Search

PURPOSE OF REVIEW: Ambient air pollution is strongly linked to cardiovascular and respiratory diseases. We summarize available published evidence regarding similar associations with diabetes across the life course. RECENT FINDINGS: We performed a life-course survey of the recent literature, including prenatal, gestational, childhood/adolescence, and adult exposures to air pollution. Oxidative stress is identified as a key factor in both metabolic dysfunction and the effects of air pollution exposure, especially from fossil fuel combustion products, providing a plausible mechanism for air pollution-diabetes associations. The global burden of diabetes attributed to air pollution exposure is substantial, with a recent estimate that ambient fine particulate matter (PM(2.5)) exposure contributes to more than 200,000 deaths from diabetes annually. SUMMARY: There is a growing body of literature linking air pollution exposure during childhood and adulthood with diabetes etiology and related cardiometabolic biomarkers. A small number of studies found that exposure to air pollution during pregnancy is associated with elevated gestational diabetes risk among mothers. Studies examining prenatal air pollution exposure and diabetes risk among the offspring, as well as potential transgenerational effects of air pollution exposure, are very limited thus far. This review provides insight into how air pollutants affect diabetes and other metabolic dysfunction-related diseases across the different life stages.

Awareness of the human health impacts of exposure to air pollution is growing rapidly. For example, it has become evident that the adverse health effects of air pollution are more pronounced in disadvantaged populations. Policymakers in many jurisdictions have responded to this evidence by enacting initiatives that lead to lower concentrations of air pollutants, such as urban traffic restrictions. In this review, we focus on the interplay between advances in environmental exposure assessment and developments in policy. We highlight recent progress in the granular measurement of air pollutants and individual-level exposures, and how this has enabled focused local policy actions. Finally, we detail an illustrative study designed to link individual-level health-relevant exposures with economic, behavioral, biological, familial, and environmental variables., How can scientists and policymakers work together to reduce the health impacts of air pollution? In this review paper, the authors discuss the interplay between advances in environmental exposure assessment and policy advances to tackle pollution in a focused way.

Exposure to fine particulate matter (PM2.5) and black carbon (BC) have been linked to negative health risks, but exposure among professional taxi drivers is understudied. This pilot study measured drivers' knowledge, attitudes, and beliefs (KAB) about air pollution compared with direct measures of exposures. Roadside and in-vehicle levels of PM2.5 and BC were continuously measured over a single shift on each subject, and exposures compared with central site monitoring. One hundred drivers completed an air pollution KAB questionnaire, and seven taxicabs participated in preliminary in-cab air sampling. Taxicab PM2.5 and BC concentrations were elevated compared with nearby central monitoring. Average PM2.5 concentrations per 15-min interval were 4-49 μg/m3. BC levels were also elevated; reaching>10 μg/m3. Fifty-six of the 100 drivers surveyed believed they were more exposed than non-drivers; 81 believed air pollution causes health problems. Air pollution exposures recorded suggest that driver exposures would likely exceed EPA recommendations if experienced for 24 h. Surveys indicated that driver awareness of this was limited. Future studies should focus on reducing exposures and increasing awareness among taxi drivers.

Personal exposures to fine particulate matter air pollution (PM(2.5)), and to its traffic-related fraction, were investigated in a group of urban children with asthma. The relationships of personal and outdoor school-site measurements of PM(2.5) and elemental carbon (EC) were characterized for a total of 40 fifth-grade children. These students, from four South Bronx, NY schools, each carried air pollution monitoring equipment with them for 24 h per day for similar to 1 month. Daily EC concentrations were estimated using locally calibrated reflectance of the PM(2.5) samples. Personal EC concentration was more closely related to outdoor school-site EC (median subject-specific: r=0.64) than was personal PM(2.5) to school-site PM(2.5) concentration (median subject-specific: r=0.33). Regression models also showed a stronger, more robust association of school site with personal measurements for EC than those for PM(2.5). High traffic pollution exposure was found to coincide with the weekday early morning rush hour, with higher personal exposures for participants living closer to a highway (

Growing evidence that ultrafine particles in ambient air can cause brain lesions in animals led us to investigate whether particulate components of air pollution may be associated with brain cancer risk in humans. Air pollution has been associated with respiratory disorders and cardiovascular morbidity and mortality, but associations between air pollutants and brain cancer have not been investigated in adults.The analyses included 1,284 deaths due to brain cancer from the Cancer Prevention Study-II, an ongoing prospective mortality study of adults in the United States and Puerto Rico conducted by the American Cancer Society. Air pollution data from national databases for metropolitan areas were combined with residential history and vital status data to estimate exposure to particulate and gaseous air pollution.We found no elevated risk for estimated measures of air pollutants, an unanticipated reduction in risk was found between gaseous air pollutants and brain cancer mortality.The findings do not provide evidence of increased risk of brain cancer mortality due to air pollutants.

Air pollution epidemiologic research has often utilized ambient air concentrations measured from centrally located monitors as a surrogate measure of exposure to these pollutants. Associations between these ambient concentrations and health outcomes such as lung function, hospital admissions, and mortality have been examined in short- and long-term cohort studies as well as in time-series and case-crossover studies. The issues related to interpreting the observed associations of ambient air pollutants with health outcomes were discussed at the US EPA sponsored workshop on December 13 and 14, 2006 in Chapel Hill, North Carolina, USA. The second session of this workshop focused on the following topics: (1) statistical methodology and study designs that may improve understanding of multipollutant health effects; (2) ambient concentrations as surrogate measures of pollutant mixtures; and (3) source-focused epidemiologic research. New methodology and approaches to better distinguish the effects of individual pollutants include multicity hierarchical modeling and the use of case-crossover analysis to control for copollutants. An alternative approach is to examine the mixture as a whole using principal component analysis. Another important consideration is to what extent the observed health associations are attributable to individual pollutants, which are often from common sources and are correlated, versus the pollutant mixtures that the pollutants are representing. For example, several ambient air concentrations, such as particulate matter mass, nitrogen dioxide, and carbon monoxide, may be serving as surrogate measures of motor vehicle exhaust. Source apportionment analysis is one method that may allow further advancement in understanding the source components that contribute to multipollutant health effects.

Assessing the long-term benefits of marginal improvements in air quality from regulatory intervention is methodologically challenging. In this study, we explore how the relative risks (RRs) of mortality from air pollution exposure change over time and whether patterns in the RRs can be attributed to air quality improvements. We employed two-stage multilevel Cox models to describe the association between air pollution and mortality for 51 cities with data from the American Cancer Society (ACS) cohort (N = 264,299, deaths = 69,819). New pollution data were computed through models that predict yearly average fine particle (PM2.5) concentrations throughout the follow-up (1982–2000). Average PM2.5 concentrations from 1999 to 2000 and sulfate concentrations from 1980 were also examined. We estimated the RRs of mortality associated with air pollution separately for five time periods (1982–1986, 1987–1990, 1991–1994, 1995–1998, and 1999–2000). Mobility models were implemented with a sub-sample of 100,557 subjects to assist with interpreting the RR estimates. Sulfate RRs exhibit a large decline from the 1980s to the 1990s. In contrast, PM2.5 RRs follow the opposite pattern, with larger RRs later in the 1990s. The reduction in sulfate RR may have resulted from air quality improvements that occurred through the 1980s and 1990s in response to the acid rain control program. PM2.5 concentrations also declined in many places, but toxic mobile sources are now the largest contributors to PM in urban areas. This may account for the heightened RR of mortality associated with PM2.5 in the 1990s. The paper concludes with a three alternative explanations for the temporal pattern of RRs, each emphasizing the uncertainty in ascribing health benefits to air quality improvements.

Source apportionment may be useful in epidemiological investigation of PM health effects, but variations and options in these methods leave uncertainties. An EPA-sponsored workshop investigated source apportionment and health effects analyses by examining the associations between daily mortality and the investigators' estimated source-apportioned PM(2.5) for Washington, DC for 1988-1997. A Poisson Generalized Linear Model (GLM) was used to estimate source-specific relative risks at lags 0-4 days for total non-accidental, cardiovascular, and cardiorespiratory mortality adjusting for weather, seasonal/temporal trends, and day-of-week. Source-related effect estimates and their lagged association patterns were similar across investigators/methods. The varying lag structure of associations across source types, combined with the Wednesday/Saturday sampling frequency made it difficult to compare the source-specific effect sizes in a simple manner. The largest (and most significant) percent excess deaths per 5-95(th) percentile increment of apportioned PM(2.5) for total mortality was for secondary sulfate (variance-weighted mean percent excess mortality=6.7% (95% CI: 1.7, 11.7)), but with a peculiar lag structure (lag 3 day). Primary coal-related PM(2.5) (only three teams) was similarly significantly associated with total mortality with the same 3-day lag as sulfate. Risk estimates for traffic-related PM(2.5), while significant in some cases, were more variable. Soil-related PM showed smaller effect size estimates, but they were more consistently positive at multiple lags. The cardiovascular and cardiorespiratory mortality associations were generally similar to those for total mortality. Alternative weather models generally gave similar patterns, but sometimes affected the lag structure (e.g., for sulfate). Overall, the variations in relative risks across investigators/methods were found to be much smaller than those across estimated source types or across lag days for these data. This consistency suggests the robustness of the source apportionment in health effects analyses, but remaining issues, including accuracy of source apportionment and source-specific sensitivity to weather models, need to be investigated.

As part of an EPA-sponsored workshop to investigate the use of source apportionment in health effects analyses, the associations between the participant's estimated source contributions of PM(2.5) for Phoenix, AZ for the period from 1995-1997 and cardiovascular and total nonaccidental mortality were analyzed using Poisson generalized linear models (GLM). The base model controlled for extreme temperatures, relative humidity, day of week, and time trends using natural spline smoothers. The same mortality model was applied to all of the apportionment results to provide a consistent comparison across source components and investigators/methods. Of the apportioned anthropogenic PM(2.5) source categories, secondary sulfate, traffic, and copper smelter-derived particles were most consistently associated with cardiovascular mortality. The sources with the largest cardiovascular mortality effect size were secondary sulfate (median estimate=16.0% per 5th-to-95th percentile increment at lag 0 day among eight investigators/methods) and traffic (median estimate=13.2% per 5th-to-95th percentile increment at lag 1 day among nine investigators/methods). For total mortality, the associations were weaker. Sea salt was also found to be associated with both total and cardiovascular mortality, but at 5 days lag. Fine particle soil and biomass burning factors were not associated with increased risks. Variations in the maximum effect lag varied by source category suggesting that past analyses considering only single lags of PM(2.5) may have underestimated health impact contributions at different lags. Further research is needed on the possibility that different PM(2.5) source components may have different effect lag structure. There was considerable consistency in the health effects results across source apportionments in their effect estimates and their lag structures. Variations in results across investigators/methods were small compared to the variations across source categories. These results indicate reproducibility of source apportionment results across investigative groups and support applicability of these methods to effects studies. However, future research will also need to investigate a number of other important issues including accuracy of results.

During the past three decades, receptor models have been used to identify and apportion ambient concentrations to sources. A number of groups are employing these methods to provide input into air quality management planning. A workshop has explored the use of resolved source contributions in health effects models. Multiple groups have analyzed particulate composition data sets from Washington, DC and Phoenix, AZ. Similar source profiles were extracted from these data sets by the investigators using different factor analysis methods. There was good agreement among the major resolved source types. Crustal (soil), sulfate, oil, and salt were the sources that were most unambiguously identified (generally highest correlation across the sites). Traffic and vegetative burning showed considerable variability among the results with variability in the ability of the methods to partition the motor vehicle contributions between gasoline and diesel vehicles. However, if the total motor vehicle contributions are estimated, good correspondence was obtained among the results. The source impacts were especially similar across various analyses for the larger mass contributors (e.g., in Washington, secondary sulfate SE=7% and 11% for traffic; in Phoenix, secondary sulfate SE=17% and 7% for traffic). Especially important for time-series health effects assessment, the source-specific impacts were found to be highly correlated across analysis methods/researchers for the major components (e.g., mean analysis to analysis correlation, r0.9 for traffic and secondary sulfates in Phoenix and for traffic and secondary nitrates in Washington. The sulfate mean r value is0.75 in Washington.). Overall, although these intercomparisons suggest areas where further research is needed (e.g., better division of traffic emissions between diesel and gasoline vehicles), they provide support the contention that PM(2.5) mass source apportionment results are consistent across users and methods, and that today's source apportionment methods are robust enough for application to PM(2.5) health effects assessments.

Many, but not all, observational epidemiological studies of ozone (O(3)) air pollution have yielded significant associations between variations in daily ambient concentrations of this pollutant and a wide range of adverse health outcomes. We evaluate some past epidemiological studies that have assessed the short-term association of O(3) with mortality, and investigate one possible reason for variations in their O(3) effect estimate, i.e., differences in their approaches to the modeling of weather influences on mortality. For all of the total mortality-air pollution time-series studies considered, the combined analysis yielded a relative risk, RR=1.036 per 100-ppb increase in daily 1-h maximum O(3) (95% CI: 1.023-1.050). However, the subset of studies that specified the nonlinear nature of the temperature-mortality association yielded a combined estimate of RR=1.056 per 100 ppb (95% CI: 1.032-1.081). This indicates that past time-series studies using linear temperature-mortality specifications have underpredicted the premature mortality effects of O(3) air pollution. For Detroit, MI, an illustrative analysis of daily total mortality during 1985-1990 also indicated that the model weather specification choice can influence the O(3) health effects estimate. Results were intercompared for alternative weather specifications. Nonlinear specifications of temperature and relative humidity (RH) yielded lower intercorrelations with the O(3) coefficient, and larger O(3) RR estimates, than a base model employing a simple linear spline of hot and cold temperature. We conclude that, unlike for particulate matter (PM) mass, the mortality effect estimates derived by time-series analyses for O(3) can be sensitive to the way that weather is addressed in the model. The same may well also be true for other pollutants with largely temperature-dependent formation mechanisms, such as secondary aerosols. Generally, we find that the O(3)-mortality effect estimate increases in size and statistical significance when the nonlinearity and the humidity interaction of the temperature-health effect association are incorporated into the model weather specification. We recommend that a minimization of the intercorrelations of model coefficients be considered (along with other critical factors such as goodness of fit, autocorrelation, and overdispersion) when specifying such a model, especially when individual coefficients are to be interpreted for risk estimation.

Efforts to tackle climate change have met significant financial and political barriers that have been difficult to overcome. Research now shows that such measures are justified on grounds other than mitigation of climate change.