Residential exposure associations with ALS risk, survival, and phenotype: a Michigan-based case-control study

Background: Environmental exposures impact amyotrophic lateral sclerosis (ALS) risk and progression, a fatal and progressive neurodegenerative disease. Better characterization of these exposures is needed to decrease disease burden. Objective: To identify exposures in the residential setting that associate with ALS risk, survival, and onset segment. Methods: ALS and control participants recruited from University of Michigan completed a survey that ascertained exposure risks in the residential setting. ALS risk was assessed using logistic regression models followed by latent profile analysis to consider exposure profiles. A case-only analysis considered the contribution of the residential exposure variables via a Cox proportional hazards model for survival outcomes and multinomial logistic regression for onset segment, a polytomous outcome. Results: This study included 367 ALS and 255 control participants. Twelve residential variables were associated with ALS risk after correcting for multiple comparison testing, with storage in an attached garage of chemical products including gasoline or kerosene (odds ratio (OR) = 1.14, padjusted < 0.001), gasoline-powered equipment (OR = 1.16, padjusted < 0.001), and lawn care products (OR = 1.15, padjusted < 0.001) representing the top three risk factors sorted by padjusted. Latent profile analysis indicated that storage of these chemical products in both attached and detached garages increased ALS risk. Although residential variables were not associated with poorer ALS survival following multiple testing corrections, storing pesticides, lawn care products, and woodworking supplies in the home were associated with shorter ALS survival using nominal p values. No exposures were associated with ALS onset segment. Conclusion: Residential exposures may be important modifiable components of the ALS susceptibility and prognosis exposome.

Metal mixtures associate with higher amyotrophic lateral sclerosis risk and mortality independent of genetic risk and correlate to self-reported exposures: a case-control study

Background

The pathogenesis of amyotrophic lateral sclerosis (ALS) involves both genetic and environmental factors. This study investigates associations between metal measures in plasma and urine, ALS risk and survival, and exposure sources.

Methods

Participants with and without ALS from Michigan provided plasma and urine samples for metal measurement via inductively coupled plasma mass spectrometry. Odds and hazard ratios for each metal were computed using risk and survival models. Environmental risk scores (ERS) were created to evaluate the association between exposure mixtures and ALS risk and survival and exposure source. ALS (ALS-PGS) and metal (metal-PGS) polygenic risk scores were constructed from an independent genome-wide association study and relevant literature-selected SNPs.

Results

Plasma and urine samples from 454 ALS and 294 control participants were analyzed. Elevated levels of individual metals, including copper, selenium, and zinc, significantly associated with ALS risk and survival. ERS representing metal mixtures strongly associated with ALS risk (plasma, OR=2.95, CI=2.38-3.62, p<0.001; urine, OR=3.10, CI=2.43-3.97, p<0.001) and poorer ALS survival (plasma, HR=1.42, CI=1.24-1.63, p<0.001; urine, HR=1.52, CI=1.31-1.76, p<0.001). Addition of the ALS-PGS or metal-PGS did not alter the significance of metals with ALS risk and survival. Occupations with high potential of metal exposure associated with elevated ERS. Additionally, occupational and non-occupational metal exposures associated with measured plasma and urine metals.

Conclusion

Metals in plasma and urine associated with increased ALS risk and reduced survival, independent of genetic risk, and correlated with occupational and non-occupational metal exposures. These data underscore the significance of metal exposure in ALS risk and progression.

Avocational exposure associations with ALS risk, survival, and phenotype: A Michigan-based case-control study

INTRODUCTION

Environmental exposures strongly influence ALS risk and identification is needed to reduce ALS burden. Participation in hobbies and exercise may alter ALS risk and phenotype, warranting an assessment to understand their contribution to the ALS exposome.

METHODS

Participants with ALS and healthy controls were recruited from University of Michigan and self-completed a survey to ascertain hobbies, exercise, and avocational exposures. Exposure variables were associated with ALS risk, survival, onset segment, and onset age.

RESULTS

ALS (n = 400) and control (n = 287) participants self-reported avocational activities. Cases were slightly older (median age 63.0 vs. 61.1 years, p = 0.019) and had a lower educational attainment (p < 0.001) compared to controls; otherwise, demographics were well balanced. Risks associating with ALS after multiple comparison correction included golfing (odds ratio (OR) 3.48, padjusted = 0.004), recreational dancing (OR 2.00, padjusted = 0.040), performing gardening or yard work (OR 1.71, padjusted = 0.040) five years prior to ALS and personal (OR 1.76, padjusted = 0.047) or family (OR 2.21, padjusted = 0.040) participation in woodworking, and personal participation in hunting and shooting (OR 1.89, padjusted = 0.040). No exposures associated with ALS survival and onset. Those reporting swimming (3.86 years, padjusted = 0.016) and weightlifting (3.83 years, padjusted = 0.020) exercise 5 years prior to ALS onset had an earlier onset age.

DISCUSSION

The identified exposures in this study may represent important modifiable ALS factors that influence ALS phenotype. Thus, exposures related to hobbies and exercise should be captured in studies examining the ALS exposome.

Occupational history associates with ALS survival and onset segment

OBJECTIVE

To identify associations between occupational settings and self-reported occupational exposures on amyotrophic lateral sclerosis (ALS) survival and phenotypes.

METHODS

All patients seen in the University of Michigan Pranger ALS Clinic were invited to complete an exposure assessment querying past occupations and exposures. Standard occupational classification (SOC) codes for each job and the severity of various exposure types were derived. Cox proportional hazards models associated all-cause mortality with occupational settings and the self-reported exposures after adjusting for sex, diagnosis age, revised El Escorial criteria, onset segment, revised ALS Functional Rating Scale (ALSFRS-R), and time from symptom onset to diagnosis. Multinomial logistic regression models with three categories, adjusted for age, assessed the association between occupational settings and exposures to onset segment.

RESULTS

Among the 378 ALS participants (median age, 64.7 years; 54.4% male), poorer survival was associated with work in SOC code "Production Occupations" and marginally with work in "Military Occupation"; poor survival associated with self-reported occupational pesticide exposure in adjusted models. Among onset segments: cervical onset was associated with ALS participants having ever worked in "Buildings and Grounds Cleaning and Maintenance Occupations," "Construction and Extraction Occupations," and "Production Occupations"; bulbar onset with self-reported occupational exposure to radiation; and cervical onset with exposure to particulate matter, volatile organic compounds, metals, combustion and diesel exhaust, electromagnetic radiation, and radiation.

CONCLUSION

Occupational settings and self-reported exposures influence ALS survival and onset segment. Further studies are needed to explore and understand these relationships, most advantageously using prospective cohorts and detailed ALS registries.

Associations of self-reported occupational exposures and settings to ALS: a case-control study

BACKGROUND

Environmental exposures contribute to the pathogenesis of amyotrophic lateral sclerosis (ALS), a fatal and progressive neurological disease. Identification of these exposures is important for targeted screening and risk factor modification.

OBJECTIVE

To identify occupational exposures that are associated with a higher risk of ALS using both survey and standard occupational classification (SOC) coding procedures, and to highlight how exposure surveys can complement SOC coding.

METHODS

ALS participants and neurologically healthy controls recruited in Michigan completed a detailed exposure assessment on their four most recent and longest held occupations. Exposure scores were generated from the exposure survey, and occupations were assigned to SOC codes by experienced exposure scientists.

RESULTS

This study included 381 ALS and 272 control participants. ALS participants reported higher duration-adjusted occupational exposure to particulate matter (OR = 1.45, 95% CI 1.19-1.78, p < 0.001), volatile organic compounds (OR = 1.22, 95% CI 1.02-1.45, p = 0.029), metals (OR = 1.48, 95% CI 1.21-1.82, p < 0.001), and combustion and diesel exhaust pollutants (OR = 1.20, 95% CI 1.01-1.43, p = 0.041) prior to ALS diagnosis, when adjusted for sex, age, and military service compared to controls. In multivariable models, only occupational exposure to metals remained significant risk (OR = 1.56, 95% CI 1.11-2.20, p = 0.011), although in an adaptive elastic net model, particulate matter (OR = 1.203), pesticides (OR = 1.015), and metals (1.334) were all selected as risk factors. Work in SOC code "Production Occupations" was associated with a higher ALS risk. SOC codes "Building and Grounds Cleaning and Maintenance Occupations", "Construction and Extraction Occupations", "Installation, Maintenance, and Repair Occupations", and "Production Occupations" were all associated with a higher exposure to metals as determined using survey data.

DISCUSSION

Occupational exposure to particulate matter, volatile organic compounds, metals, pesticides, and combustion and diesel exhaust and employment in "Production Occupations" was associated with an increased ALS risk in this cohort.

Integrating Interprofessional Collaborative Practice Into Occupational and Environmental Health and Safety Education: Results of a Feasibility Study

OBJECTIVE

Although occupational health and safety programs are expected to prepare graduates to function in an interprofessional collaborative environment, research to support training in this area is far less common in this discipline than among clinical programs. We incorporated interprofessional education (IPE) competencies into an occupational health and safety course for graduate students in diverse disciplines (public health, engineering, nursing) and evaluated effectiveness.

METHODS

We assessed course offerings for content learning and attitudes toward IPE and analyzed data using a mixed-methods approach.

RESULTS

In general, students increased their understanding of the role of other disciplines, confidence in working in interprofessional teams, and valuation of IPE.

CONCLUSION

Introducing IPE competencies into graduate education in worker health and safety is feasible and can equip students to more effectively work in occupational health-oriented multidisciplinary teams.

Opportunities and challenges in reducing personal inhalation exposure to air pollution among electronic waste recovery workers in Ghana

BACKGROUND

Informal sector electronic waste (e-waste) recovery produces toxic emissions resulting from burning e-waste to recover valuable metals.

OBJECTIVES

To identify high-risk worker groups by measuring relative levels of personal inhalation exposure to particulate matter (PM) of fine (≤2.5 µm) and coarse (2.5-10 µm) fractions (PM2.5 and PM2.5-10, respectively) across work activities among e-waste workers, and to assess how wind conditions modify levels of PM by activity and site location.

METHODS

At the Agbogbloshie e-waste site, 170 partial-shift PM samples and time-activity data were collected from participants (N = 105) enrolled in the GeoHealth cohort study. Personal sampling included continuous measures of size-specific PM from the worker's breathing zone and time-activity derived from wearable cameras. Linear mixed models were used to estimate changes in personal PM2.5 and PM2.5-10 associated with activities and evaluate effect modification by wind conditions.

RESULTS

Mean (±standard deviation) personal PM2.5 and PM2.5-10 concentrations were 80 (±  81) and 123 (± 139) µg m-3 , respectively. The adjusted mean PM2.5 concentration for burning e-waste was 88 µg m-3 , a 28% increase above concentrations during non-recovery activities (such as eating). Transportation-related and burning activities were associated with the highest PM2.5-10 concentrations. Frequent changes in wind direction were associated with higher PM2.5 concentrations when burning, and high wind speeds with higher PM2.5-10 concentrations when dismantling e-waste downwind of the burning zone.

Pollen production for 13 urban North American tree species: Allometric equations for tree trunk diameter and crown area

Estimates of airborne pollen concentrations at the urban scale would be useful for epidemiologists, land managers, and allergy sufferers. Mechanistic models could be well suited for this task, but their development will require data on pollen production across cities, including estimates of pollen production by individual trees. In this study, we developed predictive models for pollen production as a function of trunk size, canopy area, and height, which are commonly recorded in tree surveys or readily extracted from remote sensing data. Pollen production was estimated by measuring the number of flowers per tree, the number of anthers per flower, and the number of pollen grains per anther. Variability at each morphological scale was assessed using bootstrapping. Pollen production was estimated for the following species: Acer negundo, Acer platanoides, Acer rubrum, Acer saccharinum, Betula papyrifera, Gleditsia triacanthos, Juglans nigra, Morus alba, Platanus x acerfolia, Populus deltoides, Quercus palustris, Quercus rubra, and Ulmus americana. Basal area predicted pollen production with a mean R2 of 0.72 (range: 0.41 - 0.99), whereas canopy area predicted pollen production with a mean R2 of 0.76 (range: 0.50 - 0.99). These equations are applied to two tree datasets to estimate total municipal pollen production and the spatial distribution of street tree pollen production for the focal species. We present some of the first individual-tree based estimates of pollen production at the municipal scale; the observed spatial heterogeneity in pollen production is substantial and can feasibly be included in mechanistic models of airborne pollen at fine spatial scales.

Urban-scale variation in pollen concentrations: A single station is insufficient to characterize daily exposure

Epidemiological analyses of airborne allergenic pollen often use concentration measurements from a single station to represent exposure across a city, but this approach does not account for the spatial variation of concentrations within the city. Because there are few descriptions of urban-scale variation, the resulting exposure measurement error is unknown but potentially important for epidemiological studies. This study examines urban scale variation in pollen concentrations by measuring pollen concentrations of 13 taxa over 24-hr periods twice weekly at 25 sites in two seasons in Detroit, Michigan. Spatio-temporal variation is described using cumulative distribution functions and regression models. Daily pollen concentrations across the 25 stations varied considerably, and the average quartile coefficient of dispersion was 0.63. Measurements at a single site explained 3-85% of the variation at other sites, depending on the taxon, and 95% prediction intervals of pollen concentrations generally spanned one to two orders of magnitude. These results demonstrate considerable heterogeneity of pollen levels at the urban scale, and suggest that the use of a single monitoring site will not reflect pollen exposure over an urban area and can lead to sizable measurement error in epidemiological studies, particularly when a daily time-step is used. These errors might be reduced by using predictive daily pollen levels in models that combine vegetation maps, pollen production estimates, phenology models and dispersion processes, or by using coarser time-steps in the epidemiological analysis.

Time series analysis of total and direct associations between high temperatures and preterm births in Detroit, Michigan

OBJECTIVES

Preterm births (PTBs) represent significant health risks, and several studies have found associations between high outdoor temperatures and PTB. We estimated both the total and natural direct effects (independent of particulate matter, ozone and nitrogen dioxide air pollutants) of the prior 2-day mean apparent temperature (AT) on PTB. We evaluated effect modification by maternal age, race, education, smoking status and prenatal care.

DESIGN AND SETTING

We obtained birth records and meteorological data for the Detroit, Michigan, USA area, for the warm months (May to September), 1991 to 2001. We used a time series Poisson regression with splines of AT, wind speed, solar radiation and citywide average precipitation to estimate total effects. To accommodate multiple mediators and exposure-mediator interactions, AT inverse odds weights, predicted by meteorological and air pollutant covariates, were added in a subsequent model to estimate direct effects.

RESULTS

At 24.9°C relative to 18.6°C, 10.6% (95% CI: 3.8% to 17.2%) of PTBs were attributable to the total effects of AT, and 10.4% (95% CI: 2.2% to 17.5%) to direct effects. Relative excess risks of interaction indicated that the risk of PTB with increasing temperature above 18.6°C was significantly lower among black mothers and higher among mothers less than 19, older than 30, with late or no prenatal care and who smoked.

CONCLUSION

This additional evidence of a direct association between high temperature and PTB may motivate public health interventions to reduce extreme heat exposures among pregnant women, particularly among those who may have enhanced vulnerability.

Allergenic pollen production across a large city for common ragweed (Ambrosia artemisiifolia)

Predictions of airborne allergenic pollen concentrations at fine spatial scales require information on source plant location and pollen production. Such data are lacking at the urban scale, largely because manually mapping allergenic pollen producing plants across large areas is infeasible. However, modest-sized field surveys paired with allometric equations, remote sensing, and habitat distribution models can predict where these plants occur and how much pollen they produce. In this study, common ragweed (Ambrosia artemisiifolia) was mapped in a field survey in Detroit, MI, USA. The relationship between ragweed presence and habitat-related variables derived from aerial imagery, LiDAR, and municipal data were used to create a habitat distribution model, which was then used to predict ragweed presence across the study area (392 km²). The relationship between inflorescence length and pollen production was used to predict pollen production in the city. Ragweed occurs in 1.7% of Detroit and total pollen production is 312 × 10¹² pollen grains annually, but ragweed presence was highly heterogeneous across the city. Ragweed was predominantly found in in vacant lots (75%) and near demolished structures (48%), and had varying associations with land cover types (e.g., sparse vegetation, trees, pavement) detected by remote sensing. These findings also suggest several management strategies that could help reduce levels of allergenic pollen, including appropriate post-demolition management practices. Spatially-resolved predictions for pollen production will allow mechanistic modeling of airborne allergenic pollen and improved exposure estimates for use in epidemiological and other applications.

Evaluation of Changes in Lead Levels in Drinking Water Due to Replacement of Water Mains: A Comprehensive Study in Chicago, Illinois

Disturbances to water treatment and distribution systems using lead service lines have been reported to increase water lead levels. This study evaluates effects from one type of physical disturbance, namely, water mains replacement. Lead concentrations at 542 homes in Chicago, Illinois were measured using partial profile sampling (1^st, 4^th, 6^th and 5^th min 1-L samples) after 6-h stagnation, both before and after mains replacement; a subset had monthly follow-up sampling for an additional 12 months. Concentrations were correlated from month-to-month and depended on household water consumption, temperature, residence age, and other factors. The sampling event maximum yielded considerably higher concentrations than first-draw samples, and 5 min flush samples had the lowest concentration at nearly all homes. Mains replacement was associated with less than a 1 μg/L increase in median and 90^th percentile concentrations; changes were smaller or not seen after controlling for other factors. Transient lead peaks were identified in a subset of residences and visits. These findings in Chicago indicate that mains replacement did not produce large changes in Pb concentrations in samples collected 1 to 12 months following the disturbance. We recommend continued outreach to promote flushing and other actions to minimize lead exposure, and routine use of profile sampling.

Absence of PCB Hot Spot Effect in Walleye Sander vitreus from Lower Green Bay of Lake Michigan

Under certain conditions, polychlorinated biphenyl (PCB) concentration in individuals of one sex of an adult fish population may exceed that of the other sex by more than a factor of two. This phenomenon, known as the PCB hot spot effect, has been postulated to be contingent upon the following two conditions: (1) presence of a PCB hot spot in the bottom sediments of the aquatic ecosystem, such that prey PCB concentrations in the hot spot region are substantially higher than prey PCB concentrations in locations distant from the hot spot, and (2) habitat use varying between the sexes, such that individuals of one sex inhabit the hot spot region to a considerably greater degree than individuals of the other sex. To test whether PCB concentrations in walleye Sander vitreus from lower Green Bay of Lake Michigan displayed a PCB hot spot effect, whole-fish PCB concentrations were determined in ten female and ten male adult walleye from the population spawning in the Fox River, the main tributary to lower Green Bay. In addition, mark-recapture data for the Fox River walleye population were analyzed to determine differences in spatial distributions between the sexes. Results revealed that the ratio of mean PCB concentration in males to mean PCB concentration in females was only 1.13, indicating the absence of a PCB hot spot effect. This result was corroborated by the mark-recapture data analysis, which showed no significant difference in habitat use between the sexes. Thus, although condition 1 was met, condition 2 was not met. Consequently, the PCB hot spot effect was not observed in the Fox River walleye population. Lack of a significant difference in PCB congener distributions between the sexes further corroborated our conclusions.

Effect of intra-urban temperature variation on tree flowering phenology, airborne pollen, and measurement error in epidemiological studies of allergenic pollen

Temperature gradients in cities can cause inter-neighborhood differences in the timing of pollen release. However, most epidemiological studies examining allergenic pollen utilize daily measurements from a single pollen monitoring station with the implicit assumption that the measured time series of airborne pollen concentrations applies across the study areas, and that the temporal mismatch between concentrations at the counting station and elsewhere in the study area is negligible. This assumption is tested by quantifying temperature using satellite imagery, observing flowering times of oak (Quercus) and mulberry (Morus) trees at multiple sites, and collecting airborne pollen. Epidemiological studies of allergenic pollen are reviewed and temperatures within their study areas are quantified. In this one-year study, peak oak flowering time was well explained by average February nighttime temperature (R2 = 0.94), which varied by 6 °C across Detroit. This relationship was used to predict flowering phenology across the study region. Peak flowering ranged from April 20-May 13 and predicted a substantial portion of relative airborne oak pollen concentrations in Detroit (R2 = 0.46) and at the regional pollen monitoring station (R2 = 0.61). The regional pollen monitoring station was located in a cooler outlying area where peak flowering occurred around May 12 and peak pollen concentrations were measured on May 15. This provides evidence that the timing of pollen release varies substantially within a metropolitan area and challenges the assumption that pollen measurements at a single location are representative of an entire city. Across the epidemiological studies, 50% of study areas were not within 1 °C (equal to a lag or lead of 4 days in flowering time) of temperatures at the pollen measurement location. Epidemiological studies using a single pollen station as a proxy for pollen concentrations are prone to significant measurement error if the study area is climatically variable.

Influence of viral infection on the relationships between airway cytokines and lung function in asthmatic children

BACKGROUND

Few longitudinal studies examine inflammation and lung function in asthma. We sought to determine the cytokines that reduce airflow, and the influence of respiratory viral infections on these relationships.

METHODS

Children underwent home collections of nasal lavage during scheduled surveillance periods and self-reported respiratory illnesses. We studied 53 children for one year, analyzing 392 surveillance samples and 203 samples from 85 respiratory illnesses. Generalized estimated equations were used to evaluate associations between nasal lavage biomarkers (7 mRNAs, 10 proteins), lung function and viral infection.

RESULTS

As anticipated, viral infection was associated with increased cytokines and reduced FVC and FEV1. However, we found frequent and strong interactions between biomarkers and virus on lung function. For example, in the absence of viral infection, CXCL10 mRNA, MDA5 mRNA, CXCL10, IL-4, IL-13, CCL4, CCL5, CCL20 and CCL24 were negatively associated with FVC. In contrast, during infection, the opposite relationship was frequently found, with IL-4, IL-13, CCL5, CCL20 and CCL24 levels associated with less severe reductions in both FVC and FEV1.

CONCLUSIONS

In asthmatic children, airflow obstruction is driven by specific pro-inflammatory cytokines. In the absence of viral infection, higher cytokine levels are associated with decreasing lung function. However, with infection, there is a reversal in this relationship, with cytokine abundance associated with reduced lung function decline. While nasal samples may not reflect lower airway responses, these data suggest that some aspects of the inflammatory response may be protective against viral infection. This study may have ramifications for the treatment of viral-induced asthma exacerbations.

Impact of community respiratory viral infections in urban children with asthma

Background

Upper respiratory tract viral infections cause asthma exacerbations in children. However, the impact of natural colds on children with asthma in the community, particularly in the high-risk urban environment, is less well defined.

Objective

We hypothesized that children with high-symptom upper respiratory viral infections have reduced airway function and greater respiratory tract inflammation than children with virus-positive low-symptom illnesses or virus-negative upper respiratory tract symptoms.

Methods

We studied 53 children with asthma from Detroit, Michigan, during scheduled surveillance periods and self-reported respiratory illnesses for 1 year. Symptom score, spirometry, fraction of exhaled nitric oxide (FeNO), and nasal aspirate biomarkers, and viral nucleic acid and rhinovirus (RV) copy number were assessed.

Results

Of 658 aspirates collected, 22.9% of surveillance samples and 33.7% of respiratory illnesses were virus-positive. Compared with the virus-negative asymptomatic condition, children with severe colds (symptom score ≥5) showed reduced forced expiratory flow at 25% to 75% of the pulmonary volume (FEF_25%-75%), higher nasal messenger RNA expression of C-X-C motif chemokine ligand (CXCL)-10 and melanoma differentiation-associated protein 5, and higher protein abundance of CXCL8, CXCL10 and C-C motif chemokine ligands (CCL)-2, CCL4, CCL20, and CCL24. Children with mild (symptom score, 1-4) and asymptomatic infections showed normal airway function and fewer biomarker elevations. Virus-negative cold-like illnesses demonstrated increased FeNO, minimal biomarker elevation, and normal airflow. The RV copy number was associated with nasal chemokine levels but not symptom score.

Conclusion

Urban children with asthma with high-symptom respiratory viral infections have reduced FEF_25%-75% and more elevations of nasal biomarkers than children with mild or symptomatic infections, or virus-negative illnesses.

Environmental impacts of commuting modes in Lisbon: a life-cycle assessment addressing particulate matter impacts on health

A life-cycle assessment of commuting alternatives is conducted that compares six transportation modes (car, bus, train, subway, motorcycle and bicycle) for eight impact indicators. Fine particulate matter (PM_2.5) emissions and health impacts are incorporated in the assessment using intake fractions that differentiate between urban and non-urban emissions, combined with an effect factor. The potential benefits of different strategies for reducing environmental impacts are illustrated. The results demonstrate the need for comprehensive approaches that avoid problem-shifting among transportation-related strategies. Policies aiming to improve the environmental performance of urban transportation should target strategies that decrease local emissions, life-cycle impacts and health effects.

Effectiveness of Using Enhanced Filters in Schools and Homes to Reduce Indoor Exposures to PM2.5 from Outdoor Sources and Subsequent Health Benefits for Children with Asthma

Filters can reduce indoor concentrations of particulate matter (PM_2.5), but their benefits have not been well-characterized. This study investigates exposure, health, and cost impacts of high efficiency filters in homes and schools, focusing on the asthma-related outcomes. Reductions in indoor exposures to PM_2.5 from outdoor sources with enhanced filters (e.g., MERV 12) are estimated using probabilistic indoor air quality models, and avoided health impacts are quantified using health impact assessment. These methods are applied using data from Detroit, Michigan, an urban region with elevated asthma rates. Replacing inefficient filters with enhanced filters in schools would reduce the PM_2.5-attributable asthma burden by 13% annually, with higher benefits for more efficient filters. Marginal costs average $63 per classroom or $32 per child with asthma per year. In homes, using efficient furnace filters or air cleaners yields 11 to 16% reductions in the asthma burden with an annualized marginal costs of $151-494 per household. Additional benefits include reductions in health risk for adults and lower exposures to other contaminants such as PM from indoor sources. On the basis of the included health outcomes, efficient filters in schools in particular is a potentially cost-efficient way to reduce the asthma-related health burden for children.

Operational evaluation of the RLINE dispersion model for studies of traffic-related air pollutants

Exposure to traffic-related air pollutants (TRAP) remains a key public health issue, and improved exposure measures are needed to support health impact and epidemiologic studies and inform regulatory responses. The recently developed Research LINE source model (RLINE), a Gaussian line source dispersion model, has been used in several epidemiologic studies of TRAP exposure, but evaluations of RLINE's performance in such applications have been limited. This study provides an operational evaluation of RLINE in which predictions of NO_x, CO and PM_2.5 are compared to observations at air quality monitoring stations located near high traffic roads in Detroit, MI. For CO and NO_x, model performance was best at sites close to major roads, during downwind conditions, during weekdays, and during certain seasons. For PM_2.5, the ability to discern local and particularly the traffic-related portion was limited, a result of high background levels, the sparseness of the monitoring network, and large uncertainties for certain processes (e.g., formation of secondary aerosols) and non-mobile sources (e.g., area, fugitive). Overall, RLINE's performance in near-road environments suggests its usefulness for estimating spatially- and temporally-resolved exposures. The study highlights considerations relevant to health impact and epidemiologic applications, including the importance of selecting appropriate pollutants, using appropriate monitoring approaches, considering prevailing wind directions during study design, and accounting for uncertainty.

Air pollutant strategies to reduce adverse health impacts and health inequalities: a quantitative assessment for Detroit, Michigan

The development of air quality management (AQM) strategies provides opportunities to improve public health and reduce health inequalities. This study evaluates health and inequality impacts of alternate SO2 control strategies in Detroit, MI, a designated non-attainment area. Control alternatives include uniform reductions across sources, ranking approaches based on total emissions and health impacts per ton of pollutant emitted, and optimizations that meet concentration and health goals. Using dispersion modeling and quantitative health impact assessment (HIA), these strategies are evaluated in terms of ambient concentrations, health impacts, and the inequality in health risks. The health burden attributable to SO2 emissions in Detroit falls primarily among children and includes 70 hospitalizations and 6,000 asthma-related respiratory symptom-days annually, equivalent to 7 disability-adjusted life years (DALYs). The health burden disproportionately falls on Hispanic/Latino residents, residents with less than a high school diploma, and foreign-born residents. Control strategies that target smaller facilities near exposed populations provide the greatest benefit in terms of the overall health burden reductions and the inequality of attributable health risk; conventional strategies that target the largest emission sources can increase inequality and provide only modest health benefits. The assessment is novel in using spatial analyses that account for urban scale gradients in exposure, demographics, vulnerability, and population health. We show that quantitative HIA methods can be used to develop AQM strategies that simultaneously meet environmental, public health, and environmental justice goals, advancing AQM beyond its current compliance-oriented focus.

Sensitivity analysis of the near-road dispersion model RLINE - an evaluation at Detroit, Michigan

The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NO_x predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies, e.g., to estimate health impacts.

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM_2.5), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM_2.5 and O₃ exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO₂ exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO₂ exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.

Assessing concentrations and health impacts of air quality management strategies: Framework for Rapid Emissions Scenario and Health impact ESTimation (FRESH-EST)

In air quality management, reducing emissions from pollutant sources often forms the primary response to attaining air quality standards and guidelines. Despite the broad success of air quality management in the US, challenges remain. As examples: allocating emissions reductions among multiple sources is complex and can require many rounds of negotiation; health impacts associated with emissions, the ultimate driver for the standards, are not explicitly assessed; and long dispersion model run-times, which result from the increasing size and complexity of model inputs, limit the number of scenarios that can be evaluated, thus increasing the likelihood of missing an optimal strategy. A new modeling framework, called the "Framework for Rapid Emissions Scenario and Health impact ESTimation" (FRESH-EST), is presented to respond to these challenges. FRESH-EST estimates concentrations and health impacts of alternative emissions scenarios at the urban scale, providing efficient computations from emissions to health impacts at the Census block or other desired spatial scale. In addition, FRESH-EST can optimize emission reductions to meet specified environmental and health constraints, and a convenient user interface and graphical displays are provided to facilitate scenario evaluation. The new framework is demonstrated in an SO2 non-attainment area in southeast Michigan with two optimization strategies: the first minimizes emission reductions needed to achieve a target concentration; the second minimizes concentrations while holding constant the cumulative emissions across local sources (e.g., an emissions floor). The optimized strategies match outcomes in the proposed SO2 State Implementation Plan without the proposed stack parameter modifications or shutdowns. In addition, the lower health impacts estimated for these strategies suggest that FRESH-EST could be used to identify potentially more desirable pollution control alternatives in air quality management planning.

Non-stationary spatio-temporal modeling of traffic-related pollutants in near-road environments

A problem often encountered in environmental epidemiological studies assessing the health effects associated with ambient exposure to air pollution is the spatial misalignment between monitors' locations and subjects' actual residential locations. Several strategies have been adopted to circumvent this problem and estimate pollutants concentrations at unsampled sites, including spatial statistical or geostatistical models that rely on the assumption of stationarity to model the spatial dependence in pollution levels. Although computationally convenient, the assumption of stationarity is often untenable for pollutants concentration, particularly in the near-road environment. Building upon the work of Fuentes (2001) and Schmidt et al. (2011), in this paper we present a non-stationary spatio-temporal model for three traffic-related pollutants in a localized near-road environment. Modeling each pollutant separately and independently, we express each pollutant's concentration as a mixture of two independent spatial processes, each equipped with a non-stationary covariance function with covariates driving the non-stationarity and the mixture weights.

Measurement and Comparison of Organic Compound Concentrations in Plasma, Whole Blood, and Dried Blood Spot Samples

The preferred sampling medium for measuring human exposures of persistent organic compounds (POPs) is blood, and relevant sample types include whole blood, plasma, and dried blood spots (DBS). Because information regarding the performance and comparability of measurements across these sample types is limited, it is difficult to compare across studies. This study evaluates the performance of POP measurements in plasma, whole blood and DBS, and presents the distribution coefficients needed to convert concentrations among the three sample types. Blood samples were collected from adult volunteers, along with demographic and smoking information, and analyzed by GC/MS for organochlorine pesticides (OCPs), chlorinated hydrocarbons (CHCs), polychlorinated biphenyls (PCBs), and brominated diphenyl ethers (PBDEs). Regression models were used to evaluate the relationships between the sample types and possible effects of personal covariates. Distribution coefficients also were calculated using physically-based models. Across all compounds, concentrations in plasma were consistently the highest; concentrations in whole blood and DBS samples were comparable. Distribution coefficients for plasma to whole blood concentrations ranged from 1.74 to 2.26 for pesticides/CHCs, averaged 1.69 ± 0.06 for the PCBs, and averaged 1.65 ± 0.03 for the PBDEs. Regression models closely fit most chemicals (R (2) > 0.80), and whole blood and DBS samples generally showed very good agreement. Distribution coefficients estimated using biologically-based models were near one and did not explain the observed distribution. Among the study population, median concentrations of several pesticides/CHCs and PBDEs exceeded levels reported in the 2007-2008 National Health and Nutrition Examination Survey, while levels of other OCPs and PBDEs were comparable or lower. Race and smoking status appeared to slightly affect plasma/blood concentration ratios for several POPs. The experimentally-determined distribution coefficients can be used to compare POP exposures across studies using different types of blood-based matrices.

Air Pollution Exposure Model for Individuals (EMI) in Health Studies: Evaluation for Ambient PM2.5 in Central North Carolina

Air pollution health studies of fine particulate matter (diameter ≤2.5 μm, PM2.5) often use outdoor concentrations as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and time indoors can induce exposure errors. We developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. We linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Tier 2), indoor concentrations (Tier 3), personal exposure factors (Tier 4), and personal exposures (Tier 5) for ambient PM2.5. Using cross-validation, individual predictions were compared to 591 daily measurements from 31 homes (Tiers 1-3) and participants (Tiers 4-5) in central North Carolina. Median absolute differences were 39% (0.17 h(-1)) for Tier 1, 18% (0.10) for Tier 2, 20% (2.0 μg/m(3)) for Tier 3, 18% (0.10) for Tier 4, and 20% (1.8 μg/m(3)) for Tier 5. The capability of EMI could help reduce the uncertainty of ambient PM2.5 exposure metrics used in health studies.

Health impact metrics for air pollution management strategies

Health impact assessments (HIAs) inform policy and decision making by providing information regarding future health concerns, and quantitative HIAs now are being used for local and urban-scale projects. HIA results can be expressed using a variety of metrics that differ in meaningful ways, and guidance is lacking with respect to best practices for the development and use of HIA metrics. This study reviews HIA metrics pertaining to air quality management and presents evaluative criteria for their selection and use. These are illustrated in a case study where PM2.5 concentrations are lowered from 10 to 8μg/m(3) in an urban area of 1.8 million people. Health impact functions are used to estimate the number of premature deaths, unscheduled hospitalizations and other morbidity outcomes. The most common metric in recent quantitative HIAs has been the number of cases of adverse outcomes avoided. Other metrics include time-based measures, e.g., disability-adjusted life years (DALYs), monetized impacts, functional-unit based measures, e.g., benefits per ton of emissions reduced, and other economic indicators, e.g., cost-benefit ratios. These metrics are evaluated by considering their comprehensiveness, the spatial and temporal resolution of the analysis, how equity considerations are facilitated, and the analysis and presentation of uncertainty. In the case study, the greatest number of avoided cases occurs for low severity morbidity outcomes, e.g., asthma exacerbations (n=28,000) and minor-restricted activity days (n=37,000); while DALYs and monetized impacts are driven by the severity, duration and value assigned to a relatively low number of premature deaths (n=190 to 230 per year). The selection of appropriate metrics depends on the problem context and boundaries, the severity of impacts, and community values regarding health. The number of avoided cases provides an estimate of the number of people affected, and monetized impacts facilitate additional economic analyses useful to policy analysis. DALYs are commonly used as an aggregate measure of health impacts and can be used to compare impacts across studies. Benefits per ton metrics may be appropriate when changes in emissions rates can be estimated. To address community concerns and HIA objectives, a combination of metrics is suggested.

Effects of fuels, engine load and exhaust after-treatment on diesel engine SVOC emissions and development of SVOC profiles for receptor modeling

Diesel exhaust emissions contain numerous semivolatile organic compounds (SVOCs) for which emission information is limited, especially for idling conditions, new fuels and the new after-treatment systems. This study investigates exhaust emissions of particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs (NPAHs), and sterane and hopane petroleum biomarkers from a heavy-duty (6.4 L) diesel engine at various loads (idle, 600 and 900 kPa BMEP), with three types of fuel (ultra-low sulfur diesel or ULSD, Swedish low aromatic diesel, and neat soybean biodiesel), and with and without a diesel oxidation catalyst (DOC) and diesel particulate filter (DPF). Swedish diesel and biodiesel reduced emissions of PM_2.5, Σ₁₅PAHs, Σ₁₁NPAHs, Σ₅Hopanes and Σ₆Steranes, and biodiesel resulted in the larger reductions. However, idling emissions increased for benzo[k]fluoranthene (Swedish diesel), 5-nitroacenaphthene (biodiesel) and PM_2.5 (biodiesel), a significant result given the attention to exposures from idling vehicles and the toxicity of high-molecular-weight PAHs and NPAHs. The DOC + DPF combination reduced PM_2.5 and SVOC emissions during DPF loading (>99% reduction) and DPF regeneration (83-99%). The toxicity of diesel exhaust, in terms of the estimated carcinogenic risk, was greatly reduced using Swedish diesel, biodiesel fuels and the DOC + DPF. PAH profiles showed high abundances of three and four ring compounds as well as naphthalene; NPAH profiles were dominated by nitro-naphthalenes, 1-nitropyrene and 9-nitroanthracene. Both the emission rate and the composition of diesel exhaust depended strongly on fuel type, engine load and after-treatment system. The emissions data and chemical profiles presented are relevant to the development of emission inventories and exposure and risk assessments.

Multimedia model for polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs in Lake Michigan

Polycyclic aromatic hydrocarbon (PAH) contamination in the U.S. Great Lakes has long been of concern, but information regarding the current sources, distribution, and fate of PAH contamination is lacking, and very little information exists for the potentially more toxic nitro-derivatives of PAHs (NPAHs). This study uses fugacity, food web, and Monte Carlo models to examine 16 PAHs and five NPAHs in Lake Michigan, and to derive PAH and NPAH emission estimates. Good agreement was found between predicted and measured PAH concentrations in air, but concentrations in water and sediment were generally under-predicted, possibly due to incorrect parameter estimates for degradation rates, discharges to water, or inputs from tributaries. The food web model matched measurements of heavier PAHs (≥5 rings) in lake trout, but lighter PAHs (≤4 rings) were overpredicted, possibly due to overestimates of metabolic half-lives or gut/gill absorption efficiencies. Derived PAH emission rates peaked in the 1950s, and rates now approach those in the mid-19th century. The derived emission rates far exceed those in the source inventories, suggesting the need to reconcile differences and reduce uncertainties. Although additional measurements and physiochemical data are needed to reduce uncertainties and for validation purposes, the models illustrate the behavior of PAHs and NPAHs in Lake Michigan, and they provide useful and potentially diagnostic estimates of emission rates.

PAHs, nitro-PAHs, hopanes, and steranes in lake trout from Lake Michigan

The present study examines concentrations and risks of polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs (NPAHs), steranes, and hopanes in lake trout collected in Lake Michigan. A total of 74 fish were collected in 2 seasons at 3 offshore sites. The total PAH concentration (Σ9 PAH) in whole fish ranged from 223 pg/g to 1704 pg/g wet weight, and PAH concentrations and profiles were similar across season, site, and sex. The total NPAH (Σ9 NPAH) concentrations ranged from 0.2 pg/g to 31 pg/g wet weight, and carcinogenic compounds, including 1-nitropyrene and 6-nitrochrysene, were detected. In the fall, NPAH concentrations were low at the Illinois site (0.2-0.5 pg/g wet wt), and site profiles differed considerably; in the spring, concentrations and profiles were similar across sites, possibly reflecting changes in fish behavior. In the fall, the total sterane (Σ5 Sterane) and total hopane (Σ2 Hopane) levels reached 808 pg/g and 141 pg/g wet weight, respectively, but concentrations in the spring were 10 times lower. Concentrations in eggs (fall only) were on the same order of magnitude as those in whole fish. These results demonstrate the presence of target semivolatile organic compounds in a top predator fish, and are consistent with PAH biodilution observed previously. Using the available toxicity information for PAHs and NPAHs, the expected cancer risk from consumption of lake trout sampled are low. However, NPAHs contributed a significant portion of the toxic equivalencies in some samples. The present study provides the first measurements of NPAHs in freshwater fish, and results suggest that additional assessment is warranted.

PAHs (polycyclic aromatic hydrocarbons), nitro-PAHs, and hopane and sterane biomarkers in sediments of southern Lake Michigan, USA

PAHs in the Great Lakes basin are of concern due to their toxicity and persistence in bottom sediments. Their nitro derivatives, nitro-PAHs (NPAHs), which can have stronger carcinogenic and mutagenic activity than parent PAHs, may follow similar transport routes and also are accumulated in sediments. Limited information exists regarding the current distribution, trends and loadings of these compounds, especially NPAHs, in Lake Michigan sediments. This study characterizes PAHs, NPAHs, and biomarkers steranes and hopanes in surface sediments collected at 24 offshore sites in southern Lake Michigan. The ΣPAH14 (sum of 14 compounds) ranged from 213 to 1,291 ng/g dry weight (dw) across the sites, levels that are 2 to 10 times lower than those reported 20 to 30 years earlier. Compared to consensus-based sediment quality guidelines, PAH concentrations suggest very low risk to benthic organisms. The ΣNPAH5 concentration ranged from 2.9 to 18.6 ng/g dw, and included carcinogenic compounds 1-nitropyrene and 6-nitrochrysene. ΣSterane6 and ΣHopane5 concentrations ranged from 6.2 to 36 and 98 to 355 ng/g dw, respectively. Based on these concentrations, Lake Michigan is approximately receiving 11, 0.16, 0.25 and 3.6 metrictons per year (t/yr) of ΣPAH14, ΣNPAH5, ΣSterane6 and ΣHopane5, respectively. Maps of OC-adjusted concentrations display that concentrations decline with increasing off-shore distance. The major sources of PAHs and NPAHs are pyrogenic in nature, based on diagnostic ratios. Using chemical mass balance models, sources were apportioned to emissions from diesel engines (56 ± 18%), coal power plants (27 ± 14%), coal-tar pavement sealants (16 ± 11%), and coke ovens (7 ± 12%). The biomarkers identify a combination of petrogenic and biogenic sources, with the southern end of the lake more impacted by petroleum. This first report of NPAH levels in sediments of Lake Michigan reveals several carcinogenic compounds at modest concentrations, and a need for further work to assess potential risks to aquatic organisms.

GPS-based microenvironment tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation in central North Carolina

A critical aspect of air pollution exposure assessment is the estimation of the time spent by individuals in various microenvironments (ME). Accounting for the time spent in different ME with different pollutant concentrations can reduce exposure misclassifications, while failure to do so can add uncertainty and bias to risk estimates. In this study, a classification model, called MicroTrac, was developed to estimate time of day and duration spent in eight ME (indoors and outdoors at home, work, school; inside vehicles; other locations) from global positioning system (GPS) data and geocoded building boundaries. Based on a panel study, MicroTrac estimates were compared with 24-h diary data from nine participants, with corresponding GPS data and building boundaries of home, school, and work. MicroTrac correctly classified the ME for 99.5% of the daily time spent by the participants. The capability of MicroTrac could help to reduce the time-location uncertainty in air pollution exposure models and exposure metrics for individuals in health studies.

Environmental risk factors and amyotrophic lateral sclerosis (ALS): a case-control study of ALS in Michigan

An interim report of a case-control study was conducted to explore the role of environmental factors in the development of amyotrophic lateral sclerosis (ALS). Sixty-six cases and 66 age- and gender-matched controls were recruited. Detailed information regarding residence history, occupational history, smoking, physical activity, and other factors was obtained using questionnaires. The association of ALS with potential risk factors, including smoking, physical activity and chemical exposure, was investigated using conditional logistic regression models. As compared to controls, a greater number of our randomly selected ALS patients reported exposure to fertilizers to treat private yards and gardens and occupational exposure to pesticides in the last 30 years than our randomly selected control cases. Smoking, occupational exposures to metals, dust/fibers/fumes/gas and radiation, and physical activity were not associated with ALS when comparing the randomly selected ALS patients to the control subjects. To further explore and confirm results, exposures over several time frames, including 0-10 and 10-30 years earlier, were considered, and analyses were stratified by age and gender. Pesticide and fertilizer exposure were both significantly associated with ALS in the randomly selected ALS patients. While study results need to be interpreted cautiously given the small sample size and the lack of direct exposure measures, these results suggest that environmental and particularly residential exposure factors warrant close attention in studies examining risk factors of ALS.

Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons

BACKGROUND

As public awareness of consequences of environmental exposures has grown, estimating the adverse health effects due to simultaneous exposure to multiple pollutants is an important topic to explore. The challenges of evaluating the health impacts of environmental factors in a multipollutant model include, but are not limited to: identification of the most critical components of the pollutant mixture, examination of potential interaction effects, and attribution of health effects to individual pollutants in the presence of multicollinearity.

METHODS

In this paper, we reviewed five methods available in the statistical literature that are potentially helpful for constructing multipollutant models. We conducted a simulation study and presented two data examples to assess the performance of these methods on feature selection, effect estimation and interaction identification using both cross-sectional and time-series designs. We also proposed and evaluated a two-step strategy employing an initial screening by a tree-based method followed by further dimension reduction/variable selection by the aforementioned five approaches at the second step.

RESULTS

Among the five methods, least absolute shrinkage and selection operator regression performs well in general for identifying important exposures, but will yield biased estimates and slightly larger model dimension given many correlated candidate exposures and modest sample size. Bayesian model averaging, and supervised principal component analysis are also useful in variable selection when there is a moderately strong exposure-response association. Substantial improvements on reducing model dimension and identifying important variables have been observed for all the five statistical methods using the two-step modeling strategy when the number of candidate variables is large.

CONCLUSIONS

There is no uniform dominance of one method across all simulation scenarios and all criteria. The performances differ according to the nature of the response variable, the sample size, the number of pollutants involved, and the strength of exposure-response association/interaction. However, the two-step modeling strategy proposed here is potentially applicable under a multipollutant framework with many covariates by taking advantage of both the screening feature of an initial tree-based method and dimension reduction/variable selection property of the subsequent method. The choice of the method should also depend on the goal of the study: risk prediction, effect estimation or screening for important predictors and their interactions.

Composition and Integrity of PAHs, Nitro-PAHs, Hopanes and Steranes In Diesel Exhaust Particulate Matter

Diesel exhaust particulate matter contains many semivolatile organic compounds (SVOCs) of environmental and health significance. This study investigates the composition, emission rates, and integrity of 25 SVOCs, including polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs (NPAHs), and diesel biomarkers hopanes and steranes. Diesel engine particulate matter (PM), generated using an engine test bench, three engine conditions, and ultra-low sulfur diesel (ULSD), was collected on borosilicate glass fiber filters. Under high engine load, the PM emission rate was 0.102 g/kWh, and emission rate of ΣPAHs (10 compounds), ΣNPAHs (6 compounds), Σhopanes (2 compounds), and Σsteranes (2 compounds) were 2.52, 0.351, 0.02 ~ 2 and 1μg/kWh, respectively. Storage losses were evaluated for three cases: conditioning filters in clean air at 25 °C and 33% relative humidity (RH) for 24 h; storing filter samples (without extraction) wrapped in aluminum foil at 4 °C for up to one month; and storing filter extracts in glass vials capped with Teflon crimp seals at 4 °C for up to six months. After conditioning filters for 24 h, 30% of the more volatile PAHs were lost, but lower volatility NPAHs, hopanes and steranes showed negligible changes. Storing wrapped filters and extracts at 4 °C for up to one month did not lead to significant losses, but storing extracts for five months led to significant losses of PAHs and NPAHs; hopanes and steranes demonstrated greater integrity. These results suggest that even relatively brief filter conditioning periods, needed for gravimetric measurements of PM mass, and extended storage of filter extracts can lead to underestimates of SVOC concentrations. Thus, SVOC sampling and analysis protocols should utilize stringent criteria and performance checks to identify and limit possible biases occurring during filter and extract processing.

Characterization of allergens and airborne fungi in low and middle-income homes of primary school children in Durban, South Africa

BACKGROUND

The South Durban Health Study (SDHS) is a population-based study that examined the relationship between exposure to ambient air pollutants and respiratory disease among school children with high prevalence of asthma who resided in two purposely selected communities in north and south Durban, KwaZulu-Natal, South Africa.

METHODS

From the SDHS participants, a subgroup of 135 families was selected for investigation of household characteristics potentially related to respiratory health. In these households, a walkthrough investigation was conducted, and settled dust and air samples were collected for allergen and fungal measurements using standardized techniques.

RESULTS

Asp f1 allergen was detected in all homes, and Bla g1 allergen was detected in half of the homes. House dust allergens, Der f1 and Der p1 exceeded concentrations associated with risk of sensitization and exacerbation of asthma in 3% and 13%, respectively, of the sampled homes, while Bla g1 exceeded guidance values in 13% of the homes. Although airborne fungal concentrations in sleep areas and indoors were lower than outdoor concentrations, they exceeded 1,000 CFU/m(3) in 29% of the homes. Multivariate analyses identified several home characteristics that were predictors of airborne fungal concentrations, including moisture, ventilation, floor type, and bedding type. Airborne fungal concentrations were similar indoors and outdoors, which likely reduced the significance of housing and indoor factors as determinants of indoor concentrations.

CONCLUSION

Allergen concentrations were highly variable in homes, and a portion of the variability can be attributed to easily recognized conditions.

Gaseous and Particulate Emissions from Diesel Engines at Idle and under Load: Comparison of Biodiesel Blend and Ultralow Sulfur Diesel Fuels

Diesel exhaust emissions have been reported for a number of engine operating strategies, after-treatment technologies, and fuels. However, information is limited regarding emissions of many pollutants during idling and when biodiesel fuels are used. This study investigates regulated and unregulated emissions from both light-duty passenger car (1.7 L) and medium-duty (6.4 L) diesel engines at idle and load and compares a biodiesel blend (B20) to conventional ultralow sulfur diesel (ULSD) fuel. Exhaust aftertreatment devices included a diesel oxidation catalyst (DOC) and a diesel particle filter (DPF). For the 1.7 L engine under load without a DOC, B20 reduced brake-specific emissions of particulate matter (PM), elemental carbon (EC), nonmethane hydrocarbons (NMHCs), and most volatile organic compounds (VOCs) compared to ULSD; however, formaldehyde brake-specific emissions increased. With a DOC and high load, B20 increased brake-specific emissions of NMHC, nitrogen oxides (NOx), formaldehyde, naphthalene, and several other VOCs. For the 6.4 L engine under load, B20 reduced brake-specific emissions of PM2.5, EC, formaldehyde, and most VOCs; however, NOx brake-specific emissions increased. When idling, the effects of fuel type were different: B20 increased NMHC, PM2.5, EC, formaldehyde, benzene, and other VOC emission rates from both engines, and changes were sometimes large, e.g., PM2.5 increased by 60% for the 6.4 L/2004 calibration engine, and benzene by 40% for the 1.7 L engine with the DOC, possibly reflecting incomplete combustion and unburned fuel. Diesel exhaust emissions depended on the fuel type and engine load (idle versus loaded). The higher emissions found when using B20 are especially important given the recent attention to exposures from idling vehicles and the health significance of PM2.5. The emission profiles demonstrate the effects of fuel type, engine calibration, and emission control system, and they can be used as source profiles for apportionment, inventory, and exposure purposes.

Variability of indoor and outdoor VOC measurements: an analysis using variance components

This study examines concentrations of volatile organic compounds (VOCs) measured inside and outside of 162 residences in southeast Michigan, U.S.A. Nested analyses apportioned four sources of variation: city, residence, season, and measurement uncertainty. Indoor measurements were dominated by seasonal and residence effects, accounting for 50 and 31%, respectively, of the total variance. Contributions from measurement uncertainty (<20%) and city effects (<10%) were small. For outdoor measurements, season, city and measurement variation accounted for 43, 29 and 27% of variance, respectively, while residence location had negligible impact (<2%). These results show that, to obtain representative estimates of indoor concentrations, measurements in multiple seasons are required. In contrast, outdoor VOC concentrations can use multi-seasonal measurements at centralized locations. Error models showed that uncertainties at low concentrations might obscure effects of other factors. Variance component analyses can be used to interpret existing measurements, design effective exposure studies, and determine whether the instrumentation and protocols are satisfactory.

Air pollutant exposure and preterm and term small-for-gestational-age births in Detroit, Michigan: long-term trends and associations

Studies in a number of countries have reported associations between exposure to ambient air pollutants and adverse birth outcomes, including low birth weight, preterm birth (PTB) and, less commonly, small for gestational age (SGA). Despite their growing number, the available studies have significant limitations, e.g., incomplete control of temporal trends in exposure, modest sample sizes, and a lack of information regarding individual risk factors such as smoking. No study has yet examined large numbers of susceptible individuals. We investigated the association between ambient air pollutant concentrations and term SGA and PTB outcomes among 164,905 singleton births in Detroit, Michigan occurring between 1990 and 2001. SO(2), CO, NO(2), O(3) and PM(10) exposures were used in single and multiple pollutant logistic regression models to estimate odds ratios (OR) for these outcomes, adjusted for the infant's sex and gestational age, the mother's race, age group, education level, smoking status and prenatal care, birth season, site of residence, and long-term exposure trends. Term SGA was associated with CO levels exceeding 0.75ppm (OR=1.14, 95% confidence interval=1.02-1.27) and NO(2) exceeding 6.8ppb (1.11, 1.03-1.21) exposures in the first month, and with PM(10) exceeding 35μg/m(3) (1.22, 1.03-1.46) and O(3) (1.11, 1.02-1.20) exposure in the third trimester. PTB was associated with SO(2) (1.07, 1.01-1.14) exposure in the last month, and with (hourly) O(3) exceeding 92ppb (1.08, 1.02-1.14) exposure in the first month. Exposure to several air pollutants at modest concentrations was associated with adverse birth outcomes. This study, which included a large Black population, suggests the importance of the early period of pregnancy for associations between term SGA with CO and NO(2), and between O(3) with PTB; and the late pregnancy period for associations between term SGA and O(3) and PM(10), and between SO(2) with PTB. It also highlights the importance of accounting for individual risk factors such as maternal smoking, maternal race, and long-term trends in air pollutant levels and adverse birth outcomes in evaluating relationships between pollutant exposures and adverse birth outcomes.

VOC composition of current motor vehicle fuels and vapors, and collinearity analyses for receptor modeling

The formulation of motor vehicle fuels can alter the magnitude and composition of evaporative and exhaust emissions occurring throughout the fuel cycle. Information regarding the volatile organic compound (VOC) composition of motor fuels other than gasoline is scarce, especially for bioethanol and biodiesel blends. This study examines the liquid and vapor (headspace) composition of four contemporary and commercially available fuels: gasoline (<10% ethanol), E85 (85% ethanol and 15% gasoline), ultra-low sulfur diesel (ULSD), and B20 (20% soy-biodiesel and 80% ULSD). The composition of gasoline and E85 in both neat fuel and headspace vapor was dominated by aromatics and n-heptane. Despite its low gasoline content, E85 vapor contained higher concentrations of several VOCs than those in gasoline vapor, likely due to adjustments in its formulation. Temperature changes produced greater changes in the partial pressures of 17 VOCs in E85 than in gasoline, and large shifts in the VOC composition. B20 and ULSD were dominated by C(9) to C(16)n-alkanes and low levels of the aromatics, and the two fuels had similar headspace vapor composition and concentrations. While the headspace composition predicted using vapor-liquid equilibrium theory was closely correlated to measurements, E85 vapor concentrations were underpredicted. Based on variance decomposition analyses, gasoline and diesel fuels and their vapors VOC were distinct, but B20 and ULSD fuels and vapors were highly collinear. These results can be used to estimate fuel related emissions and exposures, particularly in receptor models that apportion emission sources, and the collinearity analysis suggests that gasoline- and diesel-related emissions can be distinguished.

Concentrations and speciation of polybrominated diphenyl ethers in human amniotic fluid

Polybrominated diphenyl ethers (PBDEs) are persistent organic chemicals used as flame retardants in textiles, plastics, and consumer products. Although PBDE accumulation in humans has been noted since the 1970s, few studies have investigated PBDEs within the gestational compartment, and none to date has identified levels in amniotic fluid. The present study reports congener-specific brominated diphenyl ether (BDE) concentrations in second-trimester clinical amniotic fluid samples collected in 2009 from fifteen women in southeast Michigan, USA. Twenty-one BDE congeners were measured by GC/MS/NCI. The average total PBDE concentration was 3795 pg/ml amniotic fluid (range: 337-21,842 pg/ml). BDE-47 and BDE-99 were identified in all samples. Based on median concentrations, the dominant congeners were BDE-208, 209, 203, 206, 207, and 47 representing 23, 16, 12, 10, 9 and 6%, respectively, of the total detected PBDEs. PBDE concentrations were identified in all amniotic fluid samples from southeast Michigan, supporting a need for further investigations of fetal exposure pathways and potential impacts on perinatal health.

Permeation of gasoline, diesel, bioethanol (E85), and biodiesel (B20) fuels through six glove materials

Biofuels and conventional fuels differ in terms of their evaporation rates, permeation rates, and exhaust emissions, which can alter exposures of workers, especially those in the fuel refining and distribution industries. This study investigated the permeation of biofuels (bioethanol 85%, biodiesel 20%) and conventional petroleum fuels (gasoline and diesel) through gloves used in occupational settings (neoprene, nitrile, and Viton) and laboratories (latex, nitrile, and vinyl), as well as a standard reference material (neoprene sheet). Permeation rates and breakthrough times were measured using the American Society for Testing and Materials F739-99 protocol, and fuel and permeant compositions were measured by gas chromatography/mass spectrometry. In addition, we estimated exposures for three occupational scenarios and recommend chemical protective clothing suitable for use with motor fuels. Permeation rates and breakthrough times depended on the fuel-glove combination. Gasoline had the highest permeation rate among the four fuels. Bioethanol (85%) had breakthrough times that were two to three times longer than gasoline through neoprene, nitrile Sol-Vex, and the standard reference materials. Breakthrough times for biodiesel (20%) were slightly shorter than for diesel for the latex, vinyl, nitrile examination, and the standard neoprene materials. The composition of permeants differed from neat fuels, e.g., permeants were significantly enriched in the lighter aromatics including benzene. Viton was the best choice among the tested materials for the four fuels tested. Among the scenarios, fuel truck drivers had the highest uptake via inhalation based on the personal measurements available in the literature, and gasoline station attendants had highest uptake via dermal exposure if gloves were not worn. Appropriate selection and use of gloves can protect workers from dermal exposures; however, current recommendations from the National Institute for Occupational Safety and Health should be revised to account for contemporary fuel formulations that routinely contain ethanol.

Prediction and analysis of near-road concentrations using a reduced-form emission/dispersion model

BACKGROUND

Near-road exposures of traffic-related air pollutants have been receiving increased attention due to evidence linking emissions from high-traffic roadways to adverse health outcomes. To date, most epidemiological and risk analyses have utilized simple but crude exposure indicators, most typically proximity measures, such as the distance between freeways and residences, to represent air quality impacts from traffic. This paper derives and analyzes a simplified microscale simulation model designed to predict short- (hourly) to long-term (annual average) pollutant concentrations near roads. Sensitivity analyses and case studies are used to highlight issues in predicting near-road exposures.

METHODS

Process-based simulation models using a computationally efficient reduced-form response surface structure and a minimum number of inputs integrate the major determinants of air pollution exposures: traffic volume and vehicle emissions, meteorology, and receptor location. We identify the most influential variables and then derive a set of multiplicative submodels that match predictions from "parent" models MOBILE6.2 and CALINE4. The assembled model is applied to two case studies in the Detroit, Michigan area. The first predicts carbon monoxide (CO) concentrations at a monitoring site near a freeway. The second predicts CO and PM2.5 concentrations in a dense receptor grid over a 1 km2 area around the intersection of two major roads. We analyze the spatial and temporal patterns of pollutant concentration predictions.

RESULTS

Predicted CO concentrations showed reasonable agreement with annual average and 24-hour measurements, e.g., 59% of the 24-hr predictions were within a factor of two of observations in the warmer months when CO emissions are more consistent. The highest concentrations of both CO and PM(2.5) were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind speeds) and high emissions (e.g., weekday rush hour). The spatial and temporal variation among predicted concentrations was significant, and resulted in unusual distributional and correlation characteristics, including strong negative correlation for receptors on opposite sides of a road and the highest short-term concentrations on the "upwind" side of the road.

CONCLUSIONS

The case study findings can likely be generalized to many other locations, and they have important implications for epidemiological and other studies. The reduced-form model is intended for exposure assessment, risk assessment, epidemiological, geographical information systems, and other applications.

Time allocation shifts and pollutant exposure due to traffic congestion: an analysis using the national human activity pattern survey

Traffic congestion increases air pollutant exposures of commuters and urban populations due to the increased time spent in traffic and the increased vehicular emissions that occur in congestion, especially "stop-and-go" traffic. Increased time in traffic also decreases time in other microenvironments, a trade-off that has not been considered in previous time activity pattern (TAP) analyses conducted for exposure assessment purposes. This research investigates changes in time allocations and exposures that result from traffic congestion. Time shifts were derived using data from the National Human Activity Pattern Survey (NHAPS), which was aggregated to nine microenvironments (six indoor locations, two outdoor locations and one transport location). After imputing missing values, handling outliers, and conducting other quality checks, these data were stratified by respondent age, employment status and period (weekday/weekend). Trade-offs or time-shift coefficients between time spent in vehicles and the eight other microenvironments were then estimated using robust regression. For children and retirees, congestion primarily reduced the time spent at home; for older children and working adults, congestion shifted the time spent at home as well as time in schools, public buildings, and other indoor environments. Changes in benzene and PM(2.5) exposure were estimated for the current average travel delay in the U.S. (9 min day(-1)) and other scenarios using the estimated time shifts coefficients, concentrations in key microenvironments derived from the literature, and a probabilistic analysis. Changes in exposures depended on the duration of the congestion and the pollutant. For example, a 30 min day(-1) travel delay was determined to account for 21+/-12% of current exposure to benzene and 14+/-8% of PM(2.5) exposure. The time allocation shifts and the dynamic approach to TAPs improve estimates of exposure impacts from congestion and other recurring events.

Concentrations and emissions of polybrominated diphenyl ethers from U.S. houses and garages

Concentrations of polybrominated diphenyl ethers (PBDEs) and other brominated flame retardants (BFRs) have been rapidly increasing in fish, birds, sediments, indoor environments, and humans, but emission sources and exposure pathways of these pollutants remain poorly understood. The many BFR-containing materials in buildings constitute a large reservoir of these compounds, and in-use releases from this reservoir may be a significant environmental source. To estimate in-use releases from building materials and contents in residences, we monitored 12 houses and garages in two seasons and combined measurements of BFRs in air and settled dust, air exchange rates, and other information in an approach that utilized the building as a "natural" test chamber. Results were scaled to provide a first estimate of aggregate emission rates from U.S. houses. PBDE releases total about 4 microg h(-1) per house or 20 ng m(-2) h(-1), and U.S. houses and garages collectively release about 4100 kg y(-1). Most of these releases are settled floor dust, but about 20% are released directly to the ambient environment via airborne vapor and particulate matter. These screening-level estimates are subject to considerable uncertainty, but they have an advantage in that they reflect real-world conditions based on mass balance calculations.