Julian D. Marshall

Improving health through policies that promote active travel: A review of evidence to support integrated health impact assessment

BACKGROUND Substantial policy changes to control obesity, limit chronic disease, and reduce air pollution emissions, including greenhouse gasses, have been recommended. Transportation and planning policies that promote active travel by walking and cycling can contribute to these goals, potentially yielding further co-benefits. Little is known, however, about the interconnections among effects of policies considered, including potential unintended consequences. OBJECTIVES AND METHODS We review available literature regarding health impacts from policies that encourage active travel in the context of developing health impact assessment (HIA) models to help decision-makers propose better solutions for healthy environments. We identify important components of HIA models of modal shifts in active travel in response to transport policies and interventions. RESULTS AND DISCUSSION Policies that increase active travel are likely to generate large individual health benefits through increases in physical activity for active travelers. Smaller, but population-wide benefits could accrue through reductions in air and noise pollution. Depending on conditions of policy implementations, risk tradeoffs are possible for some individuals who shift to active travel and consequently increase inhalation of air pollutants and exposure to traffic injuries. Well-designed policies may enhance health benefits through indirect outcomes such as improved social capital and diet, but these synergies are not sufficiently well understood to allow quantification at this time. CONCLUSION Evaluating impacts of active travel policies is highly complex; however, many associations can be quantified. Identifying health-maximizing policies and conditions requires integrated HIAs.

Addressing Global Mortality from Ambient PM2.5

Ambient fine particulate matter (PM2.5) has a large and well-documented global burden of disease. Our analysis uses high-resolution (10 km, global-coverage) concentration data and cause-specific integrated exposure-response (IER) functions developed for the Global Burden of Disease 2010 to assess how regional and global improvements in ambient air quality could reduce attributable mortality from PM2.5. Overall, an aggressive global program of PM2.5 mitigation in line with WHO interim guidelines could avoid 750 000 (23%) of the 3.2 million deaths per year currently (ca. 2010) attributable to ambient PM2.5. Modest improvements in PM2.5 in relatively clean regions (North America, Europe) would result in surprisingly large avoided mortality, owing to demographic factors and the nonlinear concentration-response relationship that describes the risk of particulate matter in relation to several important causes of death. In contrast, major improvements in air quality would be required to substantially reduce mortality from PM2.5 in more polluted regions, such as China and India. Moreover, forecasted demographic and epidemiological transitions in India and China imply that to keep PM2.5-attributable mortality rates (deaths per 100 000 people per year) constant, average PM2.5 levels would need to decline by ∼20-30% over the next 15 years merely to offset increases in PM2.5-attributable mortality from aging populations. An effective program to deliver clean air to the worlds most polluted regions could avoid several hundred thousand premature deaths each year.

Healthy neighborhoods: walkability and air pollution.

Background The built environment may influence health in part through the promotion of physical activity and exposure to pollution. To date, no studies have explored interactions between neighborhood walkability and air pollution exposure. Methods We estimated concentrations of nitric oxide (NO), a marker for direct vehicle emissions), and ozone (O3) and a neighborhood walkability score, for 49,702 (89% of total) postal codes in Vancouver, British Columbia, Canada. NO concentrations were estimated from a land-use regression model, O3 was estimated from ambient monitoring data; walkability was calculated based on geographic attributes such as land-use mix, street connectivity, and residential density. Results All three attributes exhibit an urban–rural gradient, with high walkability and NO concentrations, and low O3 concentrations, near the city center. Lower-income areas tend to have higher NO concentrations and walkability and lower O3 concentrations. Higher-income areas tend to have lower pollution (NO and O3). “Sweet-spot” neighborhoods (low pollution, high walkability) are generally located near but not at the city center and are almost exclusively higher income. Policy implications Increased concentration of activities in urban settings yields both health costs and benefits. Our research identifies neighborhoods that do especially well (and especially poorly) for walkability and air pollution exposure. Work is needed to ensure that the poor do not bear an undue burden of urban air pollution and that neighborhoods designed for walking, bicycling, or mass transit do not adversely affect resident’s exposure to air pollution. Analyses presented here could be replicated in other cities and tracked over time to better understand interactions among neighborhood walkability, air pollution exposure, and income level.

Electric Vehicles in China: Emissions and Health Impacts

E-bikes in China are the single largest adoption of alternative fuel vehicles in history, with more than 100 million e-bikes purchased in the past decade and vehicle ownership about 2× larger for e-bikes as for conventional cars; e-car sales, too, are rapidly growing. We compare emissions (CO(2), PM(2.5), NO(X), HC) and environmental health impacts (primary PM(2.5)) from the use of conventional vehicles (CVs) and electric vehicles (EVs) in 34 major cities in China. CO(2) emissions (g km(-1)) vary and are an order of magnitude greater for e-cars (135-274) and CVs (150-180) than for e-bikes (14-27). PM(2.5) emission factors generally are lower for CVs (gasoline or diesel) than comparable EVs. However, intake fraction is often greater for CVs than for EVs because combustion emissions are generally closer to population centers for CVs (tailpipe emissions) than for EVs (power plant emissions). For most cities, the net result is that primary PM(2.5) environmental health impacts per passenger-km are greater for e-cars than for gasoline cars (3.6× on average), lower than for diesel cars (2.5× on average), and equal to diesel buses. In contrast, e-bikes yield lower environmental health impacts per passenger-km than the three CVs investigated: gasoline cars (2×), diesel cars (10×), and diesel buses (5×). Our findings highlight the importance of considering exposures, and especially the proximity of emissions to people, when evaluating environmental health impacts for EVs.

The impact of daily mobility on exposure to traffic-related air pollution and health effect estimates

Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79–0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ∼16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63–0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0–2 h spent at least 10 km away, 0.66 for ≥10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates.

Intake fraction of primary pollutants: motor vehicle emissions in the South Coast Air Basin

The intake fraction is defined for a specific species and emission source as the ratio of attributable population intake to total emissions. Focusing on Californias South Coast Air Basin (SoCAB) as a case study, we combine ambient monitoring data with time-activity patterns to estimate the population intake of carbon monoxide and benzene emitted from motor vehicles during 1996-1999. In addition to exposures to ambient concentrations, three microenvironments are considered in which the exposure concentration of motor vehicle emissions is higher than in ambient air: in and near vehicles, inside a building that is near a freeway, and inside a residence with an attached garage. Incorporating data on motor vehicle emissions estimated by the EMFAC2000 model, we estimate that the 15 million people in the SoCAB inhale 0.003-0.009% (34 85 per million, with a best estimate of 47 per million) of primary, nonreactive compounds emitted into the basin by motor vehicles. This population intake of primary motor vehicle emissions is approximately 50% higher than the average ambient concentration times the average breathing rate, owing to higher concentrations in the three microenviromments and also to the temporal and spatial correlation among breathing rates, concentrations, and population densities. The approach demonstrated here can inform policy decisions requiring a metric of population exposure to airborne pollutants

National Patterns in Environmental Injustice and Inequality: Outdoor NO2 Air Pollution in the United States

We describe spatial patterns in environmental injustice and inequality for residential outdoor nitrogen dioxide (NO2) concentrations in the contiguous United States. Our approach employs Census demographic data and a recently published high-resolution dataset of outdoor NO2 concentrations. Nationally, population-weighted mean NO2 concentrations are 4.6 ppb (38%, p<0.01) higher for nonwhites than for whites. The environmental health implications of that concentration disparity are compelling. For example, we estimate that reducing nonwhites’ NO2 concentrations to levels experienced by whites would reduce Ischemic Heart Disease (IHD) mortality by ∼7,000 deaths per year, which is equivalent to 16 million people increasing their physical activity level from inactive (0 hours/week of physical activity) to sufficiently active (>2.5 hours/week of physical activity). Inequality for NO2 concentration is greater than inequality for income (Atkinson Index: 0.11 versus 0.08). Low-income nonwhite young children and elderly people are disproportionately exposed to residential outdoor NO2. Our findings establish a national context for previous work that has documented air pollution environmental injustice and inequality within individual US metropolitan areas and regions. Results given here can aid policy-makers in identifying locations with high environmental injustice and inequality. For example, states with both high injustice and high inequality (top quintile) for outdoor residential NO2 include New York, Michigan, and Wisconsin.

Life cycle air quality impacts of conventional and alternative light-duty transportation in the United States

Significance Our assessment of the life cycle air quality impacts on human health of 10 alternatives to conventional gasoline vehicles finds that electric vehicles (EVs) powered by electricity from natural gas or wind, water, or solar power are best for improving air quality, whereas vehicles powered by corn ethanol and EVs powered by coal are the worst. This work advances the current debate over the environmental impacts of conventional versus alternative transportation options by combining detailed spatially and temporally explicit emissions inventories with state-of-the-science air quality impact analysis using advanced chemical transport modeling. Our results reinforce previous findings that air quality-related health damages from transportation are generally comparable to or larger than climate change-related damages. Commonly considered strategies for reducing the environmental impact of light-duty transportation include using alternative fuels and improving vehicle fuel economy. We evaluate the air quality-related human health impacts of 10 such options, including the use of liquid biofuels, diesel, and compressed natural gas (CNG) in internal combustion engines; the use of electricity from a range of conventional and renewable sources to power electric vehicles (EVs); and the use of hybrid EV technology. Our approach combines spatially, temporally, and chemically detailed life cycle emission inventories; comprehensive, fine-scale state-of-the-science chemical transport modeling; and exposure, concentration–response, and economic health impact modeling for ozone (O3) and fine particulate matter (PM2.5). We find that powering vehicles with corn ethanol or with coal-based or “grid average” electricity increases monetized environmental health impacts by 80% or more relative to using conventional gasoline. Conversely, EVs powered by low-emitting electricity from natural gas, wind, water, or solar power reduce environmental health impacts by 50% or more. Consideration of potential climate change impacts alongside the human health outcomes described here further reinforces the environmental preferability of EVs powered by low-emitting electricity relative to gasoline vehicles.

Long-Term Ozone Exposure and Mortality in a Large Prospective Study

RATIONALE Tropospheric ozone (O3) is potentially associated with cardiovascular disease risk and premature death. Results from long-term epidemiological studies on O3 are scarce and inconclusive. OBJECTIVES In this study, we examined associations between chronic ambient O3 exposure and all-cause and cause-specific mortality in a large cohort of U.S. adults. METHODS Cancer Prevention Study II participants were enrolled in 1982. A total of 669,046 participants were analyzed, among whom 237,201 deaths occurred through 2004. We obtained estimates of O3 concentrations at the participants residence from a hierarchical Bayesian space-time model. Estimates of fine particulate matter (particulate matter with an aerodynamic diameter of up to 2.5 μm [PM2.5]) and NO2 concentrations were obtained from land use regression. Cox proportional hazards regression models were used to examine mortality associations adjusted for individual- and ecological-level covariates. MEASUREMENTS AND MAIN RESULTS In single-pollutant models, we observed significant positive associations between O3, PM2.5, and NO2 concentrations and all-cause and cause-specific mortality. In two-pollutant models adjusted for PM2.5, significant positive associations remained between O3 and all-cause (hazard ratio [HR] per 10 ppb, 1.02; 95% confidence interval [CI], 1.01-1.04), circulatory (HR, 1.03; 95% CI, 1.01-1.05), and respiratory mortality (HR, 1.12; 95% CI, 1.08-1.16) that were unchanged with further adjustment for NO2. We also observed positive mortality associations with both PM2.5 (both near source and regional) and NO2 in multipollutant models. CONCLUSIONS Findings derived from this large-scale prospective study suggest that long-term ambient O3 contributes to risk of respiratory and circulatory mortality. Substantial health and environmental benefits may be achieved by implementing further measures aimed at controlling O3 concentrations.

National Satellite-Based Land-Use Regression: NO2 in the United States

Land-use regression models (LUR) estimate outdoor air pollution at high spatial resolution. Previous LURs have generally focused on individual cities. Here, we present an LUR for year-2006 ground-level NO(2) concentrations throughout the contiguous United States. Our approach employs ground- and satellite-based NO(2) measurements, and geographic characteristics such as population density, land-use (based on satellite data), and distance to major and minor roads. The results provide reliable estimates of ambient NO(2) air pollution as measured by the U.S. EPA (R(2) = 0.78; bias = 22%) at a spatial resolution (∼ 30 m) that is capable of capturing within-urban and near-roadway gradients in NO(2). We explore several aspects of temporal (time-of-day; day-of-week; season) and spatial (urban versus rural; U.S. region) variability in the model. Results are robust to spatial autocorrelation, to selection of an alternative input data set, and to minor perturbations in input data (using 90% of the data to predict the remaining 10%). The modeled population-weighted (unweighted) mean outdoor concentration in the United States is 10.7 (4.8) ppb. Our approach could be implemented in other areas of the world given sufficient road network and pollutant monitoring data. To facilitate future use and evaluation of the results, concentration estimates for the ∼ 8 million U.S. Census blocks in the contiguous United States are publicly available via the Supporting Information.