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Abstract: Using the data on all live births (∼400,000) and criteria pollutants from the Chicago Metropolitan Statistical Area (MSA) between 2000 and 2004, this paper empirically demonstrates how mismatches in the spatiotemporal scales of health and air pollution data can result in inconsistency and uncertainty in the linkages between air pollution and birth outcomes. This paper suggests that the risks of low birth weight associated with air pollution exposure changes significantly as the distance interval (around the monitoring stations) used for exposure estimation changes. For example, when the analysis was restricted within 3 miles distance of the monitoring stations the odds of LBW (births <2500 g) increased by a factor of 1.045 (±0.0285 95% CI) with a unit increase in the average daily exposure to PM10 (in μg m−3) during the gestation period; the value dropped to 1.028 when the analysis was restricted within 6 miles distance of air pollution monitoring stations. The effect of PM10 exposure on LBW became null when controlled for confounders. But PM2.5 exposure showed a significant association with low birth weight when controlled for confounders. These results must be interpreted with caution, because the distance to monitoring station does not influence the risks of adverse birth outcomes, but uncertainty in exposure increases with the increase in distance from the monitoring stations, especially for coarse particles such as PM10 that settle with gravity within short distance and time interval. The results of this paper have important implications for the research design of environmental epidemiological studies, and the way air pollution (and potentially other environmental) and health data are collocated to compute exposure. While this paper challenges the findings of pervious epidemiological studies that have relied on coarse resolution air pollution data (such as county level aggregated data), the paper also calls for time-space resolved estimate of air pollution to minimize uncertainty in exposure estimation. [Copyright &y& Elsevier]

It has been well documented that air pollution adversely affects health, and epidemiological pollution-health studies utilise pollution data from automatic monitors. However, these automatic monitors are small in number and hence spatially sparse, which does not allow an accurate representation of the spatial variation in pollution concentrations required for these epidemiological health studies. Nitrogen dioxide (NO 2 ) diffusion tubes are also used to measure concentrations, and due to their lower cost compared to automatic monitors are much more prevalent. However, even combining both data sets still does not provide sufficient spatial coverage of NO 2 for epidemiological studies, and modelled concentrations on a regular grid from atmospheric dispersion models are also available. This paper proposes the first modelling approach to using all three sources of NO 2 data to make fine scale spatial predictions for use in epidemiological health studies. We propose a geostatistical fusion model that regresses combined NO 2 concentrations from both automatic monitors and diffusion tubes against modelled NO 2 concentrations from an atmospheric dispersion model in order to predict fine scale NO 2 concentrations across our West Central Scotland study region. Our model exhibits a 47% improvement in fine scale spatial prediction of NO 2 compared to using the automatic monitors alone, and we use it to predict NO 2 concentrations across West Central Scotland in 2006. [ABSTRACT FROM AUTHOR]

Abstract: Because people spend the majority of their time indoors, the variable efficiency with which ambient PM2.5 penetrates and persists indoors is a source of error in epidemiologic studies that use PM2.5 concentrations measured at central-site monitors as surrogates for ambient PM2.5 exposure. To reduce this error, practical methods to model indoor concentrations of ambient PM2.5 are needed. Toward this goal, we evaluated and refined an outdoor-to-indoor transport model using measured indoor and outdoor PM2.5 species concentrations and air exchange rates from the Relationships of Indoor, Outdoor, and Personal Air Study. Herein, we present model evaluation results, discuss what data are most critical to prediction of residential exposures at the individual-subject and populations levels, and make recommendations for the application of the model in epidemiologic studies. This paper demonstrates that not accounting for certain human activities (air conditioning and heating use, opening windows) leads to bias in predicted residential PM2.5 exposures at the individual-subject level, but not the population level. The analyses presented also provide quantitative evidence that shifts in the gas-particle partitioning of ambient organics with outdoor-to-indoor transport contribute significantly to variability in indoor ambient organic carbon concentrations and suggest that methods to account for these shifts will further improve the accuracy of outdoor-to-indoor transport models. [Copyright &y& Elsevier]

Abstract: Exposure to roadway emissions is an emerging area of research because of recent epidemiological studies reporting association between living within a few hundred meters of high-traffic roadways and adverse health effects. The air quality impact of roadway emissions has been studied in a number of field experiments, most of which have not fully considered the impact of wind direction on near-road concentrations. This paper examines the role of wind direction by using a dispersion model to analyze data from three field studies that include measurements under varying wind directions: 1) a tracer study conducted adjacent to highway 99 in Sacramento, CA in 1981–82, 2) a field study next to a highway in Raleigh, North Carolina in 2006, and 3) a field study conducted next to a depressed highway in Las Vegas, Nevada in 2010. We find that wind direction is an important variable in characterizing exposure to roadway emissions. Under stable conditions, the near-surface concentrations at receptors up to 100 m from the road increase with wind angle before dropping off at angles close to parallel to the road. It is only for pollutants with short life times does the maximum concentration occur when the wind direction is normal to the road. We also show that current dispersion models are reliable tools for interpreting observations and for formulating plans for field studies. [Copyright &y& Elsevier]

Abstract: Many cohort studies in environmental epidemiology require accurate modeling and prediction of fine scale spatial variation in ambient air quality across the U.S. This modeling requires the use of small spatial scale geographic or “land use” regression covariates and some degree of spatial smoothing. Furthermore, the details of the prediction of air quality by land use regression and the spatial variation in ambient air quality not explained by this regression should be allowed to vary across the continent due to the large scale heterogeneity in topography, climate, and sources of air pollution. This paper introduces a regionalized national universal kriging model for annual average fine particulate matter (PM2.5) monitoring data across the U.S. To take full advantage of an extensive database of land use covariates we chose to use the method of Partial Least Squares, rather than variable selection, for the regression component of the model (the “universal” in “universal kriging”) with regression coefficients and residual variogram models allowed to vary across three regions defined as West Coast, Mountain West, and East. We demonstrate a very high level of cross-validated accuracy of prediction with an overall R 2 of 0.88 and well-calibrated predictive intervals. In accord with the spatially varying characteristics of PM2.5 on a national scale and differing kriging smoothness parameters, the accuracy of the prediction varies by region with predictive intervals being notably wider in the West Coast and Mountain West in contrast to the East. [Copyright &y& Elsevier]

Abstract: This paper presents a Bayesian hierarchical spatiotemporal method of interpolation, termed as Markov Cube Kriging (MCK). The classical Kriging methods become computationally prohibitive, especially for large datasets due to the O(n3) matrix decomposition. MCK offers novel and computationally efficient solutions to address spatiotemporal misalignment, mismatch in the spatiotemporal scales and missing values across space and time in large spatiotemporal datasets. MCK is flexible in that it allows for non-separable spatiotemporal structure and nonstationary covariance at the hierarchical spatiotemporal scales. Employing MCK we developed estimates of daily concentration of fine particulates matter ≤2.5 μm in aerodynamic diameter (PM2.5) at 2.5 km spatial grid for the Cleveland Metropolitan Statistical Area, 2000 to 2009. Our validation and cross-validation suggest that MCK achieved robust prediction of spatiotemporal random effects and underlying hierarchical and nonstationary spatiotemporal structure in air pollution data. MCK has important implications for environmental epidemiology and environmental sciences for exposure quantification and collocation of data from different sources, available at different spatiotemporal scales. [Copyright &y& Elsevier]

Abstract: The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates long-term effects of exposure to air pollution on human health in Europe. This paper documents the spatial variation of measured NO2 and NO x concentrations between and within 36 ESCAPE study areas across Europe. In all study areas NO2 and NO x were measured using standardized methods between October 2008 and April 2011. On average, 41 sites were selected per study area, including regional and urban background as well as street sites. The measurements were conducted in three different seasons, using Ogawa badges. Average concentrations for each site were calculated after adjustment for temporal variation using data obtained from a routine monitor background site. Substantial spatial variability was found in NO2 and NO x concentrations between and within study areas; 40% of the overall NO2 variance was attributable to the variability between study areas and 60% to variability within study areas. The corresponding values for NO x were 30% and 70%. The within-area spatial variability was mostly determined by differences between street and urban background concentrations. The street/urban background concentration ratio for NO2 varied between 1.09 and 3.16 across areas. The highest median concentrations were observed in Southern Europe, the lowest in Northern Europe. In conclusion, we found significant contrasts in annual average NO2 and NO x concentrations between and especially within 36 study areas across Europe. Epidemiological long-term studies should therefore consider different approaches for better characterization of the intra-urban contrasts, either by increasing of the number of monitors or by modelling. [Copyright &y& Elsevier]

Abstract: Recent epidemiological and toxicological studies have suggested that short-term elevations of ambient fine particle mass concentrations (aerodynamic diameter <2.5μm, PM2.5) can increase cardiac and pulmonary health risks. Thus, examining temporal variations of chemical changes in ambient PM2.5 that could pose the greatest health risks and identifying its sources is critical so that the most toxic categories can be controlled. In this study we collected detailed air quality data in Steubenville, Ohio in August 2006 with the ultimate goal to evaluate associations between cardiovascular (CV) parameters measured in exposed laboratory animals and the chemical and elemental composition of PM2.5. Current approaches using radiotelemetry to measure CV parameters in conscious laboratory animals are capable of collecting continuous recordings. To provide a robust and analogous dataset that can be better matched with CV responses, we have incorporated a highly time-resolved sampling method to characterize trace elements and thereby obtain more robust input data to determine potential emission sources. We applied positive matrix factorization (PMF) to trace element concentrations from 30-minute ambient PM2.5 samples in Steubenville, Ohio, an area designated as a non-attainment area for the PM2.5 National Ambient Air Quality Standards by the Environmental Protection Agency. The average ambient PM2.5 filter-based mass concentration during the 8-hour summer exposure study period was 26±11μgm−3. Results from PMF indicated that six major factors contributed to the ambient PM2.5 mass during this time: coal combustion/secondary (39±46%), mobile sources (12±14%), metal coating/processing (10±11%), iron and steel manufacturing (5±5%), Pb factor (5±8%), and incineration/smelting (1±3%). The objectives of this paper are (1) to present chemical composition of ambient PM2.5 and its potential emission sources in Steubenville; and (2) to evaluate the PMF modeling results using observed meteorological data. These semi-continuous sampling approaches to determine potential emission sources have significant advantages over similar analyses using samples averaged over 8–24h, and are being utilized by our group to determine associations of PM with acute CV responses from animal inhalation toxicology field studies. [Copyright &y& Elsevier]

Abstract: The development of geographical information system techniques has opened up a wide array of methods for air pollution exposure assessment. The extent to which these provide reliable estimates of air pollution concentrations is nevertheless not clearly established. Nor is it clear which methods or metrics should be preferred in epidemiological studies. This paper compares the performance of ten different methods and metrics in terms of their ability to predict mean annual PM10 concentrations across 52 monitoring sites in London, UK. Metrics analysed include indicators (distance to nearest road, traffic volume on nearest road, heavy duty vehicle (HDV) volume on nearest road, road density within 150 m, traffic volume within 150 m and HDV volume within 150 m) and four modelling approaches: based on the nearest monitoring site, kriging, dispersion modelling and land use regression (LUR). Measures were computed in a GIS, and resulting metrics calibrated and validated against monitoring data using a form of grouped jack-knife analysis. The results show that PM10 concentrations across London show little spatial variation. As a consequence, most methods can predict the average without serious bias. Few of the approaches, however, show good correlations with monitored PM10 concentrations, and most predict no better than a simple classification based on site type. Only land use regression reaches acceptable levels of correlation (R 2 = 0.47), though this can be improved by also including information on site type. This might therefore be taken as a recommended approach in many studies, though care is needed in developing meaningful land use regression models, and like any method they need to be validated against local data before their application as part of epidemiological studies. [Copyright &y& Elsevier]

Abstract: Numerous epidemiological studies have shown health effects related to short- and long-term exposure to elevated levels of ambient particulate matter (PM). It is not clear however which specific characteristics (e.g., size, components) or sources of PM are responsible for the observed effects. The aim of RAPTES (Risk of Airborne Particles: a Toxicological–Epidemiological hybrid Study) was to investigate which specific physical, chemical or oxidative characteristics of ambient PM are associated with adverse effects of PM on health. This was done by performing experimental exposure of human volunteers to air pollution at several real-world settings that had high contrast and low correlation between several PM characteristics. For this goal, eight sites in the Netherlands that differed in local PM emission sources were chosen for extensive air pollution characterization. Measurement sites included an underground train station, three different road traffic sites, an animal farm, a sea harbor, a site located in the vicinity of steelworks, and an urban background site. Five- to six-hours average concentration measurements at each site were made between June 2007 and October 2009. We measured PM10, PM2.5, particle number concentration (PNC), oxidative potential of PM, absorbance, endotoxin content, as well as elemental and chemical composition of PM, and gaseous pollutants concentrations. This paper presents a detailed characterization of particulate air pollution at the sampling sites. We found significant differences in all PM characteristics between the sites. The underground train station, compared to each outdoor location, had substantially higher concentrations of nearly all PM characteristics. The average PM10 and PM2.5 mass concentrations at the underground train station were 394 μg m−3 and 137 μg m−3, respectively, which was 14.1 and 7.6 times higher than the urban background. The sum of the concentrations of trace metals in fine and coarse PM was nearly 20 times above the outdoor levels. Elemental carbon (EC) was elevated at the underground site in the fine but also in the coarse mode, in contrast to the traffic sites where EC was predominantly found in fine PM. The highest concentrations and contrasts in PNC were at the traffic sites (between 45,000 and 80,000 particles cm−3), which was several times higher than measured at any other site. Correlations of PNC with metals, PM10, PM2.5 and absorbance were low to moderate, while correlations between PM10, PM2.5 and the metals Cu and Fe were high. After excluding the underground train station data, correlations between PM10, EC and metals decreased whereas the correlation between PNC and EC increased. We conclude that we were able to successfully identify and characterize real-world situations with very different particle characteristics. High contrast and low correlations between PM characteristics, as well as consistency of these differences across sampling campaigns, provide a good basis for identifying health relevant PM characteristics in the upcoming analysis. [Copyright &y& Elsevier]

Abstract: Epidemiologic evidence indicates a relationship between outdoor particle exposure and adverse health effects, while most people spend 85–90% of their time indoors, thus understanding the relationship between indoor and outdoor particles is quite important. This paper aims to provide an up-to-date revision for both experiment and modeling on relationship between indoor and outdoor particles. The use of three different parameters: indoor/outdoor (I/O) ratio, infiltration factor and penetration factor, to assess the relationship between indoor and outdoor particles were reviewed. The experimental data of the three parameters measured both in real houses and laboratories were summarized and analyzed. The I/O ratios vary considerably due to the difference in size-dependent indoor particle emission rates, the geometry of the cracks in building envelopes, and the air exchange rates. Thus, it is difficult to draw uniform conclusions as detailed information, which make I/O ratio hardly helpful for understanding the indoor/outdoor relationship. Infiltration factor represents the equilibrium fraction of ambient particles that penetrates indoors and remains suspended, which avoids the mixture with indoor particle sources. Penetration factor is the most relevant parameter for the particle penetration mechanism through cracks and leaks in the building envelope. We investigate the methods used in previously published studies to both measure and model the infiltration and penetration factors. We also discuss the application of the penetration factor models and provide recommendations for improvement. [ABSTRACT FROM AUTHOR]

Abstract: Semivolatile organic compounds (SVOCs) are a major class of indoor pollutants. Understanding SVOC partitioning between the gas phase and settled dust is important for characterizing the fate of these species indoors and the pathways by which humans are exposed to them. Such knowledge also helps in crafting measurement programs for epidemiological studies designed to probe potential associations between exposure to these compounds and adverse health effects. In this paper, we analyze published data from nineteen studies that cumulatively report measurements of dustborne and airborne SVOCs in more than a thousand buildings, mostly residences, in seven countries. In aggregate, measured median data are reported in these studies for 66 different SVOCs whose octanol-air partition coefficients (K oa) span more than five orders of magnitude. We use these data to test a simple equilibrium model for estimating the partitioning of an SVOC between the gas phase and settled dust indoors. The results demonstrate, in central tendency, that a compound’s octanol-air partition coefficient is a strong predictor of its abundance in settled dust relative to its gas phase concentration. Using median measured results for each SVOC in each study, dustborne mass fractions predicted using K oa and gas-phase concentrations correlate reasonably well with measured dustborne mass fractions (R 2 =0.76). Combined with theoretical understanding of SVOC partitioning kinetics, the empirical evidence also suggests that for SVOCs with high K oa values, the mass fraction in settled dust may not have sufficient time to equilibrate with the gas phase concentration. [ABSTRACT FROM AUTHOR]

Abstract: Valuation of health effects of air pollution is becoming a critical component of the performance of cost–benefit analysis of pollution control measures, which provides a basis for setting priorities for action. Beijing has focused on control of transport emission as vehicular emissions have recently become an important source of air pollution, particularly during Olympic games and Post-games. In this paper, we conducted an estimation of health effects and economic cost caused by road transport-related air pollution using an integrated assessment approach which utilizes air quality model, engineering, epidemiology, and economics. The results show that the total economic cost of health impacts due to air pollution contributed from transport in Beijing during 2004–2008 was 272, 297, 310, 323, 298 million US$ (mean value), respectively. The economic costs of road transport accounted for 0.52, 0.57, 0.60, 0.62, and 0.58% of annual Beijing GDP from 2004 to 2008. Average cost per vehicle and per ton of PM10 emission from road transport can also be estimated as 106 US $/number and 3584 US $ t−1, respectively. These findings illustrate that the impact of road transport contributed particulate air pollution on human health could be substantial in Beijing, whether in physical and economic terms. Therefore, some control measures to reduce transport emissions could lead to considerable economic benefit. [Copyright &y& Elsevier]