Rozprawy doktorskie na temat „URBAN TRAFFIC EMISSION”

La circulation routière est une source majeure de pollution atmosphérique dans les zones urbaines. Les décideurs insistent pour qu’on leur propose de nouvelles solutions, y compris de nouvelles stratégies de management qui pourraient directement faire baisser les émissions de polluants. Pour évaluer les performances de ces stratégies, le calcul des émissions de pollution devrait tenir compte de la dynamique spatiale et temporelle du trafic. L’utilisation de capteurs traditionnels sur route (par exemple, capteurs inductifs ou boucles de comptage) pour collecter des données en temps réel est nécessaire mais pas suffisante en raison de leur coût de mise en oeuvre très élevé. Le fait que de telles technologies, pour des raisons pratiques, ne fournissent que des informations locales est un inconvénient. Certaines méthodes devraient ensuite être appliquées pour étendre cette information locale à une grande échelle. Ces méthodes souffrent actuellement des limites suivantes : (i) la relation entre les données manquantes et la précision de l’estimation ne peut être facilement déterminée et (ii) les calculs à grande échelle sont énormément coûteux, principalement lorsque les phénomènes de congestion sont considérés. Compte tenu d’une simulation microscopique du trafic couplée à un modèle d’émission, une approche innovante de ce problème est mise en oeuvre. Elle consiste à appliquer des techniques de sélection statistique qui permettent d’identifier les emplacements les plus pertinents pour estimer les émissions des véhicules du réseau à différentes échelles spatiales et temporelles. Ce travail explore l’utilisation de méthodes statistiques intelligentes et naïves, comme outil pour sélectionner l’information la plus pertinente sur le trafic et les émissions sur un réseau afin de déterminer les valeurs totales à plusieurs échelles. Ce travail met également en évidence quelques précautions à prendre en compte quand on calcul les émissions à large échelle à partir des données trafic et d’un modèle d’émission. L’utilisation des facteurs d’émission COPERT IV à différentes échelles spatio-temporelles induit un biais en fonction des conditions de circulation par rapport à l’échelle d’origine (cycles de conduite). Ce biais observé sur nos simulations a été quantifié en fonction des indicateurs de trafic (vitesse moyenne). Il a également été démontré qu’il avait une double origine : la convexité des fonctions d’émission et la covariance des variables de trafic
Road traffic is a major source of air pollution in urban areas. Policy makers are pushing for different solutions including new traffic management strategies that can directly lower pollutants emissions. To assess the performances of such strategies, the calculation of pollution emission should consider spatial and temporal dynamic of the traffic. The use of traditional on-road sensors (e.g. inductive sensors) for collecting real-time data is necessary but not sufficient because of their expensive cost of implementation. It is also a disadvantage that such technologies, for practical reasons, only provide local information. Some methods should then be applied to expand this local information to large spatial extent. These methods currently suffer from the following limitations: (i) the relationship between missing data and the estimation accuracy, both cannot be easily determined and (ii) the calculations on large area is computationally expensive in particular when time evolution is considered. Given a dynamic traffic simulation coupled with an emission model, a novel approach to this problem is taken by applying selection techniques that can identify the most relevant locations to estimate the network vehicle emissions in various spatial and temporal scales. This work explores the use of different statistical methods both naïve and smart, as tools for selecting the most relevant traffic and emission information on a network to determine the total values at any scale. This work also highlights some cautions when such traffic-emission coupled method is used to quantify emissions due the traffic. Using the COPERT IV emission functions at various spatial-temporal scales induces a bias depending on traffic conditions, in comparison to the original scale (driving cycles). This bias observed in our simulations, has been quantified in function of traffic indicators (mean speed). It also has been demonstrated to have a double origin: the emission functions’ convexity and the traffic variables covariance

The level of air pollution in urban areas, which is largely affected by road traffic, is an issue of high political relevance. Congestion is most prevalent in urban areas and a common and increasingly present phenomenon worldwide. The first four chapters of this study have investigated how and to what extent models, which are used to predict emissions on road links in urban road networks, include the effects of congestion on emissions. In order to make this assessment, traffic engineering literature and empirical studies have been examined and used as a basis to review (current) emission models that exist or have been used around the world. Congestion causes changes in driving patterns of individual vehicles in a traffic stream, and these changes are subsequently reflected in changes in congestion indicators and changes in emission levels. This consideration and a literature review has led to a proposed 'congestion typology' of emission models, which reflects the different ways in which and the extent to which congestion has been incorporated in these models. The typology clarifies that six of in total ten families of emission models that were investigated in this thesis explicitly consider congestion in the modelling process (i.e. model variables are related to congestion), although this is done in different ways. For the remaining four families of emission models it was not possible to determine the extent to which congestion has been incorporated on the basis of literature review alone. Two families fell beyond the scope of this work since they cannot be used to predict emission on road links. For the other two families it became clear in the course of the thesis that the extent can be determined through analysis of driving pattern data (and other information with respect to e.g. data collection) that were used in the model development. A new methodology is presented in this thesis to perform this analysis and to assess the mean level of congestion in driving patterns (driving cycles). The analysis has been carried out for one important family of emission models, the so-called travel speed models ('average speed models'), which are used extensively in urban network modelling. For four current models (COPERT III, MOBILE 6, QGEPA 2002, EMFAC 2000), it is concluded that these models implicitly (i.e. congestion is inherently considered) take varying levels of congestion into account, but that this conclusion is subject to a number of limitations. It became clear in the course of this study that prediction of (the effects of) congestion in both traffic models and emission models is generally restricted to certain modelling dimensions. As a consequence, the effects of congestion are only partially predicted in current air emission modelling. Chapter 5 has attempted to address the question whether congestion is actually an important issue in urban network emission modelling or not. It also addressed the question if different types of emission models actually predict different results. On the basis of a number of selection criteria, two types of models were compared, i.e. one explicit model (TEE-KCF 2002) and two implicit models (COPERT III, QGEPA 2002). The research objectives have been addressed by applying these emission models to a case-study urban network in Australia (Brisbane) for which various model input attributes were collected from different sources (both modelled and field data). The findings are limited by the fact that they follow from one urban network with particular characteristics (fleet composition, signal settings, speed limits) and application of only a few particular emission models. The results therefore indicate that: 1. Changes in traffic activity (i.e. distribution of vehicle kilometres travelled on network links) over the day appear to have the largest effect on predicted traffic emissions. 2. Congestion is an important issue in the modelling of CO and HC emissions. This appears not to be the case for NOx emissions, where basic traffic composition is generally a more important factor. For the most congested parts in the urban network that have been investigated, congestion can more than double predicted emissions of CO and HC. 3. Different types of emission models can produce substantially different results when absolute (arithmetic) differences are considered, but can produce similar results when relative differences (ratio or percent difference) are considered.

The level of air pollution in urban areas, which is largely affected by road traffic, is an issue of high political relevance. Congestion is most prevalent in urban areas and a common and increasingly present phenomenon worldwide. The first four chapters of this study have investigated how and to what extent models, which are used to predict emissions on road links in urban road networks, include the effects of congestion on emissions. In order to make this assessment, traffic engineering literature and empirical studies have been examined and used as a basis to review (current) emission models that exist or have been used around the world. Congestion causes changes in driving patterns of individual vehicles in a traffic stream, and these changes are subsequently reflected in changes in congestion indicators and changes in emission levels. This consideration and a literature review has led to a proposed 'congestion typology' of emission models, which reflects the different ways in which and the extent to which congestion has been incorporated in these models. The typology clarifies that six of in total ten families of emission models that were investigated in this thesis explicitly consider congestion in the modelling process (i.e. model variables are related to congestion), although this is done in different ways. For the remaining four families of emission models it was not possible to determine the extent to which congestion has been incorporated on the basis of literature review alone. Two families fell beyond the scope of this work since they cannot be used to predict emission on road links. For the other two families it became clear in the course of the thesis that the extent can be determined through analysis of driving pattern data (and other information with respect to e.g. data collection) that were used in the model development. A new methodology is presented in this thesis to perform this analysis and to assess the mean level of congestion in driving patterns (driving cycles). The analysis has been carried out for one important family of emission models, the so-called travel speed models ('average speed models'), which are used extensively in urban network modelling. For four current models (COPERT III, MOBILE 6, QGEPA 2002, EMFAC 2000), it is concluded that these models implicitly (i.e. congestion is inherently considered) take varying levels of congestion into account, but that this conclusion is subject to a number of limitations. It became clear in the course of this study that prediction of (the effects of) congestion in both traffic models and emission models is generally restricted to certain modelling dimensions. As a consequence, the effects of congestion are only partially predicted in current air emission modelling. Chapter 5 has attempted to address the question whether congestion is actually an important issue in urban network emission modelling or not. It also addressed the question if different types of emission models actually predict different results. On the basis of a number of selection criteria, two types of models were compared, i.e. one explicit model (TEE-KCF 2002) and two implicit models (COPERT III, QGEPA 2002). The research objectives have been addressed by applying these emission models to a case-study urban network in Australia (Brisbane) for which various model input attributes were collected from different sources (both modelled and field data). The findings are limited by the fact that they follow from one urban network with particular characteristics (fleet composition, signal settings, speed limits) and application of only a few particular emission models. The results therefore indicate that: 1. Changes in traffic activity (i.e. distribution of vehicle kilometres travelled on network links) over the day appear to have the largest effect on predicted traffic emissions. 2. Congestion is an important issue in the modelling of CO and HC emissions. This appears not to be the case for NOx emissions, where basic traffic composition is generally a more important factor. For the most congested parts in the urban network that have been investigated, congestion can more than double predicted emissions of CO and HC. 3. Different types of emission models can produce substantially different results when absolute (arithmetic) differences are considered, but can produce similar results when relative differences (ratio or percent difference) are considered.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Environmental Planning
Full Text

The dissertation deals with the issue of of transport air pollution and proposes the construction of an instantaneous emission model, then applied to the Torino’s urban context. The work has been focused on the analysis of the different parameters on the pollutant emissions and the construction of computer tools and abacus for the energetic and environmental assessment of the policies and technologies having an impact on the traffic conditions. This led to the definition of an emission model for the evaluation of car hot pollutant emissions and fuel consumption. The emission model was partly developed during a period spent as visiting researcher (Marie Curie grant holder) at INRETS (Institute National de Recherche sur les Transports et leur Securité - France). This later allowed, an experimentation in the city of Torino to define relationships between traffic and air pollution. An application of the istanataneous emission model and the application of a box model (dspersion model) has allowed to obtain a tool for traffic planning and management.

京都大学
0048
新制・課程博士
博士(工学)
甲第22766号
工博第4765号
新制||工||1745(附属図書館)
京都大学大学院工学研究科都市環境工学専攻
(主査)教授 米田 稔, 教授 高野 裕久, 准教授 藤森 真一郎
学位規則第4条第1項該当
Doctor of Philosophy (Engineering)
Kyoto University
DFAM

Motor vehicles are a major source of gaseous and particulate matter pollution in urban areas, particularly of ultrafine sized particles (diameters < 0.1 µm). Exposure to particulate matter has been found to be associated with serious health effects, including respiratory and cardiovascular disease, and mortality. Particle emissions generated by motor vehicles span a very broad size range (from around 0.003-10 µm) and are measured as different subsets of particle mass concentrations or particle number count. However, there exist scientific challenges in analysing and interpreting the large data sets on motor vehicle emission factors, and no understanding is available of the application of different particle metrics as a basis for air quality regulation. To date a comprehensive inventory covering the broad size range of particles emitted by motor vehicles, and which includes particle number, does not exist anywhere in the world. This thesis covers research related to four important and interrelated aspects pertaining to particulate matter generated by motor vehicle fleets. These include the derivation of suitable particle emission factors for use in transport modelling and health impact assessments; quantification of motor vehicle particle emission inventories; investigation of the particle characteristic modality within particle size distributions as a potential for developing air quality regulation; and review and synthesis of current knowledge on ultrafine particles as it relates to motor vehicles; and the application of these aspects to the quantification, control and management of motor vehicle particle emissions. In order to quantify emissions in terms of a comprehensive inventory, which covers the full size range of particles emitted by motor vehicle fleets, it was necessary to derive a suitable set of particle emission factors for different vehicle and road type combinations for particle number, particle volume, PM1, PM2.5 and PM1 (mass concentration of particles with aerodynamic diameters < 1 µm, < 2.5 µm and < 10 µm respectively). The very large data set of emission factors analysed in this study were sourced from measurement studies conducted in developed countries, and hence the derived set of emission factors are suitable for preparing inventories in other urban regions of the developed world. These emission factors are particularly useful for regions with a lack of measurement data to derive emission factors, or where experimental data are available but are of insufficient scope. The comprehensive particle emissions inventory presented in this thesis is the first published inventory of tailpipe particle emissions prepared for a motor vehicle fleet, and included the quantification of particle emissions covering the full size range of particles emitted by vehicles, based on measurement data. The inventory quantified particle emissions measured in terms of particle number and different particle mass size fractions. It was developed for the urban South-East Queensland fleet in Australia, and included testing the particle emission implications of future scenarios for different passenger and freight travel demand. The thesis also presents evidence of the usefulness of examining modality within particle size distributions as a basis for developing air quality regulations; and finds evidence to support the relevance of introducing a new PM1 mass ambient air quality standard for the majority of environments worldwide. The study found that a combination of PM1 and PM10 standards are likely to be a more discerning and suitable set of ambient air quality standards for controlling particles emitted from combustion and mechanically-generated sources, such as motor vehicles, than the current mass standards of PM2.5 and PM10. The study also reviewed and synthesized existing knowledge on ultrafine particles, with a specific focus on those originating from motor vehicles. It found that motor vehicles are significant contributors to both air pollution and ultrafine particles in urban areas, and that a standardized measurement procedure is not currently available for ultrafine particles. The review found discrepancies exist between outcomes of instrumentation used to measure ultrafine particles; that few data is available on ultrafine particle chemistry and composition, long term monitoring; characterization of their spatial and temporal distribution in urban areas; and that no inventories for particle number are available for motor vehicle fleets. This knowledge is critical for epidemiological studies and exposure-response assessment. Conclusions from this review included the recommendation that ultrafine particles in populated urban areas be considered a likely target for future air quality regulation based on particle number, due to their potential impacts on the environment. The research in this PhD thesis successfully integrated the elements needed to quantify and manage motor vehicle fleet emissions, and its novelty relates to the combining of expertise from two distinctly separate disciplines - from aerosol science and transport modelling. The new knowledge and concepts developed in this PhD research provide never before available data and methods which can be used to develop comprehensive, size-resolved inventories of motor vehicle particle emissions, and air quality regulations to control particle emissions to protect the health and well-being of current and future generations.

Motor vehicles are a major source of gaseous and particulate matter pollution in urban areas, particularly of ultrafine sized particles (diameters < 0.1 µm). Exposure to particulate matter has been found to be associated with serious health effects, including respiratory and cardiovascular disease, and mortality. Particle emissions generated by motor vehicles span a very broad size range (from around 0.003-10 µm) and are measured as different subsets of particle mass concentrations or particle number count. However, there exist scientific challenges in analysing and interpreting the large data sets on motor vehicle emission factors, and no understanding is available of the application of different particle metrics as a basis for air quality regulation. To date a comprehensive inventory covering the broad size range of particles emitted by motor vehicles, and which includes particle number, does not exist anywhere in the world. This thesis covers research related to four important and interrelated aspects pertaining to particulate matter generated by motor vehicle fleets. These include the derivation of suitable particle emission factors for use in transport modelling and health impact assessments; quantification of motor vehicle particle emission inventories; investigation of the particle characteristic modality within particle size distributions as a potential for developing air quality regulation; and review and synthesis of current knowledge on ultrafine particles as it relates to motor vehicles; and the application of these aspects to the quantification, control and management of motor vehicle particle emissions. In order to quantify emissions in terms of a comprehensive inventory, which covers the full size range of particles emitted by motor vehicle fleets, it was necessary to derive a suitable set of particle emission factors for different vehicle and road type combinations for particle number, particle volume, PM1, PM2.5 and PM1 (mass concentration of particles with aerodynamic diameters < 1 µm, < 2.5 µm and < 10 µm respectively). The very large data set of emission factors analysed in this study were sourced from measurement studies conducted in developed countries, and hence the derived set of emission factors are suitable for preparing inventories in other urban regions of the developed world. These emission factors are particularly useful for regions with a lack of measurement data to derive emission factors, or where experimental data are available but are of insufficient scope. The comprehensive particle emissions inventory presented in this thesis is the first published inventory of tailpipe particle emissions prepared for a motor vehicle fleet, and included the quantification of particle emissions covering the full size range of particles emitted by vehicles, based on measurement data. The inventory quantified particle emissions measured in terms of particle number and different particle mass size fractions. It was developed for the urban South-East Queensland fleet in Australia, and included testing the particle emission implications of future scenarios for different passenger and freight travel demand. The thesis also presents evidence of the usefulness of examining modality within particle size distributions as a basis for developing air quality regulations; and finds evidence to support the relevance of introducing a new PM1 mass ambient air quality standard for the majority of environments worldwide. The study found that a combination of PM1 and PM10 standards are likely to be a more discerning and suitable set of ambient air quality standards for controlling particles emitted from combustion and mechanically-generated sources, such as motor vehicles, than the current mass standards of PM2.5 and PM10. The study also reviewed and synthesized existing knowledge on ultrafine particles, with a specific focus on those originating from motor vehicles. It found that motor vehicles are significant contributors to both air pollution and ultrafine particles in urban areas, and that a standardized measurement procedure is not currently available for ultrafine particles. The review found discrepancies exist between outcomes of instrumentation used to measure ultrafine particles; that few data is available on ultrafine particle chemistry and composition, long term monitoring; characterization of their spatial and temporal distribution in urban areas; and that no inventories for particle number are available for motor vehicle fleets. This knowledge is critical for epidemiological studies and exposure-response assessment. Conclusions from this review included the recommendation that ultrafine particles in populated urban areas be considered a likely target for future air quality regulation based on particle number, due to their potential impacts on the environment. The research in this PhD thesis successfully integrated the elements needed to quantify and manage motor vehicle fleet emissions, and its novelty relates to the combining of expertise from two distinctly separate disciplines - from aerosol science and transport modelling. The new knowledge and concepts developed in this PhD research provide never before available data and methods which can be used to develop comprehensive, size-resolved inventories of motor vehicle particle emissions, and air quality regulations to control particle emissions to protect the health and well-being of current and future generations.

The aim of this project was to provide a more accurate representation of road traffic carbon dioxide (CO2) emissions in urban areas, whilst remaining within limited Local Government Authority (LGA) resources. Tailpipe emissions from vehicles on urban roads have damaging impacts, with the problem exacerbated by the common occurrence of congestion. The scope of the project was CO2 because it is by far the largest constituent (99%) of road traffic greenhouse gas emissions. LGAs are typically responsible for facilitating mitigation of these emissions and must engage in emissions modelling to quantify the impact of transport interventions. A review of relevant literature identified a research gap, which constituted an investigation into whether a Traffic Variable Emissions Model (EM) (i.e. based on input data aggregated at traffic level rather than disaggregated at vehicle level) represented optimal complexity for LGAs, improving on the ability of wellestablished Average Speed EMs to capture the influence on emissions of congestion, whilst remaining within resource constraints. British LGAs (n=34) were surveyed to discover general attitudes to emissions modelling. Results showed that resource scarcity is important, with particular importance attached to EM reusability and convenient input data sources. Data sources rated highly for convenience were Urban Traffic Control (UTC) systems and Road Traffic Models (RTMs). A new Traffic Variable EM was developed termed the Practical EM for Local Authorities (PEMLA). Using Southampton as a testbed, 514 real-world GPS driving patterns (1Hz speed-time profiles) were collected from 49 drivers of different vehicle types and used as inputs to a detailed, instantaneous EM to calculate accurate vehicle CO2 emissions (assumed to represent 'real-world' emissions). Concurrent data were collected from Inductive Loop Detectors (ILDs installed as part of UTC systems) crossed by vehicles during their journeys and used to calculate values for selected traffic variables. Relationships between traffic variables (predictor variables) and accurate emissions (outcome variable) were examined using statistical analysis. Results showed that PEMLA outperformed the well-established, next-best alternative EM available to LGAs (an Average Speed EM), with mean predictions of PEMLA found to be 2% greater than observed values, whilst mean predictions of the alternative EM were 12% less. PEMLA's contribution is two-fold. Firstly, it is closer to optimal complexity than the well-established Average Speed EM alternative. This was for two reasons: (1) PEMLA was more accurate through using as inputs other traffic variable congestion indicators (in addition to traffic average speed), which improved its ability to capture the influence of congestion on emissions; and (2) PEMLA consumes similar (or potentially lower) resources to operate because inputs are generated from ILD data, which are a by-product of UTC systems or can be readily simulated in RTMs. Secondly, it possesses attributes that addressed the identified limitations of other Traffic Variable EM alternatives. These two contributions make PEMLA a suitable option to be recommended for LGA use.

Le but de ce travail est de connaitre la repartition spatio-temporelle des concentrations des polluants emis par les vehicules automobiles dans differents types de voierie (autoroute, route et rue encaissee), afin de determiner le niveau previsible de la pollution a laquelle est soumise la population

Este estudo tem por objetivo propor um método para aprimorar a estimativa de emissões veiculares em áreas urbanas através da utilização de modelagem híbrida de tráfego associada a modelos de previsão de emissões. A modelagem híbrida agrega as vantagens individuais das abordagens agregada e desagregada de tráfego, uma vez que combina a micro-simulação de tráfego em áreas específicas com a simulação agregada em uma área de estudo mais abrangente. O método proposto neste trabalho foi consolidado a partir do desenvolvimento de um estudo de caso que consistiu na modelagem de uma rede viária com características distintas de infraestrutura e operação viárias. Os resultados do estudo de caso permitiram a identificação de trechos da rede viária nos quais as estimativas de emissões provenientes de modelos agregados foram significativamente diferentes das estimativas derivadas de modelos microscópicos, demonstrando a importância de uma abordagem híbrida. A utilização do método proposto pode embasar a elaboração e implementação de políticas de transportes que busquem reduzir a ocorrência de eventos responsáveis pela geração de elevados níveis de emissões.
This study aims to propose a method to improve the vehicle emissions estimation in urban area. The method associates hybrid traffic flow models with emission models. Hybrid traffic modeling combines the specific advantages of aggregate and disaggregated approaches, since they integrate traffic microssimulation in specific areas with agregated simulation in a wide area. The development of the proposed method was based on a case study consisting in the modeling a road network with different operations and infrastructure characteristics. Case study results indicated that emission estimates obtained from aggregated models were significantly different from emission estimates derived from microscopic models on some road segments, emphasizing the importance of a hybrid approach adopted in the method proposed in this work. The proposed method can be used to guide the development and implementation of transportation policies that aim to reduce the number of traffic events responsible for high levels of emissions.

BACKGROUND: A growing body of research has suggested that exposure to traffic-related emissions is associated with numerous adverse health effects including prevalence and severity of symptoms of asthma, hospitalizations for acute myocardial infarctions, and cardiovascular-related mortality. No previous studies have assessed the association between proximity to traffic and respiratory and cardiovascular outcomes across all age groups. OBJECTIVE: The purpose of this study was to assess the association between proximity to traffic emissions within the City of Atlanta and respiratory and cardiovascular 911 Emergency Management Service (EMS) calls and subsequent emergency department (ED) visits. METHODS: Case and control diagnostic groups were established for 5,450 EMS calls received between 2004 and 2008 from residents of the City of Atlanta based on ICD-9 codes assigned within the ED. Case diagnostic groups included asthma, cardiovascular outcomes, and stroke. Gastrointestinal diagnostic groups were selected as controls. Cumulative traffic within a 100 m buffer of the call origination location was used as an indicator of exposure to traffic emissions. Using a case-control study design, the associations between exposure to traffic emissions and the case diagnostic groups were evaluated using logistic regression, controlling for potential confounding factors including age, gender, ethnicity, and socio-economic status (SES). Subgroup analyses were performed to evaluate differences by select age categories, gender, and SES. P-values of <0.05 and 95% confidence intervals (CI) were used to determine statistical significance. RESULTS: Increased cumulative traffic near the call location was associated with an increase in the odds of an EMS call and ED visit for cardiovascular outcomes compared to the control diagnostic group even after adjustment for confounding factors (OR = 1.07; 95% CI ,1.01-1.12). The strongest effects were among men and individuals aged 40-75 years. Increased cumulative traffic was also associated with an increased odds of an EMS call and ED visit for stroke among individuals aged 18-39 years after adjusting for confounding (OR = 1.16; 95% CI, 1.01-1.34). No statistically significant associations were found between increased cumulative traffic and the odds of an EMS call and ED visit for asthma. CONCLUSION: These results provide additional evidence that proximity to traffic is associated with adverse cardiovascular outcomes and stroke in certain age groups.

In this work we address our attention to the estimation of the contribution of non-exhaust sources, like brake abrasion, tire and road wear and resuspension of particles, to the final PM air concentration; particularly we focus our investigation on the resuspension of PM deposited on road pavement surfaces and raised by the air turbulence produced by the vehicles flux, under urban and extra-urban traffic conditions. Our approach to the problem is based on modeling techniques. We refer to measurement data from literature to determine the selected empirical parameters contained in our models. Analytical models based on algebraic eddy diffusivity hypothesis are applied to describe the mean statistical component of flow generated by air recirculation inside a canyon and by the far-wake structure besides moving vehicles of simplified geometrical shapes. The analysis of the far wake solutions is suitable to the description of vehicle wakes interaction, which permits to apply our analysis to different driving cycles conditions. Numerical simulations based on finite element discretization of suitable two-equation turbulence models are employed to describe near-wake structures, which cause the strongest mixing of atmospheric pollutants and resuspension of road dust. These different components of turbulence fields at different scales of the street geometry are composed to define a set of operational and numerical models for the dispersion dynamics at the canyon scale of two classes of PM10 pollutants, corresponding to a Soot and a road dust components. The deposition and the resuspension of pollutants are described by resistance and filtration models on porous asphalts, inserting the corresponding terms in the dispersion equations as suitable boundary conditions on the ground. We estimate the resuspension fraction of traffic-related PM10 emissions at the tailpipe, through a simplified linear-emission model, considering representative data describing traffic statistics coming from empirical data. Profile laws of resuspension factors are drawn, for different vehicles geometries and velocities, and how resuspension changes with different asphalt characteristics. The results are applied to typical traffic situations in the city of Milan, studying the effect of implementations of different reduction scenarios to the total amount of traffic-related PM10 emissions. The results point at a new approach to the local PM10 reduction policies, based on more effective asphalt design and maintenance. Finally, we apply one of the dispersion operational models to the case of a congested urban traffic configuration in a canyon street, in order to obtain the pollutant spatial distribution.

Urban air pollution is responsible for high levels of morbidity and mortality in exposed populations due to its effects on cardiovascular and respiratory function. Transportation-related air pollutants account for the majority of harmful air pollution in urban areas. Forests are known to reduce air pollution through their ability to facilitate dry deposition and atmospheric gas exchange. This work characterizes the interactions between transportation air pollutants and urban forests in Hillsborough County, Florida. A highly spatially resolved passive air sampling campaign was conducted to characterize local concentrations of nitrogen oxides, benzene, toluene, ethylbenzene, and xylenes (BTEX) in Hillsborough County, Florida. Sampling locations included a proportion of densely forested urban areas in order to determine the effects of Hillsborough County's urban forest resources on localized concentrations of selected transportation pollutants. Recommended approaches for the use of urban forests as an effective air pollution mitigation technique in Hillsborough County were generated based on results from the sampling campaign. Results show mean concentrations of 2.1 parts per billion and 6.5 µg/m3 for nitrogen oxides and total BTEX, respectively. High spatial variability in pollutant concentrations across Hillsborough County was observed, with the coefficient of variation found to be 0.61 for nitrogen oxides and 0.79 for total BTEX. Higher concentrations were observed along interstate highways, in urban areas of the county, and near select point sources in rural areas. Differences in concentrations within forested areas were observed, but were not statistically significant at the 95%#37; confidence level. These results can be used to identify elements of urban design which contribute to differences in concentrations and exposures. This information can be used to create more sustainable urban designs which promote health and equity of the population.

La Pianura Padana, situata nella parte settentrionale dell'Italia, è una delle aree più critiche del paese per livelli d’inquinamento. La ragione di questo problema non è solo legata all'elevata densità di popolazione con relative attività antropiche, ma è anche dovuta alla conformazione orografica del territorio, delimitato dalla catena alpina ad ovest e a nord e dagli Appennini a sud. Queste caratteristiche geografiche determinano condizioni meteorologiche sfavorevoli alla dispersione atmosferica, quali: velocità medie annue del vento inferiori a 2 m s-1, inversioni termiche ricorrenti nei primi strati di atmosfera a contatto con il suolo, ridotte altezze dello strato rimescolato e persistenti nebbie durante il periodo invernale. Uno degli inquinanti atmosferici più rilevanti per effetti critici sulla salute umana è il biossido di azoto (NO2), i cui livelli negli ultimi anni hanno superato i limiti nazionali e dell’OMS in molte aree urbane della Pianura Padana, esponendo la popolazione al rischio di patologie legate all’inquinamento. L’obiettivo principale di questo studio è stato lo sviluppo di un sistema di modellazione multi-scala in grado di fornire campi di concentrazione oraria di NOx (NO + NO2) sulla città di Modena ad una scala spaziale in grado di risolvere gli effetti dovuti alla presenza degli edifici, al fine di supportare politiche ambientali, studi epidemiologici e di aiutare la pianificazione della mobilità urbana. Il sistema di modellazione si basa su due diversi tool: il modello euleriano di chimica e di trasporto WRF-Chem, in grado di calcolare campi di concentrazione su un dominio regionale considerando specifici scenari di emissione, e Parallel Micro SWIFT e SPRAY (PMSS) suite modellistica sviluppata per risolvere i fenomeni di dispersione all'interno di ambienti urbani. PMSS è stato utilizzato per simulare la dispersione di NOx prodotta dai flussi di traffico urbano nella città di Modena, mentre il modello WRF-Chem è stato applicato per stimare le concentrazioni di NOx di fondo su più domini innestati fra loro, utilizzando emissioni a scala regionale ed escludendo allo stesso tempo le fonti di emissioni da traffico entro la città di Modena. Nella prima parte del lavoro il sistema di modellazione è stato impiegato per riprodurre le concentrazioni comprese nell’arco temporale tra il 28 ottobre e l'8 novembre 2016, corrispondente al periodo in cui è stata condotta una campagna di rilevazione dei flussi di traffico, attraverso radar Doppler su una strada di Modena a quattro corsie, al fine di riprodurre una modulazione temporale delle emissioni il più possibile realistica. Nella seconda parte dello studio lo stesso sistema di modellazione è stato utilizzato per produrre previsioni orarie delle concentrazioni di NO2 e NO, fino ad un giorno in avanti, per tutto il mese di febbraio 2019 sulla città di Modena. Le concentrazioni orarie simulate e osservate mostrano un andamento molto concorde fra loro, specialmente per il sito di traffico urbano, dove le stime dettagliate sulle emissioni del traffico si sono dimostrate molto efficaci nel riprodurre la tendenza osservata. Nella stazione urbana di fondo, nonostante una generale sottostima delle concentrazioni osservate, la combinazione di WRF-Chem con PMSS ha fornito comunque un andamento medio giornaliero in linea con le osservazioni. Infine, l'analisi statistica ha mostrato che il sistema di modellazione, in entrambi i siti urbani (di traffico e di fondo), soddisfa i criteri di accettazione standard per la valutazione dei modelli di dispersione urbana, confermando che tale sistema può essere impiegato come strumento per verificare gli effetti delle politiche locali riguardanti il traffico e a supporto di valutazioni di impatto sulla salute umana.
In Europe, emissions of many air pollutants have decreased substantially over the past decades, resulting in improved air quality across the region. However, air pollutant concentrations are still too high, and air quality problems persist. The Po Valley, located in the northern part of Italy, is one of the most critical area of the country in terms of pollution level. The reason to this problem is not only related to the high population density with its related activities, but it is also due to the orographic conformation of the territory which appears surrounded by mountains on three sides: the Alps to the west and to the north and the Apennines to the south. These geographical characteristics lead to meteorological conditions unfavorable to the atmospheric dispersion: average annual wind speed less than 2 m s-1, recurrent thermal inversions at low altitude, low mixing layer heights and persistent foggy and hazy events during winter time. One of the main critical air pollutants in terms of health effects is nitrogen dioxide (NO2), whose levels in the last years exceeded national and WHO (World Health Organization) standards in many urban areas across the Po Valley, exposing urban population to the risk of pollution-related diseases and health conditions. The main goal of this study was to develop a multi-scale modelling system able to provide hourly NOx (NO + NO2) concentration fields at a building-resolving scale in the urban area of Modena, a city in the middle of the Po Valley, in order to support environmental policies, epidemiological studies and urban mobility planning. The modelling system relied on two different models: the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), which is able to compute concentration fields over regional domain by considering specific emission scenarios, and the Parallel Micro SWIFT and SPRAY (PMSS) modelling suite accounting for dispersion phenomena within the urban area. The PMSS modelling suite was used to simulate at building-scale resolution the NOx dispersion produced by urban traffic flows in the city of Modena. Conversely, the WRF-Chem model was selected to estimate the NOx background concentrations on multiple domains with a nesting technique, in order to take into account emissions both at regional and local scale by excluding traffic emissions sources over the city of Modena. In the first part of the work the modelling system was performed for the period between 28 October and 8 November 2016, the same period whereby a direct vehicle flow measurement campaign was carried out continuously with 4 Doppler radar counters in a four-lane road in Modena, in order to reproduce emission hourly modulation rates. In second section of the study the modelling system was set-up with the aim of produce hourly forecast of NO2 and NO concentrations, up to one day ahead, for the city of Modena for the entire month of February 2019. Simulated and observed hourly concentrations exhibited a large agreement in particular for urban traffic site where detailed traffic emission estimations proved to be very successful in reproducing the observed trend. At urban background stations, despite a general underestimation of the observed concentrations, the combination of WRF-Chem with PMSS provided daily pattern in line with observations. Finally, the statistical analysis showed that PMSS combined with WRF-Chem at both traffic and background sites fulfilled standard acceptance criteria for urban dispersion model evaluation, confirming that the proposed multi-modelling system can be employed as a tool to support human exposures and health impact assessments as well as the effects of local traffic policies on urban air quality.

The measurement of submicrometre (< 1.0 m) and ultrafine particles (diameter < 0.1 m) number concentration have attracted attention since the last decade because the potential health impacts associated with exposure to these particles can be more significant than those due to exposure to larger particles. At present, ultrafine particles are not regularly monitored and they are yet to be incorporated into air quality monitoring programs. As a result, very few studies have analysed their long-term and spatial variations in ultrafine particle concentration, and none have been in Australia. To address this gap in scientific knowledge, the aim of this research was to investigate the long-term trends and seasonal variations in particle number concentrations in Brisbane, Australia. Data collected over a five-year period were analysed using weighted regression models. Monthly mean concentrations in the morning (6:00-10:00) and the afternoon (16:00-19:00) were plotted against time in months, using the monthly variance as the weights. During the five-year period, submicrometre and ultrafine particle concentrations increased in the morning by 105.7% and 81.5% respectively whereas in the afternoon there was no significant trend. The morning concentrations were associated with fresh traffic emissions and the afternoon concentrations with the background. The statistical tests applied to the seasonal models, on the other hand, indicated that there was no seasonal component. The spatial variation in size distribution in a large urban area was investigated using particle number size distribution data collected at nine different locations during different campaigns. The size distributions were represented by the modal structures and cumulative size distributions. Particle number peaked at around 30 nm, except at an isolated site dominated by diesel trucks, where the particle number peaked at around 60 nm. It was found that ultrafine particles contributed to 82%-90% of the total particle number. At the sites dominated by petrol vehicles, nanoparticles (< 50 nm) contributed 60%-70% of the total particle number, and at the site dominated by diesel trucks they contributed 50%. Although the sampling campaigns took place during different seasons and were of varying duration these variations did not have an effect on the particle size distributions. The results suggested that the distributions were rather affected by differences in traffic composition and distance to the road. To investigate the occurrence of nucleation events, that is, secondary particle formation from gaseous precursors, particle size distribution data collected over a 13 month period during 5 different campaigns were analysed. The study area was a complex urban environment influenced by anthropogenic and natural sources. The study introduced a new application of time series differencing for the identification of nucleation events. To evaluate the conditions favourable to nucleation, the meteorological conditions and gaseous concentrations prior to and during nucleation events were recorded. Gaseous concentrations did not exhibit a clear pattern of change in concentration. It was also found that nucleation was associated with sea breeze and long-range transport. The implications of this finding are that whilst vehicles are the most important source of ultrafine particles, sea breeze and aged gaseous emissions play a more important role in secondary particle formation in the study area.

The measurement of submicrometre (< 1.0 m) and ultrafine particles (diameter < 0.1 m) number concentration have attracted attention since the last decade because the potential health impacts associated with exposure to these particles can be more significant than those due to exposure to larger particles. At present, ultrafine particles are not regularly monitored and they are yet to be incorporated into air quality monitoring programs. As a result, very few studies have analysed their long-term and spatial variations in ultrafine particle concentration, and none have been in Australia. To address this gap in scientific knowledge, the aim of this research was to investigate the long-term trends and seasonal variations in particle number concentrations in Brisbane, Australia. Data collected over a five-year period were analysed using weighted regression models. Monthly mean concentrations in the morning (6:00-10:00) and the afternoon (16:00-19:00) were plotted against time in months, using the monthly variance as the weights. During the five-year period, submicrometre and ultrafine particle concentrations increased in the morning by 105.7% and 81.5% respectively whereas in the afternoon there was no significant trend. The morning concentrations were associated with fresh traffic emissions and the afternoon concentrations with the background. The statistical tests applied to the seasonal models, on the other hand, indicated that there was no seasonal component. The spatial variation in size distribution in a large urban area was investigated using particle number size distribution data collected at nine different locations during different campaigns. The size distributions were represented by the modal structures and cumulative size distributions. Particle number peaked at around 30 nm, except at an isolated site dominated by diesel trucks, where the particle number peaked at around 60 nm. It was found that ultrafine particles contributed to 82%-90% of the total particle number. At the sites dominated by petrol vehicles, nanoparticles (< 50 nm) contributed 60%-70% of the total particle number, and at the site dominated by diesel trucks they contributed 50%. Although the sampling campaigns took place during different seasons and were of varying duration these variations did not have an effect on the particle size distributions. The results suggested that the distributions were rather affected by differences in traffic composition and distance to the road. To investigate the occurrence of nucleation events, that is, secondary particle formation from gaseous precursors, particle size distribution data collected over a 13 month period during 5 different campaigns were analysed. The study area was a complex urban environment influenced by anthropogenic and natural sources. The study introduced a new application of time series differencing for the identification of nucleation events. To evaluate the conditions favourable to nucleation, the meteorological conditions and gaseous concentrations prior to and during nucleation events were recorded. Gaseous concentrations did not exhibit a clear pattern of change in concentration. It was also found that nucleation was associated with sea breeze and long-range transport. The implications of this finding are that whilst vehicles are the most important source of ultrafine particles, sea breeze and aged gaseous emissions play a more important role in secondary particle formation in the study area.

Outdoor air pollution is a major contributor to adverse health effects of citizens, in particular those living in urban environments. Air quality monitoring networks are set up to measure air quality in different environments in compliance with national and European legislation. Generally, only a few fixed monitoring sites are located within a city and thus cannot represent air pollutant concentrations in urban areas accurately enough to allow for a detailed human exposure assessment. Other approaches to derive detailed urban air pollutant concentration estimates exist, such as dispersion models and land-use regression (LUR) models. Low-cost portable air quality monitors are also emerging, which have the potential to add value to existing monitoring networks by providing measurements at greater spatial resolution and also to provide individual-level exposure assessment. The aim of this thesis is to demonstrate how measurements and modelling in combination allow detailed investigations of the variability of air pollutants in space and time in urban area, and in turn improve on the current exposure assessment methods. Three types of low-cost portable monitors measuring NO2, O3 (Aeroqual monitors) and PM2.5 (microPEM monitor) were evaluated against their respective reference instruments. The Aeroqual O3 monitor showed very good correlation (r2 > 0.9) with the respective reference instruments, but biases in the slope and intercept coefficients indicated that calibration of Aeroqual O3 monitor was needed. The Aeroqual NO2 monitor was subject to cross-sensitivity from O3, which, as demonstrated, can be effectively corrected by making O3 and NO2 measurements in tandem. Correlation between the microPEM monitor and its reference instrument was poor (r2 < 0.1) when PM2.5 concentrations were low (< 10 μg m-3), but significantly improved (r2 > 0.69) during periods with elevated PM2.5 concentrations. Relative humidity was not found to affect the raw results of PM2.5 measurements in a consistent manner. All three types of monitors cannot be used as equivalent or indicative methods instead of reference methods in studies that require quantification of absolute pollutant concentrations. However, the generally good correlations with reference instruments reassure their application in studies of relative trends of air pollution. Concentrations of PM2.5, ultrafine particles (UFP) and black carbon (BC) were quantified using portable monitors through a combination of mobile and static measurements in the city of Edinburgh, UK. The spatial variability of UFP and BC was large, of similar magnitude and about 3 times higher than the spatial variability of PM2.5. Elevated concentrations of UFP and BC were observed along streets with high traffic volumes whereas PM2.5 showed less variation between streets and a footpath without road traffic. Both BC and UFP significantly correlated with traffic counts, while no significant correlation between PM2.5 and traffic counts was observed. The relationships between UFP, NO2 and inorganic components of PM2.5 were further investigated through long-term measurements at roadside, urban background and rural sites. UFP moderately correlated with NOx (NO2 + NO) and showed varying relationships with NOx depending on the particle size distribution. Principal component analysis and air-mass back trajectory analysis revealed that PM2.5 concentrations were dominated by long-range transport of secondary inorganic aerosols, whereas UFP were mainly related to varying local emissions and meteorological conditions. These findings imply the need for different policies for managing human exposure to these different particle components: control of much BC and UFP appears to be manageable at local scale by restricting traffic emissions; however, abatement of PM2.5 requires a more strategic approach, in cooperation with other regions and countries on emissions control to curb long-range transport of PM2.5 precursors. A dispersion model (ADMS-Urban) was used to simulate high resolution NO2 and O3 concentrations in Edinburgh. The effects of different emission and meteorological input datasets on the resulting modelled NO2 concentrations were investigated. The modelled NO2 and O3 concentrations using the optimal model setup were validated against reference instrument and diffusion tube measurements. Temporal variability of NO2 was predicted well at locations that were not heavily influenced by local effects, such as road junctions and bus stops. Temporal variability of O3 was predicted better than for NO2. Long-term spatial variability of NO2 was found to correlate well with diffusion tube measurements, while modelled spatial variability of O3 in ADMS-Urban compared poorly with diffusion tube measurements. However, it was found that the O3 diffusion tube measurements may be subject to some unidentified biases affecting their accuracy. Land-use regression (LUR) models are widely used to estimate exposure to air pollution in urban areas. An appropriately sized and designed monitoring network is an important component for the development of a robust LUR model. Concentrations of NO2 were simulated by ADMS-Urban at ‘virtual’ monitoring sites in 54 different network designs of varying numbers and types of site, using a 25 km2 area including much of the Edinburgh city area. Separate LUR models were developed for each network. These LUR models were then used to estimate ambient NO2 concentrations at all residential addresses, which were evaluated against the ADMS-Urban modelled concentration at these addresses. The improvement in predictive capability of the LUR models was insignificant above ~30 monitoring sites, although more sites tended to yield more precise LUR models. Monitoring networks containing sites located within highly populated areas better estimated NO2 concentrations across all residential locations. LUR models constructed from networks containing more roadside sites better characterised the high end of residential NO2 concentrations but had increased errors when considering the whole range of concentrations. No particular composition of monitoring network resulted in good estimation simultaneously across all residential NO2 concentration and of the highest NO2 levels implying a lack of spatial contrast in LUR-modelled pollution surface compared with the dispersion model. Finally, the results from the measurement and modelling studies presented in thesis are synthesised in the context of current exposure assessment studies. Low-cost air-quality monitors currently do not possess and are unlikely in the near future to provide the robustness and accuracy to replace the existing routine monitoring network. Development of the low-cost air-quality should be aiming at upgrading them as the indicative method as defined in the data quality objective in the EU directive. The monitoring sites used to build LUR models should capture well the population distribution in the study area as opposed to capturing the greatest pollution contrast. The traditional methods of evaluating LUR models are also ineffective in characterising the models’ capability at estimating pollutant concentration at residential address. Given that the dispersion models are also subject to the availability and uncertainties in the input data, future air quality model development should endeavour to incorporate both dispersion and land-use regression models, where the uncertainty in the input data can be reduced by using LUR models built on actual measurements, and the limitation in the statistical modelling can be replaced by adopting the deterministic approach used in the dispersion model.

Vehicular emissions contribute significantly to poor air quality in urban areas. An integrated modelling approach is adopted to estimate microscale urban traffic emissions. The modelling framework consists of a traffic microsimulation model, a microscopic emissions model, and two dispersion models. This framework is applied to a traffic network in downtown Toronto to evaluate summer time morning peak traffic emissions during weekdays for carbon monoxide and nitrogen oxide. The model predicted results are validated against sensor observations with a reasonably good fit. Availability of local estimates of ambient concentration is useful for accurate comparisons of total predicted concentrations with observed concentrations. Both predicted and observed concentrations are significantly smaller than the National Ambient Air Quality Objectives established by Environment Canada. Sensitivity analysis is performed on a set of input parameters and horizontal wind speed is found to be the most influential factor in pollutant dispersion.

博士
國立臺灣大學
職業醫學與工業衛生研究所
97
Traffic emissions contribute importantly to air pollution and are reported to negatively affect human health. This study aimed to investigate formation, exposure and health effects of traffic emissions. The ozone problem has drawn attention during the past years. However, most studies focused so far on ozone event and non-event days while less attention was given to ozone formation potentials. Therefore, the first part aimed to collect volatile organic compounds data from photochemical assessment stations in central Taiwan to analyze ozone formation potentials in urban, suburban and rural areas. The results showed that transported ozone and precursors, i.e. volatile organic compound (VOC) from upwind to downwind producing elevated ozone levels in the suburban and rural areas. We also used the VOC data to investigate the relationship between mortality and traffic-related VOCs, which was a novel approach. Our findings indicated that daily cardiovascular mortality is associated with acute exposure to traffic-related VOCs. Moreover, fuel combustion and road transport were identified as the dominant sources of particulate matters (PM). The third part of this study assessed the particle levels when commuters use different transportation modes during rush hours. Motorcycle commuters were exposed to the highest concentrations of PM and they also the highest exposure dose even though they had the shortest travel time. By contrast, car commuters were exposed to the lowest PM concentrations and doses. The last part of this study compared number concentrations of submicron and ultrafine particles in public transportation stations, i.e. bus station, train station and mass rapid transportation station. The highest average particle number concentration was found in Taipei bus station, where more diesel engine buses were driving by, and the lowest average particle number concentration was found in Taipei MRT station. In conclusion, our study findings provided evidence for traffic-related emission, health effects and exposure data.

Dissertação de Mestrado Integrado em Engenharia do Ambiente apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra
As emissões de poluentes do tráfego rodoviário são uma das principais causas da degradação da qualidade do ar nas áreas urbanas. Assim, torna-se fundamental a caraterização da poluição atmosférica (origem, dispersão e composição) nas áreas urbanas, através da utilização de técnicas de modelação que se revelam importantes na caraterização da poluição, no cumprimento da legislação e na gestão da qualidade do ar. O principal objetivo no âmbito desta dissertação foi avaliar a qualidade do ar numa área urbana do concelho de Coimbra, através da aplicação de um modelo de qualidade do ar Lagrangeano que é adotado pela Agência Federal Alemã do Ambiente, o AUSTAL2000. A utilização deste modelo prevê a introdução de dados de entrada, nomeadamente das emissões, meteorologia, topografia do terreno e edifícios. Para caraterização das emissões, elaborou-se uma campanha de contagens de tráfego, no dia 12 de Junho de 2014, para possibilitar uma estimativa dos volumes de tráfego na zona de estudo. Os volumes de tráfego estimados foram posteriormente utilizados no cálculo das emissões através do modelo de emissões TREM. Os dados meteorológicos foram fornecidos pelo Observatório Geofísico e Astronómico da Universidade de Coimbra para o dia das contagens, enquanto a topografia do terreno e os edifícios foram introduzidos no modelo com recurso a ferramentas SIG. Por fim, após a obtenção dos resultados foi possível analisá-los espacialmente e compará-los com os valores medidos na estação de qualidade do ar da Avenida Fernão de Magalhães. Os valores máximos de concentração, obtidos pelo modelo na zona de estudo, de NO2 e PM10 foram 87 e 31 μg/m³. Os resultados obtidos permitiram concluir que os resultados da modelação, assim como os dados medidos, horários para NO2 e diário para PM10 (34 μg/m³ e 41 μg/m³), cumpriram os valores limite (50 μg/m³ e 200 μg/m³, respetivamente) estabelecidos pela Diretiva 2008/50/CE. Por outro lado, os resultados da modelação apresentaram um bom desempenho, uma vez que, além de se observar uma boa concordância entre os valores medidos e os modelados, o tratamento estatístico dos dados revela uma boa correlação dos dados medidos e modelados, principalmente de PM10.
Pollutant emissions from road traffic are a major cause of degradation of air quality in urban areas. Thus, the characterization of air pollution (source, dispersion and composition) in urban areas becomes essential, through the use of modelling techniques that prove important in the characterization of pollution, in compliance with legislation and the management of air quality. The main objective in the context of this dissertation was to evaluate the air quality in an urban area of the municipality of Coimbra, through the application of a Lagrangian air quality model that is adopted by the German Federal Environment Agency, the AUSTAL2000. The use of this model predicts the introduction of input data, including emissions, meteorology, topography and buildings. For characterization of emissions, we prepared a campaign of traffic counts, on June 12, 2014, to allow an estimate of traffic volumes in the referred area. The estimated traffic volumes were then used to calculate emissions through the TREM emissions model. The meteorological data was provided by the Geophysical and Astronomical Observatory of the University of Coimbra for the campaign day, while the topography of the land and buildings was introduced in the model using GIS tools. Finally, after obtaining the results it was possible to analyse them spatially and compare them with the values measured in the air quality station of Fernão de Magalhães Avenue. The maximum concentration values, obtained by the model in the stated area, of NO2 and PM10 were 87 and 31 μg/m³. The results showed that the modelling results, as well as the measured data, hourly for NO2 and daily for PM10 (34 μg/m³ and 41 μg/m³), met the limit values (50 μg/m³ and 200 μg/m³, respectively) established by the Directive 2008/50/CE. Moreover, the results of the modelling showed good performance, since, in addition to observe some agreement between the measured and modelled values, statistical processing of the data revealed a good correlation of the measured and modelled data, especially PM10

Feng, Xiaofei.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2013.
Includes bibliographical references (leaves 157-168).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstracts also in Chinese.

This research develops and applies a methodology to calculate vehicle activity inputs for modelling of emissions from on-road vehicles using traffic count data. The thesis: (1) provides an understanding of emissions modelling in Canada and the U.S. and discusses the traffic activity data inputs required by vehicle emissions modelling software; (2) develops a methodology to collect and prepare vehicle activity data for an urban centre and applies this methodology by estimating vehicle activity for Winnipeg and Saskatoon; and (3) estimates vehicle emissions and then compares the sensitivity of estimating emissions using locally developed vehicle activity to estimating emissions using default vehicle activity. The methodology this research develops and applies to Winnipeg and Saskatoon is applicable to any jurisdiction in need of developing their own vehicle activity inputs for emissions modelling. The emissions estimates calculated using these different inputs emphasizes the importance of obtaining jurisdiction-specific input values for emissions modelling.

Transportation planning and urban development in the United States have synchronously emerged over the past few decades to encompass goals associated with sustainability, improved connectivity, complete streets and mitigation of environmental impacts. These goals have evolved in tandem with some of the relatively more traditional objectives of supply-side improvements such as infrastructure and capacity expansion. Apart from the numerous federal regulations in the US transportation sector that reassert sustainability motivations, metropolitan planning organizations and civic societies face similar concerns in their decision-making and policy implementation. However, overall transportation planning to incorporate these wide-ranging objectives requires characterization of large-scale transportation systems and traffic flow through them, which is dynamic in nature, computationally intense and a non-trivial problem. Thus, these contemporary questions lie at the interface of transportation planning, urban development and sustainability planning. They have the potential of being effectively addressed through state-of-the-art transportation modeling tools, which is the main motivation and philosophy of this thesis. From the research standpoint, some of these issues have been addressed in the past typically from the urban design, built-environment, public health and vehicle technology and mostly qualitative perspectives, but not as much from the traffic engineering and transportation systems perspective---a gap in literature which the thesis aims to fill. Specifically, it makes use of simulation-based dynamic traffic assignment (DTA) to develop modeling paradigms and integrated frameworks to seamlessly incorporate these in the transportation planning process. In addition to just incorporating them in the planning process, DTA-based paradigms are able to accommodate numerous spatial and temporal dynamics associated with system traffic, which more traditional static models are not able to. Besides, these features are critical in the context of the planning questions of this study. Specifically, systemic impacts of suburban and urban street pattern developments typically found in US cities in past decades of the 20th century have been investigated. While street connectivity and design evolution is mostly regulated through local codes and subdivision ordinances, its impacts on traffic and system congestion requires modeling and quantitative evidence which are explored in this thesis. On the environmental impact mitigation side, regional emission inventories from the traffic sector have also been quantified. Novel modeling approaches for the street connectivity-accessibility problem are proposed. An integrated framework using the Environmental Protection Agency's regulatory MOVES model has been developed, combining it with mesoscopic-level DTA simulation. Model demonstrations and applications on real and large-sized study areas reveal that different levels of connectivity and accessibility have substantial impacts on system-wide traffic---as connectivity levels reduce, traffic and congestion metrics show a gradually increasing trend. As regards emissions, incorporation of dynamic features leads to more realistic emissions inventory generation compared to default databases and modules, owing to consideration of the added dynamic features of system traffic and region-specific conditions. Inter-dependencies among these sustainability planning questions through the common linkage of traffic dynamics are also highlighted. In summary, the modeling frameworks, analyses and findings in the thesis contribute to some ongoing debates in planning studies and practice regarding ideal urban designs, provisions of sustainability and complete streets. Furthermore, the integrated emissions modeling framework, in addition to sustainability-related contributions, provides important tools to aid MPOs and state agencies in preparation of state implementation plans for demonstrating conformity to national ambient air-quality standards in their regions and counties. This is a critical condition for them to receive federal transportation funding.
text