1 US - EPA



## Approach to Assessing Non- cancer Health Effects from Formaldehyde and Benefits from

Reducing Non- cancer Health Effects as a Result of Im plementing Formaldehyde Emission Limits for Composite Wood Products2 

# Table of Contents **Title Page**  

## Section 1: Background and Scope **4 1.0 Background**  4 **1.1 Scope of the Document**  **5** 

## Section 2 **: Non -**  **cancer Health Effects**
**— Derivation of Concentration**  **- Response Functions**  **6 2.0 Background on the Identification of Non**  **- Cancer Health Effects**  6 **2.1 Sensory Irritation** 7 2.1.1 Sensory Irritation: Background 7 2.1.2 Sensory Irritation: Relevant Studies 7 2.1.3 Sensory Irritation: Derivation of Concentration
-Response Relationship for Eye Irritation 8 2.1.4 Sensory Irritation: Discussion of Uncertainties 14 **2. 2 Asthma** 1 6 2.2.1 Asthma: Background 16 2.2.2 Asthma: Relevant Studies 16 2.2.3 Asthma: Derivation of Concentration
-Response Relationship 17 2.2.4 Asthma: Discussion of Uncertainties 19 **2.3 Female Reproductive Toxicity** 21 2.3.1 Female Reproductive Toxicity: Background 21 2.3.2 Female Reproductive Toxicity: Relevant Studies 21 2.3.3 Female Reproductive Toxicity: Derivation of Concentration
-Response Relationship 22 2.3.4 Female Reproductive Toxicity: Discussion of Uncertainties 2 53

**2.4 References** 30 

## Section 3: Benefits Assessment **Approach 33**  **3.1 Introduction/Background** 33 3.1.1 General Model 33 3.1.2 Simplified Model 34 **3.2 Sensory Irritation** 3 7 3.2.1 Monetization 37 3.2.2 Annual approach 39 3.2.3 Example calculation 40 3.2.4 Discussion of uncertainties 46 **3.3 Asthma incidence** 4 7 3.3.1 Monetization 47 3.3.2 Annual approach 52 3.3.3 Example calculation 58 3.3.4 Discussion of uncertainties 62 **3.4 Red uced Fertility**  6 2 3.4.1 Monetization 6 2 3.4.2 Annual Approach 66 3.4.3 Example Calculation 6 8 3.4.4 Discussion of uncertainties 71 **3.5 References** 744



## Section 1: Background and Scope **1.0 Background**  In March 2008, the Sierra Club and other organizations and individuals petitioned EPA under Section 21 of the Toxic Substances Control Act (TSCA). The petitioners requested that EPA use TSCA Section 6 to adopt a newly-promulgated California Air Resources Board (CARB) Airborne Toxic Control Measure (ATCM) 1 as a national standard for formaldehyde emissions from hardwood plywo od, particleboard, and medium- density f iberboard products. The petitioners expressed particular concern over the levels of formaldehyde found in emergency housing provided for Hurricane Katrina survivors, but noted that there are no federal regulations on formaldehyde emissions from composite wood products other than those applicable to manufactured housing regulated b y the U.S. Department of Housing and Urban Development . In response to the petition, EPA announced in the Federal Register on Ju ne 27, 2008 (73 FR 36504) 2 that EPA would initiate a proceeding to investigate whether and what ty pe of regulatory or other action might be appropriate to protect against risks that may b e posed by

f ormaldehyde emitted from composite wood products. In December 2008, EPA published an Advance Notice of Proposed Rulemaking (ANPR) to publicly initiate the investigation and obtain stakeholder input (73 FR 73620). 3 On July 7 , 2010, the Formaldehyde Standards for Composite Wood Products Act (FSCWPA) was signed into law. This statute , which adds a Title VIto TSCA, establishes formaldehyde emission standards for hardwood plywood, particleboard, and medium- density f iberboard that are identical to the CARB ATCM Phase II standards. 4 TSCA Title VI directs EPA to promulgate implementing regulations by J anuary 1, 2013 that address: sell -through dates for products; stockpiling; third-party testing and certification; auditing and reporting of third-party certifiers; recordkeeping; chain of custody; l abeling; enforcement; products made with no - added formaldehyde (NAF) and ultra- low emitting formaldehyde (ULEF) resins; laminated products; finished goods; hardboard; and products containing de minimis amounts of composite wood products. In support of the implementing regulations, as with other rulemaking s that are determined to be “significant regulatory

action[s]” under Executive Order 12866

— Regulatory Planning and Review , EPA is required to conduct an economic analys is of the costs and benefits associated with the rulemaking. On June 2, 2010, EPA released a draft document titled Toxicological Review of Formaldehyde - Inhalation Assessment: InS upport of Summary Information on the Integrated Risk Information System (IRIS). 5 This draft IRIS assessment, which recently underwent independent scientific peer review by the National Academy o f Sciences (NAS) 6 , addresses both non- cancer and cancer human health effects that may result from chronic inhalation exposure to formaldehyde. However, this draft IRIS assessment does not include
1 Available at http://www.arb.ca.gov/toxics/comp wo od/com pwood.htm 2 Available at http://www.gpo.gov/fdsys/pkg/FR- 2008 - 06 - 27/pdf/E8 - 14618.pdf 3 Available at http://www.gpo.gov/fdsys/pkg/FR- 2008 - 12 - 03/pdf/E8 - 28585.pdf 4 Available at http://www.arb.ca.gov/toxics/comp wo od/com pwood.htm 5 Available at http://cfpub.epa.gov/ncea/iris_drafts/recordisplay.cfm?deid=223614 6 Available at http://www.nap.edu/catalog.php?record_id=131425

concentration-response functions for non- cancer health effects, which are needed for the benefits assessment. Therefore, this draft report titled Approach to Assessing Non
- cancer Health Effects from Formaldehyde and Benefits from Reducing Non
- cancer Health Effects as a Result of Implementing Formaldehyde Emission Limits for Composite Wo od Products (referred to hereafter as draft Approach Document) was developed, which provides discussion of several non- cancer endpoints as well as a discussion of the derivation of concentration-response functions, uncertainties, and EPA’s proposed approach for using the concentration-response functions in the benefits assessment. **1.1 Scope of the Document**  The economic analys is to support EPA’s implementing regulations under the TSCA Title VI will weigh the costs of complyingwith the regulations against the benefits to society. B enefits from both reduced cancer cases and non- cancer health effects will be assessed; however, this draft Approach Document does not include a discussion of EPA’s a pproach to assessing benef its from reduced cancer cases. As part of the economic analys is, EPA is also assessing inhalation exposure to formaldehyde. EPA has developed an indoor air model, which has already been peer reviewed, to assess exposure to formaldehyde in different ty pes of homes. EPA has also assessed occupational exposure to formaldehyde during resin manufacture, composite wood product manufacture, and manufacture of products made with composite wood (e.g., furniture), as well as exposure from wholesale and retail of composite wood products and products made with composite wood, and exposure to formaldehyde in office buildings. U s ing the indoor air model and the occupational exposure assessment, EPA is estimating formaldehyde inhalation exposure to the general population at baseline (prior to implementing the rule) and the change in exposure as a result of the rule. The occupational and general population exposure assessments are discussed in detail in other documents, which will be publicly available at www.regulations.gov when the proposed rule is published. This draft Approach Document uses preliminary e stimates from the exposure assessment as examples, but it does not describe how they w ere derived, as the derivation is covered in a separate document. The purpose of this draft Approach Document is to describe how EPA derived concentrationresponse functions for formaldehyde effects on sensory irritation, asthma, and female reproductive toxicity as well as the approach EPA will take to use these concentration-response functions to estimate benefits from the contribution of reduced non - c ancer effects as a result of the rule. This document describes in detail how the concentration-response functions were derived as well as uncertainties. This document also briefly describes the epidemiological studies that were used to derive the concentration-response functions ; however, a detailed analys is of these studies (as well as others) is included in the draft IRIS assessment for formaldehyde, which was already peer reviewed by the NAS, and therefore is not provided here. This draft Approach Document also describes how EPA plans to use the concentration-response functions to estimate benefits. It provides sample calculations but not the actual benefits assessment. The full benefits assessment will be provided in the economic analys is that will be publicly available (at www.regulations.gov) when the proposed rule is published.6



## Section 2: Non**- cancer Health Effects** 
**— Derivation of Concentration** **- Response Functions**  **2.0 Background on the Identification of Non**  **- Cancer Health Effects**  In 2010, EPA released a draft IRIS assessment of adverse cancer and non- cancer health effects from inhalation of formaldehyde (U.S. EPA, 2010). This draft identified non- cancer health effects for which quantitative concentration-response relationships were sought for the purpose of deriving a reference concentration (RfC), which represents an exposure level expected to be without an appreciable risk of deleterious effects in human, including susceptible populations, with little uncertainty (within an order of magnitude). The draft IRIS assessment has since been externally peer-reviewed by

the National Academy

of Sciences (NAS, 2011). This section is largely a recapitulation of portions of the text from EPA’s draft IRIS assessment specific to the identified non- cancer health effects, taking the external peer-review comments into consideration. The aim of the current effort is to describe the quantitative inhalation concentration-response relationship (i.e., the concentration-response function) for each of the identified non- cancer health effects, where possible, to inform the economic benefits assessment of potential economic welfare gains which could reasonably be expected to be attributable to decrements in formaldehyde exposures resulting from the propose d implementing regulations under FSCWPA. The following subsections describe the studies of non- cancer health outcomes which were judged in the draft IRIS assessment to be the most informative for candidate RfC derivation within health effect categories. These studies were judged to have been adequately conducted, and all were published in the peer-reviewed literature. The relative merits and limitations of these studies were evaluated with respect to the characteristics of the study p opulation, the quality and frequency

o f exposure measurements, and the exposure levels at which effects were observed. The studies identified in the draft IRIS assessment as the best available for candidate RfC derivation also represent the best available studies from which to quantitatively estimate the concentration-response relationship between inhalation exposures and non- cancer health effects for the purposes of the economic benefits assessment . Additional details on specific studies discussed here may

be found in the draft IRIS assessment (Chapters 4 and 5 of U.S. EPA, 2010 ) . Only the studies reviewed in the draft IRIS assessment were further evaluated for use in the concentration-response modeling in support of the economic benefits assessment. It should be noted that one of the comments in the NAS report was that it was not always clear how EPA selected and advanced studies for further consideration in RfC development. In two specific instances, the NAS peer-review report recommended not using particular studies for the derivation of candidate RfCs and, consequently, those studies were not used here to estimate quantitative concentration-response relationships. The draft IRIS assessment identified seven categories of health outcomes for potential RfC derivation including: 1) Sensory Irritation; 2) Upper Respiratory Tract Pathology ; 3) Pulmonary Function Effects; 4) Asthma and Allergic Sensitization (Atopy); 5) Immune Function Effects; 6)7

Neurological and Behavioral Toxicity; 7) Developmental and Reproductive Toxicity. From those categories, the draft IRIS assessment proposed RfCs based on four effects: sensory irritation, pulmonary

f unction effects, asthma and allergic sensitization (atopy) , and reproductive toxicity. The NAS supported the derivation of candidate RfCs for each of these four endpoints based on human epidemiologic data. For some non- cancer endpoints, it was possible to derive a candidate RfC for a particular health outc ome based on a no - observed- adverse - effect level (NOAEL) or a lowest- observed- adverse - effect level (LOAEL) , but those same data may not have supported a more fully characterized concentration-response function. At this time, only three of the health effect categories were judged to have sufficient data for quantitative concentration-response modeling in support of the economic benefits assessment: sensory irritation, asthma, and female reproductive toxicity . The available data on pulmonary f unction effects showing an association between increased formaldehyde concentrations and decreased peak expiratory

f low rates could not be advanced in this Approach document because it was not possible to link specific decrements in pulmonary function with specific economic costs. **2.1 Sensory Irritation** 2.1.1 Sensory Irritation: Background Formaldehyde, a chemical irritant, binds to protein receptors of the trigeminal nerve, triggering a burning and painful sensation in humans. This process is distinct from taste and smell (Nielsen 1991; Cometto - Muniz and Cain, 1992). S timulation of the trigeminal nerve may also result in reflex responses including lacrimation, coughing, and sneezing. B oth the reflex responses and sensations such as burning, pain, and itching of the eyes, nose, and throat are considered adverse. Formaldehyde - induced eye, nose, and throat irritation has been well documented in a wide range of epidemiologic and other human studies. C ommon effects of chemically induced sensory irritation include lacrimation, burning of the eyes and nose, rhinitis, burning of the throat, and cough (Feron et al., 2001). S tudies examining these endpoints were either controlled chamber studies with a defined population (e.g., healthy volunteers or sensitive individuals), worker/student studies, or general population studies (e.g., residential). Chamber studies, by design, are acute studies, although some researchers have investigated the outcomes after repeated exposures. O ccupational, student, and residential exposures are generally o f longer duration, although there is variability in exposure level and duration among subjects. The endpoints for assessing irritation include self -reporting of sy

mptoms (e.g., pain, burning, itching) and objective measures of irritation (e.g., eye -blink counts, lacrimation). 2.1.2 Sensory Irritation: Relevant Studies Eye irritation is the most sensitive of reported effects in human studies. Two different short-term chamber studies provide similar benchmark concentrations for 10% extra risk of eye irritation of 560 ppb and 240 ppb for 3 and 5 hour exposures, respectively (Kulle, 1993; Andersen and Molhave, 1983, modeled by Arts et al., 2006b). Various occupational studies have noted increased eye irritation for average exposures ranging from 180 ppb to 690 ppb ( e.g., Horvath et8

al., 1988, Alexandersson and Hedenstiera, 1988; Holmstrom and Wilhelmsson, 1988). The results of residential studies of the general population, where in-home formaldehyde levels are used as the exposure metric, indicate eye irritation may increase with increasing exposure from less than 100 ppb to more than 500 ppb for these chronic exposure scenarios (Ritchie and Lehnen, 1987, Hanrahan et al., 1984; Liu et al., 1991.) Details of the human studies reporting associations between formaldehyde inhalation exposures and sensory

irritation and a review of their findings are presented in Chapter 4 of the draft IRIS assessment of formaldehyde (U . S . EPA, 2010) along with experimental animal evidence supporting the plausibility o f these epidemiologic findings. As ynthesis of the most relevant findings and conclusions is also presented in Chapter 4 (U . S . EPA, 2010). Although exposure concentrations are well - defined in the chamber studies, the chamber studies are not appropriate for estimating the effects of chronic exposures because they are acute studies . The participants in the chamber studies also ty pically represent healthier individuals (although some studies do include asthmatics) and do not include either young or old people. The occupational studies also ty pically represent healthier individuals and do not include either young or old people , and both chamber and occupational studies involve higher exposure levels than occur in most residential settings, which are the focus of the formaldehyde rule. Additionally, occupational exposures often include short-term “peak” exposures of higher concentrations. Thus , the human epidemiology

studies of lower, chronic residential exposures are more relevant and convey l ess uncertainty than the acute chamber studies or the occupational studies and are preferred for the purposes of application in the economic analys is. Of the various independent sensory irritation effects for which one may wish to estimate economic benefits ensuing from the formaldehyde rule, only eye irritation had adequate quantitative data from residential studies for the derivation of a concentration-response function. 2.1.3 Sensory Irritation: Derivation of Concentration
-Response Relationship for Eye Irritation There are three studies that report sensory irritation in humans from chronic exposures in a residential environment and provide sufficient exposure data to support quantitative assessment (Ritchie and Lehnen, 1987; Hanrahan et al., 1984; Liu et al., 1991). E ach study reports site specific exposure measurements and presents some metric of individual exposure. These studies employ in-home measurements for each study participant, either as average exposure level in the home (Ritchie and Lehnen, 1987; Hanrahan et al., 1984) or as calculated cumulative exposure based on the time in the home (Liu et al., 1991). Eyeirritation is the most sensitive endpoint representing sensory

irritation and is reported at similar levels of residential formaldehyde exposure in the three studies (U.S. EPA, 2010, Figures 5 - 8 and 5 - 9). E ach study provides an estimated concentration-response relationship for prevalence of eye irritation in relation to inhome formaldehyde exposure based on individual - level data, including levels of exp osure below 100 ppb. Liu et al. (1991) additionally evaluated responses by

s eason (summer and winter). These studies generally assume a constant concentration-response over time which may have been attenuated due to acclimatization to relatively c onstant formaldehyde exposure levels in residential environments. It has been shown that short-term responses to formaldehyde exposures including reductions in pulmonary f unction, and irritation of the eyes, nose and throat can diminish over time with repeated episodic exposure (Kriebel et al., 1993; 2001). The9

detailed exposure information and chronic nature of the exposures support the selection of these residential epidemiology

studies for concentration- response modeling. While the concentration-response relation reported in Ritchie and Lehnen (1987) was consistent with those reported in the two other studies, it is possible that the Ritchie and Lehnen results were impacted by

p otential selection bias − owing to the need for a doctor’s note to be able to parti cipate; thus, these results may not accurately

describe the concentration-response in the general public and are not considered further here . The two remaining studies report eye irritation in humans from chronic exposures in a residential environment and provide sufficient exposure data to support quantitative assessment (Liu et al., 1991; Hanrahan et al., 1984). The Liu et al. (1991) residential study is a random- sample study o f over 1,000 mobile home residents across the State of California (1,394 in the summer; 1,096 in the winter) that included both young children (< 4 ye ars) and the elderly (> 65 ye ars). Cumulative weekly

exposures were based on in-home formaldehyde sampling and a participant survey o f time spent at home. Air sampling was conducted for a 7 - day p eriod using 2 passive samplers in each home ( for both summer and winter). One passive sampler was placed in the kitchen and one in the master bedroom; however, there was no significant difference in exposure levels between the two locations, so the two values were averaged to reflect the household concentration. The resulting estimates of cumulative exposure assumed no formaldehyde exposure outside of the home. The authors based their measure of exposure on the product of the measured concentration of formaldehyde in the home and the number of hours per week the individual spent in the home. Liu et al. (1991) reported that this measure of cumulative formaldehyde exposure was a significant predictor of numerous irritant sy mptoms in a multivariate linear logistic regression, including “burning eyes” ( p < 0.05). The highest prevalence rates were reported for those aged 20 −64 ye ars and the prevalence of eye irritation increased with increasing cumulative exposure in a categorical analys is of participants in this age group for both summer and winter exposure estimates , as shown in Figure 1 below (U.S. EPA, 2010, Figure 5 - 9). Liu et al. (1991) presented results for three categorical levels of cumulative exposure. The lowest exposure group extended from 0 to 7.0 ppm × hours/week and did not constitute a ‘nonexposed’ reference group but rather a comparative lower exposure level. The mid- level exposure group encompassed exposure from 7.0 to 12.0 ppm × hours/week and the high exposure group covered exposure greater than 12.0 ppm× hours/week. Liu et al . reported that the average concentration of formaldehyde in the homes was 88 ppb in the summer and 89 ppb in the winter. The range of cumulative exposures extended from 0 to 53.4 ppm × hours in the summer and 0 to 40.9 ppm × hours in the winter, with respective average cumulative exposures of 9.8 ppm × hours and 9.9 ppm × hours . D ividing the average 7 - day cumulative exposures reported by Liu et al. (1991) by the average concentrations, the average time spent in the homes can be estimated as approximately

1 11 hours (about 16 hours per day). The prevalence of eye irritation was above 10% in the lowest exposure group (0

–7.0 ppm × hours/week) and increased to 17.1% in the midexposure group (7.0 −12.0 ppm × hours/week) and to 21.4 % in the high- exposure group (12.0 ppm × hours/week) in the summer survey time; winter rates were slightly l ower but showed a similar increase with increasing cumulative exposure.10 13.3% 17.1% 21.4%

10.8% 14.7% 20.6%

0% 5% 10% 15% 20% 25%

< 7 7 - 12 > 12 **Formal dehy** **de Exposure (ppm-hr per w eek)** **P r** **e v** **a l** **e n** **c e** **o f** **b u** **r n** **i n** **g /** **t e** **a r** **i n** **g e** **y e** **s (** **% )**  Su mme r Winter

**Figure 1.**  See Figure 5 - 9 U.S. EPA (2010). P ositive concentration-response relationships reported for in-home formaldehyde exposures and sensory irritation (burning eyes). Cumulative formaldehyde exposure was estimated for each participant from measured in-home formaldehyde levels (7 - day

p assive air sample s ) and reported hours spent in the home . Prevalence rates are given for the 20 - 64 ye ar- old age group for both summer (n = 739 ) and winter (n = 587 ) survey periods. Adapted from Liu et al. (1991). While these data are not well suited to developing continuous concentration-response relationship s because there are only three categories of exposure and the distributions of exposures and times spent indoors within each category are unknown, this study

does provide the most specific information on the relative timing of exposure to formaldehyde and the occurrence of sensory

irritation sy

mptoms , which is important for the economic benefits assessment. The described methodology

states that the sy

mptoms were rep orted for a two -week period that included the week preceding and the week of exposure monitoring . K nowledge of the timing of exposure is an important aspect for the application of a concentration-response function to inform the economic benefits assessment, and the data from the paper by Liu et al. (1991) provide supportive information on the shape of the concentration-response function as well as an estimate of the general prevalence of sensory irritation in a highly

relevant population. Hanrahan et al. (1984) reported a concentration-response relationship for burning eyes in a study of 61 teenage and adult residents of mobile homes in Wisconsin . Note that ocular discomfort was recorded in two related ways as ‘eye irritation’ and as ‘burning eyes’; however, only the concentration-response relationship for ‘burning eyes’ was reported and this endpoint was used by EPA to represent eye irritation more broadly. In-home formaldehyde measurements were obtained for all participants, and measured formaldehyde levels (average of two approximately 1 -hour air samples

– one from the kitchen or living room and one from a bedroom) were used to characterize average in-home exposures. M easured residential formaldehyde concentrations11

ranged from less than 100 ppb to 800 ppb. The median (geometric mean) was reported as 160 ppb. No information was provide d on the arithmetic mean or standard deviation of the indoor measurements . However, the standard deviation of the outdoor measurements was reported as 30 ppb and effectively serves as a lower bound on the standard deviation of the indoor concentrations be cause indoor levels are generally considerably higher and more variable than outdoor levels. Given the information provided by H anrahan et al. (1984) on the indoor geometric mean and a lower bound estimate on the indoor arithmetic standard deviation, the percentages of indoor formaldehyde measurements below 100 ppb and below 50 ppb can be estimated . U sing an estimated expected value (arithmetic mean) of 163 ppb based on an arithmetic standard deviation of 30 ppb [for log-normally distributed concentrations , the arithmetic mean is a function of the geometric mean and the standard deviation], and an assumption that the measured concentrations were log-normally distributed, 44% of actual residential measurements are estimated to have been below 100 ppb with 36% below 50 ppb. If the actual, but unreported, standard deviation of the indoor measurement s was greater than 30 ppb, then these percentage s would be expected to increase. This information is important , as it shows that the underlying residential exposure data which inform the concentration-response relationship are relevant to concentrations modeled in the exposure assessment for use in the economic analysis . Different distributional assumptions would produce different estimates of the proportion of measured samples which were below 50 ppb, but, under the assumption of a lognormal distribution, which is common for exposure estimates, at least one third of the measurements informing the concentration-response function were in the range that is most applicable to the economic analys is. Hanrahan et al. (1984) reported that eye irritation was associated with in-home formaldehyde exposures (

p < 0.05) (both as “burning eyes” and “eye irritation”), and the authors provided a graphical representation of the best- fitting logistic regression model results of predicted prevalence of “burning eyes” for exposures from 100 to 800 ppb. From inspection of this graph (shown here as Figure 2), EPA determined the prevalence of eye irritation predicted at 100 ppb is approximately

4 %. Although, as discussed above, a large percentage (44%) of the indoor formaldehyde measurements were estimated to have been below 100 ppb, the predicted model results were simply not shown below 100 ppb. At the time of pub lication, the World H ealth Organization standard was 300 ppb and the central conclusion of Hanrahan et al. ( 1984) was that indoor formaldehyde levels below 100 ppb would lower the risk of sensory irritation for most residents. Nonetheless, it is possible to approximate the functional form of the concentrationresponse relationship below 100 ppb from the graphical results provided in the plot from Hanrahan et al. (1984) presented below in Figure 2 because a single functional form was used to plot the results shown and reconstruction of that form can show the results of the Hanrahan et al. (1984) model where they were omitted below 100 ppb .12

**Figure 2.**  See Figure 5 - 8 U.S. EPA (2010). Regression of prevalence of ”burning eyes” versus indoor formaldehyde concentration (ppm) in mobile homes ( approximately 1 -hour air sample s ). Dashed lines show upper and lower 95th percentile confidence intervals on model results. Model based on reported eye irritation from individuals in 42 mobile homes. Adapted from Hanrahan et al. (1984). Inthis figure, the dependent variable is displayed as predicted % prevalence of eye irritation. However, the method used by H anrahan et al. (1984) to model prevalence was logistic regression, which predicts the log odds of prevalence, which the authors then transformed to prevalence for graphing. In order to describe the underlying functional form of the results displayed, EPA converted the prevalence data back to prevalence odds. T able 1 shows the prevalence values which EPA visually estimated from the plot as well as the associated prevalence odds, which EPA calculated as estimated prevalence divided by the complement of estimated prevalence, that is p/(1 -p). F igure 3a plots the estimated prevalence of eye irritation against the residential concentration of formaldehyde. F igure 3b plots the estimated prevalence odds against the residential concentration of formaldehyde. **Table 1. Concentration -**  **response information extracted from Hanrahan et al. (1984).**

Residential Formaldehyde Concentration (ppb) Prevalence (p) Prevalence Odds

(p/[1 -p]) 100 0.0375 0.039 200 0.175 0.212 300 0.35 0.538 400 0.52 1.08 500 0.66 1.86 600 0.725 2.64 700 0.8 4 800 0.85 5.6713

**Figure 3a & b.** Plot of residential concentration-response information from Table 1. Using MS Excel® , the EPA fit a simple trend line to the discrete prevalence odds data in Figure 3b and not the continuous raw data and found that a power function could be fit which yielded an R2 value of 0.9997. This indicates nearly a p erfect fit. A similar fit would not have been achieved from analys is of the raw data (unavailable), but the objective here was to recreate the functional form of the modeled data presented by H anrahan et al. (19 84). Note that this power function will asy

mptotically

a pproach zero for a zero formaldehyde exposure concentration. The following describes the functional form for the prevalence odds:

= 7 ∗ 10 ∗ ( ) . (2 - 1) Solving for prevalence (

p ) itself:



### = ∗ ∗ ( ) . [ ∗ ∗ ( ) . ] (2 - 2) Figure 4 a shows the predicted prevalence odds from the functional form above overlaid on the prevalence odds estimates extracted from Hanrahan et al. (1984). This concentration-response function is clearly

c onsistent with the reported model results and provides an estimate of the concentration-response relationship below 100 ppb residential exposure, in the region of exposures of interest for the economic benefits analysis. Figure 4b shows the same plot with greater focus on the lower range of the concentration-re sponse relationship.14

**Figure 4**  **a .**  Relationship between residential formaldehyde exposures and the predicted prevalence of sensory

irritation (burning eyes) as report by H anrahan et al. (1984) [red squares] and the continuous concentration-response relationship as estimated by EPA.

**Figure 4b**  **.** Relationship between residential formaldehyde exposures and the predicted prevalence of sensory

irritation (burning eyes) as report by H anrahan et al. (1984) [red square ] and the continuous concentration-response relationship as estimated by EPA for formaldehyde concentration below 150 ppb . 2.1.4 Sensory Irritation: Discussion of Uncertainties Both studies (Liu et al., 1991; Hanrahan et al., 1984) have the strength of having individual inhome exposure measurements, and each demonstrates a positive concentration-response relationship for eye irritation within a range of residential formaldehyde exposures ( for both conventional and mobile homes). The assessment of formaldehyde concentrations in these studies were by

two different methods. Liu et al. (1991) measured formaldehyde concentration using passive monitors over seven days. Hanrahan et al. (1984) based their exposure assessment on short-term samples taken over approximately one hour. S hort-term sampling is a less precise methodology

and the resulting measures can be reasonably expected to include a greater degree of exposure measurement error than those from week- long passive monitoring. The short-term15

nature of the exposure sampling in Hanrahan et al . (1984) is a limitation; however, the results in Liu et a. (1991) based on the stronger exposure assessment methodology ( although still prone to some measurement error) are well aligned and strengthen the validity o f the Hanrahan et al. (1984) results. One weakness of these studies was that the assessment of sensory irritation was conducted by surveys and not objectively assessed by

trained observers of physiological measurements. While it was considered a common strength that these studies did have in-home measurement of formaldehyde exposures in temporal proximity to the assessment of irritation, those measurements were of relatively short sampling duration and they do not reflect daily fluctuations which may

include relative peak exposures . B oth studies were based on randomly selected people representative of the general population , although the study s ize in Hanrahan et al. (1984) was small with just 61 participants. Both studies did control for major potential confounders but did not measure and control for all the factors that could potentially b e confounders. Liu et al. (1991) controlled for the potentially confounding effects of age, gender and smoking as well as controlling for chronic respiratory and allergy

problems. The analysis by H anrahan et al. (1984) controlled for age, gender and smoking , and the authors reported that the presence of either smokers or gas appliances was not associated with indoor concentrations of formaldehyde, making those factors unlikely confounders. Nonetheless, there is always the potential for other possible confounders , such as molds or other volatile organic compounds , to have played a role in the observed concentrationresponse relations described by Liu et al. (1991) and H anrahan et al. (1984). No data on these potential confounders were noted in these papers, but the reported concentration-response relationships are strong evidence of a formaldehyde effect and, for these reported relationships to have been the spurious product of confounding, those unmeasured confounding factors would need to have been strongly associated with both eye irritation and the concentrations of formaldehyde. Both studies support a finding of increased eye irritation with increasing exposures to formaldehyde at concentrations starting below 100 ppb. Liu et al. (1991) reported an average concentration of 88 ppb, with measured concentrations from 10 ppb (the limit o f detection) to 460 ppb. H anrahan et al. (1984) reported exposures ranging from less than 100 ppb to 800 ppb, with a median of 160 ppb. While it is not clear from the published report what the distribution of exposures below 100 ppb was, if it can reasonably b e assumed that the formaldehyde concentrations were log-normally distributed with median of 160 ppb and a standard deviation of 30 ppb (based on the reported standard deviation from the outdoor measurements), then it would be expected that about 44% of the measured indoor samples were below 100 ppb, with 36% below 50 ppb. Given that the measured indoor levels were likely to have been more variable than the reported outdoor levels, the true indoor standard deviation would likely have be en higher than 30 ppb and, consequently, the percentages below 100 ppb and below 50 ppb would have been greater . The results as graphed in Hanrahan et al. (1984) did not show the shape of the concentrationresponse curve below 100 ppb even though the study had exposure data at these exposure levels. EPA was able to derive a concentration-response function that closely a pproximated the results from Hanrahan et al. (1984) and, from this functional form, to infer the shape of the published16

concentration-response function be low 100 ppb

– a range EPA considers to encompass at least 44% of the indoor measurements. Thus, while there is clearly uncertainty in estimating effects below 100 ppb residential exposure , these effects can be estimated from the published concentration-response curve . Re -modeling of the reported results from Hanrahan et al. (1984) provided the best available estimate of the adverse response below 100 ppb , and this concentration-response function can be used in the economic benefits assessment. These studies provided a reasonable basis for quantitative concentration-response modeling, with Liu et al. (1991) supporting the modeling based on Hanrahan et al. (1984). Additionally, the study p opulations have been drawn from the general population, including children and the elderly, and have not been limited to those healthy enough for full -time employ ment (as is often the case in occupational cohorts). The results in these studies are further supported by the consistent findings of an extended concentration-re sponse relationship reported in some nonresidential studies at higher levels of exposure. F or example, Kriebel et al. (2001) reported that increasing formaldehyde exposures were positively associated with sy

mptoms of eye irritation (p<0.001) among anatomy students with measured exposure concentrations having a geometric mean of 700 ppb but with 25% of exposures below 300 ppb. **2. 2 Asthma** 2.2.1 Asthma: Background Asthma is a chronic respiratory disease characterized by inflammation of the airways. Sensitization to inhalational chemical exposure may manifest as an allergic or asthmatic response that is characterized by

bronchial constriction or bronchial hy per-responsiveness. This may b e a result of immune involvement, as in the case of hy persensitivity, or a neurogenic sensitization, where a chemical may

directly stimulate inflammation. In the United States, the prevalence of asthma is common. The Centers for Disease Control and Prevention estimated that in 2005 approximately

9 % of children had asthma (6.5 million children) and that the prevalence rate of asthma in children had more than doubled from 1980 to the mid- 1990s and has remained at historically

high levels (Akinbami, 2006). In 2009, it was estimated that nearly 10% of children had asthma (CDC, 2009). 2.2.2 Asthma: Relevant Studies Multiple epidemiologic studies have reported associations between formaldehyde inhalation exposures and childhood asthma. D etails of these epidemiologic studies and a review of their findings are presented in Chapter 4 of the draft IRIS assessment of formaldehyde (U . S . EPA, 2010) along with experimental animal evidence supporting the plausibility o f these epidemiologic findings. Asynthesis of the most relevant findings and conclusions is presented also in Chapter 4 (U . S . EPA, 2010). The study

that is most relevant for developing a concentration-response function for use in the economic benefits assessment is the meta- analys is of formaldehyde - associated asthma conducted by M cGwin et al. (2010) based on seven peer- reviewed studies providing quantitative17

concentration-response results for children. All of the studies were observational in nature and were predominantly

cross - sectional or case - control in design. The mean ages of the children in the seven studie s ranged from 1.9 ye ars up to 14.3 ye ars. Details of the individual studies were provided by

the authors in their Table 1 reproduced below (see Table 2 of this document). For the meta- analys is, odds ratios (ORs) and confidence intervals (CIs) were abstracted from the studies , and effect estimates were standardized to OR per 10 μg/m3 increase in formaldehyde concentration. Funnel plots were used to assess publication bias and did not show such a bias (McGwin et al., 2010). F ixed- and random- effects model s were used to calculate pooled ORs and 95% CIs following a test of heterogeneity. Af ixed- effect model assumes that all the individual studies provided estimates of the same effect or slope, while the random- effects model allows for different effects or slopes in the source studies that may reflect differences in baseline risk factors within the study p opulations. McGwin et al. (2010) preferred the fixed- effect model when heterogeneity

was low and the random- effects model when the data were more heterogeneous. Both models were presented, as the degree of heterogeneity, measured by the Q test and the I2 statistic, indicated the presence of low to moderate heterogeneity, depending on the analys is . The reference d Q -test is also known as Cochran’s Q and is a classical measure of heterogeneity

c alculated as the sum of the weighted squared differences between the individual study e ffects and the pooled effect across studies, where the weights are those used in the pooling method. The I2 statistic described the percentage of variation across studies that is due to heterogeneity rather than chance and is computed as 100%*(Q

– degrees of freedom)/Q. Table 2. Reproduction of McGwin et al. (2010) Table 1: Summary o f studies selected for inclusion in meta- analys is.

2.2.3 Asthma: Derivation of Concentration
-Response Relationship Of the seven studies that were included in the McGwin et al. meta- analysis, six reported increased risks of asthma associated with exposure to formaldehyde. The results of the randomef fects model showed an overall effect estimate of OR = 1.17 per 10 μg/m3 formaldehyde (95% CI : 1.01 - 1.36) (see U . S . EPA , 2010: Figure 4 - 2). The three studies with the highest statistical weights based on the inverse of the variance of the study ORs were the studies by Rumchev et al. (2002), Garrett et al. (1999) and Krzyzanowski et al. (1990). Higher weights reflect the narrower confidence intervals in these studies, which implied that the studies were able to yi eld more precise effects estimates, warranting greater weight in the meta- analys is. Note that the two18

results from the Zhao et al. (2008) study, including the school results, which showed a health protective effect of formaldehyde, had such large confidence intervals that their weights in the meta- analys is were virtually zero.

Source: McGwin et al. (2010) , Fig. 2 . McGwin et al. (2010) noted that the study b y Rumchev et al. (2002) may have had ‘undue influence on the study

data’ and recomputed the fixed- and random- effects model results without that study . They

also suggested that one aspect of Rumchev et al. (2002) which was unique among the seven studies was the focus on very young children. According to the National Heart Lung and Blood Institute, a diagnosis of asthma is most reliable when it i s based on a physical exam, a medical history

and a test of pulmonary f unction. However, conducting lung function tests on young children is difficult and doctors may rely on signs and sy

mptoms rather than spirometry

in making diagnostic decisions (U.S. HHS, 2007 ). In the peer-review report of the draft IRIS formaldehyde assessment (U . S . EPA, 2010), the NAS (2011) noted that many of the studies of childhood asthma in the epidemiologic literature were decades old and that the understanding of asthma was not characterized as well then as it is now. The NAS report (2011) noted that while Rumchev and coauthors (2 002) interpreted the health outcome in their study as a diagnosis of asthma, study p articipants of such young ages are now thought to have been more likely to have actually had lower respiratory i llnesses with wheezing of an infectious etiology, a diagnosi s which could often have been mislabeled as asthma. For these reasons, the results from the Rumchev et al. (2002) study are not included in the approach proposed here to estimate the concentration-response relationship for asthma-related formaldehyde expo sures. Excluding Rumchev et al. (2002) from the meta- analys is, McGwin et al. (2010) reported the summary me asure of the concentration-response relationship based on the random- effects model as OR = 1.24 per 10 μg/m3 formaldehyde (95% CI : 1.07 - 1.45) with a p - value of 0.0026, while for the fixed- effect model they reported an OR = 1.24 (95% CI : 1.09 - 1.42) with a p -value of19

0.0013. Without including Rumchev et al. (2002), the heterogeneity statistics, Q and I2 , were 6.76 and 11. 3 , respectively, which indicate low heterogeneity . C onsistent with the low heterogeneity, both models estimated a similar magnitude of effect (the same OR estimate in this instance) but with the fixed- effect estimate having a narrower confidence interval. This concentration-response relationship (OR = 1.24 per 10 μg/m3 ) is recommended for application in the economic benefits assessment for the formaldehyde rule. The units of this concentration-response relationship can be converted to show the effects per part per billion (ppb) of for

maldehyde. C onverting from μg/m3 to ppb depends on the molecular weight of formaldehyde, which is 30.03 g/mol. The formula to convert concentrations in air (at 25°C) from μg/m3 to ppb is: ppb = (μg/m3 ) × (24.45 L) / (gram molecular weight of the compound). The number 24.45 in the equations above is the volume (liters) of a mole (gram molecular weight) of a gas or vapor when the pressure is at 1 atmosphere (760 torr or 760 mm Hg) and at 25°C. F or formaldehyde, 1 μg/m3 = 0.814 ppb. Therefore, the effect estimate from McGwin et al. (2010) of OR = 1.24 per 10 μg/m3 is equivalent to OR = 1.24 per 8.14 ppb. This odds ratio represents the exponentiated linear slope (beta) derived from the fixed- effect regression model. In order to rescale this effect to one ppb, the slope (beta) should be rescaled to represent the change in response per unit increase in exposure. The natural log of 1.24 is 0.215 and this represents the slope for each 8.14 ppb increase in exposure. D ividing by 8.14 yi elds a slope (beta) per one ppb increase in exposure of 0.0264. Exponentiating back to the odds ratio scale, the rescaled OR = 1.027 per one ppb increase in formaldehyde exposure. The variance of the rescaled OR based on the random- effects model and the associated 95% CI can also be scaled by the same methods. This OR = 1.027 per one ppb increase in formaldehyde exposure has a 95% CI : 1.008 - 1.047. The p - value for the rescaled OR remains unchanged (p=0.0026). Note that this function is in terms of residential or (in- school) exposure levels. 2.2.4 Asthma: Discussion of Uncertainties As noted by

the NAS (2011), the health endpoints described as asthma constitute a broad range of respiratory

e ffects that differ in cause, and the definition of asthma has changed over time, as the asthma phenotype has become better characterized. The meta- analys is included studies from a wide geographic range, and there are some inconsistencies in the studies included in the metaanalys is, including different effect sizes across different exposure ranges. The studies that contributed to the McGwin et al. (2010) meta- analysis span a time period from 1990 to 2008 and include both self

-reported and physician- diagnosed asthma. Due to this heterogeneity of location, effect size, exposure range, calendar time and diagnostic criteria, there is some uncertainty in summarizing all these findings in a single summary c oncentration-response measure as representative of the effect on asthma. However, once the study b y Rumchev et al. (2002) was excluded from the meta- analysis, McGwin et al. (2010) did not observe that the various findings were heterogeneous in the magnitude of the effect estimates. This observation might mitigate some potential uncertainty regarding the different ways in which the related health endpoints may

have been defined. This appears to have been further substantiated by additional random- effects models which were fit by M cGwin et al. (2010) based on diagnostic criteria

— studies in which the ORs were for self - reported asthma yi elded a summary O R = 1.26 (95% CI : 0.97 −1.64), which was very similar to the overall summary OR = 1.24.20

In addition to differences in the calendar ye ar of each source study in the meta- analysis, there were also differences in the exposure settings and in the range of exposure values. Some studies were based on measured residential exposures, while others were based on exposures measured in schools. B oth exposure settings appear to be relevant for the economic benefits assessment approach proposed here. It also appears that the range of exposures in the studies on which the concentration-response function f or formaldehyde - associated asthma was based is appropriate for use in the economic analysis described in this document. Exposure concentrations in the source studies for the meta- analys is ranged from 1 μg/m3 up to more than 87.5 μg/m3 (1 ppb to 70+ ppb). Another difference among the studies was the differing levels of adjustment for potential confounders. M cGwin et al. (2010) noted that some of the source studies provided adjusted estimates but others did not. Thus, they stated that their pooled results may besubject to residual confounding. This is a weakness that reflects the limitations of the data available for the metaanalys is. However, a strength of the McGwin analysis is that it incorporated different studies from different investigators in different countries over different time frames. S uch diversity c an be considered a strength since if there were any operative biases in the study design, such as confounding, they

would be less likely

to be replicated throughout all the studies in the same fashion. Finally, these data describe the effects in children but do not provide information on potential effects among adults. A s the onset and diagnosis of asthma is more common before a person reaches the age of 18 ye ars, many fewer persons would be expected to develop asthma and be diagnosed in adulthood. While the identified concentration-response relationship discussed here is limited to children, any p otential adult- onset asthma incidence that is associated with formaldehyde exposure is considered to be relatively small. Excluding the study by R umchev et al. (2002), the range of the mean ages of the participants in the included studies was from 8.1 to 14.3 ye ars and is considered to be appropriate for application to all but the youngest children in the economic analys is. In summary, the meta- analysis results in McGwin et al. (2010) estimated an increased risk of asthma among exposed children for incremental increases in formaldehyde inhalation concentration. While the effect estimates from the f ixed- effect modeling and the random- effects modeling were equivalent and the lack of appreciable heterogeneity led McGwin et al. to select the fixed- effect result, the observation of some heterogeneity and the remaining uncertainties in the individual studies support the selection of the random- effects result, with its wider confidence interval. The magnitude of this effect was reported by M cGwin et al. (2010) as an odds ratio of 1.24 per 10 μg/m3 increase in formaldehyde concentration (95% CI1 .09

– 1.42) with a p -value of 0.0026 for the random- effects analysis. Scaling this finding to parts per billion, the odds ratio is 1.027 per ppb increase in formaldehyde concentration (95% CI : 1.008 - 1.047) with p -value of 0.0026. **2.3 Female Reproductive Toxicity** 2. 3.1 Female Reproductive Toxicity: Background21

Although the available database for assessment of the effects of formaldehyde exposure on the female reproductive system was limited, epidemiologic studies suggest an association between occupational exposure to formaldehyde and adverse reproductive outcomes in women. S everal of these studies examine d the risks of spontaneous abortion following maternal occupational formaldehyde exposure with increased relative risk estimates for those exposed versus those unexp osed in the range of 1.7 to more than 3.0 (Taskinen et al., 1999, 1994; John et al., 1994; Seitz and Baron, 1990; Hemminki et al., 1985, 1982; Axelsson et al., 1984). However, other studies (Hemminki et al., 1985, 1982) did not identify an association between occupational formaldehyde exposure and spontaneous abortion. Ag eneral weakness of these studies is the lack of rigorous exposure assessment, which could diminish the power to detect a true association and also limit the ability o f the studies to appropriately c ontrol for any

p otential confounders. Nonetheless, these studies of formaldehyde exposure are generally consistent in their reporting of adverse reproductive effects, an association similar to other air pollutants such as particulate matter ( Glinianaia et al., 2004a; Glinianaia et al., 2004b) and reactive gaseous air pollutants such as nitrogen oxides, sulfur dioxide, carbon monoxide (Lacasana et al., 2005; Maisonet et al. 2004; Sram et al., 2005). Details of the epidemiologic studies reporting associations between formaldehyde inhalation exposures and reproductive and developmental outcomes and a review of their findings are presented in Chapter 4 of the draft IRIS assessment of formaldehyde (U . S . EPA, 2010) along with supportive experimental animal evidence of these epidemiologic findings. Asynthesis of the most relevant findings and conclusions is also presented in Chapter 4 (U . S . EPA, 2010). 2.3.2 Female Reproductive Toxicity: Relevant Studies On review of the developmental and reproductive toxicity studies in humans and laboratory animals in the draft IRIS assessment (U . S . EPA, 2010), the Taskinen et al. (1999) Finnish retrospective cohort study w as considered to represent the best available study evidence of female reproductive toxicity for the purpose of deriving a concentration-response relationship for use in the economic benefits assessment. The subjects, recruited from a woodworkers’ union and other businesses involving wood processing, were linked to a national register of births. Women were included if they were born between 1946 and 1975, had a live birth at age 20 −40 ye ars during 1985 −1995, had worked in the wood processing industry f or at least 1 month, and had first employ

ment in the wood processing industry b eginning at least 6 months before the index pregnancy . The first pregnancy that fulfilled the above criteria was the index pregnancy. There were 1,094 women with these criteria. Information about personal characteristics, pregnancies, and exposures were collecte d from mailed questionnaires for which the response rate was 64%. After other exclusions (primarily infertility history, unknown time to pregnancy, and contraceptive failure), the final sample included 602 women. Estimates of mean daily exposure to formal dehyde were based on measurements taken at the women’s factories of employ ment during the early 1990s. Where measurements were unavailable, measurements from equivalent industries were used. An exposure index representing a time -weighted average exposure (TWA) was established for every p erson in the study b ased on the concentration of their workplace formaldehyde multiplied by the proportion of the workday

exposed to formaldehyde . The investigators categorized the time -weighted22

average formaldehyde exposure into categories of low (mean of 18 ppb), medium (mean of 76 ppb), and high (mean of 219 ppb) exposure. P ersonal protection equipment included gloves and respirators. T askinen et al. (1999) reported that 40% of the women noted that they always wore gl oves, with 19% wearing them occasionally, and that respirators were rarely used (4 - 6%). Time -to -pregnancy

data were analyzed by

a discrete proportional hazards regression procedure with a fecundability

density ratio (FDR) as the outcome. In this method, a time scale (typically age or calendar time) is divided into discrete units to allow for the comparison of exposures and events within narrow time periods thereby e ffectively

c ontrolling for time. T askinen et al. (1999) do not state explicitly that age was used as the timescale in the proportional hazards regression analys es but this is standard practice and would effectively control for age by design. The FDR compares the average incidence density of pregnancies for exposed women with that of the employe d, unexposed women. A s explained by T askinen et al. (1999), an FDR significantly below unity

suggests that conception was delayed

– and indicates reduced fertility . The employ ment- , smoking- , alcohol consumption- , number of children- , and menstrual irregularityadjusted FDR was 0.64 (95% CI : 0.43 −0.92) for women exposed to high formaldehyde levels compared with the unexposed controls, indicating that there was a substantial, and statistically significant, delay

in time to conception in this group of women . At the medium exposure, there was a smaller adverse effect on fecundity that appeared to be concentration - dependent. The investigators further conducted an analys is of the risk of spontaneous abortion , which also showed a trend towards increased risk associated with increased air concentrations of formaldehyde . The risk of spontaneous abortion was reported to be elevated with formaldehyde exposure in the low exposure group (OR = 2.4 [95% CI : 1.2 −4.8]), in the medium exposure group (OR = 1.8 [95% CI : 0. 8 −4.0]), and in the high exposure group (OR = 3.2 [95% CI : 1.2 −8.3]). Endometriosis was also associated with the highest formaldehyde level (OR = 4.5 [95% CI : 1.0 −20.0]). 2.3.3 Female Reproductive Toxicity: Derivation of Concentration
-Response Relationsh ip The available evidence that formaldehyde exposure could be associated with spontaneous abortion and reduced fertility ( subfertility) is a serious concern. Given this association of formaldehyde with a reduction in fertility, based on the statistically significant reduced fecundity density ratio, one method to estimate the monetary c ost of infertility

p otentially

attributable to exposure to formaldehyde is to define the concentration-response relationship for the inability to conceive within 12 months . An inability to conceive within 12 months is the generally accepted definition of infertility ; however, since the Taskinen et al. study is only of women who did at some point conceive and give birth to a live child, this study i s measuring subfertility but not all forms of infertility . Table 5 in Taskinen et al. (1999) provides data on the number of women whose time to pregnancy exceeded 12 months at four occupational levels of exposure to formaldehyde: ‘non- exposed’ (0 ppb), ‘low exposure’ (mean of ~18 ppb), ‘medium exposure’ (mean of ~76 ppb), and ‘high’ exposure (mean of ~219 ppb). Typi cally, exposures from occupational studies are adj usted to account for the daily breathing volume appropriate to an environmental (versus occupational) setting and for continuous exposure across the total exposure period (U.S. EPA, 1993). However, with formaldehyde, there is potential for exposure outside of work from in-home and environmental sources of formaldehyde. A contemporaneous23

study o f formaldehyde exposures in Finland reports an average exposure level of 21.4 ppb (measured over 48 hours with a personal monitor) (Jurvelin et al., 2001). It shoul d be noted that both the mean exposure (18 ppb 8hr TWA) and lowest reported exposure (10 ppb 8hr TWA) of the ‘low exposed’ category in the Taskinen et al. (1999) study are below the reported average exposure level for Finland (21.4 ppb). The investigators in Taskinen et al. (1999) appear to have assumed that while the women were away f rom their “exposed” work areas their exposure to formaldehyde was zero. Thus, the reported occupational exposure levels did not account for levels of formaldehyde outside the workplace or away f rom “exposed” work stations within the workplace . This would explain why b oth the mean exposure and the lower end of work shift exposures for women in the low exposure group were reported at and below expected average exposure levels. The draft IRIS assessment (Table 5 - 5 of U . S . EPA, 2010) provides details of the exposure adjustments needed to convert the reported exposure levels from Taskinen et al. (1999) to 8 -hour TWA exposure levels taking estimated background (i.e., “non- exposed” work area) exposure levels in the workplace into account . Jurvelin et al . (2001) reported average formaldehyde exposures of 21.4 ppb among 15 randomly s elected residents of Helsinki, Finland in 1997 based on 48 -hour measurements with personal monitors . While major metropolitan formaldehyde concentrations likely exceed those of other locations in the country where the study subjects lived and worked and the 1997 sampling date occurred after the study p eriod (1985 - 1995), 21.4 ppb serves as a reasonable estimate of the non-work- station exposure experienced by

the study

subjects. Factoring in the average non-work- station exposure concentration of 21.4 ppb (Jurvelin et al., 2001) for all times when the study subjects were not working in “exposed” workplace areas , the 8 -hr TWA exposure levels are ‘non- exposed’ (21 ppb), ‘low exposure’ (mean of 34 ppb), ‘medium exposure’ (mean of 86 ppb), and ‘high’ exposure (mean of ~226 ppb). The same approach can be used to derive 24 -hour TWAs , with the average exposure level of 21.4 ppb from Jurvelin et al. (2001) serving as an estimate of background exposure levels outside of work. The adjusted 24 -hour TWA exposure levels are ‘non- exposed’ (21 ppb), ‘low exposure’ (mean of 26 ppb), ‘medium exposure’ (mean of 43 ppb), and ‘high’ exposure (mean of 89 ppb). These data are summarized in Table 2 below .

**Table 2.**  **C oncentration**  **- response**  **data on**  **time to**  **pregnancy from**  **Taskinen et**  **al. (1999)**  **Table 5 with reported and adjusted exposure concentration s**  **.**

**< 1 year**  **≥ 1**

**year Total**  **Risk of**

**Not Conceiving**  **within 12 months**  **RR F**  **orm aldehyde**  **conc w/o background**  **(ppb) 8**  **- hour** 

**TWA exposure**  **conc adjusted**  **for background**  **(ppb) 24**  **- hour** 

**TWA exposure**  **conc adjusted**  **for background**  **(ppb) Unexposed**  **261 27**  **288 0.094**  **1.00 0**  **21 21** 24

**Low 107**  **12 119**  **0.101 1.08**  **18 34**  **26 Medium**  **68 9**  **77 0.117**  **1.25 76**  **86 43**  **High 31**  **8 39**  **0.205 2.19**  **219 226**  **89** Regression equations for linear and exponential fits to the adjusted 24 -hour TWA data in Table 2 are provided below . The estimated concentration- response functions are shown in Figure 6.

**Figure 6.**  **Linear and**  **exponential c**  **oncentration -**  **response relationship**  **s between**  **formaldehyde exposure**  **and sub**  **fertility (** **24 -**  **hour TWA**  **exposure with**  **adjustment for**  **background exposure concentration) based on data from Table 5**  **of Taskinen et al. (1999).**  The linear function of subfertility risk based on estimated 24 -hour TWA exposure concentrations adjusted for background exposures yielded a measure of fit of R 2=0. 9 87 indicating a good fit to these data. The linear function can be expressed as:

= 0 . 056 + 0 . 0016 ( ) (2 - 3) Here the estimated background risk of sub fertility is 5.6 % (in the hy pothetical absence of any ambient formaldehyde) , which is somewhat lower than reported rates for the general United States population . S tephen and Chandra (1998) cite data from the National Survey o f Family Growth showing the infertility rates in the United States range from 4 - 6% among females age 15 - 24 ye ars, from 10 - 11% among those aged 25 - 34 ye ars and from 11 - 13% among those aged 35 -44 years.

0 0.05 0.1 0.15 0.2 0.25

0 20 40 60 80 100**R i** **s k** **o f** **N o** **t C** **o n** **c e** **i v** **i n** **g W** **i t** **h i** **n 1** **2 M** **o n** **t h** **s**

**24 -**  **hour TWA Formaldehyde Concentration (ppb)** obs risk linear model exponential model25

The exponential function of risk (which is equivalent to the linear function of ln(risk)) yi elded a measure of fit of R2=0. 99 7 , also indicating a good fit to these data. The exponential function can be expressed as:

= 0 . 074 ∗ . ( ) (2 -4) Here the background risk of sub fertility is 7. 4 %, which is closer to the reported rates cited above . The results based on the estimated 24 -hour TWA exposures adjusted for background are considered the most applicable for use in the economic benefits assessment . Both the linear and exponential models appear to slightly underestimate background rates for subfertility f or a hy pothetical zero exposure concentration for formaldehyde; however, exposure scenarios for the benefits analys is are more in the range of the data (i.e., 24 -hour ADCs near or > 20 ppb), and both models fit well in that range . Thus, the linear function is preferre d (see Equation 2 - 3 ) because of its ease of applicability in the benefits analysis . Note that this function is based on formaldehyde concentration in terms of a 24 -hour average exposure; this is in contrast to the functions provided for sensory irritation and asthma above, where the formaldehyde concentrations are in terms of residential (and some in- school for asthma) concentration levels. 2.3.4 Female Reproductive Toxicity: Discussion of Uncertainties Systemic effects of formal dehyde beyond the portal of entry are unlikely

to arise from the systemic delivery

of formaldehyde; however, it was noted by the NAS (2011) that it is important to differentiate between systemic delivery and systemic effects and that the delivery o f formal dehyde to distal sites was not a prerequisite for systemic effects. A s mentioned previously, adverse reproductive effects of other air pollutants, including other reactive gases, have been reported in the literature. The study b y Taskinen et al. (1999) was a well - designed population-based study that appears to have been well executed and analyz ed using appropriate methods . The study p opulation of Finnish women appears well defined and adequately s elected so as to allow for meaningful comparisons of health effects between individuals with different levels of exposure to formaldehyde , with qualifications discussed below . The participation rate was 64%, which is low enough to raise a concern about the potential for selection bias. However, the authors noted that selection bias has not influenced the results of other reproductive epidemiology studies reporting results on smoking, irregular menstruation, and earlier miscar riages, which are known to lengthen the time to pregnancy (Bolumar et al., 1996; Sallmén et al., 1995; Baird and Wilcox, 1985). Furthermore, there is no evidence to support the conjecture that an individual’s decision to participate in this study would be differential with respect to their workplace formaldehyde exposures while being nondifferential with respect to the other exposures examined in the study, including organic solvents, wood dust, and phenols. S imilarly, since the women who chose to participate in this study were not likely to be aware of the specific hy potheses under investigation, they

c ould not have known their formaldehyde exposures, which were independently estimated by an industrial hygi enist. Thus, selection bias is not a likely expl anation for the findings of adversity. The findings by T askinen et al. (1999) of reduced fertility and increased risk of spontaneous abortion are internally consistent and coherent with26

other reports of increased risk of pregnancy loss associated with exp osure to formaldehyde (John et al., 1994; Taskinen et al., 1994; Seitz and Baron, 1990; Axelsson et al., 1984). Other strengths include the linking of workers with the national database of birth outcomes and the exposure assessment by workplace measurement and an industrial hygienist. It is possible, however, that the women in the study m ay have inaccurately recalled their workplace histories. Weaknesses include the collection by questionnaire of data on pregnancy history, including spontaneous abortions and time to pregnancy . However, spontaneous abortion is the most common adverse outcome of pregnancy (Kline et al., 1989), and retrospective self -report of spontaneous abortion has been found to match well with prospectively collected reproductive histories (Wilcox and Horney, 1984). M any spontaneous abortions, however, are missed with self -reporting, with the magnitude likely exceeding 25%, but only rarely do women self

-report false positive events (Wilcox and Horney, 1984). The effect of such an undercount is to cause a bias towards the null when the likelihood of undercounting is unrelated to exposure. The implication is that the observed association of increased risk of spontaneous abortion associated with occupational exposure to formaldehyde may be an underestimation of the true risk. Taskinen et al. (1999) state that only a f ew women could not report the length of time to pregnancy, so it is unlikely that there could have been differential accuracy in this outcome variable by

difference in formaldehyde exposures , and, therefore , any systematic bias is also unlikely . Non- differential errors in reporting of outcomes is generally understood to obscure any true associations making them more difficult to detect statistically . There is a possibility

o f exposure measurement error in the computation of mean daily exposure concentrations. The exposure assessment was based on a detailed questionnaire on workplace, occupation, and work tasks, which were matched to industrial hygiene measurements from the s ame factory, when available, or from other work places of the same industrial activity . Differential recall of workplaces, occupations and work tasks as an explanation for the reported findings is a realistic concern in general. However, it is unclear why recall would have been differential for formaldehyde in such a way as to create bias for that exposure but not for exposures to phenols or dusts which were also evaluated as potential exposures of concern. H ad there been differential recall, such a phenomenon would likely have been across all exposures. As the industrial hygi ene exposure estimates of all exposures were done without knowledge of the pregnancy

history, any

measurement error would likely have been non - differential. It is well known that non- differential (random) measurement error tends to create a bias towards the null. In this case, the implication would be that the true effect of the formaldehyde exposures may have been an even lower FDR, which was attenuated towards the null value of 1 (unity). It is difficult to estimate the magnitude of such exposure measurement bias. Recent methodologic al work which imposed substantial measurement error on simulated exposure values showed that attenuation was as high as 50% (Bateson and Wright, 201 0), with more modest error resulting in a 20% attenuation. F or the concentration-response function derived here, the true slope may b e somewhat steeper than estimated due to potential exposure measurement error. There is also uncertainty in the adjustment for non-work- station exposures, which was based on the average exposure concentration from 48 -hour sampling of 15 individuals (who were not part of the Taskinen et al. study). Inaddition, there is uncertainty regarding the extent to which the effect of formaldehyde on FDR involves inhalation and/or dermal exposure. While there did appear to be a trend towards greater27

reductions in fertility

associated with greater air concentrations of formaldehyde, only the most highly exposed group showed a statistically significant effect, with a FDR of 0.64 [95% CI : 0.43 - 0.92]. T askinen et al. (1999) noted that of the 39 women with the highest exposures, 22 wore gloves. The FDR for the 17 women who did not wear gloves was 0.51 [95% CI : 0.28 −0.92]. The FDR for women who did wear gloves was 0.79 [95% CI : 0.47 −1.23]. Results stratified by respirator use were not presented. The FDR estimates stratified by glove use can be interpreted in at least three ways. F irst, these results may

indicate that there was some protective effect of glove use since when the highly exposed group was separated based on glove use, the FDR for women who did wear gloves appeared higher and was not significantly different from one (FDR=0.79 [95% CI : 0.47 −1.23]). If the route of exposure were dermal rather than by inhalation, then the use of protective gloves may have a beneficial effect on the time to pregnancy . Second, while only

the results for all highly exposed women (FDR = 0.64; 95% CI : 0.43 - 0.92) and those who did not wear gloves are statistically s ignificantly

different from 1, which may suggest a role for dermal contact in the observed subfertility associated with formaldehyde exposure, all 3 results are below 1 and the confidence intervals have substantial overlap with each other. The substantial overlap of the confidence intervals suggests that there is not a statistically

s ignificant difference between the results with and without gloves. Thus, the difference in adverse effect estimates based on gl ove usage may s imply b e the result of random variability

in subdividing the 39 highly exposed women into smaller groups of 17 and 22. Third, it is well understood that workplace safety protocols will result in the correlation of use of protective equipment. Therefore, it is plausible that women who were more likely to wear gloves were also more likely to wear respirators and that the difference in FDR estimates between those who used gloves and those who did not may b e partially explained by the joint usage of gloves and respirators. EPA used a general form of a Wald statistic to test the homogeneity hy pothesis that the FDR of 0.64 reported for all highly exposed women was uniform across both strata of glove use. The Wald statistic is the sum of the squared differences between the stratum- specific effect estimates and the hy

pothesized common value of the effect estimate divided by the variance of the stratum- specific effect estimate (Rothman and Greenland, 1998). F or ratio measures such as the FDR, the natural logarithm should be taken . The variances of the stratum- specific ln(FD R) were calculated by

extracting the standard errors from the confidence intervals. The Wald statistic was computed as follows:

= [ ( . ) ( . ) ]

. + [ ( . ) ( . ) ]

. = 1 . 30 (2 - 5) The Wald statistic has one less degree of freedom than the number of strata. H ere the p -value is 0.25, so the hy

pothesis of homogeneity o f effect across strata of reported glove use is not rejected. This assessment relied on the facts that the a priori null hy pothesis that air concentrations of formaldehyde were not associated with reduced fertility was statistically

rejected based on the28

FDR for all highly exposed women and that , in a sub - group analys is of glove use, a statistical test of the homogeneity of effect across levels of glove use did not show that the effect was modified by

glove use . Therefore, this assessment developed a concentration-response function assuming that the reduced fertility e ffects as measured by

the increased time to pregnanc y are attributable to inhalation exposure, recognizing that there are potentially important uncertainties which remain. Lastly, although Taskinen et al. (1999) controlled for several possible confounding variables ( employ ment, smoking, alcohol consumpti on , number of children , and menstrual irregularity), it is not explicitly

c lear that they

c ontrolled for the age of the women in the study (which ranged from 20 - 40 ye ars) in the proportional hazards regression model. However, the authors did explicitly state that age was controlled for in the other analyses which were presented. It is well known that age is negatively c orrelated with female fertility . Consequently, if, for any reason, older women tended to have higher exposures and they did not control f

or age, then this may lead to a misattribution of the fertility e ffect to formaldehyde rather than age . Exposure to solvents, wood dust and other dusts, and phenols was not associated with decreased fecundability and these agents would not be expected to have been confounders. Xylene was reported to be associated with an increased risk of spontaneous abortion in an earlier study in laboratory workers (Taskinen et al., 1994) but was not mentioned as an exposure for the wood workers. As noted at the outset of this section, the database on the effects of formaldehyde exposure on the female reproductive system was limited. The available evidence from epidemiologic studies is suggestive of an association with an increased risk of spontaneous abortion, which is coherent with and contributory

to diminished fertility

and increased infertility since the loss of a pregnancy

delays the time until delivery o f a live born child. Evidence from animal toxicology studies is also supportive of the observed association in humans. While the evidence base is small and somewhat varied, the potential impact of the suggested association, however uncertain, remains important. In their peer-review of the draft IRIS assessment, the NAS (2011) affirmed that the derivation of quantitative concentration-response estimates from such available evidence is in keeping with EPA’s non- cancer practices even if there is uncertainty a bout the effect (U . S . EPA, 2002). In summary, Taskinen et al. (1999) reported a finding of an association between formaldehyde exposure and reduced fertility that appeared to be concentration dependent. While this may only be suggestive of an association, it does appear to be consistent with the relatively small data base of other studies of female reproductive toxicity. Evidence that formaldehyde could be associated with reduced fertility

( subfertility) is a serious concern and more research would be helpful in reducing uncertainty in the hazard identification and quantifying any concentration-response relationship . The available data from Taskinen et al. (1999) do allow for the derivation of a concentration-response function which can provide a basis for use in the economic analys es .29

**2.4 References**  Akinbami LJ . (2006) The State of childhood asthma, United States, 1980

–2005. Advance data from vital and health statistics; no 381, Hyattsville, MD: National Center for Health Statistics. Alexandersson, R; Hedenstierna, G. (1988) Respiratory hazards associated with exposure to formaldehyde and solvents in acid- curing paints. Arch Environ Health 43:222

–227. Andersen, I; Mølhave, L. (1983) Controlled human studies with formaldehyde. In: Gibson, JE; ed. Formaldehyde toxicity . Washington, DC: Hemisphere Publishing Corporation; pp. 154

– 165. Arts, JH; Dröge, SC; Spanhaak, S; et al. (1997) Local ly mph node activation and IgE responses in brown Norway

and Wistar rats after dermal application of sensitizing and non- sensitizing chemicals. Toxicology

117:229

–234. Axels son, G; Lűtz, C; Ryl ander, R. (1984) Exposure to solvents and outcome of pregnancy in university laboratory

employees. Br J Ind Med 41:305

–312. Baird, DD; Wilcox, AJ . (1985) Cigarette smoking associated with delayed conception. JAm Med Assoc 253:2979 - 2983. Bateson, TF; Wright JM. (2010) Regression calibration for classical exposure measurement error in environmental epidemiology studies using multiple local surrogate exposures. Am J Epidemiology

172: 344 - 52. Bolumar, F; Olsen, J; Boldsen, J. ( 1996) Smoking reduces fecundity : A European multicenter study on fertility

and subfecundity. The European study group on infertility

and subfecundity. Am J Epidemiol 143:578

–587. CDC.(2009) http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6017a4.htm?s_cid=mm6017a4_w Cometto - Muňiz, JE; Cain, WS. (1992) Sensory irritation. Relation to indoor air pollution. Ann NY Acad Sci 641:137

– 151. Feron, VJ; Arts, JH; Kuper, CF; et al. (2001) Health risks associated with inhaled nasal toxicants. Crit Rev Toxicol 31:313

–347. Garrett, MH; Hooper, MA; Hooper, BM; et al. (1999a) Increased risk of allergy in children due to formaldehyde exposure in homes. Allergy 34:330

–337. Glinianaia, S.V.; Rankin, J. ; Bell, R.; et al. (2004a) Particulate Air Pollution and Fetal Health: a Systematic Review of the Epidemiologic Evidence. Epidemiology 15, 36 -45. Glinianaia, S.V.; Rankin, J.; Bell, R.; et al. (2004b) Does Particulate Air Pollution Contribute to Infant Death? A Systematic Review. Environ. Health Perspect. 112, 1365 - 1371. Hanrahan, LP ; Dally, KA; Anderson, HA; et al. (1984) Formaldehyde vapor in mobile homes: a cross sectional survey

of concentrations and irritant effects. Am J Public He alth 74(9):1026

–27.30

Hemminki, K; Kyyrönen, P; Lindbohm, ML . (1985) Spontaneous abortions and malformations in the offspring of nurses exposed to anaesthetic gases, cy tostatic drugs, and other potential hazards in hospitals, based on registered information of outcome. JEpidemiol Community Health 39:141

– 147. Hemminki, K; Mutanen, P; Salonienmi, I; et al. (1982) Spontaneous abortions in hospital staff engaged in sterilizing instruments with chemical agents. Br Med J 285:1461

– 1463. Holmström, M; Wilhelmsson, B. (1988) Respiratory sy mptoms and pathophysiological effects of occupational exposure to formaldehyde and wood dust. S cand J Work Environ Health 14:306 −311. Horvath, E, Jr; Anderson, J; Pierce, WE; et al. (1988) Effects of formaldehyde on the mucous membranes and lungs: A study of an industrial population. JAMA 259:701

–707. John, EM; Savitz, DA; Shy, CM. (1994) Spontaneous abortions among cosmetologists. Epidemiology

5:147

– 155. Jurvelin, J; Vartiainen, M; Jantunen, M; et al. (2001) Personal exposure levels and microenvironmental concentrations of formaldehyde and acetaldehyde in the Helsinki metropolitan area. J Air Waste Manag Assoc 51:17

–24. Kline J, Stein Z, Susser M. Conception to birth: epidemiology o f prenatal development. New York: Oxford University

Press, 1989. Kriebel, D; Sama, SR; Cocanour, B. (1993) Reversible pulmonary responses to formaldehyde. A study of clinical anatomy students. Am Rev Respir Dis 148:1509

– 1515. Kriebel, D; Myers, D; Cheng, M; et al. (2001) Short-term effects of formaldehyde on peak expiratory

flow and irritant sy

mptoms. Arch Environ Health 56(1):11

– 18. Krzy zanowski, M; Quackenboss, JJ; Lebowitz, MD. (1990) Chronic respiratory e ffects of indoor formaldehyde exposure. Environ Res 52:117

– 125. Kulle, TJ. (1993) Acute odor and irritation response in healthy nonsmokers with formaldehyde exposure. Inhal Toxicol 5:323

–332. Lacasana, M.; Esplugues, A.; Ballester, F. (2005) Exposure to Ambient Air Pollution and Prenatal and Early Childhood Health Effects. Eur. J. Epidemiol. 2 0, 183 - 199. Liu, KS; Huang, FY; Hay ward, SB ; et al. (1991) Irritant effects of formaldehyde exposure in mobile homes. Environ Health Perspect 94:91

–94. Maisonet, M.; Correa, A.; Misra, D.; Jaakkola, J. J. (2004) A Review of the Literature on the Effects of Ambient Air Pollution on Fetal Growth. Environ. Res. 95, 106 - 115. McGwin, G; Li enert, J; Kennedy, JI . (2010) Formaldehyde Exposure and Asthma in Children: A Systematic Review. Environ Health Perspect 118:313

–317.31

National Academy of Science. (2011) Review of the Environmental Protection Agency ’s Draft IRIS Assessment of Formaldehyde. National Research Council. Nielsen, GD. (1991) Mechanisms of activation of the sensory irritant receptor by

airborne chemicals. Crit Rev Toxicol 21:183

–208. Ritchie, IM ; Lehnen, RG. (1987) Formaldehyde -related health complaints of residents living in mobile and conventional homes. Am J Public Health 77:323

–328. Rothman, KJ; Greenland, S. (1998) Modern Epidemiology, 2 nd edition. Lippincott Williams & Wilkins, Baltimore. Rumchev, KB; Spickett, JT ; Bulsara, MK; et al. (2002) Domestic exposure to formaldehyde significantly increases the risk of asthma in young children. Eur Respir J 20:403

–408. Sallmén, M; Lindbohm, ML ; Kyyronen, P; et al. (1995) Reduced fertility among women exposed to organic solvents. Am J Ind Med 27(5):699

–713. Seitz, T; Baron, S. (1990) Health hazard evaluation report: Rockcastle Manufacturing, Mount Vernon, Kentucky. National Institute for Occupational Safety and Health, U.S. Department of Health and Human Services, Cincinnati, OH; HETA 87 - 349 - 2022. Sram, R.J.; Binkova, B.; Dejmek, J.; Bobak, M. (2005) Ambient Air Pollution and Pregnancy Outcomes: a Review of the Literature. Environ. Health Perspect. 113 , 375 - 382. Stephen E.H.; Chandra A. (1998) Updated projections of infertility in the United States: 1995

–2025. Fertility and Sterility

10:30 - 34. Taskinen, H; Kyyronen, P; Hemminki, K. (1994) Laboratory work and pregnancy outcome. J Occup Med 36:311 - 319. Taskinen, HK; Kyyronen, P; Sallmen, M; et al. (1999) Reduced fertility among female wood workers exposed to formaldehyde. Am J Ind Med 36:206

–212. U.S. EPA. (2010) Toxicological Review of Formaldehyde

– Inhalation Assessment. EPA/635/R10/002A. Integrated Risk Information System (IRI S). National Center for Environmental Assessment, Washington, DC. Available online at http://www.epa.gov/iris . U.S. HHS. (2007) Asthma Education and Prevention Program Expert Panel Report 3: Guidel ines for the Diagnosis and Management of Asthma. National In stitutes of Health. National Heart Lung and Blood Institute. NIH Publication Number 08 - 5846. Wilcox, AJ; Horney, LF. (1984) Accuracy o f spontaneous abortion recall. Am J Epidemiol 120:727

–733. Zhao, Z; Zhang, Z; Wang, Z; et al. (2008) Asthmatic sy mptoms among pupils in relation to winter indoor and outdoor air pollution in schools in Taiyaun, China. Environ Health Perspect 116:90

–97.32



## Section 3: Benefits Assessment **Approach 3.1**  **Introduction/Background** The economic benefit of a ri sk reduction to an individual can be approximated by his or her willingness to pay

to avoid a small change in the risk times the reduction in risk. This willingness to pay is denominated in dollars and represents the amount of money the individual would pay to obtain the risk reduction and be just as well off as before the change. For example , if an individual would pay

$ 0.10 to avoid an additional 1% risk of having a bad headache on the job and some polic y reduced the chance of experiencing that headache by 25% , then the value of that policy change to the individual would be $2.50. In some cases, the cost of the illness can be used as a proxy

for the willingness to pay but, as should be obvious in the case of a headache, this cost of illness may

b e less than the true willingness to pay to avoid the outcome. This is all relatively straightforward if the risk reduction occurs in the current time period. Evaluating the economic value of a series of changes in risk that occur to a number of individuals in the future, however, can be tricky because one must keep track of the benefit to each individual over time and then discount back to the present. 3.1.1 General Model Following the life - cycle consumption literature (Cropper and Portney 1 990; Cropper and Sussman 1990), an individual’s willingness to pay for a future risk reduction is the discounted willingness to pay

in the period in which the risk reduction occurs, discounted at the consumption rate of interest , which could vary over time. This must be done for all risk reductions throughout an individual’s life. From an intergenerational standpoint, a future individual’s discounted willingness to pay must be discounted back to the present using a social rate of discount, which could also vary over time. Mathematically, the total willingness to pay b y an individual , n , at time 0 for a risk reduction at age a is 

##     ,0 , , ,0 , TotalW TP WTP , , a n n a n a n n a

r e e       , ( 1 ) where WTP n,a is the marginal willingness to pay of individual n for a risk reduction at age a . λ is a standard exponential discount factor equal to 1/(1+δ), where δ is a constant consumption rate of discount. Δr

n,a(en,0 ,...,en,a) is the change in risk at age a , which could depend upon the exposure , en,t, from the beginning of the individual’s life, k=0, until age a . If we look at the total willingness to pay from an intergenerational perspective, then we further discount the individual’s value back to the present using the social rate of discount. For simplicity, we assume that the social rate of discount equals the consumption rate of discount, and that both are constant over time. 7 If we denominate each generational cohort, g, by the ye ar 7 Technically, the social rate of discount, αi , does not have to equal the consumption rate of discount, δ k, and both can vary over time. In this case, the correct discount factor for an individual of age a would be 0 0 1 1 1 1 g a i k

i k                   

###   .33

in which the cohort was born, with g=0 for individuals born this ye ar, then the total willingness to pay at time 0 for a risk reduction for individual ng (that is, individual n of generation g) at age a would be 

#     ( ) ,0 , , ,0 , Total WTP WTP , , g g g g g

a g n n a n a n n a r e e      

. (2 ) The willingness to pay

f or all risk reductions that this individual will receive over his or her lifetime, from age 0 to death at age An , would be 

#     ( ) ,0 , , ,0 , 0 Total WTP WTP , , n g g g g g A a g n n a n a n n a a r e e  

    

#  

. ( 3 ) To get a comprehensive measure of the total willingness to pay f or any s et of future risk reductions, this exercise must be carried out for all individuals in a generation, ng = 1,...,N g, for every year of each individual’s life, a=0,...,A n , and across all generations, g = 0,...,G . The total willingness to pay

for all future risk reductions would then be



#     ( )
, , ,0 , 0 1 0 To talW TP WTP , , g n g g g g g N A G a g n a n a n n a g n a r e e

 

       

##  

. (4 ) 3.1.2 Simplified Model Equation (4) represents the general model, in which the willingness to pay for each individual is calculated as the sum of discounted willingness to pay f or the risk reduction experienced in each period. The difficulty

with equation (4) is that is requires us to know both the risk reduction and the willingness to pay

f or each individual in each period. Given that this is generally impractical, we modify the problem to make it tractable . For example, if every individual in a given generation were to experience the same risk reduction, and if we assume that the real (as opposed to nominal) willingness to pay value is constant across time and individuals, then equation (4) simplifies to 

##   

#   ( )
, , 0 , 0 0 

### Total W TP WTP , ,

n A G a g g g a g g a g a 

### Pop r e e  

  

###      

#   , ( 5 ) where Pop g is the population of individuals of generation g. An example might be the value of a reduction in exposure to a uniformly mixed pollutant in an airshed . Each individual experiences the same exposure reduction and, by assumption, the same risk reduction. If we use an average marginal willingness to pay to obtain this risk reduction, then we can estimate the economic However, following Cropper and Sussman (1990) , if the individual can invest his or her income in any period at a riskless rate of interest, d, and the individual is not wealth constrained (that is, he or she is never a net borrower), then the consumption rate of interest is equal to the riskl ess rate of interest for all periods. That is, δ k = d for all k. If the intergenerational social discount rate is equal to the consumption rate of discount (that is, if we discount future generations at the same rate that we discount our own generation), then d = αi = δ k in every period. In this case, a fixed discount factor of λ(a+g) can be used.34

benefit of the action using equation (5 ). This is important because we no longer have to aggregate across individuals. Instead, we can simply c alculate the average benefits in any given ye ar, 

##   

#  

, , 0 , 

### WT P , , g a g g a 

### r e e   , multiply times the population in that ye ar, Ng , and discount back to the present using the discount factor λ(a+g)

. Inthe case of formaldehyde exposure, however, we cannot assume that each individual experiences the same risk reduction. However, we can still avoid aggregating across individuals through two simplifying assumptions . First, if the risk reduction is only a f unction of the exposure at age a rather than the lifetime exposure, then the change in risk for individual ng, is 

#    

, , 0 , , , 

### , , g g g g g n a n n a n a n a 

### r e e r e   

 . Second, if we assume a linear concentration-response function (or approximate linearity around the change in exposure), then the change in risk is some proportion of the change in exposure. That is, 

#  

, , , g g g n a n a n a 

### r e e

   

. With these two changes, equation (4) simplifies to 

##   

#   ( )
, 0 0 

### Total W TP WTP

n g A G a g g n a g a 

### Pop e   

  

###      

# 

. ( 6 ) This is the correct formula if every individual experiences the same change in exposure at the same age , but can be expanded if different groups experience different exposure changes. For formaldehyde, individuals in different housing ty pes (e.g., detached single family homes, attached single family

homes, apartments, mobile homes, and campers/RVs) experience different levels of formaldehyde based on the amount of composite wood products in these housing units and the air volume. Inaddition, different age groups (infants, children, adult workers, and retirees) experience different exposure levels based on the amount of time they are in the home and where they

go when they

are not in the home. Dividing the population up into exposure groups based on age c ohorts, c=0,...,C, and housing ty pe, h = 1,...,H

, and letting t=a+g, the simplified equation for total willingness to pay c an be written as 

##   

#  

, , , , 0 1 1 TotalWTP WTP T H C t hct hct t h c Pop e  

         

##   

, ( 7 ) where Pop h,c,t is the population in each exposure group at time t and Δeh,c,t is the change in exposure experienced by

that group. The advantage to this approach is that the willingness to pay can be calculated for each ye ar individually and the total willingness to pay will be calculated as the sum of discounted values. Equation (7) can now be applied to each endpoint to obtain the total willingness to pay f or a policy change. The change in exposure in any given ye ar will vary b y individual depending on the ty pe of housing structure, the age of that housing, and the climate (specifically, the temperature and humidity) . The complete benefits analys is will use approximately 220 exposure scenarios associated with four housing ty pes (mobile homes, apartments, single family attached35

homes, and single family detached homes), eleven age - of -unit profiles (new structure, 1 - year- old structure, ..., 10 ye ar- old structure), and five geographic climate zones. (illustrated in Figure 8) .

**Figure 8 .**  The U.S. Energy Information Administration’s five climate zones based on the average number of heating degree - days (HDD) and cooling degree - days (CDD) over a 30 - ye ar period. Climate zones do not necessarily f ollow State boundaries, because climate is affected by elevation, proximity

to coast lines, latitude, and other physical features. To simpli fy

this exposition, however, we illustrate the benefits analys is using just three housing profiles: a new mobile home in climate zone 1, a 1 -ye ar- old mobile home in climate zone 1, and a new single family

detached home in climate zone 1. The population in each housing ty pe will be the population for that climate zone, estimated from the Residential Energy C onsumption Survey (EIA 2008) and scaled by aged to match the age distribution for the general population (U.S. Bureau of Census 2009) . An example of this application is given below for each of the three endpoints (sensory irritation, asthma incidence, and reduced fertility) . The discussion for each endpoint is organized into four parts: monetization; the application of the annual approach , a worked example, and a discussion of the uncertainties. The se three endpoints represent the non- cancer effects for which a quantitative concentration exposure -response relationship can be derived. It is possible, even likely, that there are other non- cancer health impacts associated with formaldehyde exposure for which concentration exposure -response relationships cannot derived36

due to lack of data or sufficient research. If this is true, then the total economic benefits may be underestimated in the analysis here. **3.2 S ensory Irritation**  The valuation of sensory irritation is the most straightforward of the three non- cancer endpoints because the monetization has been used in numerous EPA rules and the concentration-response relationship is for a time period less than the one year time step of the exposure model. 3.2.1 Monetization Loehman and others conducted a survey of 396 respondents to estimate the willingness to pay to avoid one sy

mptom- day of shortness of breath, coughing and sneezing, and head congestion (inc luding eye, ear, and throat irritation). Asurvey o f this sort, in which respondents are asked to state their willingness to pay b ased on the policy

described is called stated preference survey . Loehman used a specific ty pe of stated preference survey c alled a contingent valuation survey . They report a median willingness to pay o f $2 - $79 per avoided sy mptom- day . The value for severe head congestion (which includes eye irritation) was $8.17 for one sy mptom- day . ( Green et al. 1978; Loehman et al.1979 ) Toll ey et al. (1986) conducted a contingent valuation study f rom the personal interviews of 176 respondents in 1984 - 1985 regarding the willingness to pay to avoid seven “ light” (i.e., less severe) sy

mptoms. The sy

mptoms were coughing, stuffed sinuses, throat c ongestion, itching eyes, drowsiness, headache, and nausea. The willingness to pay to relieve one sy

mptom- day

of a light sy mptom ranged from $25 to $50 , with a mean value of $27.73 and a median value of $12.50. (Kenkel, Berger, and Blomquist 1994) Berg er et al. (1987) reanalyzed these data to compare the mean willingness to pay and the private cost of illness for these sy mptoms and found similar values. Dickie and Gerking (1991 and 1998) used a household production function approach to estimate the willingne ss to pay to avoid 26 respiratory symptoms related to air pollution in 1985 and 1986. The sy mptoms included were cough; throat irritation; husky voice; phlegm, sputum or mucous; chest tightness; could not take a deep breath; pain on deep respiration; out o f breath easily; breathing sounds, wheezing or whistling; eye irritation; could not see as well as usual; eyes sensitive to bright light; ringing in ears; pain in ears; sinus pain; nosebleed; dry

and painful nose; runny nose; fast heartbeat at rest; tired easily ; faintness or dizziness; felt spaced out or disoriented; headache; chills or fever; nausea; and swollen glands. They found a range of estimates between $0.49 and $1 90 to re lieve one s ymptom for one day . Industrial Economics Incorporated, IE c (1993 a and 1993b ), conducted a survey o f the literature and provided a range of willingness to pay e stimates for a variety of morbidity e ffects, including throat congestion, head and sinus congestion, coughing, asthma attack, chronic allergy attack, eye irritati on, headache, shortness of breath, chest tightness, nausea, drowsiness, chronic bronchitis, and angina pectoris. While IE c recommend averaging a number of studies for many of the health effects, they relied only on Tolley

et al. (1986) for the value for eye irritation. Based on these estimates, eye irritation had the highest (mid-range) estimate of the willingness to pay for to avoid a single sy

mptom for one day .37

IE c 1993 a, 1993b also estimated the willingness to pay to avoid a minor respiratory restricte d activity day using the willingness to pay e stimates from Tolley

et al. (1986) assuming a combination of three symptoms: coughing, throat congestion, and sinusitis. The assumption behind a minor restricted activity day i s that it is a combination of sy mptoms, but the term is not exactly defined. Krupnick and Kopp (1988) previously suggested that a reasonable lower bound for the willingness to pay f or a minor respiratory

restricted activity

day would be the willingness to pay to avoid a single mild sy mptom, since any combination of sy mptoms and various levels of restriction might be considered part of a restricted day . Since a restricted activity day i s something less than a lost work day, an upper bound would be the value of a work loss day . Based on IEc’s (1993) average values , eye irritation has the highest (mid-range) willingness to pay value. Therefore, the value to avoid eye irritation serves as the best single mild sy mptom to use to represent a lower bound on a combination of sy mptoms. These results have been used by EPA in a number of contexts. In1 997, EPA conducted a retrospective analys is of the benefits and costs of the Clean Air Act (EPA 1997), including the benefits of reduced upper respiratory s ymptoms associated with air pollution. The upper re spiratory

s ymptoms are defined by P ope et al. (1995) as consisting of one or more of the following sy

mptoms: runny

or stuffy

nose; wet cough; and burning, aching, or red eyes. A central estimate of $19 (in 1990 dollars), with a range from $7 to $33, was developed for one sy mptom- day, based on the mid-range estimate produced by IEc (1993). The value for a minor restricted activity

day i s $38, with a range between $16 and $61 (and an assumption of a triangular distribution). The lower bound of $16 is based on the IE c estimate of eye irritation. In 1999, EPA conducted a prospective analys is of the Clean Air Act (EPA 1999) and used the same values as it used in the 1997 retrospective analysis . In 2011, EPA conducted a second prospective analys is of the Clean Air Act from 1990 to 2020 (EPA 2011a). In this second prospective analys is, the values were updated to 2006 dollars and were adjusted to reflect the expected rise in income level between 1990 and 2020. The value for upper respiratory sy mptoms was $28.80 at 19 90 income levels (in 2006$) and $30.70 at 2020 income levels, with a range of $10.8 0 to $50.5 0 . The value for a Minor Restricted Activity D ays is $59 at 1990 income levels and $64 at 2010 income levels, a range of $24 to $94. As before, the lower bound of the minor restricted activity days of $24 is based on the highest estimate for a single sy mptom, which is for eye irritation. These values are also used for regulatory analysis by

EPA’s Office of Air and Radiation. While the Economic Analys is for each regulation includes a comprehensive discussion of the willingness to pay

values used, the Office of Air and Regulation (EPA 2011b) describes the most common values in its Environmental Benefits Mapping and Analys is Program (BenMAP) program. BenMAP is a publicly available GI S -based program distributed by the EPA to estimate the health impacts and economic benefits from air quality improvements. T able 3 reports the median values as given in the BenMAP documentation (EPA 2011a) The value for eye irritation is listed as $20.03 in 1999 dollars , based on the median value of Tolley et al. (1986) . Table 3: Median Willingness to Pay Estimates for Morbidity

Sy mptoms38

Source: Table J - 10 in EPA BenMAP documentation ((EPA 2011a). In summary, the willingness to pay to avoid eye irritation has been used in a number of EPA studies. The use of different base ye ars for different analys es obscures the fact that the value used is almost always based on the IE c (1993) estimate derived from Tolley et al. (1986) . Therefore, a value of $24 in 2006 dollars, as was used in the last EPA study to use this number (EPA 2011a), is the value that will be used here as the willingness to pay toreduce eye irritati on from formaldehyde exposure. 3.2.2 Annual approach The concentration-response function for the prevalence of eye irritation (as a proxy for sensory irritation, as described in Section 2.1 of this document) takes the form exposure 1 exposure

b b a p a     . Because this relationship is modeled for a time of less than one year, the total willingness to pay to reduce eye irritation for one year, t, is a straight- forward application of equation (7) above.



###   1

0 , , , , , , 1 1 , , , , exposure exposure Total WTP WTP 1 exposure 1 exposure b b t H C h c policy hcbaseline t t h c t b b t t h c hcbaseline h c policy a a Pop a a 

                                  

##   

, ( 8 ) where39



##   , , , , , , , , , , exposure exposure WTP 1 exposure 1 exposure b b h c policy hcbaseline hct b b hcbaseline h c policy

a a Pop a a        
                     ( 9 ) is the willingness to pay

f or any given housing and age cohort. Pop h,c,t is the population in that particular cohort, and WTP is the estimate of the WTP for eye irritation (described above). This willingness to pay

c an then be calculated for each ye ar of analysis. If we were to assess the total benefits of a policy

action, we would need to discount each year’s benefits back to the base ye ar using the discount factor λ.



###   , , , , , , 0 1 1 , , , , exposure exposure Total WTP WTP 1 exposure 1 exposure b b T H C h c policy hcbaseline t hct b b t h c hcbaseline h c policy a a Pop a a 

                                  

##   

. ( 10 ) 3.2.3 Example calculation The concentration-response function for the prevalence of eye irritation given in Section 2.1 of this document is 

#  

  7 2 .3743 7 2 .3743 

### 7 1 0 exposure 1 7 10 exposure p 

 

###       based on Hanrahan et al. (1984). While Hanrahan does not provide the relevant time period for this relationship, Liu et al. (1991) produced qualitatively similar results for a two -week time period. Therefore, the following development assumes that the concentration- response relationship for eye irritation is for a two week period. This means that for one ye ar, 26 two -week periods, the concentration-response function would be 

##   

#  

 

7 2 .3743 7 2 .3743 

### 26 7 1 0 exposure 1 7 10 exposure p 

 

###        . Table 4 , bel ow, lists the exposure for three housing profiles for both the baseline and a “CARB2” policy s cenario (i.e., emission limits set at the California phase 2 levels, which are also the emission limits mandated by F SCWPA) . This exposure assessment comes from a spreadsheet model based on an application of the Matthews model (Hawthorne and Matthews 1987). A model housing structure is simulated for each housing profile using assumptions about the emissions from the wood found in each housing ty pe for the baseline scenario . The policy scenario is then simulated by assuming that any composite wood in the baseline scenario that is not compliant with California’s CARB phase 2 standard is made to be compliant. Any wood [product] that already meets and has lower emissions than specified by CARB2 is assumed to remain the same. In spreadsheet form the formaldehyde exposure results are reported in mg/m3 ; for display

in Table 1, the results have been converted to ppb by assuming that 814 ppb formaldehyde = 1 mg/m3 . This assumption corresponds to a temperature of 25°C, a pressure of 1 atm, and a molecular weight for formaldehyde of 30.03 g/mol., which is line with the approach for performing unit conversions as described by the National Research Council’s standing operating procedures for developing acute exposure guideline levels for hazardous chemicals (NRC 2001) . Table 4 : Illustrative formaldehyde exposure scenarios using three housing profiles40

Scenario Housing Profile Infants Avg. ADC Preschool /

School Child Avg. ADC Non- Industrial

Worker Avg. ADC Retirees Avg.

ADC Weighted average ADC (mg/m3 ) during current exposure year Baseline New

Mobile Home 0.0394 0.0344 0.0349 0.0419 1 -year- old Mobile Home 0.0280 0.0248 0.0256 0.0296 New Detached Home 0.0356 0.0312 0.0319 0.0381 CARB2 New

Mobile Home 0.0375 0.0328 0.0333 0.0398 1 -year- old Mobile Home 0.0268 0.0238 0.0247 0.0283 New Detached Home 0.0354 0.0311 0.0318 0.0379 Weighted average ADC (ppb) during current exposure year Baseline New

Mobile Home 32.07 28.00 28.41 34.11 1 -year- old Mobile Home 22.79 20.19 20.84 24.09 New Detached Home 28.98 25.40 25.97 31.01 CARB2 New

Mobile Home 30.53 26.70 27.11 32.40 1 -year- old Mobile Home 21.82 19.37 20.11 23.04 New Detached Home 28.82 25.32 25.89 30.85 Weighted average ADC (ppb) during current exposure year Change New

Mobile Home - 1.55 - 1.30 - 1.30 - 1.71 1 -year- old Mobile Home - 0.98 - 0.81 - 0.73 - 1.06 New Detached Home - 0.16 - 0.08 - 0.08 - 0.16 * Note: These are preliminary estimates and will likely change in the final exposure and benefits assessments. ADC = Average Daily Concentration. The average daily concentration (ADC) for four categories of classes of individuals (infants, age 0 - 2; preschool and school - aged children, age 2 - 16; non- industrial workers, age 16 - 65; and retirees, age 65 - 100 ) are reported in Table 1. Each individual is assumed to enter the next age group at the birthday

on the lower bound of the age category . So, for example, an individual is classified as an infant until his or her second birthday, whereupon that individual is classified as a preschool/school - aged child. Individuals in different age categories but living in the same housing ty

pe have different ADCs because they have different levels of exposure to formaldehyde outside of the home. The exposure model accounts for formaldehyde concentrations in schools and daycare, in automobiles, workplace exposure, outside. The final benefits analys is will include exposure for a larger group of individuals (e.g., workers exposed to higher levels of formaldehyde in the workplac e), but the four classifications given here are sufficient to illustrate the benefits methodology . Individuals in a new mobile home have the highest baseline exposure of these three profiles, based on ppb, ranging from 34.11 ppb for retirees (in the last c olumn of Table 1) to 28.00 ppb for school - aged children (in the fourth column of Table 1). The exposure is different for each individual because of the amount of time each individual spends in the home compared to other activities. For example, children sp end more time outside where formaldehyde exposure is comparatively lower than in the home. The policy s cenario results in a reduction of 1.71 ppb to 1.30 ppb for new mobile homes.41

Individuals in new single - family detached home have the next highest exposure in this example, ranging from 31.01 ppb to 25.40 ppb. New single family homes have lower emissions than new mobile homes because of the larger ratio of air volume to surface area of wood in the home. A 1 - ye ar- old mobile home has a baseline range of exposure from 24.09 ppb to 20.19 ppb. The 1 - yearold mobile home has lower emissions than a new mobile home because the stepwise approximation of off

- gassing of formaldehyde, with the largest emissions coming from the first ye ar and declining exponentially thereafter. Notice, however, that even though a new detached home has higher overall emissions than a 1 - ye ar- old mobile home, the emissions reductions from the policy scenario are larger in the 1 -ye ar- old mobile home. This is because the volume of wood which is assumed to be affected by the rule and the effect that it has in the unit differs by housing ty pe, as predicted by

the Matthews model. Table 5 , below, illustrates how this exposure assessment and the c oncentration-response function for eye irritation would be used in a benefits analysis for these three housing profiles in climate zone 1. Each profile has an 8 - column block in the table. The first column block represents the population of 21,670 individuals living in new mobile homes (EIA 2008 ) . This population is distributed across ages (represented by rows) in proportion to the age distribution for the nation as a whole (U.S. Bureau of Census 2009) . The baseline exposure, policy s cenario exposure, and associated change in ppb are listed for each age. The number of cases for the baseline, policy, and the change in cases (in absolute value) for each age is then calculated using the difference in the concentration- response function at baseline and CARB2 exp osure levels. Using a willingness to pay o f $24 per case, the willingness to pay at any age is given by

e quation (9). For example, for an individual age 0 (that is, an infant before his or her first birthday) living in a new mobile home, this would be



###       

##    



##       

##    



##     2.3743 2.3743 7 7 0 2.3743 2.3743 7 7 26 7 1 0 3 2.07 26 7 1 0 3 0.53 Total WTP $24 302 $55 1 7 10 32.07 1 7 10 30.53

 

                        

              In total, 129.3 cases (the sum of all rows from age 0 on) of eye irritation would be avoided from formaldehyde exposure reduction in new mobile homes in climate zone 1 for one ye ar . The total willingness to pay

for this housing prof

ile across all ages would be $3,103 .42

Table 5 : Illustrative benefits calculations for sensory irritation using three housing profiles

Ag e Population Base line

Ex pos ure (ppb) CA RB 2 Exposure (ppb) Exposure Re duc tio n (ppb) Base line

Ca ses Po lic y Cas es Change in Cases WTP Po pulation Base line Exposure (ppb) CA RB 2 Ex pos ure (ppb) Exposu re Reduc tion (ppb) Baseline

Cas es Policy Cas es Change in

Cas es WTP Po pulation Baseline Exposu re (ppb) CA RB2 Expos ure (ppb) Exposu re Reduc tion (ppb) Baseline

Cas es Po licy Ca ses Change inCas es WTP **21, 670**  **129. 3**  **$3, 103**  **21, 670**  **48. 9**  **$1, 174**  **302, 507**  **115. 4**  **$2, 770**  0 302 32. 07 30. 53 -1. 55 20. 75 18. 44 2.30 $55 302 22. 79 21. 82 -0. 98 9. 21 8. 30 0.91 $22 4, 213 28. 98 28. 82 -0. 16 227. 49 224. 46 3. 03 $73 1 299 32. 07 30. 53 -1. 55 20. 58 18. 30 2.28 $55 299 22. 79 21. 82 -0. 98 9. 13 8. 23 0.90 $22 4, 180 28. 98 28. 82 -0. 16 225. 71 222. 70 3. 01 $72 2 302 28. 00 26. 70 -1. 30 15. 02 13. 42 1.61 $39 302 20. 19 19. 37 -0. 81 6. 90 6. 26 0.64 $15 4, 214 25. 40 25. 32 -0. 08 166. 27 165. 01 1. 26 $30 3 304 28. 00 26. 70 -1. 30 15. 15 13. 53 1.62 $39 304 20. 19 19. 37 -0. 81 6. 96 6. 31 0.65 $16 4, 249 25. 40 25. 32 -0. 08 167. 65 166. 37 1. 27 $31 4 296 28. 00 26. 70 -1. 30 14. 76 13. 18 1.58 $38 296 20. 19 19. 37 -0. 81 6. 78 6. 15 0.63 $15 4, 139 25. 40 25. 32 -0. 08 163. 30 162. 06 1. 24 $30 5 293 28. 00 26. 70 -1. 30 14. 61 13. 04 1.56 $38 293 20. 19 19. 37 -0. 81 6. 71 6. 08 0.63 $15 4, 097 25. 40 25. 32 -0. 08 161. 64 160. 41 1. 23 $30 6 294 28. 00 26. 70 -1. 30 14. 62 13. 06 1.57 $38 294 20. 19 19. 37 -0. 81 6. 72 6. 09 0.63 $15 4, 101 25. 40 25. 32 -0. 08 161. 81 160. 58 1. 23 $30 7 290 28. 00 26. 70 -1. 30 14. 46 12. 91 1.55 $37 290 20. 19 19. 37 -0. 81 6. 64 6. 02 0.62 $15 4, 055 25. 40 25. 32 -0. 08 159. 98 158. 76 1. 22 $29 8 288 28. 00 26. 70 -1. 30 14. 35 12. 81 1.54 $37 288 20. 19 19. 37 -0. 81 6. 59 5. 98 0.61 $15 4, 025 25. 40 25. 32 -0. 08 158. 79 157. 58 1. 21 $29 9 292 28. 00 26. 70 -1. 30 14. 55 13. 00 1.56 $37 292 20. 19 19. 37 -0. 81 6. 69 6. 06 0.62 $15 4, 082 25. 40 25. 32 -0. 08 161. 07 159. 84 1. 22 $29 10 281 28. 00 26. 70 -1. 30 14. 00 12. 50 1.50 $36 281 20. 19 19. 37 -0. 81 6. 43 5. 83 0.60 $14 3, 928 25. 40 25. 32 -0. 08 154. 99 153. 81 1. 18 $28 11 277 28. 00 26. 70 -1. 30 13. 78 12. 31 1.48 $35 277 20. 19 19. 37 -0. 81 6. 33 5. 74 0.59 $14 3, 866 25. 40 25. 32 -0. 08 152. 54 151. 38 1. 16 $28 12 277 28. 00 26. 70 -1. 30 13. 76 12. 29 1.47 $35 277 20. 19 19. 37 -0. 81 6. 32 5. 73 0.59 $14 3, 861 25. 40 25. 32 -0. 08 152. 33 151. 17 1. 16 $28 13 278 28. 00 26. 70 -1. 30 13. 82 12. 34 1.48 $36 278 20. 19 19. 37 -0. 81 6. 35 5. 76 0.59 $14 3, 876 25. 40 25. 32 -0. 08 152. 93 151. 76 1. 16 $28 14 283 28. 00 26. 70 -1. 30 14. 09 12. 58 1.51 $36 283 20. 19 19. 37 -0. 81 6. 47 5. 87 0.60 $14 3, 952 25. 40 25. 32 -0. 08 155. 92 154. 73 1. 19 $28 15 288 28. 00 26. 70 -1. 30 14. 32 12. 78 1.53 $37 288 20. 19 19. 37 -0. 81 6. 58 5. 96 0.61 $15 4, 015 25. 40 25. 32 -0. 08 158. 42 157. 21 1. 20 $29 16 290 28. 41 27. 11 -1. 30 14. 96 13. 38 1.58 $38 290 20. 84 20. 11 -0. 73 7. 16 6. 58 0.58 $14 4, 055 25. 97 25. 89 -0. 08 168. 65 167. 40 1. 25 $30 17 297 28. 41 27. 11 -1. 30 15. 30 13. 69 1.62 $39 297 20. 84 20. 11 -0. 73 7. 32 6. 73 0.60 $14 4, 147 25. 97 25. 89 -0. 08 172. 49 171. 21 1. 28 $31 18 303 28. 41 27. 11 -1. 30 15. 61 13. 96 1.65 $40 303 20. 84 20. 11 -0. 73 7. 47 6. 86 0.61 $15 4, 231 25. 97 25. 89 -0. 08 175. 97 174. 66 1. 31 $31 19 309 28. 41 27. 11 -1. 30 15. 91 14. 23 1.68 $40 309 20. 84 20. 11 -0. 73 7. 62 7. 00 0.62 $15 4, 313 25. 97 25. 89 -0. 08 179. 40 178. 07 1. 33 $32 20 316 28. 41 27. 11 -1. 30 16. 26 14. 55 1.72 $41 316 20. 84 20. 11 -0. 73 7. 78 7. 15 0.63 $15 4, 408 25. 97 25. 89 -0. 08 183. 32 181. 96 1. 36 $33 21 306 28. 41 27. 11 -1. 30 15. 74 14. 08 1.66 $40 306 20. 84 20. 11 -0. 73 7. 54 6. 92 0.61 $15 4, 267 25. 97 25. 89 -0. 08 177. 48 176. 16 1. 32 $32 22 302 28. 41 27. 11 -1. 30 15. 57 13. 92 1.64 $39 302 20. 84 20. 11 -0. 73 7. 45 6. 84 0.61 $15 4, 220 25. 97 25. 89 -0. 08 175. 50 174. 20 1. 31 $31 23 301 28. 41 27. 11 -1. 30 15. 48 13. 84 1.63 $39 301 20. 84 20. 11 -0. 73 7. 41 6. 80 0.60 $14 4, 195 25. 97 25. 89 -0. 08 174. 48 173. 19 1. 30 $31 24 303 28. 41 27. 11 -1. 30 15. 63 13. 97 1.65 $40 303 20. 84 20. 11 -0. 73 7. 48 6. 87 0.61 $15 4, 235 25. 97 25. 89 -0. 08 176. 14 174. 83 1. 31 $31 25 305 28. 41 27. 11 -1. 30 15. 73 14. 07 1.66 $40 305 20. 84 20. 11 -0. 73 7. 53 6. 91 0.61 $15 4, 263 25. 97 25. 89 -0. 08 177. 31 175. 99 1. 32 $32 26 300 28. 41 27. 11 -1. 30 15. 47 13. 83 1.63 $39 300 20. 84 20. 11 -0. 73 7. 40 6. 80 0.60 $14 4, 191 25. 97 25. 89 -0. 08 174. 33 173. 03 1. 30 $31 27 305 28. 41 27. 11 -1. 30 15. 70 14. 04 1.66 $40 305 20. 84 20. 11 -0. 73 7. 51 6. 90 0.61 $15 4, 254 25. 97 25. 89 -0. 08 176. 95 175. 63 1. 32 $32 28 306 28. 41 27. 11 -1. 30 15. 76 14. 10 1.67 $40 306 20. 84 20. 11 -0. 73 7. 55 6. 93 0.62 $15 4, 272 25. 97 25. 89 -0. 08 177. 70 176. 37 1. 32 $32 29 308 28. 41 27. 11 -1. 30 15. 86 14. 19 1.68 $40 308 20. 84 20. 11 -0. 73 7. 59 6. 97 0.62 $15 4, 300 25. 97 25. 89 -0. 08 178. 84 177. 51 1. 33 $32 30 306 28. 41 27. 11 -1. 30 15. 76 14. 10 1.66 $40 306 20. 84 20. 11 -0. 73 7. 54 6. 93 0.62 $15 4, 272 25. 97 25. 89 -0. 08 177. 67 176. 34 1. 32 $32 31 291 28. 41 27. 11 -1. 30 14. 97 13. 39 1.58 $38 291 20. 84 20. 11 -0. 73 7. 16 6. 58 0.58 $14 4, 057 25. 97 25. 89 -0. 08 168. 74 167. 49 1. 26 $30 32 282 28. 41 27. 11 -1. 30 14. 52 12. 99 1.53 $37 282 20. 84 20. 11 -0. 73 6. 95 6. 38 0.57 $14 3, 935 25. 97 25. 89 -0. 08 163. 67 162. 45 1. 22 $29 33 277 28. 41 27. 11 -1. 30 14. 28 12. 78 1.51 $36 277 20. 84 20. 11 -0. 73 6. 84 6. 28 0.56 $13 3, 871 25. 97 25. 89 -0. 08 161. 02 159. 82 1. 20 $29 34 270 28. 41 27. 11 -1. 30 13. 89 12. 43 1.47 $35 270 20. 84 20. 11 -0. 73 6. 65 6. 11 0.54 $13 3, 766 25. 97 25. 89 -0. 08 156. 63 155. 46 1. 17 $28 35 277 28. 41 27. 11 -1. 30 14. 25 12. 75 1.51 $36 277 20. 84 20. 11 -0. 73 6. 82 6. 27 0.56 $13 3, 863 25. 97 25. 89 -0. 08 160. 66 159. 46 1. 20 $29 36 268 28. 41 27. 11 -1. 30 13. 81 12. 35 1.46 $35 268 20. 84 20. 11 -0. 73 6. 61 6. 07 0.54 $13 3, 743 25. 97 25. 89 -0. 08 155. 69 154. 53 1. 16 $28 37 274 28. 41 27. 11 -1. 30 14. 11 12. 62 1.49 $36 274 20. 84 20. 11 -0. 73 6. 76 6. 20 0.55 $13 3, 825 25. 97 25. 89 -0. 08 159. 10 157. 92 1. 18 $28 38 287 28. 41 27. 11 -1. 30 14. 77 13. 21 1.56 $37 287 20. 84 20. 11 -0. 73 7. 07 6. 49 0.58 $14 4, 004 25. 97 25. 89 -0. 08 166. 53 165. 29 1. 24 $30 39 302 28. 41 27. 11 -1. 30 15. 57 13. 93 1.64 $39 302 20. 84 20. 11 -0. 73 7. 45 6. 85 0.61 $15 4, 220 25. 97 25. 89 -0. 08 175. 53 174. 23 1. 31 $31 40 308 28. 41 27. 11 -1. 30 15. 85 14. 18 1.67 $40 308 20. 84 20. 11 -0. 73 7. 59 6. 97 0.62 $15 4, 297 25. 97 25. 89 -0. 08 178. 71 177. 38 1. 33 $32 41 291 28. 41 27. 11 -1. 30 14. 98 13. 40 1.58 $38 291 20. 84 20. 11 -0. 73 7. 17 6. 59 0.58 $14 4, 060 25. 97 25. 89 -0. 08 168. 88 167. 62 1. 26 $30 42 285 28. 41 27. 11 -1. 30 14. 69 13. 14 1.55 $37 285 20. 84 20. 11 -0. 73 7. 03 6. 46 0.57 $14 3, 982 25. 97 25. 89 -0. 08 165. 62 164. 39 1. 23 $30 43 286 28. 41 27. 11 -1. 30 14. 71 13. 16 1.55 $37 286 20. 84 20. 11 -0. 73 7. 04 6. 47 0.57 $14 3, 988 25. 97 25. 89 -0. 08 165. 87 164. 64 1. 23 $30 44 293 28. 41 27. 11 -1. 30 15. 11 13. 52 1.60 $38 293 20. 84 20. 11 -0. 73 7. 23 6. 64 0.59 $14 4, 096 25. 97 25. 89 -0. 08 170. 36 169. 09 1. 27 $30 45 313 28. 41 27. 11 -1. 30 16. 12 14. 42 1.70 $41 313 20. 84 20. 11 -0. 73 7. 72 7. 09 0.63 $15 4, 370 25. 97 25. 89 -0. 08 181. 75 180. 40 1. 35 $32 46 317 28. 41 27. 11 -1. 30 16. 35 14. 62 1.73 $41 317 20. 84 20. 11 -0. 73 7. 83 7. 19 0.64 $15 4, 431 25. 97 25. 89 -0. 08 184. 30 182. 93 1. 37 $33 47 316 28. 41 27. 11 -1. 30 16. 28 14. 56 1.72 $41 316 20. 84 20. 11 -0. 73 7. 79 7. 16 0.64 $15 4, 412 25. 97 25. 89 -0. 08 183. 49 182. 12 1. 36 $33 48 317 28. 41 27. 11 -1. 30 16. 31 14. 59 1.72 $41 317 20. 84 20. 11 -0. 73 7. 81 7. 17 0.64 $15 4, 420 25. 97 25. 89 -0. 08 183. 85 182. 48 1. 37 $33 49 319 28. 41 27. 11 -1. 30 16. 45 14. 71 1.74 $42 319 20. 84 20. 11 -0. 73 7. 87 7. 23 0.64 $15 4, 459 25. 97 25. 89 -0. 08 185. 46 184. 08 1. 38 $33 50 325 28. 41 27. 11 -1. 30 16. 72 14. 96 1.77 $42 325 20. 84 20. 11 -0. 73 8. 00 7. 35 0.65 $16 4, 533 25. 97 25. 89 -0. 08 188. 52 187. 12 1. 40 $34 **Ne w Mobile H**  **ome, C limat** **e Z one**  **1 1**  **Ye ar**  **Old Mobile H**  **ome, C limat** **e Z one**  **1 Ne**  **w Single**  **Fa mily D** **et ac**  **he d H**  **ome, C limat** **e Z one**  **1**43

Table 5 : Illustrative benefits calculations for sensory irritation using three housing profiles (cont.)

Ag e Population Base line

Ex pos ure (ppb) CA RB 2 Exposure (ppb) Exposure Re duc tio n (ppb) Base line

Ca ses Po lic y Cas es Change in Cases WTP Po pulation Base line Exposure (ppb) CA RB 2 Ex pos ure (ppb) Exposu re Reduc tion (ppb) Baseline

Cas es Policy Cas es Change in

Cas es WTP Po pulation Baseline Exposu re (ppb) CA RB2 Expos ure (ppb) Exposu re Reduc tion (ppb) Baseline

Cas es Po licy Ca ses Change inCas es WTP 51 311 28. 41 27. 11 -1. 30 16. 02 14. 33 1.69 $41 311 20. 84 20. 11 -0. 73 7. 67 7. 04 0.63 $15 4, 343 25. 97 25. 89 -0. 08 180. 62 179. 28 1. 34 $32 52 311 28. 41 27. 11 -1. 30 16. 00 14. 31 1.69 $41 311 20. 84 20. 11 -0. 73 7. 66 7. 03 0.62 $15 4, 336 25. 97 25. 89 -0. 08 180. 35 179. 01 1. 34 $32 53 306 28. 41 27. 11 -1. 30 15. 78 14. 11 1.67 $40 306 20. 84 20. 11 -0. 73 7. 55 6. 94 0.62 $15 4, 277 25. 97 25. 89 -0. 08 177. 89 176. 57 1. 32 $32 54 294 28. 41 27. 11 -1. 30 15. 14 13. 54 1.60 $38 294 20. 84 20. 11 -0. 73 7. 25 6. 65 0.59 $14 4, 103 25. 97 25. 89 -0. 08 170. 64 169. 38 1. 27 $30 55 295 28. 41 27. 11 -1. 30 15. 17 13. 57 1.60 $38 295 20. 84 20. 11 -0. 73 7. 26 6. 67 0.59 $14 4, 112 25. 97 25. 89 -0. 08 171. 04 169. 76 1. 27 $31 56 281 28. 41 27. 11 -1. 30 14. 48 12. 95 1.53 $37 281 20. 84 20. 11 -0. 73 6. 93 6. 37 0.57 $14 3, 924 25. 97 25. 89 -0. 08 163. 23 162. 01 1. 21 $29 57 271 28. 41 27. 11 -1. 30 13. 96 12. 49 1.47 $35 271 20. 84 20. 11 -0. 73 6. 68 6. 14 0.54 $13 3, 784 25. 97 25. 89 -0. 08 157. 38 156. 21 1. 17 $28 58 261 28. 41 27. 11 -1. 30 13. 45 12. 03 1.42 $34 261 20. 84 20. 11 -0. 73 6. 44 5. 91 0.53 $13 3, 646 25. 97 25. 89 -0. 08 151. 67 150. 54 1. 13 $27 59 254 28. 41 27. 11 -1. 30 13. 06 11. 68 1.38 $33 254 20. 84 20. 11 -0. 73 6. 25 5. 74 0.51 $12 3, 539 25. 97 25. 89 -0. 08 147. 19 146. 09 1. 09 $26 60 252 28. 41 27. 11 -1. 30 12. 97 11. 60 1.37 $33 252 20. 84 20. 11 -0. 73 6. 21 5. 70 0.51 $12 3, 516 25. 97 25. 89 -0. 08 146. 26 145. 17 1. 09 $26 61 241 28. 41 27. 11 -1. 30 12. 44 11. 12 1.31 $32 241 20. 84 20. 11 -0. 73 5. 95 5. 47 0.49 $12 3, 371 25. 97 25. 89 -0. 08 140. 19 139. 15 1. 04 $25 62 238 28. 41 27. 11 -1. 30 12. 26 10. 97 1.30 $31 238 20. 84 20. 11 -0. 73 5. 87 5. 39 0.48 $11 3, 323 25. 97 25. 89 -0. 08 138. 20 137. 18 1. 03 $25 63 248 28. 41 27. 11 -1. 30 12. 78 11. 43 1.35 $32 248 20. 84 20. 11 -0. 73 6. 12 5. 62 0.50 $12 3, 464 25. 97 25. 89 -0. 08 144. 07 143. 00 1. 07 $26 64 184 28. 41 27. 11 -1. 30 9. 50 8. 49 1.00 $24 184 20. 84 20. 11 -0. 73 4. 55 4. 18 0.37 $9 2, 574 25. 97 25. 89 -0. 08 107. 06 106. 26 0. 80 $19 65 183 34. 11 32. 40 -1. 71 14. 53 12. 86 1.67 $40 183 24. 09 23. 04 -1. 06 6. 36 5. 71 0.64 $15 2, 549 31. 01 30. 85 -0. 16 161. 78 159. 76 2. 01 $48 66 179 34. 11 32. 40 -1. 71 14. 21 12. 57 1.64 $39 179 24. 09 23. 04 -1. 06 6. 21 5. 59 0.63 $15 2, 492 31. 01 30. 85 -0. 16 158. 15 156. 18 1. 97 $47 67 183 34. 11 32. 40 -1. 71 14. 56 12. 89 1.68 $40 183 24. 09 23. 04 -1. 06 6. 37 5. 73 0.65 $15 2, 555 31. 01 30. 85 -0. 16 162. 12 160. 10 2. 02 $48 68 160 34. 11 32. 40 -1. 71 12. 75 11. 28 1.47 $35 160 24. 09 23. 04 -1. 06 5. 58 5. 01 0.56 $14 2, 237 31. 01 30. 85 -0. 16 141. 96 140. 19 1. 77 $42 69 147 34. 11 32. 40 -1. 71 11. 73 10. 38 1.35 $32 147 24. 09 23. 04 -1. 06 5. 13 4. 61 0.52 $12 2, 057 31. 01 30. 85 -0. 16 130. 55 128. 92 1. 63 $39 70 142 34. 11 32. 40 -1. 71 11. 29 9. 99 1.30 $31 142 24. 09 23. 04 -1. 06 4. 94 4. 44 0.50 $12 1, 980 31. 01 30. 85 -0. 16 125. 67 124. 11 1. 56 $38 71 134 34. 11 32. 40 -1. 71 10. 63 9. 40 1.22 $29 134 24. 09 23. 04 -1. 06 4. 65 4. 18 0.47 $11 1, 864 31. 01 30. 85 -0. 16 118. 31 116. 83 1. 47 $35 72 129 34. 11 32. 40 -1. 71 10. 30 9. 11 1.19 $28 129 24. 09 23. 04 -1. 06 4. 51 4. 05 0.46 $11 1, 807 31. 01 30. 85 -0. 16 114. 68 113. 25 1. 43 $34 73 120 34. 11 32. 40 -1. 71 9. 59 8. 48 1.10 $27 120 24. 09 23. 04 -1. 06 4. 19 3. 77 0.42 $10 1, 682 31. 01 30. 85 -0. 16 106. 72 105. 39 1. 33 $32 74 117 34. 11 32. 40 -1. 71 9. 27 8. 20 1.07 $26 117 24. 09 23. 04 -1. 06 4. 06 3. 65 0.41 $10 1, 626 31. 01 30. 85 -0. 16 103. 21 101. 93 1. 28 $31 75 115 34. 11 32. 40 -1. 71 9. 13 8. 08 1.05 $25 115 24. 09 23. 04 -1. 06 3. 99 3. 59 0.40 $10 1, 601 31. 01 30. 85 -0. 16 101. 61 100. 35 1. 26 $30 76 103 34. 11 32. 40 -1. 71 8. 23 7. 28 0.95 $23 103 24. 09 23. 04 -1. 06 3. 60 3. 24 0.36 $9 1, 444 31. 01 30. 85 -0. 16 91. 64 90. 50 1. 14 $27 77 102 34. 11 32. 40 -1. 71 8. 09 7. 15 0.93 $22 102 24. 09 23. 04 -1. 06 3. 54 3. 18 0.36 $9 1, 418 31. 01 30. 85 -0. 16 90. 01 88. 89 1. 12 $27 78 98 34. 11 32. 40 -1. 71 7. 82 6. 92 0.90 $22 98 24. 09 23. 04 -1. 06 3. 42 3. 07 0.35 $8 1, 372 31. 01 30. 85 -0. 16 87. 07 85. 98 1. 08 $26 79 95 34. 11 32. 40 -1. 71 7. 57 6. 70 0.87 $21 95 24. 09 23. 04 -1. 06 3. 31 2. 98 0.34 $8 1, 328 31. 01 30. 85 -0. 16 84. 27 83. 22 1. 05 $25 80 94 34. 11 32. 40 -1. 71 7. 46 6. 60 0.86 $21 94 24. 09 23. 04 -1. 06 3. 26 2. 93 0.33 $8 1, 308 31. 01 30. 85 -0. 16 83. 01 81. 98 1. 03 $25 81 86 34. 11 32. 40 -1. 71 6. 83 6. 04 0.79 $19 86 24. 09 23. 04 -1. 06 2. 99 2. 68 0.30 $7 1, 198 31. 01 30. 85 -0. 16 76. 01 75. 07 0. 95 $23 82 82 34. 11 32. 40 -1. 71 6. 55 5. 79 0.75 $18 82 24. 09 23. 04 -1. 06 2. 86 2. 57 0.29 $7 1, 148 31. 01 30. 85 -0. 16 72. 87 71. 97 0. 91 $22 83 77 34. 11 32. 40 -1. 71 6. 11 5. 40 0.70 $17 77 24. 09 23. 04 -1. 06 2. 67 2. 40 0.27 $6 1, 071 31. 01 30. 85 -0. 16 67. 98 67. 14 0. 85 $20 84 70 34. 11 32. 40 -1. 71 5. 60 4. 95 0.64 $15 70 24. 09 23. 04 -1. 06 2. 45 2. 20 0.25 $6 982 31. 01 30. 85 -0. 16 62. 29 61. 51 0. 78 $19 85 66 34. 11 32. 40 -1. 71 5. 24 4. 64 0.60 $15 66 24. 09 23. 04 -1. 06 2. 29 2. 06 0.23 $6 920 31. 01 30. 85 -0. 16 58. 37 57. 64 0. 73 $17 86 59 34. 11 32. 40 -1. 71 4. 68 4. 14 0.54 $13 59 24. 09 23. 04 -1. 06 2. 05 1. 84 0.21 $5 821 31. 01 30. 85 -0. 16 52. 13 51. 48 0. 65 $16 87 52 34. 11 32. 40 -1. 71 4. 11 3. 63 0.47 $11 52 24. 09 23. 04 -1. 06 1. 80 1. 61 0.18 $4 720 31. 01 30. 85 -0. 16 45. 71 45. 14 0. 57 $14 88 46 34. 11 32. 40 -1. 71 3. 64 3. 22 0.42 $10 46 24. 09 23. 04 -1. 06 1. 59 1. 43 0.16 $4 639 31. 01 30. 85 -0. 16 40. 56 40. 05 0. 50 $12 89 40 34. 11 32. 40 -1. 71 3. 15 2. 79 0.36 $9 40 24. 09 23. 04 -1. 06 1. 38 1. 24 0.14 $3 553 31. 01 30. 85 -0. 16 35. 07 34. 63 0. 44 $10 90 33 34. 11 32. 40 -1. 71 2. 62 2. 32 0.30 $7 33 24. 09 23. 04 -1. 06 1. 15 1. 03 0.12 $3 460 31. 01 30. 85 -0. 16 29. 18 28. 82 0. 36 $9 91 26 34. 11 32. 40 -1. 71 2. 06 1. 82 0.24 $6 26 24. 09 23. 04 -1. 06 0. 90 0. 81 0.09 $2 362 31. 01 30. 85 -0. 16 22. 95 22. 67 0. 29 $7 92 22 34. 11 32. 40 -1. 71 1. 72 1. 52 0.20 $5 22 24. 09 23. 04 -1. 06 0. 75 0. 68 0.08 $2 301 31. 01 30. 85 -0. 16 19. 12 18. 88 0. 24 $6 93 16 34. 11 32. 40 -1. 71 1. 31 1. 16 0.15 $4 16 24. 09 23. 04 -1. 06 0. 57 0. 51 0.06 $1 229 31. 01 30. 85 -0. 16 14. 54 14. 36 0. 18 $4 94 13 34. 11 32. 40 -1. 71 1. 04 0. 92 0.12 $3 13 24. 09 23. 04 -1. 06 0. 45 0. 41 0.05 $1 182 31. 01 30. 85 -0. 16 11. 58 11. 43 0. 14 $3 95 10 34. 11 32. 40 -1. 71 0. 81 0. 71 0.09 $2 10 24. 09 23. 04 -1. 06 0. 35 0. 32 0.04 $1 142 31. 01 30. 85 -0. 16 8. 98 8. 87 0. 11 $3 96 7 34. 11 32. 40 -1. 71 0. 60 0. 53 0.07 $2 7 24. 09 23. 04 -1. 06 0. 26 0. 23 0.03 $1 105 31. 01 30. 85 -0. 16 6. 63 6. 55 0. 08 $2 97 5 34. 11 32. 40 -1. 71 0. 43 0. 38 0.05 $1 5 24. 09 23. 04 -1. 06 0. 19 0. 17 0.02 $0 76 31. 01 30. 85 -0. 16 4. 84 4. 78 0. 06 $1 98 4 34. 11 32. 40 -1. 71 0. 30 0. 27 0.03 $1 4 24. 09 23. 04 -1. 06 0. 13 0. 12 0.01 $0 53 31. 01 30. 85 -0. 16 3. 34 3. 30 0. 04 $1 99 3 34. 11 32. 40 -1. 71 0. 21 0. 19 0.02 $1 3 24. 09 23. 04 -1. 06 0. 09 0. 08 0.01 $0 37 31. 01 30. 85 -0. 16 2. 34 2. 31 0. 03 $1 100 5 34. 11 32. 40 -1. 71 0. 38 0. 33 0.04 $1 5 24. 09 23. 04 -1. 06 0. 16 0. 15 0.02 $0 66 31. 01 30. 85 -0. 16 4. 20 4. 14 0. 05 $1 **Ne w Mobile H**  **ome, C limat** **e Z one**  **1 1**  **Ye ar**  **Old Mobile H**  **ome, C limat** **e Z one**  **1 Ne**  **w Single**  **Fa mily D** **et ac**  **he d H**  **ome, C limat** **e Z one**  **1**44

The second column block of Table 5 represents the benefits to individuals living in a 1 - year- old mobile home. Here assume again that there are 21,670 individuals living in this ty pe of structure (an assumption of convenience due to lack of data), so the population distribution across ages is exactly the same as for new mobile homes. Inf act, the only

change between column block 1 and column block 2 is the smaller exposure due to the fact that the wood in a one - ye ar- old structure has had time to off

- gas. In total, a projected 48.9 cases of eye irritation annually would be reduced in this housing profile, with a total benefit of $ 1,174 for any ye ar of analys is. The third column blo ck of Table 5 represents the benefits to individuals living in a new single - family detached home. An estimated 302,507 individuals live in this ty pe of home . Even though the magnitude of the exposure reduction experienced in new detached homes is smaller than that experience d in either mobile home profile presented, the number of cases reduced is larger because the exposure reduction is applied to a larger population. An estimated 115.4 cases of eye irritation are projected to be reduced each ye ar by this p olicy change, with a benefit of $2,770 . The benefits of all housing profiles are then summed to produce the total benefits in one year. If we assume that the populations and distributions across housing ty pes are stable, then the benefits value for each housing cohort will the same for every year except the first one. In other words, individuals may move from one house to another and we are not capable of predicting those movements, but, if we assume that there will be 21,670 individuals living in a new mob ile home in climate zone 1 during every

year of our analysis, then we will continue to see $3,149 worth of benefits from this housing profile every year. An additional conclusion from this analys is is that even after the policy is implemented, there will b e a lag in its effect based on the age of the housing structure. This is why, in the example here, the first ye ar is different from the others. A policy requiring “CARB2” compliant wood will affect new houses immediately . It will, however, not affect the existing housing stock, unless those existing houses are remodeled with new wood products. (The proposed rule will include this remodeling impact, but it has not been included here to simplify the exposition.) The effect of the policy will not be seen in one -ye ar- old homes until one year after its implementation. Table 6 , to the right, illustrates how the total estimated benefits of the rule will be calculated for 30 ye ars using a discount rate of 3% (implying a discount factor of 0.9709). 8 Inthe ye ar imme diately

8 EPA generally expresses costs and benefits that occur over time as present values by exponentially discounting them to a common base year. Most EPA rules use a 3% discount rate, which is approximately the return to Government-backed securities. This rate reflects how individuals discount future consumption and how government should value policy-related consumption changes. Most EPA analyses also include present values suing a 7% Tab le6 : Dis counted Willin gnes s to Pay Ye ar Annual WTP Dis counted

WTP **$144, 009**  0 $5,873 $5,873 1 $7,048 $6,842 2 $7,048 $6,643 3 $7,048 $6,450 4 $7,048 $6,262 5 $7,048 $6,079 6 $7,048 $5,902 7 $7,048 $5,730 8 $7,048 $5,563 9 $7,048 $5,401 10 $7,048 $5,244 11 $7,048 $5,091 12 $7,048 $4,943 13 $7,048 $4,799 14 $7,048 $4,659 15 $7,048 $4,524 16 $7,048 $4,392 17 $7,048 $4,264 18 $7,048 $4,140 19 $7,048 $4,019 20 $7,048 $3,902 21 $7,048 $3,788 22 $7,048 $3,678 23 $7,048 $3,571 24 $7,048 $3,467 25 $7,048 $3,366 26 $7,048 $3,268 27 $7,048 $3,173 28 $7,048 $3,080 29 $7,048 $2,991 30 $7,048 $2,90445

following the policy

implementation, ye ar 0 in Table 3, the annual benefits are the sum of the benefits for new mobile homes and new detached houses, which is ($3,103 + $2,770 =) $5,873 . The benefits in the second ye ar, and each ye ar thereafter are the benefits from all three housing ty pes, ($3,103 + $1,174 + $2,770 =) $7,048 . Discounting these benefits to present value terms by multiplying them times 0.9709 t , where t is the year, produces total benefits of $144,009 for this policy change. 3.2.4 Discussion of uncertainties The example above assumes that the population is constant from year to year (births = deaths + net migrations) and that the future distribution across housing ty pes is exactly

the same as the present distribution, neither of whi ch is likely to be true. However, the method described above can be modified to accommodate a growing population and a different housing and age distribution by

changing the population values for each ye ar. The concentration-response function used here is non- linear. This means that annual approach may not produce an exact measure of the benefits. However, given that the change in exposure is relatively small, the results from using the non- linear concentration-response function are probably very similar to the results if we were to use a linear function. Furthermore, because the non- linear concentration-response function applies to a period of time less than one ye ar, there should be no problems in applying the annual approach. The contingent valuation estimates of willingness to pay to avoid eye irritation are rather dated . The values obtained by

T olley et al. , which are the underlying values used in the analysis, are 25 ye ars old. While EPA still uses these estimates, there have been a number of advances over the ye ars in the application of stated preference methods aimed at reducing hy pothetical bias which causes stated preference estimates of willingness to pay to be too high. On the other hand, the same willingness to pay

value is attributed to both ad ults and children. There is some evidence that parents are willing to pay more to avoid adverse health effects in their children than they are to avoid the same adverse health effects in themselves (Dickie and Gerking, 2003; Dickie and Ulery, 2002; Liu et al., 2000). EPA (2000a) reviewed the results of several contingent valuation studies (Loehman et al. 1979, 1979; Rowe and Chestnut 1986; and Tolley et al. 1986) for various sy mptoms. Both Loehman et al. (1979) and Tolley et al. (1986) found that the willingness to pay to avoid multiple days of a condition is greater than the willingness to pay to avoid one sy mptom- day . However, the willingness to pay p er sy mptom- day

declined as the number of sy mptom- days avoided increased. In other words, the value to reduce two sy mptom- days of some effect is generally less than two times the value for one sy mptom- day. This point was also made in Kenkel, Berger, and Blomquist (1994) and declining marginal willingness to pay f or avoided illnesses was also found more recently in Bosworth, Cameron, D eShazo (2009) . Therefore, multiplying the marginal willingness to pay f or one sy mptom- day

times the number of sy mptom- days, as has been done here, may overstate the total value and, hence, the benefit of the policy change. However, a reasonable upper bound of the annual value of the total effect for one sy mptom- day

would be the sum of the willingness to pay for one sy mptom- day

o f each discount rate, reflecting opportunity costs in terms of foregone investments and therefore future consumption. Alternate approaches to exponential discounting are discussed in EPA 2011a.46

effect individually . This would be what have reported in Table 5 if we assume that a different set of people experience these sy mptoms in each of the two week periods . This analys is only valued one sy mptom, eye irritation, largely because this is the only s ymptom for which an effective concentration-response function could be derived. However, there is evidence that there are other forms of irritation (e.g. nose, throat irritation) associated with formaldehyde exposure. If this is true and the reduction of formaldehyde exposure reduces these sy mptoms concurrently, then the values of those other minor effects should, in theory, be included in the analysis . This limitation will be acknowledged qualitatively in the economic analys i s for the proposed rule. The report by

Tolley et al. (1986) suggested that the impact of multiple symptoms is close to, but slightly lower than the aggregate sum of the willingness to pay f or individual effects. That is, the willingness to pay

f or a group of three to five symptoms jointly was slightly

less than the sum of the willingness to pay

f or each sy

mptom individually . Therefore, if we were to produce a concentration-response function for all of these sy mptoms of sensory

irritation associated with for maldehyde exposure, then a reasonable upper bound of the total effect for one sy mptom- day would be the sum of the willingness to pay f or one sy mptom- day

o f each effect individually . EPA (2000a) suggested that a reasonable lower bound would be the high- end value for one sy mptom. Since eye irritation is the lower end of the median values as reported by IEc ( 1993a and 1993b) , the values used here likely probably represent a lower bound on the tr ue effect of multiple sy

mptoms. **3.3 Asthma incidence**  Valuing asthma incidence is more difficult than sensory irritation. First, monetization is more difficult because the valuation is based on the incidence of asthma . Most economic analysis of asthma value the exacerbation of asthma sy mptoms rather than deve lopment of new cases . Second, the concentration-response relationship is based on an odds ratio for asthma prevalence, whereas we are need an annual incidence rate . Third, as illustrated in the sensory irritation section, the exposure model produces results for one - year of exposure to formaldehyde for each age, housing ty

pe, climate cohort. If the concentration-response relationship produces an incidence rate for a time period longer than this one ye ar window, then an adjustment to the simple model given b y equation (7) needs to be made. 3.3.1 Monetization EPA has included asthma as a health effect in many o f its economic analys e s. However, this valuation usually

takes the form of valuing an increase, or exacerbation, of asthma sy mptoms rather than an increase in the incidence of asthma. The Office of Air uses two values to monetize the effect of asthma exacerbation in adults and three values for asthma exacerbation in children. Rowe and Chestnut (1986) estimate a mean willingness to pay o f $43 in 2000$ for avoiding a “bad asthma day” as $43 in 2000$, with a range of $16 to $71. This unit value is applicable to both adults and chi ldren. A second value for adults is $74, derived from Dickie and Ulery (2002) and Dickie and Messman (2004), assuming that asthma exacerbation consists of 1 sy mptom- day .47

Two unit values for children are available from Dickie and Ulery (2002) and Dickie and Messman (2004). One of these is $86 , twice the adult unit value for adults based on evidence that parents are willing to pay a bout twice as much to avoid sy mptoms and illness in their children as in themselves. The other value for children is $156, based on a statistical model including a dummy variable for children. These values were used in all three analys es of the benefits and costs of the Clean Air Act (EPA 1997, 1999, and 2011a) and are summarized in the EPA’s Office of Air’s BenMAP program (EPA 201 1b). The problem with applying these values to formaldehyde exposure is that, as of yet, a statistical relationship between formaldehyde exposure and asthma exacerbation has not been sufficiently well established to develop a concentration-response function. As described in the section on concentration-response functions, however, there is a statistical relationship between formaldehyde exposure and asthma occurrence or incidence. While valuing asthma incidence is less common in EPA analyses than valuing asthma exacerbation, it has been done. This approach was used in the first prospective study of the benefits and costs of the Clean Air Act (EPA 1999). The number of new cases of chronic asthma as a function of ozone exposure was estimated based on a study by M cDonnell et al. (1999). Using an approach similar to the one that EPA uses to value reductions in chronic bronchitis, the willingness to pay to avoid chronic asthma was based as the net present value of a willingness to pay to avoid the disease over a lifetime. The central estimate (in 1990 dollars) was $25,000, with a triangular distribution and a range between $19,000 and $30,000. This range and distribution was based on two studies that estimated the willingness to pay to avoid chronic asthma in adults. Blumenschein and Johannesson (1998) used two different contingent valuation methods to estimate mean willingness to pay f or a cure for asthma. Using their mean estimate of $2,268 per ye ar, a three percent discount rate, and 1997 life expectancies for males in the United States, they e stimated a present discounted value of the stream of annual willingness to pay

values of $47,637 (in 2000$). O’Conor and Blomquist (1997) estimated the willingness to pay

to avoid chronic asthma by o ffering respondents a choice between hy pothetical medications which differed in safety and efficacy . They then combined this risk-risk tradeoff with a statistical value of life to derive an annual willingness to pay to avoid asthma. EPA (2011b) reports that, using a value of a statistical life of $5,894,400 (in 1997$), the implied annual value of avoiding chronic asthma, based on O’Conor and Blomquist, would be $1,474. Assuming a three percent discount rate and 1997 life expectancies for males in the United States, the present di scounted value of the stream of annual WTPs would be $30,257 (in 2000$). The implied annual value of asthma control of $1,474 (in 1995 $) is also close to the best estimates from more recent work by Blomquist, Dickie, and O’Conor (2011) . For the average age adult (45 ye ars old) the annual value of asthma control is $1,960 in 2007 dollars, or $1,441 in 1995 dollars . These willingness to pay values are higher, in real terms, than the direct medical costs for an average asthma patient reported by EPA (2000 d) and repeated below in Table 7 . Table 7: Lifetime Direct Medical Costs for Asthma (in 1999$)48

Source: Table IV.2 - 17 in EPA 2000 d The cost- of

- illness estimates in this table are based on the cost of medical services, reimbursement by

M edicare, and national recommendations and private sector costs regarding drug therapy . The values were obtained by multiplying the probabilities of patients utilizing various services times the cost per service, and then discounting over time to form a present value at various discount rates. One important assumption embedded in the estimate for the average patient is that 30 percent will become asy mptomatic as they

move into adulthood, so they do not incur costs beyond their seventeenth year, and 70 percent will have the disease throughout their life. All of the high-use patients are assumed to have asthma throughout their life. It is not necessarily

surprising that the direct medical costs are below the willingness to pay estimates. Ingeneral, cost- of - illness estimates understate the willingness to pay to avoid a given health effect because they often omit averting expenditures and the value of lost utility associated with pain and suffering. Although the cost- of - illness approach could be used to value asthma incidence, the use of willingness to pay values is considered to be a more appropriate measure (EPA 2010). Curiously, beside the prospective analysis of the Clean Air Act, monetization of asthma incidence has not been used in economic analys es of regulatory action. This is probably

d ue to a lack of an appropriate concentration-response relationship. Nevertheless, the EPA’s Office of Air includes rec ommended values for the monetization of chronic asthma in its BenMAP documentation (EPA 2011b) based on Blumenschein and Johannesson (1998) and O’Conor and Blomquist (1997). The value of avoiding one statistical case of chronic asthma is the average of the two estimates, based on a three percent discount rate, or $38,947 (in 2000$). This is the value that will be used as the willingness to pay f or reduced asthma incidence from formaldehyde exposure. There are two reasons why this is not the appropriate value to use for an asthma case developed in the 6 - 17 age range. First, the value reported in BenMAP represents the average value for adult- onset asthma. The method used to calculate the willingness to pay i s based on the present discounted value of a stream o f annual values derived from Blumenschein and Johannesson (1998) and O’Conor and Blomquist (1997). Using this method, the willingness to pay to avoid child- onset asthma should be larger because of more years associated with having asthma.49

The present disc ounted value of the stream of annual WTPs for someone who gets asthma at age a is 1 1 (1 ) a LE annual a j j WT P pdv dr 

         

##  ( 10 ) where annual 

### WTP denotes the annual WTP, a denotes the age at onset of asthma (e.g., a = 6, 7, ..., or 17),

a 

### LE denotes the life expectancy for an individual age a, and dr denotes the discount rate, Note that this implicitly

assumes that an individual who develops asthma will suffer with the disease for the rest of his or her life (i.e., he or she will never become as ymptomatic). A weighted average pdv can be calculated using the percentages of the population in the age categories as the weights. If j 

### p denotes the percent of children in the age range [6 - 17] who are age j , j = 6, ..., 17 and assuming that any child in the age range is eq ually likely to have asthma, then

17 [6 17 ] 6 j j

j pdv p pdv

   

#  ( 10 ) Inflating the willingness to pay values for adult- onset asthma derived from the annual values of Blumenschein and Johannesson (1998) and O’Conor and Blomquist (1997) using the CPI-U implies values of $60,322 and $38,314 (2010$) respectively . In contrast, the willingness to pay to avoid child onset- asthma between the ages of 6 and 17 using equation (12) would be $93,34 1 and $59,287 (2010$) respectively . The second reason why the values reported in BenMAP may not be appropriate is that there is substantial evidence that non-trivial percentages of asthmatics become as ymptomatic at some point in their lives. The 2009 population and prevalence rate results for the total U.S. population in the current asthma and lifetime asthma tables are available online from the Centers for Disease Control, National Health Interview Survey (NHIS) and are shown in Table 8 below .50

Table 8 : Results of the 2009 National Health Interview Survey Questions on Asthma and Implied Percentage of Asymptomatic Individuals

**Characteristic Total**  **Population Children (<18)**  **Adults (18+)** Lifetime asthma population (in 1000s) ( CDC Table 1 - 1 ) 39,930 10,196 29,734 Current asthma population (in 1000s) ( CDC Table 3 - 1 ) 24,567 7,111 17,456 Percent of asthmatics who still have asthma: 61.5% 69.7% 58.7% Percent of asthmatics who are now asymptomatic: 38.5% 30.3% 41.3% Source: Centers for Disease Control and Prevention 2009 The percent of asthmatics who still have asthma can be calculated b y dividing the “current asthma population” by the “lifetime asthma population. ” The percent of asthmatics that are asy mptomatic is found by

subtracting this value from 100%. For example, the adult age group (age 18+) , 29,734 ,000 individuals said they had been diagnosed with asthma; of these, 17,456 ,000

– or 58.7 % -- said they still have asthma . That implies that 100% - 58.7 % = 41.3 % of asthmatics in this age group are as ymptomatic. For simplicity, we assume that an asthmatic child who becomes asy mptomatic is equally

likely to do so at any

age after childhood. However, rather than calculate this probability for each age, we discretely

represent this equal probability 10 year intervals , starting with age 17

– ages 17, 27, 37, 47, 57, and 67. Since the total proportion of child asthmatics who will become asy mptomatic at some point after childhood is assumed to be 41%, and each age is assumed to have an equal probability of being the age at which the individual becomes asymptomatic, we assume that (41%/6 =) 6.8% o f child asthmatics become as ymptomatic at each of the 6 representative ages (17, 27, 37 , 47, 57, and 67) . For each of the representative ages (17, 27, 37, 47, 57, and 67), the analysis calculated , given that age -when- asthma- stops, using equations (27) and (28) above and replacing in equation (27) with the representative age, for all value s of a. If we assume that asthma starts at the beginning of an age and stops in the middle of an age, then if a child gets asthma at age x and becomes asy mptomatic at age y, the number of years the child has asthma is y

– x + 0.5. For example, the present discounted value of willingness to pay values for an asthmatic child of age a that become as ymptomatic at age 27 is 

##   27 0.5 1 1 for each in 6 1 7 . (1 ) a annual a j j WTP pdv a

dr  

           

##  ( 10 ) E quation **Error! Reference source not found.** can then be used to calculate the resulting value for ]

17 6 [

 

### pdv
. The weighted PDV (in 2010$) of willingness to pay values using this method is51

$79,214 and $50,314 using the derived annual values of Blumenschein and Johannesson (1998) and O’Conor and Blomquist (1997) respectively . The average of two studies is $64,764 (in 2010$). This is the value that will be used in this analysis. 3.3.2 Annual approach The concentration-response relationship for asthma incidence is given as an odds ratio for children exposed to formaldehyde. An odds ratio is a way o f comparing the probability o f an event, in this case asthma diagnosis, occurring between two groups and is commonly used in epidemiology

and risk assessment. Mathematically, “odds” is the ratio of the probability that an event will occur, P, to the probability that the event will not occur, (1 - P). That is, odds = P/(1 - P). The “odds ratio” is simply the ratio of the odds of the event occurring to an exposed group to the odds of the event occurring in the control group. That is exposed exposed control control 1 odds ratio 1 P

P P P    

       

   ( 11 ) An odds ratio is used to estimate how strongly exposure is associated with the probability that an event, such as asthma diagnosis, occurs. For example, if the odds ratio is 1.02 7 f or a 1 ppb change in formaldehyde exposure and we see 100,000 cases of asthma in our control population, then we would expect to see 102, 7 00 cases of asthma in a population exposed to one additional ppb of formaldehyde. The odds ratio can be related to the logistic model . The logistic function ( ) ( ) 

### ( ) 1 y x

y x 

### e F x e   varies between 0 and 1 and is used to represent the cumulative density function of some event occurring. y(x) is often expressed as a linear function of the form y = β 0 + β 1 ∙ x. So, in our case, F(x) is the probability

o f being diagnosed with asthma, P, and x is exposure to formaldehyde. The logit of P is then defined as the natural logarithm of the odds. That is, logit(P) = ln(P/(1 - P)) = β 0 + β 1 ∙ x. Therefore, the odds of asthma incidence for the entire population is e (β0 + β1 ∙x) , and the log- odds is β 0 + β 1 ∙ x. This means that the result of a logistic regression is nothing more than the log of the odds, or the log - odds. The odds ratio of an exposed group, x = x 1 , to some control group, x = x 0 , is then52       1 1 0 1 1 1 0 0 0 1 1 0 1 0 0 exposed exposed control control 1 odds ratio 1 x x x x x x P P e e e P e P              



       

          

   ( 12 ) So β 1 gives the impact of a one unit change in exposure (that is, where x 1 – x 0 = 1). Furthermore, β 1 can be recovered b y taking the log of the odds ratio given in the epidemiology literature. So, if the odds ratio of a 1 ppb increase in formaldehyde is 1.02 7 , then the β 1 in the implied logistic model would be ln(1.02 7 ) = 0.0 266 . Thi s can be used to determine the willingness to pay f or any change in emissions. Given a fixed willingness to pay

f or one case of asthma, the willingness to pay f or a change in exposure over some period of time, t0 -t 1 , is given b y a modified version of equation (7).



##   1 0 1 0 0 1 0 1 , , 1 1 Probability Ef fectiv e Total W TP WTP 1 of A sthm a Odds Ratio t H C t hct t t t h c t t t t

Pop 

   

                       

  

##    ( 13 ) The willingness to pay

for any given housing and age cohort is 

##   0 1 0 1 0 1

, , Probability Ef fectiv e WTP 1

of A st hm a Odds Ratio hct t t t t t Pop



       

             

  ( 14 ) The “effective odds ratio” is defined here as the odds ratio for a given change in exposure based on the odds ratio from the literature   0 1
0 1 ln exposure Effective Odds Ratio t t Odds Ratio t t e 

       

      

       ( 15 ) So, continuing with the example above, if the odds ratio is 1.02 for a 1 ppb change in formaldehyde exposure and we would like to determine the effect of a - 1.28 ppb change, the effective odds ratio would be e ln(1.02 7 )*( - 1.28) = 0.9 66 . A problem arises when the time period, t, is longer than the time step of the exposure model. This can be illustrated using chi ldhood asthma incidence and formaldehyde exposure. The odds ratio for asthma prevelance is based on the random effects model of McGwin et al. (2010) , excluding Rumchev et al. (2002) . The mean age for the six remaining studies in McGwin et a.. ranges from 8 to 14 ye ars old. The minimum age across the six studies was 6 and the maximum was 17 . This is the age range for which we estimate benefits in this analys is. We assume that probability o f being diagnosed with asthma can be taken from the prevalence rate reported in the 2008 National Health Interview Survey and reported in Table 9 . Table 9 : Lifetime Asthma Prevalence, 2008

**All ages 0**  **- 4**  **5 -**  **14 15**  **- 19**  **20 -**  **24 25**  **- 34**  **35 -**  **64 65+** 53

Asthmatics 38,450 1,499 6,576 3,653 3,609 5,119 13,887 4,107 Prevalence Rate 12.9 7.2 16.4 17.4 17.4 12.7 11.7 11 Population 298,062 20,819 40,098 20,994 20,741 40,307 118,692 37,336 Percentage 100.00% 3.90% 17.10% 9.50% 9.39% 13.31% 36.12% 10.68% Source: CDC 2008 (Tables 1 - 1 and 2 - 1) The data indicate that the asthma prevalence among 0 -4 ye ars olds is 7.2 percent and asthma prevalence among 5 - 14 ye ar olds is 16.2 percent . We assumes that once a child gets asthma, he or she does not outgrow it within that age range. F or example, for ages 0 - 4, the prevalence rate is 7.2%. This means that 7.2% of individuals ages 0 - 4 have asthma. S ince this age range starts at birth, all of these asthma cases must have started within the age range – i.e., in this case, prevalence equals cumulative incidence. F or any age range that starts at an age greater than 0, however, prevalence does not equal incidence, since some of the prevalent cases may have started at an age less than the lower bound of the age range. F or example, the 16.4% of individuals with asthma between the ages of 5 and 14 include those individuals whose asthma started during ages 0 -4. The incidence rate for the age range 5 - 14 was estimated by subtracting 7.2 percent from 16.4 percent to estimate that 9.2 percent of individuals in the age range of 5 - 14 are newly diagnosed with asthma. For this analys is, we assume that this 9.2 percent applies to the 12 ye ars that we are analyzing and that new cases are uniformly distributed over the se 11 ye ars. Thus, the annual incidence rate for our analys is is 0.092/12 = 0.007 6, or 0. 7 6% for children aged 6 to 17. Using this assumption, the expected number of asthma cases that we would expect to se e in the baseline per 100,000 children age 6 to 17 is illustrated by the red and blue areas in Figure 9 . We would expect 760 cases per year each ye ar for twelve years , for a sum total of 9 ,200 cases of If the odds ratio for a 1 ppb reduction is 1.02 7 , then we would expect a 2 .7 % reduction in the area under this curve for a 1 ppb reduction in exposure. That is, we would see 242 fewer asthma case s , found by

multiplying 9,200 times the effective odds ratio for a 1 ppb reduction, [1 - e ln(1.02 7 ) ∙( - 1)]. If we had a 10 ppb reduction, we would see 9,200 ∙ [1 - e ln(1.02 7 ) ∙( - 10)] = 2,152 fewer cases, an 23 % reduction. If we were to allocate these 2,152 fewer cases uniformly then we would see approximately 179 fewer cases in each . This is illustrated by the dark red areas in Figure 9 .54

**Figure 8**  **.** Number of asthma cases per 100,000 from ages 6 to 17, assuming an odds ratio of 1.02 and a 10 ppb change in formaldehyde exposure Allocating cases uniformly across the age brackets given by the CDC data provides us with a way to calculate the annual benefits of any exposure reduction that occurs for the entire time period9

. We now ask the more difficult question of how to model the annual reduction if the representative individual does not experience the exposure re duction over the entire time period. For example, how do we calculate the benefits to an individual who is 15 years old at the beginning of the policy

and experiences only one ye ar of the 10 ppb exposure reduction. We consider two possible approaches. The first approach is to assume that the odds ratio can be applied to the one ye ar reduction in the same fashion as it was applied for the 1 2 year time period. This is illustrated in Figure 9 . In the baseline, we expect 760 cases of asthma among 1 7 year- olds. Applying the odds ratio to the one ye ar interval implies that a 10 ppb reduction would produce 760 ∙ [1 - e ln(1.02 7 ) ∙( - 10)] = 178 fewer cases, represented by

the red area in Figure 9 . This is same area as the red area for the 15 to 16 age bracket in Figure 8 .

9 A more sophisticated approach than this uniform distribution could be developed by making assumptions about the nature of the concentration-response relationship for each year. For example, if the concentration-response relationship is linear for a 12 year period, the health can occur more than once, and the probability is dependent only on the exposure in one year, then the probability of the event not occurring over a 12 year period would be the product of the probability not occurring in each year. If we assume that the concentration-response relation ship is linear for one year, than this can be solved for the probability of the event in each year. A similar calculation could be done if the probability of an event is a function of the cumulative exposure. This analysis may be extended in this fashion f or the final rule.55

**Figure 8**  **.** Upper bound estimate of the reduced number of asthma cases per 100,000, assuming an odds ratio of 1.02 and a 10 ppb change in formaldehyde exposure for one ye ar The problem with this first approach is that it effectively treats the exposure as an one year effect. The odds ratio that indicated an 23 % reduction in asthma cases from a 10 ppb reduction in formaldehyde exposure over 16 ye ars is treated as implying an 2 3 % reduction in asthma case for a 10 ppb reduction in formaldehyde in one single ye ar. However, it serves as an upper bound to the number of asthma cases that might be avoided by an exposure reduction of one year . At the other extreme, we might assume that the exposure reduction of 10 ppb for one ye ar produces the same effect as if that 10 ppb was spread out over the entire 12 ye ar time period. In other words, a 10 ppb reduction in one ye ar is modeled like a 10/12 = 0.833 ppb reduction for 12 ye ars. This is illustrated in Figure 8 . The baseline is represented by the red and blue areas. A 0.625 ppb exposure reduction experienced over 16 years would result in 9,200 ∙ [1 - e ln(1.02 7 ) ∙( - 0.833 ) ] = 202 fewer cases, represented by the light and dark red areas. However, only 17 of those cases would occur in the 17 th year . This would be the number of fewer asthma cases we would see in a population of 760 fifteen- ye ar- old children who experience only one ye ar of a 10 ppb exposure reduction. This second approach provides a lower bound to the number of asthma cases that might be avoided from an exposure reduction over one ye ar .56

**Figure 8**  **.** Lower bound estimate of the reduced number of asthma cases per 100,000, assuming an odds ratio of 1.02 and a 10 ppb change in formaldehyde exposure for one ye ar This same logic for both the upper and lower bound can be used to determine the benefits of an exposure reduction for any individual who does not experience the exposure reduction for the full period. Mathematically, this means that for any time period 0 1



### [ , ] t t t  , the willingness to pay for an individual who is age k at the time of policy implementation would be Upper bound: 

###    
, , 0 1 0 1 ln exposure exposure

, , Probability WTP 1

of Asthma policy t b aseline t odds Ratio t h c t t t t WTP Pop e  

     



        

      

       

  ( 16 ) Lower bound: 

##     1
, ,

1 0 0 1 0 1 exposure exposure ln

, , Probability WTP 1

of Asthma t policy t b aseline t k odds Ratio t t t h c t t t t WTP Pop e  

    

  

    



                          

  ( 17 ) The reason that this adjustment is necessary is because there are individuals between the ages of 6 and 17 who are alive when the policy is implemented and we must calculate the benefits of the exposure reduction to them. In other words, we need this to model the transition path as we move from the ye ar after policy implementation, where the benefits are presumably l ower, to a time when everyone is enjoying the full benefit of the policy . After 12 ye ars, every individual will experience the full 12 years of exposure reduction (in one housing ty pe or another) , so won’t need to worry

a bout bounding the willingness to pay . Note that the upper bound estimate reflects57

full benefit for the 1 7 th ye ar, so it is the same as if the individual had experienced the full exposure reduction for the 1 1 ye ars prior. While we allocated the cases proportionally across age brackets in this case, calculating the transition path of the willingness to pay as the rule is fully phased may b e important if the probability

o f the outcome is small in the final years. For example, consider the case of lifetime cancer risk. The risk of being diagnosed with cancer is greatest in the middle years and smaller in the last year of one’s life. If we were to stop dosing an individual with a carcinogen in the final ye ar of his or her life, the upper bound estimate suggests that he or she would receive the same benefit in this last ye ar of life as an individual who had experienced the exposure reduction for his or her entire life. The lower bound estimate would treat this final ye ar exposure reduction as if it had been spread out over the entire lifetime and then allocates the last year’s benefit. 3.3.3 Example calculation The odds ratio for asthma incidence given earlier in the document is 1.027 per one ppb increase in formaldehyde exposure over a 16 ye ar period. Using the CDC data from Table 9 , above, we assume that the probability

o f being diagnosed with asthma between ag es 6 and 17 is 9 .2% and that this probability i s uniformly distributed across the age bracket. This can now be used for the benefits analys is using equations ( 1 9 ) and (20 ) above.For this example, we will use the same three housing profiles in climate zone 1 that we used for sensory

irritation: a new mobile home; a 1 -ye ar- old mobile home; and a new single family detached. We also use the same exposure changes for these three housing profiles, listed in Table 10 for the demographics of interest: school - aged children, age 2 - 16 , and non- industrial workers. Table 11 lists the benefits for the assumed exposure reductions in these three housing profiles. The first block represents the 3,436 children living in new mobile homes in climate zone 1 (EIA 2008, U.S. Bureau of Census 2009) . Notice that the population assumptions are the same as those that we assumed for the sensory irritation analysis, but we are only c oncentrating on the children ag e 6 - 17 . Under the baseline, we expect 26.34 asthma cases Table 10 : Illustrative formaldehyde exposure scenarios using three housing profiles Scenario Housing Profile Preschool / School Child Avg. ADC NonIndustrial Worker Avg. ADC Weighted average ADC (mg/m3 ) during current exposure year Baseline New

Mobile Home 0.0344 0.0349 1 -year- old Mobile Home 0.0248 0.0256 New Detached Home 0.0312 0.0319 CARB2 New

Mobile Home 0.0328 0.0333 1 -year- old Mobile Home 0.0238 0.0247 New Detached Home 0.0311 0.0318 Weighted average ADC (ppb) during current exposure year Baseline New

Mobile Home 28.00 28.41 1 -year- old Mobile Home 20.19 20.84 New Detached Home 25.40 25.97 CARB2 New

Mobile Home 26.70 27.11 1 -year- old Mobile Home 19.37 20.11 New Detached Home 25.32 25.89 Weighted average ADC (ppb) during current exposure year Change New

Mobile Home - 1.30 - 1.30 1 -year- old Mobile Home - 0.81 - 0.73 New Detached Home - 0.08 - 0.0858

for this housing profile. Given the assumed exposure reductions , we would expect 0.898 fewer asthma cases. Table 11 : Illustrative benefits calculations for asthma incidence using three housing profiles. **New Mobile Home, Cli mate Zone 1**  Age Population Baseline Exposure (ppb) CARB2

Exposure (ppb) Exposure

Reduction (ppb) Expected

Baseline Cases Effective

Odds Ratio Cases

Avoided WTP **3,436 26.34**  **0.898 $58,181**  6 294 28.00 26.70 - 1.30 2.2524 0.9659 0.077 $4,975 7 290 28.00 26.70 - 1.30 2.2270 0.9659 0.076 $4,919 8 288 28.00 26.70 - 1.30 2.2104 0.9659 0.075 $4,882 9 292 28.00 26.70 - 1.30 2.2421 0.9659 0.076 $4,952 10 281 28.00 26.70 - 1.30 2.1574 0.9659 0.074 $4,765 11 277 28.00 26.70 - 1.30 2.1234 0.9659 0.072 $4,690 12 277 28.00 26.70 - 1.30 2.1204 0.9659 0.072 $4,683 13 278 28.00 26.70 - 1.30 2.1288 0.9659 0.073 $4,702 14 283 28.00 26.70 - 1.30 2.1704 0.9659 0.074 $4,794 15 288 28.00 26.70 - 1.30 2.2052 0.9659 0.075 $4,871 16 290 28.41 27.11 - 1.30 2.2270 0.9659 0.076 $4,919 17 297 28.41 27.11 - 1.30 2.2777 0.9659 0.078 $5,031 **1 Year Old Mobile Home, Climate Zone 1** Age Population Baseline Exposure (ppb) CARB2

Exposure (ppb) Exposure

Reduction (ppb) Expected

Baseline Cases Effective

Odds Ratio Cases

Avoided WTP **3,436 26.34**  **0.556 $35,980**  6 294 20.19 19.37 - 0.81 2.2524 0.9785 0.048 $3,129 7 290 20.19 19.37 - 0.81 2.2270 0.9785 0.048 $3,094 8 288 20.19 19.37 - 0.81 2.2104 0.9785 0.047 $3,071 9 292 20.19 19.37 - 0.81 2.2421 0.9785 0.048 $3,115 10 281 20.19 19.37 - 0.81 2.1574 0.9785 0.046 $2,998 11 277 20.19 19.37 - 0.81 2.1234 0.9785 0.046 $2,950 12 277 20.19 19.37 - 0.81 2.1204 0.9785 0.045 $2,946 13 278 20.19 19.37 - 0.81 2.1288 0.9785 0.046 $2,958 14 283 20.19 19.37 - 0.81 2.1704 0.9785 0.047 $3,015 15 288 20.19 19.37 - 0.81 2.2052 0.9785 0.047 $3,064 16 290 20.84 20.11 - 0.73 2.2270 0.9807 0.043 $2,788 17 297 20.84 20.11 - 0.73 2.2777 0.9807 0.044 $2,851 **New Single Fam ily Detached Home, Climate Zone 1**  Age Population Baseline Exposure (ppb) CARB2

Exposure (ppb) Exposure

Reduction (ppb) Expected

Baseline Cases Effective

Odds Ratio Cases

Avoided WTP **47,964 367.72**  **0.797 $51,591**  6 4,101 25.40 25.32 - 0.08 31.4420 0.9978 0.068 $4,411 7 4,055 25.40 25.32 - 0.08 31.0871 0.9978 0.067 $4,361 8 4,025 25.40 25.32 - 0.08 30.8555 0.9978 0.067 $4,329 9 4,082 25.40 25.32 - 0.08 31.2980 0.9978 0.068 $4,391 10 3,928 25.40 25.32 - 0.08 30.1165 0.9978 0.065 $4,225 11 3,866 25.40 25.32 - 0.08 29.6410 0.9978 0.064 $4,159 12 3,861 25.40 25.32 - 0.08 29.5995 0.9978 0.064 $4,153 13 3,876 25.40 25.32 - 0.08 29.7162 0.9978 0.064 $4,169 14 3,952 25.40 25.32 - 0.08 30.2970 0.9978 0.066 $4,251 15 4,015 25.40 25.32 - 0.08 30.7836 0.9978 0.067 $4,319 16 4,055 25.97 25.89 - 0.08 31.0880 0.9978 0.067 $4,362 17 4,147 25.97 25.89 - 0.08 31.7959 0.9978 0.069 $4,46159

The effective odds ratio and willingness to pay are calculated using the upper bound, equation (16) above, assuming a willingness to pay to avoid one asthma case of $64,764 . For example, the willingness to pay

for a six -ye ar old child in this housing profile would be 

##        



#   ln 1.027 26.70 28.00 0 9.2% $64, 764 294 1 $ 4, 975

12 WTP e            

      The total willingness to pay for this housing profile would be $ 58,181 . The second block of Table 11 represents the benefits to individuals living in a 1 - ye ar- old mobile home. As with sensory irritation, we assume that the population distribution across ages is exactly the same as for new mobile homes but the exposure profile is different. Intotal, 26.34 asthma case s would be expected in the baseline (the same as for new mobile homes because the age distribution is the same). 0.556 cases would be avoided from the policy intervention, and the total willingness to pay

for this intervention would be $ 35,980 . The third block of Table 11 represents the benefits to individuals living in a new single - family detached home. An estimated 47,964 children live in this ty pe of home and the exposure reduction expected from the policy i s smaller than for mobile homes. An estimated 367.72 asthma case s would be expected in the baseline, 0.797 cases would be avoided from the policy intervention, and the total benefits of this action would be $ 51,591 . As was true in the case for sensory irritation, the sum of these willingness to pay values for new mobile homes and new detached houses ($ 58,181 + $ 51,591 = $ 109,772 ) is the willingness to pay for the reduction in asthma incidence in the first ye ar. The sum of the willingness to pay f or all three housing ty

pes ($ 58,181 + $ 35,980 + $ 51,591 = $ 145,751 ) is the wil lingness to pay f or any given year of analys is after the first year, assuming that the population remains constant and the distribution across housing types is the same in the future as it is now. These aggregate results are appropriate for the case where every individual is afforded the exposure reduction over their entire 12 ye ar exposure , but it may not be valid for the early ye ars of policy implementation. To calculate the transition path from initial to full policy implantation, we use the upper and l ower bound equations, equations ( 19 ) and (20 ) respectively. The results of these calculations are given in Table 12 . The left hand block of Table 12 gives the upper bound estimate of the policy f or thirty years of implementation. Inthis case, the undiscounted willingness to pay i s the same value, $ 145,751 , for each ye ar after the first ye ar. This is because the upper bound estimate assumes that the odds ratio is appropriate for an exposure reduction experience for one ye ar . So, for example, a child who is seventeen years old in the first year of policy implementation will only enjoy one ye ar of exposure reduction associated with this concentration-response function. Inthe upper bound estimate, that child receive s the same benefit in that year as all seventeen-ye ar- old children do in every year thereafter. Assuming a discount rate of 3%, the present discounted value of 30 years of the policy

is about $2 .9 million dollars for these three housing profiles.60

Table 12 : Upper and lower bound benefits calculations for asthma incidence using three housing profiles **Upper Bound Estimate Lower Bound Estimate**  Year New

Mobile Home 1 -Year Old

Mobile Home New SF

Detached Home Total WTP Total Discounted WTP New

Mobile Home 1 -Year Old

Mobile Home New SF

Detached Home Total WTP Total Discounted WTP **$1,803,609 $1,079,385**  **$1,599,309 $4,482,303**  **$2,966,556 $1,681,291**  **$1,023,670 $1,496,535**  **$4,201,497 $2,706,869**  0 $58,181 $51,591 $109,772 $109,772 $31,019 $27,971 $58,990 $58,990 1 $58,181 $35,980 $51,591 $145,751 $141,506 $35,452 $22,080 $31,902 $89,434 $86,829 2 $58,181 $35,980 $51,591 $145,751 $137,384 $39,455 $24,607 $35,470 $99,532 $93,818 3 $58,181 $35,980 $51,591 $145,751 $133,383 $43,082 $26,878 $38,676 $108,636 $99,417 4 $58,181 $35,980 $51,591 $145,751 $129,498 $46,279 $28,887 $41,517 $116,683 $103,671 5 $58,181 $35,980 $51,591 $145,751 $125,726 $49,067 $30,646 $44,005 $123,718 $106,720 6 $58,181 $35,980 $51,591 $145,751 $122,064 $51,461 $32,158 $46,147 $129,766 $108,677 7 $58,181 $35,980 $51,591 $145,751 $118,509 $53,454 $33,425 $47,942 $134,822 $109,622 8 $58,181 $35,980 $51,591 $145,751 $115,057 $55,059 $34,447 $49,390 $138,896 $109,646 9 $58,181 $35,980 $51,591 $145,751 $111,706 $56,271 $35,218 $50,485 $141,974 $108,811 10 $58,181 $35,980 $51,591 $145,751 $108,452 $57,073 $35,735 $51,219 $144,027 $107,170 11 $58,181 $35,980 $51,591 $145,751 $105,294 $58,181 $35,980 $51,591 $145,751 $105,294 12 $58,181 $35,980 $51,591 $145,751 $102,227 $58,181 $35,980 $51,591 $145,751 $102,227 13 $58,181 $35,980 $51,591 $145,751 $99,249 $58,181 $35,980 $51,591 $145,751 $99,249 14 $58,181 $35,980 $51,591 $145,751 $96,359 $58,181 $35,980 $51,591 $145,751 $96,359 15 $58,181 $35,980 $51,591 $145,751 $93,552 $58,181 $35,980 $51,591 $145,751 $93,552 16 $58,181 $35,980 $51,591 $145,751 $90,827 $58,181 $35,980 $51,591 $145,751 $90,827 17 $58,181 $35,980 $51,591 $145,751 $88,182 $58,181 $35,980 $51,591 $145,751 $88,182 18 $58,181 $35,980 $51,591 $145,751 $85,613 $58,181 $35,980 $51,591 $145,751 $85,613 19 $58,181 $35,980 $51,591 $145,751 $83,120 $58,181 $35,980 $51,591 $145,751 $83,120 20 $58,181 $35,980 $51,591 $145,751 $80,699 $58,181 $35,980 $51,591 $145,751 $80,699 21 $58,181 $35,980 $51,591 $145,751 $78,348 $58,181 $35,980 $51,591 $145,751 $78,348 22 $58,181 $35,980 $51,591 $145,751 $76,066 $58,181 $35,980 $51,591 $145,751 $76,066 23 $58,181 $35,980 $51,591 $145,751 $73,851 $58,181 $35,980 $51,591 $145,751 $73,851 24 $58,181 $35,980 $51,591 $145,751 $71,700 $58,181 $35,980 $51,591 $145,751 $71,700 25 $58,181 $35,980 $51,591 $145,751 $69,612 $58,181 $35,980 $51,591 $145,751 $69,612 26 $58,181 $35,980 $51,591 $145,751 $67,584 $58,181 $35,980 $51,591 $145,751 $67,584 27 $58,181 $35,980 $51,591 $145,751 $65,616 $58,181 $35,980 $51,591 $145,751 $65,616 28 $58,181 $35,980 $51,591 $145,751 $63,704 $58,181 $35,980 $51,591 $145,751 $63,704 29 $58,181 $35,980 $51,591 $145,751 $61,849 $58,181 $35,980 $51,591 $145,751 $61,849 30 $58,181 $35,980 $51,591 $145,751 $60,048 $58,181 $35,980 $51,591 $145,751 $60,048 The right hand block of Table 12 gives the lower bound of the policy. The undiscounted total willingness to pay

in the first ye ar, $ 58,990 , reflects the fact that most of the individuals receiving benefits have already been exposed to the baseline level of formaldehyde for part of their life and will not experience the full benefit of the program. Each ye ar, a new cohort of children is added who will experience the full exposure reduction, so the undiscounted willingness to pay

grows for the next 1 2 years, until it stabilizes at $ 145,751 . The present discounted value of 30 years of the policy in this case would be about $ 2.7 million.61

3.3.4 Discussion of uncertainties As with sensory

irritation, this ex ample assumes that the population is constant and that the future distribution across housing ty pes is exactly

the same as the present distribution. Accommodating a growing population and a different housing and age distribution would be implemented in the same way as described above, by

s imply b y changing the population values for each ye ar. As with sensory

irritation, the concentration-response function is non- linear. One way to deal with this would be to linearize this concentration-response function over the relevant range of exposure, for example with a first order Taylor approximation. However, given that the upper and lower bound calculations require us to form an annual change in risk and the change in exposure is relatively

small, the results from using the non- linear concentration-response function are probably very similar to the results if we were to use a linear function. The prevalence rates from the CDC are higher than those used for the most recent prospective analys is of the benefits and costs of the Clean Air Act (EPA 2011a) as reported by IEc (2011). IE c assumed a prevalence rate of 5.27% for children less than 18 ye ars of age, based on data from the American Lung Association (2002). The rates are also higher than the prevalence rates recommended by

the EPA’s Office of Air for their BenMAP program (EPA 2011b). The Office of Air suggests a prevalence rate of 6.14% for children less than 5 ye ars old and 10.7% for children 5 - 17 b ased on current prevalence rates in 2008 American Lung Association (2010). **3.4 Reduced Fertility**  Valuing reductions in fertility i s the most difficult of the three non - cancer effects. Similar to valuing asthma incidence, the concentration-response relationship is based on a period of time longer than the modeled one ye ar exposure window, so adjustments will have to be made similar to those for asthma incidence. Monetization, based on either the cost of illness or a willingness to pay framework, has been di scussed in the literature but has not been included in EPA rules. 3.4.1 Monetization The effect of pollution on pregnant women is a legitimate economic endpoint and has been included qualitatively

in a number of contexts. The Safe Drinking Water Act (SDWA 1974) explicitly mentions special consideration of pregnant women when setting priorities for maximum contaminant level goals for public drinking water systems. However, this is generally interpreted as evaluating the health impact on the mother rather than the ability to become pregnant. For example, EPA’s Ground Water Rule (EPA 2006) and Proposed Revised Total Coliform Rule (EPA 2010) both evaluate the effect of pollutants on the health of pregnant women but do not discuss potential impacts on fertility or other reproductive issues. When reproductive issues are monetized, they almost always take the form of valuing the health impact on the fetus rather than the ability to become pregnant. For example, both the Lead- Based Paint Hazard Standards (EPA 2000c) and the EPA Clean Air Mercury Rule (EPA 2005a)62

monetize the impact of IQ losses from in utero exposure to the pollutant, but not fertility e ffects. In the case of lead, there was an explicit recognition that exposure can cause reproductive difficulty in b oth men and women (EPA, CPSC, and HUD 2003), but the endpoint was not included in the quantitative analys is. Ina f ew cases, fertility

impacts have been included in the economic analysis, but without full monetization. For example, the Arsenic in Drinking Water rule explicitly mentioned “reproductive and developmental effects” (including spontaneous abortions, perinatal death, and stillbirth) as “potential non- quantifiable health benefit” (EPA 2000b). This was based on a National Research Council review of the EPA’s 1988 risk assessment for arsenic in drinking water (NRC 1999). However, the EPA chose not to monetize the endpoint because of a lack of human data or strong evidence on the risks of this health effect. The furthest that the EPA has gone in the past in valuing fertility impacts is the quantification, but not monetization , of fetal losses in the Stage 2 Disinfectants and Disinfection By products Rule (EPA 2005b). Inthis analysis, EPA calculated the odds -ratio or relative risk ratio for fetal loss from four population-based epidemiological studies. Because the exposure in these four studies was significantly greater than the exposure for the general population, survey data were used to derive a Population Attributable Risk (PAR) values for the general population. Using the annual incidence of fetal loss, EPA estimated a range of 0 to 18,700 annual fetal losses attributable to exposure to chlorinated drinking water. EPA chose neither to monetize these impacts nor to include them in the primary benefits analys is because it believed that the scientific knowledge on reproductive and developmental health effects was not strong enough to quantify risk in the primary

b enefits analysis. In particular, the lower 95 percent confidence intervals for the PAR for all four studies were less than zero, meaning that the possibility o f no effect could not be ruled out with 95 percent confidence. However, the calculations did indicate that the potential monetized value of this effect could be significant. In the same rule, EPA also mentioned other potential adverse reproductive impacts. For example, disinfectants are associated with miscarriages, which have associated medical costs as well as associated pain, suffering, and loss. These costs were estimated to range from $5,000 to $11,000 depending on the conditions of the miscarriage. For the case of formaldehyde, exposure has been associated with delayed conception (Taskinen et al. 1999) rather than fetal loss. Fertility problems are not uncommon during an individual’s reproductive cycle. Garceau et al. (2002) claims that one in six couples experience fertility difficulty at some point in time in their reproductive lives. The CDC (2005) reports that 7% of married couples of reproductive age reported not using contraception for 12 months and not becoming pregnant and 12% of women of childbearing age in the United States have used an infertility service at some point in their reproductive cycle. While obtaining some fertility service is common, only

3 .8 percent obtain ovul ation drugs, 1.1 percent undergo artificial insemination and 0.3 percent engages in assisted reproductive technology, primarily in-vitro fertilization (IVF). The remainder seeks advice, undergoes tests, or obtains medical help to prevent miscarriages or surgery

for blocked tubes. Ovulation drugs, artificial insemination, and IVF are considered cycle -based treatments (Katz 2011). Artificial insemination is a process in which the sperm is placed in either the cervix (intracervical insemination, or IC I) or the uterus (intrauterine insemination, or IUI) by artificial63

means. IVF is a process by which human egg cells are fertilized by sperm outside the body

and then transferred back to the patient's uterus. Success rates have been rising over time, with about 40% of couple s undergoing IVF ultimately b ecoming pregnant (de La Rochebrochard 2009). In an 18 month cohort study, Katz (2011) finds a 23.5% success rate for medication only ; a 38.5 - 55.6% success rate for IU I, depending on the accompanying fertility drug used ; and a 47.3 - 52.4% rate for IVF, depending on whether or not donor eggs were used. Katz (2011) reports median treatment costs, successful or not, of $1,182 for medication only; $3,595 - $8,594 for IU I; and $24,373 - $38,015 for IVF. The median treatment costs per successful outcome were $925 for medi cation; $4,124 - $8,542 for IUI; and $24,010 - $38,545 for IVF. This is roughly consistent with the cost estimates reported from other studies. The average cost per IVF cycle and per live -birth were estimated by Omurtag (2009) to be $9,226 and $56,419, respectively, and were estimated by Chambers (2009) to be $12,513 and $41,132, respectively. While there appear to be good data on the cost of treatment, there are few studies on the willingness to pay

to reduce delayed conception. Most economic valuation studies regarding fertility f ocus on an individual or a couples’ willingness to pay f or infertility

treatment, particularly in-vitro fertilization (IVF). Granberg et al. (1995) conducted a study o f 40 couples in Sweden using open- ended questions about the maximum number of treatments that they would be willing to undergo at various price level and their willingness to pay to have a child. The combined direct and indirect cost at the time of the study

was ₤3,880 per treatment and ₤11,490 per delivery . The willingness to pay f or a child ranged from ₤0 to ₤25,000, with more than half of the couple ’ s willing to pay more than ₤10,000. Ryan (1996) conducted a contingent valuation survey o f 339 women seeking fertility s ervices in Australia and found a mean willingness to pay o f about $2,500 per IVF attempt. Because this study included a subset of women who had completed their treatment, it allowed Ryan to estimate the willingness to pay with certainty and to distinguish between undergoing treatment and having a child versus completing the treatment but ending childless. Having a child increased the willingness to pay by about 30%. A second study

(Ryan, 1997) surveyed 458 women using a fertility s ervice in Scotland. Using a randomized bid and a dichotomous choice model, Ryan estimates a mean willingness to pay o f just over ₤5,000 per IVF attempt. There was a lower willingness to pay among individuals who had completed their treatment, likely reflecting the lower v alue associated with undergoing the treatment with the certainty of ending the process with no child. Note that both Ryan studies reflect a sample of individuals who are already s eeking infertility services. This suggests a potential sample selection bias for individual s with a preference for increasing their chances of having a child. Neumann and Johannesson (1994) conducted a study o f 231 nurses, doctors, parents, students , and administrative staff in the Boston area. The contingent valuation survey used a pay

ment card and a randomized bid to obtain the willingness to pay f or IVF treatment with varying probabilities of success. The survey c ompared the respondents' willingness to pay f or treatment in the event that they

are infertile (an ex -post analysis) with the willingness to pay f or insurance,64

assuming that they

do not know their infertility status (an ex - ante analysis). The ex -post willingness to pay

f or a 10% chance of success was $17,730, suggesting about $175,000 per statistical pregnancy . The mean ex - ante willingness to pay was $865 per IVF treatment with a 10% chance of success. The authors take this latter value to imply a willingness to pay

o f $1.7 million per statistical pregnancy, but this is based on the authors’ subjective assumption about couples’ ex - ante belief that they will need IVF treatment. Curiously, the willingness to pay p er statistical successful outcome drops as the level of certainly o f success rises. The authors estimate a willingness to pay o f $40,000 per statistical pregnancy in the ex -post case and $216,000 in the ex - ante case given 100% certainty o f ending with a child. Again, the ex - ante value is based on the author’s assessment of a couple’s ex - ante belief that they will need IVF treatment. All of these studies reflect willingness to pay f or fertility

treatment. The one study that comes close to valuing a reduction in delayed conception is Van Houtven and Smith (1999). They conducted a pilot study o f 188 individuals in four cities using a mall intercept survey . Respondents were asked to consider taking a completely s afe medication on a weekly basis which would delay

the increase in infertility risk for up to five ye ars based on randomly assigned monthly p ay ment amounts. They

f ound a willingness to pay o f willingness to pay

o f $16,500 to $49,400 per statistical pregnancy using a 3% discount rate (and $15,200 to $45,700 using a 5% rate). Valuing difficulty in conceiving due to formaldehyde exposure could be done using either the cost of treatment or some measure of the willingnes s to pay. Conceptually, the willingness to pay approach is preferable and could be deduced from the studies described above. For example, the van Houtven and Smith ( 199 9 ) value of approximately $ 15,000 - $50,000 for a statistical pregnancy

and a probability

o f success of 25% from ovulation drugs suggests a willingness to pay o f between $3,750 and $12,50 0 for fertility treatment. Alternatively, the Ryan (1996) willingness to pay

o f $2,500 per IVF attempt plus 30% for certainty, an average of 3.5 IVF cycles per patient, and a 50% success rate for IVF suggests a willingness to pay o f just under $6,000. However, given that there is there has been less research on these willingness to pay values than other values and the fact that the values have not b een used in previous economic analysis, it may b e more straight- forward to use a cost of illness approach. Futhermore, many of these willingness to pay studies apply to the more serious infertility rather than to the delayed fertility being modeled here . In this respect, the willingness to pay studies are not suitable for benefits transfer. For this analys is, we will use rounded values of the midpoint from the median cost of treatment values for fertility s ervices taken from Katz (2011): $ 1,000 for medication; $ 6,000 for IU I; and $ 30,000 for IFV. 3.4.2 Annual Approach The linear concentration-response function accounting for background exposures based on Taskinen et al. (1999) provides the most direct application of equation (7).65 

###   

##   

##   1

0 , , , , , , 1 1 Total W TP WTP exposure exposure

t H C t t h c t hcbaseline h c policy t t h c Pop     

                

##    ( 18 ) And the willingness to pay for any given housing and age cohort is



##   

#     

#  

, , , , , , Total WTP WTP exposure exposure

t h c t hcbaseline h c policy

Pop              

  ( 19 ) The number of women in the U.S. wishing to become pregnant can be estimated using the number of women of child-bearing age and information on pregnancy and contraception use (C DC 2010). The Taskinen (1999) study examined the fecundity of women age 20 - 40, so this is the age group which we will consider. The percentage of women seeking pregnancy over the course of a ye ar can be estimated as the sum of the percentage of women who reported not using contraception because they were seeking pregnancy and the percentage who are intentionally pregnant or postpartum after an intentional pregnancy . The percentages of women who reported not using contraception because they w ere seeking pre gnancy

were 4.3%, 6.3%, 5.9%, and 5.1% for age ranges 20 - 24, 25 - 29, 30 - 34, and 35 - 39, respectively. The percentages of women who were pregnant or postpartum were 10.0%, 7.7%, 8.1%, and 1.9% for age ranges 20 - 24, 25 - 29, 30 - 34, and 35 - 39, respectively . S ince half of pregnancies are intended, according to CDC (2010), the percentage of women seeking pregnancy over the course of a ye ar was estimated as the percentage of women currently seeking pregnancy plus half of the percentage of women who are pregnant or postpartum. These estimates are 9.3%, 10.2%, 10.0%, and 6.1% for age ranges 20 - 24, 25 - 29, 30 - 34, and 35 - 39, respectively . The percent of women seeking fertility treatment varies by age group . Table 13 presents the probabilities of seeking pregnancy and of obtaining cycle -based treatment based on tables provided by

the CDC (2005 and 2010) . Note that the last three columns, which present the percentages of women using a particular fertility treatment conditional on seeking some fertility treatment, do not sum to one hundred percent. This is because the costs associated with fertility treatments other than the three presented in the table were not estimated. Thus, these costs are implicitly assumed to be zero for the purpose of this analysis.66

Table 13: Probability of Obtaining Cycle -Based Treatment by Age Group **Percent Obtaining Treatm ent**  **Percent Obtaining Treatm ent**  **Conditional on Some Fertility Treatm** **ent Being Sought Age**  **Group Percent** 

**Seeking Pregnancy**  **Any**

**Fertility Service**  **Ovulation**

**drugs IUI**  **IVF Ovulation**  **drugs IUI**  **IVF** 20 - 24 9 .3% 4.9% 1.0% 0.2% 0.0% 20.41% 4.08% 0.00% 25 - 29 10.2 % 30 - 34 10 .0% 17.7% 5.3% 1.8% 0.4% 29.94% 10.17% 2.26% 35 - 39 6.1 % 17.3% 4.6% 1.4% 0.3% 23.96% 7.29% 3.65% Source: Percent seeking pregnancy is from CDC 2010 and percent obtaining treatment is from CDC 2005 The willingness to pay

for an exposure change for some time period, t0 to t 1 , would then be: 

#  

0 1 0 1 0 1

0 1 0 1 0

, , , , , Prob of seeking Total B aseline Policy

pregnancy WT P exposure exposure Cost of Cost of Pr( Meds ) P r( IUI ) Pr( IVF)

Meds IUI t t t t t t h c c h c h c

t t t t t t Pop 







                       

             

    

        1 Cost of Meds    

         (20 ) where Pr( ∙) is the probability o f needing the cycle -based treatment conditional upon requiring fertility assistance. As with asthma prevalence, the time period associated with the exposure -response relationship is longer than the 20 ye ar time step examined by Taskinen (1999) . Therefore, the linear model estimated above would be the probability o f having difficulty conceiving based on an exposure for that time period. As before, this causes a problem if we have an individual who does not experience the exposure reduction for the full time period. We can form an upper and lower bound of this willingness to pay as we did for asthma prevalence. The upper bound is based on the assumption that the linear concentration-response function holds for an exposure reduction of one ye ar. The lower bound assumes that an exposure reduction for one ye ar represents 1/20 th of an exposure red

uction for the entire 20 year time period. (For convenience, the 20 -40 time period is considered to be inclusive, so we are only modeling up to the individual’s 40 th birthday.) This means that for any time period 0 1 

### [ , ] t t t  , the willingness to pay f or an individual who is age k at the time of policy

implementation would be Upper bound: 

#  

0 1 0 1 0 1

0 1 0 1 0

, , , , , Prob of seeking Total Baseline Policy

pregnancy WTP exposure exposure Cost of Cost of Pr( Me ds) Pr( IU I) Pr( IVF)

Me ds IU I t t t t t t h c c h c h c

t t t t t t Pop 







                       

             

    

        1 Cost of Me ds    

         (21 )67

Lower bound: 

#   1

0 1 0 1 0 1

0 1 0 1

, , , 1 0 Baseline Policy Prob of seeking exposure exposure Total pregnancy WTP Cost of Cost of

Pr( Meds ) Pr(IUI)

Meds IU t t t t t t t k t t h c c

t t t t Pop t t 







         

              

       

 

      

   

    

## 

0 1 Cost of Pr( IVF) I Meds t t       

              (22 )

3.4.3 Example Calculation The linear concentration -response function for the risk of having difficulty conceiving is Risk = 0.0 5 6 + 0. 00 16 ∙ (exposure) We will use the same three housing profiles as we did in the previous examples. The assumed ADC for formaldehyde exposure for a woman age 20 -40 is given in Table 14 . This can be combined with the probability o f reduced fecundity to produce a probability o f undergoing fertility

treatment. The willingness to pay for fertility treatment will be treated in an expected value context. The average cost and probability o f obtaining various cycle -based fertility treatments at various ages is given in Table 1 4 . The first column gives the age ranges and the second column provides the proabililty that a woman is seeking pregnancy . Women between 20 and 34 ye ars old have around a 10% probability o f seeking pregnancy . From 35 to 40, the probability o f a woman seeking pregnancy drops to 6.1%. The final three columns give the probability o f obtaining treatment, conditional upon seeking some fertility treatment. Women seeking fertility treatment have no chance of being re commended for the more invasive and expensive IVF in their 20s, but have an increasing chance of obtaining this procedure in their 30s. Inc ontrast, obtaining medication and IU I peaks between ages 30 - 34. It is true that women older than age 39 also obtain fertility treatment, but the costs and probabilities have been truncated at age 39 to remain consistent with Taskinen et al. (1999). The average cost of the three ty

pes of cycle -based fertility treatment, based on Katz (2010), is given in the final row. Table 14 : Illustrative formaldehyde exposure (is ppb) using three housing profiles Scenario Housing Profile NonIndustrial Worker Avg. ADC Weighted average ADC (ppb) during current exposure year Baseline New

Mobile Home 28.41 1 -year- old Mobile Home 20.84 New Detached Home 25.97 CARB2 New

Mobile Home 27.11 1 -year- old Mobile Home 20.11 New Detached Home 25.8968

Table 16 shows the illustrative benefits calculation for fertility effects in women living in the three housing ty

pes in climate zone 1. The population size is assumed to be half of the total population living in this housing structure because we are only dealing with women. The first seven columns of Table 1 6 represents the 2,943 women living in new mobile. Given reduced emissions of 1. 30 ppb for every year, we would expect 0.548 fewer cases of women having difficulty conceiving for this housing profile. The total willingnes s to pay

to obtain this reduction is $ 543. The next six columns represent women living in one ye ar old mobile homes. Since the emission reduction is smaller, we expect to see 0.308 fewer cases, with a willingness to pay of $ 300 . The last six columns reflect the impact on women of child-bearing age in new homes. Here we expect 0.478 fewer cases and estimate a total willingness to pay

of $ 465 . Table 1 7 gives the upper and lower bound estimate of the policy f or the three housing ty

pes for thirty years of implementation. As before, the first ye ar of implementation (year 0) does not affect individuals in 1 - ye ar- old homes. The upper bound estimate (left hand block of Table 1 7 ) produces the same willingness to pay f or each ye ar after the first ye ar. The lower bound estimate (right hand block of Table 1 7 ) produces steadily increasing undiscounted willingness to pay estimates until the 20th year, reflecting the fact that earlier ye ars were accounting for women who only experience part of the benefit of the exposure reduction. Discounting these values at 3%, the total discounted willingness to pay from this illustration is between $ 26,461 and $ 21,779 . Table 1 5 : Average cost and probability of obtaining cycle -based fertility treatment **Age Group**  **Percent**

**Seeking Pregnancy**  **Percent Obtaining Treatm ent** 

**Conditional on Some Fertility Treatm** **ent Being Sought Ovulation**  **drugs IUI**  **IVF** 20 - 24 9.30% 20.41% 4.08% 0.00% 25 - 29 10.20% 30 - 34 10.00% 29.94% 10.17% 2.26% 35 - 39 6.10% 23.96% 7.29% 3.65% Average Cost $1,000 $6,000 $30,000 Source: Percent seeking pregnancy is from CDC 2010 and percent69

Table 1 6 : Illustrative benefit calculations for reduced fertility effects to women living in new mobile homes **New Mobile Home, Climate Zone 1 1 Year Old Mobile Home, Climate Zone 1**  **New Single Family Detached Home, Climate Zone 1** Age Population Baseline

Exposure (ppb) CARB2 Exposure (ppb) Exposure Reduction (ppb) Cases

Avoided WTP Population Baseline Exposure (ppb) CARB2 Exposure (ppb) Exposure Reduction (ppb) Cases

Avoided WTP Population Baseline Exposure (ppb) CARB2 Exposure (ppb) Exposure Reduction (ppb) Cases

Avoided WTP **2,943 0.548**  **$533 2,943**  **0.308 $300**  **41,081 0.478**  **$465** 20 158 28.41 27.11 - 1.30 0.0306 $14 158 20.84 20.11 - 0.73 0.0172 $8 2204 25.97 25.89 - 0.08 0.0267 $12 21 153 28.41 27.11 - 1.30 0.0296 $13 153 20.84 20.11 - 0.73 0.0167 $7 2134 25.97 25.89 - 0.08 0.0258 $12 22 151 28.41 27.11 - 1.30 0.0293 $13 151 20.84 20.11 - 0.73 0.0165 $7 2110 25.97 25.89 - 0.08 0.0256 $11 23 150 28.41 27.11 - 1.30 0.0291 $13 150 20.84 20.11 - 0.73 0.0164 $7 2098 25.97 25.89 - 0.08 0.0254 $11 24 152 28.41 27.11 - 1.30 0.0294 $13 152 20.84 20.11 - 0.73 0.0165 $7 2117 25.97 25.89 - 0.08 0.0256 $12 25 153 28.41 27.11 - 1.30 0.0325 $15 153 20.84 20.11 - 0.73 0.0183 $8 2132 25.97 25.89 - 0.08 0.0283 $13 26 150 28.41 27.11 - 1.30 0.0319 $14 150 20.84 20.11 - 0.73 0.0179 $8 2096 25.97 25.89 - 0.08 0.0278 $12 27 152 28.41 27.11 - 1.30 0.0324 $15 152 20.84 20.11 - 0.73 0.0182 $8 2127 25.97 25.89 - 0.08 0.0283 $13 28 153 28.41 27.11 - 1.30 0.0325 $15 153 20.84 20.11 - 0.73 0.0183 $8 2136 25.97 25.89 - 0.08 0.0284 $13 29 154 28.41 27.11 - 1.30 0.0327 $15 154 20.84 20.11 - 0.73 0.0184 $8 2150 25.97 25.89 - 0.08 0.0286 $13 30 153 28.41 27.11 - 1.30 0.0319 $51 153 20.84 20.11 - 0.73 0.0179 $28 2136 25.97 25.89 - 0.08 0.0278 $44 31 145 28.41 27.11 - 1.30 0.0303 $48 145 20.84 20.11 - 0.73 0.0170 $27 2029 25.97 25.89 - 0.08 0.0264 $42 32 141 28.41 27.11 - 1.30 0.0294 $47 141 20.84 20.11 - 0.73 0.0165 $26 1968 25.97 25.89 - 0.08 0.0256 $41 33 139 28.41 27.11 - 1.30 0.0289 $46 139 20.84 20.11 - 0.73 0.0163 $26 1936 25.97 25.89 - 0.08 0.0252 $40 34 135 28.41 27.11 - 1.30 0.0281 $45 135 20.84 20.11 - 0.73 0.0158 $25 1883 25.97 25.89 - 0.08 0.0245 $39 35 138 28.41 27.11 - 1.30 0.0176 $31 138 20.84 20.11 - 0.73 0.0099 $18 1931 25.97 25.89 - 0.08 0.0153 $27 36 134 28.41 27.11 - 1.30 0.0170 $30 134 20.84 20.11 - 0.73 0.0096 $17 1872 25.97 25.89 - 0.08 0.0149 $26 37 137 28.41 27.11 - 1.30 0.0174 $31 137 20.84 20.11 - 0.73 0.0098 $17 1913 25.97 25.89 - 0.08 0.0152 $27 38 143 28.41 27.11 - 1.30 0.0182 $32 143 20.84 20.11 - 0.73 0.0103 $18 2002 25.97 25.89 - 0.08 0.0159 $28 39 151 28.41 27.11 - 1.30 0.0192 $34 151 20.84 20.11 - 0.73 0.0108 $19 2110 25.97 25.89 - 0.08 0.0168 $3070

Table 1 7 : Upper and Lower bound benefits for reduced fertility e ffects using three housing profiles **Upper**

**Bound Estimate**  **Low er Bound**  **Estimate** Year New Mobile

Home 1 -Year Old

Mobile Home New SF

Detached Home Total WTP Total Discounted WTP New

Mobile Home 1 -Year Old

Mobile Home New SF

Detached Home Total WTP Total Discounted WTP **$16,538 $9,003**  **$14,429 $39,970**  **$26,461 $14,272**  **$7,901 $12,450**  **$34,623 $21,779**  0 $533 $465 $999 $999 $226 $197 $422 $422 1 $533 $300 $465 $1,299 $1,261 $252 $141 $219 $612 $595 2 $533 $300 $465 $1,299 $1,224 $277 $156 $242 $674 $635 3 $533 $300 $465 $1,299 $1,189 $302 $170 $263 $734 $672 4 $533 $300 $465 $1,299 $1,154 $317 $178 $284 $779 $692 5 $533 $300 $465 $1,299 $1,121 $349 $196 $304 $850 $733 6 $533 $300 $465 $1,299 $1,088 $372 $209 $324 $905 $758 7 $533 $300 $465 $1,299 $1,056 $393 $221 $343 $958 $779 8 $533 $300 $465 $1,299 $1,025 $415 $233 $362 $1,010 $797 9 $533 $300 $465 $1,299 $996 $446 $251 $380 $1,077 $826 10 $533 $300 $465 $1,299 $967 $455 $256 $397 $1,107 $824 11 $533 $300 $465 $1,299 $938 $472 $265 $412 $1,149 $830 12 $533 $300 $465 $1,299 $911 $487 $274 $425 $1,185 $831 13 $533 $300 $465 $1,299 $885 $499 $281 $436 $1,215 $828 14 $533 $300 $465 $1,299 $859 $509 $286 $444 $1,240 $820 15 $533 $300 $465 $1,299 $834 $517 $291 $451 $1,259 $808 16 $533 $300 $465 $1,299 $810 $524 $295 $457 $1,275 $795 17 $533 $300 $465 $1,299 $786 $528 $297 $461 $1,287 $779 18 $533 $300 $465 $1,299 $763 $532 $299 $464 $1,295 $761 19 $533 $300 $465 $1,299 $741 $533 $300 $465 $1,299 $741 20 $533 $300 $465 $1,299 $719 $533 $300 $465 $1,299 $719 21 $533 $300 $465 $1,299 $698 $533 $300 $465 $1,299 $698 22 $533 $300 $465 $1,299 $678 $533 $300 $465 $1,299 $678 23 $533 $300 $465 $1,299 $658 $533 $300 $465 $1,299 $658 24 $533 $300 $465 $1,299 $639 $533 $300 $465 $1,299 $639 25 $533 $300 $465 $1,299 $620 $533 $300 $465 $1,299 $620 26 $533 $300 $465 $1,299 $602 $533 $300 $465 $1,299 $602 27 $533 $300 $465 $1,299 $585 $533 $300 $465 $1,299 $585 28 $533 $300 $465 $1,299 $568 $533 $300 $465 $1,299 $568 29 $533 $300 $465 $1,299 $551 $533 $300 $465 $1,299 $551 30 $533 $300 $465 $1,299 $535 $533 $300 $465 $1,299 $535

3.4.4 Discussion of uncertainties As with the previous endpoints, this example assumes that the population is constant and that the future distribution across housing ty pes is exactly

the same as the present distribution. Correcting for trends in population and housing could be done in the same fashion as for the other endpoints.71

The willingness to pay

e stimates above reflects the positive value that women who have trouble conceiving would obtain by not suffering this difficulty . However, it should be pointed out that there is probably a much smaller opposite effect associated with an increase in pregnancy b y women who do not want to become pregnant. A complete analysis would incorporate this opposite effect, but is much more difficult and controversial to estimate and value. The equation for probability o f having trouble conceiving is used to produce an estimate of the number of women who whose time to pregnancy will exceed 12 months. The valuation treated this estimate as equal to the number of women who seek fertility treatment. This might be justified as an upper bound estimate, but there are certainly women to have trouble becoming pregnant and do not seek fertility treatment. This is not reflected in this analysis. The willingness to pay

values are based on the average cost of obtaining fertility services. While this is not the theoretically c orrect value to use for monetization, it is a reasonable estimate if most fertility

treatment costs are paid for out- of -pocket by the patients rather than by insurance. Inactually

paying the cost, the patient reveal s that the treatments is worth at least as much as she paid. The maximum willingness to pay, however, would be expected to be greater than these costs because many consumers receive more utility f rom service than they

p ay f or it . Note that this does not take into account any utility or disutility

of treatments. This suggests using cost- ofillness will produce an underestimate. On the other hand, the se values are being applied to all women who are exposed to formaldehyde , but not all of the women whose fertility i s affected will pay

the market prices for the treatments. In other words, we have an estimate of the willingness to pay for the women who choose to undergo these treatments. However, we do not have an estimate of the willingness to pay f or the women whose fertility i s affected but choose not to undertake the treatment . Their values are likely to be positive, but they are less than the costs. For ex ample, it may b e that women with lower household incomes are less likely to pay for treatments at the going prices. In this sense, the estimate of fertility benefits using the average cost of obtaining fertility s ervice may be biased upward. Also, all of the women in the Taskanen study whose pregnancies were delayed ultimately had successful pregnancies. The “health endpoint” being modeled is therefore delayed fertility, not infertility . If women would be willing to pay more to increase the chance of getting pregnant than they would to decrease the time to pregnancy then the cost- of - illness estimates based on a successful pregnancy

used here would overstate the true willingness to pay . The willingness to pay

f or reduced time to conceive could, theoretically, be estimated with a stated preference survey, but this would require women to place an ex - ante value on their willingness to pay

to undergo various ty

pes of fertility

treatment, each with an uncertain outcome. Since the women producing this ex - ante value will likely have had little or no experience with the various fertility tre atments, such a study would require extensive pre -testing to assure that the women understand the commodity . There are also reasons to believe that the cost of treatment may not even reflect the correct endpoint . For example, women are willing to pay to reduced time to conceive but , at the same time , are reluctant to undergo fertility treatment . Furthermore, the cycle -based tre atments72

described here may

not even be the appropriate treatment to reduce delayed fertility associated with formaldehyde exposure . Finally, the values reflect that ages reported in the Taskinen (1999) study, ages 20 -40. However, women older than age 40 obtain fertility treatment. The CDC (2005) study that was used to produce probabilities for the 20 -40 age group reports 19.2% of the women studied who were age 40 to 44 had obtained some ty pe of infertility

treatment, with 8.4% obtaining ovulation drugs, 2.6% undergoing artificial insemination, and 0.7% utilitizing assisted reproductive technology (assumed to be IVF). The value of reduced infertility e ffects from reduced formaldehyde exposure is not reflected in this analysis in order to remain consistent with Taskinen (1999), but could be included b y simply extending the ages considered.73

**3.5 References**  American Lung Association. 2002. Trends in Asthma Morbidity and Mortality . American Lung Association, Best Practices and Program Services, Epidemiology and Statistics Unit. American Lung Association. 2010. Trends in Asthma Morbidity and Mortality . American Lung Association, Best Practices and Program Services, Epidemiology and Statistics Unit. Accessed on April 4, 2011 http://www.lungusa.org/finding- cures/our-research/trend-reports/asthma-trendreport.pdf Berger, Mark C. et al. 1987. "Valuing Changes in Health Risks: A Comparison of Alternative Measures." The Southern Economic Journal 53: 977 - 984. 1987. Bloomquist, Glenn C. , Mark Dickie, and Richard M. O’Conor. 2011. “Willingness to Pay for Reducing Fatality Risks and Asthma Symptoms: Values for Children and Adults of All Ages” Resource and Energy Economics . 33 (May): 410 - 425. Blumenschein, K. and M. Johannesson. 1998. Relationship between quality of life instruments , health state utilities, and willingness to pay in patients with asthma. Ann Allergy Asthma Immunol. Vol. 80 (2): 189 - 94. Bosworth, Ryan, Trudy Ann Cameron, and J.R. DeShazo. 2009. “Demand for Environmental Policies to Improve Health: Evaluating Community- Level Policy Scenarios” Journal of Environmental Economics and Management 57: 293 - 308. Center for Disease Control and Prevention (CDC) . 2005. Fertility, Family Planning, and Reproductive Health of U.S.Women: Data From the 2002 National Survey of Family Growth. DHHS Publication No. (PHS) 2006 - 1977. U.S. Hyattsville, MD: National Center for Health Statistics Center for Disease Control and Prevention (CDC) . 2008. National Health Interview Survey . Accessed March 31, 2011. http://www.cdc.gov/asthma/nhis/08/data.htm . Centers for Disease Control and Prevention. 2009. National Health Interview Survey. Available at: http://www.cdc.gov/asthma/nhis/09/data.htm (Accessed Mar ch 31, 2011) Centers for Disease Control and Prevention (CDC). 2010 . Use of Contraception in the United States: 1982

–2008, National Center for Health Statistics. Vital and Health Statistics 23. Chambers G, Sullivan E, Ishihara O, Chapman M, Adamson G. 20 09. “The economic impact of assisted reproductive technology : a review of selected developed countries.” Fertility and Sterility . 91:2281

–94. Cropper, Maureen and Paul Portney . 1990. “Discounting and the Evaluation of Life - Saving Programs.” Journal of Risk and Uncertainty . 3:360 - 380.74

Cropper, Maureen and Frances Sussman. 1990. “Valuing Future Risks to Life.” Journal of Environmental Economics and Management. 19:160 - 174. de La Rochebrochard, Elise, Celine Quelen, Rusudan Peikrishvili, Juliette Guibert, and Jean Bouyer. 2009. “Long-term outcome of parenthood project during in vitro fertilization and after discontinuation of unsuccessful in vitro fertilization.” Fertility and Sterility. 92(1): 149 - 156. Dickie, M. and S. Gerking. 1987. Reconciling Averting Behavior and Contingent Valuation Benefit Estimates of Reducing S ymptoms ofOzone Exposure (draft), as cited in Neumann, J. E., M. Dickie, and R.E. Unsworth. 1994. Prepared by Industrial Economics. Prepared for JimDeMocker, U.S. EPA, Office ofAir and Radiation. March 31. Dickie, Mark and Shelby Gerking. 1991. “Willingness to Pay for Reduced Morbidity : A Household Production Approach” Southern Economic Journal . 57(3): 1991. Dickie, Mark and Shelby Gerking. 1998. “Valuing Nonmarket Goods: A Household Prod uction Approach.” Unpublished manuscript. Dickie, Mark and Victoria L . Messman. 2004. “Parental altruism and the value of avoiding acute illness: are kids worth more than parents?” Journal of Environmental Economics and Management. 48(3): 1146 - 1174. Dickie, M., and V. L. Ulery . 2002. “Parental Altruism and the Value of Avoiding Acute Illness: Are Kids Worth More than Parents?” Working paper. Department of Economics, University of Central Florida, Orlando. Accessed April 4, 2011. http://www.econ.ucsb.edu/~tedb/Courses/UCSBpf/readings/dickieulery .pdf Fleurence, Rachael L . and Christopher S. Hollenbeak. 2007. “Rates and Probabilities in Economic Modelling, Transformation, Translation and Appropriate Application” Pharmacoeconomics . 25(1): 3 - 6. Freeman and Hutchinson. 1980. “Prevalence, Incidence, and Duration” American Journal of Epidemiology. 12(5): 707 - 723. Fombonne, Eric. 2007. “Epidemiology .” In Lewis's Child and Adolescent Psychiatry : A Comprehensive Textbook, Andres Martin and Fred R. Volkmar, eds. 4th Edition: pp. 150 - 171. Garceau, L., J.Henderson, L .J.Davis, S.Petrou, L .R.Henderson, E.McVeigh , D.H.Barlow, and L. L.Davidson. 2002. “Economic implications of assisted reproductive techniques: a systematic review.” Human Reproduction . 17(12): pp. 3090

–3109. Gastmeier, Petra; Helga Bräuer; Dorit Sohr; Christine Geffers; Dietmar H. Forster; Franz Das chner; and Henning Rüden. 2001. “Converting Incidence and Prevalence Data of Nosocomial Infections: Results from Eight Hospitals.” Infection Control and Hospital Epidemiology . 22(1): pp. 31 - 34.75

Granberg, Maria, Matt Swikland, Lars Nilsson, and Lars Hamberger. 1995. “Couples’ willingness to pay

for IVF/ET.” Acta Obstet Gynecol Scand. 74: 199 - 202. Green, A. E. S., Berg, S. V., Loehman, E. T., Shaw, M. E., Fahien, R. W., Hedinger, R. H., Arro yo, A. A., and De, V. H. 1978. An Interdisciplinary Study of the Health, Social and Environmental Economics of Sulfur Oxide Pollution in Florida . Interdisciplinary Center for Aeronomy and (other) Atmospheric Sciences, University of Florida, Gainesville, Florida (1978). Hanrahan, Lawrence P.; Dally, Kay A.; Anderson, Henry A.; Kanarek, Marty S.; and Rankin, John. (1984) “Formaldehyde vapor in mobile homes: a cross sectional survey of concentrations and irritant effects.” Am. J. Public Health . 74(9): 1026 - 1027. Hawthorne, A.R., Matthews, T.G. 1987. “Models for estimating organic emissions from building materials: Formaldehyde example” Atmospheric Environment. 21(2): pp 419 -424. Industrial Economics, Incorporated (IEc). 1993 a . “ Comparison of Morbidity, Visibility, and Forest Valuation Studies to Contingent Valuation Guidelines ” Memorandum to Jim DeMocker, Office of Policy

Analys is and Review, Office of Air and Radiation, U.S. Environmental Protection Agency, prepared by Jim Neumann, Lisa Robinson, and Bob Unsworth. September 30. Industrial Economics, Incorporated (IEc). 199 3 b . “Review of Existing Value of Morbidity Avoidance Estimates: Draft Valuation Document” Memorandum to Jim DeMocker, Office of Policy Analys is and Review, Office of Air and Radiation, U.S. Environmental Protection Agency, prepared by

Jim Neumann and Bob Unsworth. September 30. Industrial Economics, Incorporated (IEc). 2011. “Health and Welfare Benefits Analyses to Support the Second Section 812 Benefit- Cost Analysis of the Clean Air Act.” Final report prepared for the Office of Air and Radiation, US Environmental Protection Agency . February . Katz, Patricia, Jonathan Showstack, James F. Smith, Robert D. Nachtigall, Susan G. Millstein, Holly Wing, Michael L. Eisenberg, Lauri A. Pasch, Mary S. Croughan, and Nancy Adler. 2011. “Costs of infertility

treatment: results from an 18 - month prospective cohort study .” Fertility and Sterility . 95(3): 915 - 921. Kenkel, Donald; Berger, Mark; and Blomquist, Glenn. 1994. “Contingent Valuation in Health,” In Valuing Health for Policy: An Economic Approach . Tolley, Kenkel , and Fabian eds. Chicago, Il: University

of Chicago Press. Kriebel, D; Sama, SR; Cocanour, B. (1993) Reversible pulmonary responses to formaldehyde. A study of clinical anatomy students. Am Rev Respir Dis 148:1509

– 1515. Kriebel, D; Myers, D; Cheng, M; et al. (2001) Short-term effects of formaldehyde on peak expiratory

flow and irritant sy

mptoms. Arch Environ Health 56(1):11

– 18.76

Krupnick, A. J., and R. J. Kopp. 1988. "The Health and Agricultural Benefits of Reductions in Ambient Ozone in the United States," Appendix to Office of Technology Assessment, U.S. Congress, 1989, Catching Our Breath: Next Step for Reducing Urban Ozone (Washington, D.C.: U.S. Government Printing Office). Krzy zanowski, M; Quackenboss, JJ; Lebowitz, MD. (1990) Chronic respiratory effects of indoor formaldehyde exposure. Environ Res 52:117

– 125. Jurvelin, J., Vartiainen, M., Jantunen, M., Pasanen, P., 2001. “Personal exposure levels and microenvironmental concentrations of formaldehyde and acetaldehyde in the Helsinki metropolitan area.” Finland. Journal of Air and Waste Management. 51: 17

–24. Liu, K.S., F.Y. Huang, S.B. Hay ward, J. Wesolowski, and K. Sexton. 1991. “Irritant effects of formaldehyde exposure in mobile homes.” Environ Health Perspect. 94: 91

–94. Loehman, E.T., S.V . Berg, A.A. Arro yo, R.A. Hedinger, J.M. Schwartz, M.E. Shaw, R.W. Fahien, V.H. De, R.P. Fishe, D.E. Rio, W.F. Rossley, and A.E.S. Green. 1979. “Distributional Analysis of Regional Benefits and Cost of Air Quality Control.” Journal of Environmental Economics and Management . 6:222 - 243. McDonnell, William F., David E. Abbey, Naomi Nishino, Michael D. Lebowitz. 1999. “LongTerm Ambient Ozone Concentration and the Incidence of Asthma in Nonsmoking Adults: The Ahsmog Study” Environmental Research Section A. 80: 110 - 121. Mortimer, K.M., L.M. Neas, D.W. Dockery, S. Redlinez, and I .B. Tager. 2002. “The effect of air pollution on inner- city

children with asthma.” European Respiratory Journal. 19: 699

–705. National Research Council (NRC) 1999. Arsenic in Drink ing W ater . Washington, DC: National Academies Press. Nati onal Research Council (NRC) 2001 . Standing Operating Procedures for Developing Acute Exposure Guideline Levels for Hazardous Chemicals . Washington, DC: National Academies Press. Neumann, Peter J., and Magnus Johannesson. 1994. “The Willingness to Pay for in Vitro Fertilization: A Pilot Study Using Contingent Valuation.” Medical Care . 32(7): 686 - 699. O'Conor, R. M. and G. C. Blomquist. 1997. Measurement of Consumer- Patient Preferences Using a Hy

brid Contingent Valuation Method. Journal of Health Economics. Vol. 16: 667 - 683. Omurtag, Kenan R., Aaron K. Styer, Donna Session, and Thomas L. Toth. 2009. “Economic Implications of Insurance C overage for In Vitro Fertilization in the United States: A Review.” The Journal of Reproductive Medicine . 5(11 - 12): pp. 661 - 8.77

Ostro, B.D., M.J. Lipsett, J.K. Mann, H. Braxton- Owens, and M.C. White. 2001. “Air Pollution and Asthma Exacerbations Among Afri can American Children in Los Angeles.” Epidemiology . 12(2): 200 - 8. Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D.W. Dockery, J.S. Evans, F.E. Speizer, and C.W. Heath, Jr. 1995. “Particulate Air Pollution as a Predictor of Mortality in a Prospective Study of U.S. Adults.” Am. J. Respir. Crit. Care Med. 151: 669 - 674. Rowe, R.D. and L.G. Chestnut. 1986. “Oxidants and Asthmatics in Los Angeles: A Benefits Analysis - Executive Summary .” Prepared by Energy and Resource Consultants, Inc. Report to the U.S. EPA, Office of Policy Analys is. EPA- 230 - 09 - 86 - 018. Washington, D.C. March. Ryan, Mandy. 1996. “Using Willingness to Pay to Assess the Benefits of Assisted Reproductive Techniques.” Health Economics . 5: 543 - 558. Ryan, Mandy. 1997. “Should government fund assisted reproductive techniques? A study using willingness to pay .” Applied Economics. 29,:841 - 849. The Safe Drinking Water Act (SDWA). 1974. 42 U.S.C. § 300g

– 1. Taskinen, H.K., P. Kyy

ronen, M. Sallmen, S.V. Virtanen, T.A. Liuldwnen, O. Huida, M. - L. Lindbohm, and A. Anttila. 1999. “Reduced Fertility Among Female Wood Workers Exposed to Formaldehyde” American Journal of Industrial Medicine . 36:202 - 121. Tolley, G., et al. Valuation of Reductions in Human Health Symptoms and Risks . Volumes 1 - 4.Final Report to the US Environmental Protection Agency . University of Chicago, Chicago, IL.January

1986. Accessed March 25, 2011. Volume 1: http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE - 0092a- 1.doc/$file/EE - 0092a1.doc Volume 2: http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE - 0092B - 01.pdf/$file/EE - 0092B 01.pdf Volume 3: http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE - 0092C.pdf/$file/EE - 0092C.pdf Volume 4: http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE - 0092C.pdf/$file/EE - 0092C.pdf U.S. Census Bureau. 2009 . 2007 - 2009 American Community Survey (ACS) 3 -Year Estimates. U.S. Energy

Information Administration (EIA). 2008. 2005 Residential Energy Consumption Survey . http://www.eia.gov/emeu/consumption/ U.S. Environmental Protection Agency (EPA). 1997. The Benefits and Costs of the Clean Air Act: 1970 to 1990. U.S. EPA, Office of Air and Radiation, Office of Policy, Planning and Evaluation. Washington, DC. EPA 410 - R- 97 - 002. October. http://www.epa.gov/airprogm/oar/sect812/index.html .78

U.S. Environmental Protection Agency (EPA). 1999. The Benefits and Costs of the Clean Air Act: 1990 to 2010. U.S. EPA, Office of Air and Radiation, Office of Policy. Washington, DC. EPA 410 - R- 99 - 001. November. http://www.epa.gov/airprogm/oar/sect812/prospective1.html U.S. Environmental Protection Agency (EPA). 2000a. Handbook for Non
- Cancer Health Effects Valuation . Report of the Non- Cancer Health Effects Valuation Subcommittee of the EPA Social Science Discussion Group. November. Accessed March 25, 2011. http://www.epa.gov/spc/pdfs/chapters.pdf U.S. Environmental Protection Agency (EPA). 2000b. Arsenic in Drinking Water Rule Economic Analysis . U.S. EPA, Office of Ground Water and Drinking Water. EPA 815 - R- 00 - 026 December. http://water.epa.gov/drink/info/arsenic/upload/2005_11_22_arsenic_econ_analys is.pdf . U.S. Environmental Protection Agency (EPA). 2000c. Economic Analys is of Toxic Substance Control Act Section 403: Lead- Based Paint Hazard Standards. U.S. EPA, Office of Pollution Prevention and Toxics. Washington, DC. December 21. http://www.epa.gov/lead/pubs/403_ea_d21.pdf . U.S. Environmental Protection Agency (EPA). 2000d . Cost of Illness Handbook. Accessed November 2, 2011. http://www.epa.gov/oppt/coi/ U.S. Environmental Protection Agency (EPA). 2005a. Regulatory Impact Analysis of the Clean Air Mercury

Rule. U.S. EPA, Office of Air Quality Planning and Standards. Washington, DC. EPA-452/R- 05 - 003. March. http://www.epa.gov/ttn/atw/utility /ria_final.pdf

. U.S. Environmental Protection Agency (EPA). 2005b. Economic Analys is for the Final Stage 2 Disinfectants and Disinfection By products Rule. U.S. EPA, Office of Water. Washington, DC. EPA 815 - R- 05 - 010. December. http://water.epa.gov/lawsregs/rulesregs/sdwa/stage2/upload/2006_03_17_disinfection_stage2_an aylsis_stage2_ecconomic_main.pdf . U.S. Environmental Protection Agency (EPA). 2006. Economic Analys is for the Proposed Revised Total Coliform Rule. U.S. EPA, Office of Water. Washington, DC. EPA 815 - R- 06 - 014. October. http://www.epa.gov/ogwdw000/disinfection/gwr/pdfs/support_gwr_economicanalys is.pdf . U.S. Environmental Protection Agency (EPA). 2010. Economic Analys is for the Final Ground Water Rule. U.S. EPA, Office of Water. Washington, DC. EPA 815 - R- 10 - 001. June. http://www.epa.gov/safewater/disinfection/tcr/pdfs/Proposed%20RTCR%20EA.pdf . U.S. Environmental Protection Agency (EPA). 2011a. Guidelines for Preparing Economic Analyses . Office of the Administrator. Washington, DC. December. EPA 240 - R- 10 - 001 http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html79

U.S. Environmental Protection Agency (EPA). 2011a. The Benefits and Costs of the Clean Air Act: 1990 to 2020. U.S. EPA, Office of Air and Radiation. Washington, DC. March. http://www.epa.gov/airprogm/oar/sect812/prospective2.html U.S. Environmental Protection Agency (EPA). 2011b. Environmental Benefits Mapping and Analysis Program (BenMAP). http://www.epa.gov/air/benmap/ U.S. Environmental Protection Agency (EPA), U.S. Consumer Product Safety Commission (CPSC), and U.S. Department of Housing and Urban Development (HUD) . 2003. Protect Your Family From Lead In Your Home. U.S. EPA. Washington, DC. EPA747 - K- 99 - 001. June. http://www.epa.gov/lead/pubs/leadpdfe.pdf . Van Houtven, George, and V. Kerry Smith. 1999. “Willingness to Pay for Reductions in Inferti lity Risks: A Contingent Valuation Study .” In Shi - Ling Hsu (ed.) Valuing Heath for Environmental Policy with Special Emphasis on Children’s Health Protection: Proceedings of the Second Workshop in the Environmental Policy and Economic Workshop Series . http://yosemite.epa.gov/ee/epa/eerm.nsf/a2c59a4b95cc92e4852564d3004e4ee2/d811c47399e896 c48525679d0074ed76?OpenDocument .