Browsing by Author "Foroutan, Hosein"

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    Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere
    Bullock, Orren Russell, Jr.; Foroutan, Hosein; Gilliam, Robert C.; Herwehe, Jerold A. (Copernicus Publications, 2018-07-16)
    The Model for Prediction Across Scales – Atmosphere (MPAS-A) has been modified to allow fourdimensional data assimilation (FDDA) by the nudging of temperature, humidity, and wind toward target values predefined on the MPAS-A computational mesh. The addition of nudging allows MPAS-A to be used as a global-scale meteorological driver for retrospective air quality modeling. The technique of “analysis nudging” developed for the Penn State/National Center for Atmospheric Research (NCAR) Mesoscale Model, and later applied in the Weather Research and Forecasting model, is implemented in MPAS-A with adaptations for its polygonal Voronoi mesh. Reference fields generated from 1° x 1° National Centers for Environmental Prediction (NCEP) FNL (Final) Operational Global Analysis data were used to constrain MPAS-A simulations on a 92–25 km variable-resolution mesh with refinement centered over the contiguous United States. Test simulations were conducted for January and July 2013 with and without FDDA, and compared to reference fields and near-surface meteorological observations. The results demonstrate that MPAS-A with analysis nudging has high fidelity to the reference data while still maintaining conservation of mass as in the unmodified model. The results also show that application of FDDA constrains model errors relative to 2m temperature, 2m water vapor mixing ratio, and 10m wind speed such that they continue to be at or below the magnitudes found at the start of each test period.
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    Air Quality in Southeast Brazil during COVID-19 Lockdown: A Combined Satellite and Ground-Based Data Analysis
    Brandao, Rayssa; Foroutan, Hosein (MDPI, 2021-05-01)
    With the current 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 São Paulo (SP) and Rio de Janeiro (RJ) were addressed using a combination of satellite and ground-based daily data analysis. We explored nitrogen dioxide (NO2) and fine particulate matter (PM2.5) daily levels for the month of May from 2015–2020. Daily measurements of NO2 column concentrations from the Ozone Monitoring Instrument (OMI) aboard NASA’s Aura satellite were analyzed and decreases of 42% and 49.6% were found for SP and RJ, respectively, during the year 2020 compared to the 2015–2019 average. Besides NO2 column retrievals, ground-based data measured by the Brazilian States Environmental Institutions were analyzed and correlated with satellite retrievals. Correlation coefficients between year-to-year changes in satellite column and ground-based concentrations were 77% and 53% in SP and RJ, respectively. Ground-based data showed 13.3% and 18.8% decrease in NO2 levels for SP and RJ, respectively, in 2020 compared to 2019. In SP, no significant change in PM2.5 was observed in 2020 compared to 2019. To further isolate the effect of emissions reduction due to the lockdown, meteorological data and number of wildfire hotspots were analyzed. NO2 concentrations showed negative and positive correlations with wind speed and temperature, respectively. PM2.5 concentration distributions suggested an influence by the wildfires in the southeast region of the country. Synergistic analyses of satellite retrievals, surface level concentrations, and weather data provide a more complete picture of changes to pollutant levels.
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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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    Computational Analysis of Internal Coral Hydrodynamics
    Hossain, Md monir (Virginia Tech, 2020-07-30)
    Knowledge of the detailed flow dynamics at the interior of branching corals is critical for a full understanding of nutrient uptake, mass transport, wave dissipation, and other essential processes. These physiological processes depend on the local velocity field, local concentration gradients of nutrients and waste, and the turbulent stresses developed on and above the coral surface. Though the large-scale hydrodynamics over coral reefs are well studied, the interior hydrodynamics, between the branches, remains uncharacterized due to limited optical and acoustic access to the interior. In the current thesis, a three-dimensional immersed boundary method in the large eddy simulation framework was used to compute the flow inside several branching coral colony geometries in order to study the effects of branch density and surface structure on the flow fields in the coral interiors. Two different Pocillopora colony species were studied at different Reynolds numbers. A ray-tracing algorithm was used for capturing the arbitrary branches of these complex geometries to obtain the three-dimensional flow fields within these colonies for the first time. The analysis showed the formation of vortices at the colony interior that stir the water column and thus passively enhance mass transport, compensating for the reduced mean velocity magnitude compared to the free stream value, within the densely branched Pocillopora meandrina colony. Further analysis showed that the mean streamwise velocity profile changes shape along the streamwise direction inside P. meandrina, whereas the mean velocity profile did not change shape from the front to the back for the loosely branched Pocillopora colony, Pocillopora eydouxi. Moreover, turbulent flow field quantities were computed for both these structures, and for two almost identical Montipora capitata colony geometries, one with, and one without roughness elements called verrucae. The analyses demonstrated significant differences in the mean velocity profiles, Reynolds stress, and other flow quantities with changes in colony branch density and surface structure.
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    Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs)
    Nolan, Peter J.; Pinto, James; González-Rocha, Javier; Jensen, Anders; Vezzi, Christina N.; Bailey, Sean C. C.; de Boer, Gijs; Diehl, Constantin; Laurence, Roger; Powers, Craig W.; Foroutan, Hosein; Ross, Shane D.; Schmale, David G. III (MDPI, 2018-12-15)
    Concentrations of airborne chemical and biological agents from a hazardous release are not spread uniformly. Instead, there are regions of higher concentration, in part due to local atmospheric flow conditions which can attract agents. We equipped a ground station and two rotary-wing unmanned aircraft systems (UASs) with ultrasonic anemometers. Flights reported here were conducted 10 to 15 m above ground level (AGL) at the Leach Airfield in the San Luis Valley, Colorado as part of the Lower Atmospheric Process Studies at Elevation—a Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) campaign in 2018. The ultrasonic anemometers were used to collect simultaneous measurements of wind speed, wind direction, and temperature in a fixed triangle pattern; each sensor was located at one apex of a triangle with ∼100 to 200 m on each side, depending on the experiment. A WRF-LES model was used to determine the wind field across the sampling domain. Data from the ground-based sensors and the two UASs were used to detect attracting regions (also known as Lagrangian Coherent Structures, or LCSs), which have the potential to transport high concentrations of agents. This unique framework for detection of high concentration regions is based on estimates of the horizontal wind gradient tensor. To our knowledge, our work represents the first direct measurement of an LCS indicator in the atmosphere using a team of sensors. Our ultimate goal is to use environmental data from swarms of sensors to drive transport models of hazardous agents that can lead to real-time proper decisions regarding rapid emergency responses. The integration of real-time data from unmanned assets, advanced mathematical techniques for transport analysis, and predictive models can help assist in emergency response decisions in the future.
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    Coupling of organic and inorganic aerosol systems and the effect on gas-particle partitioning in the southeastern US
    Pye, Havala O. T.; Zuend, Andreas; Fry, Juliane L.; Isaacman-VanWertz, Gabriel; Capps, Shannon L.; Appel, K. Wyat; Foroutan, Hosein; Xu, Lu; Ng, Nga L.; Goldstein, Allen H. (European Geophysical Union, 2018-01-12)
    Several models were used to describe the partitioning of ammonia, water, and organic compounds between the gas and particle phases for conditions in the southeastern US during summer 2013. Existing equilibrium models and frameworks were found to be sufficient, although additional improvements in terms of estimating pure-species vapor pressures are needed. Thermodynamic model predictions were consistent, to first order, with a molar ratio of ammonium to sulfate of approximately 1.6 to 1.8 (ratio of ammonium to 2 x sulfate, R-N/2S approximate to 0.8 to 0.9) with approximately 70% of total ammonia and ammonium (NHx) in the particle. Southeastern Aerosol Research and Characterization Network (SEARCH) gas and aerosol and Southern Oxidant and Aerosol Study (SOAS) Monitor for AeRosols and Gases in Ambient air (MARGA) aerosol measurements were consistent with these conditions. CMAQv5.2 regional chemical transport model predictions did not reflect these conditions due to a factor of 3 overestimate of the nonvolatile cations. In addition, gas-phase ammonia was overestimated in the CMAQ model leading to an even lower fraction of total ammonia in the particle. Chemical Speciation Network (CSN) and aerosol mass spectrometer (AMS) measurements indicated less ammonium per sulfate than SEARCH and MARGA measurements and were inconsistent with thermodynamic model predictions. Organic compounds were predicted to be present to some extent in the same phase as inorganic constituents, modifying their activity and resulting in a decrease in [H+](air) (H+ in mu g m(-3) air), increase in ammonia partitioning to the gas phase, and increase in pH compared to complete organic vs. inorganic liquid-liquid phase separation. In addition, accounting for nonideal mixing modified the pH such that a fully interactive inorganic-organic system had a pH roughly 0.7 units higher than predicted using traditional methods (pH = 1.5 vs. 0.7). Particle-phase interactions of organic and inorganic compounds were found to increase partitioning towards the particle phase (vs. gas phase) for highly oxygenated (O : C >= 0.6) compounds including several isoprene-derived tracers as well as levoglucosan but decrease particle-phase partitioning for low O : C, monoterpene-derived species.
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    Drone-based particle monitoring above two harmful algal blooms (HABs) in the USA
    Bilyeu, Landon; Bloomfield, Bryan; Hanlon, Regina; González-Rocha, Javier; Jacquemin, Stephen J.; Ault, Andrew P.; Birbeck, Johnna A.; Westrick, Judy A.; Foroutan, Hosein; Ross, Shane D.; Powers, Craig W.; Schmale, David G. III (Royal Society of Chemistry, 2022-09-26)
    Little is known about the transport and fate of aerosolized particles associated with harmful algal blooms (HABs). An Airborne DROne Particle-monitoring System (AirDROPS) was developed and used to monitor, collect, and characterize airborne particles over two HABs in Grand Lake St Marys (GLSM) and Lake Erie (LE), Ohio USA in August 2019. The AirDROPS consisted of an impinging device (ID) and an optical particle counter (OPC) mounted on a large commercial quadcopter (DJI Inspire 2). The sensor package was mounted above the airframe to limit the effects of propeller downwash that can corrupt measurements taken below the drone. Nineteen flights were conducted 10 m above water level (AWL) at GLSM, and five flights were conducted 10 m AWL at LE. The sampling height was chosen to minimize the effects of propwash on aerosolization from the lake surface. One intercomparison flight was conducted at GLSM over land adjacent to a sonic anemometer mounted on the top of a flagpole 15 m above ground level (AGL). Particle counts generally decreased from morning to afternoon flights, ranging from >4000 in the morning to <1000 later in the day. Decreased particle counts were associated with an increase in windspeed that corresponded with time of day, ranging from >4000 below 4 m s−1 to <2500 above 4 m s−1. Flow cytometry was used to image particles trapped in a liquid impinger onboard the AirDROPS. Sixty percent (15/25) of the impinger samples contained at least one biotic (fluorescent) object. Impinger samples were also analyzed for a suite of potential cyanotoxins using liquid chromatography-mass spectrometry (LC-MS/MS), but no cyanotoxins were detected in any of these air samples (water samples collected during a similar time contained greater than 20 μg L−1 microcystins). Additional work is needed to understand the environmental factors associated with the potential aerosolization and transport of cyanobacterial cells and toxins in aquatic environments.
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    Drone-based water sampling and characterization of three freshwater harmful algal blooms in the United States
    Hanlon, Regina; Jacquemin, Stephen J.; Birbeck, Johnna A.; Westrick, Judy A.; Harb, Charbel; Gruszewski, Hope; Ault, Andrew P.; Scott, Durelle T.; Foroutan, Hosein; Ross, Shane D.; González-Rocha, Javier; Powers, Craig; Pratt, Lowell; Looney, Harry; Baker, Greg; Schmale, David G. III (Frontiers, 2022-08-24)
    Freshwater harmful algal blooms (HABs), caused mostly by toxic cyanobacteria, produce a range of cyanotoxins that threaten the health of humans and domestic animals. Climate conditions and anthropogenic influences such as agricultural run-off can alter the onset and intensity of HABs. Little is known about the distribution and spread of freshwater HABs. Current sampling protocols in some lakes involve teams of researchers that collect samples by hand from a boat and/or from the shoreline. Water samples can be collected from the surface, from discrete-depth collections, and/or from depth-integrated intervals. These collections are often restricted to certain months of the year, and generally are only performed at a limited number of collection sites. In lakes with active HABs, surface samples are generally sufficient for HAB water quality assessments. We used a unique DrOne Water Sampling SystEm (DOWSE) to collect water samples from the surface of three different HABs in Ohio (Grand Lake St Marys, GLSM and Lake Erie) and Virginia (Lake Anna), United States in 2019. The DOWSE consisted of a 3D-printed sampling device tethered to a drone (uncrewed aerial system, or UAS), and was used to collect surface water samples at different distances (10–100 m) from the shore or from an anchored boat. One hundred and eighty water samples (40 at GLSM, 20 at Lake Erie, and 120 at Lake Anna) were collected and analyzed from 18 drone flights. Our methods included testing for cyanotoxins, phycocyanin, and nutrients from surface water samples. Mean concentrations of microcystins (MCs) in drone water samples were 15.00, 1.92, and 0.02 ppb for GLSM, Lake Erie, and Lake Anna, respectively. Lake Anna had low levels of anatoxin in nearly all (111/120) of the drone water samples. Mean concentrations of phycocyanin in drone water samples were 687, 38, and 62 ppb for GLSM, Lake Erie, and Lake Anna, respectively. High levels of total phosphorus were observed in the drone water samples from GLSM (mean of 0.34 mg/L) and Lake Erie (mean of 0.12 mg/L). Lake Anna had the highest variability of total phosphorus with concentrations that ranged from 0.01 mg/L to 0.21 mg/L, with a mean of 0.06 mg/L. Nitrate levels varied greatly across sites, inverse with bloom biomass, ranging from below detection to 3.64 mg/L, with highest mean values in Lake Erie followed by GLSM and Lake Anna, respectively. Drones offer a rapid, targeted collection of water samples from virtually anywhere on a lake with an active HAB without the need for a boat which can disturb the surrounding water. Drones are, however, limited in their ability to operate during inclement weather such as rain and heavy winds. Collectively, our results highlight numerous opportunities for drone-based water sampling technologies to track, predict, and respond to HABs in the future.
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    Electric versus Gasoline Vehicle Particulate Matter and Greenhouse Gas Emissions: Large-scale Analysis
    Rakha, Hesham A.; Farag, Mohamed; Foroutan, Hosein (2024-07-31)
    This study addresses the contentious issue of non-exhaust particulate matter (PM) emissions from battery electric vehicles (BEVs) compared to internal combustion engine vehicles (ICEVs) by developing models to quantify tire and brake PM emissions and incorporate them in a microscopic traffic simulation environment. Furthermore, exhaust greenhouse gas (GHG) emissions are quantified to develop a comprehensive picture of vehicle network emissions. The key findings are: 1) BEVs emit more tire and less brake PM emissions, thus necessitating a comprehensive analysis to avoid erroneous conclusions. 2) If at least 15% of travel is city driving, BEVs produce less non-exhaust PM emissions. 3) For the freeway section analyzed, a volume-to-capacity ratio of at least 0.25 is required for BEVs to produce less non-exhaust PM emissions. By incorporating these detailed models into traffic simulations, the study provides a tool for policymakers to better understand and manage vehicle emissions at a city level.
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    Experimental and Theoretical Developments in the Application of Lagrangian Coherent Structures to Geophysical Transport
    Nolan, Peter Joseph (Virginia Tech, 2019-04-15)
    The transport of material in geophysical fluid flows is a problem with important implications for fields as diverse as: agriculture, aviation, human health, disaster response, and weather forecasting. Due to the unsteady nature of geophysical flows, predicting how material will be transported in these systems can often be challenging. Tools from dynamical systems theory can help to improve the prediction of material transport by revealing important transport structures. These transport structures reveal areas of the flow where fluid parcels, and thus material transported by those parcels, are likely to converge or diverge. Typically, these transport structures have been uncovered by the use of Lagrangian diagnostics. Unfortunately, calculating Lagrangian diagnostics can often be time consuming and computationally expensive. Recently new Eulerian diagnostics have been developed. These diagnostics are faster and less expensive to compute, while still revealing important transport structures in fluid flows. Because Eulerian diagnostics are so new, there is still much about them and their connection to Lagrangian diagnostics that is unknown. This dissertation will fill in some of this gap and provide a mathematical bridge between Lagrangian and Eulerian diagnostics. This dissertation is composed of three projects. These projects represent theoretical, numerical, and experimental advances in the understanding of Eulerian diagnostics and their relationship to Lagrangian diagnostics. The first project rigorously explores the deep mathematical relationship that exists between Eulerian and Lagrangian diagnostics. It proves that some of the new Eulerian diagnostics are the limit of Lagrangian diagnostics as integration time of the velocity field goes to zero. Using this discovery, a new Eulerian diagnostic, infinitesimal-time Lagrangian coherent structures is developed. The second project develops a methodology for estimating local Eulerian diagnostics from wind velocity data measured by a fixed-wing unmanned aircraft system (UAS) flying in circular arcs. Using a simulation environment, it is shown that the Eulerian diagnostic estimates from UAS measurements approximate the true local Eulerian diagnostics and can predict the passage of Lagrangian diagnostics. The third project applies Eulerian diagnostics to experimental data of atmospheric wind measurements. These are then compared to Eulerian diagnostics as calculated from a numerical weather simulation to look for indications of Lagrangian diagnostics.
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    Experimental development of a lake spray source function and its model implementation for Great Lakes surface emissions
    Harb, Charbel; Foroutan, Hosein (European Geosciences Union, 2022-09-12)
    Lake spray aerosols (LSAs) are generated from freshwater breaking waves in a mechanism similar to their saltwater counterparts, sea spray aerosols (SSAs). Unlike the well-established research field pertaining to SSAs, studying LSAs is an emerging research topic due to their potential impacts on regional cloud processes and their association with the aerosolization of freshwater pathogens. A better understanding of these climatic and public health impacts requires the inclusion of LSA emission in atmospheric models, yet a major hurdle to this inclusion is the lack of a lake spray source function (LSSF), namely an LSA emission parameterization. Here, we develop an LSSF based on measurements of foam area and the corresponding LSA emission flux in a marine aerosol reference tank (MART). A sea spray source function (SSSF) is also developed for comparison. The developed LSSF and SSSF are then implemented in the Community Multiscale Air Quality (CMAQ) model to simulate particle emissions from the Great Lakes surface from 10 to 30 November 2016. Measurements in the MART revealed that the average SSA total number concentration was 8 times higher than that of LSA. Over the 0.01–10 µm aerosol diameter size range, the developed LSSF was around 1 order of magnitude lower than the SSSF and around 2 orders of magnitude lower for aerosols with diameters between 1 and 3 µm. Model results revealed that LSA emission flux from the Great Lakes surface can reach ∼105 m−2 s−1 during an episodic event of high wind speeds. These emissions only increased the average total aerosol number concentrations in the region by up to 1.65 %, yet their impact on coarse-mode aerosols was much more significant, with up to a 19-fold increase in some areas. The increase in aerosol loading was mostly near the source region, yet LSA particles were transported up to 1000 km inland. Above the lakes, LSA particles reached the cloud layer, where the total and coarse-mode particle concentrations increased by up to 3 % and 98 %, respectively. Overall, this study helps quantify LSA emission and its impact on regional aerosol loading and the cloud layer.
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    A General Observational Strategy for Validation of Satellite NO₂ Retrievals using Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS)
    Earley, Jeffrey D. (Virginia Tech, 2022-06-21)
    This thesis analyzes the effectiveness of spatially averaged Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements at regular azimuth angle intervals on an hourly basis to validate satellite based DOAS measurements. Off-Axis MAX-DOAS Measurements taken in Blacksburg, Virginia, between November 2021 and April 2022 with an evenly distributed set of measurements were averaged every hour and compared to Direct Sun measurements, also averaged every hour. Comparisons of the difference in average measurement from both measuring strategies, as well as the distribution standard deviations of hourly measurements suggests that the NO₂ distribution around Blacksburg is homogeneous. In order to test the effectiveness of this sampling strategy,in an inhomogeneous location, the LOTOS-EUROS high resolution (1kmx1km) chemical transport model was used to simulate profiles and vertical column densities of real measurements taken during the TROLIX'19 Field Campaign. The LOTOs-EUROS model was used to simulate vertical profiles as well as Vertical Column Densities based on real MAX-DOAS measurements as well as TROPOMI viewing geometry. While the individual ground measurements were not equal to the TROPOMI profile, the TROPOMI profile is approximately the average of the profiles of measurements made within the hour of TROPOMI overpass.
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    Harmful algal blooms and toxic air: The economic value of improved forecasts
    Moeltner, Klaus; Fanara, Tracy; Foroutan, Hosein; Hanlon, Regina; Lovko, Vince; Ross, Shane D.; Schmale, David G. III (2021-02)
    The adverse economic impacts of harmful algal blooms can be mitigated via tailored forecasting methods. Adequate provision of these services requires knowledge of the losses avoided, or, in other words, the economic benefits they generate. The latter can be difficult to measure for broader population segments, especially if forecasting services or features do not yet exist. We illustrate how Stated Preference tools and Choice Experiments are well-suited for this case. Using as example forecasts of respiratory irritation levels associated with airborne toxins caused by Florida red tide, we show that 24-hour predictions of spatially and temporally refined air quality conditions are valued highly by the underlying population. This reflects the numerous channels and magnitude of red tide impacts on locals' life and activities, which are also highlighted by our study. Our approach is broadly applicable to any type of air quality impediment with risk of human exposure.
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    How mobility restrictions policy and atmospheric conditions impacted air quality in the State of Sao Paulo during the COVID-19 outbreak
    Rudke, A. P.; Martins, J. A.; de Almeida, D. S.; Martins, L. D.; Beal, A.; Hallak, R.; Freitas, E. D.; Andrade, M. F.; Foroutan, Hosein; Baek, B. H.; Albuquerque, T. T. de A. (Academic Press-Elsevier, 2021-07-01)
    Mobility restrictions are among actions to prevent the spread of the COVID-19 pandemic and have been pointed as reasons for improving air quality, especially in large cities. However, it is crucial to assess the impact of atmospheric conditions on air quality and air pollutant dispersion in the face of the potential variability of all sources. In this study, the impact of mobility restrictions on the air quality was analyzed for the most populous Brazilian State, São Paulo, severely impacted by COVID-19. Ground-based air quality data (PM10, PM2.5, CO, SO2, NOx, NO2, NO, and O3) were used from 50 automatic air quality monitoring stations to evaluate the changes in concentrations before (January 01 - March 25) and during the partial quarantine (March 16 - June 30). Rainfall, fires, and daily cell phone mobility data were also used as supplementary information to the analyses. The Mann-Whitney U test was used to assess the heterogeneity of the air quality data during and before mobility restrictions. In general, the results demonstrated no substantial improvements in air quality for most of the pollutants when comparing before and during restrictions periods. Besides, when the analyzed period of 2020 is compared with the year 2019, there is no significant air quality improvement in the São Paulo State. However, special attention should be given to the Metropolitan Area of São Paulo (MASP), due to the vast population residing in this area and exposed to air pollution. The region reached an average decrease of 29% in CO, 28% in NOx, 40% in NO, 19% in SO2, 15% in PM2.5, and 8% in PM10 concentrations during the mobility restrictions period compared to the same period in 2019. The only pollutant that showed an increase in concentration was ozone, with a 20% increase compared to 2019 during the mobility restrictions period. Before the mobility restrictions period, the region reached an average decrease of 30% in CO, 39% in NOx, 63% in NO, 12% in SO2, 23% in PM2.5, 18% in PM10, and 16% in O3 concentrations when compared to the same period in 2019. On the other hand, Cubatão, a highly industrialized area, showed statistically significant increases above 20% for most monitored pollutants in both periods of 2020 compared to 2019. This study reinforces that the main driving force of pollutant concentration variability is the dynamics of the atmosphere at its various time scales. An abnormal rainy season, with above average rainfall before the restrictions and below average after it, generated a scenario in which the probable significant reductions in emissions did not substantially affect the concentration of pollutants.
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    Hydraulic Characterization of Mounded Gravel Fish Nests: Incipient Motion Criteria and Despiking Acoustic Doppler Velocimeter Data
    Kraus, Samuel Aloysius (Virginia Tech, 2024-06-06)
    The bluehead chub (Nocomis leptocephalus) is a keystone species, an ecosystem engi- neer that constructs mounded gravel nests for spawning. Chubs provide benefits for other spawning fishes, predators, and benthic organisms through their nest construction. This study seeks to apply sediment transport models to find incipient motion criteria and erosion susceptibility of chubs nests. Field water flow velocities were measured with an acoustic Doppler velocimeter (ADV) in Tom's Creek, Blacksburg, Virginia, USA. ADVs are often used to collect in-situ turbulent velocity data. In almost all applications of ADVs, erroneous spikes are recorded during collection, which can significantly distort turbulence statistics de- rived from velocity fluctuations. In this study, a bivariate kernel density estimation despiking algorithm is compared to a novel univariate simplification developed as part of this work. Despiking methods are evaluated using field ADV and direct numerical simulation (DNS) data of a turbulent boundary layer. Visual assessment of despiked velocity time series and power spectra and corresponding changes in statistical moments, as well as response to arti- ficial spiking of DNS data, yield valid performance of the univariate method. After despiking chub nest data, multiple methods of finding bed shear stress from velocity vertical profiles are evaluated. Bed shear stress is found over the profile of 26 field nests. The ambient to peak flow stress amplification due to a nest's bed protrusion is found to be a proportion of τ = 1.66τ to determine a critical ambient Shields parameter of approximately τ∗ = 0.03 pa c,a for nests.
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    Cannabis pollen dispersal across the United States
    Nimmala, Manu; Ross, Shane D.; Foroutan, Hosein (Nature Portfolio, 2024-09-04)
    For the recently legalized US hemp industry (Cannabis sativa), cross-pollination between neighboring fields has become a significant challenge, leading to contaminated seeds, reduced oil yields, and in some cases, mandated crop destruction. As a step towards assessing hemp cross-pollination risk, this study characterizes the seasonal and spatial patterns in windborne hemp pollen dispersal spanning the conterminous United States (CONUS). By leveraging meteorological data obtained through mesoscale model simulations, we have driven Lagrangian Stochastic models to simulate wind-borne hemp pollen dispersion across CONUS on a county-by-county basis for five months from July to November, encompassing the potential flowering season for industrial hemp. Our findings reveal that pollen deposition rates escalate from summer to autumn due to the reduction in convective activity during daytime and the increase in wind shear at night as the season progresses. We find diurnal variations in pollen dispersion: nighttime conditions favor deposition in proximity to the source, while daytime conditions facilitate broader dispersal albeit with reduced deposition rates. These shifting weather patterns give rise to specific regions of CONUS more vulnerable to hemp cross-pollination.
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    Improving the simulation of convective dust storms in regional-to-global models
    Foroutan, Hosein; Pleim, Jonathan E. (AGU Publications, 2017-09-05)
    Convective dust storms have significant impacts on atmospheric conditions and air quality and are a major source of dust uplift in summertime. However, regional-to-global models generally do not accurately simulate these storms, a limitation that can be attributed to (1) using a single mean value for wind speed per grid box, i.e., not accounting for subgrid wind variability and (2) using convective parametrizations that poorly simulate cold pool outflows. This study aims to improve the simulation of convective dust storms by tackling these two issues. Specifically, we incorporate a probability distribution function for surface wind in each grid box to account for subgrid wind variability due to dry and moist convection. Furthermore, we use lightning assimilation to increase the accuracy of the convective parameterization and simulated cold pool outflows. This updated model framework is used to simulate a massive convective dust storm that hit Phoenix, AZ, on 6 July 2011. The results show that lightning assimilation provides a more realistic simulation of precipitation features, including timing and location, and the resulting cold pool outflows that generated the dust storm. When those results are combined with a dust model that accounts for subgrid wind variability, the prediction of dust uplift and concentrations are considerably improved compared to the default model results. This modeling framework could potentially improve the simulation of convective dust storms in global models, regional climate simulations, and retrospective air quality studies.
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    Interactions Between Dust and Ecosystem, and Landscape at Multiple Scales
    Huang, Xinyue (Virginia Tech, 2024-09-05)
    Atmospheric dust is the largest contributor to global aerosols from land. Dust emissions rate and properties are influenced by meteorological conditions, parent soil, and landscape, and in turn, it affects impacts on climate, ecosystems, and human societies through various pathways. This dissertation aims to explore the coupled dynamics of dust particle emissions and their essential properties in relation to topography, ecosystem, and atmospheric conditions by integrating information across multiple scales. Specifically, three research projects are pursued. First, the modulation of dust emissions by non-photosynthetic vegetation (NPV) is evaluated by implementing a satellite-based total vegetation dataset, which includes NPV, into a regional atmospheric chemistry model. Simulations of the entire year 2016 over the conterminous United States demonstrate that NPV reduces dust emissions by 10-70% from most dust sources in the southwest, particularly in spring. Second, the relationship between topographic wind conditions (i.e., speed and direction with respect to surface slope) and dust particle size distribution is investigated using a decade's worth of dust reanalysis data covering North Africa. Findings indicate that the fraction of coarse dust in emissions increases with wind speed and slope, particularly under uphill winds, the latter highlighting the role of topography in enhancing vertical transport for larger particles. These positive correlations weaken during the afternoon and summer events, suggesting that turbulence associated with haboob events suspends coarse particles. Finally, a series of air samples collected in Tenerife, Spain is revisited for a detailed study on the associated dust plume characteristics, which would facilitate the understanding of how environmental factors during transport influence airborne microbial assemblages. Using back trajectory analysis and dust optical depth reanalysis data, air samples impacted by African dust are identified. Seasonal variations in trajectories and associated environmental conditions reveal highly variable trans-Atlantic airflows. Elevated altitudes, higher temperatures, and lower relative humidity (RH) along summer trajectories implied the presence of Saharan Air Layer, whereas the frequent occurrence of higher RH (> 40%) and light precipitation in spring indicate more deposition of dust and associated microbes during transport. Overall, this work highlights the importance of accurately representing of various environmental elements that interact with the dust cycle, such as vegetation and topographic winds, which improves our ability to predict and manage the impacts of dust as well as other components of the Earth system.