No description available.

No description available.

PM2.5 variability within the neighborhood scale has not been thoroughly studied for wood burning communities. High variability in near-field PM2.5 concentration may lead to harmful public exposure since monitoring does not occur on that scale. This study measures near-field PM2.5 variability by measuring black carbon (BC), a component of PM2.5, in a 1 km2 area located in Cambria, California. BC and meteorological data (when meteorological instruments were available) were measured over thirteen 12-hour intensive operation periods (IOPs) occurring over the winters of 2009 and 2010. Near-field BC variability was measured to understand the type of exposures found in communities where many homes are burning wood simultaneously within a small area. In addition, relationships between meteorological, geographical, and burning source characteristics and BC were observed as tools for understanding BC concentration. The computer air dispersion modeling programs, ISC-PRIME and ISCST3, were also evaluated for applicability to the near field. BC concentrations were measured using 1- to 2-minute resolution aethalometers and 12 hour resolution Personal Environmental Monitors (PEMs). On average, over all IOPs and sites, aethalometer and PEM BC averages were very similar, ranging between 200 and 250 ng/m3, or 4 and 5 µg/m3 for PM2.5, and standard deviations were often high. Averaging all BC measurements, aethalometer BC standard deviation values were 360 percent of the average BC concentration and PEM BC standard deviations were 120 percent the average BC concentration. The average standard deviation detected during each IOP was 190 percent of the average BC concentration for aethalometers and 79 percent of the average BC concentration for PEMs. The average standard deviation detected at each site was 220 percent of the average BC concentration for aethalometers and 76 percent of the average BC concentration for PEMs. The larger standard deviations measured by higher resolution aethalometers demonstrated that low resolution instruments, such as PEMs, are unable to detect high concentrations that may occur. In addition to examining BC variability, multiple linear regression analyses were conducted to determine the impact of meteorological variables and geographic and burning source characteristics on BC concentration and a weighted BC deviation function (BC standard deviation divided by average BC concentration). Time impacts, humidity, and wind speed, accounted for about 50 percent of variability in aethalometer average BC and BC deviation. However, because all model assumptions were not satisfied, improvements are needed. Regression models based on PEM BC found wind speed and direction to account for about 80 percent of average PEM BC variability and number of burning sources to account for about 30 percent of PEM BC deviation. Although PEM BC models accounted for a high percentage of BC variability, few data points were available for the PEM analyses and more IOPs are needed to determine their accuracy. When evaluating correlations between geographic and burning source characteristics and PEM BC concentrations, specific IOP and PEM sampling location explained almost 70 percent of variability in BC concentration, though model residuals suggested model bias. IOP likely explained variation in burning patterns and meteorology over each night while sampling location was likely a proxy for housing density, tree coverage, and/or elevation. Because all regression model assumptions could not be satisfied, the predictors were also observed graphically. Plotting BC concentration versus the number of burning sources suggested that number of burning sources may affect BC concentration in areas of low tree coverage and high housing density and in the case that the level of surrounding vegetation and structures are minimal. More data points will be needed to determine whether or not these relationships are significant. ISC-PRIME and ISCST3 modeling overall tended to under predict BC concentrations with average modeled-to-measured ratios averaging 0.25 and 0.15, for ISC-PRIME and ISCST3, respectively. Correction factors of 9.75 and 18.2 for ISC-PRIME and ISCST3, respectively, were determined to bring modeled BC concentrations closer to unity, but the range of ratios was still high. Both programs were unable to consistently capture BC variability in the area and more investigation will be needed to improve models. The results of the study indicate high BC variability exists on the near-field scale, but that the variability is not clearly explained by existing regression and air dispersion models. To prevent public exposure to harmful concentrations, more investigation will be needed to determine factors that largely influence pollutant variability on the neighborhood scale.

Increasing levels of air pollution are prompting researchers to develop more reliable air pollution modeling approaches in order to protect the public and the environment from toxic contaminants and airborne pathogens. Although land use regression has long been used to assess exposure to air pollution, researchers are increasingly using machine learning algorithms to quantify the concentration of harmful pollutants—for this study black carbon (BC) and particle number (PN). Additionally, researchers are moving away from using fixed-site data in favor of using mobile monitoring data in a variety of locations to develop hourly empirical models of particulate air pollution. This study uses secondary data describing BC and PN pollutant levels, which are obtained from roads that bikers share in the more rural location of Blacksburg (VA). Machine learning (ML) algorithms are then built to develop accurate and reliable short-term empirical prediction models. Different pre-processing methods for the mobile monitoring data and various input variables are tested to assess how ML can be used effectively in this process. Three types of time-average models are developed (daytime, hourly average, and one second models). Various combinations of spatial and temporal input variables are used in the short-term models. The impact of adding more spatiotemporal variables (e.g., emissions) to machine learning models to improve model performance is assessed in the short-term models. Incorporating spatial and temporal autocorrelation is intended to develop more sophisticated validation approaches for identifying ML performance patterns—the goal of which is to predict concentration levels more accurately in comparison to using raw data without data reprocessing. The results show that the model developed using refined disaggregated data is able to detect the spatial distribution of the pollutant concentration at equivalent levels as the smoothed data models, although the latter display fewer errors. The performance of the short-term model including all variables is equivalent to the model omitting emissions. The ML results are compared to earlier stepwise regression model results, suggesting that ML has the ability to improve both long-term and short-term model accuracy. Our findings indicate that ML demonstrates higher predictive capacity in comparison to stepwise regression. The results from this study may be useful in enhancing the performance of ML through the incorporation of different data preprocessing tasks, as well as showing how different input variables contribute to the ML modeling process. The findings from this study could be used toward the development of environmental/eco-friendly routes that would decrease the risk for exposure to harmful vehicle-related emissions. / Doctor of Philosophy / Air pollution is a major environmental threat to human health, claiming the lives of millions of people each year, primarily as a result of fine particulate matter entering the respiratory system. As such, it is important to develop reliable and accurate air pollution modeling approaches in order to protect the public and the environment from toxic contaminants and pathogens in the air. Although an approach known as land use regression has long been used to assess exposure to air pollution, researchers are increasingly using machine learning (ML) algorithms to quantify the concentration of harmful pollutants—for this study black carbon and particle number, which is a generic assessment that captures a number of known airborne hazards. Additionally, researchers are moving away from using fixed-site data in favor of using mobile monitoring data in a variety of locations to develop hourly empirical models of particulate air pollution. In this study, machine learning algorithms are developed using secondary data collected from roads that bikers share, which are representative of pollution levels of particle number and black carbon in the more rural location of Blacksburg (VA), in order to develop accurate and reliable short-term empirical prediction models. Different pre-processing methods of the mobile monitoring data and various input variables are tested to assess how machine learning can be efficiently used in this process. Our findings indicate that machine learning demonstrates higher predictive capacity in comparison to stepwise regression. The results from this study are expected to be useful in enhancing the performance of machine learning through the incorporation of different data preprocessing tasks, as well as how different input variables contribute to the machine learning modeling process. The findings from this study could assist transportation planners and other stakeholders better assess pollution risks for bike riders and pedestrians. As such, this study's findings could be used toward the development of environmental/eco-friendly routes that would decrease the risk for exposure to harmful vehicle-related emissions.

Air quality management strategies in the US are developed largely from estimates of emissions, some highly uncertain, rather than actual measurements. Improved knowledge based on measurements of real-world emissions is needed to increase the effectiveness of these strategies. Consequently, the objectives of this research were to (1) quantify relationships among urban emissions sources, land use, and demographics, (2) determine the spatial and temporal variability of emissions, and (3) evaluate the accuracy of official emissions estimates. These objectives guided three field campaigns that employed a unique mobile laboratory equipped to measure pollutant fluxes by eddy covariance. The first campaign, conducted in Norfolk, Virginia, represented the first time fluxes of nitrogen oxides (NO_x) were measured by eddy covariance in an urban environment. Fluxes agreed to within 10% of estimates in the National Emissions Inventory (NEI), but were three times higher than those of an inventory used for air quality modeling and planning. Additionally, measured fluxes were correlated with road density and increased development. The second campaign took place in the Tijuana-San Diego border region. Distinct spatial differences in fluxes of carbon dioxide (CO₂), NO_x, and particles were revealed across four sampling locations with the lowest fluxes occurring in a residential neighborhood and the highest ones at a port of entry characterized by heavy motor vehicle traffic. Additionally, observed emissions of NO_x and carbon monoxide were significantly higher than those in emissions inventories, suggesting the need for further refinement of the inventories. The third campaign focused on emissions at a regional airport in Roanoke, Virginia. NOx and particle number emissions indices (EIs) were calculated for aircraft, in terms of grams of pollutant emitted per kilogram of fuel burned. Observed NO_x EIs were ~20% lower than those in an international databank. NO_x EIs from takeoffs were significantly higher than those from taxiing, but relative differences for particle EIs were mixed. Observed NO_x fluxes at the airport agreed to within 25% of estimates derived from the NEI. The results of this research will provide greater knowledge of urban impacts to air quality and will improve associated management strategies through increased accuracy of official emissions estimates. / Ph. D.

Diversos estudos tiveram como objetivo determinar e caracterizar o aerossol atmosférico na cidade de São Paulo, quanto a seu tamanho e composição química, bem como encontrar as suas fontes emissoras e contribuições em massa para a região estudada. A coleta dos constituintes atmosféricos foi realizada na estação de amostragem do Laboratório de Análises dos Processos Atmosféricos (LAPAt) do Instituto de Astronomia, Geofísica e Ciências Atmosféricas (IAG) da Universidade de São Paulo (USP), localizada na zona oeste da cidade de São Paulo, geograficamente em 23°3334 S e 46°4400 O. O experimento foi realizado de 15 de agosto a 16 de setembro de 2016. Foram realizadas coletas de material particulado para análise da concentração em massa de sua fração fina inalável e composição química. A distribuição de tamanho para massa de material particulado foi determinada através da coleta com um impactador em cascata. A distribuição de tamanho para número foi obtida a partir de medidas com um Scanning Mobility Particle Sampler (SMPS) com o cálculo da concentração número de partículas (PNC) para o intervalo de 9 a 450 nm de diâmetro. Para estudar as relações entre os gases presentes na região amostrada com a radiação ultravioleta e com o PNC utilizamos os valores horários de concentrações dos gases (O3, NO, NO2 e NOX) e UV medidos na Rede Telemétrica da CETESB (Companhia de Tecnologia Ambiental do Estado de São Paulo). Os filtros coletados foram analisados pela técnica de Fluorescência de Raios-X dispersivo em energia (EDX). As concentrações de Black Carbon (BC) foram obtidas por refletância. Para a determinação das fontes de material particulado fino (MP2,5) foram utilizados os seguintes modelos receptores: Análise de Componentes Principais (ACP) e Fatoração de Matriz Positiva (FMP). Para análise de dispersão do poluente, utilizamos dados meteorológicos da estação climatológica do IAG situada no Parque do Estado. A concentração média de MP2,5 foi de 18,6 (±12,5) g/m³ e a concentração média de BC foi de 1,9 (±1,5) g/m³. As principais fontes encontradas, por ambos modelos receptores ACP e FMP, foram: veículos pesados (a diesel), veículos leves, queima de biomassa, ressuspensão de poeira de solo, pavimentos e construção, processos secundários e misturas de fontes. Os elementos-traço foram definidos em diferentes modas de tamanho: Al, Ca, Si e Ti com picos nas modas de acumulação, traçadores de ressuspensão de pavimento; Fe, Mn, P, K e Cr com picos na fração mais grossa da moda de acumulação, traçadores de emissões veiculares e queima de biomassa. Cu, Zn, Br, Pb, S e BC apresentam picos na fração mais fina da moda de acumulação, traçadores de emissões veiculares e queima de biomassa. / Several studies aimed to determine and characterize the atmospheric aerosol in the city of São Paulo, not only to its size and chemical composition, but as well as to find its emitting sources and mass contributions in the studied area. The atmospheric constituents were collected at the Laboratório de Análise dos Processos Atmosféricos (LAPAt) of the Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG) of the University of São Paulo (USP), located in the western zone of the city of São Paulo Paulo, geographically at 23°33\'34\"S and 46°44\'00\" W. The experiment was conducted from August 15 to September 16 of 2016. Samples of particulate matter were collected to analyze the mass concentration and chemical composition of its inhalable fine fraction. The particulate mass size distribution was determined through the collection with a cascade impactor. The number size distribution was obtained from measurements with a Scanning Mobility Particle Sampler (SMPS) with the calculated number of particle concentration (PNC) for the range of 9 to 450 nm of the diameter. In order to study the relationships among the compounds present in the region and the PNC, we used the hourly values of the gaseous concentrations (O3, NO, NO2 and NOx) and UV measured in CETESB\'s Air Quality Telemetric Network in the State of São Paulo. The sampled filters were analyzed by the energy dispersive X-ray Fluorescence (EDX) technique to determine the elemental composition. The concentrations of Black Carbon (BC) were obtained by reflectance analysis. In order to determine the sources of fine particulate matter (PM2.5), the following Receptors Models were used: Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF). For air pollution dispersion analysis, we used meteorological data from the IAG climatological station located in the Southeast of the city. The mean MP2.5 concentration was 18.6 (± 12.5) g/m³ and the mean concentration of BC was 1.9 (± 1.5) g/m³ for the sampling period. The main sources found by both ACP and PMF models were heavy-duty vehicles (diesel), light-duty vehicles, biomass burning, resuspension of soil dust, pavements and construction, secondary processes and mixed sources. The trace elements were defined at different size distributions: Al, Ca, Si and Ti with peaks in accumulation fraction (related to pavement resuspension tracers); Fe, Mn, P, K and Cr with peaks in the largest fraction of accumulation mode, characteristic of vehicular emissions tracer and biomass burning. Cu, Zn, Br, Pb, S and BC presented peaks in the finer fraction of the accumulation mode, related to vehicle emissions tracer and biomass burning.

Diversos estudos tiveram como objetivo determinar e caracterizar o aerossol atmosférico na cidade de São Paulo, quanto a seu tamanho e composição química, bem como encontrar as suas fontes emissoras e contribuições em massa para a região estudada. A coleta dos constituintes atmosféricos foi realizada na estação de amostragem do Laboratório de Análises dos Processos Atmosféricos (LAPAt) do Instituto de Astronomia, Geofísica e Ciências Atmosféricas (IAG) da Universidade de São Paulo (USP), localizada na zona oeste da cidade de São Paulo, geograficamente em 23°3334 S e 46°4400 O. O experimento foi realizado de 15 de agosto a 16 de setembro de 2016. Foram realizadas coletas de material particulado para análise da concentração em massa de sua fração fina inalável e composição química. A distribuição de tamanho para massa de material particulado foi determinada através da coleta com um impactador em cascata. A distribuição de tamanho para número foi obtida a partir de medidas com um Scanning Mobility Particle Sampler (SMPS) com o cálculo da concentração número de partículas (PNC) para o intervalo de 9 a 450 nm de diâmetro. Para estudar as relações entre os gases presentes na região amostrada com a radiação ultravioleta e com o PNC utilizamos os valores horários de concentrações dos gases (O3, NO, NO2 e NOX) e UV medidos na Rede Telemétrica da CETESB (Companhia de Tecnologia Ambiental do Estado de São Paulo). Os filtros coletados foram analisados pela técnica de Fluorescência de Raios-X dispersivo em energia (EDX). As concentrações de Black Carbon (BC) foram obtidas por refletância. Para a determinação das fontes de material particulado fino (MP2,5) foram utilizados os seguintes modelos receptores: Análise de Componentes Principais (ACP) e Fatoração de Matriz Positiva (FMP). Para análise de dispersão do poluente, utilizamos dados meteorológicos da estação climatológica do IAG situada no Parque do Estado. A concentração média de MP2,5 foi de 18,6 (±12,5) g/m³ e a concentração média de BC foi de 1,9 (±1,5) g/m³. As principais fontes encontradas, por ambos modelos receptores ACP e FMP, foram: veículos pesados (a diesel), veículos leves, queima de biomassa, ressuspensão de poeira de solo, pavimentos e construção, processos secundários e misturas de fontes. Os elementos-traço foram definidos em diferentes modas de tamanho: Al, Ca, Si e Ti com picos nas modas de acumulação, traçadores de ressuspensão de pavimento; Fe, Mn, P, K e Cr com picos na fração mais grossa da moda de acumulação, traçadores de emissões veiculares e queima de biomassa. Cu, Zn, Br, Pb, S e BC apresentam picos na fração mais fina da moda de acumulação, traçadores de emissões veiculares e queima de biomassa. / Several studies aimed to determine and characterize the atmospheric aerosol in the city of São Paulo, not only to its size and chemical composition, but as well as to find its emitting sources and mass contributions in the studied area. The atmospheric constituents were collected at the Laboratório de Análise dos Processos Atmosféricos (LAPAt) of the Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG) of the University of São Paulo (USP), located in the western zone of the city of São Paulo Paulo, geographically at 23°33\'34\"S and 46°44\'00\" W. The experiment was conducted from August 15 to September 16 of 2016. Samples of particulate matter were collected to analyze the mass concentration and chemical composition of its inhalable fine fraction. The particulate mass size distribution was determined through the collection with a cascade impactor. The number size distribution was obtained from measurements with a Scanning Mobility Particle Sampler (SMPS) with the calculated number of particle concentration (PNC) for the range of 9 to 450 nm of the diameter. In order to study the relationships among the compounds present in the region and the PNC, we used the hourly values of the gaseous concentrations (O3, NO, NO2 and NOx) and UV measured in CETESB\'s Air Quality Telemetric Network in the State of São Paulo. The sampled filters were analyzed by the energy dispersive X-ray Fluorescence (EDX) technique to determine the elemental composition. The concentrations of Black Carbon (BC) were obtained by reflectance analysis. In order to determine the sources of fine particulate matter (PM2.5), the following Receptors Models were used: Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF). For air pollution dispersion analysis, we used meteorological data from the IAG climatological station located in the Southeast of the city. The mean MP2.5 concentration was 18.6 (± 12.5) g/m³ and the mean concentration of BC was 1.9 (± 1.5) g/m³ for the sampling period. The main sources found by both ACP and PMF models were heavy-duty vehicles (diesel), light-duty vehicles, biomass burning, resuspension of soil dust, pavements and construction, secondary processes and mixed sources. The trace elements were defined at different size distributions: Al, Ca, Si and Ti with peaks in accumulation fraction (related to pavement resuspension tracers); Fe, Mn, P, K and Cr with peaks in the largest fraction of accumulation mode, characteristic of vehicular emissions tracer and biomass burning. Cu, Zn, Br, Pb, S and BC presented peaks in the finer fraction of the accumulation mode, related to vehicle emissions tracer and biomass burning.

Aerosols change the Earth's energy balance. Black carbon (BC) aerosols are a product of incomplete combustion of fossil fuels and biomass burning and cause a net warming through aerosol radiation interactions (ari) and aerosol cloud interactions (aci). BC aerosols have potentially strong implications on the Arctic climate, yet the net global climate effect of BC is very uncertain. Best estimates assume a net warming effect, roughly half to that of CO2. However, the time scales during which CO2 emissions affect the global climate are on the order of hundreds of years, while BC is a short-lived climate pollutant (SLCP) with atmospheric life times of days to weeks. Climate models or atmospheric transport models struggle to emulate the seasonality and amplitude of BC concentrations in the Arctic, which are low in summer and high in winter/spring during the so called Arctic haze season. The high uncertainties regarding BC's climate impact are not only related to ari and aci, but also due to model parameterizations of BC lifetime and transport, and the highly uncertain estimates of global and regional BC emissions. Given the high uncertainties in technology-based emission inventories (EI), there is a need for an observation-based assessment of sources of BC in the atmosphere. We study short-term and long-term observations of elemental carbon (EC), the mass-based analog of optically-defined BC. EC aerosol concentrations and carbon-isotope-based (δ13C and ∆14C) sources were constrained (top-down) for three Arctic receptor sites in Abisko (northern Sweden), Tiksi (East Siberian Russia), and Zeppelin (on Svalbard, Norway). The radiocarbon (∆14C) signature allows to draw conclusion on the EC sources (fossil fuels vs. biomass burning) with high accuracy (&lt;5% variation). Stable carbon isotopic fingerprints (δ13C) give qualitative information of the consumed fuel type, i.e. coal, C3-plants (wood), liquid fossil fuels (diesel) or gas flaring (methane and non-methane hydrocarbons). These fingerprints can be used in conjunction with Bayesian statistics, to estimate quantitative source contributions of the sources. Finally, our observations were compared to predictions from a state of the art atmospheric transport model (coupled to BC emissions), conducted by our collaborators at NILU (Norwegian Institute for Air Research). Observed BC concentrations showed a high seasonality throughout the year, with elevated concentrations in the winter, at all sites. The highest concentrations were measured on Svalbard during a short campaign (Jan-Mar 2009) focusing on BC pollution events. Long-term observations showed that Svalbard (2013) had overall the lowest annual BC concentrations, followed by Abisko (2012) and Tiksi (2013). Isotope constraints on BC combustion sources exhibited a high seasonality and big amplitude all across the Eurasian Arctic. Uniform seasonal trends were observed in all three year-round studies, showing fractions of biomass burning of 60-70% in summer and 10-40% in winter. Europe was the major source region (&gt;80%) for BC emissions arriving at Abisko and the main sources were liquid fossil fuels and biomass burning (wood). The model agreed very well with the Abisko observations, showing good model skill and relatively well constrained sources in the European regions of the EI. However, for the Svalbard and East Siberian Arctic observatories the model-observation agreement was not as good. Here, Russia, Europe and China were the major contributors to the mostly liquid fossil and biomass burning BC emissions. This showed that the EI still needs to be improved, especially in regions where emissions are high but observations are scarce (low ratio of observations to emitted pollutant quantity). Strategies for BC mitigation in the (Eurasian) Arctic are probably most efficient, if fossil fuel (diesel) emissions are tackled during winter and spring periods, all across Eurasia. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript.</p>

La combustion du noir de carbone (NC) dans le creuset d'une thermobalance est contrôlée à la fois par la réaction et par le transport de l'oxygène jusqu'à la surface du lit et à l'intérieur du lit poreux de NC.Les expériences menées en modifiant la masse de NC ont montré que la concentration en oxygène peut tomber à zéro avant d'atteindre le fond du lit. Ainsi, à un instant donné, seule une partie du lit est en combustion. Cette masse, appelée masse critique (mc) dépend de la température. Elle passe de 35 mg à 570°C à 17,5 mg à 650°C.Un gradient d'oxygène s'établi donc dans le lit. La modélisation du transport interne de l'oxygène a montré que la diffusion de Fick constitue une bonne approximation pour représenter ce transport.Des conseils pour extraire correctement une constante cinétique à partir d'expériences thermogravimétrique sont donnés. La procédure est adaptée en fonction de la précision souhaitée.Ainsi, l'utilisation de faibles masses afin de réduire au mieux l'effet de la masse et l'exothermicité de la réaction est fortement conseillée. L'influence de la diffusion externe du gaz peut être réduite en utilisant des creusets de très faibles hauteurs, ou en mettant l'échantillon le plus proche de la bouche du creuset en remplissant le fond du creuset avec un matériau inerte. / Combustion of carbon black (CB) in the crucible of a thermobalance is controlled by both carbon reactivity and oxygen transport from the oxidizing flux to the surface of the bed and within the porous bed.The experiments conducted by changing the mass of CB showed that the oxygen concentration can fall to zero before the bottom of the bed. Thus, at a given time, only a part of the bed is burning. This mass, called critical mass (mc), depends to temperature. It went from 35 mg at 570°C to 17.5 mg at 650°C.An oxygen gradient is thus established in the bed. The Modelling of the internal transport of oxygen showed that the Fick diffusion is a good approximation to represent the transport.Advices to correctly extract a kinetic constant using thermogravimetric experiments are given. The procedure is adjusted depending to the precision desired.Thus, the use of low masses to best reduce the mass and exothermic reaction effects is strongly recommended. The influence of stagnant gas can be reduced by using crucibles with very low height, or by placing the sample closest to the mouth of the crucible by filling the bottom of the crucible with an inert material.

Black carbon (BC) is a significant component of fine particulate matter (PM2.5) air pollution, which has been linked to a series of adverse health effects, in particular premature mortality. Recent scientific research indicates that BC also plays an important role in climate change. Therefore, controlling black carbon emissions provides an opportunity for a double dividend. This study quantifies the national burden of mortality and morbidity attributable to exposure to ambient BC in the United States (US). We use GEOS–Chem, a global 3-D model of atmospheric composition to estimate the 2010 annual average BC levels at 0.5 x 0.667° resolution, and then re-grid to 12-km grid resolution across the continental US. Using PM2.5 mortality risk coefficient drawn from the American Cancer Society cohort study, the numbers of deaths due to BC exposure were estimated for each 12-km grid, and then aggregated to the county, state and national level. Given evidence that BC particles may pose a greater risk on human health than other components of PM2.5, we also conducted sensitivity analysis using BC-specific risk coefficients drawn from recent literature. We estimated approximately 14,000 deaths to result from the 2010 BC levels, and hundreds of thousands of illness cases, ranging from hospitalizations and emergency department visits to minor respiratory symptoms. Sensitivity analysis indicates that the total BC-related mortality could be even significantly larger than the above mortality estimate. Our findings indicate that controlling BC emissions would have substantial benefits for public health in the US.