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Research Report (Health Effects Institute)[JOURNAL]

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Part 2. Development of Enhanced Statistical Methods for Assessing Health Effects Associated with an Unknown Number of Major Sources of Multiple Air Pollutants.

Park ES, Symanski E, Han D … +1 more , Spiegelman C

Res Rep Health Eff Inst · 2015 Jun · PMID 26333239

A major difficulty with assessing source-specific health effects is that source-specific exposures cannot be measured directly; rather, they need to be estimated by a source-apportionment method such as multivariate rece... A major difficulty with assessing source-specific health effects is that source-specific exposures cannot be measured directly; rather, they need to be estimated by a source-apportionment method such as multivariate receptor modeling. The uncertainty in source apportionment (uncertainty in source-specific exposure estimates and model uncertainty due to the unknown number of sources and identifiability conditions) has been largely ignored in previous studies. Also, spatial dependence of multipollutant data collected from multiple monitoring sites has not yet been incorporated into multivariate receptor modeling. The objectives of this project are (1) to develop a multipollutant approach that incorporates both sources of uncertainty in source-apportionment into the assessment of source-specific health effects and (2) to develop enhanced multivariate receptor models that can account for spatial correlations in the multipollutant data collected from multiple sites. We employed a Bayesian hierarchical modeling framework consisting of multivariate receptor models, health-effects models, and a hierarchical model on latent source contributions. For the health model, we focused on the time-series design in this project. Each combination of number of sources and identifiability conditions (additional constraints on model parameters) defines a different model. We built a set of plausible models with extensive exploratory data analyses and with information from previous studies, and then computed posterior model probability to estimate model uncertainty. Parameter estimation and model uncertainty estimation were implemented simultaneously by Markov chain Monte Carlo (MCMC*) methods. We validated the methods using simulated data. We illustrated the methods using PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) speciation data and mortality data from Phoenix, Arizona, and Houston, Texas. The Phoenix data included counts of cardiovascular deaths and daily PM2.5 speciation data from 1995-1997. The Houston data included respiratory mortality data and 24-hour PM2.5 speciation data sampled every six days from a region near the Houston Ship Channel in years 2002-2005. We also developed a Bayesian spatial multivariate receptor modeling approach that, while simultaneously dealing with the unknown number of sources and identifiability conditions, incorporated spatial correlations in the multipollutant data collected from multiple sites into the estimation of source profiles and contributions based on the discrete process convolution model for multivariate spatial processes. This new modeling approach was applied to 24-hour ambient air concentrations of 17 volatile organic compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, during years 2000 to 2005. Simulation results indicated that our methods were accurate in identifying the true model and estimated parameters were close to the true values. The results from our methods agreed in general with previous studies on the source apportionment of the Phoenix data in terms of estimated source profiles and contributions. However, we had a greater number of statistically insignificant findings, which was likely a natural consequence of incorporating uncertainty in the estimated source contributions into the health-effects parameter estimation. For the Houston data, a model with five sources (that seemed to be Sulfate-Rich Secondary Aerosol, Motor Vehicles, Industrial Combustion, Soil/Crustal Matter, and Sea Salt) showed the highest posterior model probability among the candidate models considered when fitted simultaneously to the PM2.5 and mortality data. There was a statistically significant positive association between respiratory mortality and same-day PM2.5 concentrations attributed to one of the sources (probably industrial combustion). The Bayesian spatial multivariate receptor modeling approach applied to the VOC data led to a highest posterior model probability for a model with five sources (that seemed to be refinery, petrochemical production, gasoline evaporation, natural gas, and vehicular exhaust) among several candidate models, with the number of sources varying between three and seven and with different identifiability conditions. Our multipollutant approach assessing source-specific health effects is more advantageous than a single-pollutant approach in that it can estimate total health effects from multiple pollutants and can also identify emission sources that are responsible for adverse health effects. Our Bayesian approach can incorporate not only uncertainty in the estimated source contributions, but also model uncertainty that has not been addressed in previous studies on assessing source-specific health effects. The new Bayesian spatial multivariate receptor modeling approach enables predictions of source contributions at unmonitored sites, minimizing exposure misclassification and providing improved exposure estimates along with their uncertainty estimates, as well as accounting for uncertainty in the number of sources and identifiability conditions.
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Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents.

Coull BA, Bobb JF, Wellenius GA … +4 more , Kioumourtzoglou MA, Mittleman MA, Koutrakis P, Godleski JJ

Res Rep Health Eff Inst · 2015 Jun · PMID 26333238

INTRODUCTION: The United States Environmental Protection Agency (U.S. EPA*) currently regulates individual air pollutants on a pollutant-by-pollutant basis, adjusted for other pollutants and potential confounders. Howeve... INTRODUCTION: The United States Environmental Protection Agency (U.S. EPA*) currently regulates individual air pollutants on a pollutant-by-pollutant basis, adjusted for other pollutants and potential confounders. However, the National Academies of Science concluded that a multipollutant regulatory approach that takes into account the joint effects of multiple constituents is likely to be more protective of human health. Unfortunately, the large majority of existing research had focused on health effects of air pollution for one pollutant or for one pollutant with control for the independent effects of a small number of copollutants. Limitations in existing statistical methods are at least partially responsible for this lack of information on joint effects. The goal of this project was to fill this gap by developing flexible statistical methods to estimate the joint effects of multiple pollutants, while allowing for potential nonlinear or nonadditive associations between a given pollutant and the health outcome of interest. METHODS: We proposed Bayesian kernel machine regression (BKMR) methods as a way to simultaneously achieve the multifaceted goals of variable selection, flexible estimation of the exposure-response relationship, and inference on the strength of the association between individual pollutants and health outcomes in a health effects analysis of mixtures. We first developed a BKMR variable-selection approach, which we call component-wise variable selection, to make estimating such a potentially complex exposure-response function possible by effectively using two types of penalization (or regularization) of the multivariate exposure-response surface. Next we developed an extension of this first variable-selection approach that incorporates knowledge about how pollutants might group together, such as multiple constituents of particulate matter that might represent a common pollution source category. This second grouped, or hierarchical, variable-selection procedure is applicable when groups of highly correlated pollutants are being studied. To investigate the properties of the proposed methods, we conducted three simulation studies designed to evaluate the ability of BKMR to estimate environmental mixtures responsible for health effects under potentially complex but plausible exposure-response relationships. An attractive feature of our simulation studies is that we used actual exposure data rather than simulated values. This real-data simulation approach allowed us to evaluate the performance of BKMR and several other models under realistic joint distributions of multipollutant exposure. The simulation studies compared the two proposed variable-selection approaches (component-wise and hierarchical variable selection) with each other and with existing frequentist treatments of kernel machine regression (KMR). After the simulation studies, we applied the newly developed methods to an epidemiologic data set and to a toxicologic data set. To illustrate the applicability of the proposed methods to human epidemiologic data, we estimated associations between short-term exposures to fine particulate matter constituents and blood pressure in the Maintenance of Balance, Independent Living, Intellect, and Zest in the Elderly (MOBILIZE) Boston study, a prospective cohort study of elderly subjects. To illustrate the applicability of these methods to animal toxicologic studies, we analyzed data on the associations between both blood pressure and heart rate in canines exposed to a composition of concentrated ambient particles (CAPs) in a study conducted at the Harvard T. H. Chan School of Public Health (the Harvard Chan School; formerly Harvard School of Public Health; Bartoli et al. 2009). RESULTS: We successfully developed the theory and computational tools required to apply the proposed methods to the motivating data sets. Collectively, the three simulation studies showed that component-wise variable selection can identify important pollutants within a mixture as long as the correlations among pollutant concentrations are low to moderate. The hierarchical variable-selection method was more effective in high-dimension, high-correlation settings. Variable selection in existing frequentist KMR models can incur inflated type I error rates, particularly when pollutants are highly correlated. The analyses of the MOBILIZE data yielded evidence of a linear and additive association of black carbon (BC) or Cu exposure with standing diastolic blood pressure (DBP), and a linear association of S exposure with standing systolic blood pressure (SBP). Cu is thought to be a marker of urban road dust associated with traffic; and S is a marker of power plant emissions or regional long-range transported air pollution or both. Therefore, these analyses of the MOBILIZE data set suggest that emissions from these three source categories were most strongly associated with hemodynamic responses in this cohort. In contrast, in the Harvard Chan School canine study, after controlling for an overall effect of CAPs exposure, we did not observe any associations between DBP or SBP and any elemental concentrations. Instead, we observed strong evidence of an association between Mn concentrations and heart rate in that heart rate increased linearly with increasing concentrations of Mn. According to the positive matrix factorization (PMF) source apportionment analyses of the multipollutant data set from the Harvard Chan School Boston Supersite, Mn loads on the two factors that represent the mobile and road dust source categories. The results of the BKMR analyses in both the MOBILIZE and canine studies were similar to those from existing linear mixed model analyses of the same multipollutant data because the effects have linear and additive forms that could also have been detected using standard methods. CONCLUSIONS: This work provides several contributions to the KMR literature. First, to our knowledge this is the first time KMR methods have been used to estimate the health effects of multipollutant mixtures. Second, we developed a novel hierarchical variable-selection approach within BKMR that is able to account for the structure of the mixture and systematically handle highly correlated exposures. The analyses of the epidemiologic and toxicologic data on associations between fine particulate matter constituents and blood pressure or heart rate demonstrated associations with constituents that are typically associated with traffic emissions, power plants, and long-range transported pollutants. The simulation studies showed that the BKMR methods proposed here work well for small to moderate data sets; more work is needed to develop computationally fast methods for large data sets. This will be a goal of future work.
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Part 4. Assessment of plasma markers and cardiovascular responses in rats after chronic exposure to new-technology diesel exhaust in the ACES bioassay.

Conklin DJ, Kong M, HEI Health Review Committee

Res Rep Health Eff Inst · 2015 Jan · PMID 25842618

Although epidemiologic and experimental studies suggest that chronic exposure to diesel exhaust (DE*) emissions causes adverse cardiovascular effects, neither the specific components of DE nor the mechanisms by which DE... Although epidemiologic and experimental studies suggest that chronic exposure to diesel exhaust (DE*) emissions causes adverse cardiovascular effects, neither the specific components of DE nor the mechanisms by which DE exposure could induce cardiovascular dysfunction and exacerbate cardiovascular disease (CVD) are known. Because advances in new technologies have resulted in cleaner fuels and decreased engine emissions, uncertainty about the relationship between DE exposure and human cardiovascular health effects has increased. To address this ever-changing baseline of DE emissions, as part of the larger Advanced Collaborative Emissions Study (ACES) bioassay studying the health effects of 2007-compliant diesel engine emissions (new-technology diesel exhaust), we examined whether plasma markers of vascular inflammation, thrombosis, cardiovascular aging, cardiac fibrosis, and aorta morphometry were changed over 24 months in an exposure-level-, sex-, or exposure-duration-dependent manner. Many plasma markers--several recognized as human CVD risk factors--were measured in the plasma of rats exposed for up to 24 months to filtered air (the control) or DE. Few changes in plasma markers resulted from 12 months of DE exposure, but significant exposure-level-dependent increases in soluble intercellular adhesion molecule 1 (sICAM-1) and interleukin-6 (IL-6) levels, as well as decreases in total and non-high-density-lipoprotein cholesterol (non-HDL) levels in plasma, were observed in female rats after 24 months of DE exposure. These effects were not observed in male rats, and no changes in cardiac fibrosis or aorta morphometry resulting from DE exposure were observed in either sex. Collectively, the significant changes may reflect an enhanced sensitivity of the female cardiovascular system to chronic DE exposure; however, this conclusion should be interpreted within both the context and limitations of the current study.
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Part 3. Assessment of genotoxicity and oxidative damage in rats after chronic exposure to new-technology diesel exhaust in the ACES bioassay.

Hallberg LM, Ward JB, Hernandez C … +3 more , Ameredes BT, Wickliffe JK, HEI Health Review Committee

Res Rep Health Eff Inst · 2015 Jan · PMID 25842617

In 2001, the U.S. Environmental Protection Agency (EPA*) and the California Air Resources Board (CARB) adopted new standards for diesel fuel and emissions from heavy-duty diesel engines. By 2007, diesel engines were requ... In 2001, the U.S. Environmental Protection Agency (EPA*) and the California Air Resources Board (CARB) adopted new standards for diesel fuel and emissions from heavy-duty diesel engines. By 2007, diesel engines were required to meet these new standards for particulate matter (PM), with other standards to follow. Through a combination of advanced compression-ignition engine technology, development of exhaust aftertreatment systems, and reformulated fuels, stringent standards were introduced. Before the 2007 standards were put in place by the EPA, human health effects linked to diesel exhaust (DE) exposure had been associated with diesel-fuel solvent and combustion components. In earlier research, diesel engine exhaust components were, in turn, linked to increased mutagenicity in cultures of Salmonella typhimurium and mammalian cells (Tokiwa and Ohnishi 1986). In addition, DE was shown to increase both the incidence of tumors and the induction of 8-hydroxy-deoxyguanosine (8-OHdG) adducts in rodents (Ichinose et al. 1997) and total DNA adducts in rats (Bond et al. 1990). Furthermore, DE is composed of a complex mixture of polycyclic aromatic hydrocarbons (PAHs) and particulates. One such PAH, 3-nitrobenzanthrone (3-NBA), is also found in urban air. 3-NBA has been observed to induce micronucleus formation in the DNA of human hepatoma cells (Lamy et al. 2004). The current study is part of the Advanced Collaborative Emissions Study (ACES), a multidisciplinary program carried out by the Health Effects Institute and the Coordinating Research Council. Its purpose was to determine whether recent improvements in the engineering of heavy-duty diesel engines reduce the toxicity associated with exposure to DE components. To this end, we evaluated potential genotoxicity and induction of oxidative stress in bioassays of serum and tissues from Wistar Han rats chronically exposed--for up to 24 months--to DE from a 2007-compliant diesel engine (new-technology diesel exhaust, or NTDE). Genotoxicity was measured as DNA strand breaks in lung tissue, using an alkaline-modified comet assay. As a correlate of possible DNA damage evaluated in the comet assay, concentrations of the free DNA adduct 8-OHdG were evaluated in serum by a competitive enzyme-linked immunosorbent assay (ELISA). The 8-OHdG fragment found in the serum is a specific biomarker for the repair of oxidative DNA damage. In addition, an assay for thiobarbituric acid reactive substances (TBARS) was used to assess oxidative stress and damage in the form of lipid peroxidation in the hippocampus region of the brains of the DE-exposed animals. These endpoints were evaluated at 1, 3, 12, and 24 months of exposure to DE or to a control atmosphere (filtered air). At the concentrations of DE evaluated, there were no significant effects of exposure in male or female rats after 1, 3, 12, or 24 months in any measure of DNA damage in the comet assay (%DNA in tail, tail length, tail moment, or olive moment). The comparison of exposure groups versus control and the comparison of groups by sex for 1 and 3 months of exposure showed no significant differences in serum 8-OHdG concentrations (P > 0.05). The concentrations of 8-OHdG in all exposure groups at 3 months were higher than those in exposure groups at any other time point (P < 0.05). Looking at the levels of 8-OHdG in serum in the 12-month and 24-month groups, we saw a significant difference from control in the 12-month group at the mid and high levels (P < 0.05), as well as some other scattered changes. Sex differences were noted in the 12-month high-level group (P < 0.05). However, these differences did not follow an exposure-dependent pattern. All other comparisons were not significant (P > 0.05). Hippocampal concentrations of TBARs, measured as malondialdehyde (MDA), showed some small and scattered changes in groups exposed to different levels of DE and at different time points, but we did not consider these to be exposure-related. We concluded that exposure to DE in these rats did not produce any significant increase in oxidative damage to lipids or damage to DNA in the form of strand breaks.
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Part 2. Assessment of micronucleus formation in rats after chronic exposure to new-technology diesel exhaust in the ACES bioassay.

Bemis JC, Torous DK, Dertinger SD … +1 more , HEI Health Review Committee

Res Rep Health Eff Inst · 2015 Jan · PMID 25842616

The formation of micronuclei (MN*) is a well-established endpoint in genetic toxicology; studies designed to examine MN formation in vivo have been conducted for decades. Conditions that cause double-strand breaks or dis... The formation of micronuclei (MN*) is a well-established endpoint in genetic toxicology; studies designed to examine MN formation in vivo have been conducted for decades. Conditions that cause double-strand breaks or disrupt the proper segregation of chromosomes during division result in increases in MN formation frequency. This endpoint is therefore commonly used in preclinical studies designed to assess the potential risks to humans of exposure to a myriad of chemical and physical agents, including inhaled diesel exhaust (DE). As part of the Advanced Collaborative Emissions Study (ACES) Phase 3B, which examined numerous additional toxicity endpoints associated with lifetime exposure to DE in a rodent model, this ancillary 24-month investigation examined the potential of inhaled DE to induce chromosome damage in chronically exposed rodents. The ACES design included exposure of both mice and rats to DE derived from heavy-duty engines that met U.S. Environmental Protection Agency (EPA) 2007 standards for diesel-exhaust emissions (new-technology diesel exhaust). The exposure conditions consisted of air (the control) and three dilutions of DE, resulting in four levels of exposure. At specific times, blood samples were collected, fixed, and shipped by the bioassay staff at Lovelace Respiratory Research Institute (LRRI) to Litron Laboratories (Rochester, NY) for further processing and analysis. In recent years, significant improvements have been made to MN scoring by using objective, automated methods such as flow cytometry, which allows the detection of micronucleated reticulocytes (MN-RET), micronucleated normochromatic erythrocytes (MN-NCE), and reticulocytes (RET) in peripheral blood samples from mice and rats. By using a simple staining procedure coupled with rapid and efficient analysis, many more cells can be examined in less time than was possible using traditional, microscopy-based MN assays. Thus, for each sample in the current study, 20,000 RET were scored for the presence of MN. In the chronic-exposure (12 and 24 months) bioassay, blood samples were obtained from separate groups of exposed animals at specific time points throughout the course of the study. The automated method using flow cytometry has found widespread use in safety assessment and is supported by regulatory guidelines, including International Conference on Harmonisation (ICH) S2(R1) (2011). Statistical analyses included the use of analysis of variance (ANOVA) to compare the effects of sex, exposure condition, and duration, as well asthe interactions between them. Analyses of blood samples from rats combined data from our earlier 1- and 3-month exposure studies (Bemis et al. 2012) with data from our current 12- and 24-month exposure studies. Consistent with findings from the preliminary studies, no sex-based differences in MN frequency were observed in the rats. An initial examination of mean frequencies across the treatment groups and durations of exposure showed no evidence of treatment-related increases in MN at any of the time points studied. Further statistical analyses did not reveal any significant exposure-related effects. An examination of the potential genotoxic effects of DE is clearly valuable as part of a large-scale chronic exposure bioassay. The results described in this report provide a comprehensive examination of chronic exposure to DE in a rodent model. Our investigation of chromosomal damage also plays an important role in the context of ACES, which was designed to assess the safety of emissions from 2007-compliant diesel engines.
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Part 1. Assessment of carcinogenicity and biologic responses in rats after lifetime inhalation of new-technology diesel exhaust in the ACES bioassay.

McDonald JD, Doyle-Eisele M, Seagrave J … +7 more , Gigliotti AP, Chow J, Zielinska B, Mauderly JL, Seilkop SK, Miller RA, HEI Health Review Committee

Res Rep Health Eff Inst · 2015 Jan · PMID 25842615

The Health Effects Institute and its partners conceived and funded a program to characterize the emissions from heavy-duty diesel engines compliant with the 2007 and 2010 on-road emissions standards in the United States... The Health Effects Institute and its partners conceived and funded a program to characterize the emissions from heavy-duty diesel engines compliant with the 2007 and 2010 on-road emissions standards in the United States and to evaluate indicators of lung toxicity in rats and mice exposed repeatedly to 2007-compliant new-technology diesel exhaust (NTDE*). The a priori hypothesis of this Advanced Collaborative Emissions Study (ACES) was that 2007-compliant on-road diesel emissions "... will not cause an increase in tumor formation or substantial toxic effects in rats and mice at the highest concentration of exhaust that can be used ... although some biological effects may occur." This hypothesis was tested at the Lovelace Respiratory Research Institute (LRRI) by exposing rats by chronic inhalation as a carcinogenicity bioassay. Indicators of pulmonary toxicity in rats were measured after 1, 3, 12, 24, and 28-30 months of exposure. Similar indicators of pulmonary toxicity were measured in mice, as an interspecies comparison of the effects of subchronic exposure, after 1 and 3 months of exposure. A previous HEI report (Mauderly and McDonald 2012) described the operation of the engine and exposure systems and the characteristics of the exposure atmospheres during system commissioning. Another HEI report described the biologic responses in mice and rats after subchronic exposure to NTDE (McDonald et al. 2012). The primary motivation for the present chronic study was to evaluate the effects of NTDE in rats in the context of previous studies that had shown neoplastic lung lesions in rats exposed chronically to traditional technology diesel exhaust (TDE) (i.e., exhaust from diesel engines built before the 2007 U.S. requirements went into effect). The hypothesis was largely based on the marked reduction of diesel particulate matter (DPM) in NTDE compared with emissions from older diesel engine and fuel technologies, although other emissions were also reduced. The DPM component of TDE was considered the primary driver of lung tumorigenesis in rats exposed chronically to historical diesel emissions. Emissions from a 2007-compliant, 500-horsepower-class engine and after treatment system operated on a variable-duty cycle were used to generate the animal inhalation test atmospheres. Four groups were exposed to one of three concentrations (dilutions) of exhaust combined with crankcase emissions, or to clean air as a negative control. Dilutions of exhaust were set to yield average integrated concentrations of 4.2, 0.8, and 0.1 ppm nitrogen dioxide (NO2). Exposure atmospheres were analyzed by daily measurements of key effects of NTDE in the present study were generally consistent with those observed previously in rats exposed chronically to NO2 alone. This suggests that NO2 may have been the primary driver of the biologic responses to NTDE in the present study. There was little evidence of effects characteristic of rats exposed chronically to high concentrations of DPM in TDE, such as an extensive accumulation of DPM within alveolar macrophages and inflammation leading to neoplastic transformation of epithelia and lung tumors. components and periodic detailed physical-chemical characterizations. Exposures were conducted 16 hours/day (overnight, during the rats' most active period), 5 days/week. Responses to exposure were evaluated via hematology, serum chemistry, bronchoalveolar lavage (BAL), lung cell proliferation, histopathology, and pulmonary function. The exposures were accomplished as planned, with average integrated exposure concentrations within 20% of the target dilutions. The major components from exhaust were the gaseous inorganic compounds, nitrogen monoxide (NO), NO2, and carbon monoxide (CO). Minor components included low concentrations of DPM and volatile and semi-volatile organic compounds (VOCs and SVOCs). Among the more than 100 biologic response variables evaluated, the majority showed no significant difference from control as a result of exposure to NTDE. The major outcome of this study was the absence of pre-neoplastic lung lesions, primary lung neoplasia, or neoplasia of any type attributable to NTDE exposure. The lung lesions that did occur were minimal to mild, occurred only at the highest exposure level, and were characterized by an increased number and prominence of basophilic epithelial cells (considered reactive or regenerative) lining distal terminal bronchioles, alveolar ducts, and adjacent alveoli (termed in this report "Hyperplasia; Epithelial; Periacinar"), which often had a minimal increase in subjacent fibrous stroma (termed "Fibrosis; Interstitial; Periacinar"). Slight epithelial metaplastic change to a cuboidal morphology, often demonstrating cilia, was also noted in some animals (termed "Bronchiolization"). In addition to the epithelial proliferation, there was occasionally a subtle accumulation of pulmonary alveolar macrophages (termed "Accumulation; Macrophage") in affected areas. The findings in the lung progressed slightly from 3 to 12 months, without further progression between 12 months and the final sacrifice at 28 or 30 months. In addition to the histologic findings, there were biochemical changes in the lung tissue and lavage fluid that indicated mild inflammation and oxidative stress. Generally, these findings were observed only at the highest exposure level. There was also a mild progressive decrease in pulmonary function, which was more consistent in females than males. Limited nasal epithelial changes resulted from NTDE exposure, including increases in minor olfactory epithelial degeneration, hyperplasia, and/or metaplasia. Increases in these findings were present primarily at the highest exposure level, and their minor and variable nature renders their biologic significance uncertain. Overall, the findings of this study demonstrated markedly less severe biologic responses to NTDE than observed previously in rats exposed similarly to TDE. Further, the effects of NTDE in the present study were generally consistent with those observed previously in rats exposed chronically to NO2 alone. This suggests that NO2 may have been the primary driver of the biologic responses to NTDE in the present study. There was little evidence of effects characteristic of rats exposed chronically to high concentrations of DPM in TDE, such as an extensive accumulation of DPM within alveolar macrophages and inflammation leading to neoplastic transformation of epithelia and lung tumors.
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Synergistic effects of particulate matter and substrate stiffness on epithelial-to-mesenchymal transition.

Barker TH, Dysart MM, Brown AC … +4 more , Douglas AM, Fiore VF, Russell AG, HEI Health Review Committee

Res Rep Health Eff Inst · 2014 Nov · PMID 25669020

Dysfunctional pulmonary homeostasis and repair, including diseases such as pulmonary fibrosis, chronic obstructive pulmonary disease (COPD*), and tumorigenesis, have been increasing steadily over the past decade, a fact... Dysfunctional pulmonary homeostasis and repair, including diseases such as pulmonary fibrosis, chronic obstructive pulmonary disease (COPD*), and tumorigenesis, have been increasing steadily over the past decade, a fact that heavily implicates environmental influences. Several investigations have suggested that the lung "precursor cell"--the alveolar type II (ATII) epithelial cell--is central in the initiation and progression of pulmonary fibrosis. Specifically, ATII cells have been shown (Iwano et al. 2002) to be capable of undergoing an epithelial-to-mesenchymal transition (EMT). EMT, the de-differentiation of an epithelial cell into a mesenchymal cell, has been theorized to increase the number of extracellular matrix (ECM)-secreting mesenchymal cells, perpetuating fibrotic conditions and resulting in increased lung tissue stiffness. In addition, increased exposure to pollution and inhalation of particulate matter (PM) have been shown to be highly correlated with an increased incidence of pulmonary fibrosis. Although both of these events are involved in the progression of pulmonary fibrosis, the relationship between tissue stiffness, exposure to PM, and the initiation and course of EMT remains unclear. The hypothesis of this study was twofold: 1. That alveolar epithelial cells cultured on increasingly stiff substrates become increasingly contractile, leading to enhanced transforming growth factor beta (TGF-β) activation and EMT; and 2. That exposure of alveolar epithelial cells to PM with an aerodynamic diameter ≤ 2.5 μm (PM2.5; also known as fine PM) results in enhanced cell contractility and EMT. Our study focused on the relationship between the micromechanical environment and external environmental stimuli on the phenotype of alveolar epithelial cells. This relationship was explored by first determining how increased tissue stiffness affects the regulation of fibronectin (Fn)-mediated EMT in ATII cells in vitro. We cultured ATII cells on substrates of increasing stiffness and evaluated changes in cell contractility and EMT. We found that stiff, but not soft, Fn substrates were able to induce EMT and that this event depended on a contractile phenotype of the cell and the subsequent activation of TGF-β. In addition, we were able to show that activation or suppression of cell contractility by way of exogenous factors was sufficient to overcome the effect of substrate stiffness. Pulse-chase experiments indicated that the effect on cell contractility is dose- and time-dependent. In response to low levels of TGF-β on soft surfaces, either added exogenously or produced through contraction induced by the stiffness agonist thrombin, cells initiate EMT; on removal of the TGF-β, they revert to an epithelial phenotype. Overall, the results from this first part of our study identified matrix stiffness or cell contractility as critical targets for the control of EMT in fibrotic diseases. For the second part of our study, we wanted to investigate whether exposure to PM2.5, which might have higher toxicity than coarser PM because of its small size and large surface-to-mass ratio, altered the observed stiffness-mediated EMT. Again, we cultured ATII cells on increasingly stiff substrates with or without the addition of three concentrations of PM2.5. We found that exposure to PM2.5 was involved in increased stiffness-mediated EMT, as shown by increases in mesenchymal markers, cell contractility, and TGF-β activation. Most notably, on substrates with an elastic modulus (E) of 8 kilopascals (kPa), a physiologically relevant range for pulmonary fibrosis, the addition of PM2.5 resulted in increased mesenchymal cells and EMT; these were not seen in the absence of the PM2.5. Overall, this study showed that there is a delicate balance between substrate stiffness, TGF-β, and EMT. Furthermore, we showed that exposure to PM2.5 is able to further mediate this interaction. The higher levels of EMT seen with exposure to PM2.5 might have been a result of a positive feedback loop, in which enhanced exposure to PM2.5 through the loss of cell-cell junctions during the initial stages of EMT led to the cells being more susceptible to the effects of surrounding immune cells and inflammatory signals that can further activate TGF-β and drive additional EMT progression. Overall, our work--showing increased cell contractility, TGF-β activation, and EMT in response to substrate stiffness and PM2.5 exposure--highlights the importance of both the micromechanical and biochemical environments in lung disease. These findings suggest that already-fibrotic tissue might be more susceptible to further damage than healthy tissue when exposed to PM2.5.
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Personal exposure to mixtures of volatile organic compounds: modeling and further analysis of the RIOPA data.

Batterman S, Su FC, Li S … +3 more , Mukherjee B, Jia C, HEI Health Review Committee

Res Rep Health Eff Inst · 2014 Jun · PMID 25145040

INTRODUCTION: Emission sources of volatile organic compounds (VOCs*) are numerous and widespread in both indoor and outdoor environments. Concentrations of VOCs indoors typically exceed outdoor levels, and most people sp... INTRODUCTION: Emission sources of volatile organic compounds (VOCs*) are numerous and widespread in both indoor and outdoor environments. Concentrations of VOCs indoors typically exceed outdoor levels, and most people spend nearly 90% of their time indoors. Thus, indoor sources generally contribute the majority of VOC exposures for most people. VOC exposure has been associated with a wide range of acute and chronic health effects; for example, asthma, respiratory diseases, liver and kidney dysfunction, neurologic impairment, and cancer. Although exposures to most VOCs for most persons fall below health-based guidelines, and long-term trends show decreases in ambient emissions and concentrations, a subset of individuals experience much higher exposures that exceed guidelines. Thus, exposure to VOCs remains an important environmental health concern. The present understanding of VOC exposures is incomplete. With the exception of a few compounds, concentration and especially exposure data are limited; and like other environmental data, VOC exposure data can show multiple modes, low and high extreme values, and sometimes a large portion of data below method detection limits (MDLs). Field data also show considerable spatial or interpersonal variability, and although evidence is limited, temporal variability seems high. These characteristics can complicate modeling and other analyses aimed at risk assessment, policy actions, and exposure management. In addition to these analytic and statistical issues, exposure typically occurs as a mixture, and mixture components may interact or jointly contribute to adverse effects. However most pollutant regulations, guidelines, and studies remain focused on single compounds, and thus may underestimate cumulative exposures and risks arising from coexposures. In addition, the composition of VOC mixtures has not been thoroughly investigated, and mixture components show varying and complex dependencies. Finally, although many factors are known to affect VOC exposures, many personal, environmental, and socioeconomic determinants remain to be identified, and the significance and applicability of the determinants reported in the literature are uncertain. To help answer these unresolved questions and overcome limitations of previous analyses, this project used several novel and powerful statistical modeling and analysis techniques and two large data sets. The overall objectives of this project were (1) to identify and characterize exposure distributions (including extreme values), (2) evaluate mixtures (including dependencies), and (3) identify determinants of VOC exposure. METHODS VOC data were drawn from two large data sets: the Relationships of Indoor, Outdoor, and Personal Air (RIOPA) study (1999-2001) and the National Health and Nutrition Examination Survey (NHANES; 1999-2000). The RIOPA study used a convenience sample to collect outdoor, indoor, and personal exposure measurements in three cities (Elizabeth, NJ; Houston, TX; Los Angeles, CA). In each city, approximately 100 households with adults and children who did not smoke were sampled twice for 18 VOCs. In addition, information about 500 variables associated with exposure was collected. The NHANES used a nationally representative sample and included personal VOC measurements for 851 participants. NHANES sampled 10 VOCs in common with RIOPA. Both studies used similar sampling methods and study periods. Specific Aim 1. To estimate and model extreme value exposures, extreme value distribution models were fitted to the top 10% and 5% of VOC exposures. Health risks were estimated for individual VOCs and for three VOC mixtures. Simulated extreme value data sets, generated for each VOC and for fitted extreme value and lognormal distributions, were compared with measured concentrations (RIOPA observations) to evaluate each model's goodness of fit. Mixture distributions were fitted with the conventional finite mixture of normal distributions and the semi-parametric Dirichlet process mixture (DPM) of normal distributions for three individual VOCs (chloroform, 1,4-DCB, and styrene). Goodness of fit for these full distribution models was also evaluated using simulated data. Specific Aim 2. Mixtures in the RIOPA VOC data set were identified using positive matrix factorization (PMF) and by toxicologic mode of action. Dependency structures of a mixture's components were examined using mixture fractions and were modeled using copulas, which address correlations of multiple components across their entire distributions. Five candidate copulas (Gaussian, t, Gumbel, Clayton, and Frank) were evaluated, and the performance of fitted models was evaluated using simulation and mixture fractions. Cumulative cancer risks were calculated for mixtures, and results from copulas and multivariate lognormal models were compared with risks based on RIOPA observations. Specific Aim 3. Exposure determinants were identified using stepwise regressions and linear mixed-effects models (LMMs). RESULTS: Specific Aim 1. Extreme value exposures in RIOPA typically were best fitted by three-parameter generalized extreme value (GEV) distributions, and sometimes by the two-parameter Gumbel distribution. In contrast, lognormal distributions significantly underestimated both the level and likelihood of extreme values. Among the VOCs measured in RIOPA, 1,4-dichlorobenzene (1,4-DCB) was associated with the greatest cancer risks; for example, for the highest 10% of measurements of 1,4-DCB, all individuals had risk levels above 10(-4), and 13% of all participants had risk levels above 10(-2). Of the full-distribution models, the finite mixture of normal distributions with two to four clusters and the DPM of normal distributions had superior performance in comparison with the lognormal models. DPM distributions provided slightly better fit than the finite mixture distributions; the advantages of the DPM model were avoiding certain convergence issues associated with the finite mixture distributions, adaptively selecting the number of needed clusters, and providing uncertainty estimates. Although the results apply to the RIOPA data set, GEV distributions and mixture models appear more broadly applicable. These models can be used to simulate VOC distributions, which are neither normally nor lognormally distributed, and they accurately represent the highest exposures, which may have the greatest health significance. Specific Aim 2. Four VOC mixtures were identified and apportioned by PMF; they represented gasoline vapor, vehicle exhaust, chlorinated solvents and disinfection byproducts, and cleaning products and odorants. The last mixture (cleaning products and odorants) accounted for the largest fraction of an individual's total exposure (average of 42% across RIOPA participants). Often, a single compound dominated a mixture but the mixture fractions were heterogeneous; that is, the fractions of the compounds changed with the concentration of the mixture. Three VOC mixtures were identified by toxicologic mode of action and represented VOCs associated with hematopoietic, liver, and renal tumors. Estimated lifetime cumulative cancer risks exceeded 10(-3) for about 10% of RIOPA participants. The dependency structures of the VOC mixtures in the RIOPA data set fitted Gumbel (two mixtures) and t copulas (four mixtures). These copula types emphasize dependencies found in the upper and lower tails of a distribution. The copulas reproduced both risk predictions and exposure fractions with a high degree of accuracy and performed better than multivariate lognormal distributions. Specific Aim 3. In an analysis focused on the home environment and the outdoor (close to home) environment, home VOC concentrations dominated personal exposures (66% to 78% of the total exposure, depending on VOC); this was largely the result of the amount of time participants spent at home and the fact that indoor concentrations were much higher than outdoor concentrations for most VOCs. In a different analysis focused on the sources inside the home and outside (but close to the home), it was assumed that 100% of VOCs from outside sources would penetrate the home. Outdoor VOC sources accounted for 5% (d-limonene) to 81% (carbon tetrachloride [CTC]) of the total exposure. Personal exposure and indoor measurements had similar determinants depending on the VOC. Gasoline-related VOCs (e.g., benzene and methyl tert-butyl ether [MTBE]) were associated with city, residences with attached garages, pumping gas, wind speed, and home air exchange rate (AER). Odorant and cleaning-related VOCs (e.g., 1,4-DCB and chloroform) also were associated with city, and a residence's AER, size, and family members showering. Dry-cleaning and industry-related VOCs (e.g., tetrachloroethylene [or perchloroethylene, PERC] and trichloroethylene [TCE]) were associated with city, type of water supply to the home, and visits to the dry cleaner. These and other relationships were significant, they explained from 10% to 40% of the variance in the measurements, and are consistent with known emission sources and those reported in the literature. Outdoor concentrations of VOCs had only two determinants in common: city and wind speed. Overall, personal exposure was dominated by the home setting, although a large fraction of indoor VOC concentrations were due to outdoor sources. City of residence, personal activities, household characteristics, and meteorology were significant determinants. Concentrations in RIOPA were considerably lower than levels in the nationally representative NHANES for all VOCs except MTBE and 1,4-DCB. Differences between RIOPA and NHANES results can be explained by contrasts between the sampling designs and staging in the two studies, and by differences in the demographics, smoking, employment, occupations, and home locations. (ABSTRACT TRUNCATED)
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Development and application of an aerosol screening model for size-resolved urban aerosols.

Stanier CO, Lee SR, HEI Health Review Committee

Res Rep Health Eff Inst · 2014 Jun · PMID 25145039

Predictive models of vehicular ultrafine particles less than 0.1 microm in diameter (UFPs*) and other urban pollutants with high spatial and temporal variation are useful and important in applications such as (1) decisio... Predictive models of vehicular ultrafine particles less than 0.1 microm in diameter (UFPs*) and other urban pollutants with high spatial and temporal variation are useful and important in applications such as (1) decision support for infrastructure projects, emissions controls, and transportation-mode shifts; (2) the interpretation and enhancement of observations (e.g., source apportionment, extrapolation, interpolation, and gap-filling in space and time); and (3) the generation of spatially and temporally resolved exposure estimates where monitoring is unfeasible. The objective of the current study was to develop, test, and apply the Aerosol Screening Model (ASM), a new physically based vehicular UFP model for use in near-road environments. The ASM simulates hourly average outdoor concentrations of roadway-derived aerosols and gases. Its distinguishing features include user-specified spatial resolution; use of the Weather Research and Forecasting (WRF) meteorologic model for winds estimates; use of a database of more than 100,000 road segments in the Los Angeles, California, region, including freeway ramps and local streets; and extensive testing against more than 9000 hours of observed particle concentrations at 11 sites. After initialization of air parcels at an upwind boundary, the model solves for vehicle emissions, dispersion, coagulation, and deposition using a Lagrangian modeling framework. The Lagrangian parcel of air is subdivided vertically (into 11 levels) and in the crosswind direction (into 3 parcels). It has overall dimensions of 10 m (downwind), 300 m (vertically), and 2.1 km (crosswind). The simulation is typically started 4 km upwind from the receptor, that is, the location at which the exposure is to be estimated. As parcels approach the receptor, depending on the user-specified resolution, step size is decreased, and crosswind resolution is enhanced through subdivision of parcels in the crosswind direction. Hourly concentrations and size distributions of aerosols were simulated for 11 sites in the Los Angeles area with large variations in proximal traffic and particle number concentrations (ranging from 6000 to 41,000/cm3). Observed data were from the 2005-2007 Harbor Community Monitoring Study (HCMS; Moore et al. 2009), in Long Beach, California, and the Coronary Health and Air Pollution Study (CHAPS; Delfino et al. 2008), in the Los Angeles area. Meteorologic fields were extracted from 1-km-resolution meteorologic simulations, and observed wind direction and speed were incorporated. Using on-road and tunnel measurements, size-resolved emission factors ranging from 1.4 x 10(15) to 16 x 10(15) particles/kg fuel were developed specifically for the ASM. Four separate size-resolved emissions were used. Traffic and emission factors were separately estimated for heavy-duty diesel and light-duty vehicles (LDV), and both cruise and acceleration emission factors were used. The light-duty cruise size-resolved number emission factor had a single prominent mode at 12 nm. The diesel cruise size-resolved number emission factor was bimodal, with a large mode at 16 nm and a secondary mode at around 100 nm. Emitted particles were assumed to be nonvolatile. Data on traffic activity came from a 2008 travel-demand model, supplemented by data on diurnal patterns. Simulated ambient number size distributions and number concentrations were compared to observations taking into account estimated losses from particle transmission efficiency in instrument inlet tubing. The skill of the model in predicting number concentrations and size distributions was mixed, with some promising prediction features and some other areas in need of substantial improvement. For long-term (-15-day) average concentrations, the variability from site to site could be modeled with a coefficient of determination (r2) of 0.76. Model underprediction was more common than overprediction. The average of the absolute normalized bias was 0.30; in other words, long-term mean particle concentrations at each site were on average predicted to within 30% of the measured values. Observed 24-hour number concentrations were simulated to within a factor of 1.6 on 48% of days at HCMS sites and 81% at CHAPS sites, lower than the original design goal of 90%. Extensive evaluation of hourly concentrations, diurnal patterns, sizedistributions, and directional patterns was performed. At two sites with heavy freeway and heavy-duty-vehicle (HDV) influences and extensive size-resolved measurements, the ASM made significant errors in the diurnal pattern, concentration, and mode position of the aerosol size distribution. Observations indicated a shift in concentrations and size distributions corresponding to the afternoon development of offshore wind at the HCMS sites. The model did not reproduce the changes in particles associated with this wind shift and suffered from overprediction for particles of less than 15 nm and underprediction for particles of between 15 and 500 nm, raising doubt about the applicability of the HDV emission factors and the model's assumptions that particles were nonvolatile. The model's temporal prediction skill at individual monitoring sites was variable; the index of agreement (IOA) for hourly values at single sites ranged from 0.30 to 0.56. The model's ability to reproduce diurnal patterns in aerosol concentrations was site dependent; midday underprediction as well as underprediction for particle sizes greater than 15 nm were typical errors. Despite some problems in model skill, the number of time periods and locations evaluated as well as the extent of our qualitative and quantitative evaluations versus physical measurements well exceeded other published size-resolved modeling efforts. As a trial of a typical application, the sensitivity of the concentrations at each receptor site to LDV traffic, HDV traffic, and various road classes was evaluated. The sensitivity of overall particle numbers to all types of traffic ranged from 0.87 at the site with the heaviest traffic to 0.28 at the site with the lightest traffic, meaning that a 1% reduction in traffic could yield a reduction in particle number of 0.87% to 0.28%. Key conclusions and implications of the study are the following: 1. That variable-resolution (down to 10 m) modeling in a relatively simple framework is feasible and can support most of the applications mentioned above; 2. That model improvements will be required for some applications, especially in the areas of the HDV emission factor and the parameterization of meteorologic dispersion; 3. That particle loss from instrument transmission efficiency can be significant for particles smaller than 50 nm, and especially significant for particles smaller than 20 nm. In cases where loss corrections are not accounted for, or are inaccurate, this loss can cause disagreements in observation-model and observation-observation comparisons. 4. That LDV traffic exposures likely exceed HDV traffic exposures in some locations; 5. That variable step size and adaptive parcel width are critical to balancing computational efficiency and resolution; and 6. That the effects of roadways on air quality depend on both traffic volume and distance--in other words, low traffic volumes at close proximity need to be considered in health and planning studies just as much as do high traffic volumes at distances up to several kilometers. Future improvements to the model have been identified. They include improved emission factors; integration with the U.S. Environmental Protection Agency (EPA) Motor Vehicle Emission Simulator (MOVES) model; nesting with three-dimensional (3D) Eulerian models such as the Community Multi-scale Air Quality (CMAQ) model; increased emission dependence on acceleration, load, grade, and speed as well as evaporation and condensation of semivolatile aerosol species; and modeling of carbon dioxide (CO2) as an on-road and near-road dilution tracer. In addition, comparison with other statistically and physically based models would be highly beneficial.
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Characterizing ultrafine particles and other air pollutants in and around school buses.

Zhu Y, Zhang Q, HEI Health Review Committee

Res Rep Health Eff Inst · 2014 Mar · PMID 24834688

Increasing evidence has demonstrated toxic effects of ultrafine particles (UFP*, diameter < 100 nm). Children are particularly at risk because of their immature respiratory systems and higher breathing rates per body mas... Increasing evidence has demonstrated toxic effects of ultrafine particles (UFP*, diameter < 100 nm). Children are particularly at risk because of their immature respiratory systems and higher breathing rates per body mass. This study aimed to characterize UFP, PM2.5 (particulate matter < or = 2.5 microm in aerodynamic diameter), and other vehicular-emitted pollutants in and around school buses. Four sub-studies were conducted, including: 1. On-road tests to measure in-cabin air pollutant levels while school buses were being driven; 2. Idling tests to determine the contributions of tailpipe emissions from idling school buses to air pollutant levels in and around school buses under different scenarios; 3. Retrofit tests to evaluate the performance of two retrofit systems, a diesel oxidation catalyst (DOC) muffler and a crankcase filtration system (CFS), on reducing tailpipe emissions and in-cabin air pollutant concentrations under idling and driving conditions; and 4. High efficiency particulate air (HEPA) filter air purifier tests to evaluate the effectiveness of in-cabin filtration. In total, 24 school buses were employed to cover a wide range of school buses commonly used in the United States. Real-time air quality measurements included particle number concentration (PNC), fine and UFP size distribution in the size range 7.6-289 nm, PM2.5 mass concentration, black carbon (BC) concentration, and carbon monoxide (CO) and carbon dioxide (CO2) concentrations. For in-cabin measurements, instruments were placed on a platform secured to the rear seats inside the school buses. For all other tests, a second set of instruments was deployed to simultaneously measure the ambient air pollutant levels. For tailpipe emission measurements, the exhaust was diluted and then measured by instruments identical to those used for the in-cabin measurements. The results show that when driving on roads, in-cabin PNC, fine and UFP size distribution, PM2.5, BC, and CO varied by engine age, window position, driving speed, driving route, and operating conditions. Emissions from idling school buses increased the PNC close to the tailpipe by a factor of up to 26.0. Under some circumstances, tailpipe emissions of idling school buses increased the in-cabin PNC by factors ranging from 1.2 to 5.8 in the 10-30 nm particle size range. Retrofit systems significantly reduced the tailpipe emissions of idling school buses. With both DOC and CFS installed, PNC in tailpipe emissions dropped by 20%-94%. No unequivocal decrease was observed for in-cabin air pollutants after retrofitting. The operation of the air conditioning (AC) unit and the pollutant concentrations in the surrounding ambient air played more important roles than retrofit technologies in determining in-cabin air quality. The use of a HEPA air purifier removed up to 50% of in-cabin particles. Because each sub-study tested only a subset of the 24 school buses, the results should be seen as more exploratory than definitive.
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New statistical approaches to semiparametric regression with application to air pollution research.

Robins JM, Zhang P, Ayyagari R … +6 more , Logan R, Tchetgen ET, Li L, Lumley T, van der Vaart A, HEI Health Review Committee

Res Rep Health Eff Inst · 2013 Nov · PMID 24400588

Abstract loading — click title to view on PubMed.

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National Particle Component Toxicity (NPACT) initiative report on cardiovascular effects.

Vedal S, Campen MJ, McDonald JD … +5 more , Larson TV, Sampson PD, Sheppard L, Simpson CD, Szpiro AA

Res Rep Health Eff Inst · 2013 Oct · PMID 24377210

Epidemiologic and toxicologic studies were carried out in concert to provide complementary insights into the compositional features of ambient particulate matter (PM*) that produce cardiovascular effects. In the epidemio... Epidemiologic and toxicologic studies were carried out in concert to provide complementary insights into the compositional features of ambient particulate matter (PM*) that produce cardiovascular effects. In the epidemiologic studies, we made use of cohort data from two ongoing studies--the Multi-Ethnic Study of Atherosclerosis (MESA) and the Women's Health Initiative--Observational Study (WHI-OS)--to investigate subclinical markers of atherosclerosis and clinical cardiovascular events. In the toxicologic study, we used the apolipoprotein E null (ApoE(-/-)) hypercholesterolemic mouse model to assess cardiovascular effects of inhalation exposure to various atmospheres containing laboratory-generated pollutants. In the epidemiologic studies, individual-level residential concentrations of fine PM, that is, PM with an aerodynamic diameter of 2.5 microm or smaller (PM2.5), PM2.5 components (primarily elemental carbon [EC] and organic carbon [OC], silicon, and sulfur but also sulfate, nitrate, nickel, vanadium, and copper), and the gaseous pollutants sulfur dioxide and nitrogen dioxide were estimated using spatiotemporal modeling and other exposure estimation approaches. In the MESA cohort data, evidence for associations with increased carotid intima-media thickness (CIMT) was found to be strongest for PM2.5, OC, and sulfur, as well as for copper in more limited analyses; the evidence for this was found to be weaker for silicon, EC, and the other components and gases. Similarly, in the WHI-OS cohort data, evidence for associations with incidence of cardiovascular mortality and cardiovascular events was found to be good for OC and sulfur, respectively, and for PM2.5; the evidence for this was found to be weaker for EC and silicon. Source apportionment based on extensive monitoring data in the six cities in the MESA analyses indicated that OC represented secondary formation processes as well as primary gasoline and biomass emissions, that sulfur represented largely secondary inorganic aerosols, and that copper represented brake dust and diesel emissions. In the toxicologic study, hypercholesterolemic mice were exposed for 50 days to atmospheres containing mixed vehicular engine emissions (MVE) consisting of mixed gasoline and diesel engine exhaust or to MVE-derived gases only (MVEG). Mice were also exposed to atmospheres containing sulfate, nitrate, or road dust, either alone or mixed with MVE or MVEG. Sulfate alone or in combination with MVE was associated with increased aortic reactivity. All exposures to atmospheres containing MVE (including a combination of MVE with other PM) were associated with increases in plasma and aortic oxidative stress; exposures to atmospheres containing only sulfate or nitrate were not. Exposure to MVE and to MVEG combinations except those containing road dust resulted in increased monocyte/macrophage sequestration in aortic plaque (a measure of plaque inflammation). Exposure to all atmospheres except those containing nitrate was associated with enhanced aortic vasoconstriction. Exposure to the MVEG was an independent driver of lipid peroxidation, matrix metalloproteinase (MMP) activation, and vascular inflammation. The epidemiologic and toxicologic study designs were intended to complement each other. The epidemiologic studies provided evidence in real-world human settings, and the toxicologic study directly assessed the biologic effects of various pollutant mixtures (in a way that is not possible in epidemiologic studies) by examining endpoints that probably underlie the subclinical and clinical cardiovascular endpoints examined in the epidemiologic studies. The epidemiologic studies were not suited to determining whether the observed associations were caused by direct effects of individual pollutants or by the mixtures in which individual pollutants are found. These studies were consistent in finding that OC and sulfate had the strongest evidence for associations with the cardiovascular disease endpoints, with much weaker evidence for EC and silicon. Both OC and sulfate reflected a large secondary aerosol component. Results from the toxicologic study indicated, for the most part, that MVE and mixtures of MVE and MVEG with other PM pollutants were important in producing the toxic cardiovascular effects found in the study. Further work on the effects of pollutant mixtures and secondary aerosols should allow better understanding of the pollution components and sources most responsible for the adverse cardiovascular effects of air pollution exposure.
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National Particle Component Toxicity (NPACT) Initiative: integrated epidemiologic and toxicologic studies of the health effects of particulate matter components.

Lippmann M, Chen LC, Gordon T … +2 more , Ito K, Thurston GD

Res Rep Health Eff Inst · 2013 Oct · PMID 24377209

Particulate matter (PM*), an ambient air criteria pollutant, is a complex mixture of chemical components; particle sizes range from nanometer-sized molecular clusters to dust particles that are too large to be aspirated... Particulate matter (PM*), an ambient air criteria pollutant, is a complex mixture of chemical components; particle sizes range from nanometer-sized molecular clusters to dust particles that are too large to be aspirated into the lungs. Although particle composition is believed to affect health risks from PM exposure, our current health-based air quality standards for PM are limited to (1) the mass concentrations of PM2.5 (particles 2.5 microm or smaller in aerodynamic diameter), which are largely attributable to combustion products; and (2) PM10 (10 microm or smaller), which includes larger-sized mechanically generated dusts. Both of these particle size fractions are regulated under the National Ambient Air Quality Standards (NAAQS) and both have been associated with excess mortality and morbidity. We conducted four studies as part of HEI's integrated National Particle Component Toxicity (NPACT) Initiative research program. Since 1999, the Chemical Speciation Network (CSN), managed by the U.S. Environmental Protection Agency (U.S; EPA), has routinely gathered air monitoring data every third or sixth day for the concentrations of numerous components of PM2.5. Data from the CSN enabled us to conduct a limited time-series epidemiologic study of short-term morbidity and mortality (Ito study); and a study of the associations between long-term average pollutant concentrations and annual mortality (Thurston study). Both have illuminated the roles of PM2.5 chemical components and source-related mixtures as potentially causal agents. We also conducted a series of 6-month subchronic inhalation exposure studies (6 hours/day, 5 days/week) of PM2.5 concentrated (nominally) 10 x from ambient air (CAPs) with apolipoprotein E-deficient (ApoE(-/-)) mice (a mouse model of atherosclerosis) (Chen study). The CAPs studies were conducted in five different U.S. airsheds; we measured the daily mass concentrations of PM2.5, black carbon (BC), and 16 elemental components in order to identify their sources and their roles in eliciting both short- and long-term health-related responses. In addition, from the same five air-sheds we collected samples of coarse (PM10-2.5), fine (PM2.5-0.2), and ultrafine (PM0.2) particles. Aliquots of these samples were administered to cells in vitro and to mouse lungs in vivo (by aspiration) in order to determine their comparative acute effects (Gordon Study). The results of these four complementary studies, and the overall integrative analyses, provide a basis for guiding future research and for helping to determine more targeted emission controls for the PM components most hazardous to acute and chronic health. Application of the knowledge gained in this work may therefore contribute to an optimization of the public health benefits of future PM emission controls. The design of each NPACT study conducted at NYU was guided by our scientific hypotheses, which were based on our reviews of the background literature and our experience in conducting studies of associations between ambient PM and health-related responses. These hypotheses guided the development and conduct of the four studies. Hypothesis 1. Coarse, fine, and ultrafine PM are each capable of producing acute health effects of public health concern, but the effects may differ according to particle size and composition. (Applies to all studies.) Hypothesis 2. Long-term PM2.5 exposures are closely associated with chronic health effects. (Applies to studies 1 and 4.) Hypothesis 3. The source-apportionment techniques that we have developed and refined in recent years provide a useful basis for identifying major categories of sources of PM in ambient air and specific chemical components that have the greatest impacts on a variety of acute and chronic health effects. (Applies to all studies.) Hypothesis 4. The health effects due to ambient PM exposures can best be seen in sensitive subgroups within overall human populations and in animal models of such populations. (Applies to studies 1, 3, and 4.) Overall, the studies have demonstrated that the toxicity of PM is driven by a complex interaction of particle size range, geographic location, source category, and season. These findings suggest that the components of PM--associated with certain categories of sources--are responsible for the observed adverse health effects. Most importantly, the responsible components and source categories vary with the health-related endpoints being assessed. Across all studies, fossil-fuel combustion source categories were most consistently associated with both short- and long-term adverse effects of PM2.5 exposure. The components that originate from the Residual Oil Combustion and Traffic source categories were most closely associated with short-term effects; and components from the Coal Combustion category were more closely associated with long-term effects.
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Effect of air pollution control on mortality and hospital admissions in Ireland.

Dockery DW, Rich DQ, Goodman PG … +5 more , Clancy L, Ohman-Strickland P, George P, Kotlov T, HEI Health Review Committee

Res Rep Health Eff Inst · 2013 Jul · PMID 24024358

During the 1980s the Republic of Ireland experienced repeated severe pollution episodes. Domestic coal burning was a major source of this pollution. In 1990 the Irish government introduced a ban on the marketing, sale, a... During the 1980s the Republic of Ireland experienced repeated severe pollution episodes. Domestic coal burning was a major source of this pollution. In 1990 the Irish government introduced a ban on the marketing, sale, and distribution of coal in Dublin. The ban was extended to Cork in 1995 and to 10 other communities in 1998 and 2000. We previously reported decreases in particulate black smoke (BS*) and sulfur dioxide (SO2) concentrations, measured as total gaseous acidity, in Dublin after the 1990 coal ban (Clancy et al. 2002). In the current study we explored and compared the effectiveness of the sequential 1990, 1995, and 1998 bans in reducing community air pollution and in improving public health. We compiled records of daily BS, total gaseous acidity (SO2), and counts of cause-specific deaths from 1981 to 2004 for Dublin County Borough (1990 ban), county Cork (1995 ban), and counties Limerick, Louth, Wexford, and Wicklow (1998 ban). We also compiled daily counts of hospital admissions for cardiovascular, respiratory, and digestive diagnoses for Cork County Borough (1991 to 2004) and counties Limerick, Louth, Wexford, and Wicklow (1993 to 2004). We compared pre-ban and post-ban BS and SO2 concentrations for each city. Using interrupted time-series methods, we estimated the change in cause-specific, directly standardized mortality rates in each city or county after the corresponding local coal ban. We regressed weekly age- and sex-standardized mortality rates against an indicator of the post- versus pre-ban period, adjusting for influenza epidemics, weekly mean temperature, and a season smooth of the standardized mortality rates in Coastal counties presumably not affected by the bans. We compared these results with similar analyses in Midlands counties also presumably unaffected by the bans. We also estimated the change in cause-specific, directly standardized, weekly hospital admissions rates normalized for underreporting in each city or county after the 1995 and 1998 bans, adjusting for influenza epidemics, weekly mean temperature, and local admissions for digestive diagnoses. Mean BS concentrations fell in all affected population centers post-ban compared with the pre-ban period, with decreases ranging from 4 to 35 microg/m3 (corresponding to reductions of 45% to 70%, respectively), but we observed no clear pattern in SO2 measured as total gaseous acidity associated with the bans. In comparisons with the pre-ban periods, no significant reduction was found in total death rates associated with the 1990 (1% reduction), 1995 (4% reduction), or 1998 (0% reduction) bans, nor for cardiovascular mortality (0%, 4%, and 1% reductions for the 1990, 1995, and 1998 bans, respectively). Respiratory mortality was reduced in association with the bans (17%, 9%, and 3%, respectively). We found a 4% decrease in hospital admissions for cardiovascular disease associated with the 1995 ban and a 3% decrease with the 1998 ban. Admissions for respiratory disease were not consistently lower after the bans; admissions for pneumonia, chronic obstructive pulmonary disease (COPD), and asthma were reduced. However, underreporting of hospital admissions data and lack of control and comparison series tempered our confidence in these results. The successive coal bans resulted in immediate and sustained decreases in particulate concentrations in each city or town; with the largest decreases in winter and during the heating season. The bans were associated with reductions in respiratory mortality but no detectable improvement in cardiovascular mortality. The changes in hospital admissions for respiratory and cardiovascular disease were supportive of these findings but cannot be considered confirming. Detecting changes in public health indicators associated even with clear improvements in air quality, as in this case, remains difficult when there are simultaneous secular improvements in the same health indicators.
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Cardiorespiratory biomarker responses in healthy young adults to drastic air quality changes surrounding the 2008 Beijing Olympics.

Zhang J, Zhu T, Kipen H … +13 more , Wang G, Huang W, Rich D, Zhu P, Wang Y, Lu SE, Ohman-Strickland P, Diehl S, Hu M, Tong J, Gong J, Thomas D, HEI Health Review Committee

Res Rep Health Eff Inst · 2013 Feb · PMID 23646463

Associations between air pollution and cardiorespiratory mortality and morbidity have been well established, but data to support biologic mechanisms underlying these associations are limited. We designed this study to ex... Associations between air pollution and cardiorespiratory mortality and morbidity have been well established, but data to support biologic mechanisms underlying these associations are limited. We designed this study to examine several prominently hypothesized mechanisms by assessing Beijing residents' biologic responses, at the biomarker level, to drastic changes in air quality brought about by unprecedented air pollution control measures implemented during the 2008 Beijing Olympics. To test the hypothesis that changes in air pollution levels are associated with changes in biomarker levels reflecting inflammation, hemostasis, oxidative stress, and autonomic tone, we recruited and retained 125 nonsmoking adults (19 to 33 years old) free of cardiorespiratory and other chronic diseases. Using the combination of a quasi-experimental design and a panel-study approach, we measured biomarkers of autonomic dysfunction (heart rate [HR*] and heart rate variability [HRV]), of systemic inflammation and oxidative stress (plasma C-reactive protein [CRP], fibrinogen, blood cell counts and differentials, and urinary 8-hydroxy-2'-deoxyguanosine [8-OHdG]), of pulmonary inflammation and oxidative stress (fractional exhaled nitric oxide [FeNO], exhaled breath condensate [EBC] pH, EBC nitrate, EBC nitrite, EBC nitrite+nitrate [sum of the concentrations of nitrite and nitrate], and EBC 8-isoprostane), of hemostasis (platelet activation [plasma sCD62P and sCD40L], platelet aggregation, and von Willebrand factor [vWF]), and of blood pressure (systolic blood pressure [SBP] and diastolic blood pressure [DBP]). These biomarkers were measured on each subject twice before, twice during, and twice after the Beijing Olympics. For each subject, repeated measurements were separated by at least one week to avoid potential residual effects from a prior measurement. We measured a large suite of air pollutants (PM2.5 [particulate matter < or = 2.5 microm in aerodynamic diameter] and constituents, sulfur dioxide [SO2], carbon monoxide [CO], nitrogen dioxide [NO2], and ozone [O3]) throughout the study at a central Beijing site near the residences and workplaces of the subjects on a daily basis. Total particle number (TPN) was also measured at a separate site. We used a time-series analysis to assess changes in pollutant concentration by period (pre-, during-, and post-Olympics periods). We used mixed-effects models to assess changes in biomarker levels by period and to estimate changes associated with increases in pollutant concentrations, controlling for ambient temperature, relative humidity (RH), sex, and the day of the week of the biomarker measurements. We conducted sensitivity analyses to assess the impact of potential temporal confounding and exposure misclassification. We observed reductions in mean concentrations for all measured pollutants except O3 from the pre-Olympics period to the during-Olympics period. On average, elemental carbon (EC) changed by -36%, TPN by -22%, SO2 by -60%, CO by -48%, and NO2 by -43% (P < 0.05 for all these pollutants). Reductions were observed in mean concentrations of PM2.5 (by -27%), sulfate (SO4(2-)) (by -13%), and organic carbon (OC) (by -23%); however, these values were not statistically significant. Both 24-hour averages and 1-hour maximums of O3 increased (by 20% and 17%, respectively) from the pre-Olympics to the during-Olympics period. In the post-Olympics period after the pollution control measures were relaxed, mean concentrations of most pollutants (with the exception of SO4(2-) and O3) increased to levels similar to or higher than pre-Olympics levels. Concomitantly and consistent with the hypothesis, we observed, from the pre-Olympics to the during-Olympics period, statistically significant (P < or = 0.05) or marginally significant (0.05 < P < 0.1) decreases in HR (-1 bpm or -1.7% [95% CI, -3.4 to -0.1]), SBP (-1.6 mmHg or -1.8% [95% CI, -3.9 to 0.4]), 8-OHdG (-58.3% [95% CI, -72.5 to -36.7]), FeNO (-60.3% [95% CI, -66.0 to -53.6]), EBC nitrite (-30.0% [95% CI, -39.3 to -19.3]), EBC nitrate (-21.5% [95% CI, -35.5 to -4.5]), EBC nitrite+nitrate (-17.6% [95% CI, -28.4 to -5.1]), EBC hydrogen ions (-46% [calculated from EBC pH], or +3.5% in EBC pH [95% CI, 2.2 to 4.9]), sCD62P (-34% [95% CI, -38.4 to -29.2]), sCD40L (-5.7% [95% CI, -10.5 to -0.7]), and vWF (-13.1% [95% CI, -18.6 to -7.5]). Moreover, the percentages of above-detection values out of all observations were significantly lower for plasma CRP and EBC 8-isoprostane in the during-Olympics period compared with the pre-Olympics period. In the post-Olympics period, the levels of the following biomarkers reversed (increased, either with or without statistical significance) from those in the during-Olympics period: SBP (10.7% [95% CI, 2.8 to 18.6]), fibrinogen (4.3% [95% CI, -1.7 to 10.2), neutrophil count (4.7% [95% CI, -7.7 to 17.0]), 8-OHdG (315% [95% CI, 62.0 to 962]), FeNO (130% [95% CI, 62.5 to 225]), EBC nitrite (159% [95% CI, 71.8 to 292]), EBC nitrate (161% [95% CI, 48.0 to 362]), EBC nitrite+nitrate (124% [95% CI, 50.9 to 233]), EBC hydrogen ions (146% [calculated from EBC pH] or -4.8% in EBC pH [95% CI, -9.4 to -0.21), sCD62P (33.7% [95% CI, 17.7 to 51.8]), and sCD40L (9.1% [95% CI, -3.7 to 23.5]). Furthermore, these biomarkers also showed statistically significant associations with multiple pollutants across different lags after adjusting for meteorologic parameters. The associations were in the directions hypothesized and were consistent with the findings from the comparisons between periods, providing further evidence that the period effects were due to changes in air quality, independent of season and meteorologic conditions or other potential confounders. Contrary to our hypothesis, however, we observed increases in platelet aggregation, red blood cells (RBCs) and white blood cells (WBCs) associated with the during-Olympics period, as well as significant negative associations of these biomarkers with pollutant concentrations. We did not observe significant changes in any of the HRV indices and DBP by period. However, we observed associations between a few HRV indices and pollutant concentrations. Changes in air pollution levels during the Beijing Olympics were associated with acute changes in biomarkers of pulmonary and systemic inflammation, oxidative stress, and hemostasis and in measures of cardiovascular physiology (HR and SBP) in healthy, young adults. These changes support the prominently hypothesized mechanistic pathways underlying the cardiorespiratory effects of air pollution.
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Selective detection and characterization of nanoparticles from motor vehicles.

Johnston MV, Klems JP, Zordan CA … +3 more , Pennington MR, Smith JN, HEI Health Review Committee

Res Rep Health Eff Inst · 2013 Feb · PMID 23614271

Numerous studies have shown that exposure to motor vehicle emissions increases the probability of heart attacks, asthma attacks, and hospital visits among at-risk individuals. However, while many studies have focused on... Numerous studies have shown that exposure to motor vehicle emissions increases the probability of heart attacks, asthma attacks, and hospital visits among at-risk individuals. However, while many studies have focused on measurements of ambient nanoparticles near highways, they have not focused on specific road-level domains, such as intersections near population centers. At these locations, very intense spikes in particle number concentration have been observed. These spikes have been linked to motor vehicle activity and have the potential to increase exposure dramatically. Characterizing both the contribution and composition of these spikes is critical in developing exposure models and abatement strategies. To determine the contribution of the particle spikes to the ambient number concentration, we implemented wavelet-based algorithms to isolate the particle spikes from measurements taken during the summer and winter of 2009 in Wilmington, Delaware, adjacent to a roadway intersection that approximately 28,000 vehicles pass through daily. These measurements included both number concentration and size distributions recorded once every second by a condensation particle counter (CPC*; TSI, Inc., St. Paul, MN) and a fast mobility particle sizer (FMPS). The high-frequency portion of the signal, consisting of a series of abrupt spikes in number concentration that varied in length from a few seconds to tens of seconds, accounted for 3% to 35% of the daily ambient number concentration, with spike contributions sometimes greater than 50% of hourly number concentrations. When the data were weighted by particle volume, this portion of the signal contributed an average of 10% to 20% to the daily concentration of particulate matter (PM) < or = 0.1 microm in aerodynamic diameter (PM0.1). The preferred locations for observing particle concentration spikes were those surrounding the measurement site at which motor vehicles accelerated after a red traffic light turned green. As the distance or transit time from emission to sampling increased, the size distribution shifted to a larger particle size, which confirmed the source assignments. To determine the distribution of emissions from individual vehicles, we correlated camera images with the spike contribution to particle number concentration at each time point. A small percentage of motor vehicles were found to emit a disproportionally large concentration of nanoparticles, and these high emitters included both spark-ignition (SI) and heavy-duty diesel (HDD) vehicles. In addition to characterizing the contribution of the spikes (local sources) to the ambient number concentration, we developed a method to determine the net contribution of motor vehicles (all sources) to the total mass concentration of ambient nanoparticles. To do this, we correlated the concentration of spikes with measurements of fast changes in the chemical composition of nanoparticles measured with the nano aerosol mass spectrometer (NAMS; built by the Johnston group). The NAMS irradiates individual, size-selected nanoparticles with a high-energy laser pulse to generate a mass spectrum consisting of multiply charged atomic ions. The elemental composition of each particle was determined from the ion signal intensities of each element. However, overlapping mass-to-charge ratios (m/z) at 4 m/z (O(+4) and C(+3)) and at 8 m/z (O(+2) and S(+4)) needed to be separated into their component ions to obtain a representative composition. To do this, we developed a method to deconvolute these ion signals using sucrose and ammonium sulfate [(NH4)2SO4] as calibration standards. With this approach, the differences between the expected and measured elemental mole fractions of carbon (C), oxygen (O), nitrogen (N), and sulfur (S) for a variety of test particles were generally much less than 10%. Ambient nanoparticles were found to consist mostly of C, O, N, and S. Many particles also contained silicon (Si). The elemental compositions were apportioned into molecular species that are commonly found in ambient aerosol: sulfate (SO4(2-)), nitrate (NO3-), ammonium (NH4+), carbonaceous matter, and when present, silicon dioxide (SiO2). Correlating NAMS chemical-composition measurements with spike contributions allowed for the development of a chemical profile representing motor vehicle emissions, which could be used to apportion their total contribution to the ambient nanoparticle mass. Particles originating from motor vehicles had compositions dominated by unoxidized carbonaceous matter, whereas non-motor vehicle particles consisted mostly of SO42-, NO3-, and oxidized carbonaceous matter. Motor vehicles were found to contribute up to 48% and 60% of the nanoparticle mass and number concentrations, respectively, in the winter measurement period, but only 16% and 49% of the nanoparticle mass and number concentrations, respectively, in the summer period. Chemical-composition profiles and contributions of SI versus HDD vehicles to the nanoparticle mass concentration were estimated by correlating still camera images, chemical composition, and spike contributions at each time point. The total mass contributions from SI and HDD vehicles were roughly equal, but the uncertainty in the split was large. The results of this study suggest that nanoparticle concentrations will be higher adjacent to an intersection than along the same roadway but further from an intersection. Possible ways to reduce the motor vehicle contribution to ambient nanoparticulate matter include minimizing stop-and-go activity at an intersection (i.e., vehicles accelerating after a red light turns green) and identifying the small fraction of motor vehicles that emit a disproportionally large number of nanoparticles.
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Potential air toxics hot spots in truck terminals and cabs.

Smith TJ, Davis ME, Hart JE … +4 more , Blicharz A, Laden F, Garshick E, HEI Health Review Committee

Res Rep Health Eff Inst · 2012 Dec · PMID 23409510

INTRODUCTION: Hot spots are areas where concentrations of one or more air toxics--organic vapors or particulate matter (PM)--are expected to be elevated. The U.S. Environmental Protection Agency's (EPA*) screening values... INTRODUCTION: Hot spots are areas where concentrations of one or more air toxics--organic vapors or particulate matter (PM)--are expected to be elevated. The U.S. Environmental Protection Agency's (EPA*) screening values for air toxics were used in our definition of hot spots. According to the EPA, a screening value "is used to indicate a concentration of a chemical in the air to which a person could be continually exposed for a lifetime ... and which would be unlikely to result in a deleterious effect (either cancer or noncancer health effects)" (U.S. EPA 2006). Our characterization of volatile organic compounds (VOCs; namely 18 hydrocarbons, methyl tert-butyl ether [MTBE], acetone, and aldehydes) was added onto our ongoing National Cancer Institute-funded study of lung cancer and particulate pollutant concentrations (PM with an aerodynamic diameter < or = 2.5 microm [PM2.5], elemental carbon [EC], and organic carbon [OC]) and source apportionment of the U.S. trucking industry. We focused on three possible hot spots within the trucking terminals: upwind background areas affected by nearby industrial parks; downwind areas affected by upwind and terminal sources; and the loading docks and mechanic shops within terminal as well as the interior of cabs of trucks being driven on city, suburban, and rural streets and on highways. METHODS: In Phase 1 of our study, 15 truck terminals across the United States were each visited for five consecutive days. During these site visits, sorbent tubes were used to collect 12-hour integrated samples of hydrocarbons and aldehydes from upwind and downwind fence-line locations as well as inside truck cabs. Meteorologic data and extensive site information were collected with each sample. In Phase 2, repeat visits to six terminals were conducted to test the stability of concentrations across time and judge the representativeness of our previous measurements. During the repeat site visits, the sampling procedure was expanded to include real-time sampling for total hydrocarbon (HC) and PM2.5 at the terminal upwind and downwind sites and inside the truck cabs, two additional monitors in the yard for four-quadrant sampling to better characterize the influence of wind, and indoor sampling in the loading dock and mechanic shop work areas. RESULTS: Mean and median concentrations of VOCs across the sampling locations in and around the truck terminals showed significant variability in the upwind concentrations as well as in the intensity of exposures for drivers, loading-dock workers, and mechanics. The area of highest concentrations varied, although the lowest concentrations were always found in the upwind background samples. However, the downwind samples, which included the terminal's contribution, were on average only modestly higher than the upwind samples. In the truck terminal, the mechanic-shop-area concentrations were consistently elevated for many of the VOCs (including the xylenes, alkanes, and acetone) and particulates; the loading-dock concentrations had relatively high concentrations of 1,3-butadiene, formaldehyde, and acetaldehyde; and nonsmoking driver exposures were elevated for benzene, MTBE, styrene, and hexane. Also, the loading dock and yard background concentrations for EC and PM2.5 were highly correlated with many of the VOCs (50% of pairs tested with Spearman r > 0.5 and 75% with r > 0.4); in the mechanic shop VOCs were correlated with EC but not PM2.5 (r = 0.4-0.9 where significant); and for driver exposures VOC correlations with EC and PM2.5 were relatively low, with the exception of a few aromatics, primarily benzene (r = 0.4-0.5). A principal component analysis of background source characteristics across the terminal locations that had repeat site visits identified three different groupings of variables (the "components"). This analysis suggested that a strong primary factor for hydrocarbons (alkanes and aromatics) was the major contributor to VOC variability in the yard upwind measurement. Aldehydes and acetone, which loaded onto the second and third components, were responsible for a smaller contribution to VOC variability. A multi-layer exposure model was constructed using structural equation modeling techniques that significantly predicted the yard upwind concentrations of individual VOCs as a function of wind speed, road proximity, and regional location (R2 = 0.5-0.9). This predicted value for the yard background concentration was then used to calculate concentrations for the loading dock and mechanic shop. Finally, we conducted a detailed descriptive analysis of the real-time data collected in the yard and in truck cabs during the six repeat site visits, which included more than 50 12-hour sessions at each sampling location. The real-time yard monitoring results suggested that under some conditions there was a clear upwind-to-downwind trend indicating a terminal contribution, which was not apparent in the integrated sampling data alone. They also suggested a nonlinear relationship with wind speed: calm conditions (wind speed < 2 mph) were associated with erratic upwind-downwind differences, lower wind speeds (2 to 10 mph) favored transport with little dilution, and higher wind speeds (> 10 mph) favored dilution and dispersal (more so for VOCs than for PM). Finally, an analysis of the real-time data for driver exposures in trucks with a global positioning system (GPS) matched with geographic information system (GIS) data suggested a clear influence of traffic and industrial sources along a given route with peaks in driver exposures. These peaks were largely associated with traffic, major intersections, idling at the terminals, and pickup and delivery (P&D) periods. However, VOCs and PM2.5 had different exposure patterns: VOCs exposures increased when the vehicle was stopped, and PM2.5 exposures increased during travel in traffic. CONCLUSIONS: All three types of testing sites--upwind and downwind fence-line locations and inside truck cabs while in heavy traffic--met the established definition for a hot spot by having periods with concentrations of pollutants that exceeded the EPA's screening values. Most frequently, the pollutants with concentrations exceeding the screening values were formaldehyde, acetaldehyde, and EC (which serves as a marker for diesel particulate); less frequently they were 1,3-butadiene and benzene. In the case of the downwind location of a single truck terminal without an aggregation of other sources, high concentrations of VOCs and PM were infrequent. Using structural equation modeling, a model was developed that could identify combinations of conditions and factors likely to produce hot spots. Source apportionment analyses showed that EC came predominantly from diesel emissions. As expected from the sites studied, organic vapors associated with vehicle emissions (C6-C8 alkanes and aromatics) were the predominant components of VOCs, followed by formaldehyde and acetaldehyde. For driver exposures, high VOC values were associated with stopped vehicles, and high PM2.5 values were associated with conditions during driving.
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Accountability analysis of title IV phase 2 of the 1990 Clean Air Act Amendments.

Morgenstern RD, Harrington W, Shih JS … +2 more , Bell ML, HEI Health Review Committee

Res Rep Health Eff Inst · 2012 Nov · PMID 23409509

In this study, we sought to assess what portion, if any, of the reductions in ambient concentrations of particulate matter (PM*) < or = 2.5 microm in aerodynamic diameter (PM2.5) that occurred in the United States betwee... In this study, we sought to assess what portion, if any, of the reductions in ambient concentrations of particulate matter (PM*) < or = 2.5 microm in aerodynamic diameter (PM2.5) that occurred in the United States between the years 1999 and 2006 can be attributed to reductions in emissions of sulfur dioxide (SO2) and nitrogen oxides (NOx) resulting from implementation of Phase 2 of Title IV of the 1990 Clean Air Act Amendments. To this end, a detailed statistical model linking sources and monitors over time and space was used to estimate associations between the observed emissions reductions and improvements in air quality. Overall, it turned out to be quite feasible to use relatively transparent statistical methods to assess these outcomes of the Phase 2 program, which was designed to reduce long-range transport of emissions. Associations between changes in emissions from individual power plants and monitor-specific estimates of changes in concentrations of PM2.5, our indicator pollutant, were highly significant and were mostly of the expected relative magnitudes with respect to distances and directions from sources. Originally estimated on monthly data for a set of 193 monitors between 1999 and 2005, our preferred model performed equally well using data for the same 193 monitors for 2006 as well as for an additional 217 monitors not in the original set in 2006. Although substantial model uncertainty was observed, we were able to estimate that the Title IV Phase 2 emissions reduction program implemented between 1999 and 2005 reduced PM2.5 concentrations in the eastern United States by an average of 1.07 microg/m3 (standard deviation [SD] = 0.11 microg/m3) compared with a counterfactual case defined as there having been no change in emission rates per unit of energy input (1 million British thermal units [BTUs]). On a population-weighted basis, the comparable reduction in PM2.5 was 0.89 microg/m3. Compared with the air quality fate and transport models used by the U.S. Environmental Protection Agency (EPA) to estimate air quality improvements associated with the Clean Air Interstate Rule (CAIR) for 2010 and 2015, when baseline PM2.5 concentrations were expected to be about one-third lower, our statistical model yielded roughly similar results per ton of SO2 reduced, well within the estimated confidence intervals of the models. We have proposed a number of steps to advance air quality outcomes research using statistical methods. Specifically, we have emphasized the value of updating our analysis with post-2005 data to try to corroborate our findings. We have also recommended extending the work on air quality outcomes to include changes in health outcomes that might be associated with the implementation of Title IV Phase 2.
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Impact of the 1990 Hong Kong legislation for restriction on sulfur content in fuel.

Wong CM, Rabl A, Thach TQ … +8 more , Chau YK, Chan KP, Cowling BJ, Lai HK, Lam TH, McGhee SM, Anderson HR, Hedley AJ

Res Rep Health Eff Inst · 2012 Aug · PMID 23316618

INTRODUCTION: After the implementation of a regulation restricting sulfur to 0.5% by weight in fuel on July 1, 1990, in Hong Kong, sulfur dioxide (SO2*) levels fell by 45% on average and as much as 80% in the most pollut... INTRODUCTION: After the implementation of a regulation restricting sulfur to 0.5% by weight in fuel on July 1, 1990, in Hong Kong, sulfur dioxide (SO2*) levels fell by 45% on average and as much as 80% in the most polluted districts (Hedley et al. 2002). In addition, a reduction of respiratory symptoms and an improvement in bronchial hyperresponsiveness in children were observed (Peters et al. 1996; Wong et al. 1998). A recent time-series study (Hedley et al. 2002) found an immediate reduction in mortality during the cool season at six months after the intervention, followed by an increase in cool-season mortality in the second and third years, suggesting that the reduction in pollution was associated with a delay in mortality. Proportional changes in mortality trends between the 5-year periods before and after the intervention were measured as relative risks and used to assess gains in life expectancy using the life table method (Hedley et al. 2002). To further explore the relation between changes in pollution-related mortality before and after the intervention, our study had three objectives: (1) to evaluate the short-term effects on mortality of changes in the pollutant mix after the Hong Kong sulfur intervention, particularly with changes in the particulate matter (PM) chemical species; (2) to improve the methodology for assessment of the health impact in terms of changes in life expectancy using linear regression models; and (3) to develop an approach for analyzing changes in life expectancy from Poisson regression models. A fourth overarching objective was to determine the relation between short- and long-term benefits due to an improvement in air quality. METHODS: For an assessment of the short-term effects on mortality due to changes in the pollutant mix, we developed Poisson regression Core Models with natural spline smoothers to control for long-term and seasonal confounding variations in the mortality counts and with covariates to adjust for temperature (T) and relative humidity (RH). We assessed the adequacy of the Core Models by evaluating the results against the Akaike Information Criterion, which stipulates that, at a minimum, partial autocorrelation plots should be between -0.1 and 0.1, and by examining the residual plots to make sure they were free from patterns. We assessed the effects for gaseous pollutants (NO2, SO2, and O3), PM with an aerodynamic diameter < or = 10 microm (PM10), and its chemical species (aluminum [Al], iron [Fe], manganese [Mn], nickel [Ni], vanadium [V], lead [Pb], and zinc [Zn]) using the Core Models, which were developed for the periods 5 years (or 2 years in the case of the sensitivity analysis) before and 5 years after the intervention, as well as in the10-year (or 7-year in the case of the sensitivity analysis) period pre- and post-intervention. We also included an indicator to separate the pre- and post-intervention periods, as well as the product of the indicator with an air pollution concentration variable. The health outcomes were mortality for all natural causes and for cardiovascular and respiratory causes, at all ages and in the 65 years or older age group. To assess the short- and long-term effects, we developed two methods: one using linear regression models reflecting the age-standardized mortality rate D(j) at day j, divided by a reference D(ref); and the other using Poisson regression models with daily mortality counts as the outcome variables. We also used both models to evaluate the relation between outcome variables and daily air pollution concentrations in the current day up to all previous days in the past 3 to 4 years. In the linear regression approach, we adjusted the data for temperature and relative humidity. We then removed season as a potential confounder, or deseasonalized them, by calculating a standard seasonal mortality rate profile, normalized to an annual average of unity, and dividing the mortality rates by this profile. Finally, to correct for long-term trends, we calculated a reference mortality rate D(ref)(j) as a moving average of the corrected and deseasonalized D(j) over the observation window. Then we regressed the outcome variable D(j)/D(ref) on an entire exposure sequence {c(i)} with lags up to 4 years in order to obtain impact coefficient f(i) from the regression model shown below: deltaD(j)/D (ref) = i(max)sigma f(i) c(j - i)(i = 0). The change in life expectancy (LE) for a change of units (deltac) in the concentration of pollutants on T(day)--representing the short interval (i.e., a day)--was calculated from the following equation (deltaL(pop) = average loss in life expectancy of an entire population): deltaL(pop) = -deltac T(day) infinity sigma (j = 0) infinity sigma f(i) (i = 0). In the Poisson regression approach, we fitted a distributed-lag model for exposure to previous days of up to 4 years in order to obtain the cumulative lag effect sigma beta(i). We fit the linear regression model of log(LE*/LE) = gamma(SMR - 1) + alpha to estimate the parameter gamma by gamma, where LE* and LE are life expectancy for an exposed and an unexposed population, respectively, and SMR represents the standardized mortality ratio. The life expectancy change per Ac increase in concentration is LE {exp[gamma delta c(sigma beta(i))]-1}. RESULTS: In our assessment of the changes in pollutant levels, the mean levels of SO2, Ni, and V showed a statistically significant decline, particularly in industrial areas. Ni and V showed the greatest impact on mortality, especially for respiratory diseases in the 5-year pre-intervention period for both the all-ages and 65+ groups among all chemical species. There were decreases in excess risks associated with Ni and V after the intervention, but they were nonsignificant. Using the linear regression approach, with a window of 1095 days (3 years), the losses in life expectancy with a 10-microg/m3 increase in concentrations, using two methods of estimation (one with adjustment for temperature and RH before the regression against pollutants, the other with adjustment for temperature and RH within the regression against pollutants), were 19.2 days (95% CI, 12.5 to 25.9) and 31.4 days (95% CI, 25.6 to 37.2) for PM10; and 19.7 days (95% CI, 15.2 to 24.2) and 12.8 days (95% CI, 8.9 to 16.8) for SO2. The losses in life expectancy in the current study were smaller than the ones implied by Elliott and colleagues (2007) and Pope and colleagues (2002) as expected since the observation window in our study was only 3 years whereas these other studies had windows of 16 years. In particular, the coefficients used by Elliott and colleagues (2007) for windows of 12 and 16 years were non-zero, which suggests that our window of at most 3 years cannot capture the full life expectancy loss and the effects were most likely underestimated. Using the Poisson regression approach, with a window of 1461 days (4 years), we found that a 10-microg/m3 increase in concentration of PM10 was associated with a change in life expectancy of -69 days (95% CI, -140 to 1) and a change of -133 days (95% CI, -172 to -94) for the same increase in SO2. The effect estimates varied as expected according to most variations in the sensitivity analysis model, specifically in terms of the Core Model definition, exposure windows, constraint of the lag effect pattern, and adjustment for smoking prevalence or socioeconomic status. CONCLUSIONS: Our results on the excess risks of mortality showed exposure to chemical species to be a health hazard. However, the statistical power was not sufficient to detect the differences between the pre- and post-intervention periods in Hong Kong due to the data limitations (specifically, the chemical species data were available only once every 6 days, and data were not available from some monitoring stations). Further work is needed to develop methods for maximizing the information from the data in order to assess any changes in effects due to the intervention. With complete daily air pollution and mortality data over a long period, time-series analysis methods can be applied to assess the short- and long-term effects of air pollution, in terms of changes in life expectancy. Further work is warranted to assess the duration and pattern of the health effects from an air pollution pulse (i.e., an episode of a rapid rise in air pollution) so as to determine an appropriate length and constraint on the distributed-lag assessment model.
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Multicity study of air pollution and mortality in Latin America (the ESCALA study).

Romieu I, Gouveia N, Cifuentes LA … +10 more , de Leon AP, Junger W, Vera J, Strappa V, Hurtado-Díaz M, Miranda-Soberanis V, Rojas-Bracho L, Carbajal-Arroyo L, Tzintzun-Cervantes G, HEI Health Review Committee

Res Rep Health Eff Inst · 2012 Oct · PMID 23311234

INTRODUCTION: The ESCALA* project (Estudio de Salud y Contaminación del Aire en Latinoamérica) is an HEI-funded study that aims to examine the association between exposure to outdoor air pollution and mortality in nine L... INTRODUCTION: The ESCALA* project (Estudio de Salud y Contaminación del Aire en Latinoamérica) is an HEI-funded study that aims to examine the association between exposure to outdoor air pollution and mortality in nine Latin American cities, using a common analytic framework to obtain comparable and updated information on the effects of air pollution on several causes of death in different age groups. This report summarizes the work conducted between 2006 and 2009, describes the methodologic issues addressed during project development, and presents city-specific results of meta-analyses and meta-regression analyses. METHODS: The ESCALA project involved three teams of investigators responsible for collection and analysis of city-specific air pollution and mortality data from three different countries. The teams designed five different protocols to standardize the methods of data collection and analysis that would be used to evaluate the effects of air pollution on mortality (see Appendices B-F). By following the same protocols, the investigators could directly compare the results among cities. The analysis was conducted in two stages. The first stage included analyses of all-natural-cause and cause-specific mortality related to particulate matter < or = 10 pm in aerodynamic diameter (PM10) and to ozone (O3) in cities of Brazil, Chile, and México. Analyses for PM10 and O3 were also stratified by age group and O3 analyses were stratified by season. Generalized linear models (GLM) in Poisson regression were used to fit the time-series data. Time trends and seasonality were modeled using natural splines with 3, 6, 9, or 12 degrees of freedom (df) per year. Temperature and humidity were also modeled using natural splines, initially with 3 or 6 df, and then with degrees of freedom chosen on the basis of residual diagnostics (i.e., partial autocorrelation function [PACF], periodograms, and a Q-Q plot) (Appendix H, available on the HEI Web site). Indicator variables for day-of-week and holidays were used to account for short-term cyclic fluctuations. To assess the association between exposure to air pollution and risk of death, the PM10 and O3 data were fit using distributed lag models (DLMs). These models are based on findings indicating that the health effects associated with air pollutant concentrations on a given day may accumulate over several subsequent days. Each DLM measured the cumulative effect of a pollutant concentration on a given day (day 0) and that day's contribution to the effect of that pollutant on multiple subsequent (lagged) days. For this study, exposure lags of up to 3, 5, and 10 days were explored. However, only the results of the DLMs using a 3-day lag (DLM 0-3) are presented in this report because we found a decreasing association with mortality in various age-cause groups for increasing lag effects from 3 to 5 days for both PM10 and O3. The potential modifying effect of socioeconomic status (SES) on the association of PM10 or O3 concentration and mortality was also explored in four cities: Mexico City, Rio de Janeiro, São Paulo, and Santiago. The methodology for developing a common SES index is presented in the report. The second stage included meta-analyses and metaregression. During this stage, the associations between mortality and air pollution were compared among cities to evaluate the presence of heterogeneity and to explore city-level variables that might explain this heterogeneity. Meta-analyses were conducted to combine mortality effect estimates across cities and to evaluate the presence of heterogeneity among city results, whereas meta-regression models were used to explore variables that might explain the heterogeneity among cities in mortality risks associated with exposures to PM10 (but not to O3). RESULTS: The results of the mortality analyses are presented as risk percent changes (RPC) with a 95% confidence interval (CI). RPC is the increase in mortality risk associated with an increase of 10 microg/m3 in the 24-hour average concentration of PM10 or in the daily maximum 8-hour moving average concentration of O3. Most of the results for PM10 were positive and statistically significant, showing an increased risk of mortality with increased ambient concentrations. Results for O3 also showed a statistically significant increase in mortality in the cities with available data. With the distributed lag model, DLM 0-3, PM10 ambient concentrations were associated with an increased risk of mortality in all cities except Concepci6n and Temuco. In Mexico City and Santiago the RPC and 95% CIs were 1.02% (0.87 to 1.17) and 0.48% (0.35 to 0.61), respectively. PM10 was also significantly associated with increased mortality from cardiopulmonary, respiratory, cardiovascular, cerebrovascular-stroke, and chronic obstructive lung diseases (COPD) in most cities. The few nonsignificant effects generally were observed in the smallest cities (Concepción, Temuco, and Toluca). The percentage increases in mortality associated with ambient O3 concentrations were smaller than for those associated with PM10. All-natural-cause mortality was significantly related to O3 in Mexico City, Monterrey, São Paulo and Rio de Janeiro. Increased mortality risks for some specific causes were also observed in these cities and in Santiago. In the analyses stratified by season, different patterns in mortality and O3 were observed for cold and warm seasons. Risk estimates for the warm season were larger and significant for several causes of death in São Paulo and Rio de Janeiro. Risk estimates for the cold season were larger and significant for some causes of death in Mexico City, Monterrey, and Toluca. In an analysis stratified by SES, the all-natural-cause mortality risk in Mexico City was larger for people with a medium SES; however we observed that the risk of mortality related to respiratory causes was larger among people with a low SES, while the risk of mortality related to cardiovascular and cerebrovascular-stroke causes was larger among people with medium or high SES. In São Paulo, the all-natural-cause mortality risk was larger in people with a high SES, while in Rio de Janeiro the all-natural-cause mortality risk was larger in people with a low SES. In both Brazilian cities, the risks of mortality were larger for respiratory causes, especially for the low- and high-SES groups. In Santiago, all-natural-cause mortality risk did not vary with level of SES; however, people with a low SES had a higher respiratory mortality risk, particularly for COPD. People with a medium SES had larger risks of mortality from cardiovascular and cerebrovascular-stroke disease. The effect of ambient PM10 concentrations on infant and child mortality from respiratory causes and lower respiratory infection (LRI) was studied only for Mexico City, Santiago, and São Paulo. Significant increased mortality risk from these causes was observed in both Santiago (in infants and older children) and Mexico City (only in infants). For O3, an increased mortality risk was observed in Mexico City (in infants and older children) and in São Paulo (only in infants during the warm season). The results of the meta-analyses confirmed the positive and statistically significant association between PM10 and all-natural-cause mortality (RPC = 0.77% [95% CI: 0.60 to 1.00]) using the random-effects model. For mortality from specific causes, the percentage increase in mortality ranged from 0.72% (0.54 to 0.89) for cardiovascular disease to 2.44% (1.36 to 3.59) for COPD, also using the random-effects model. For O3, significant positive associations were observed using the random-effects model for some causes, but not for all natural causes or for respiratory diseases in people 65 years or older (> or = 65 years), and not for COPD and cerebrovascular-stroke in the all-age and the > or = 65 age groups. The percentage increase in all-natural-cause mortality was 0.16% (-0.02 to 0.33). In the meta-regression analyses, variables that best explained heterogeneity in mortality risks among cities were the mean average of temperature in the warm season, population percentage of infants (< 1 year), population percentage of children at least 1 year old but < 5 years (i.e., 1-4 years), population percentage of people > or = 65 years, geographic density of PM10 monitors, annual average concentrations of PM10, and mortality rates for lung cancer. CONCLUSIONS: The ESCALA project was undertaken to obtain information for assessing the effects of air pollutants on mortality in Latin America, where large populations are exposed to relatively high levels of ambient air pollution. An important goal was to provide evidence that could inform policies for controlling air pollution in Latin America. This project included the development of standardized protocols for data collection and for statistical analyses as well as statistical analytic programs (routines developed in R by the ESCALA team) to insure comparability of results. The analytic approach and statistical programming developed within this project should be of value for researchers carrying out single-city analyses and should facilitate the inclusion of additional Latin American cities within the ESCALA multicity project. Our analyses confirm what has been observed in other parts of the world regarding the effects of ambient PM10 and 03 concentrations on daily mortality. They also suggest that SES plays a role in the susceptibility of a population to air pollution; people with a lower SES appeared to have an increased risk of death from respiratory causes, particularly COPD. Compared with the general population, infants and young children appeared to be more susceptible to both PM10 and O3, although an increased risk of mortality was not observed in these age groups in all cities. (ABSTRACT TRUNCATED)
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