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To address limitations of observational epidemiology studies of air pollution and health effects, including residual confounding by temporal and spatial factors, several studies have taken advantage of ‘natural experiments’, where an environmental policy or air quality intervention has resulted in reductions in ambient air pollution concentrations. Researchers have examined whether the population impacted by these air quality improvements, also experienced improvements in various health indices (e.g. reduced morbidity/mortality). In this paper, I review key accountability studies done previously and new studies done over the past several years in Beijing, Atlanta, London, Ireland, and other locations, describing study design and analysis strengths and limitations of each. As new ‘natural experiment’ opportunities arise, several lessons learned from these studies should be applied when planning a new accountability study. Comparison of health outcomes during the intervention to both before and after the intervention in the population of interest, as well as use of a control population to assess whether any temporal changes in the population of interest were also seen in populations not impacted by air quality improvements, should aid in minimizing residual confounding by these long term time trends. Use of either detailed health records for a population, or prospectively collected data on relevant mechanistic biomarkers coupled with such morbidity/mortality data may provide a more thorough assessment of if the intervention beneficially impacted the health of the community, and if so by what mechanism(s). Further, prospective measurement of a large suite of air pollutants may allow a more thorough understanding of what pollutant source(s) is/are responsible for any health benefit observed. The importance of using multiple statistical analysis methods in each paper and the difference in how the timing of the air pollution/outcome association may impact which of these design features is most important is also discussed. Based on these and other lessons learned, researchers may provide a more epidemiologically rigorous evaluation of cause-specific health impacts of an air quality intervention or action. [ABSTRACT FROM AUTHOR]

The European Study of Cohorts for Air Pollution Effects (ESCAPE) is a13-nation study of long-term health effects of air pollution based on subjects pooled from up to 22 cohorts that were intended for other purposes. Twenty-five papers have been published on associations of various health endpoints with long-term exposures to NOx, NO2, traffic indicators, PM10, PM2.5 and PM constituents including absorbance (elemental carbon). Seven additional ESCAPE papers found moderate correlations (R2 = 0.3–0.8) between measured air quality and estimates based on land-use regression that were used; personal exposures were not considered. I found no project summaries or comparisons across papers; here I conflate the 25 ESCAPE findings in the context of other recent European epidemiology studies. Because one ESCAPE cohort contributed about half of the subjects, I consider it and the other 18 cohorts separately to compare their contributions to the combined risk estimates. I emphasize PM2.5 and confirm the published hazard ratio of 1.14 (1.04–1.26) per 10 μg/m3 for all-cause mortality. The ESCAPE papers found 16 statistically significant (p < 0.05) risks among the125 pollutant-endpoint combinations; 4 each for PM2.5 and PM10, 1 for PM absorbance, 5 for NO2, and 2 for traffic. No PM constituent was consistently significant. No significant associations were reported for cardiovascular mortality; low birthrate was significant for all pollutants except PM absorbance. Based on associations with PM2.5, I find large differences between all-cause death estimates and the sum of specific-cause death estimates. Scatterplots of PM2.5 mortality risks by cause show no consistency across the 18 cohorts, ostensibly because of the relatively few subjects. Overall, I find the ESCAPE project inconclusive and I question whether the efforts required to estimate exposures for small cohorts were worthwhile. I suggest that detailed studies of the large cohort using historical exposures and additional cardiovascular risk factors might be productive. [ABSTRACT FROM AUTHOR]

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: 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: 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: 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]