded that the AQCD (U.S. EPA, 1991, 

017643) “. . . provides a scientifically balanced and defensible summary of current knowledge of the 

effects of this pollutant and provides an adequate basis for the EPA to make a decision as to the 

appropriate primary NAAQS for CO.” A revised Staff Paper subsequently was reviewed by CASAC 

and the public, and in a “closure letter” (McClellan, 1992, 194667) sent to the Administrator, 

CASAC stated “. . . that a standard of the present form and with a numerical value similar to that of 

the present standard would be supported by the present scientific data on health effects of exposure 

to carbon monoxide.” Based on the revised AQCD (U.S. EPA, 1991, 017643) and staff conclusions 

and recommendations contained in the revised Staff Paper (U.S. EPA, 1992, 084191), the 

Administrator announced the final decision (59 FR 38906) on August 1, 1994, that revision of the 

primary NAAQS for CO was not appropriate at that time. 

In 1997, revisions to the 1991 AQCD (U.S. EPA, 1991, 017643) were initiated. A workshop 

was held in September 1998 to review and discuss material contained in the revised draft AQCD. On 

June 9, 1999, CASAC held a public meeting to review the draft AQCD and a draft exposure analysis 

methodology document. Comments from CASAC and the public were considered in a second draft 

AQCD, which was reviewed at a CASAC meeting, held on November 18, 1999. After revision of the 

second draft AQCD, the final AQCD (U.S. EPA, 2000, 000907) was released in August 2000. EPA 

put the review on hold when Congress called on the National Research Council (NRC) to conduct a 

review of the impact of meteorology and topography on ambient CO concentrations in high altitude 

and extreme cold regions of the U.S. In response, the NRC convened the committee on Carbon 

Monoxide Episodes in Meteorological and Topographical Problem Areas, which focused on 

January 2010 1-3 
Fairbanks, Alaska as a case study in an interim report, which was completed in 2002. A final report, 

Managing Carbon Monoxide Pollution in Meteorological and Topographical Problem Areas, was 

published in 2003 (National Research Council, 2003, 042550) and offered a wide range of 

recommendations on management of CO air pollution, cold start emissions standards, oxygenated 

fuels, and CO monitoring. EPA did not complete the NAAQS review which started in 1997. 

1.3. ISA Development 

EPA initiated the current review of the NAAQS for CO on September 13, 2007 with a call for 

information from the public (72 FR 52369). In addition to the call for information, publications were 

identified through an ongoing literature search process that includes extensive computer database 

mining on specific topics. Literature searches were conducted routinely to identify studies published 

since the last review, focusing on publications from 1999 to May 2009. Search strategies were 

iteratively modified to optimize identification of pertinent publications. Additional papers were 

identified for inclusion in several ways: review of pre-publication tables of contents for journals in 

which relevant papers may be published; independent identification of relevant literature by expert 

authors; and identification by the public and CASAC during the external review process. 

Publications considered for inclusion in the ISA were added to the Health and Environmental 

Research Online (HERO) database recently developed by EPA (http://cfpub.epa.gov/ncea/hero/); 

note that all references in the ISA include a HERO ID that provides a link to the database. Typically, 

only information that had undergone scientific peer review and had been published or accepted for 

publication was considered, along with analyses conducted by EPA using publicly available data. 

This review has attempted to evaluate all relevant data published since the last review pertaining to 

the atmospheric science of CO, human exposure to ambient CO, and epidemiologic, controlled 

human exposure, and animal toxicological studies on CO, including those related to exposureresponse relationships, mode(s) of action (MOA), or susceptible populations. Added to the body of 

research on CO effects were EPA’s analyses of air quality and emissions data, studies on atmospheric 

chemistry, transport, and fate of these emissions, as well as issues related to exposure to CO. An 

extensive literature search for data on the ecological effects of ambient CO did not identify any 

relevant information published since the review of the ecological effects evidence in the 1979 CO 

AQCD (U.S. EPA, 1979, 017687). 

In general, in assessing the scientific quality and relevance of health and environmental effects 

studies, the following considerations have been taken into account when selecting studies for 

inclusion in the ISA or its annexes. The selection process for studies included in this ISA is shown in 

Figure 1-1. 

 Are the study populations, subjects, or animal models adequately selected and are they 

sufficiently well defined to allow for meaningful comparisons between study or exposure 

groups? 

 Are the statistical analyses appropriate, properly performed, and properly interpreted? 

Are likely covariates adequately controlled or taken into account in the study design and 

statistical analysis? 

 Are the air quality data, exposure, or dose metrics of adequate quality and sufficiently 

representative of information regarding ambient CO? 

 Are the health or welfare effect measurements meaningful and reliable? 

January 2010 1-4 
Figure 1-1. Identification of studies for inclusion in the ISA. 

In selecting epidemiologic studies, EPA considered whether a given study presented 

information on associations with short- or long-term CO exposures at or near ambient levels of CO; 

considered approaches to evaluate issues related to potential confounding by other pollutants; 

assessed potential effect modifiers; addressed health endpoints and populations not previously 

extensively researched; and evaluated important methodologic issues (e.g., lag or time period 

between exposure and effects, model specifications, thresholds, mortality displacement) related to 

interpretation of the health evidence. Among the epidemiologic studies selected, particular emphasis 

was placed on those studies most relevant to the review of the NAAQS. Specifically, studies 

conducted in the United States (U.S.) or Canada were discussed in more detail than those from other 

geographical regions. Particular emphasis was placed on: (1) recent multicity studies that employ 

standardized analysis methods for evaluating effects of CO and that provide overall estimates for 

effects based on combined analyses of information pooled across multiple cities; (2) studies that help 

understand quantitative relationships between exposure concentrations and effects; (3) new studies 

that provide evidence on effects in susceptible populations; and (4) studies that consider and report 

CO as a component of a complex mixture of air pollutants. 

January 2010 1-5 
Criteria for the selection of research evaluating controlled human exposure or animal 

toxicological studies included a focus on studies conducted using relevant pollutant exposures. For 

both types of studies, relevant pollutant exposures are considered to be those generally within one or 

two orders of magnitude of ambient CO concentrations. Studies in which higher doses were used 

may also be considered if they provide information relevant to understanding MOAs or mechanisms, 

as noted below. 

Evaluation of controlled human exposure studies focused on those that approximated expected 

human exposure conditions in terms of concentration and duration. In the selection of controlled 

human exposure studies, emphasis is placed on studies that (1) investigate potentially susceptible 

populations such as people with cardiovascular diseases; (2) address issues such as concentrationresponse or time-course of responses; (3) include control exposures to filtered air; and (4) have 

sufficient statistical power to assess findings. 

Review of the animal toxicological evidence focused on studies that approximate expected 

human dose conditions, which will vary depending on the toxicokinetics and biological sensitivity of 

the particular laboratory animal species or strains studied. Due to resource constraints on exposure 

duration and numbers of animals tested, animal studies typically utilize high-concentration 

exposures to acquire data relating to mechanisms and assure a measureable response. Such studies 

were considered to the extent that they provided useful information to inform our understanding of 

interspecies differences and potential sensitivity differences between healthy and susceptible human 

populations. 

These criteria provide benchmarks for evaluating various studies and for focusing on the 

policy-relevant studies in assessing the body of health and welfare effects evidence. Detailed critical 

analysis of all CO health and welfare effects studies, especially in relation to the above 

considerations, is beyond the scope of this document. Of most relevance for evaluation of studies is 

whether they provide useful qualitative or quantitative information on exposure-effect or 

exposure-response relationships for effects associated with current ambient air concentrations of CO 

that can inform decisions on whether to retain or revise the standards. 

In developing the CO ISA, EPA began by reviewing and summarizing the evidence on 

atmospheric sciences and exposure and the health effects evidence from in vivo and in vitro 

toxicological studies, controlled human exposure studies, and epidemiologic studies. In November 

2008, EPA invited EPA staff and other researchers with expertise in CO to a teleconference to review 

the scientific content of preliminary draft materials for the draft ISA and the annexes. The purpose of 

the initial peer review teleconference was to ensure that the ISA is up to date and focused on the 

most policy-relevant findings, and to assist EPA with integration of evidence within and across 

disciplines. Subsequently, EPA addressed comments and completed the initial integration and 

synthesis of the evidence. 

The integration of evidence on health or welfare effects involves collaboration between 

scientists from various disciplines. As described in the section below, the ISA organization is based 

on health effect categories. As an example, an evaluation of health effects evidence would include 

summaries of findings from epidemiologic, controlled human exposure, and toxicological studies, 

and integration of the results to draw conclusions based on the causal framework described below. 

Using the causal framework described in Section 1.6, EPA scientists consider aspects such as 

strength, consistency, coherence and biological plausibility of the evidence, and develop draft 

causality judgments on the nature of the relationships. The draft integrative synthesis sections and 

conclusions are reviewed by EPA internal experts and, as appropriate, by outside expert authors. In 

practice, causality determinations often entail an iterative process of review and evaluation of the 

evidence. The draft ISA is released for review by the CASAC and the public, and comments received 

on the characterization of the science as well as the implementation of the causal framework are 

carefully considered in revising and completing the ISA. 

1.4. Document Organization 

The ISA is composed of five chapters. This introductory chapter presents background 

information and provides an overview of EPA’s framework for making causal judgments. Chapter 2 

is an integrated summary of key findings and conclusions regarding the source to dose paradigm, 

MOA, and important health effects of CO, including cardiovascular, nervous system, 

January 2010 1-6 
perinatal/developmental, respiratory, and mortality outcomes. Chapter 3 highlights key concepts and 

evidence relevant to understanding the sources, ambient concentrations, atmospheric behavior, and 

exposure to ambient CO. Chapter 4 describes the dosimetry and pharmacokinetics of CO, including 

formation and fate of carboxyhemoglobin (COHb). Chapter 5 presents a discussion of the MOA of 

CO and evaluates and integrates epidemiologic, human clinical, and animal toxicological 

information on health effects related to short-term exposures (i.e., hours, days, or weeks) and longterm exposures (i.e., months or years) to CO, including cardiovascular and systemic effects, central 

nervous system (CNS) effects, birth outcomes and developmental effects, respiratory effects, and 

mortality. 

A series of annexes supplement this ISA. The annexes provide tables summarizing additional 

details of the pertinent literature published since the last review, as well as selected older studies of 

particular interest. These annexes contain information on: 

 atmospheric chemistry of CO, sampling and analytic methods for measurement of CO 

concentrations, emissions, sources and human exposure to CO (Annex A); 

 studies on the dosimetry and pharmacokinetics of CO (Annex B); 

 epidemiologic studies of health effects from short- and long-term exposure to CO 

(Annex C); 

 controlled human exposure studies of health effects related to exposure to CO (Annex 

D); and 

 toxicological studies of health effects in laboratory animals (Annex E) 

Within Annexes B through E, detailed information about methods and results of health studies 

is summarized in tabular format, and generally includes information about concentrations of CO and 

averaging times, study methods employed, results and comments, and quantitative results for 

relationships between effects and exposure to CO. As noted in the section above, the most pertinent 

results of this body of studies are brought into the ISA. 

1.5. Document Scope 

For the current review of the primary CO standards, relevant scientific information on human 

exposures and health effects associated with exposure to ambient CO has been assessed. Health 

effects resulting from accidental exposures to very high concentrations of non-ambient CO (i.e., CO 

poisoning) are not directly relevant to ambient exposures, and as such, a discussion of these effects 

has deliberately been excluded from this document. For a detailed review of the effects of high-level 

exposures to CO, the reader is referred to the extensive body of literature related to CO poisoning 

(Ernst and Zibrak, 1998, 049822; Penney, 2007, 194668; Raub et al., 2000, 002180). In addition, 

results of studies investigating the relationship between blood COHb concentrations and health 

effects (e.g., Hedblad et al., 2006, 199512) may be informative regarding the biological plausibility 

of health effects associated with changes in COHb concentrations. However, the lack of data on 

ambient concentrations and the likely contribution of non-ambient CO to COHb in these studies 

complicates the interpretation of the results with respect to ambient CO exposure, and therefore these 

studies will not be discussed in this review. The possible influence of other atmospheric pollutants on 

the interpretation of the role of CO in health effects studies is considered in this assessment. This 

includes other pollutants with the potential to co-occur in the environment (e.g., nitrogen dioxide 

[NO2], sulfur dioxide [SO2], ozone [O3], and particulate matter [PM]). 

The review also assesses relevant scientific information associated with known or anticipated 

public welfare effects that may be identified. The 1979 CO AQCD (U.S. EPA, 1979, 017687) 

reviewed research on the effects of CO on vegetation and soil microflora, which showed that visible 

symptoms and effects on growth, yield, and reproduction were observed in some studies at very high 

CO concentrations (1,000-10,000 ppm or greater), while biochemical and physiological responses, 

including reduced nitrogen fixation, were observed at lower concentrations (1,000 ppm and below). 

As discussed in Section 1.3, a critical review of the ecological effects literature identified no 

January 2010 1-7 
information published since the 1979 CO AQCD (U.S. EPA, 1979, 017687) pertinent to ambient CO 

exposures; hence, no section on ecological effects appears in this assessment. The reader is referred 

to the 1979 CO AQCD (U.S. EPA, 1979, 017687) for a detailed discussion of the effects of high CO 

concentrations on plants and microorganisms. The definition of public welfare for the NAAQS 

includes considerations of climate. Thus, the climate forcing effects of CO are summarized in 

Chapter 2 and are discussed in detail in Chapter 3, where distinctions are drawn between globalscale conclusions related to climate and the strongly variable continental and regional climate 

forcing effects from CO. 

1.6. EPA Framework for Causal Determination 

The EPA has developed a consistent and transparent basis to evaluate the causal nature of air 

pollution-induced health or environmental effects. The framework described below establishes 

uniform language concerning causality and brings more specificity to the findings. This standardized 

language was drawn from across the federal government and wider scientific community, especially 

from the recent National Academy of Sciences (NAS) Institute of Medicine (IOM) document, 

Improving the Presumptive Disability Decision-Making Process for Veterans, (2008, 156586) the 

most recent comprehensive work on evaluating causality. 

This introductory section focuses on the evaluation of health effects evidence. While focusing 

on human health outcomes, the concepts are also generally relevant to causality determination for 

welfare effects. This section: 

 describes the kinds of scientific evidence used in establishing a general causal 

relationship between exposure and health effects; 

 defines cause, in contrast to statistical association; 

 discusses the sources of evidence necessary to reach a conclusion about the existence of 

a causal relationship; 

 highlights the issue of multifactorial causation; 

 identifies issues and approaches related to uncertainty; and 

 provides a framework for classifying and characterizing the weight of evidence in 

support of a general causal relationship. 

Approaches to assessing the separate and combined lines of evidence (e.g., epidemiologic, 

human clinical, and animal toxicological studies) have been formulated by a number of regulatory 

and science agencies, including the IOM of the NAS (2008, 156586), International Agency for 

Research on Cancer (2006, 093206), EPA Guidelines for Carcinogen Risk Assessment (2005, 

086237), Centers for Disease Control and Prevention (2004, 056384), and National Acid 

Precipitation Assessment Program (1991, 095894). These formalized approaches offer guidance for 

assessing causality. The frameworks are similar in nature, although adapted to different purposes, 

and have proven effective in providing a uniform structure and language for causal determinations. 

Moreover, these frameworks have supported decision-making under conditions of uncertainty. 

1.6.1. Scientific Evidence Used in Establishing Causality 

Causality determinations are based on the evaluation and synthesis of evidence from across 

scientific disciplines; the type of evidence that is most important for such determinations will vary 

by pollutant or assessment. The most compelling evidence of a causal relationship between pollutant 

exposures and human health effects comes from human clinical studies. This type of study 

experimentally evaluates the health effects of administered exposures in human volunteers under 

highly controlled laboratory conditions. 

January 2010 1-8 
In epidemiologic or observational studies of humans, the investigator does not control 

exposures or intervene with the study population. Broadly, observational studies can describe 

associations between exposures and effects. These studies fall into several categories: 

cross-sectional, prospective cohort, and time-series studies. “Natural experiments” offer the 

opportunity to investigate changes in health with a change in exposure; these include comparisons of 

health effects before and after a change in population exposures, such as closure of a pollution 

source. 

Experimental animal data can help characterize effects of concern, exposure-response 

relationships, susceptible populations and MOAs. In the absence of controlled human exposure or 

epidemiologic data, animal data alone may be sufficient to support a likely causal determination, 

assuming that humans respond similarly to the experimental species. 

1.6.2. Association and Causation 

“Cause” is a significant, effectual relationship between an agent and an effect on health or 

public welfare. “Association” is the statistical dependence among events, characteristics, or other 

variables. An association is prima facie evidence for causation; alone, however, it is insufficient 

proof of a causal relationship between exposure and disease. Unlike an association, a causal claim 

supports the creation of counterfactual claims; that is, a claim about what the world would have been 

like under different or changed circumstances (IOM, 2008, 156586). Much of the newly available 

health information evaluated in this ISA comes from epidemiologic studies that report a statistical 

association between ambient exposure and health outcome. 

Many of the health and environmental outcomes reported in these studies have complex 

etiologies. Diseases such as asthma, coronary heart disease (CHD) or cancer are typically initiated 

by multiple agents. Outcomes depend on a variety of factors, such as age, genetic susceptibility, 

nutritional status, immune competence, and social factors (Gee and Payne-Sturges, 2004, 093070; 

IOM, 2008, 156586). Effects on ecosystems are often also multifactorial with a complex web of 

causation. Further, exposure to a combination of agents could cause synergistic or antagonistic 

effects. Thus, the observed risk represents the net effect of many actions and counteractions. 

1.6.3. Evaluating Evidence for Inferring Causation 

Moving from association to causation involves the elimination of alternative explanations for 

the association. In estimating the causal influence of an exposure on health or environmental effects, 

it is recognized that scientific findings incorporate uncertainty. "Uncertainty" can be defined as a 

state of having limited knowledge where it is impossible to exactly describe an existing state or 

future outcome, e.g., the lack of knowledge about the correct value for a specific measure or 

estimate. Uncertainty characterization and uncertainty assessment are two activities that lead to 

different degrees of sophistication in describing uncertainty. Uncertainty characterization generally 

involves a qualitative discussion of the thought processes that lead to the selection and rejection of 

specific data, estimates, scenarios, etc. Uncertainty assessment is more quantitative. The process 

begins with simpler measures (e.g., ranges) and simpler analytical techniques and progresses, to the 

extent needed to support the decision for which the assessment is conducted, to more complex 

measures and techniques. Data will not be available for all aspects of an assessment and those data 

that are available may be of questionable or unknown quality. In these situations, evaluation of 

uncertainty can include professional judgment or inferences based on analogy with similar situations. 

The net result is that the assessment will be based on a number of assumptions with varying degrees 

of uncertainty. Uncertainties commonly encountered in evaluating health evidence for the criteria air 

pollutants are outlined below for epidemiologic and experimental studies. Various approaches to 

evaluating uncertainty include classical statistical methods, sensitivity analysis, or probabilistic 

uncertainty analysis, in order of increasing complexity and data requirements. The ISA generally 

evaluates uncertainties qualitatively in assessing the evidence from across studies; in some situations 

quantitative analysis approaches, such as metaregression, may be used. 

Meta-analysis may be a valuable tool for evaluating evidence by combining results from a 

body of studies. Blair et al. (1995, 079190) observe that meta-analysis can enhance understanding of 

associations between exposures and effects that are not readily apparent in examination of individual 

study results and can be particularly useful for formally examining sources of heterogeneity. 

January 2010 1-9 
However, these authors note that meta-analysis may not be useful when the relationship between the 

exposure and outcome is obvious, when only a few studies are available for a particular exposureoutcome relationship, where there is limited access to data of sufficient quality, or where there is 

substantial variation in study design or population. In addition, important differences in effect 

estimates, exposure metrics, or other factors may limit or even preclude quantitative statistical 

combination of multiple studies. 

Controlled human exposure studies evaluate the effects of exposures to a variety of pollutants 

in a highly controlled laboratory setting. Also referred to as human clinical studies, these 

experiments allow investigators to expose subjects to known concentrations of air pollutants under 

carefully regulated environmental conditions and activity levels. In some instances, controlled 

human exposure studies can also be used to characterize concentration-response relationships at 

pollutant concentrations relevant to ambient conditions. Controlled human exposures are typically 

conducted using a randomized crossover design, with subjects exposed both to CO and a clean air 

control. In this way, subjects serve as their own controls, effectively controlling for many potential 

confounders. However, human clinical studies are limited by a number of factors, including a small 

sample size and short exposure times. The repetitive nature of ambient CO exposures at levels that 

can vary widely may lead to cumulative health effects, but this type of exposure is not practical to 

replicate in a laboratory setting. In addition, although subjects do serve as their own controls, 

personal exposure to pollutants in the hours and days preceding the controlled exposures may vary 

significantly between and within individuals. Endogenous production of CO creates a body burden 

of CO that, together with personal exposure from nonambient sources, contributes to baseline COHb 

levels. Endogenous production rates vary within and among individuals, particularly for individuals 

with diseases such as hemolytic anemia or chronic inflammation. This body burden of CO and 

COHb limits the lower range of exposures that can be practically covered in controlled human 

exposure studies. Finally, human clinical studies require investigators to adhere to stringent health 

criteria for a subject to be included in the study, and therefore the results cannot necessarily be 

generalized to an entire population. Although some human clinical studies have included healthcompromised individuals such as those with coronary artery disease (CAD), these individuals must 

also be relatively healthy and do not represent the most sensitive individuals in the population. Thus, 

a lack of observation of effects from human clinical studies does not necessarily mean that a causal 

relationship does not exist. While human clinical studies provide important information on the 

biological plausibility of associations observed between air pollutant exposure and health outcomes 

in epidemiologic studies, observed effects in these studies may underestimate the response in certain 

populations. 

Epidemiologic studies provide important information on the associations between health 

effects and exposure of human populations to ambient air pollution. In the evaluation of 

epidemiologic evidence, one important consideration is potential confounding. Confounding is “. . . 

a confusion of effects. Specifically, the apparent effect of the exposure of interest is distorted because 

the effect of an extraneous factor is mistaken for or mixed with the actual exposure effect (which 

may be null)” (Rothman and Greenland, 1998, 086599). One approach to remove spurious 

associations due to possible confounders is to control for characteristics that may differ between 

exposed and unexposed persons; this is frequently termed “adjustment.” Scientific judgment is 

needed regarding likely sources and magnitude of confounding, together with consideration of how 

well the existing constellation of study designs, results, and analyses address this potential threat to 

inferential validity. One key consideration in this review is evaluation of the potential contribution of 

CO to health effects when it is a component of a complex air pollutant mixture. Reported CO effect 

estimates in epidemiologic studies may reflect independent CO effects on health outcomes. Ambient 

CO may also be serving as an indicator of complex ambient air pollution mixtures that share the 

same source as CO (e.g., motor vehicle emissions). Alternatively, copollutants may mediate the 

effects of CO or CO may influence the toxicity of copollutants. 

Another important consideration in the evaluation of epidemiologic evidence is effect 

modification. “Effect-measure modification differs from confounding in several ways. The main 

difference is that, whereas confounding is a bias that the investigator hopes to prevent or remove 

from the effect estimate, effect-measure modification is a property of the effect under study . . . In 

epidemiologic analysis one tries to eliminate confounding but one tries to detect and estimate effectmeasure modification” (Rothman and Greenland, 1998, 086599). Examples of effect modifiers in 

some of the studies evaluated in this ISA include environmental variables, such as temperature or 

humidity, individual risk factors, such as education, cigarette smoking status, age in a prospective 

January 2010 1-10 
cohort study, and community factors, such as percent of population > 65 yr old. It is often possible to 

stratify the relationship between health outcome and exposure by one or more of these risk factor 

variables. For variables that modify the association, effect estimates in each stratum will be different 

from one another and different from the overall estimate, indicating a different exposure-response 

relationship may exist in populations represented by these variables. Effect modifiers may be 

encountered (a) within single-city time-series studies or (b) across cities in a two-stage hierarchical 

model or meta-analysis. 

Several statistical methods are available to detect and control for potential confounders, with 

none of them being completely satisfactory. Multivariable regression models constitute one tool for 

estimating the association between exposure and outcome after adjusting for characteristics of 

participants that might confound the results. The use of multipollutant regression models has been 

the prevailing approach for controlling potential confounding by copollutants in air pollution health 

effects studies. Finding the likely causal pollutant from multipollutant regression models is made 

difficult by the possibility that one or more air pollutants may be acting as a surrogate for an 

unmeasured or poorly measured pollutant or for a particular mixture of pollutants. In addition, more 

than one pollutant may exert similar health effects, resulting in independently observed associations 

for multiple pollutants. For example, PM2.5 and NO2 have each been linked to cardiovascular effects 

in epidemiologic studies. Correlation between CO concentrations and various copollutants, such as 

PM2.5 and NO2, makes it difficult to quantitatively interpret associations between different pollutant 

exposures and health effects. Thus, results of models that attempt to distinguish CO effects from 

those of copollutants must be interpreted with caution. The number and degree of diversity of 

covariates, as well as their relevance to the potential confounders, remain matters of scientific 

judgment. Despite these limitations, the use of multipollutant models is still the prevailing approach 

employed in most air pollution epidemiologic studies and provides some insight into the potential for 

confounding or interaction among pollutants. 

Another way to adjust for potential confounding is through stratified analysis, i.e., examining 

the association within homogeneous groups with respect to the confounding variable. The use of 

stratified analyses has an additional benefit: it allows examination of effect modification through 

comparison of the effect estimates across different groups. If investigators successfully measured 

characteristics that distort the results, adjustment of these factors help separate a spurious from a true 

causal association. Appropriate statistical adjustment for confounders requires identifying and 

measuring all reasonably expected confounders. Deciding which variables to control for in a 

statistical analysis of the association between exposure and disease or health outcome depends on 

knowledge about possible mechanisms and the distributions of these factors in the population under 

study. Identifying these mechanisms makes it possible to control for potential sources that may result 

in a spurious association. 

Adjustment for potential confounders can be influenced by differential exposure measurement 

error. There are several components that contribute to exposure measurement error in epidemiologic 

studies, including the difference between true and measured ambient concentrations, the difference 

between average personal exposure to ambient pollutants and ambient concentrations at central 

monitoring sites, and the use of average population exposure rather than individual exposure 

estimates. Consideration of issues important for evaluation of exposure to ambient CO include: (1) 

spatial variability of CO concentrations across urban areas, particularly with respect to highly 

traveled roadways; (2) location of CO monitors at varying distances from roads; and (3) the 

detection limit of instruments in the CO monitoring network. Previous AQCDs have examined the 

role of measurement error for non-reactive pollutants in time-series epidemiologic studies using 

simulated data and mathematical analyses and suggested that transfer of effects from the “causal” 

variable to the confounder would only occur under unusual circumstances (i.e., “true” predictors 

having high positive or negative correlation; substantial measurement error; or extremely negatively 

correlated measurement errors) (U.S. EPA, 2004, 056905). 

Confidence that unmeasured confounders are not producing the findings is increased when 

multiple studies are conducted in various settings using different subjects or exposures, each of 

which might eliminate another source of confounding from consideration. Thus, multicity studies 

which use a consistent method to analyze data from across locations with different levels of 

covariates can provide insight on potential confounding in associations. Intervention studies, because 

of their quasi-experimental nature, can be particularly useful in characterizing causation. 

In addition to clinical and epidemiologic studies, the tools of experimental biology have been 

valuable for developing insights into human physiology and pathology. Laboratory tools have been 

January 2010 1-11 
extended to explore the effects of putative toxicants on human health, especially through the study of 

model systems in other species. These studies evaluate the effects of exposures to a variety of 

pollutants in a highly controlled laboratory setting and allow exploration of MOAs or mechanisms 

by which a pollutant may cause effects. Background knowledge of the biological mechanisms by 

which an exposure might or might not cause disease can prove crucial in establishing or negating a 

causal claim. Consideration of evidence on the non-hypoxic effects of CO via cell signaling and 

alteration of heme protein function along with evidence on COHb-mediated hypoxic stress, provides 

a more complete understanding of the biological response to CO. There are, however, uncertainties 

associated with quantitative extrapolations between laboratory animals and humans on the 

pathophysiological effects of any pollutant. Animal species can differ from each other in 

fundamental aspects of physiology and anatomy (e.g., metabolism, airway branching, hormonal 

regulation) that may limit extrapolation. 

Interpretations of experimental studies of air pollution effects in laboratory animals, as in the 

case of environmental comparative toxicology studies, are affected by limitations associated with 

extrapolation models. The differences between humans and rodents with regard to pollutant 

absorption and distribution profiles based on metabolism, hormonal regulation, breathing pattern, 

exposure dose, and differences in lung structure and anatomy, all have to be taken into consideration. 

Also, in spite of a high degree of homology and the existence of a high percentage of orthologous 

genes across humans and rodents (particularly mice), extrapolation of molecular alterations at the 

gene level is complicated by species-specific differences in transcriptional regulation. Given these 

molecular differences, at this time there are uncertainties associated with quantitative extrapolations 

between laboratory animals and humans of observed pollutant-induced pathophysiological 

alterations under the control of widely varying biochemical, endocrine, and neuronal factors. 

1.6.4. Application of Framework for Causal Determination 

EPA uses a two-step approach to evaluate the scientific evidence on health or environmental 

effects of criteria pollutants. The first step determines the weight of evidence in support of causation 

and characterizes the strength of any resulting causal classification. The second step includes further 

evaluation of the quantitative evidence regarding the concentration-response relationships and the 

loads or levels, duration and pattern of exposures at which effects are observed. 

To aid judgment, various “aspects”1

 of causality have been discussed by many philosophers 

and scientists. The most widely cited aspects of causality in epidemiology, and public health, in 

general, were articulated by Sir Austin Bradford Hill (1965, 071664) and have been widely used 

(CDC, 2004, 056384; IARC, 2006, 093206; IOM, 2008, 156586; U.S. EPA, 2005, 086237).These 

aspects (Hill, 1965, 071664) have been modified (Table 1-2) for use in causal determinations 

specific to health and welfare effects or pollutant exposures (U.S. EPA, 2009, 179916).2

 Some 

aspects are more likely than others to be relevant for evaluating evidence on the health or 

environmental effects of criteria air pollutants. For example, the analogy aspect does not always 

apply, especially for the gaseous criteria pollutants, and specificity would not be expected for multietiologic health outcomes, such as asthma or cardiovascular disease, or ecological effects related to 

acidification. Aspects that usually play a larger role in determination of causality are consistency of 

results across studies, coherence of effects observed in different study types or disciplines, biological 

plausibility, exposure-response relationship, and evidence from “natural” experiments. 

 

1

 The “aspects” described by Hill (1965, 071664) have become, in the subsequent literature, more commonly described as “criteria.” The 

original term “aspects” is used here to avoid confusion with ‘criteria’ as it is used, with different meaning, in the Clean Air Act. 

2

 The Hill aspects were developed for interpretation of epidemiologic results. They have been modified here for use with a broader array of 

data, i.e., epidemiologic, controlled human exposure, and animal toxicological studies, as well as in vitro data, and to be more consistent 

with EPA’s Guidelines for Carcinogen Risk Assessment. 

January 2010 1-12 
Table 1-1. Aspects to aid in judging causality. 

Consistency of the 

observed association 

An inference of causality is strengthened when a pattern of elevated risks is observed across several independent studies. The 

reproducibility of findings constitutes one of the strongest arguments for causality. If there are discordant results among 

investigations, possible reasons such as differences in exposure, confounding factors, and the power of the study are 

considered. 

Coherence 

An inference of causality from epidemiologic associations may be strengthened by other lines of evidence (e.g., clinical and 

animal studies) that support a cause-and-effect interpretation of the association. Evidence on ecological or welfare effects may 

be drawn from a variety of experimental approaches (e.g., greenhouse, laboratory, and field) and subdisciplines of ecology 

(e.g., community ecology, biogeochemistry and paleological/historical reconstructions). The coherence of evidence from 

various fields greatly adds to the strength of an inference of causality. The absence of other lines of evidence, however, is not a 

reason to reject causality. 

Biological plausibility. 

An inference of causality tends to be strengthened by consistency with data from experimental studies or other sources 

demonstrating plausible biological mechanisms. A proposed mechanistic linking between an effect and exposure to the agent is 

an important source of support for causality, especially when data establishing the existence and functioning of those 

mechanistic links are available. A lack of biologic understanding, however, is not a reason to reject causality. 

Biological gradient 

(exposure-response 

relationship) 

A well characterized exposure-response relationship (e.g., increasing effects associated with greater exposure) strongly 

suggests cause and effect, especially when such relationships are also observed for duration of exposure (e.g., increasing 

effects observed following longer exposure times). There are, however, many possible reasons that a study may fail to detect 

an exposure-response relationship. Thus, although the presence of a biologic gradient may support causality, the absence of 

an exposure-response relationship does not exclude a causal relationship. 

Strength of the observed 

association 

The finding of large, precise risks increases confidence that the association is not likely due to chance, bias, or other factors. However, given a truly causal agent, a small magnitude in the effect could follow from a lower level of exposure, a lower 

potency, or the prevalence of other agents causing similar effects. While large effects support causality, modest effects 

therefore do not preclude it. 

Experimental evidence. The strongest evidence for causality can be provided when a change in exposure brings about a change in occurrence or 

frequency of health or welfare effects. 

Temporal relationship of 

the observed association 

Evidence of a temporal sequence between the introduction of an agent, and appearance of the effect, constitutes another 

argument in favor of causality. 

Specificity of the observed 

association 

As originally intended, this refers to increased inference of causality if one cause is associated with a single effect or disease 

(Hill, 1965, 071664). Based on our current understanding, this is now considered one of the weaker guidelines for causality; for 

example, many agents cause respiratory disease and respiratory disease has multiple causes. At the scale of ecosystems, as 

in epidemiology, complexity is such that single agents causing single effects, and single effects following single causes, are 

extremely unlikely. The ability to demonstrate specificity under certain conditions remains, however, a powerful attribute of 

experimental studies. Thus, although the presence of specificity may support causality, its absence does not exclude it. 

Analogy 

Structure activity relationships and information on the agent’s structural analogs can provide insight into whether an association 

is causal. Similarly, information on mode of action for a chemical, as one of many structural analogs, can inform decisions 

regarding likely causality. 

 

Although these aspects provide a framework for assessing the evidence, they do not lend 

themselves to being considered in terms of simple formulas or fixed rules of evidence leading to 

conclusions about causality (Hill, 1965, 071664). For example, one cannot simply count the number 

of studies reporting statistically significant results or statistically nonsignificant results and reach 

credible conclusions about the relative weight of the evidence and the likelihood of causality. Rather, 

these important considerations are taken into account with the goal of producing an objective 

appraisal of the evidence, informed by peer and public comment and advice, which includes 

weighing alternative views on controversial issues. In addition, it is important to note that the aspects 

in Table 1-1 cannot be used as a strict checklist, but rather to determine the weight of the evidence 

for inferring causality. In particular, not meeting one or more of the principles does not automatically 

preclude a determination of causality (See discussion in CDC, 2004, 056384). 

1.6.5. Determination of Causality 

In the ISA, EPA assesses the results of recent relevant publications, building upon evidence 

available during the previous NAAQS review, to draw conclusions on the causal relationships 

between relevant pollutant exposures and health or environmental effects. This ISA uses a five-level 

hierarchy that classifies the weight of evidence for causation, not just association1

; that is, whether 

the weight of scientific evidence makes causation at least as likely as not, in the judgment of the 

reviewing group. In developing this hierarchy, EPA has drawn on the work of previous evaluations, 

most prominently the IOM’s Improving the Presumptive Disability Decision-Making Process for 

Veterans (2008, 156586), EPA’s Guidelines for Carcinogen Risk Assessment (2005, 086237), and the 

 

1

 It should be noted that the CDC and IOM frameworks use a four-category hierarchy for the strength of the evidence. A five-level 

hierarchy is used here to be consistent with the EPA Guidelines for Carcinogen Risk Assessment and to provide a more nuanced set of 

categories. 

January 2010 1-13 
U.S. Surgeon General’s smoking reports (CDC, 2004, 056384). In the ISA, EPA uses a series of five 

descriptors to characterize the weight of evidence for causality. This weight of evidence evaluation is 

based on various lines of evidence from across the health and environmental effects disciplines. 

These separate judgments are integrated into a qualitative statement about the overall weight of the 

evidence and causality. The five descriptors for causal determination are described in Table 1-2. 

Table 1-2. Weight of evidence for causal determination. 

Health Effects Ecological and Welfare Effects 

Causal 

relationship 

Evidence is sufficient to conclude that there is a causal relationship 

with relevant pollutant exposures. That is, the pollutant has been 

shown to result in health effects in studies in which chance, bias, and 

confounding could be ruled out with reasonable confidence. For 

example: a) controlled human exposure studies that demonstrate 

consistent effects; or b) observational studies that cannot be 

explained by plausible alternatives or are supported by other lines of 

evidence (e.g., animal studies or mode of action information). 

Evidence includes replicated and consistent high-quality studies by 

multiple investigators. 

Evidence is sufficient to conclude that there is a causal relationship 

with relevant pollutant exposures. That is, the pollutant has been 

shown to result in effects in studies in which chance, bias, and 

confounding could be ruled out with reasonable confidence. 

Controlled exposure studies (laboratory or small- to medium-scale 

field studies) provide the strongest evidence for causality, but the 

scope of inference may be limited. Generally, determination is based 

on multiple studies conducted by multiple research groups, and 

evidence that is considered sufficient to infer a causal relationship is 

usually obtained from the joint consideration of many lines of 

evidence that reinforce each other. 

Likely to be a 

causal 

relationship 

Evidence is sufficient to conclude that a causal relationship is likely to 

exist with relevant pollutant exposures, but important uncertainties 

remain. That is, the pollutant has been shown to result in health 

effects in studies in which chance and bias can be ruled out with 

reasonable confidence but potential issues remain. For example: a) 

observational studies show an association, but copollutant exposures 

are difficult to address and/or other lines of evidence (controlled 

human exposure, animal, or mode of action information) are limited 

or inconsistent; or b) animal toxicological evidence from multiple 

studies from different laboratories that demonstrate effects, but 

limited or no human data are available. Evidence generally includes 

replicated and high-quality studies by multiple investigators. 

Evidence is sufficient to conclude that there is a likely causal 

association with relevant pollutant exposures. That is, an association 

has been observed between the pollutant and the outcome in studies 

in which chance, bias and confounding are minimized, but 

uncertainties remain. For example, field studies show a relationship, 

but suspected interacting factors cannot be controlled, and other 

lines of evidence are limited or inconsistent. Generally, determination 

is based on multiple studies in multiple research groups. 

Suggestive of 

a causal 

relationship 

Evidence is suggestive of a causal relationship with relevant pollutant 

exposures, but is limited because chance, bias and confounding 

cannot be ruled out. For example, at least one high-quality 

epidemiologic study shows an association with a given health 

outcome but the results of other studies are inconsistent. 

Evidence is suggestive of a causal relationship with relevant pollutant 

exposures, but chance, bias and confounding cannot be ruled out. 

For example, at least one high-quality study shows an effect, but the 

results of other studies are inconsistent. 

Inadequate to 

infer a causal 

relationship 

Evidence is inadequate to determine that a causal relationship exists 

with relevant pollutant exposures. The available studies are of 

insufficient quantity, quality, consistency or statistical power to permit 

a conclusion regarding the presence or absence of an effect. 

The available studies are of insufficient quality, consistency or 

statistical power to permit a conclusion regarding the presence or 

absence of an effect. 

Not likely to 

be a causal 

relationship 

Evidence is suggestive of no causal relationship with relevant 

pollutant exposures. Several adequate studies, covering the full 

range of levels of exposure that human beings are known to 

encounter and considering susceptible populations, are mutually 

consistent in not showing an effect at any level of exposure. 

Several adequate studies, examining relationships with relevant 

exposures, are consistent in failing to show an effect at any level of 

exposure. 

For the CO ISA, determination of causality involved the evaluation of evidence for different 

types of health effects associated with short- and long-term exposure periods. In making 

determinations of causality for CO, evidence was evaluated for health outcome categories, such as 

cardiovascular effects, and then conclusions were drawn based upon the integration of evidence from 

across disciplines (e.g., epidemiology, clinical studies and toxicology) and also across the suite of 

related individual health outcomes. To accomplish this integration, evidence from multiple and 

various types of studies was considered. Response was evaluated over a range of observations which 

was determined by the type of study and methods of exposure or dose and response measurements. 

Results from different protocols were compared and contrasted. 

In drawing judgments regarding causality for the criteria air pollutants, EPA focuses on 

evidence of effects at relevant pollutant exposures. To best inform reviews of the NAAQS, these 

evaluations go beyond a determination of causality at any dose or concentration to emphasize the 

relationship apparent at relevant pollutant exposures. Concentrations generally within an order of 

magnitude or two of ambient pollutant measurements are considered to be relevant for this 

determination. Building upon the determination of causality are questions relevant to quantifying 

health or environmental risks based on our understanding of the quantitative relationships between 

pollutant exposures and health or welfare effects. While the causality determination is based 

primarily on evaluation of health or environmental effects evidence, EPA also evaluates evidence 

related to the doses or levels at which effects are observed. Considerations relevant to evaluation of 

quantitative relationships for health and environmental effects are summarized below. 

January 2010 1-14 
1.6.5.1. Effects on Human Populations 

Once a determination is made regarding the causal relationship between the pollutant and 

outcome category, important questions regarding quantitative relationships include: 

 What is the concentration-response, exposure-response, or dose-response relationship in 

the human population? 

 What is the interrelationship between incidence and severity of effect? 

 What exposure conditions (dose or exposure, duration and pattern) are important? 

 What populations appear to be differentially affected (i.e., more susceptible to effects)? 

To address these questions, the entirety of policy-relevant quantitative evidence is evaluated to 

best quantify those concentration-response relationships that exist. This requires evaluation of 

pollutant concentrations and exposure durations at which effects were observed for exposed 

populations, including potentially susceptible populations. This integration of evidence resulted in 

identification of a study or set of studies that best approximated the concentration-response 

relationships between health outcomes and CO, given the current state of knowledge and the 

uncertainties that surrounded these estimates. To accomplish this, evidence is considered from 

multiple and diverse types of studies. To the extent available, the ISA evaluates results from across 

epidemiologic studies that use various methods to evaluate the form of relationships between CO 

and health outcomes and draws conclusions on the most well-supported shape of these relationships. 

Animal data may also inform evaluation of concentration-response relationships, particularly relative 

to MOAs and characteristics of susceptible populations. Chapter 2 presents the integrated findings 

informative for evaluation of population risks. 

An important consideration in characterizing the public health impacts associated with 

exposure to a pollutant is whether the concentration-response relationship is linear across the full 

concentration range encountered or if nonlinear relationships exist along any part of this range. Of 

particular interest is the shape of the concentration-response curve at and below the level of the 

current standards. The shape of the concentration-response curve varies, depending on the type of 

health outcome, underlying biological mechanisms and dose. At the human population level, 

however, various sources of variability and uncertainty, such as the low data density in the lower 

concentration range, possible influence of exposure measurement error, and individual differences in 

susceptibility to air pollution health effects, tend to smooth and “linearize” the 

concentration-response function. In addition, many chemicals and agents may act by perturbing 

naturally occurring background processes that lead to disease, which also linearizes population 

concentration-response relationships (Clewell and Crump, 2005, 156359; Crump et al., 1976, 

003192; Hoel, 1980, 156555). These attributes of population dose-response may explain why the 

available human data at ambient concentrations for some environmental pollutants (e.g., PM, O3, 

lead [Pb], environmental tobacco smoke [ETS], radiation) do not exhibit evident thresholds for 

cancer or noncancer health effects, even though likely mechanisms include nonlinear processes for 

some key events. These attributes of human population dose-response relationships have been 

extensively discussed in the broader epidemiologic literature (Rothman and Greenland, 1998, 

086599). 

Publication bias is a source of uncertainty regarding the magnitude of health risk estimates. It 

is well understood that studies reporting non-null findings are more likely to be published than 

reports of null findings, and publication bias can also result in overestimation of effect estimate sizes 

(Ioannidis, 2008, 188317). For example, effect estimates from single-city epidemiologic studies have 

been found to be generally larger than those from multicity studies (Anderson et al., 2005, 087916) 

Although publication bias commonly exists for many research areas, it may be present to a lesser 

degree for epidemiologic studies on CO. In general, epidemiologic studies have focused on the 

effects of PM, and CO was largely considered as a potentially confounding copollutant of PM. Thus, 

CO effect estimates may have been presented in these studies regardless of the statistical significance 

of the results. 

Finally, identification of the susceptible population groups contributes to an understanding of 

the public health impact of pollutant exposures. In this ISA, the term “susceptible population” will 

be used as an overarching concept to encompass populations variously described as susceptible, 

January 2010 1-15 
vulnerable, or sensitive. “Susceptible populations” is defined here as those populations that have a 

greater likelihood of experiencing health effects related to exposure to an air pollutant (e.g., CO) due 

to a variety of factors including but not limited to: genetic or developmental factors, race, gender, 

lifestage, lifestyle (e.g., smoking status and nutrition) or preexisting disease; as well as populationlevel factors that can increase an individual's exposure to an air pollutant (e.g., CO) such as 

socioeconomic status [SES], which encompasses reduced access to health care, low educational 

attainment, residential location, and other factors. Epidemiologic studies can help identify 

susceptible populations by evaluating health responses in the study population. Examples include 

stratified analyses for subsets of the population under study or testing for interactions or effect 

modification by factors such as gender, age group, or health status. Experimental studies using 

animal models of susceptibility or disease can also inform the extent to which health risks are likely 

greater in specific population groups. Further discussion of these groups is presented in Section 5.7. 

1.6.5.2.

1.6.6. 

Effects on Ecosystems or Public Welfare 

Key questions for understanding the quantitative relationships between exposure (or 

concentration or deposition) to a pollutant and risk to ecosystems or the public welfare include: 

 What elements of the ecosystem (e.g., types, regions, taxonomic groups, populations, 

functions, etc.) appear to be affected, or are more sensitive to effects? 

 Under what exposure conditions (amount deposited or concentration, duration and 

pattern) are effects seen? 

 What is the shape of the concentration-response or exposure-response relationship? 

Evaluations of causality generally consider the probability of quantitative changes in 

ecological and welfare effects in response to exposure. A challenge to the quantification of exposureresponse relationships for ecological effects is the great regional and local variability in ecosystems. 

Thus, exposure-response relationships are often determined for a specific ecological system and 

scale, rather than at the national or even regional scale. Quantitative relationships therefore are 

available site by site. For example, an ecological response to deposition of a given pollutant can 

differ greatly between ecosystems. Where results from greenhouse or animal ecotoxicological 

studies are available, they may be used to aid in characterizing exposure-response relations, 

particularly relative to mechanisms of action, and characteristics of sensitive biota. 

Concepts in Evaluating Adversity of Health Effects 

In evaluating the health evidence, a number of factors can be considered in determining the 

extent to which health effects are “adverse” for health outcomes such as changes in lung function or 

in cardiovascular health measures. Some health outcome events, such as hospitalization for 

respiratory or cardiovascular diseases, are clearly considered adverse; what is more difficult is 

determining the extent of change in the more subtle health measures that is adverse. What constitutes 

an adverse health effect may vary between populations. Some changes in healthy individuals may 

not be considered adverse while those of a similar type and magnitude are potentially adverse in 

more susceptible individuals. 

For example, the extent to which changes in lung function are adverse has been discussed by 

the American Thoracic Society (ATS) in an official statement titled What Constitutes an Adverse 

Health Effect of Air Pollution? (2000, 011738). This statement updated the guidance for defining 

adverse respiratory health effects that had been published 15 years earlier (ATS, 1985, 006522), 

taking into account new investigative approaches used to identify the effects of air pollution and 

reflecting concern for impacts of air pollution on specific susceptible groups. In the 2000 update, 

there was an increased focus on quality of life measures as indicators of adversity and a more 

specific consideration of population risk. Exposure to air pollution that increases the risk of an 

adverse effect to the entire population is viewed as adverse, even though it may not increase the risk 

of any identifiable individual to an unacceptable level. For example, a population of asthmatics 

could have a distribution of lung function such that no identifiable individual has a level associated 

with significant impairment. Exposure to air pollution could shift the distribution such that no 

January 2010 1-16 
identifiable individual experiences clinically relevant effects. This shift toward decreased lung 

function, however, would be considered adverse because individuals within the population would 

have diminished reserve function and therefore would be at increased risk to further environmental 

insult. 

It is important to recognize that the more subtle health outcomes may be linked to health 

events that are clearly adverse. For example, air pollution has been shown to affect markers of 

transient myocardial ischemia such as ST-segment abnormalities and onset of exertional angina. In 

some cases, these effects are silent yet may still increase the risk of a number of cardiac events, 

including MI and sudden death. 

1.7. Summary 

This ISA is a concise evaluation and synthesis of the most policy-relevant science for 

reviewing the NAAQS for CO, and it is the chief means for communicating the critical science 

judgments relevant to that NAAQS review. It reviews the most policy-relevant evidence from 

atmospheric science, exposure, and health and environmental effects studies and includes 

mechanistic evidence from basic biological science. This final ISA incorporates clarification and 

revisions based on public comments and advice and comments provided by EPA’s CASAC on the 

first and second draft ISAs (Brain and Samet, 2009, 194669; Brain and Samet, 2010, 202840). 

Annexes to the ISA provide additional details of the literature published since the last review. A 

framework for making critical judgments concerning causality was presented in this chapter. It relies 

on a widely accepted set of principles and standardized language to express evaluation of the 

evidence. This approach can bring rigor and clarity to current and future assessments. This ISA 

should assist EPA and others, now and in the future, to accurately represent what is presently known 

and what remains unknown concerning the effects of CO on human health and public welfare. 

January 2010 1-17 
References 

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reference to epidemiologic studies of air pollution. Am Rev Respir Dis, 131: 666-668. 006522

ATS (2000). What constitutes an adverse health effect of air pollution? Official statement of the American Thoracic 

Society. Am J Respir Crit Care Med, 161: 665-673. 011738

Blair A; Burg J; Foran J; Gibb H; Greenland S; Morris R; Raabe G; Savitz D; Teta J; Wartenberg D; Wong O; Zimmerman 

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Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Information System (IRIS). 

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