A major opportunity for using satellite observations of tropospheric chemical concentrations is to improve our scientific understanding of atmospheric processes by integrated analysis of satellite, aircraft, and ground-based observations with global and regional scale models. One endpoint of such efforts is to reduce modeling biases and uncertainties. The idea of coupling these observations with a regional scale air quality model was the starting point of this research. The overall objective of this research was to improve the NOₓ emission inventories by integrating observations from different platforms and regional air quality modeling. Specific objectives were: 1) Comparison of satellite NO₂ retrievals with simulated NO₂ by the regional air quality model. Comparison of simulated tropospheric gas concentrations simulated by the regional air quality model, with aircraft and ground-based observations; 3) Assessment of the uncertainties in comparing satellite NO₂ retrievals with NOₓ emissions estimates and model simulations; 4) Identification of biases in emission inventories by data assimilation of satellite NO₂ retrievals, and ground-based NO, NO₂ and O₃ observations with an iterative inverse method using the regional air quality model coupled with sensitivity calculations; 5) Improvement of our understanding of NOₓ emissions, and the interaction between regional and global air pollution by an integrated analysis of satellite NO₂ retrievals with the regional air quality model. Along with these objectives, a lightning NOₓ emission inventory was prepared for two months of summer 2004 to account for a significant upper level NOₓ source. Spatially-resolved weekly NO₂ variations from satellite retrievals were compared with estimated NOₓ emissions for different region types. Data assimilation of satellite NO₂ retrievals, and ground-based NO, NO₂ and O₃ observations were performed to evaluate the NOₓ emission inventory. This research contributes to a better understanding of the use of satellite NO₂ retrievals in air quality modeling, and improvements in the NOₓ emission inventories by correcting some of the inconsistencies that were found in the inventories. Therefore, it may provide groups that develop emissions estimates guidance on areas for improvement. In addition, this research indicates the weaknesses and the strengths of the satellite NO₂ retrievals and offers suggestions to improve the quality of the retrievals for further use in the tropospheric air pollution research.

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.

Air pollution has changed from an urban environmental problem to a phenomenon spreading to state, country and even global scales. In response, a variety of regulations, standards, and policies have been enacted world-wide. Policy-making and development of efficient and effective control strategies requires understanding of air quality impacts from different sources, which are usually estimated using source-oriented air quality models and their corresponding uncertainties should be addressed. This thesis evaluates emission uncertainties and their impacts on air quality modeling (Models-3/Community Multiscale Air Quality Model (CMAQ)), with special attention to biomass burning. Impacts of uncertainties in ozone precursors (mainly NOX and VOC) emissions from different sources and regions on ozone formation and emission control efficiencies are evaluated using Monte Carlo methods. Instead of running CMAQ multiple of times, first and higher order ozone sensitivities calculated by Higher-order Decoupled Direct method in Three Dimensions (CMAQ-HDDM-3D) are employed to propagate emission uncertainties. Biomass burning is one of the major sources for PM2.5. Impacts of uncertainties in biomass burning emissions, including total amount, temporal and spatial characteristics, and speciation on air quality modeling are investigated to identify emission shortcomings. They are followed by estimation of seasonal PM2.5 source contributions over the southeastern US focusing on Georgia. Results show that prescribed forest fires are the largest individual biomass burning source. Forest fire emissions under different forest management practices and ensuing air quality impacts are further studied. Forest management practices considered here include different burning seasons, fire-return intervals (FRIs), and controlling emissions during smoldering. Finally, uncertainties in prescribed forest fire emissions are quantified by propagation of uncertainties in burned area, fuel consumption and emission factors, which are required inputs for emissions estimation and quantified by various fire behavior models and methods. In summary, this thesis has provided important insights regarding emission uncertainties and their impacts on air quality modeling.

Ambient and indoor air pollution is a major cause of premature mortality, and has been associated with more than three million preventative deaths per year worldwide. Most of these health impacts are from the effects from fine particulate matter. It is suspected that PM2.5 health effects vary by composition, which depends on the mixture of pollutants emitted by sources. This has led to efforts to estimate relationships between sources of PM2.5 and health effects. The health effects of PM2.5 may be preferentially dependent on specific species; however, recent work has suggested that health impacts may actually be caused by the net effect of the mixture of pollutants which make up PM2.5. Recently, there have been efforts to use source impacts from source apportionment (SA) studies as a proxy for these multipollutant effects. Source impacts can be quantified using both receptor and chemical transport models (RMs and CTMs), and have both advantages and limitations for their use in health studies. In this work, a technique is developed that reconciles differences between source apportionment (SA) models by ensemble-averaging source impacts results from several SA models. This method uses a two-step process to calculate the ensemble average. An initial ensemble average is used calculate new estimates of uncertainties for the individual SA methods that are used in the ensemble. Next, an updated ensemble average is calculated using the SA method uncertainties as weights. Finally, uncertainties of the ensemble average are calculated using propagation of errors that includes covariance terms. The ensemble technique is extended to include a Bayesian formulation of weights used in ensemble-averaging source impacts. In a Bayesian approach, probabilistic distributions of the parameters of interest are estimated using prior distributions, along with information from observed data. Ensemble averaging results in updated estimates of source impacts with lower uncertainties than individual SA methods. Overall uncertainties for ensemble-averaged source impacts were ~45 - 74%. The Bayesian approach also captures the expected seasonal variation of biomass burning and secondary impacts. Sensitivity analysis found that using non-informative prior weighting performed better than using weighting based on method-derived uncertainties. The Bayesian-based source impacts for biomass burning correlate better with observed levoglucosan (R2=0.66) and water soluble potassium (R2=0.63) than source impacts estimated using more traditional methods, and more closely agreed with observed total mass. Power spectra of the time series of biomass burning source impacts suggest that profiles/factors associated with this source have the greatest variability across methods and locations. A secondary focus of this work is to examine the impacts of biomass burning. First a field campaign was undertaken to measure emissions from prescribed fires. An emissions factor of 14±17 g PM2.5/kg fuel burned was determined. Water soluble organic carbon (WSOC) was highly correlated with potassium (K) (R2=.93) and levoglucosan (R2=0.98). Results using a biomass burning source profile derived from this work further indicate that source apportionment is sensitive to levels of potassium in biomass burning source profiles, underscoring the importance of quantifying local biomass burning source profiles. Second, the sensitivity of ambient PM2.5 to various fire and meteorological parameters in was examined using the method of principle components regression (PCR) to estimate sensitivity of PM2.5 to fire data and, observed and forecast meteorological parameters. PM2.5 showed significant sensitivity to PB, with a unit-based sensitivity of 3.2±1 µg m-3 PM2.5 per 1000 acres burned. PM2.5 had a negative sensitivity to dispersive parameters such as wind speed.

Particles reduce visibility, change climate, and affect human health. In 1997, the National Ambient Air Quality Standard (NAAQS) for PM2.5 (particles less than 2.5 mm) was promulgated. The annual mean PM2.5 mass concentrations in Atlanta, Georgia exceed the standard, and control is needed. The first goal of this study is to develop the control strategies of PM2.5 in Atlanta, Georgia. Based on the statistical analysis of measured data, from 22% to 40% of emission reductions are required to meet the NAAQS at 95% CI. The estimated control levels can be tested using the Community Multiscale Air Quality (CMAQ) model to better assess if the proposed levels will achieve sufficient reduction in PM2.5. The second goal of this study is to analyze various uncertainties residing in CMAQ. For the model to be used in such applications with confidence, it needs to be evaluated. The model performance is calculated by the relative agreement between volume-averaged predictions and point measurements. Up to 14% of the model error for PM2.5 mass is due to the different spatial scales of the two values. CMAQ predicts PM2.5 mass concentrations reasonably well, but CMAQ significantly underestimates PM2.5 number concentrations. Causes of the underestimation include that assumed inaccurate particle density and particle size of the primary emissions in CMAQ, in addition to the expression of the particle size with three lognormal distributions. Also, the strength and limitations of CMAQ in performing PM2.5 source apportionment are compared with those of the Chemical Mass Balance with Molecular Markers. Finally, the accuracy of emissions, one of the important inputs of CMAQ, is evaluated by the inverse modeling. Results show that base level emissions for CO and SO2 sources are relatively accurate, whereas NH3, NOx, PEC and PMFINE emissions are overestimated. The emission adjustment for POA and VOC emissions is significantly different among regions.

L'objectif principal de cette thèse est le développement d'outils numériques peu coûteux pour la cartographie de concentrations de polluants a partir de mesures et de modèles déterministes avancés. Le développement mondial et l'urbanisation des populations génèrent une hausse d’émissions et d'expositions. A n d'estimer les expositions individuelles et évaluer leur association à des pathologies diverses, les campagnes de mesure de qualité de l'air, et des études épidémiologiques sur les effets de santé de la pollution sont devenues plus courantes. Cependant, les concentrations de pollution de l'air sont très variables en temps et en espace. La sensibilité et la précision de ces études est souvent détériorée par de mauvais classements des expositions dus aux estimations grossières des expositions individuelles. Les méthodes d'assimilation de données intègrent des données de mesures et des modèles mathématiques a n de mieux approximer le champ de concentration. Quand ces méthodes sont basées sur un modèle de qualité de l'air (AQM) déterministe avancé, elles sont capables de fournir des approximations détaillées et de petite échelle. Ces informations précises permettront de meilleures estimations d'exposition. Néanmoins, ces méthodes sont souvent tr es coûteuses. Elles nécessitent la résolution a plusieurs reprises du modèle, qui peut être coûteux soi-même. Dans ce travail nous enquêtons sur la combinaison des méthodes des bases réduites (RB) et d'assimilation de données pour des AQM avancés a l'échelle urbaine. Nous souhaitons diminuer le coût de résolution en exploitant les RB, et incorporer des données de mesure a n d'améliorer la qualité de la solution. On étend la méthode de Parameterized-Background Data-Weak (PBDW) pour des AQMs basés sur la physique. Cette méthode est capable d'estimer de façon rapide et "online" des concentrations de polluants à l'échelle du quartier. Elle se sert des AQMs disponibles dans une procédure non intrusive et efficace par rapport aux temps de calculs pour réduire le coût de résolution par des centaines de fois. Les résultats de PBDW sont comparés à la méthode d'interpolation empirique généralisée (GEIM) et à une méthode inverse usuelle, la méthode adjointe, a n de mesurer l'efficacité de la PBDW. Cette comparaison montre la possibilité d'augmenter la précision de la solution, et d'une grande réduction en temps de calcul par rapport à des méthodes classiques. Dans nos applications sur un modèle imparfait, l'étude a fourni des estimations d'état avec erreur d'approximation de moins de 10% presque partout. Les résultats se montrent prometteurs pour la reconstruction en temps réel de champs de pollution sur de grands domaines par la PBDW / The principal objective of this thesis is the development of low-cost numerical tools for spatial mapping of pollutant concentrations from field observations and advanced deterministic models. With increased pollutant emissions and exposure due to mass urbanization and development worldwide, air quality measurement campaigns and epidemiology studies of the association between air pollution and adverse health effects have become increasingly common. However, as air pollution concentrations are highly variable spatially and temporally, the sensitivity and accuracy of these epidemiology studies is often deteriorated by exposure misclassi cation due to poor estimates of individual exposures. Data assimilation methods incorporate available measurement data and mathematical models to provide improved approximations of the concentration. These methods, when based on an advanced deterministic air quality models (AQMs), could provide spatially-rich small-scale approximations and can enable better estimates of effects and exposures. However, these methods can be computationally expensive. They require repeated solution of the model, which could itself be costly. In this work we investigate a combined reduced basis (RB) data assimilation method for use with advanced AQMs on urban scales. We want to diminish the cost of resolution, using RB arguments, and incorporate measurement data to improve the quality of the solution. We extend the Parameterized-Background Data-Weak (PBDW) method to physically-based AQMs. This method can rapidly estimate "online" pollutant concentrations at urban scale, using available AQMs in a non-intrusive and computationally effcient manner, reducing computation times by factors up to hundreds. We apply this method in case studies representing urban residential pollution of PM2.5, and we study the stability of the method depending on the placement or air quality sensors. Results from the PBDW are compared to the Generalized Empirical Interpolation Method (GEIM) and a standard inverse problem, the adjoint method, in order to measure effciency of the method. This comparison shows possible improvement in precision and great improvement in computation cost with respect to classical methods. We fi nd that the PBDW method shows promise for the real-time reconstruction of a pollution eld in large-scale problems, providing state estimation with approximation error generally under 10% when applied to an imperfect model

Among the most ubiquitous and persistent air quality problems facing urban areas are high concentrations of gas phase oxidants and fine particulate matter. Ozone and particulate matter concentrations in urban areas are significantly influenced by other factors in addition to local emissions, such as regional transport spanning distances as large as 1000 kilometers. Despite the importance of regional transport in meeting air quality standards, to date most analyses of regional transport have focused only on short duration episodes, or semi-quantitative assessments. The development and evaluation of seasonal, quantitative assessments of regional pollutant transport, based on modeling calculations and observational data is the topic of this dissertation. The observational data available through the Texas Air Quality Studies in 2000 and 2006 provide a unique opportunity to develop, evaluate, and improve methods for characterizing regional air pollutant transport. Measurements collected during these studies are used as the primary observational basis for characterizing regional ozone transport and to evaluate the performance of photochemical models. Results suggest that measurements (from aircraft and surface monitors) and the photochemical model provide consistent estimates of the magnitude of ozone transport. On this basis, photochemical modeling is used to determine potential impacts of regional ozone transport in Texas, under varying meteorological and photochemical conditions, as well as to characterize the dominant chemical and physical processes within urban plumes. While qualitative studies and limited quantitative analyses have been performed to assess regional ozone transport, this work includes the first detailed quantitative characterization of the importance of ozone transport over the course of an entire ozone season using both photochemical modeling and ambient data. Results demonstrate that urban plumes in Texas are capable of transporting significant amounts of ozone over distances spanning hundreds of kilometers. Furthermore, on a seasonal basis, there are a number of days characterized by high contributions from inter-city transport coinciding with high total ozone concentrations, suggesting that the role of inter-city transport will remain significant for many areas to demonstrate attainment of the NAAQS for ozone. Results also indicate that reductions in the impacts of inter-city transport are possible by decreases in emissions from source regions. / text