Alan Vette

Health and household air pollution from solid fuel use: the need for improved exposure assessment.

Background: Nearly 3 billion people worldwide rely on solid fuel combustion to meet basic household energy needs. The resulting exposure to air pollution causes an estimated 4.5% of the global burden of disease. Large variability and a lack of resources for research and development have resulted in highly uncertain exposure estimates. Objective: We sought to identify research priorities for exposure assessment that will more accurately and precisely define exposure–response relationships of household air pollution necessary to inform future cleaner-burning cookstove dissemination programs. Data Sources: As part of an international workshop in May 2011, an expert group characterized the state of the science and developed recommendations for exposure assessment of household air pollution. Synthesis: The following priority research areas were identified to explain variability and reduce uncertainty of household air pollution exposure measurements: improved characterization of spatial and temporal variability for studies examining both short- and long-term health effects; development and validation of measurement technology and approaches to conduct complex exposure assessments in resource-limited settings with a large range of pollutant concentrations; and development and validation of biomarkers for estimating dose. Addressing these priority research areas, which will inherently require an increased allocation of resources for cookstove research, will lead to better characterization of exposure–response relationships. Conclusions: Although the type and extent of exposure assessment will necessarily depend on the goal and design of the cookstove study, without improved understanding of exposure–response relationships, the level of air pollution reduction necessary to meet the health targets of cookstove interventions will remain uncertain. Citation: Clark ML, Peel JL, Balakrishnan K, Breysse PN, Chillrud SN, Naeher LP, Rodes CE, Vette AF, Balbus JM. 2013. Health and household air pollution from solid fuel use: the need for improved exposure assessment. Environ Health Perspect 121:1120–1128; http://dx.doi.org/10.1289/ehp.1206429

The design and field implementation of the Detroit Exposure and Aerosol Research Study

The US Environmental Protection Agency recently conducted the Detroit Exposure and Aerosol Research Study (DEARS). The study began in 2004 and involved community, residential, and personal-based measurements of air pollutants targeting 120 participants and their residences. The primary goal of the study was to evaluate and describe the relationship between air toxics, particulate matter (PM), PM constituents, and PM from specific sources measured at a central site monitor with those from the residential and personal locations. The impact of regional, local (point and mobile), and personal sources on pollutant concentrations and the role of physical and human factors that might influence these concentrations were investigated. A combination of active and passive sampling methodologies were employed in the collection of PM mass, criteria gases, semivolatile organics, and volatile organic compound air pollutants among others. Monitoring was conducted in six selected neighborhoods along with one community site using a repeated measure design. Households from each of the selected communities were monitored for 5 consecutive days in the winter and again in the summer. Household, participant and a variety of other surveys were utilized to better understand human and household factors that might affect the impact of ambient-based pollution sources upon personal and residential locations. A randomized recruitment strategy was successful in enrolling nearly 140 participants over the course of the study. Over 36,000 daily-based environmental data points or records were ultimately collected. This paper fully describes the design of the DEARS and the approach used to implement this field monitoring study and reports select preliminary findings.

The relationships between personal PM exposures for elderly populations and indoor and outdoor concentrations for three retirement center scenarios

Personal exposures, indoor and outdoor concentrations, and questionnaire data were collected in three retirement center settings, supporting broader particulate matter (PM)-health studies of elderly populations. The studies varied geographically and temporally, with populations studied in Baltimore, MD in the summer of 1998, and Fresno, CA in the winter and spring of 1999. The sequential nature of the studies and the relatively rapid review of the mass concentration data after each segment provided the opportunity to modify the experimental designs, including the information collected from activity diary and baseline questionnaires and influencing factors (e.g., heating, ventilation, and air-conditioning (HVAC) system operation, door and window openings, air exchange rate) measurements. This paper highlights both PM2.5 and PM10 personal exposure data and interrelationships across the three retirement center settings, and identifies the most probable influencing factors. The current limited availability of questionnaire results, and chemical speciation data beyond mass concentration for these studies, provided only limited capability to estimate personal exposures from models and apportion the personal exposure collections to their sources. The mean personal PM2.5 exposures for the elderly in three retirement centers were found to be consistently higher than the paired apartment concentrations by 50% to 68%, even though different facility types and geographic locations were represented. Mean personal-to-outdoor ratios were found to 0.70, 0.82, and 1.10, and appeared to be influenced by the time doors and windows were open and aggressive particle removal by the HVAC systems. Essentially identical computed mean PM2.5 personal clouds of 3 μg/m3 were determined for two of the studies. The proposed significant contributing factors to these personal clouds were resuspended particles from carpeting, collection of body dander and clothing fibers, personal proximity to open doors and windows, and elevated PM levels in nonapartment indoor microenvironments.

Exploration of the Rapid Effects of Personal Fine Particulate Matter Exposure on Arterial Hemodynamics and Vascular Function during the Same Day

Background Levels of fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM2.5)] are associated with alterations in arterial hemodynamics and vascular function. However, the characteristics of the same-day exposure–response relationships remain unclear. Objectives We aimed to explore the effects of personal PM2.5 exposures within the preceding 24 hr on blood pressure (BP), heart rate (HR), brachial artery diameter (BAD), endothelial function [flow-mediated dilatation (FMD)], and nitroglycerin-mediated dilatation (NMD). Methods Fifty-one nonsmoking subjects had up to 5 consecutive days of 24-hr personal PM2.5 monitoring and daily cardiovascular (CV) measurements during summer and/or winter periods. The associations between integrated hour-long total personal PM2.5 exposure (TPE) levels (continuous nephelometry among compliant subjects with low secondhand tobacco smoke exposures; n = 30) with the CV outcomes were assessed over a 24-hr period by linear mixed models. Results We observed the strongest associations (and smallest estimation errors) between HR and TPE recorded 1–10 hr before CV measurements. The associations were not pronounced for the other time lags (11–24 hr). The associations between TPE and FMD or BAD did not show as clear a temporal pattern. However, we found some suggestion of a negative association with FMD and a positive association with BAD related to TPE just before measurement (0–2 hr). Conclusions Brief elevations in ambient TPE levels encountered during routine daily activity were associated with small increases in HR and trends toward conduit arterial vasodilatation and endothelial dysfunction within a few hours of exposure. These responses could reflect acute PM2.5-induced autonomic imbalance and may factor in the associated rapid increase in CV risk among susceptible individuals.

Air Quality Modeling in Support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)

A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. A hybrid air quality modeling approach was used to estimate exposure to traffic-related air pollutants in support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) conducted in Detroit (Michigan, USA). Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and Research LINE-source dispersion model for near-surface releases (RLINE) dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the Community Multi-scale Air Quality (CMAQ) and the Space-Time Ordinary Kriging (STOK) models. To capture the near-road pollutant gradients, refined “mini-grids” of model receptors were placed around participant homes. Exposure metrics for CO, NOx, PM2.5 and its components (elemental and organic carbon) were predicted at each home location for multiple time periods including daily and rush hours. The exposure metrics were evaluated for their ability to characterize the spatial and temporal variations of multiple ambient air pollutants compared to measurements across the study area.

Can Personal Exposures to Higher Nighttime and Early Morning Temperatures Increase Blood Pressure

J Clin Hypertens (Greenwich). 2011;13:881–888. ©2011 Wiley Periodicals, Inc.

Separating the air quality impact of a major highway and nearby sources by nonparametric trajectory analysis.

Nonparametric Trajectory Analysis (NTA), a receptor-oriented model, was used to assess the impact of local sources of air pollution at monitoring sites located adjacent to highway I-15 in Las Vegas, NV. Measurements of black carbon, carbon monoxide, nitrogen oxides, and sulfur dioxide concentrations were collected from December 2008 to December 2009. The purpose of the study was to determine the impact of the highway at three downwind monitoring stations using an upwind station to measure background concentrations. NTA was used to precisely determine the contribution of the highway to the average concentrations measured at the monitoring stations accounting for the spatially heterogeneous contributions of other local urban sources. NTA uses short time average concentrations, 5 min in this case, and constructed local back-trajectories from similarly short time average wind speed and direction to locate and quantify contributions from local source regions. Averaged over an entire year, the decrease of concentrations with distance from the highway was found to be consistent with previous studies. For this study, the NTA model is shown to be a reliable approach to quantify the impact of the highway on local air quality in an urban area with other local sources.

Modeling Spatial and Temporal Variability of Residential Air Exchange Rates for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)

Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. The residential air exchange rate (AER), which is the rate of exchange of indoor air with outdoor air, is an important determinant for house-to-house (spatial) and temporal variations of air pollution infiltration. Our goal was to evaluate and apply mechanistic models to predict AERs for 213 homes in the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS), a cohort study of traffic-related air pollution exposures and respiratory effects in asthmatic children living near major roads in Detroit, Michigan. We used a previously developed model (LBL), which predicts AER from meteorology and questionnaire data on building characteristics related to air leakage, and an extended version of this model (LBLX) that includes natural ventilation from open windows. As a critical and novel aspect of our AER modeling approach, we performed a cross validation, which included both parameter estimation (i.e., model calibration) and model evaluation, based on daily AER measurements from a subset of 24 study homes on five consecutive days during two seasons. The measured AER varied between 0.09 and 3.48 h−1 with a median of 0.64 h−1. For the individual model-predicted and measured AER, the median absolute difference was 29% (0.19 h‑1) for both the LBL and LBLX models. The LBL and LBLX models predicted 59% and 61% of the variance in the AER, respectively. Daily AER predictions for all 213 homes during the three year study (2010–2012) showed considerable house-to-house variations from building leakage differences, and temporal variations from outdoor temperature and wind speed fluctuations. Using this novel approach, NEXUS will be one of the first epidemiology studies to apply calibrated and home-specific AER models, and to include the spatial and temporal variations of AER for over 200 individual homes across multiple years into an exposure assessment in support of improving risk estimates.

Applicability of the Environmental Relative Moldiness Index for Quantification of Residential Mold Contamination in an Air Pollution Health Effects Study

The Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) investigated the impact of exposure to traffic-related air pollution on the respiratory health of asthmatic children in Detroit, Michigan. Since indoor mold exposure may also contribute to asthma, floor dust samples were collected in participants homes (n = 112) to assess mold contamination using the Environmental Relative Moldiness Index (ERMI). The repeatability of the ERMI over time, as well as ERMI differences between rooms and dust collection methods, was evaluated for insights into the application of the ERMI metric. ERMI values for the standard settled floor dust samples had a mean ± standard deviation of 14.5 ± 7.9, indicating high levels of mold contamination. ERMI values for samples collected from the same home 1 to 7 months apart (n = 52) were consistent and without systematic bias. ERMI values for separate bedroom and living room samples were highly correlated (r = 0.69, n = 66). Vacuum bag dust ERMI values were lower than for floor dust but correlated (r = 0.58, n = 28). These results support the use of the ERMI to evaluate residential mold exposure as a confounder in air pollution health effects studies.

The Importance of Exposure in Addressing Current and Emerging Air Quality Issues

The air quality issues that we face today and will face in the future are becoming increasingly more complex and require an improved understanding of human exposure to be effectively addressed. The objectives of this paper are (1) to discuss how concepts of human exposure and exposure science and should be applied to improve air quality management practices, and (2) to show how air quality modeling tools can be used to improve exposures estimates used for under- standing associations between air quality and human health. Data from a large human exposure monitoring study is presented to demonstrate the value of exposure in understanding important air quality issues, such as health effects associated with exposure to components of particulate matter (PM), to PM of different size fractions (coarse and ultrafine), and to air pollution in near roadway environments. Various approaches for improving estimates of exposure via application of air quality modeling are discussed and results from example modeling applications are pre- sented. These air quality modeling approaches include: the integration of regional scale eulerian air quality models with local scale gaussian dispersion models; the fusion of modeled estimates with air quality observations; the integration of air quality and human exposure modeling tools; and the use of exposure factors, such as housing ventilation, to adjust modeled estimates of ambient air quality.