Browsing by Author "Marr, Linsey C."

Now showing 1 - 20 of 141
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    Accelerating Atmospheric Modeling Through Emerging Multi-core Technologies
    Linford, John Christian (Virginia Tech, 2010-05-05)
    The new generations of multi-core chipset architectures achieve unprecedented levels of computational power while respecting physical and economical constraints. The cost of this power is bewildering program complexity. Atmospheric modeling is a grand-challenge problem that could make good use of these architectures if they were more accessible to the average programmer. To that end, software tools and programming methodologies that greatly simplify the acceleration of atmospheric modeling and simulation with emerging multi-core technologies are developed. A general model is developed to simulate atmospheric chemical transport and atmospheric chemical kinetics. The Cell Broadband Engine Architecture (CBEA), General Purpose Graphics Processing Units (GPGPUs), and homogeneous multi-core processors (e.g. Intel Quad-core Xeon) are introduced. These architectures are used in case studies of transport modeling and kinetics modeling and demonstrate per-kernel speedups as high as 40x. A general analysis and code generation tool for chemical kinetics called "KPPA" is developed. KPPA generates highly tuned C, Fortran, or Matlab code that uses every layer of heterogeneous parallelism in the CBEA, GPGPU, and homogeneous multi-core architectures. A scalable method for simulating chemical transport is also developed. The Weather Research and Forecasting Model with Chemistry (WRF-Chem) is accelerated with these methods with good results: real forecasts of air quality are generated for the Eastern United States 65% faster than the state-of-the-art models.
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    Advancing Nanoplasmonics-enabled Regenerative Spatiotemporal Pathogen Monitoring at Bio-interfaces
    Garg, Aditya (Virginia Tech, 2024-05-09)
    Non-invasive and continuous spatiotemporal pathogen monitoring at biological interfaces (e.g., human tissue) holds promise for transformative applications in personalized healthcare (e.g., wound infection monitoring) and environmental surveillance (e.g., airborne virus surveillance). Despite notable progress, current receptor-based biosensors encounter inherent limitations, including inadequate long-term performance, restricted spatial resolutions and length scales, and challenges in obtaining multianalyte information. Surface-enhanced Raman spectroscopy (SERS) has emerged as a robust analytical method, merging the molecular specificity of Raman spectroscopy's vibrational fingerprinting with the enhanced detection sensitivity from strong light-matter interaction in plasmonic nanostructures. As a receptor-free and noninvasive detection tool capable of capturing multianalyte chemical information, SERS holds the potential to actualize bio-interfaced spatiotemporal pathogen monitoring. Nonetheless, several challenges must be addressed before practical adoption, including the development of plasmonic bio-interfaces, sensitive capture of multianalyte information from pathogens, regeneration of nanogap hotspots for long-term sensing, and extraction of meaningful information from spatiotemporal SERS datasets. This dissertation tackles these fundamental challenges. Plasmonic bio-interfaces were created using innovative nanoimprint lithography-based scalable nanofabrication methods for reliable bio-interfaced spatiotemporal measurements. These plasmonic bio-interfaces feature sensitive, dense, and uniformly distributed plasmonic transducers (e.g., plasmonic nano dome arrays, optically-coupled plasmonic nanodome and nanohole arrays, self-assembled nanoparticle micro patches) on ultra-flexible and porous platforms (e.g., biomimetic polymeric meshes, textiles). Using these plasmonic bio-interfaces, advancements were made in SERS signal transduction, machine-learning-enabled data analysis, and sensor regeneration. Large-area multianalyte spatiotemporal monitoring of bacterial biofilm components and pH was demonstrated in in-vitro biofilm models, crucial for wound biofilm diagnostics. Additionally, novel approaches for sensitive virus detection were introduced, including monitoring spectral changes during viral infection in living biofilms and direct detection of decomposed viral components. Spatiotemporal SERS datasets were analyzed using unsupervised machine-learning methods to extract biologically relevant spatiotemporal information and supervised machine-learning tools to classify and predict biological outcomes. Finally, a sensor regeneration method based on plasmon-induced nanocavitation was developed to enable long-term continuous detection in protein-rich backgrounds. Through continuous implementation of spatiotemporal SERS signal transduction, machine-learning-enabled data analysis, and sensor regeneration in a closed loop, our solution has the potential to enable spatiotemporal pathogen monitoring at the bio-interface.
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    Aerosol microdroplets exhibit a stable pH gradient
    Wei, Haoran; Vejerano, Eric P.; Leng, Weinan; Huang, Qishen; Willner, Marjorie R.; Marr, Linsey C.; Vikesland, Peter J. (2018-07-10)
    Suspended aqueous aerosol droplets (< 50 mu m) are microreactors for many important atmospheric reactions. In droplets and other aquatic environments, pH is arguably the key parameter dictating chemical and biological processes. The nature of the droplet air/water interface has the potential to significantly alter droplet pH relative to bulk water. Historically, it has been challenging to measure the pH of individual droplets because of their inaccessibility to conventional pH probes. In this study, we scanned droplets containing 4-mercaptobenzoic acid-functionalized gold nanoparticle pH nanoprobes by 2D and 3D laser confocal Raman microscopy. Using surface-enhanced Raman scattering, we acquired the pH distribution inside approximately 20-mu m-diameter phosphate-buffered aerosol droplets and found that the pH in the core of a droplet is higher than that of bulk solution by up to 3.6 pH units. This finding suggests the accumulation of protons at the air/water interface and is consistent with recent thermodynamic model results. The existence of this pH shift was corroborated by the observation that a catalytic reaction that occurs only under basic conditions (i.e., dimerization of 4-aminothiophenol to produce dimercaptoazobenzene) occurs within the high pH core of a droplet, but not in bulk solution. Our nanoparticle probe enables pH quantification through the cross-section of an aerosol droplet, revealing a spatial gradient that has implications for acid-base-catalyzed atmospheric chemistry.
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    Aerosolization and Atmospheric Transformation of Engineered Nanoparticles
    Tiwari, Andrea Jean (Virginia Tech, 2014-04-04)
    While research on the environmental impacts of engineered nanoparticles (ENPs) is growing, the potential for them to be chemically transformed in the atmosphere has been largely ignored. The overall objective of this work was to assess the atmospheric transformation of carbonaceous nanoparticles (CNPs). The research focuses on C₆₀ fullerene because it is an important member of the carbonaceous nanoparticle (CNP) family and is used in a wide variety of applications. The first specific objective was to review the potential of atmospheric transformations to alter the environmental impacts of CNPs. We described atmospheric processes that were likely to physically or chemically alter aerosolized CNPs and demonstrated their relevance to CNP behavior and toxicity in the aqueous and terrestrial environment. In order to investigate the transformations of CNP aerosols under controlled conditions, we developed an aerosolization technique that produces nano-scale aerosols without using solvents, which can alter the surface chemistry of the aerosols. We demonstrated the technique with carbonaceous (C₆₀) and metal oxide (TiO₂, CeO₂) nanoparticle powders. All resulting aerosols exhibited unimodal size distributions and mode particle diameters below 100 nm. We used the new aerosolization technique to investigate the reaction between aerosolized C₆₀ and atmospherically realistic levels of ozone (O₃) in terms of reaction products, reaction rate, and oxidative stress potential. We identified C₆₀O, C₆₀O2, and C₆₀O3 as products of the C₆₀-O3 reaction. We demonstrated that the oxidative stress potential of C₆₀ may be enhanced by exposure to O3. We found the pseudo-first order reaction rate to be 9 x 10⁻⁶ to 2 x 10⁻⁵ s⁻¹, which is several orders of magnitude lower than the rate for several PAH species under comparable conditions. This research has demonstrated that a thorough understanding of atmospheric chemistry of ENPs is critical for accurate prediction of their environmental impacts. It has also enabled future research in that vein by developing a novel technique to produce nanoscale aerosols from nanoparticle powders. Results of this research will help guide the formulation of appropriate environmental policy concerning the regulation of ENPs.
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    Aerosolization of Drinking Water Metals to Indoor Air and Assessment of Human Taste and Visual Thresholds for Manganese
    Sain, Amanda Elizabeth (Virginia Tech, 2013-04-17)
    Exposure to excess manganese via drinking water raises concerns due to potential for adverse neurological impacts, particularly in children. Manganese is ubiquitous in US groundwaters above the SMCL = 0.05 mg/L. Manganese is an essential nutrient, but exposures to elevated manganese have neurotoxic effects. Chapter 2 focuses on human senses\' ability to detect manganese in drinking water. Findings indicate human senses cannot be relied upon to detect excess Mn(II) in drinking water. Mn(IV) is easily visually detected, but cannot be tasted at 10 times the SMCL. Chapter 3 is an assessment the ability of an ultrasonic humidifier to expel drinking water impurities in aerosols. The quality of the water used to charge the humidifier reservoir affects the composition of elements in the aerosols and condensate. Findings indicate condensed humidifier aerosols contain 85% of elements present in the reservoir water for a variety of water types if there is no precipitation. Waters with high concentration of hardness or iron formed precipitates that decreased the concentrations of these metals in the aerosols causing variable results for other elements that were initially present at < 1mg/L in the charge water. This indicates that humidifiers could be a source of inhalation exposure for source water contaminants.
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    Aerosolization of Ebola Virus Surrogates in Wastewater Systems
    Lin, Kaisen (Virginia Tech, 2016-09-26)
    Recent studies have shown that Ebola virus can persist in wastewater, and the potential for the virus to be aerosolized and pose a risk of inhalation exposure has not been evaluated. We considered this risk for three wastewater systems: toilets, a lab-scale model of an aeration basin, and a lab-scale model of converging sewer pipes. We measured the aerosol size distribution generated by each system, spiked Ebola virus surrogates into each system, and determined the emission rate of viruses into the air. While the number of aerosols released ranged from 105 to 107 per flush from the toilets or per minute from the lab-scale models, the total volume of aerosols generated by these systems was ~10-8 to 10-7 mL per flush or per minute in all cases. The Ebola virus surrogates MS2 and Phi6, spiked into toilets at an initial concentration of 107 PFU mL-1, were not detected in air after flushing. Airborne concentrations of MS2 and Phi6 were ~20 PFU L-1 and ~0.1 PFU L-1, respectively, associated with the aeration basin and sewer models. This corresponds to emission rates of 547 PFU min-1 and 3.8 PFU min-1 of MS2 and Phi6, respectively, for the aeration basin and 79 PFU min-1 and 0.3 PFU min-1 for the sewer model. Since information on the aerosolization of Ebola virus is quite limited, these emission rates can greatly help inform risk assessment of inhalation exposure to Ebola virus.
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    Air Quality in Mexico City: Spatial and Temporal Variations of Particulate Polycyclic Aromatic Hydrocarbons and Source Apportionment of Gasoline-Versus-Diesel Vehicle Emissions
    Thornhill, Dwight Anthony Corey (Virginia Tech, 2007-07-26)
    The Mexico City Metropolitan Area (MCMA) is one of the largest cities in the world, and as with many megacities worldwide, it experiences serious air quality and pollution problems, especially with ozone and particulate matter. Ozone levels exceed the health-based standard, which is equivalent to the U.S. standard, on approximately 80% of all days, and concentrations of particulate matter 10 μm and smaller (PM10) exceed the standard on more than 40% of all days in most years. Particulate polycyclic aromatic hydrocarbons (PAHs) are a class of semi-volatile compounds that are formed during combustion and many of these compounds are known or suspected carcinogens. Recent studies on PAHs in Mexico City indicate that very high concentrations have been observed there and may pose a serious health hazard. The first part of this thesis describes results from the Megacities Initiative: Local and Regional Observations (MILAGRO) study in Mexico City in March 2006. During this field campaign, we measured PAH and aerosol active surface area (AS) concentrations at six different locations throughout the city using the Aerodyne Mobile Laboratory (AML). The different sites encompassed a mix of residential, commercial, industrial, and undeveloped land use. The goals of this research were to describe spatial and temporal patterns in PAH and AS concentrations, to gain insight into sources of PAHs, and to quantify the relationships between PAHs and other pollutants. We observed that the highest measurements were generally found at sites with dense traffic networks. Also, PAH concentrations varied considerably in space. An important implication of this result is that for risk assessment studies, a single monitoring site will not adequately represent an individual's exposure. Source identification and apportionment are essential for developing effective control strategies to improve air quality and therefore reduce the health impacts associated with fine particulate matter and PAHs. However, very few studies have separated gasoline- versus diesel-powered vehicle emissions under a variety of on-road driving conditions. The second part of this thesis focuses on distinguishing between the two types of engine emissions within the MCMA using positive matrix factorization (PMF) receptor modeling. The Aerodyne Mobile Laboratory drove throughout the MCMA in March 2006 and measured on-road concentrations of a large suite of gaseous and particulate pollutants, including carbon dioxide, carbon monoxide (CO), nitric oxide (NO), benzene (C6H6), formaldehyde (HCHO), ammonia (NH3), fine particulate matter (PM2.5), PAHs, and black carbon (BC). These pollutant species served as the input data for the receptor model. Fuel-based emission factors and annual emissions within Mexico City were then calculated from the source profiles of the PMF model and fuel sales data. We found that gasoline-powered vehicles were responsible for 90% of mobile source CO emissions and 85% of VOCs, while diesel-powered vehicles accounted for almost all of NO emissions (99.98%). Furthermore, the annual emissions estimates for CO and VOC were lower than estimated during the MCMA-2003 field campaign. The number of megacities is expected to grow dramatically in the coming decades. As one of the world's largest megacities, Mexico City serves as a model for studying air quality problems in highly populated, extremely polluted environments. The results of this work can be used by policy makers to improve air quality and reduce related health risks in Mexico City and other megacities.
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    Airborne Nanoparticles: Generation, Characterization, and Occupational Exposure
    Yeganeh Talab, Behnoush (Virginia Tech, 2007-03-14)
    Despite the rapid growth in nanotechnology, very little is known about the unintended health or environmental effects of manufactured nanomaterials. The development of nanotechnology risk assessments and regulations requires quantitative information on the potential for exposure to nanomaterials. In addition, to facilitate life-cycle assessments and inhalation toxicology studies, robust methods are needed to generate aerosolized engineered nanoparticles. We conducted a set of field studies to measure the fine particle mass concentrations (PM2.5) as well as nanoparticle number concentrations and size distributions in two nanomaterial manufacturing facilities. Measurements were performed near the reactor, in the breathing zone, and at a background site. Increases in PM2.5 and particle number concentrations were associated with physical handling of nanomaterials. The highest PM2.5 concentration observed was 2700 ug m-3 during sweeping of the reactor in the commercial plant. In most cases, an increase in the number of sub-100 nm particles accounted for the increase in total number concentrations. The results of this research can be used to develop guidelines for workplace regulations to minimize workers' exposure to nanoparticles. Furthermore, we used an atomizer to aerosolize C60 aggregates from a fullerene-water suspension. Measurement of particle size distributions and number concentrations showed that increasing the initial fullerene concentration resulted in increased number of aerosolized particles, while the average size of particles remained relatively constant. To return the aerosolized fullerenes into water, we passed the aerosol sample through an impinger. Reducing the flow rate through the impinger resulted in an increase in the collection efficiency of airborne nanoparticles.
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    Airborne Transmission of Influenza a Virus in Indoor Environments
    Yang, Wan (Virginia Tech, 2012-03-30)
    Despite formidable advances in virology and medicine in recent decades, we know remarkably little about the dynamics of the influenza virus in the environment during transmission between hosts. There is still controversy over the relative importance of various transmission routes, and the seasonality of influenza remains unexplained. This work focuses on developing new knowledge about influenza transmission via the airborne route and the virus' inter-host dynamics in droplets and aerosols. We measured airborne concentrations of influenza A viruses (IAVs) and size distributions of their carrier aerosols in a health center, a daycare center, and airplanes. Results indicate that the majority of viruses are associated with aerosols smaller than 2.5 µm and that concentrations are sufficient to induce infection. We further modeled the fate and transport of IAV-laden droplets expelled from a cough into a room, as a function of relative humidity (RH) and droplet size. The model shows that airborne concentrations of infectious IAV vary with RH through its influence on virus inactivation and droplet size, which shrinks due to evaporation. IAVs associated with large droplets are removed mostly by settling, while those associated with aerosols smaller than 5 µm are removed mainly by ventilation and inactivation. To investigate the relationship between RH and influenza transmission further, we measured the viability of IAV in droplets at varying RHs. Results suggest that there exist three regimes defined by RH: physiological conditions (~100% RH) with high viability, concentrated conditions (~50% to ~99% RH) with lower viability, and dry conditions (<~50% RH) with high viability. A droplet's extent of evaporation, which is determined by RH, affects solute concentrations in the droplet, and these appear to influence viability. This research considerably advances the current understanding of the dynamics of the influenza virus while it is airborne and provides an explanation for influenza's seasonality. Increased influenza activity in winter in temperate regions could be due to greater potential for IAV carrier aerosols to remain airborne and higher viability of IAV at low RH. In tropical regions, transmission could be enhanced due to better survival of IAV at extremely high RH.
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    Ammonia Emissions from Dairy Manure Storage Tanks Affected by Diets and Manure Removal Practices
    Li, Lifeng (Virginia Tech, 2009-08-07)
    The objectives of this study were to determine: 1) ammonia emission rates from stored scraped and flushed manure from dairy cows fed either normal or low N diet; and 2) seasonal effects on ammonia emission rates from stored scraped and flushed dairy manure. Four pilot-scale tanks were used for manure storage with different treatments - scraped manure for normal diet (NS), flushed manure for normal diet (NF), scraped manure for low N diet (LS), and flushed manure for low N diet (LF). The first part of the study lasted for 1 month and four treatments were all investigated; the second part of the study lasted for 12 months and two tanks with treatments NS and NF were investigated. Dynamic flux chambers and a photoacoustic gas analyzer were used to measure ammonia emission rates. There was no significant change of the N content of manure as the dietary N content is reduced (from 17.8% to 15.9% crude protein). However, ammonia emission rates from manure storage tanks were reduced by 33% (from 27.4 ± 38.1 to 18.4 ± 21.9 mg m⁻²h⁻¹; P<0.0001 based on paired t-test). Flushing manure reduced emission rates by 72% compared to scraping manure (from 35.6 ± 39.6 to 10.1 ± 8.2 mg m⁻²h⁻¹; P<0.0001 based on paired t-test). Ammonia emission rates for NS, NF, LS and LF were 43.9 ± 48.0, 10.9 ± 8.7, 27.4 ± 27.3, and 9.3 ± 7.8 mg m-2 h-1, respectively. The chamber headspace temperature for NS, NF, LS and LF were 26.0 ± 6.9, 25.8 ± 6.8, 26.6 ± 6.5, and 27.2 ± 6.7 °C, respectively. The manure pH for NS, NF, LS, and LF were 6.3 ± 0.1, 6.4 ± 0.3, 6.4 ± 0.1, and 6.1 ± 0.1, respectively. Both dietary N reduction and manure flushing are recommended to reduce ammonia emission rates from dairy manure storage tanks. Ammonia emission rates were higher in summer and fall, due to higher air temperature and higher manure pH. The pH of scraped manure was 7.2 ± 0.6, 6.7 ± 0.2, 6.5 ± 0.3 and 7.0 ± 0.3 for fall, winter, spring and summer, respectively. The pH of flushed manure was 6.8 ± 0.4, 6.7 ± 0.4, 6.4 ± 0.3 and 6.8 ± 0.4 for fall, winter, spring and summer, respectively. Ammonia emission rates from scraped manure for fall, winter, spring, and summer were 7.4 ± 8.6, -0.5 ± 1.2, 1.1 ± 1.9, and 5.8 ± 2.7 mg m⁻²h⁻¹, respectively. Ammonia emission rates from flushed manure for fall, winter, spring, and summer were 3.9 ± 4.2, -0.5 ± 0.9, 0.8 ± 1.4, and 4.4 ± 1.2 mg m⁻²h⁻¹, respectively. Seasonal changes of air temperature and manure pH were key factors affecting ammonia emissions from manure storage in this study. Seasonal climate conditions including precipitations (rainstorms and snows) and icing can cause reduction of ammonia emissions from manure storage in open air. More attention should be paid to reduce ammonia emissions in warmer seasons, e.g., by covering the storage facilities.
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    An Analysis of Short-Term Load Forecasting on Residential Buildings Using Deep Learning Models
    Suresh, Sreerag (Virginia Tech, 2020-07-07)
    Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since the residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting at the building level. Although, other studies have looked at using deep learning models for building energy forecasting, most of those studies have looked at limited number of homes or an aggregate load of a collection of homes. This study aims to address this gap and serve as an investigation on selecting the better deep learning model architecture for short term load forecasting on 3 communities of residential buildings. The deep learning models CNN and LSTM have been used in the study. For 15-min ahead forecasting for a collection of homes it was found that homes with a higher variance were better predicted by using CNN models and LSTM showed better performance for homes with lower variances. The effect of adding weather variables on 24-hour ahead forecasting was studied and it was observed that adding weather parameters did not show an improvement in forecasting performance. In all the homes, deep learning models are shown to outperform the simple ANN model.
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    Application of a Mobile Flux Lab for the Atmospheric Measurement of Emissions (FLAME)
    Moore, Tim Orland II (Virginia Tech, 2009-09-08)
    According to the World Health Organization, urban air pollution is a high public health priority due its linkage to cardio-pulmonary disease and association with increased mortality and morbidity (1, 2). Additionally, air pollution impacts climate change, visibility, and ecosystem health. The development of effective strategies for improving air quality requires accurate estimates of air pollutant emissions. In response to the need for new approaches to measuring emissions, we have designed a mobile Flux Lab for the Atmospheric Measurement of Emissions (FLAME) that applies a proven, science-based method known as eddy covariance for the direct quantification of anthropogenic emissions to the atmosphere. The mobile flux lab is a tool with novel, multifaceted abilities to assess air quality and improve the fidelity of emission inventories. Measurements of air pollutant concentrations in multiple locations at the neighborhood scale can provide much greater spatial resolution for population exposure assessments. The lab's mobility allows it to target specific sources, and plumes from these can be analyzed to determine emission factors. Through eddy covariance, the lab provides the new ability to directly measure emissions of a suite of air pollutants. We have deployed the FLAME to three different settings: a rural Appalachian town where coal transport is the dominant industry; schools in the medium-sized city of Roanoke, Virginia; and the large urban areas around Norfolk, Virginia, to measure neighborhood-scale emissions of air pollution. These areas routinely experience high ozone and particulate matter concentrations and include a diverse array of residential neighborhoods and industries. The FLAME is able to capture emissions from all ground-based sources, such as motor vehicles, rail and barge traffic, refuse fires and refueling stations, for which no direct measurement method has been available previously. Experiments focus on carbon dioxide (CO₂), the principal greenhouse gas responsible for climate change; nitrogen oxides (NOx), a key ingredient in ground-level ozone and acid rain; volatile organic compounds (VOCs), a second key ingredient in ozone and many of which are air toxics; and fine particulate matter (PM2.5), a cause of mortality, decreased visibility, and climate change. This research provides some of the first measurements of neighborhood-scale anthropogenic emissions of CO₂, NOx, VOCs and PM2.5 and as a result, the first opportunity to validate official emission inventories directly. The results indicate that a mobile eddy covariance system can be used successfully to measure fluxes of multiple pollutants in a variety of urban settings. With certain pollutants in certain locations, flux measurements confirmed inventories, but in others, they disagreed by factors of up to five, suggesting that parts of the inventory may be severely over- or underestimated. Over the scale of a few kilometers within a city, emissions were highly heterogeneous in both space and time. FLAME-based measurements also confirmed published emission factors from coal barges and showed that idling vehicles are the dominant source of emissions of air toxics around seven schools in southwest Virginia. Measurements from this study corroborate existing emission inventories of CO₂ and NOx and suggest that inventories of PM2.5 may be overestimated. Despite the tremendous spatial and temporal variability in emissions found in dense urban areas, CO₂ fluxes on average are very similar across the areas in this study and other urban areas in the developed world. Nevertheless, the high level of variability in spatial and temporal patterns of emissions presents a challenge to air quality modelers. The finding that emissions from idling vehicles at schools are likely responsible for creating hot spots of air toxics adds to the urgency of implementing no-idling and other rules to reduce the exposure of children to such pollutants. Ultimately, the results of this study can be used in combination with knowledge from existing emission inventories to improve the science and policies surrounding air pollution.
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    Application of positive matrix factorization to on-road measurements for source apportionment of diesel- and gasoline-powered vehicle emissions in Mexico City
    Thornhill, D. A.; Williams, A. E.; Onasch, T. B.; Wood, E.; Herndon, S. C.; Kolb, C. E.; Knighton, W. B.; Zavala, M.; Molina, L. T.; Marr, Linsey C. (Copernicus Publications, 2010)
    The goal of this research is to quantify diesel- and gasoline-powered motor vehicle emissions within the Mexico City Metropolitan Area (MCMA) using on-road measurements captured by a mobile laboratory combined with positive matrix factorization (PMF) receptor modeling. During the MCMA-2006 ground-based component of the MILAGRO field campaign, the Aerodyne Mobile Laboratory (AML) measured many gaseous and particulate pollutants, including carbon dioxide, carbon monoxide (CO), nitrogen oxides (NOx), benzene, toluene, alkylated aromatics, formaldehyde, acetaldehyde, acetone, ammonia, particle number, fine particulate mass (PM2.5), and black carbon (BC). These serve as inputs to the receptor model, which is able to resolve three factors corresponding to gasoline engine exhaust, diesel engine exhaust, and the urban background. Using the source profiles, we calculate fuel-based emission factors for each type of exhaust. The MCMA's gasoline-powered vehicles are considerably dirtier, on average, than those in the US with respect to CO and aldehydes. Its diesel-powered vehicles have similar emission factors of NOx and higher emission factors of aldehydes, particle number, and BC. In the fleet sampled during AML driving, gasoline-powered vehicles are found to be responsible for 97% of total vehicular emissions of CO, 22% of NOx, 95-97% of each aromatic species, 72-85% of each carbonyl species, 74% of ammonia, negligible amounts of particle number, 26% of PM2.5, and 2% of BC; diesel-powered vehicles account for the balance. Because the mobile lab spent 17% of its time waiting at stoplights, the results may overemphasize idling conditions, possibly resulting in an underestimate of NOx and overestimate of CO emissions. On the other hand, estimates of the inventory that do not correctly account for emissions during idling are likely to produce bias in the opposite direction.The resulting fuel-based estimates of emissions are lower than in the official inventory for CO and NOx and higher for VOCs. For NOx, the fuel-based estimates are lower for gasoline-powered vehicles but higher for diesel-powered ones compared to the official inventory. While conclusions regarding the inventory should be interpreted with care because of the small sample size, 3.5 h of driving, the discrepancies with the official inventory agree with those reported in other studies.
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    Assessing Human Exposure to Emissions from Ultrasonic Humidifiers
    Yao, Wenchuo (Virginia Tech, 2021-09-14)
    Portable ultrasonic humidifiers add moisture into room air, but they simultaneously add exposure risks of aerosolized metals from drinking water used as fill water. The inhalation exposure from emitted metals can be overlooked, and thus, co-exposure of inhalation and ingestion and co-exposure to multiple inorganic metals is investigated. The objectives of this work are: 1) predict airborne metal concentrations and particle sizes in four realistic room scenarios (33 m3 small or 72 m3 large, with varying ventilation rates from 0.2/hr -1.5/hr), and the investigated metals are arsenic, cadmium, chromium, copper, lead, and manganese; 2) characterize exposure doses and consequent risks for adults and 0.25, 1, 2.5, and 6 yr old children, when using identical drinking water ingested and as fill water, including inhalation of fine, respirable particles generated at the frequency of 8 hrs/day (equals 121.67 days/yr) and daily ingestion, under four realistic room scenarios. The risk assessment includes non-cancer [calculation of average daily dose (ADD) and hazard quotient (HQ)] and cancer risk evaluation; 3) quantify deposition fraction and deposited doses of multiple metals in human adult's and children's respiratory tract, using multi-path particle dosimetry (MPPD) model. Results show airborne-particle-bound metal concentrations increase proportionally with water metals, and a poorly ventilated room causes greater exposure. Ingestion ADDs are 2 magnitudes higher than inhalation ADD, at identical water metal concentrations and daily exposure frequency. However, in the worse-case scenario of 33 m3 small room with low air exchange rate, the consequent inhalation HQs are all greater than 1 for children and adults, except for lead, indicating significant non-cancer risks when exposed to humidifier particles under the worse-case scenario. The cancer risks for arsenic, cadmium, chromium, and lead metals reveal are greater than acceptable one case in a million population (1E-6) produced from inhalation of the humidifier emitted metal-containing particles only. The MPPD model results indicate inhaled metal-containing airborne particles deposit primarily in head and pulmonary regions, and a greater dose (unit in µg/kg body weight/day) deposits in children than adults. Inhalation of ultrasonic humidifier aerosolized metals results in additional, and potentially greater risks (indicated by HQinhalation >1, and greater deposited dose) than ingestion at the same aqueous metal concentration, especially for children. Room conditions (i.e. volume and ventilation) influence risks. Both inhalation and ingestion exposures require consideration for eliminating multiple metal exposures and health-based environmental policy making. Consumers should be aware that they may be degrading their indoor air quality by using ultrasonic humidifiers even when filling with acceptable water quality for drinking.
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    Assessing the Global Threat of Coastal Flooding: A Mortality Risk Model
    Timilsina, Saurav (Virginia Tech, 2024-06-14)
    Coastal flooding, caused by sea level rise (SLR), storm surge, and tropical cyclones, is a growing threat. Previous studies have documented mortality associated with historical coastal flooding and developed predictions of mortality risk based on SLR and human development. This study updates those estimates and provides a new model by including new mortality data from events between 2010 and 2020 and an updated method for estimating the population exposed to coastal flooding events. Primary data sources include the Emergency Events Database (EM-DAT) and the Sea Level Impacts Input Dataset by Elevation, Region, and Scenario (SLIIDERS) model. We first characterize trends in exposed populations and mortality associated with coastal flooding between 1990 and 2020. A mixed effect regression model estimates mortality associated with coastal flooding and investigates the influence of variables including Human Development Index (HDI), country population, and event frequency. The frequency of coastal flooding events between 1990 and 2020 has increased, while there was an overall decrease in recorded deaths associated with coastal flooding events. The association between mortality and coastal flood exposure is reduced in countries with higher populations. This result suggests countries with larger populations may buffer risks in exposed regions. Results showed significant reduction in mortality risk, by approximately 34% (95% CI, 17-47%), associated with an increase of approximately 61 million in country-level population. Additionally, a 7% increase (95% CI, 3-11%) in mortality risk with each additional occurrence of coastal flooding events was observed. By leveraging this knowledge, decision-makers can develop targeted policies and interventions to enhance community preparedness, reduce vulnerability, and ultimately save lives in the face of increasing coastal flooding risks.
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    Atmospheric Impact of Biogenic Volatile Organic Compounds: Improving Measurement and Modeling Capabilities
    Panji, Namrata Shanmukh (Virginia Tech, 2024-08-23)
    Biogenic volatile organic compounds (BVOCs) are naturally occurring organic compounds emitted by plants, trees, and ecosystems, exerting a profound influence on the Earth's atmosphere, air quality, climate, and ecosystem dynamics. This research project aims to advance our understanding of BVOC emissions and their implications through a comprehensive and multi-faceted investigation. We investigate the dynamics of BVOCs in the atmosphere through three key objectives. First, we introduce a novel enriching inlet that uses selective permeation to preconcentrate reactive organic gases in small sample flows for atmospheric gas sampling, enhancing the sensitivity and detection limits of analytical instruments. Enrichments between 4640% and 111% were measured for major reactive atmospheric gases at ultra low flow rates and roughly several hundred percent for ambient samples at moderately low flow rates. Second, we constrain light-dependency in BVOC emissions models by comparing modeled and long-term observed BVOC concentrations measured at a mid-canopy monitoring site in a southeastern US forest. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Framework for 0-D Atmospheric Modeling (F0AM) were utilized to simulate emissions and chemical transformations, respectively to disentangle the time- and species-specificity of light dependency for various BVOC (α-pinene, camphene, and α-fenchene are completely light-independent and limonene, β-thujene, sabinene, and γ-terpinene are seasonally light-dependent). Finally, we examine these models deeper to investigate uncertainties and highlight current limitations due to variability in planetary boundary layer height (PBLH) datasets. We highlight the significance of simultaneous PBLH and BVOC measurements for improving the accuracy of BVOC concentration models. We show that a lack of co-located measurements is a large source of uncertainty in modeling BVOC concentrations. The successful completion of these objectives contributes to a better understanding of the complex interactions between BVOC emissions and atmospheric chemistry.
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    Atmospheric Pollutant Levels in Southeast Brazil During COVID-19 Lockdown: Combined Satellite and Ground-based Data Analysis
    Cruvinel Brandao Fonseca Marinho, Rayssa (Virginia Tech, 2021-01-22)
    With the ongoing COVID-19 pandemic being spread all over the world, lockdown measures are being implemented making air pollution levels go down in several countries. In this context, the air quality changes in the highly populated and trafficked Brazilian states of Sao Paulo (SP) and Rio de Janeiro (RJ) are hereby going to be addressed using a combination of satellite and ground-based data analysis. We explored nitrogen dioxide (NO2) and particulate matter (PM2.5) daily levels for the month of May during different years within 2015-2020. Daily measurements of NO2 column concentrations from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite were also gathered and averaged decreases of 42% and 49.6% were found for the year of 2020 compared to previous averaged 2015-2019 years. In parallel to the NO2 column retrieval, the ground-based data, measured by the Brazilian States Environmental Institutions, is analyzed, and correlated with satellite retrievals. Correlation coefficients between column and ground-based concentrations were 77% and 53% in SP and RJ, respectively. It was found a 13.3% (p-value = 0.099) and 18.8% (p-value = 0.077) decrease in NO2 levels for SP and RJ, respectively, in 2020 compared to 2019. For PM2.5, no significant change was observed for the same time period in the SP region, although the high number of fire burnings in the Southeast region seemed to be affecting PM2.5 levels. In addition to natural emissions (fire burnings), the combined data was also evaluated taking meteorological parameters, such as temperature and wind speed, into account. No interference of weather or fire was found in 2020 NO2 ground levels compared to previous years, This integrated analysis is innovative and has yet to be more explored in Brazilian studies. This is true specifically because the ground-based stations are spatially and temporally sparse in Brazil.
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    Automated 2D Detection and Localization of Construction Resources in Support of Automated Performance Assessment of Construction Operations
    Memarzadeh, Milad (Virginia Tech, 2012-12-10)
    This study presents two computer vision based algorithms for automated 2D detection of construction workers and equipment from site video streams. The state-of-the-art research proposes semi-automated detection methods for tracking of construction workers and equipment. Considering the number of active equipment and workers on jobsites and their frequency of appearance in a camera's field of view, application of semi-automated techniques can be time-consuming. To address this limitation, two new algorithms based on Histograms of Oriented Gradients and Colors (HOG+C), 1) HOG+C sliding detection window technique, and 2) HOG+C deformable part-based model are proposed and their performance are compared to the state-of-the-art algorithm in computer vision community. Furthermore, a new comprehensive benchmark dataset containing over 8,000 annotated video frames including equipment and workers from different construction projects is introduced. This dataset contains a large range of pose, scale, background, illumination, and occlusion variation. The preliminary results with average performance accuracies of 100%, 92.02%, and 89.69% for workers, excavators, and dump trucks respectively, indicate the applicability of the proposed methods for automated activity analysis of workers and equipment from single video cameras. Unlike other state-of-the-art algorithms in automated resource tracking, these methods particularly detects idle resources and does not need manual or semi-automated initialization of the resource locations in 2D video frames.
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    Automated Vision-Based Tracking and Action Recognition of Earthmoving Construction Operations
    Heydarian, Arsalan (Virginia Tech, 2012-04-30)
    The current practice of construction productivity and emission monitoring is performed by either manual stopwatch studies which are significantly labor intensive and subject to human errors, or by the use of RFID and GPS tracking devices which may be costly and impractical. To address these limitations, a novel computer vision based method for automated 2D tracking, 3D localization, and action recognition of construction equipment from different camera viewpoints is presented. In the proposed method, a new algorithm based on Histograms of Oriented Gradients and hue-saturation Colors (HOG+C) is used for 2D tracking of the earthmoving equipment. Once the equipment is detected, using a Direct Linear Transformation followed by a non-linear optimization, their positions are localized in 3D. In order to automatically analyze the performance of these operations, a new algorithm to recognize actions of the equipment is developed. First, a video is represented as a collection of spatio-temporal features by extracting space-time interest points and describing each with a Histogram of Oriented Gradients (HOG). The algorithm automatically learns the distributions of these features by clustering their HOG descriptors. Equipment action categories are then learned using a multi-class binary Support Vector Machine (SVM) classifier. Given a novel video sequence, the proposed method recognizes and localizes equipment actions. The proposed method has been exhaustively tested on 859 videos from earthmoving operations. Experimental results with an average accuracy of 86.33% and 98.33% for excavator and truck action recognition respectively, reflect the promise of the proposed method for automated performance monitoring.
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    Building Interdisciplinary Partnerships for Community-Engaged Environmental Health Research in Appalachian Virginia
    Satterwhite, Emily M.; Bell, Shannon E.; Marr, Linsey C.; Thompson, Christopher K.; Prussin, Aaron J. II; Buttling, Lauren G.; Pan, Jin; Gohlke, Julia M. (MDPI, 2020-03-05)
    This article describes a collaboration among a group of university faculty, undergraduate students, local governments, local residents, and U.S. Army staff to address long-standing concerns about the environmental health effects of an Army ammunition plant. The authors describe community-responsive scientific pilot studies that examined potential environmental contamination and a related undergraduate research course that documented residents’ concerns, contextualized those concerns, and developed recommendations. We make a case for the value of resource-intensive university–community partnerships that promote the production of knowledge through collaborations across disciplinary paradigms (natural/physical sciences, social sciences, health sciences, and humanities) in response to questions raised by local residents. Our experience also suggests that enacting this type of research through a university class may help promote researchers’ adoption of “epistemological pluralism”, and thereby facilitate the movement of a study from being “multidisciplinary” to “transdisciplinary”.