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Detailed photochemical modeling is used to guide air quality management activities around the world. These models use condensed chemical mechanisms to describe the multiphase processes that lead to chemical transformations in the atmosphere. Condensed mechanisms have generally not included the reactions of halogens, yet an expanding body of ambient observational evidence indicates that halogen chemistry, particularly chlorine chemistry, can be important in urban environments. This thesis is focused on the development, implementation, and evaluation of condensed chemical mechanisms that incorporate chlorine chemistry pathways. Gas phase reactions involving molecular chlorine and nitryl chloride (ClNO₂), as well as heterogeneous reactions involving particulate chloride species are addressed. The predictions of the modeling work presented here are compared to environmental chamber experiments and field observations. / text

Urban air quality management is a system for governments to lead cities towards achieving good air quality standards in an efficient way. Good air quality can avoid many environmental issues which are regarding air problems. At least, reduce environmental impacts efficiently in some extent. Carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), volatile organic compounds (VOC) and particulate matter (PM) are the common elements of air pollution. Topography, weather, the physical and chemical properties of pollutants and emission sources are also accomplices of air pollution. Östersund was a case study in this thesis because it has satisfactory air quality and won the European Mobility Week Award in 2014. Weather, winds, transportation and heating systems are the factors that influence urban air quality in Östersund. Green Traffic, Green Energy, and Green Highway are efficient projects in connection with air quality improvement in Östersund. Through successful technical application and institutional management, Östersund became one of the best climate cities in Sweden. This study is main focus on how Östersund municipality manages the local urban air quality then tries to find the suitable parts for China to learn. Here learn means find the suitable ways to improve urban air quality in China. It doesn’t mean copy all these projects. Emissions from vehicles, dust and the old style structures of energy are the main factors to reduce urban air quality in China. China did similar projects like Östersund did to improve urban air quality but the results were not so distinct so far. Vast land and large population are significant characteristics in China which make China’s ability slow to solve the air problem. Controlling the dust and emission from vehicles, using renewable resources and clean energy, optimizing industrial structure and complete legislations are beneficial projects to improve urban air quality in China. The projects of Green Traffic and Green Highway, and public participations are significant parts in Östersund which worth to learn for Chinese cities.

This dissertation investigates whether higher levels of “governmental fragmentation” in metropolitan statistical areas (MSA) leads to worse environmental outcomes. Fragmentation refers to the number of local governments in a given region or MSA as defined by the census. This research contributes to two bodies of literature, that of environmental federalism and that of urban growth and local government form. In the area of environmental federalism this dissertation extends the collective action model to include local governments. An empirical framework is developed that includes cross-sectional and panel data. In the urban growth and local government form literature, this dissertation comprehensively tests many existing measures of local government fragmentation within an environmental policy framework. It also modifies and extends some of the fragmentation variables. The results suggest that local government fragmentation does hinder MSAs from attaining the ozone standard. This dissertation extends the literature by examining the effect that local government fragmentation has on regional environmental quality. Six local government structure variables, jurisdiction count, special district dominance, central city dominance, county primacy, central city growth, and metropolitan power diffusion index are comprehensively tested to determine which might affect regional environmental quality. In addition, this research extends the use of the computationally complex measure of metropolitan power diffusion index to include additional local government expenditures as well as additional years of panel data. Two empirical estimation strategies were implemented, a cross-sectional approach and a panel data approach. The cross-sectional approach estimates the effects that long-term changes in local government structure have on attaining the ozone standard by measuring differences across MSAs. The panel data model’s primary purpose was that of a robustness check on the cross-sectional results. Three of the six tested fragmentation variables were found to have statistically significant effects on MSA attainment of the ozone standard in the cross-sectional model. Higher levels of metropolitan power diffusion index and jurisdiction count were found to hinder attainment of the ozone standard, while greater values of central city growth aided in reaching the attainment standard. Generally, the panel data results’ supported the results from the cross-sectional models. In addition, the panel model resolved some important estimation issues. Metropolitan power diffusion index was found to be correlated with unobservables in the random effects model, indicating that the cross-sectional results for metropolitan power diffusion index may be biased as well. This was not an issue for the variable jurisdiction count. Metropolitan power diffusion index and jurisdiction count are highly correlated with each other and this relationship was used to estimate a reasonable range for the effect metropolitan power diffusion index might have on the attainment of the ozone standard.

La pollution de l'air est un problème environnemental et social majeur. Parallèlement, c'est un problème complexe qui pose de multiples défis en termes de gestion et d'atténuation des polluants atmosphériques. Ceux-ci sont émis par des sources anthropiques et naturelles. Ils peuvent être soit émis directement (polluants primaires) soit formés dans l'atmosphère (en tant que polluants secondaires). Leurs impacts sur la santé, les écosystèmes, l'environnement, et le climat est avéré. Une action efficace pour réduire les impacts de la pollution de l'air nécessite une bonne compréhension de ses causes, de la manière dont les polluants sont transportés et transformés dans l'atmosphère et de leur impact sur l’Homme, les écosystèmes, le climat, la société, l'économie et le bâti. Aujourd’hui, les politiques et les plans d’aménagements visent à rendre les villes durables. Cela implique la prise en compte des interactions internes qui font de la ville un système complexe. Il est nécessaire de considérer les déterminants de la qualité de l’air. La modélisation s’impose comme un des principaux outils d’aide à la décision. Il existe actuellement peu de travaux de modélisation intégrant plusieurs champs disciplinaires en termes de qualité de l’air. Les travaux de recherche visent à développer une approche innovante de la modélisation de la qualité de l’air en intégrant des composantes sociales, économiques et de logistique de transport / Air pollution is a major environmental and social problem and, at the same time, it is a complex problem that poses multiple challenges in terms of management and mitigation of air pollutants. Air pollutants are emitted by anthropogenic and natural sources. They can be either emitted directly (primary pollutants) or formed in the atmosphere (secondary pollutants). Their impacts on health, ecosystems, the urban texture and the climate are proven. Effective action to reduce the impacts of air pollution requires a good understanding of its causes, how pollutants are transported and transformed in the atmosphere and their impact on humans, ecosystems, climate, society, the economy and buildings. Today, policies and development plans aim to make cities sustainable which involves taking into account the internal interactions that make the city a complex system. It is necessary to consider the determinants of air quality. Modeling is one of the most important tools for decision support. There is currently little modeling work integrating several disciplinary fields in terms of air quality. This research aims to develop an innovative approach to the modeling of air quality by integrating social, economic and transportation logistics

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.

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.

Doctor of Philosophy(PhD) / Abstract This thesis employs a statistical regression method to estimate models for testing the hypothesis of the thesis of vehicle emissions interdependencies. The thesis at the beginnings, reviews critically the formation of emissions in gasoline-fuelled engines, and also reviews existing and emerging models of automotive emissions. The thesis then, presents the relationships between the urban transport system and vehicle emissions. Particularly, it summarises different types of emissions and the contributory factors of the urban transport system to such emissions. Subsequently, the thesis presents the theory of vehicle emissions interdependencies and the empirical framework for testing the hypothesis of the thesis. The scope of testing the hypothesis of the thesis is only limited to gasoline-fuelled conventional vehicles in the urban transport environment. We use already available laboratory-based testing dataset of 542 passenger vehicles, to investigate the hypothesis of the thesis of vehicle emissions interdependencies. HC, CO, and NOX emissions were collected under six test drive-cycles, for each vehicle before and after vehicles were tuned. Prior to using any application, we transform the raw dataset into actionable information. We use three steps, namely conversion, cleaning, and screening, to process the data. We use classification and regression trees (CART) to narrow down the input number of variables in the models formulated for investigating the hypothesis of the thesis. We then, utilise initial results of the analysis to fix any remaining problems in the data. We employ three stage least squares (3SLS) regression to test the hypothesis of the thesis, and to estimate the maximum likelihood of vehicle variables and other emissions to influence HC, CO, and NOX emissions simultaneously. We estimate twelve models, each of which consists of a system of three simulations equations that accounts for the endogenous relations between HC, CO and NOX emissions when estimating vehicle emissions simultaneously under each test drive-cycle. The major contribution of the thesis is to investigate the inter-correlations between vehicle emissions within a well controlled data set, and to test the hypothesis of vehicle emissions interdependencies. We find that HC, CO, and NOX are endogenously or jointly dependent in a system of simultaneous-equations. The results of the analysis demonstrate that there is strong evidence against the null hypothesis (H0) in favour of the alternative hypothesis (H1) that HC, CO, and NOX are statistically significantly interdependent. We find, for the thesis sample, that NOX and CO are negatively related, whereas HC and CO emissions are positively related, and HC and NOX are positively related. The results of the thesis yield new insights. They bridge a very important gap in the current knowledge on vehicle emissions. They advance not only our current knowledge that HC, CO, and NOX should be predicted jointly since they are produced jointly, but also acknowledge the appropriateness of using 3SLS regression for estimating vehicle emissions simultaneously. The thesis measures the responses of emissions to changes with respect to changes in the other emissions. We investigate emission responses to a one percent increase in an emission with respect to the other emissions. We find the relationship between CO and NOX is of special interest. After vehicles were tuned, we find those vehicles that exhibit a one percent increase in NOX exhibit simultaneously a 0.35 percent average decrease in CO. Similarly, we find that vehicles which exhibit a one percent increase in CO exhibit simultaneously a 0.22 percent average decrease in NOX. We find that the responses of emission to changes with respect to other emissions vary with various test drive-cycles. Nonetheless, a band of upper and lower limits contains these variations. After vehicle tuning, a one percent increase in HC is associated with an increase in NOX between 0.5 percent and 0.8 percent, and an increase in CO between 0.5 percent and one percent Also, for post-tuning vehicles, a one percent increase in CO is associated with an increase in HC between 0.4 percent and 0.9 percent, and a decrease in NOX between 0.07 percent and 0.32 percent. Moreover, a one percent increase in NOX is associated with increase in HC between 0.8 percent and 1.3 percent, and a decrease in CO between 0.02 percent and 0.7 percent. These measures of the responses are very important derivatives of the hypothesis investigated in the thesis. They estimate the impacts of traffic management schemes and vehicle operations that target reducing one emission, on the other non-targeted emissions. However, we must be cautious in extending the results of the thesis to the modern vehicles fleet. The modern fleet differs significantly in technology from the dataset that we use in this thesis. The dataset consists of measurements of HC, CO, and NOX emissions for 542 gasoline-fuelled passenger vehicles, under six test drive-cycles, before and after the vehicles were tuned. Nevertheless, the dataset has a number of limitations such as limited model year range, limited representations of modal operations, and limitations of the measurements of emissions based only on averages of test drive-cycles, in addition to the exclusion of high-emitter emission measurements from the dataset. The dataset has a limited model year range, i.e., between 1980 and 1991. We highlight the age of the dataset, and acknowledge that the present vehicle fleet varies technologically from the vehicles in the dataset used in this thesis. Furthermore, the dataset has a limited number of makes - Holden, Ford, Toyota, Nissan, and Mitsubishi. There are also a limited number of modal operations. The model operations presented in the dataset are cold start, warming-up, and hot stabilised driving conditions. However, enrichment episodes are not adequately presented in the test-drive cycles of the dataset. Moreover, the dataset does not take into account driving behaviour influences, and all measurements are cycle-based averages. The emission measurements of laboratory-based testings are aggregated over a test drive cycle, and the test drive-cycle represents an average trip over an average speed. The exclusion of the measurements of high emitting vehicles from the dataset introduces further limitations. Remote sensing studies show that 20 percent of the on-road vehicle fleet is responsible for 80 percent of HC and CO emissions. The findings of the thesis assist in the identification of the best strategies to mitigate the most adverse effects of air-pollution, such as the most severe pollution that have the most undesirable pollution effects. Also, they provide decision-makers with valuable information on how changes in the operation of the transport system influence the urban air-quality. Moreover, the thesis provides information on how vehicle emissions affect the chemistry of the atmosphere and degrade the urban air-quality.

Doctor of Philosophy(PhD) / Abstract This thesis employs a statistical regression method to estimate models for testing the hypothesis of the thesis of vehicle emissions interdependencies. The thesis at the beginnings, reviews critically the formation of emissions in gasoline-fuelled engines, and also reviews existing and emerging models of automotive emissions. The thesis then, presents the relationships between the urban transport system and vehicle emissions. Particularly, it summarises different types of emissions and the contributory factors of the urban transport system to such emissions. Subsequently, the thesis presents the theory of vehicle emissions interdependencies and the empirical framework for testing the hypothesis of the thesis. The scope of testing the hypothesis of the thesis is only limited to gasoline-fuelled conventional vehicles in the urban transport environment. We use already available laboratory-based testing dataset of 542 passenger vehicles, to investigate the hypothesis of the thesis of vehicle emissions interdependencies. HC, CO, and NOX emissions were collected under six test drive-cycles, for each vehicle before and after vehicles were tuned. Prior to using any application, we transform the raw dataset into actionable information. We use three steps, namely conversion, cleaning, and screening, to process the data. We use classification and regression trees (CART) to narrow down the input number of variables in the models formulated for investigating the hypothesis of the thesis. We then, utilise initial results of the analysis to fix any remaining problems in the data. We employ three stage least squares (3SLS) regression to test the hypothesis of the thesis, and to estimate the maximum likelihood of vehicle variables and other emissions to influence HC, CO, and NOX emissions simultaneously. We estimate twelve models, each of which consists of a system of three simulations equations that accounts for the endogenous relations between HC, CO and NOX emissions when estimating vehicle emissions simultaneously under each test drive-cycle. The major contribution of the thesis is to investigate the inter-correlations between vehicle emissions within a well controlled data set, and to test the hypothesis of vehicle emissions interdependencies. We find that HC, CO, and NOX are endogenously or jointly dependent in a system of simultaneous-equations. The results of the analysis demonstrate that there is strong evidence against the null hypothesis (H0) in favour of the alternative hypothesis (H1) that HC, CO, and NOX are statistically significantly interdependent. We find, for the thesis sample, that NOX and CO are negatively related, whereas HC and CO emissions are positively related, and HC and NOX are positively related. The results of the thesis yield new insights. They bridge a very important gap in the current knowledge on vehicle emissions. They advance not only our current knowledge that HC, CO, and NOX should be predicted jointly since they are produced jointly, but also acknowledge the appropriateness of using 3SLS regression for estimating vehicle emissions simultaneously. The thesis measures the responses of emissions to changes with respect to changes in the other emissions. We investigate emission responses to a one percent increase in an emission with respect to the other emissions. We find the relationship between CO and NOX is of special interest. After vehicles were tuned, we find those vehicles that exhibit a one percent increase in NOX exhibit simultaneously a 0.35 percent average decrease in CO. Similarly, we find that vehicles which exhibit a one percent increase in CO exhibit simultaneously a 0.22 percent average decrease in NOX. We find that the responses of emission to changes with respect to other emissions vary with various test drive-cycles. Nonetheless, a band of upper and lower limits contains these variations. After vehicle tuning, a one percent increase in HC is associated with an increase in NOX between 0.5 percent and 0.8 percent, and an increase in CO between 0.5 percent and one percent Also, for post-tuning vehicles, a one percent increase in CO is associated with an increase in HC between 0.4 percent and 0.9 percent, and a decrease in NOX between 0.07 percent and 0.32 percent. Moreover, a one percent increase in NOX is associated with increase in HC between 0.8 percent and 1.3 percent, and a decrease in CO between 0.02 percent and 0.7 percent. These measures of the responses are very important derivatives of the hypothesis investigated in the thesis. They estimate the impacts of traffic management schemes and vehicle operations that target reducing one emission, on the other non-targeted emissions. However, we must be cautious in extending the results of the thesis to the modern vehicles fleet. The modern fleet differs significantly in technology from the dataset that we use in this thesis. The dataset consists of measurements of HC, CO, and NOX emissions for 542 gasoline-fuelled passenger vehicles, under six test drive-cycles, before and after the vehicles were tuned. Nevertheless, the dataset has a number of limitations such as limited model year range, limited representations of modal operations, and limitations of the measurements of emissions based only on averages of test drive-cycles, in addition to the exclusion of high-emitter emission measurements from the dataset. The dataset has a limited model year range, i.e., between 1980 and 1991. We highlight the age of the dataset, and acknowledge that the present vehicle fleet varies technologically from the vehicles in the dataset used in this thesis. Furthermore, the dataset has a limited number of makes - Holden, Ford, Toyota, Nissan, and Mitsubishi. There are also a limited number of modal operations. The model operations presented in the dataset are cold start, warming-up, and hot stabilised driving conditions. However, enrichment episodes are not adequately presented in the test-drive cycles of the dataset. Moreover, the dataset does not take into account driving behaviour influences, and all measurements are cycle-based averages. The emission measurements of laboratory-based testings are aggregated over a test drive cycle, and the test drive-cycle represents an average trip over an average speed. The exclusion of the measurements of high emitting vehicles from the dataset introduces further limitations. Remote sensing studies show that 20 percent of the on-road vehicle fleet is responsible for 80 percent of HC and CO emissions. The findings of the thesis assist in the identification of the best strategies to mitigate the most adverse effects of air-pollution, such as the most severe pollution that have the most undesirable pollution effects. Also, they provide decision-makers with valuable information on how changes in the operation of the transport system influence the urban air-quality. Moreover, the thesis provides information on how vehicle emissions affect the chemistry of the atmosphere and degrade the urban air-quality.

Atmospheric aerosols have adverse effects on human health, air quality, and visibility and frequently result in severe pollution events, particularly in urban areas. However, the sources of aerosols and the processes governing their behaviour in the atmosphere, including those which lead to high concentrations, are not well understood thus limit our ability to accurately assess and forecast air quality. Presented here are the first long-term chemical composition measurements from an urban environment using an Aerodyne compact Time-of-Flight Aerosol Mass Spectrometer (cToF-AMS). Organic aerosols (OA) were observed to account for a significant fraction (44%) of the total non-refractory submicron mass during 2012 at the urban background site in North Kensington, London, followed by nitrate (28%), sulphate (14%), ammonium (13%), and chloride (1%). The sources and components of OA were determined using Positive Matrix Factorisation (PMF) and attributed as hydrocarbon-like OA (HOA), cooking OA (COA), solid fuel OA (SFOA), type 1 oxygenated OA (OOA1), and type 2 oxygenated OA (OOA2), where HOA, COA, and SFOA were observed to be of equal importance across the year. The concentration of secondary OA increased during the summer yet the extent of oxidation, as defined by the oxygen content, showed no variability during the year. The main factors governing the diurnal, monthly, and seasonal trends observed in all organic and inorganic species were meteorological conditions, specific nature of the sources, and availability of precursors. Regional and transboundary pollution influenced total aerosol concentrations and high concentration events were observed to be governed by different factors depending on season. High-Resolution ToF-AMS measurements were used to further probe OA behaviour, where two SFOA factors were derived from PMF analysis in winter, which likely represent differences in burn conditions. In the summer an OA factor was identified, likely of primary origin, which was observed to be strongly associated with organic nitrates and anthropogenic emissions. This work uses instruments and techniques that have not previously been used in this way in an urban environment, where the results further the understanding of the chemical components of urban aerosols. Aerosol sources are likely to change in the future with increases in solid fuel burning as vehicular emissions decrease, with significant implications on air quality and health. Thus it is important to understand aerosol sources and behaviour in order to develop effective pollution abatement strategies.