This thesis presents the use of data assimilation with optimal interpolation (OI) to develop atmospheric aerosol concentration estimates for the United States at high spatial and temporal resolutions. Concentration estimates are highly desirable for a wide range of applications, including visibility, climate, and human health. OI is a viable data assimilation method that can be used to improve Community Multiscale Air Quality (CMAQ) model fine particulate matter (PM2.5) estimates. PM2.5 is the mass of solid and liquid particles with diameters less than or equal to 2.5 μm suspended in the gas phase. OI was employed by combining model estimates with satellite and surface measurements. The satellite data assimilation combined 36 x 36 km aerosol concentrations from CMAQ with aerosol optical depth (AOD) measured by MODIS and AERONET over the continental United States for 2002. Posterior model concentrations generated by the OI algorithm were compared with surface PM2.5 measurements to evaluate a number of possible data assimilation parameters, including model error, observation error, and temporal averaging assumptions. Evaluation was conducted separately for six geographic U.S. regions in 2002. Variability in model error and MODIS biases limited the effectiveness of a single data assimilation system for the entire continental domain. The best combinations of four settings and three averaging schemes led to a domain-averaged improvement in fractional error from 1.2 to 0.97 and from 0.99 to 0.89 at respective IMPROVE and STN monitoring sites. For 38% of OI results, MODIS OI degraded the forward model skill due to biases and outliers in MODIS AOD. Surface data assimilation combined 36 × 36 km aerosol concentrations from the CMAQ model with surface PM2.5 measurements over the continental United States for 2002. The model error covariance matrix was constructed by using the observational method. The observation error covariance matrix included site representation that scaled the observation error by land use (i.e. urban or rural locations). In theory, urban locations should have less effect on surrounding areas than rural sites, which can be controlled using site representation error. The annual evaluations showed substantial improvements in model performance with increases in the correlation coefficient from 0.36 (prior) to 0.76 (posterior), and decreases in the fractional error from 0.43 (prior) to 0.15 (posterior). In addition, the normalized mean error decreased from 0.36 (prior) to 0.13 (posterior), and the RMSE decreased from 5.39 μg m-3 (prior) to 2.32 μg m-3 (posterior). OI decreased model bias for both large spatial areas and point locations, and could be extended to more advanced data assimilation methods. The current work will be applied to a five year (2000-2004) CMAQ simulation aimed at improving aerosol model estimates. The posterior model concentrations will be used to inform exposure studies over the U.S. that relate aerosol exposure to mortality and morbidity rates. Future improvements for the OI techniques used in the current study will include combining both surface and satellite data to improve posterior model estimates. Satellite data have high spatial and temporal resolutions in comparison to surface measurements, which are scarce but more accurate than model estimates. The satellite data are subject to noise affected by location and season of retrieval. The implementation of OI to combine satellite and surface data sets has the potential to improve posterior model estimates for locations that have no direct measurements.

Air quality in the Mexico City Metropolitan Area (MCMA) is the subject of many studies due to concerns from high emissions and their adverse effects on public health and the environment. In this study, a high resolution simulation is performed with the Community Multi-scale Air Quality modeling system (CMAQ) using meteorology generated by the Weather Research Forecasting system (WRF). The boundary conditions for CMAQ are provided by the Goddard Earth Observing System-CHEMistry model (GEOS-Chem). The simulation period was March 2-7, 2006. Hourly species concentrations of O3, NOx, CO, SO2, PM10, and PM2.5 for the period were provided by the Automatic Air Quality Monitoring Network (labeled as RAMA). Preliminary evaluation showed GEOS-Chem and CMAQ being in good agreement with their predicted concentrations. In comparison with the base case boundary conditions, the GEOS-Chem case performs better and predicts closer to the observed values of O3, NOx, PM10, PM2.5, and SO2. Particle trajectory analysis was performed using the HYbrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) to ascertain the major sources of SO2 emitters and their impact on the MCMA.

Both long-term and short-term exposure to fine particulate matter (PM2.5) has been shown to increase the rate of respiratory and cardiovascular illness, premature death, and hospital admissions from respiratory causes. It is important to understand what contributes to ambient PM2.5 level to establish effective regulation, and air quality model can provide guidance based on the best scientific understanding available. However, PM2.5 simulations in air quality models have often found performance less than desirable, particularly for organic carbon levels. Here, some of major shortcomings of current air quality model will be addressed and improved by using CMAQ, receptor models, and regression analysis. Detailed source apportionment of PM2.5 performed using the CMAQ-tracer method suggests that wood combustion and mobile sources are the largest sources of PM2.5, followed by meat cooking and industrial processes. Biases in emission estimates are investigated using tracer species, such as organic molecular markers and trace metals that are used in receptor models. Comparison of simulated and observed tracer species shows some consistent discrepancies, which enables us to quantify biases in emissions and improve CMAQ simulations. Secondary organic aerosol (SOA) is another topic that is investigated. CMAQ studies on organic aerosol usually underestimate organic carbon with larger than a 50% bias. Formation of aged aerosol from multigenerational semi-volatile organic carbon is added to CMAQ, significantly improving performance of organic aerosol simulations.

Windblown dust events can be defined as windblown dust emitted from the Earth's surface to the atmosphere. These events have significant impact on local air quality. Predicting the location and magnitude of these events is vital for Utah air quality assessment and planning. Previous modeling studies have focused only on past dust events. This work utilized a state-of-the-science software framework based on the Community Multiscale Air Quality (CMAQ) v5.3.1 modeling system to predict dust events in Utah. The framework was verified using previous studies for dust events in April 2017 and March 2010. Once verified, the framework was used to predict the impact of future land use properties on dust events. Two scenarios were studied – shrinking of the Great Salt Lake and the addition of large-scale solar farms west of the Wasatch Front. Both showed increases in dust concentrations overpopulated areas using the meteorological conditions from the April 2017 dust event. Such information from future impact studies can assess potential impacts from climate change and can guide government water and land use policies to mitigate dust event impacts.

Air quality forecasts are of great value since several pollutants in our environment effect both human health, global climate, vegetation, crop yields, animals, materials and acidification of forests and lakes. Air-quality forecasts help to make people aware of the presence and the quantity of pollutants, and give them a chance to protect themselves, their business and the Earth. Many different air-quality models are in daily use all over the world, providing citizens with forecasts of air quality and warnings of unhealthy air quality if recommended highest concentrations are exceeded. This study adapts the WRF meteorological model (Weather research and Forecasting model) to be a driver of the CMAQ air-quality model (models-3 Community Multiscale Air Quality model). Forecasts of ozone and nitrogen dioxide concentrations from this coupled WRF/CMAQ modelling system are tested against observed data during a four-day period in May, 2006. The Lower Fraser Valley study area is a fertile valley surrounded by mountain chains in southwest British Columbia, Canada. The valley stretches from the Pacific coast eastwards towards the Rocky Mountains. This valley hosts more than 2 million people and it is west Canada’s fastest growing region. The Lower Fraser Valley holds a big city, Vancouver, several suburbs, numerous industries and a widespread agricultural production. During the analysed four-day period in May, a synoptic high-pressure built over the region, favoring high concentrations of pollutants as ozone and nitrogen dioxide. The created WRF/CMAQ model forecasted an acceptable magnitude of nitrogen dioxide but the daily variations are not recreated properly by the model. The WRF/CMAQ model forecasts the daily variation of ozone in a satisfying way, but the forecasted concentrations are overestimated by between 20 and 30 ppb throughout the study. Factors that could contribute to the elevated ozone concentrations were investigated, and it was found that the weather forecasting model WRF was not generating fully reliable meteorological values, which in turn hurt the air-quality forecasts. As the WRF model usually is a good weather forecasting model, the short spin-up time for the model could be a probable cause for its poor performance. / Prognoser över luftkvaliteten är mycket värdefulla, då flera luftföroreningar i vår närmiljö påverkar människans hälsa, det globala klimatet, vegetation, djur, material och bidrar till försurning av skog och vattendrag. Luftkvalitetsprognoser gör människan mer medveten om närvaron av luftföroreningar och i vilken mängd de finns. De ger människan en chans att vidta skyddsåtgärder för att skydda sig själv, sitt eventuella levebröd, och Jorden. Många olika luftkvalitetsmodeller används idag dagligdags över hela världen och förser invånare med prognoser för luftkvaliteten och varningar om koncentrationerna av föroreningar överstiger rekommenderade värden. I denna studie används väderprognosmodellen WRF (Weather Research and Forecasting model) för att driva luftkvalitetsmodellen CMAQ (models-3 Community Multiscale Air Quality model). Prognoser av ozon- och kvävedioxidhalterna i luften från den kopplade WRF/CMAQ modellen analyseras mot observerade data under en fyra dagars period i maj, 2006. Studieområdet Lower Fraser Valley är en bördig dalgång som är omgiven av bergskedjor i sydvästra British Columbia, Kanada. Dalen sträcker sig från Stilla havskusten och österut mot Klippiga bergen. I denna dalgång bor mer än 2 miljoner människor och det är västra Kanadas snabbast växande region. Lower Fraser Valley rymmer en storstad, Vancouver, flera förorter, många industrier och även stora jordbruksområden. Den fyra dagars period i maj som analyseras karaktäriseras av ett högtrycksbetonat synoptiskt väderläge med lokala variationer, vilka tillsammans är gynnsamma för att uppmäta höga koncentrationer av luftföroreningar som ozon och kvävedioxid. Den skapade WRF/CMAQ modellen prognostiserar godtagbar magnitud hos kvävedioxid men den dagliga variationen återskapas inte av modellen. Modellen prognostiserar den dagliga variationen av ozonkoncentration på ett tillfredsställande sätt, men storleksmässigt ligger koncentrationerna en faktor 20-30 ppb för högt rakt av under hela studien. Kringliggande faktorer som kan påverka koncentrationen ozon studeras närmare och det framkommer att den meteorologiska prognosmodellen WRF inte genererar fullt tillförlitliga värden för en rättvisande luftkvalitetsprognos. Då WRF modellen vanligtvis är en bra prognosmodell kan den korta initialiseringstiden för modellen vara en trolig orsak till dess otillräckliga prestation.

The Models-3/Community Multi-scale Air Quality modeling system (CMAQ), coupled with Goddard Institute for Space Studies (GISS) atmospheric General Circulation Model (GCM), fifth Generation Mesoscale Model system (MM5), and Goddard Earth Observing System-CHEMistry (GEOS-Chem), was used to simulate atmospheric concentration of ozone and particulate matter over the continental United States 12-km and 36-km (CONUS) domains at year 2000 and year 2050. In the study, GISS GCM model outputs interfaced with MM5 were utilized to supply the current and future meteorological conditions for CMAQ. The conventional CMAQ profile initial and boundary conditions were replaced by time-varied and layer-varied GEOS-Chem outputs. The future emission concentrations were estimated using year 2000 based emissions with emission projections suggested by the IPCC A1B scenario. Multi-scenario statistical analyses were performed to investigate the effects of climate change and change of anthropogenic emissions toward 2050. The composite effects of these changes were broken down into individual effects and analyzed on three distinct regions (i.e., Midwest, Northeast and Southeast). The results of CMAQ hourly and 8-hour average concentrations indicate the maximum ozone concentration in the Midwest is increased slightly from year 2000 to year 2050, as a result of increasing average and maximum temperatures by 2 to 3 degrees Kelvin. In converse, there is an observed reduction of surface ozone concentration in the Southeast caused by the decrease in solar radiation. For the emission reduction scenario, the decline of anthropogenic emissions causes reductions of both ozone and PM2.5 for all regions. The emission reduction has compensated the effect of increasing temperature. The overall change on the maximum daily 8-hr ozone and average PM2.5 concentrations in year 2050 were estimated to be 10% and 40% less than the values in year 2000, respectively. The modeling results indicates the effect of emissions reduction has greater impact than the effect of climate change.

Durant les últimes dècades la quantitat de gasos i partícules contaminants que s’han injectat a l’atmosfera ha augmentat considerablement. Evidència d’aquesta informació són les elevades concentracions de diòxid de nitrogen o partícules amb una grandària inferior a 10µm (PM10) que es mesuren habitualment en zones urbanes i industrials, o les també altes concentracions d’ozó en moltes zones rurals situades a sobrevent de zones urbanes o industrials, on les concentracions dels seus precursors, fonamentalment òxids de nitrogen, són molt baixes en condicions normals. Paral•lelament però, a l’augment de la concentració de contaminats, s’ha potenciat de forma considerable la sensibilització de la població i de l’administració a la qualitat de l’aire que respirem. D’aquesta manera és ja una necessitat en els països desenvolupats el poder disposar d’eines de control, gestió i avaluació de la contaminació atmosfèrica. Aquest fet ha impulsat que en els darrers anys s’hagin produït importants avenços en els sistemes per a la modelització de la qualitat de l’aire fins a esdevenir eines de gestió i pronòstic ambiental que ofereixen resultats força precisos. A més, la modelització de la qualitat de l’aire s’ha convertit en una eina totalment necessària per poder conèixer els nivells d’immissió a zones on no es disposa d’estacions de mesura i per poder crear plans d’actuació per tal de poder combatre nivells elevats dels diversos contaminants. És per aquest motiu que en aquesta tesi doctoral es descriu la recerca i el desenvolupament d’un sistema de modelització de la qualitat de l’aire. S’ha utilitzat com a punt de partida els treballs anteriors, i es descriu l’acoblament dels diferents models que el componen. Els models són adaptats a la zona d’estudi mitjançant l’ajust de diversos paràmetres i són utilitzats de forma depenent entre ells. L’avaluació estadística dels pronòstics del sistema s’utilitza com el camí per a la localització d’incerteses i posterior refinament del model. Aquest procés d’acoblament i execució del sistema per períodes llargs de temps, validació dels resultats, localització d’incerteses i anàlisi de sensibilitat, esdevé un procés continu i cíclic durant la tasca de recerca del doctorand, fins a obtenir resultats dels paràmetres estadístics dins de les recomanacions de la comunitat científica i acomplint els requeriments de la legislació per a la utilització operativa del sistema. Degut a la problemàtica comentada en l’apartat anterior, cada vegada són més els estudis que es realitzen per investigar iniciatives de reducció de la contaminació atmosfèrica i per implementar models de pronòstic de qualitat de l’aire. És per aquests motius que l’objectiu final i fonamental d’aquesta tesi ha estat implementar un sistema de pronòstic de la qualitat sobre l’àrea d’interès que sigui actual, àgil i efectiu. Assolir aquest ambiciós objectiu ha suposat per al doctorand anar assolint reptes i metes durant la fase de recerca. El document s’estructura en deu capítols diferents, incloent la introducció i la bibliografia. / During the last decades the amount of gaseous and particulate pollutants that have been injected into the atmosphere has increased significantly. Evidence of this information is the high concentration of NO2 and aerosols that are usually measured in urban and industrial areas, or high concentrations of ozone in many rural areas. Parallel to this increase in concentration of pollutants, has been enhanced significantly the sensibility and management to the air quality. Thus it is necessary now in developed countries have the tools to control, management and evaluation of air pollution. In this way, the modelling of air quality is absolutely necessary to know the levels of gases and aerosols in areas where there are not measurement stations, and also to combat high levels of these pollutants. The photochemical models are tools of environmental management and forecasting that in recent years have been refined to provide quite accurate results. We need to adapt the models to each area by adjusting parameters and the study of characteristics of pollution episodes. For this reason, this doctoral thesis describes research and development of a air quality modelling system. We have been used as a starting point the previous work and we describe the coupling of different models. The models are adapted to the area of interest by setting different parameters and are used in a dependent relationship between them. The statistical evaluation of the forecasting system is used as a way to locate uncertain and further refinement of the model. This process of engagement and implementing the system for long periods of time, validation of results, location of uncertainties and sensitivity analysis is an ongoing, cyclical during the doctoral research.

A base case modelling investigation was conducted to explore the chemical and physical behaviour of ground-level ozone (O3) and its precursor nitrogen dioxide (NO2) in Ontario using the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model. Two related studies were completed to evaluate the performance of CMAQ in reproducing the behaviour of these species in both rural and urban environments by comparing to surface measurements collected by the Ontario Ministry of the Environment (MOE) network of air quality stations. The first study was a winter examination and the second study was conducted for a period during the summer of the same year. The municipality of North Bay was used to represent a rural setting given its smaller population relative to the city of Ottawa which was the base of the urban site. Statistical and graphical analyses were used to validate the model output. CMAQ was found to replicate the spatial variation of O3 and NO2 over the domain in both the winter and summer, but showed some difficulty in simulating the temporal allocation of the species. Validation statistics for North Bay and Ottawa showed overall O3 mean biases (MB) of 3.35 ppb and 2.25 ppb, respectively, and overall NO2 MB of -8.75 ppb and -4.37 ppb, respectively for the winter. Summer statistics generated O3 MB of 4.66 ppb (North Bay) and 10.05 ppb (Ottawa) while both MB for NO2 were between -2.20 ppb to -2.55 ppb. Graphical analysis showed that the model was not able to reproduce the lower levels of O3, especially at night, or the higher levels of NO2 during the day at the North Bay site for either season. This was expected since the comparisons were made between point measurements and 36 km grid-averaged model results. The presence of high amounts of NO2 emissions local to the monitoring sites compared to the levels represented in the emissions inventory may also be a contributing factor. The simulations for Ottawa demonstrated better agreement between model results and measurements as CMAQ provided a more accurate reproduction of both the higher and lower mixing ratios of O3 and NO2 during the winter and summer seasons. Results indicate that CMAQ is able to simulate urban environments better than rural ones.

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.

A base case modelling investigation was conducted to explore the chemical and physical behaviour of ground-level ozone (O3) and its precursor nitrogen dioxide (NO2) in Ontario using the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model. Two related studies were completed to evaluate the performance of CMAQ in reproducing the behaviour of these species in both rural and urban environments by comparing to surface measurements collected by the Ontario Ministry of the Environment (MOE) network of air quality stations. The first study was a winter examination and the second study was conducted for a period during the summer of the same year. The municipality of North Bay was used to represent a rural setting given its smaller population relative to the city of Ottawa which was the base of the urban site. Statistical and graphical analyses were used to validate the model output. CMAQ was found to replicate the spatial variation of O3 and NO2 over the domain in both the winter and summer, but showed some difficulty in simulating the temporal allocation of the species. Validation statistics for North Bay and Ottawa showed overall O3 mean biases (MB) of 3.35 ppb and 2.25 ppb, respectively, and overall NO2 MB of -8.75 ppb and -4.37 ppb, respectively for the winter. Summer statistics generated O3 MB of 4.66 ppb (North Bay) and 10.05 ppb (Ottawa) while both MB for NO2 were between -2.20 ppb to -2.55 ppb. Graphical analysis showed that the model was not able to reproduce the lower levels of O3, especially at night, or the higher levels of NO2 during the day at the North Bay site for either season. This was expected since the comparisons were made between point measurements and 36 km grid-averaged model results. The presence of high amounts of NO2 emissions local to the monitoring sites compared to the levels represented in the emissions inventory may also be a contributing factor. The simulations for Ottawa demonstrated better agreement between model results and measurements as CMAQ provided a more accurate reproduction of both the higher and lower mixing ratios of O3 and NO2 during the winter and summer seasons. Results indicate that CMAQ is able to simulate urban environments better than rural ones.