Comparative Potency Method for Cancer Risk Assessment: Application to Diesel Particulate Emissions1

An estimation of the human lung cancer “unit risk” from diesel engine particulate emissions has been made using a comparative potency approach. This approach involves evaluating the tumorigenic and mutagenic potencies of the particulates from four diesel and one gasoline engine in relation to other combustion and pyrolysis products (coke oven, roofing tar, and cigarette smoke) that cause lung cancer in humans. The unit cancer risk is predicated on the linear nonthreshold extrapolation model and is the individual lifetime excess lung cancer risk from continuous exposure to 1 μg carcinogen per m³ inhaled air. The human lung cancer unit risks obtained from the epidemiologic data for coke oven workers, roofing tar applicators, and cigarette smokers were, respectively, 9.3 × 10^?4, 3.6 × 10^?4, and 2.2 × 10^?6 per μg particulate organics per m³ air. The comparative potencies of these three materials and the diesel and gasoline engine exhaust particulates (as organic extracts) were evaluated by in vivo tumorigenicity bioassays involving skin initiation and skin carcinogenicity in SENCAR mice and by the in vitro bioassays that proved suitable for this analysis: Ames Salmonella microsome bioassay, L5178Y mouse lymphoma cell mutagenesis bioassay, and sister chromatid exchange bioassay in Chinese hamster ovary cells. The relative potencies of the coke oven, roofing tar, and cigarette smoke emissions, as determined by the mouse skin initiation assay, were within a factor of 2 of those determined using the epidemiologic data. The relative potencies, from the in vitro bioassays as compared to the human data, were similar for coke oven and roofing tar, but for the cigarette smoke condensate the in vitro tests predicted a higher relative potency. The mouse skin initiation bioassay was used to determine the unit lung cancer risk for the most potent of the diesel emissions. Based on comparisons with coke oven, roofing tar, and cigarette smoke, the unit cancer risk averaged 4.4 × 10^?4. The unit lung cancer risks for the other, less potent motor-vehicle emissions were determined from their comparative potencies relative to the most potent diesel using three in vitro bioassays. There was a high correlation between the in vitro and in vivo bioassays in their responses to the engine exhaust particulate extracts. The unit lung cancer risk per μg particulates per m³ for the automotive diesel and gasoline exhaust particulates ranged from 0.20 × 10^?4 to 0.60 × 10^?4; that for the heavy-duty diesel engine was 0.02 × 10^?4. These unit risks provide the basis for a future assessment of human lung cancer risks when combined with human population exposure to automotive emissions.     

Lung Cancer Risk from Radon in Marcellus Shale Gas in Northeast U.S. Homes

The amount of radon in natural gas varies with its source. Little has been published about the radon from shale gas to date, making estimates of its impact on radon‐induced lung cancer speculative. We measured radon in natural gas pipelines carrying gas from the Marcellus Shale in Pennsylvania and West Virginia. Radon concentrations ranged from 1,520 to 2,750 Bq/m³ (41–74 pCi/L), and the throughput‐weighted average was 1,983 Bq/m³ (54 pCi/L). Potential radon exposure due to the use of Marcellus Shale gas for cooking and space heating using vent‐free heaters or gas ranges in northeastern U.S. homes and apartments was assessed. Though the measured radon concentrations are higher than what has been previously reported, it is unlikely that exposure from natural gas cooking would exceed 1.2 Bq/m³ (<1% of the U.S. Environmental Protection Agency's action level). Using worst‐case assumptions, we estimate the excess lifetime (70 years) lung cancer risk associated with cooking to be 1.8×10^?4 (interval spanning 95% of simulation results: 8.5×10^?5, 3.4×10^?4). The risk profile for supplemental heating with unvented gas appliances is similar. Individuals using unvented gas appliances to provide primary heating may face lifetime risks as high as 3.9×10^?3. Under current housing stock and gas consumption assumptions, expected levels of residential radon exposure due to unvented combustion of Marcellus Shale natural gas in the Northeast United States do not result in a detectable change in the lung cancer death rates.     

Risk Perception and Human Health Risk in Rural Communities Consuming Unregulated Well Water in Saskatchewan,Canada

Rural communities dependent on unregulated drinking water are potentially at increased health risk from exposure to contaminants. Perception of drinking water safety influences water consumption, exposure, and health risk. A community‐based participatory approach and probabilistic Bayesian methods were applied to integrate risk perception in a holistic human health risk assessment. Tap water arsenic concentrations and risk perception data were collected from two Saskatchewan communities. Drinking water health standards were exceeded in 67% (51/76) of households in Rural Municipality #184 (RM184) and 56% (25/45) in Beardy's and Okemasis First Nation (BOFN). There was no association between the presence of a health exceedance and risk perception. Households in RM184 or with an annual income >$50,000 were most likely to have in‐house water treatment. The probability of consuming tap water perceived as safe (92%) or not safe (0%) suggested that households in RM184 were unlikely to drink water perceived as not safe. The probability of drinking tap water perceived as safe (77%) or as not safe (11%) suggested households in BOFN contradicted their perception and consumed water perceived as unsafe. Integration of risk perception lowered the adult incremental lifetime cancer risk by 3% to 1.3 × 10^?5 (95% CI 8.4 × 10^?8 to 9.0 × 10^?5) for RM184 and by 8.9 × 10^?6 (95% CI 2.2 × 10^?7 to 5.9 × 10^?5) for BOFN. Probability of exposure to arsenic concentrations >1:100,000, negligible cancer risk, was 23% for RM184 and 22% for BOFN.     

A Risk Assessment for Occupational Acrylonitrile Exposure Using Epidemiology Data

The extensive data from the Blair et al.((1)) epidemiology study of occupational acrylonitrile exposure among 25460 workers in eight plants in the United States provide an excellent opportunity to update quantitative risk assessments for this widely used commodity chemical. We employ the semiparametric Cox relative risk (RR) regression model with a cumulative exposure metric to model cause-specific mortality from lung cancer and all other causes. The separately estimated cause-specific cumulative hazards are then combined to provide an overall estimate of age-specific mortality risk. Age-specific estimates of the additional risk of lung cancer mortality associated with several plausible occupational exposure scenarios are obtained. For age 70, these estimates are all markedly lower than those generated with the cancer potency estimate provided in the USEPA acrylonitrile risk assessment.((2)) This result is consistent with the failure of recent occupational studies to confirm elevated lung cancer mortality among acrylonitrile-exposed workers as was originally reported by O'Berg,((3)) and it calls attention to the importance of using high-quality epidemiology data in the risk assessment process.     

Hexavalent Chromium and Lung Cancer in the Chromate Industry: A Quantitative Risk Assessment

The purpose of this investigation was to estimate excess lifetime risk of lung cancer death resulting from occupational exposure to hexavalent-chromium-containing dusts and mists. The mortality experience in a previously studied cohort of 2,357 chromate chemical production workers with 122 lung cancer deaths was analyzed with Poisson regression methods. Extensive records of air samples evaluated for water-soluble total hexavalent chromium were available for the entire employment history of this cohort. Six different models of exposure-response for hexavalent chromium were evaluated by comparing deviances and inspection of cubic splines. Smoking (pack-years) imputed from cigarette use at hire was included in the model. Lifetime risks of lung cancer death from exposure to hexavalent chromium (assuming up to 45 years of exposure) were estimated using an actuarial calculation that accounts for competing causes of death. A linear relative rate model gave a good and readily interpretable fit to the data. The estimated rate ratio for 1 mg/m3-yr of cumulative exposure to hexavalent chromium (as CrO3), with a lag of five years, was RR=2.44 (95% CI=1.54-3.83). The excess lifetime risk of lung cancer death from exposure to hexavalent chromium at the current OSHA permissible exposure limit (PEL) (0.10 mg/m3) was estimated to be 255 per 1,000 (95% CI: 109-416). This estimate is comparable to previous estimates by U.S. EPA, California EPA, and OSHA using different occupational data. Our analysis predicts that current occupational standards for hexavalent chromium permit a lifetime excess risk of dying of lung cancer that exceeds 1 in 10, which is consistent with previous risk assessments.     

The Cancer Risk Associated with Residential Exposure to Soil Containing Radioactive Coal Combustion Residuals

Coal combustion residuals (CCRs) are composed of various constituents, including radioactive materials. The objective of this study was to utilize methodology on radionuclide risk assessment from the Environmental Protection Agency (EPA) to estimate the potential cancer risks associated with residential exposure to CCR‐containing soil. We evaluated potential radionuclide exposure via soil ingestion, inhalation of soil particulates, and external exposure to ionizing radiation using published CCR radioactivity values for ²³²Th, ²²⁸Ra, ²³⁸U, and ²²⁶Ra from the Appalachia, Illinois, and Powder River coal basins. Mean and upper‐bound cancer risks were estimated individually for each radionuclide, exposure pathway, and coal basin. For each radionuclide at each coal basin, external exposure to ionizing radiation contributed the greatest to the overall risk estimate, followed by incidental ingestion of soil and inhalation of soil particulates. The mean cancer risks by route of exposure were 2.01 × 10⁻⁶ (ingestion), 6.80 × 10⁻⁹ (inhalation), and 3.66 × 10⁻⁵ (external), while the upper bound cancer risks were 3.70 × 10⁻⁶ (ingestion), 1.18 × 10⁻⁸ (inhalation), and 6.15 × 10⁻⁵ (external), using summed radionuclide‐specific data from all locations. The upper bound cancer risk from all routes of exposure was 6.52 × 10⁻⁵. These estimated cancer risks were within the EPA's acceptable cancer risk range of 1 × 10⁻⁶ to 1 × 10⁻⁴. If the CCR radioactivity values used in this analysis are generally representative of CCR waste streams, then our findings suggest that CCRs would not be expected to pose a significant radiological risk to residents living in areas where contact with CCR‐containing soils might occur.     

Unveiling Variability and Uncertainty for Better Science and Decisions on Cancer Risks from Environmental Chemicals

The National Research Council 2009 “Silver Book” panel report included a recommendation that the U.S. Environmental Protection Agency (EPA) should increase all of its chemical carcinogen (CC) potency estimates by ～7‐fold to adjust for a purported median‐vs.‐mean bias that I recently argued does not exist (Bogen KT. “Does EPA underestimate cancer risks by ignoring susceptibility differences?,” Risk Analysis, 2014; 34(10):1780–1784). In this issue of the journal, my argument is critiqued for having flaws concerning: (1) intent, bias, and conservatism of EPA estimates of CC potency; (2) bias in potency estimates derived from epidemiology; and (3) human‐animal CC‐potency correlation. However, my argument remains valid, for the following reasons. (1) EPA's default approach to estimating CC risks has correctly focused on bounding average (not median) individual risk under a genotoxic mode‐of‐action (MOA) assumption, although pragmatically the approach leaves both inter‐individual variability in CC–susceptibility, and widely varying CC‐specific magnitudes of fundamental MOA uncertainty, unquantified. (2) CC risk estimates based on large epidemiology studies are not systematically biased downward due to limited sampling from broad, lognormal susceptibility distributions. (3) A good, quantitative correlation is exhibited between upper‐bounds on CC‐specific potency estimated from human vs. animal studies (n = 24, r = 0.88, p = 2 × 10^?8). It is concluded that protective upper‐bound estimates of individual CC risk that account for heterogeneity in susceptibility, as well as risk comparisons informed by best predictions of average‐individual and population risk that address CC‐specific MOA uncertainty, should each be used as separate, complimentary tools to improve regulatory decisions concerning low‐level, environmental CC exposures.     

A Bayesian Belief Network Model Assessing the Risk to Wastewater Workers of Contracting Ebola Virus Disease During an Outbreak

During an outbreak of Ebola virus disease (EVD), hospitals’ connections to municipal wastewater systems may provide a path for patient waste bearing infectious viral particles to pass from the hospital into the wastewater treatment system, potentially posing risks to sewer and wastewater workers. To quantify these risks, we developed a Bayesian belief network model incorporating data on virus behavior and survival along with structural characteristics of hospitals and wastewater treatment systems. We applied the model to assess risks under several different scenarios of workers’ exposure to wastewater for a wastewater system typical of a mid‐sized U.S. city. The model calculates a median daily risk of developing EVD of approximately 6.1×10⁻¹² (90% confidence interval: 1.0×10⁻¹² to 5.4×10⁻⁹; mean 1.8×10⁻⁶) when no prior exposure conditions are specified. Under a worst‐case scenario in which a worker stationed in the sewer adjacent to the hospital accidentally ingests several drops (0.35 mL) of wastewater, median risk is 5.8×10⁻⁴ (90% CI: 8.8×10⁻⁷ to 9.5×10⁻²; mean 3.2×10⁻²) . Disinfection of patient waste with peracetic acid for 15 minutes prior to flushing decreases the estimated median risk to 3.8×10⁻⁷ (90% CI: 4.1×10⁻⁹ to 8.6×10⁻⁵; mean 2.9×10⁻⁵). The results suggest that requiring hospitals to disinfect EVD patient waste prior to flushing may be advisable. The modeling framework can provide insight into managing patient waste during future outbreaks of highly virulent infectious pathogens.     

Outbreak-Based Giardia Dose–Response Model Using Bayesian Hierarchical Markov Chain Monte Carlo Analysis

Giardia is a zoonotic gastrointestinal parasite responsible for a substantial global public health burden, and quantitative microbial risk assessment (QMRA) is often used to forecast and manage this burden. QMRA requires dose–response models to extrapolate available dose–response data, but the existing model for Giardia ignores valuable dose–response information, particularly data from several well-documented waterborne outbreaks of giardiasis. The current study updates Giardia dose–response modeling by synthesizing all available data from outbreaks and experimental studies using a Bayesian random effects dose–response model. For outbreaks, mean doses (D) and the degree of spatial and temporal aggregation among cysts were estimated using exposure assessment implemented via two-dimensional Monte Carlo simulation, while potential overreporting of outbreak cases was handled using published overreporting factors and censored binomial regression. Parameter estimation was by Markov chain Monte Carlo simulation and indicated that a typical exponential dose–response parameter for Giardia is r = 1.6 × 10⁻² [3.7 × 10⁻³, 6.2 × 10⁻²] (posterior median [95% credible interval]), while a typical morbidity ratio is m = 3.8 × 10⁻¹ [2.3 × 10⁻¹, 5.5 × 10⁻¹]. Corresponding (logistic-scale) variance components were σ_r = 5.2 × 10⁻¹ [1.1 × 10⁻¹, 9.6 × 10⁻¹] and σ_m = 9.3 × 10⁻¹ [7.0 × 10⁻², 2.8 × 10⁰], indicating substantial variation in the Giardia dose–response relationship. Compared to the existing Giardia dose–response model, the current study provides more representative estimation of uncertainty in r and novel quantification of its natural variability. Several options for incorporating variability in r (and m) into QMRA predictions are discussed, including incorporation via Monte Carlo simulation as well as evaluation of the current study's model using the approximate beta-Poisson.     

Uncertainty in Cancer Risk Estimates

Several existing databases compiled by Gold et al.^(1–3) for carcinogenesis bioassays are examined to obtain estimates of the reproducibility of cancer rates across experiments, strains, and rodent species. A measure of carcinogenic potency is given by the TD₅₀ (daily dose that causes a tumor type in 50% of the exposed animals that otherwise would not develop the tumor in a standard lifetime). The lognormal distribution can be used to model the uncertainty of the estimates of potency (TD₅₀) and the ratio of TD₅₀'s between two species. For near-replicate bioassays, approximately 95% of the TD₅₀'s are estimated to be within a factor of 4 of the mean. Between strains, about 95% of the TD₅₀'s are estimated to be within a factor of 11 of their mean, and the pure genetic component of variability is accounted for by a factor of 6.8. Between rats and mice, about 95% of the TD₅₀'s are estimated to be within a factor of 32 of the mean, while between humans and experimental animals the factor is 110 for 20 chemicals reported by Allen et al.⁽⁴⁾ The common practice of basing cancer risk estimates on the most sensitive rodent species-strain-sex and using interspecies dose scaling based on body surface area appears to overestimate cancer rates for these 20 human carcinogens by about one order of magnitude on the average. Hence, for chemicals where the dose-response is nearly linear below experimental doses, cancer risk estimates based on animal data are not necessarily conservative and may range from a factor of 10 too low for human carcinogens up to a factor of 1000 too high for approximately 95% of the chemicals tested to date. These limits may need to be modified for specific chemicals where additional mechanistic or pharmacokinetic information may suggest alterations or where particularly sensitive subpopu-lations may be exposed. Supralinearity could lead to anticonservative estimates of cancer risk. Underestimating cancer risk by a specific factor has a much larger impact on the actual number of cancer cases than overestimates of smaller risks by the same factor. This paper does not address the uncertainties in high to low dose extrapolation. If the dose-response is sufficiently nonlinear at low doses to produce cancer risks near zero, then low-dose risk estimates based on linear extrapolation are likely to overestimate risk and the limits of uncertainty cannot be established.     

A Quantitative Release Assessment for the Noncommercial Movement of Companion Animals: Risk of Rabies Reintroduction to the United Kingdom

In 2004, the European Union (EU) implemented a pet movement policy (referred to here as the EUPMP) under EU regulation 998/2003. The United Kingdom (UK) was granted a temporary derogation from the policy until December 2011 and instead has in place its own Pet Movement Policy (Pet Travel Scheme (PETS)). A quantitative risk assessment (QRA) was developed to estimate the risk of rabies introduction to the UK under both schemes to quantify any change in the risk of rabies introduction should the UK harmonize with the EU policy. Assuming 100 % compliance with the regulations, moving to the EUPMP was predicted to increase the annual risk of rabies introduction to the UK by approximately 60‐fold, from 7.79 × 10^?5 (5.90 × 10^?5, 1.06 × 10^?4) under the current scheme to 4.79 × 10^?3 (4.05 × 10^?3, 5.65 × 10^?3) under the EUPMP. This corresponds to a decrease from 13,272 (9,408, 16,940) to 211 (177, 247) years between rabies introductions. The risks associated with both the schemes were predicted to increase when less than 100 % compliance was assumed, with the current scheme of PETS and quarantine being shown to be particularly sensitive to noncompliance. The results of this risk assessment, along with other evidence, formed a scientific evidence base to inform policy decision with respect to companion animal movement.     

Approximations for Estimating Change in Life Expectancy Attributable to Air Pollution in Relation to Multiple Causes of Death Using a Cause Modified Life Table

There is considerable debate as to the most appropriate metric for characterizing the mortality impacts of air pollution. Life expectancy has been advocated as an informative measure. Although the life‐table calculus is relatively straightforward, it becomes increasingly cumbersome when repeated over large numbers of geographic areas and for multiple causes of death. Two simplifying assumptions were evaluated: linearity of the relation between excess rate ratio and change in life expectancy, and additivity of cause‐specific life‐table calculations. We employed excess rate ratios linking PM_2.5 and mortality from cerebrovascular disease, chronic obstructive pulmonary disease, ischemic heart disease, and lung cancer derived from a meta‐analysis of worldwide cohort studies. As a sensitivity analysis, we employed an integrated exposure response function based on the observed risk of PM_2.5 over a wide range of concentrations from ambient exposure, indoor exposure, second‐hand smoke, and personal smoking. Impacts were estimated in relation to a change in PM_2.5 from 19.5 μg/m³ estimated for Toronto to an estimated natural background concentration of 1.8 μg/m³. Estimated changes in life expectancy varied linearly with excess rate ratios, but at higher values the relationship was more accurately represented as a nonlinear function. Changes in life expectancy attributed to specific causes of death were additive with maximum error of 10%. Results were sensitive to assumptions about the air pollution concentration below which effects on mortality were not quantified. We have demonstrated valid approximations comprising expression of change in life expectancy as a function of excess mortality and summation across multiple causes of death.     

Quantitative Risk Estimation for a Legionella pneumophila Infection Due to Whirlpool Use

Quantitative microbiological risk assessment was used to quantify the risk associated with the exposure to Legionella pneumophila in a whirlpool. Conceptually, air bubbles ascend to the surface, intercepting Legionella from the traversed water. At the surface the bubble bursts into dominantly noninhalable jet drops and inhalable film drops. Assuming that film drops carry half of the intercepted Legionella, a total of four (95% interval: 1–9) and 4.5×10⁴ (4.4×10⁴ – 4.7×10⁴) cfu/min were estimated to be aerosolized for concentrations of 1 and 1,000 legionellas per liter, respectively. Using a dose‐response model for guinea pigs to represent humans, infection risks for active whirlpool use with 100 cfu/L water for 15 minutes were 0.29 (～0.11–0.48) for susceptible males and 0.22 (～0.06–0.42) for susceptible females. A L. pneumophila concentration of ≥1,000 cfu/L water was estimated to nearly always cause an infection (mean: 0.95; 95% interval: 0.9–～1). Estimated infection risks were time‐dependent, ranging from 0.02 (0–0.11) for 1‐minute exposures to 0.93 (0.86–0.97) for 2‐hour exposures when the L. pneumophila concentration was 100 cfu/L water. Pool water in Dutch bathing establishments should contain <100 cfu Legionella/L water. This study suggests that stricter provisions might be required to assure adequate public health protection.     

Risk of Fetal Mortality After Exposure to Listeria monocytogenes Based on Dose-Response Data from Pregnant Guinea Pigs and Primates

One‐third of the annual cases of listeriosis in the United States occur during pregnancy and can lead to miscarriage or stillbirth, premature delivery, or infection of the newborn. Previous risk assessments completed by the Food and Drug Administration/the Food Safety Inspection Service of the U.S. Department of Agriculture/the Centers for Disease Control and Prevention (FDA/USDA/CDC)^{( 1 )} and Food and Agricultural Organization/the World Health Organization (FAO/WHO)^{( 2 )} were based on dose‐response data from mice. Recent animal studies using nonhuman primates^{( 3 , 4 )} and guinea pigs^{( 5 )} have both estimated LD₅₀s of approximately 10⁷ Listeria monocytogenes colony forming units (cfu). The FAO/WHO^{( 2 )} estimated a human LD₅₀ of 1.9 × 10⁶ cfu based on data from a pregnant woman consuming contaminated soft cheese. We reevaluated risk based on dose‐response curves from pregnant rhesus monkeys and guinea pigs. Using standard risk assessment methodology including hazard identification, exposure assessment, hazard characterization, and risk characterization, risk was calculated based on the new dose‐response information. To compare models, we looked at mortality rate per serving at predicted doses ranging from 10^?4 to 10¹² L. monocytogenes cfu. Based on a serving of 10⁶ L. monocytogenes cfu, the primate model predicts a death rate of 5.9 × 10^?1 compared to the FDA/USDA/CDC (fig. IV‐12)^{( 1 )} predicted rate of 1.3 × 10^?7. Based on the guinea pig and primate models, the mortality rate calculated by the FDA/USDA/CDC^{( 1 )} is underestimated for this susceptible population.     

Validation of Quantitative Microbial Risk Assessment Using Epidemiological Data from Outbreaks of Waterborne Gastrointestinal Disease

The assumptions underlying quantitative microbial risk assessment (QMRA) are simple and biologically plausible, but QMRA predictions have never been validated for many pathogens. The objective of this study was to validate QMRA predictions against epidemiological measurements from outbreaks of waterborne gastrointestinal disease. I screened 2,000 papers and identified 12 outbreaks with the necessary data: disease rates measured using epidemiological methods and pathogen concentrations measured in the source water. Eight of the 12 outbreaks were caused by Cryptosporidium, three by Giardia, and one by norovirus. Disease rates varied from 5.5 × 10^?6 to 1.1 × 10^?2 cases/person‐day, and reported pathogen concentrations varied from 1.2 × 10^?4 to 8.6 × 10² per liter. I used these concentrations with single‐hit dose–response models for all three pathogens to conduct QMRA, producing both point and interval predictions of disease rates for each outbreak. Comparison of QMRA predictions to epidemiological measurements showed good agreement; interval predictions contained measured disease rates for 9 of 12 outbreaks, with point predictions off by factors of 1.0–120 (median = 4.8). Furthermore, 11 outbreaks occurred at mean doses of less than 1 pathogen per exposure. Measured disease rates for these outbreaks were clearly consistent with a single‐hit model, and not with a “two‐hit” threshold model. These results demonstrate the validity of QMRA for predicting disease rates due to Cryptosporidium and Giardia.     

Development of a Unit Risk Factor for 1,3-Butadiene Based on an Updated Carcinogenic Toxicity Assessment

The Texas Commission on Environmental Quality (TCEQ) has developed an inhalation unit risk factor (URF) for 1,3-butadiene based on leukemia mortality in an updated epidemiological study on styrene-butadiene rubber production workers conducted by researchers at the University of Alabama at Birmingham. Exposure estimates were updated and an exposure estimate validation study as well as dose-response modeling were conducted by these researchers. This information was not available to the U.S. Environmental Protection Agency when it prepared its health assessment of 1,3-butadiene in 2002. An extensive analysis conducted by TCEQ discusses dose-response modeling, estimating risk for the general population from occupational workers, estimating risk for potentially sensitive subpopulations, effect of occupational exposure estimation error, and use of mortality rates to predict incidence. The URF is 5.0 × 10⁻⁷ per μg/m³ or 1.1 × 10⁻⁶ per ppb and is based on a Cox regression dose-response model using restricted continuous data with age as a covariate, and a linear low-dose extrapolation default approach using the 95% lower confidence limit as the point of departure. Age-dependent adjustment factors were applied to account for possible increased susceptibility for early life exposure. The air concentration at 1 in 100,000 excess leukemia mortality, the no-significant-risk level, is 20 μg/m³ (9.1 ppb), which is slightly lower than the TCEQ chronic reference value of 33 μg/m³ (15 ppb) protective of ovarian atrophy. These values will be used to evaluate ambient air monitoring data so the general public is protected against adverse health effects from chronic exposure to 1,3-butadiene.     

Risk Assessment of Salmonellosis from Consumption of Alfalfa Sprouts and Evaluation of the Public Health Impact of Sprout Seed Treatment and Spent Irrigation Water Testing

We developed a risk assessment of human salmonellosis associated with consumption of alfalfa sprouts in the United States to evaluate the public health impact of applying treatments to seeds (0–5‐log₁₀ reduction in Salmonella ) and testing spent irrigation water (SIW) during production. The risk model considered variability and uncertainty in Salmonella contamination in seeds, Salmonella growth and spread during sprout production, sprout consumption, and Salmonella dose response. Based on an estimated prevalence of 2.35% for 6.8 kg seed batches and without interventions, the model predicted 76,600 (95% confidence interval (CI) 15,400 – 248,000) cases/year. Risk reduction (by 5 ‐ to 7‐fold) predicted from a 1‐log₁₀ seed treatment alone was comparable to SIW testing alone, and each additional 1‐log₁₀ seed treatment was predicted to provide a greater risk reduction than SIW testing. A 3‐log₁₀ or a 5‐log₁₀ seed treatment reduced the predicted cases/year to 139 (95% CI 33 – 448) or 1.4 (95% CI <1 – 4.5), respectively. Combined with SIW testing, a 3‐log₁₀ or 5‐log₁₀ seed treatment reduced the cases/year to 45 (95% CI 10–146) or <1 (95% CI <1 – 1.5), respectively. If the SIW coverage was less complete (i.e., less representative), a smaller risk reduction was predicted, e.g., a combined 3‐log₁₀ seed treatment and SIW testing with 20% coverage resulted in an estimated 92 (95% CI 22 – 298) cases/year. Analysis of alternative scenarios using different assumptions for key model inputs showed that the predicted relative risk reductions are robust. This risk assessment provides a comprehensive approach for evaluating the public health impact of various interventions in a sprout production system.     

Costs of Health Damage from Atmospheric Emissions of Toxic Metals: Part 2—Analysis for Mercury and Lead

In this part of the series we explain the detailed literature review and the calculations of impacts and damage costs of mercury and lead. Methodology and general assumptions are explained in the companion article, Part 1 of this series, and the spreadsheet with the calculations is available as a supplementary file of Part 1.³ For mercury, the damage cost is 22,937 €₂₀₁₃/kg if there is a no‐effect threshold, 52,129 €₂₀₁₃/kg if there is none; 91% is due to mortality from heart disease, the rest from loss of IQ points. For lead, the damage cost is 29,343 €₂₀₁₃/kg, about 80% due to mortality and 20% due to IQ loss; there does not seem to be a no‐effect threshold. These costs are per kg of emitted pollutant.     

Air Nicotine and Saliva Cotinine as Indicators of Workplace Passive Smoking Exposure and Risk1

We model nicotine from environmental tobacco smoke (ETS) in office air and salivary cotinine in nonsmoking U.S. workers. We estimate that: an average salivary cotinine level of 0.4 ng/ml corresponds to an increased lifetime mortality risk of 1/1000 for lung cancer, and 1/100 for heart disease; >95% of ETS-exposed office workers exceed OSHA's significant risk level for heart disease mortality, and 60% exceed significant risk for lung cancer mortality; 4000 heart disease deaths and 400 lung cancer deaths occur annually among office workers from passive smoking in the workplace, at the current 28% prevalence of unrestricted smoking in the office workplace.     

Finely Resolved On‐Road PM2.5 and Estimated Premature Mortality in Central North Carolina

To quantify the on‐road PM_2.5‐related premature mortality at a national scale, previous approaches to estimate concentrations at a 12‐km × 12‐km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on‐road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on‐road PM_2.5‐related premature mortality in central North Carolina with two different concentration estimation approaches—(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36‐km × 36‐km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space–time interpolation technique to provide annual average PM_2.5 concentrations at a Census‐block level (～105,000 Census blocks). The hybrid modeling approach estimated 24% more on‐road PM_2.5‐related premature mortality than CMAQ. The major difference is from the primary on‐road PM_2.5 where the hybrid approach estimated 2.5 times more primary on‐road PM_2.5‐related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on‐road PM_2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near‐road primary PM_2.5 and suggesting that previous studies may have underestimated premature mortality due to PM_2.5 from traffic‐related emissions.