Ambient monitoring and air quality modeling of air toxics concentrations at the neighborhood-scale level is a key element for human exposure and health risk assessments. Since 2005, The University of Texas at Austin (UT) has operated a dense ambient monitoring network that includes both hourly automated gas chromatographs as well as threshold triggered canister samples and meteorological data in the Corpus Christi area. Although Corpus Christi is in attainment with the National Ambient Air Quality Standards for both ozone and fine particulate matter, its significant petroleum refining complex has resulted in concerns about exposure to air toxics. The seven site network, incorporating both the industrial and residential areas in Corpus Christi, provided a unique opportunity to further the development and understanding of air quality modeling for toxic air pollutants at the neighborhood-scale level. Two air dispersion models, AERMOD and CALPUFF, were used to predict air concentrations of benzene for one of the UT operated monitoring sites (Oak Park monitoring site: C634) and the predictions were compared to the observed benzene concentration data at the Oak Park monitoring site to evaluate model performance. AERMOD and CALPUFF were also used to predict benzene concentrations in populated areas and at sensitive receptor locations such as schools and hospitals. Both AERMOD and CALPUFF were able to reproduce the early morning high benzene concentration and the northern wind effect except under strong NNE wind conditions, where the observed data indicated elevated high benzene concentration which AERMOD and CALPUFF failed to predict. These under-predictions could be due to the NNE strong wind condition at that time of these occurrences or could be attributed to different types of emissions other than the point sources emissions from the 2005 TCEQ Photochemical Modeling inventory, such as mobile sources or accidental emission events. These preliminary analyses could be expanded by modeling longer periods, by including other emission sources and by inter-comparisons with observed data from other CCNAT monitoring sites. In addition, fundamentally different modeling approaches (eulerian, rather than lagrangian) could be considered. / text

Carbon dioxide (CO2) is the leading contributor to global warming and climate change. The increases in fossil fuel emissions, deforestation, and changes of land use have resulted in increased CO2 levels in the atmosphere from 280 ppm in 1765 to 390 ppm in 2010. Carbon mitigation policies for managing the biosphere to increase net CO2 uptake are dependent upon accurate knowledge of the biosphere fluxes. However, Northern Hemisphere bottom-up and top-down biosphere flux estimates show significant discrepancies, especially in North America. In this study, we design an analysis framework that integrates observations with models with the goal of reducing some of the key uncertainties in estimating CO2 fluxes and concentrations in the Midwest, USA. In this research, the biosphere model, WRF-VPRM model (Ahmadov et al., 2007) is used to simulate CO2 biosphere fluxes and atmospheric CO2 concentrations in the Midwest, USA at high spatial resolution. Reducing uncertainties in the predictions is accomplished by improving the model transport configurations (i.e. the WRF planetary boundary layer (PBL) scheme, the number of vertical layers and the horizontal resolution), utilizing a more detailed land cover map, optimizing VPRM photosynthesis and respiratory parameters for major crops (i.e. corn and soybean) against flux towers, and integrating CO2 tall tower observations and model through a top-down data assimilation method to improve the VPRM model parameters and in turn improving the flux and concentration estimates. The WRF-VPRM model configuration with the YonSei University PBL scheme produced the most accurate CO2 concentration predictions at the WBI tower at all three tower levels with the maximum error reduction of 17.1%. Increasing the number of vertical layers improved the CO2 estimates during nighttime and early morning, especially at 30 m, where the error was reduced by a maximum of ~ 20%. The differences in the monthly average net fluxes over the State of Iowa between the high resolution WRF-VPRM model and coarse resolution Carbon Tracker were significant, 71%, 18%, and 62% in June, July, and August, respectively. The fluxes calculated by the VPRM model are primarily dependent on 4 model parameters, half saturation value of photosynthesis (PAR0), light use efficiency (ë), and respiration parameters (á and â). These parameters are specific to vegetation types, regions, and time period. The default settings do not distinguish between corn and soybean, which are major crops in the Midwest and have significant different photosynthesis rates. When corn and soybean are explicitly included in the model, the flux estimate changed by 31.3% at 12 pm and 24.5% at 12 am. Two different methods were used to optimize for the VPRM model parameters which are optimization against Ameriflux NEE and using a top-down variational method. The simulation using optimized parameters from the variational method reduced the error during the daytime from 11.6 ppm to 7.8 ppm. The average fluxes optimized using the variational method changed by 17% and 38.6% at 12 pm and 12 am, respectively. The more accurate VPRM parameters lead to the more accurate biosphere fluxes, which will ease the evaluation of benefits of different carbon mitigation policies.

This work is aimed to study the capabilities of CMAQ and WRF-Chem models for predicting the PM10 and PM2.5 pollution in the west side of Saudi Arabia. To do this fairly, one-month simulations (April, 2021) are done in both models using same initial and boundary conditions, meteorology and anthropogenic emissions. Unique configurations in both models allow to compare differences in the chemical processes and natural emissions estimation of each model. Simulated PM (PM10 and PM2.5) surface concentrations and AOD are compared with available observations to assess models’ performance. Moreover, CMAQ is used to study a real air pollution episode generated by a fire in the Rabigh Electricity Power Station between April 8 and April 11, 2021.

Photochemical grid models are used to evaluate air pollution control strategies by simulating the physical and chemical processes that influence pollutant concentrations. Their accuracy depends on the accuracy of input data used for anthropogenic and biogenic emissions, land surface characteristics, initial and boundary conditions and meteorological conditions. Evaluation of model performance requires sufficient ambient data. This work develops approaches for applying satellite data to allow more frequent and timely estimates of parameters required to estimate emissions and pollutant removal processes for regional air quality modeling. Land use and land cover (LULC) data prepared from remote sensing satellite data were evaluated for use as inputs to photochemical grid models for estimating dry deposition velocities and biogenic emissions. The results indicated that satellite-based data derived from the Moderate Resolution Imaging Spectroradiometer instrument can be used to provide periodic updates to LULC information used in photochemical models. The sensitivity of predicted ozone concentrations to LULC data used for biogenic emission estimates was examined by comparing the database currently used for modeling in southeastern Texas with a new database prepared from Landsat satellite imagery and field data. The satellite data and image classification techniques provide useful tools for mapping and monitoring changes in LULC. However, field validation is necessary to link species and biomass densities to the classification system needed for accurate biogenic emissions estimates, especially in areas that have dense concentrations of species that emit high levels of biogenic hydrocarbons. The application of NO2 measurements from the Ozone Monitoring Instrument (OMI) to validation of NOx emission estimates and identification of emission sources for regional air quality modeling for Texas was examined. OMI observations can be used to identify regions with changes in emissions over time or where estimates have large uncertainties and to evaluate the effectiveness of emission reduction strategies. For example, in the Dallas-Fort Worth area, observed NO2 column densities from OMI indicate that emission controls are less effective than anticipated due to increased area source emissions. The techniques developed in this work have broad applicability in the advancement of methods for including satellite remote sensing data in regional air quality modeling. / text

The current dissertation is motivated by the need for an improved understanding of aerosol water interactions both in subsaturated and supersaturated atmospheric conditions with a strong emphasis on air pollution and climate change modeling. A cloud droplet formation parameterization was developed to i) predict droplet formation from a lognormal representation of aerosol size distribution and composition, and, ii) include a size-dependant mass transfer coefficient for the growth of water droplets which explicitly accounts for the impact of organics on droplet growth kinetics. The parameterization unravels most of the physics of droplet formation and is in remarkable agreement with detailed numerical parcel model simulations, even for low values of the accommodation coefficient. The parameterization offers a much needed rigorous and computationally inexpensive framework for directly linking complex chemical effects on aerosol activation in global climate models. The new aerosol activation parameterization was also tested against observations from highly polluted clouds (within the vicinity of power plant plumes). Remarkable closure was achieved (much less than the 20% measurement uncertainty). The error in predicted cloud droplet concentration was mostly sensitive to updraft velocity. Optimal closure is obtained if the water vapor uptake coefficient is equal to 0.06. These findings can serve as much needed constraints in modeling of aerosol-cloud interactions in the North America. Aerosol water interactions in ambient relative humidities less than 100% were studied using a thermodynamic equilibrium model for inorganic aerosol and a three dimensional air quality model. We developed a new thermodynamic equilibrium model, ISORROPIA-II, which predicts the partitioning of semi-volatiles and the phase state of K+/Ca2+/Mg2+/NH4+/Na+/SO42-/NO3-/Cl-/H2O aerosols. A comprehensive evaluation of its performance was conducted against the thermodynamic module SCAPE2 over a wide range of atmospherically relevant conditions. Based on its computational rigor and performance, ISORROPIA-II appears to be a highly attractive alternative for use in large scale air quality and atmospheric transport models. The new equilibrium model was also used to thermodynamically characterize aerosols measured at a highly polluted area. In the ammonia-rich environment of Mexico City, nitrate and chloride primarily partition in the aerosol phase with a 20-min equilibrium timescale; PM2.5 is insensitive to changes in ammonia but is to acidic semivolatile species. When RH is below 50%, predictions improve substantially if the aerosol follows a deliquescent behavior. The impact of including crustal species (Ca2+, K+, M2+) in equilibrium calculations within a three dimensional air quality model was also studied. A significant change in aerosol water (-19.8%) and ammonium (-27.5%) concentrations was predicted when crustals are explicitly included in the calculations even though they contributed, on average, only a few percent of the total PM2.5 mass, highlighting the need for comprehensive thermodynamic calculations in the presence of dust.

One of the major sources of air pollution in the United States metropolitan areas is due to automobiles. With the huge growth of motor vehicles and, greater dependence on them, air pollution problems have been aggravated. According to the EPA, nearly 95% of carbon monoxide (CO ) (EPA 1999) in urban areas comes from mobile sources, of which 51% is contributed by on road vehicles. It is well known fact that, carbon monoxide is one of the major mobile source pollutants and CO has detrimental effects on the human health. Carbon monoxide is the result of mainly incomplete combustion of gasoline in motor vehicles (FDOT 1996). The National Environmental Policy Act (NEPA) gives important considerations to the actions to be taken. Transportation conformity . The Clean Air Act Amendments (CAAA, 1970) was an important step in meeting the National Ambient Air Quality Standards In order to evaluate the effects of CO and Particulate Matter (PM) impacts based on the criteria for NAAQS standards, it is necessary to conduct dispersion modeling of emissions for mobile source emissions. Design of transportation engineering systems (roadway design) should take care of both the flow of the traffic as well as the air pollution aspects involved. Roadway projects need to conform to the State Implementation Plan (SIP) and meet the NAAQS. EPA guidelines for air quality modeling on such roadway intersections recommend the use of CAL3QHC. The model has embedded in it CALINE 3.0 (Benson 1979) – a line source dispersion model based on the Gaussian equation. The model requires parameters with respect to the roadway geometry, fleet volume, averaging time, surface roughness, emission factors, etc. The CAL3QHC model is a DOS based model which requires the modeling parameters to be fed into an input file. The creation of input the file is a tedious job. Previous work at UCF, resulted in the development of CALQVIEW, which expedites this process of creating input files, but the task of extracting the coordinates still has to be done manually. The main aim of the thesis is to reduce the analysis time for modeling emissions from roadway intersections, by expediting the process of extracting the coordinates required for the CAL3QHC model. Normally, transportation engineers design and model intersections for the traffic flow utilizing tools such as AutoCAD, Microstation etc. This thesis was to develop advanced software allowing graphical editing and coordinates capturing from an AutoCAD file. This software was named as CALQCAD. This advanced version will enable the air quality analyst to capture the coordinates from an AutoCAD 2004 file. This should expedite the process of modeling intersections and decrease analyst time from a few days to few hours. The model helps to assure the air quality analyst to retain accuracy during the modeling process. The idea to create the standalone interface was to give the AutoCAD user full functionality of AutoCAD tools in case editing is required to the main drawing. It also provides the modeler with a separate graphical user interface (GUI).

Modeling and simulations of large-scale systems are used extensively to not only better understand a natural phenomenon, but also to predict future events. Accurate model results are critical for design optimization and policy making. They can be used effectively to reduce the impact of a natural disaster or even prevent it from happening. In reality, model predictions are often affected by uncertainties in input data and model parameters, and by incomplete knowledge of the underlying physics. A deterministic simulation assumes one set of input conditions, and generates one result without considering uncertainties. It is of great interest to include uncertainty information in the simulation. By ``Uncertainty Quantification,'' we denote the ensemble of techniques used to model probabilistically the uncertainty in model inputs, to propagate it through the system, and to represent the resulting uncertainty in the model result. This added information provides a confidence level about the model forecast. For example, in environmental modeling, the model forecast, together with the quantified uncertainty information, can assist the policy makers in interpreting the simulation results and in making decisions accordingly. Another important goal in modeling and simulation is to improve the model accuracy and to increase the model prediction power. By merging real observation data into the dynamic system through the data assimilation (DA) technique, the overall uncertainty in the model is reduced. With the expansion of human knowledge and the development of modeling tools, simulation size and complexity are growing rapidly. This poses great challenges to uncertainty analysis techniques. Many conventional uncertainty quantification algorithms, such as the straightforward Monte Carlo method, become impractical for large-scale simulations. New algorithms need to be developed in order to quantify and reduce uncertainties in large-scale simulations. This research explores novel uncertainty quantification and reduction techniques that are suitable for large-scale simulations. In the uncertainty quantification part, the non-sampling polynomial chaos (PC) method is investigated. An efficient implementation is proposed to reduce the high computational cost for the linear algebra involved in the PC Galerkin approach applied to stiff systems. A collocation least-squares method is proposed to compute the PC coefficients more efficiently. A novel uncertainty apportionment strategy is proposed to attribute the uncertainty in model results to different uncertainty sources. The apportionment results provide guidance for uncertainty reduction efforts. The uncertainty quantification and source apportionment techniques are implemented in the 3-D Sulfur Transport Eulerian Model (STEM-III) predicting pollute concentrations in the northeast region of the United States. Numerical results confirm the efficacy of the proposed techniques for large-scale systems and the potential impact for environmental protection policy making. ``Uncertainty Reduction'' describes the range of systematic techniques used to fuse information from multiple sources in order to increase the confidence one has in model results. Two DA techniques are widely used in current practice: the ensemble Kalman filter (EnKF) and the four-dimensional variational (4D-Var) approach. Each method has its advantages and disadvantages. By exploring the error reduction directions generated in the 4D-Var optimization process, we propose a hybrid approach to construct the error covariance matrix and to improve the static background error covariance matrix used in current 4D-Var practice. The updated covariance matrix between assimilation windows effectively reduces the root mean square error (RMSE) in the solution. The success of the hybrid covariance updates motivates the hybridization of EnKF and 4D-Var to further reduce uncertainties in the simulation results. Numerical tests show that the hybrid method improves the model accuracy and increases the model prediction quality. / Ph. D.

Atmospheric particulate matter (PM) is linked to various adverse environmental and health impacts. PM in the atmosphere reduces visibility, alters precipitation patterns by acting as cloud condensation nuclei (CCN), and changes the Earth’s radiative balance by absorbing or scattering solar radiation in the atmosphere. The long-range transport of pollutants leads to increase in PM concentrations even in remote locations such as polar regions and mountain ranges. One significant effect of PM on the earth’s climate occurs while light absorbing PM, such as Black Carbon (BC), deposits over snow. In the Arctic, BC deposition on highly reflective surfaces (e.g. glaciers and sea ices) has very intense effects, causing snow to melt more quickly. Thus, characterizing PM sources, identifying long-range transport pathways, and quantifying the climate impacts of PM are crucial in order to inform emission abatement policies for reducing both health and environmental impacts of PM. Chemical transport models provide mathematical tools for better understanding atmospheric system including chemical and particle transport, pollution diffusion, and deposition. The technological and computational advances in the past decades allow higher resolution air quality and weather forecast simulations with more accurate representations of physical and chemical mechanisms of the atmosphere. Due to the significant role of air pollutants on public health and environment, several countries and cities perform air quality forecasts for warning the population about the future air pollution events and taking local preventive measures such as traffic regulations to minimize the impacts of the forecasted episode. However, the costs associated with the complex air quality forecast models especially for simulations with higher resolution simulations make “forecasting” a challenge. This dissertation also focuses on applications, performance analysis, and optimization of meteorology and air quality modeling forecasting models. This dissertation presents several modeling studies with various scales to better understand transport of aerosols from different geographical sources and economic sectors (i.e. transportation, residential, industry, biomass burning, and power) and quantify their climate impacts. The simulations are evaluated using various observations including ground site measurements, field campaigns, and satellite data. The sector-based modeling studies elucidated the importance of various economical sector and geographical regions on global air quality and the climatic impacts associated with BC. This dissertation provides the policy makers with some implications to inform emission mitigation policies in order to target source sectors and regions with highest impacts. Furthermore, advances were made to better understand the impacts of light absorbing particles on climate and surface albedo. Finally, for improving the modeling speed, the performances of the models are analyzed, and optimizations were proposed for improving the computational efficiencies of the models. Theses optimizations show a significant improvement in the performance of Weather Research and Forecasting (WRF) and WRF-Chem models. The modified codes were validated and incorporated back into the WRF source code to benefit all WRF users. Although weather and air quality models are shown to be an excellent means for forecasting applications both for local and hemispheric scale, further studies are needed to optimize the models and improve the performance of the simulations.

In this thesis, I apply the adjoint for the Community Multiscale Air Quality model (hereafter CMAQ-ADJ) in a high spatial resolution study of the sensitivity of ozone to several of its precursors in the regions surrounding the Great Lakes. CMAQ-ADJ was originally developed for low spatial resolution applications. In order to use it in high spatial resolution (12 km) studies, it was necessary to resolve a conflict between the pre-set fixed output time step interval in CMAQ-ADJ and the CMAQ-calculated irregular synchronization time-step and also to modify the meteorological interface for the backward model integrations. To increase computation efficiency, the chemistry time-step in the modified CMAQ-ADJ is checkpointed instead of being re-calculated in the backward part of the model as before. I used the modified model to analyze the sensitivity of ozone to precursor species for cases of assumed high ozone episode in two target locations in southwestern and east-central Ontario. The studies examined the influence of pre-existing ozone, NO, CO, anthropogenic volatile organic compounds (VOCs) and isoprene on ozone level changes for the 69 hours immediately preceding the assumed high ozone event. The results are dominated by the long-distance advection, local meteorology (lake breezes), air temperature, the underlying surface features, and emissions in the pollutant pathway. Both production and titration of ozone by NOx is evident at different times and locations in the simulations. The industrial Midwest U.S. and Ohio Valley have been shown to be an important source of anthropogenic emission of NO and most VOCs that contribute to high ozone events in southwestern and east-central Ontario. Isoprene from the northern forest suppresses ozone in both target regions, with a greater magnitude in east-central Ontario. The response of ozone level in the two selected receptor regions in Ontario to different VOCs depends on the type of VOC, the time and location they are emitted, and the air temperature. Increasing VOC emissions in urban areas such as Toronto and Ottawa in the morning can enhance the ozone level by late afternoon. Increasing VOCs except ethylene and formaldehyde in regions with large VOC/NOx ratio in the morning tends to suppress the ozone level by late afternoon. Among all the species examined, NO has the largest impact on the target ozone level changes. CO is very unlikely to significantly influence the ozone level changes in southwestern or east-central Ontario.

In this study, emission inventory was prepared and pollutant dispersion studies were carried out for the area around &Ccedil / ayirhan Thermal Power Plant to determine the effects of the plant on the environment. Stack gas measurement results were used for the emissions from the power plant and emission factors were used for calculating the emissions from residential sources and coal stockpiles in the study region. Ground level concentrations of SO2, NOx and PM10 were estimated by using EPA approved dispersion models / namely ISCST3 and AERMOD. The ground level concentrations predicted by two models were compared with the results of ambient air pollution measurements for November 2004. Predictions of both ISCST3 and AERMOD were underestimating the ground level SO2 concentrations. However, AERMOD predictions are better than ISCST3 predictions. The results of both models had good correlation with the results of NOx measurements. It has been shown that the contribution of the power plant to SO2, NOx and PM10 pollution in the area studied is minimal.