Aaron van Donkelaar

University of Michigan, Ann Arbor, MI, United States

Are you Aaron van Donkelaar?

Claim your profile

Publications (81)310.52 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Epidemiologic and health impact studies are inhibited by the paucity of global, long-term measurements of the chemical composition of fine particulate matter. We inferred PM2.5 chemical composition at 0.1o x 0.1o spatial resolution for 2004-2008 by combining aerosol optical depth retrieved from the MODIS and MISR satellite instruments, with coincident profile and composition information from the GEOS-Chem global chemical transport model. Evaluation of the satellite-model PM2.5 composition dataset with North American in situ measurements indicated significant spatial agreement for secondary inorganic aerosol, particulate organic mass, black carbon, mineral dust and sea salt. We found that global population-weighted PM2.5 concentrations were dominated by particulate organic mass (11.9 ± 7.3 g/m3), secondary inorganic aerosol (11.1 ± 5.0 g/m3), and mineral dust (11.1 ± 7.9 g/m3). Secondary inorganic PM2.5 concentrations exceeded 30 g/m3 over East China. Sensitivity simulations suggested that population-weighted ambient PM2.5 from biofuel burning (11 g/m3) could be almost as large as from fossil fuel combustion sources (17 g/m3). These estimates offer information about global population exposure to the chemical components and sources of PM2.5.
    Environmental science & technology. 10/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: More than a decade of satellite observations offers global information about the trend and magnitude of human exposure to fine particulate matter (PM2.5).
    Environmental Health Perspectives 10/2014; · 7.26 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: For many regions around the world ground-based observations of fine particulate matter (PM2.5) have insufficient spatial coverage to assess long-term health impacts. Although satellites offer a promising avenue to enhance spatial coverage, there are limitations and outstanding questions about the accuracy and precision with which ground-level aerosol mass concentrations can be inferred from satellite remote sensing. We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates in health effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) is an emerging global federation of ground-level monitoring stations that provide hourly PM2.5 estimates in highly populated regions. Each station is collocated with an existing ground-based sun photometer to measure aerosol optical depth (AOD). SPARTAN filters are analyzed for total PM2.5 mass, black carbon, water-soluble ions and metals. A three-city pilot study has shown good agreement between SPARTAN air filters and the nephelometer. The network has now expanded to stations spread over four continents. Participating groups include those in Bangladesh, Brazil, Canada, China, India, Indonesia, Israel, Philippines, Nigeria, Vietnam, and the United States. This presentation will describe our recent aerosol and chemical speciation results and the implications for global PM2.5 concentrations.
    AAAR, Orlando, FL; 10/2014
  • [Show abstract] [Hide abstract]
    ABSTRACT: Ambient fine particulate matter (PM2.5) is a leading environmental risk factor for premature mortality. We use aerosol optical depth (AOD) retrieved from two satellite instruments, MISR and SeaWiFS, to produce a unified 15-year global time series (1998 - 2012) of ground-level PM2.5 concentration at a resolution of 1˚ x 1˚. The GEOS-Chem chemical transport model (CTM) is used to relate each individual AOD retrieval to ground-level PM2.5. Four broad areas showing significant, spatially coherent, annual trends are examined in detail: Eastern U.S. (-0.39 ± 0.10 µg m-3 yr-1), the Arabian Peninsula (0.81 ± 0.21 µg m-3 yr-1), South Asia (0.93 ±0.22 µg m-3 yr-1) and East Asia (0.79 ± 0.27 µg m-3 yr-1). Over the period of dense in situ observation (1999 - 2012), the linear tendency for the Eastern U.S. (-0.37 ± 0.13 µg m-3 yr-1) agrees well with that from in situ measurements (-0.38 ± 0.06 µg m-3 yr-1). A GEOS-Chem simulation reveals that secondary inorganic aerosols largely explain the observed PM2.5 trend over the Eastern U.S., South Asia, and East Asia, while mineral dust largely explains the observed trend over the Arabian Peninsula.
    Environmental science & technology. 09/2014;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Few studies have examined the relationship between long-term exposure to ambient fine particulate matter (PM2.5) and non-accidental mortality in rural populations. To examine the relationship between PM2.5 and non-accidental and cardiovascular mortality in the US Agricultural Health Study cohort. The cohort (n=83,378) included farmers, their spouses, and commercial pesticide applicators primarily residing in Iowa and North Carolina. Deaths occurring between enrollment (1993-1997) and December 30, 2009 were identified by record linkage. Six-year average (2001-2006) remote-sensing derived estimates of PM2.5 were assigned to participants' residences at enrollment and Cox proportional hazards models were used to estimate hazard ratios (HR) in relation to a 10 µg/m(3) increase in PM2.5 adjusted for individual-level covariates. In total, 5931 non-accidental and 1967 cardiovascular deaths occurred over a median follow-up time of 13.9 years. PM2.5 was not associated with non-accidental mortality in the cohort as a whole (HR: 0.95, 95% CI: 0.76, 1.20) but consistent inverse relationships were observed among women. Positive associations were observed between ambient PM2.5 and cardiovascular mortality among men, and these associations were strongest among men who did not move from their enrollment address (HR=1.63, 95% 0.94, 2.84). In particular, cardiovascular mortality risk in men was significantly increased when analyses were limited to non-moving participants with the most precise exposure geocoding (HR=1.87, 95% CI: 1.04, 3.36). Rural PM2.5 may be associated with cardiovascular mortality in men; however, similar associations were not observed among women. Further evaluation is required to explore these gender differences.
    Environmental Health Perspectives 03/2014; · 7.26 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Inhaling fine particles (PM2.5) can induce oxidative stress and inflammation, and may contribute to onset of preterm labor and other adverse perinatal outcomes. We examined whether outdoor PM2.5 was associated with adverse birth outcomes among 22 countries in the World Health Organization Global Survey on Maternal and Perinatal Health from 2004-2008. Long-term average (2001-2006) estimates of outdoor PM2.5 were assigned to 50 kilometer radius circular buffers around each health clinic where births occurred. We used generalized estimating equations to determine associations between clinic-level PM2.5 levels and preterm birth and low birth weight at the individual level, adjusting for seasonality and potential confounders at individual, clinic, and country levels. Country-specific associations were also investigated. Across all countries, adjusting for seasonality, PM2.5 was not associated with preterm birth, but was associated with low birth weight [odds ratio [OR] = 1.22; 95% CI: 1.07, 1.39 for fourth quartile of PM2.5 (> 20.2 µg/m(3)) compared with the first quartile (< 6.3 µg/m(3))]. In China, the country with the largest PM2.5 range, preterm birth and low birth weight both were associated with the highest quartile of PM2.5 only, which suggests a possible threshold effect (OR = 2.54; CI: 1.42, 4.55 and OR = 1.99; CI: 1.06, 3.72 for preterm birth and low birth weight, respectively, for PM2.5 ≥ 36.5 µg/m(3) compared with PM2.5 < 12.5 µg/m(3)). Outdoor PM2.5 concentrations were associated with low birth weight but not preterm birth. In rapidly developing countries, such as China, the highest levels of air pollution may be of concern for both outcomes.
    Environmental Health Perspectives 02/2014; · 7.26 Impact Factor
  • Source
    G. C. M. Vinken, K. F. Boersma, A. van Donkelaar, L. Zhang
    [Show abstract] [Hide abstract]
    ABSTRACT: We present a top-down ship NOx emission inventory for the Baltic Sea, the North Sea, the Bay of Biscay and the Mediterranean Sea based on satellite-observed tropospheric NO2 columns of the Ozone Monitoring Instrument (OMI) for 2005-2006. We improved the representation of ship emissions in the GEOS-Chem chemistry transport model, and compared simulated NO2 columns to consistent satellite observations. Relative differences between simulated and observed NO2 columns have been used to constrain ship emissions in four European seas (the Baltic Sea, the North Sea, the Bay of Biscay and the Mediterranean Sea) using a mass-balance approach, and accounting for non-linear sensitivities to changing emissions in both model and satellite retrieval. These constraints are applied to 39 % of total top-down European ship NOx emissions, which amount to 0.96 Tg N for 2005, and 1.0 Tg N for 2006 (11-15% lower than the bottom-up EMEP ship emission inventory). Our results indicate that EMEP emissions in the Mediterranean Sea are too high (by 60%) and misplaced by up to 150 km, which can have important consequences for local air quality simulations. In the North Sea ship track, our top-down emissions amount to 0.05 Tg N for 2005 (35% lower than EMEP). Increased top-down emissions were found for the Baltic Sea and the Bay of Biscay ship tracks, with totals in these tracks of 0.05 Tg N (131% higher than EMEP) and 0.08 Tg N for 2005 (128% higher than EMEP), respectively. Our study explicitly accounts for the (non-linear) sensitivity of satellite retrievals to changes in the a priori NO2 profiles, as satellite observations are never fully independent of model information (i.e. assumptions on vertical NO2 profiles). Our study provides for the first time a space-based, top-down ship NOx emission inventory, and can serve as a framework for future studies to constrain ship emissions using satellite NO2 observations in other seas.
    Atmospheric Chemistry and Physics 01/2014; 14(3). · 4.88 Impact Factor
  • Source
    Jintai Lin, Aaron van Donkelaar, Jinyuan Xin, Huizheng Che, Yuesi Wang
    [Show abstract] [Hide abstract]
    ABSTRACT: Horizontal visibility measured at ground meteorological stations provides an under-exploited source of information for studying the interdecadal variation of aerosols and their climatic impacts. Here we propose to use a 3-hourly visibility dataset to infer aerosol optical depth (AOD) over East China, using the nested GEOS-Chem chemical transport model to interpret the spatiotemporally varying relations between columnar and near-surface aerosols. Our analysis is focused in 2006 under cloud-free conditions. We evaluate the visibility-inferred AOD using MODIS/Terra and MODIS/Aqua AOD datasets, after validating MODIS data against three ground AOD measurement networks (AERONET, CARSNET and CSHNET). We find that the two MODIS datasets agree with ground-based AOD measurements, with negative mean biases of 0.05–0.08 and Reduced Major Axis regression slopes around unity. Visibility-inferred AOD roughly capture the general spatiotemporal patterns of the two MODIS datasets with negligible mean differences. The inferred AOD reproduce the seasonal variability (correlation exceeds 0.9) and the slight AOD growth from the late morning to early afternoon shown in the MODIS datasets, suggesting the validity of our AOD inference method. Future research will extend the visibility-based AOD inference to study the long-term variability of AOD.
    Atmospheric Environment 01/2014; 95:258–267. · 3.11 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Satellite remote sensing (RS) has emerged as a cutting edge approach for estimating ground level ambient air pollution. Previous studies have reported a high correlation between ground level PM2.5 and NO2 estimated by RS and measurements collected at regulatory monitoring sites. The current study examined associations between air pollution and adverse respiratory and allergic health outcomes using multi-year averages of NO2 and PM2.5 from RS and from regulatory monitoring.
    Atmospheric Environment 12/2013; · 3.11 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Laboratory studies suggest that exposure to fine particulate matter (2.5μm in diameter or less; PM2.5) can trigger a combination of pathophysiological responses that may induce the development of hypertension. However, epidemiological evidence relating PM2.5 and hypertension is sparse. We thus conducted a population-based cohort study to determine whether exposure to ambient PM2.5 is associated with incident hypertension. We assembled a cohort of 35,303 non-hypertensive adults from Ontario, Canada who responded to one of four population-based health surveys between 1996 and 2005 and were followed-up until December 31, 2010. Incident diagnoses of hypertension were ascertained from the Ontario Hypertension Database, a validated registry of persons diagnosed with hypertension in Ontario (sensitivity=72%, specificity=95%). Estimates of long-term exposure to PM2.5 at participants' postal-code residences were derived from satellite observations. We used Cox proportional hazards models, adjusting for various individual and contextual risk factors including body mass index, smoking, physical activity, and neighbourhood-level unemployment rates. We conducted various sensitivity analyses to assess the robustness of the effect estimate, such as investigating several time windows of exposure and controlling for potential changes in the risk of hypertension over time. Between 1996 and 2010, we identified 8,649 incident cases of hypertension and 2,296 deaths. For every 10µg/m(3) increase of PPM2.5, the adjusted hazard ratio (HR10) of incident hypertension was 1.13 (95% confidence interval (CI): 1.05-1.22). Estimated associations were comparable among all sensitivity analyses. This study supports an association between PM2.5 and incident hypertension.
    Circulation 11/2013; · 15.20 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Land use regression (LUR) models typically investigate within-urban variability in air pollution. Recent improvements in data quality and availability, including satellite-derived pollutant measurements, support fine-scale LUR modelling for larger areas. Here, we describe NO2 and PM10 LUR models for Western Europe (years: 2005-2007) based on >1500 EuroAirnet monitoring sites covering background, industrial, and traffic environments. Predictor variables include land use characteristics, population density, and length of major and minor roads in zones from 0.1km to 10km, altitude, and distance to sea. We explore models with and without satellite-based NO2 and PM2.5 as predictor variables and we compare two available land cover datasets (global; European). Model performance (adjusted R2) is 0.48-0.58 for NO2 and 0.22-0.50 for PM10. Inclusion of satellite data improved model performance (adjusted R2) by, on average, 0.05 for NO2 and 0.11 for PM10. Models were applied on a 100m grid across Western Europe; to support future research, these datasets are publicly available.
    Environmental Science & Technology 10/2013; · 5.48 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Land use regression (LUR) models are widely employed in health studies to characterize chronic exposure to air pollution. The LUR is essentially an interpolation technique that employs the pollutant of interest as the dependent variable with proximate land use, traffic, and physical environmental variables used as independent predictors. Two major limitations with this method have not been addressed: (1) variable selection in the model building process, and (2) dealing with unbalanced repeated measures. In this paper, we address these issues with a modeling framework that implements the deletion/substitution/addition (DSA) machine learning algorithm that uses a generalized linear model to average over unbalanced temporal observations. Models were derived for fine particulate matter with aerodynamic diameter of 2.5 microns or less (PM2.5) and nitrogen dioxide (NO2) using monthly observations. We used 4119 observations at 108 sites and 15,301 observations at 138 sites for PM2.5 and NO2, respectively. We derived models with good predictive capacity (cross-validated-R2 values were 0.65 and 0.71 for PM2.5 and NO2, respectively). By addressing these two shortcomings in current approaches to LUR modeling, we have developed a framework that minimizes arbitrary decisions during the model selection process. We have also demonstrated how to integrate temporally unbalanced data in a theoretically sound manner. These developments could have widespread applicability for future LUR modeling efforts.
    Atmospheric Environment 10/2013; 77:172–177. · 3.11 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: OBJECTIVE Recent studies suggest that chronic exposure to air pollution can promote the development of diabetes. However, whether this relationship actually translates into an increased risk of mortality attributable to diabetes is uncertain.RESEARCH DESIGN AND METHODS We evaluated the association between long-term exposure to ambient fine particulate matter (PM2.5) and diabetes-related mortality in a prospective cohort analysis of 2.1 million adults from the 1991 Canadian census mortality follow-up study. Mortality information, including ∼5,200 deaths coded as diabetes being the underlying cause, was ascertained by linkage to the Canadian Mortality Database from 1991 to 2001. Subject-level estimates of long-term exposure to PM2.5 were derived from satellite observations. The hazard ratios (HRs) for diabetes-related mortality were related to PM2.5 and adjusted for individual-level and contextual variables using Cox proportional hazards survival models.RESULTSMean PM2.5 exposure levels for the entire population were low (8.7 µg/m(3); SD, 3.9 µg/m(3); interquartile range, 6.2 µg/m(3)). In fully adjusted models, a 10-µg/m(3) elevation in PM2.5 exposure was associated with an increase in risk for diabetes-related mortality (HR, 1.49; 95% CI, 1.37-1.62). The monotonic change in risk to the population persisted to PM2.5 concentration <5 µg/m(3).CONCLUSIONS Long-term exposure to PM2.5, even at low levels, is related to an increased risk of mortality attributable to diabetes. These findings have considerable public health importance given the billions of people exposed to air pollution and the worldwide growing epidemic of diabetes.
    Diabetes care 06/2013; · 7.74 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: [1] We develop an optimal estimation (OE) algorithm based on top-of-atmosphere reflectances observed by the MODIS satellite instrument to retrieve near-surface fine particulate matter (PM2.5). The GEOS-Chem chemical transport model is used to provide prior information for the Aerosol Optical Depth (AOD) retrieval and to relate total column AOD to PM2.5. We adjust the shape of the GEOS-Chem relative vertical extinction profiles by comparison with lidar retrievals from the CALIOP satellite instrument. Surface reflectance relationships used in the OE algorithm are indexed by land type. Error quantities needed for this OE algorithm are inferred by comparison with AOD observations taken by a worldwide network of sun photometers (AERONET) and extended globally based upon aerosol speciation and cross correlation for simulated values, and upon land type for observational values. Significant agreement in PM2.5 is found over North America for 2005 (slope = 0.89; r = 0.82; 1-σ error = 1 µg/m3 + 27%), with improved coverage and correlation relative to previous work for the same region and time period, although certain subregions, such as the San Joaquin Valley of California are better represented by previous estimates. Independently derived error estimates of the OE PM2.5 values at in situ locations over North America (of ±(2.5 µg/m3 + 31%) and Europe of ±(3.5 µg/m3 + 30%) are corroborated by comparison with in situ observations, although globally (error estimates of ±(3.0 µg/m3 + 35%), may be underestimated. Global population-weighted PM2.5 at 50% relative humidity is estimated as 27.8 µg/m3 at 0.1° × 0.1° resolution.
    Journal of Geophysical Research: Atmospheres. 06/2013; 118(11).
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created an model to predict ambient particulate matter less than 2.5 microns in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 dataset included 104,172 monthly observations at 1,464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R2 values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R2 were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.
    Environmental Science & Technology 05/2013; · 5.48 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: BACKGROUND: Laboratory studies suggest that fine particulate matter (2.5µm in diameter or less; PM2.5) can activate pathophysiological responses which may induce insulin resistance and type-2 diabetes. However, epidemiological evidence relating PM2.5 and diabetes is sparse, particularly for incident diabetes. OBJECTIVES: We conducted a population-based cohort study to determine whether long-term exposure to ambient PM2.5 is associated with incident diabetes. METHODS: We assembled a cohort of 62,012 nondiabetic adults who lived in Ontario, Canada and completed one of five population-based health surveys between 1996 and 2005. Follow-up extended until December 31, 2010. Incident diabetes diagnosed between 1996 and 2010 was ascertained using the Ontario Diabetes Database, a validated registry of persons diagnosed with diabetes (sensitivity=86%, specificity=97%). Six-year average concentrations of PM2.5 at the postal codes of baseline residences were derived from satellite observations. We used Cox proportional hazards models to estimate the associations, adjusting for various individual-level risk factors and contextual covariates such as smoking, body mass index, physical activity, and neighbourhood-level household income. We also conducted multiple sensitivity analyses. In addition, we examined effect modification for selected comorbidities and sociodemographic characteristics. RESULTS: There were 6,310 incident cases of diabetes over 484,644 total person-years of follow up. The adjusted hazard ratio for a 10-µg/m(3) increase in PM2.5 was 1.11 (95% CI: 1.02, 1.21). Estimated associations were comparable among all sensitivity analyses. We did not find strong evidence of effect modification by comorbidities or sociodemographic covariates. CONCLUSIONS: This study suggests that long-term exposure to PM2.5 may contribute to the development of diabetes.
    Environmental Health Perspectives 04/2013; · 7.26 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Ships emit large quantities of nitrogen oxides (NOx = NO + NO2), important precursors for ozone (O3) and particulate matter formation. Ships burn low-grade marine heavy fuel due to the limited regulations that exist for the maritime sector in international waters. Previous studies showed that global ship NOx emission inventories amount to 3.0-10.4 Tg N per year (15-30% of total NOx emissions), with most emissions close to land and affecting air quality in densely populated coastal regions. Bottom-up inventories depend on the extrapolation of a relatively small number of measurements that are often unable to capture annual emission changes and can suffer from large uncertainties. Satellites provide long-term, high-resolution retrievals that can be used to improve emission estimates. In this study we provide top-down constraints on ship NOx emissions in major European ship routes, using observed NO2 columns from the Ozone Monitoring Instrument (OMI) and NO2 columns simulated with the nested (0.5°×0.67°) version of the GEOS-Chem chemistry transport model. We use a plume-in-grid treatment of ship NOx emissions to account for in-plume chemistry in our model. We ensure consistency between the retrievals and model simulations by using the high-resolution GEOS-Chem NO2 profiles as a priori. We find evidence that ship emissions in the Mediterranean Sea are geographically misplaced by up to 150 km and biased high by a factor of 4 as compared to the most recent (EMEP) ship emission inventory. Better agreement is found over the shipping lane between Spain and the English Channel. We extend our approach and also provide constraints for major ship routes in the Red Sea and Indian Ocean. Using the full benefit of the long-term retrieval record of OMI, we present a new Eurasian ship emission inventory for the years 2005 to 2010, based on the EMEP and AMVER-ICOADS inventories, and top-down constraints from the satellite retrievals. Our work shows that satellite retrievals can improve the characterization of emission locations, magnitudes and trends over sparsely monitored areas such as seas or oceans.
    04/2013;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time. We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. We estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specific deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2-7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5-7·0]), and alcohol use (5·5% [5·0-5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8-9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6-8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4-6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2-10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily affect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient deficiencies, fell in rank between 1990 and 2010, with unimproved water and sanitation accounting for 0·9% (0·4-1·6) of global DALYs in 2010. However, in most of sub-Saharan Africa childhood underweight, HAP, and non-exclusive and discontinued breastfeeding were the leading risks in 2010, while HAP was the leading risk in south Asia. The leading risk factor in Eastern Europe, most of Latin America, and southern sub-Saharan Africa in 2010 was alcohol use; in most of Asia, North Africa and Middle East, and central Europe it was high blood pressure. Despite declines, tobacco smoking including second-hand smoke remained the leading risk in high-income north America and western Europe. High body-mass index has increased globally and it is the leading risk in Australasia and southern Latin America, and also ranks high in other high-income regions, North Africa and Middle East, and Oceania. Worldwide, the contribution of different risk factors to disease burden has changed substantially, with a shift away from risks for communicable diseases in children towards those for non-communicable diseases in adults. These changes are related to the ageing population, decreased mortality among children younger than 5 years, changes in cause-of-death composition, and changes in risk factor exposures. New evidence has led to changes in the magnitude of key risks including unimproved water and sanitation, vitamin A and zinc deficiencies, and ambient particulate matter pollution. The extent to which the epidemiological shift has occurred and what the leading risks currently are varies greatly across regions. In much of sub-Saharan Africa, the leading risks are still those associated with poverty and those that affect children. Bill & Melinda Gates Foundation.
    The Lancet 12/2012; 380(9859):2224-60. · 39.21 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background: To better understand adverse health effects from chronic exposure to fine particulate matter (PM2.5) a need exists to derive accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but there are relatively few studies that compare remote-sensing estimates to those derived from monitor-based data. Objective: The purpose of this paper is to evaluate and compare the predictive capabilities of remote sensing and geostatistical interpolation. Methods: We develop a space-time geostatistical kriging model to predict PM2.5 over the continental United States and compare resulting predictions to estimates derived from satellite retrievals. Results: Within about 100 km of a monitoring station, the kriging estimate was more accurate, while the remote sensing estimate was more accurate for locations >100 km from a monitoring station. Based on this finding we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates. Conclusions: This study is part of a larger investigation aimed at improving the assessment of exposure to ambient air pollution for chronic health effects studies. We evaluated the estimation capability of monitor-based interpolation to monitor-free remote sensing and found that for most of the populated areas of the continental United States, geostatistical interpolation supplied more accurate estimates than remote sensing. The differences between the estimates from the two methods, however, were relatively small. We conclude that in areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.
    Environmental Health Perspectives 10/2012; · 7.26 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: We improve the accuracy of daily ground-level fine particulate matter concentrations (PM(2.5)) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport model (GEOS-Chem) calculations of the relationship between AOD and PM(2.5). This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM(2.5), and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-σ mean uncertainties are reduced across the United States and southern Canada from ± (1 μg/m(3) + 67%) to ± (1 μg/m(3) + 54%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM(2.5) estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-σ uncertainty to ±(1 μg/m(3) + 42%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM(2.5) as compared to day-specific relationships.
    Environmental Science & Technology 09/2012; · 5.48 Impact Factor

Publication Stats

2k Citations
310.52 Total Impact Points

Institutions

  • 2013
    • University of Michigan
      • Division of Cardiovascular Medicine
      Ann Arbor, MI, United States
  • 2006–2013
    • Dalhousie University
      • Department of Physics and Atmospheric Science
      Halifax, Nova Scotia, Canada
    • Harvard-Smithsonian Center for Astrophysics
      • Division of Atomic and Molecular Physics
      Cambridge, Massachusetts, United States
  • 2012
    • Health Canada
      • Environmental Health, Science and Research Bureau
      Ottawa, Ontario, Canada