Nitrous oxide (N₂O) is a powerful greenhouse gas, and its atmospheric concentration is increasing dramatically. N₂O is produced through the microbially-mediated processes of nitrification and denitrification. Since these processes have different substrates and isotopic enrichment factors, stable isotope analysis (δ¹⁵N and δ¹⁸O) of N₂O can be used to study the production of this important greenhouse gas. Although production in rivers accounts for a significant portion of the global N₂O budget, the isotopic composition of N₂O from this source is poorly characterized. Most of the previous work using stable isotopes of N₂O has been conducted in terrestrial or oceanic environments, and only one published study has measured δ¹⁵N and δ¹⁸O of N₂O produced in a riverine environment. The purpose of this research project was to use stable isotope analysis to characterize the processes responsible for N₂O production in the Grand River, Ontario, Canada, and to determine the spatial and temporal variability of the isotopic composition of the N₂O flux. To meet the study objectives, an offline “purge and trap” method was developed to collect and purify dissolved N₂O for stable isotope analysis. Using this method, δ¹⁵N and δ¹⁸O analysis of dissolved N₂O is possible for samples with concentrations as low as 6 nmol N₂O/L. Due to the isotopic effects of gas exchange and the back flux of tropospheric N₂O, there is a complex relationship between the δ¹⁵N and the δ¹⁸O of source, dissolved, and emitted N₂O in aquatic environments. A simple box model (SIDNO – Stable Isotopes of Dissolved Nitrous Oxide) was developed to properly interpret isotopic data for dissolved N₂O. Using this model, it was determined that the isotopic composition of emitted N₂O is much more representative of N₂O production in aquatic environments than the isotopic composition of dissolved N₂O. If the concentration, δ¹⁵N and δ¹⁸O of dissolved N₂O are measured, the magnitude and isotopic composition of the N₂O flux can be calculated. Sampling downstream of the major wastewater treatment plants (WWTPs) on the Grand River indicates that nitrification and denitrification in the river are strongly tied to diel changes in dissolved oxygen (DO) concentration. During the day, when DO concentrations are high, nitrification or nitrifier-denitrification is the dominant N₂O production pathway, with sediment denitrification also contributing to N₂O production. At night, when DO concentrations are low, denitrification in the sediments and at the sediment / water interface is the dominant production pathway. Using the SIDNO model, N₂O produced during the day was found to have a δ¹⁵N of -22‰ and a δ¹⁸O of 43‰. N₂O produced at night had a δ¹⁵N of -30‰ and a δ¹⁸O of 30‰. The isotopic composition of N₂O emitted from the Grand River is dominated by night-time production downstream of the Waterloo and Kitchener WWTPs during the summer. The flux and time weighted annual average isotopic composition of N₂O emitted from the Grand River is -18.5‰ and 32.7‰ for δ¹⁵N and δ¹⁸O respectively. These values are significantly more depleted than the only other published data for riverine N₂O production. If the Grand River is representative of global riverine N₂O production, these results will have significant implications for the global isotopic budget for atmospheric N₂O.

Introduction: The index of new cars’ climate impact (Trafikverket 2010) calculated a reduction factor to quantify the positive impact of renewable fuels on CO2 emissions for new cars in Sweden 2009. Although uncertainty for many input parameters is acknowledged, all calculations were made with point estimates. Aim: The aim of this study was to verify consumer recommendations based on carbon dioxide emissions of new cars by re-assessing the CO2 emissions considering the uncertainty in input data. The data uncertainty was quantified by using probabilistic risk assessment methodology. The objectives were (1) to calculate the possible reduction of CO2 emissions for new cars in Sweden accounting for uncertainties; (2) to identify the most influential factors by conducting a sensitivity analysis; and (3) to assess the implications of interval analysis and probabilistic modelling for epistemic uncertainty in this context and thereby to contribute to the discussion on which method is the most appropriate to support decision making. Method: Calculations were performed with the deterministic model proposed by Trafikverket (2010) using probability distributions (Bayesian probability distributions assigned by expert judgements) and intervals as model input. Probabilistic modelling was implemented as Monte Carlo simulation with uniform distributions and triangular distributions in Matlab. Interval calculations were done by hand. Results: The best estimate from probabilistic modelling of CO2 reduction when using E85 as a fuel of 30% (95% confidence interval = 10% to 52%) is in a similar range as the 20% given by Trafikverket (2010). The best estimate of 28% decrease for gas cars (95% confidence interval = 3% to 44%) is lower than the originally proposed 42%, but still in a similar range. The difference is due to the large extent of optimistic values taken by Trafikverket (2010). The interval analysis produced a wider range. For ethanol driven cars, a CO2 reduction of 68%, an increase of 14% or anything in between is calculated, for cars operated on gas the result is a CO2 reduction of 59%, an increase of 29% or anything in between. Conclusions: The use of biofuels most likely reduces CO2 emissions. The CO2 emissions from the production of the biofuel are the most influential factors. The recommendations of Trafikverket to the consumers are still valid after probabilistic recalculation. Probabilistic modelling is more appropriate than interval analysis to guide authority and consumer decisions as it allows conclusions to be drawn and therefore can support decision making.

Vietnam is one of the countries suffering from the most serious adverse effects due to climate change and sea level rise. The main cause of climate change is the increased activities generating greenhouse gases. Organic waste is the main source of carbon dioxide emission, which has the largest concentration among different kinds of greenhouse gases in the earth’s atmosphere. The conversion of organic waste and biomass into energy contributes not only to supply cleaner energy but also to reduce emissions of greenhouse gases. Vietnam has a large potential of biomass and agricultural by-products. The technologies to turn biomass into different kinds of bio-energies were developed and applied all over the world. Biogas was called as "brown revolution" in the field of new energy. Biogas production technology now has been studied and applied widely in the world, particularly in developing countries with warm climate that is suitable for anaerobic fermentation of organic waste. The biogas digester can be built with any capacity, needs small investment and the input materials are widely available. The biogas energy is used for many purposes such as cooking, lighting, running engines, etc. It is a production technology quite consistent with the economy of developing countries and really brings to life more civilized and convenient to rural areas. / Việt Nam là một trong những quốc gia bị tác động nghiêm trọng nhất do biến đổi khí hậu và nước biển dâng cao. Nguyên nhân chính của biến đổi khí hậu là các hoạt động gia tăng tạo ra các khí gây hiệu ứng nhà kính. Chất thải hữu cơ là nguồn chính phát thải khí carbon dioxide có nồng độ lớn nhất trong số các loại khí gây hiệu ứng nhà kính khác nhau trong bầu khí quyển của trái đất. Việc chuyển đổi chất thải hữu cơ và sinh khối thành năng lượng góp phần không chỉ cung cấp năng lượng sạch hơn mà còn giảm phát thải khí gây hiệu ứng nhà kính. Việt Nam có một tiềm năng lớn về sinh khối và phụ phẩm nông nghiệp. Các công nghệ biến sinh khối thành các loại năng lượng sinh học khác nhau đã được phát triển và áp dụng rộng rãi trên thế giới. Khí sinh học được gọi là "cuộc cách mạng màu nâu" trong lĩnh vực năng lượng mới. Công nghệ sản xuất khí sinh học đã được nghiên cứu và áp dụng rộng rãi trên thế giới, đặc biệt là ở các nước đang phát triển với nhiệt độ khí hậu nhiệt đới phù hợp cho quá trình lên men kỵ khí các chất thải hữu cơ để sản xuất khí sinh học. Bình phản ứng tạo khí sinh học có thể được xây dựng với công suất bất kỳ, nhu cầu đầu tư nhỏ, các nguyên liệu đầu vào sẵn có. Năng lượng khí sinh học đã được sử dụng cho nhiều mục đích như thắp sáng, nấu ăn, chạy động cơ, v.v... Đây là hoạt động sản xuất khá phù hợp với nền kinh tế của các nước đang phát triển và thực sự đem lại cuộc sống văn minh hơn và tiện lợi đến các khu vực nông thôn.

Methane (CH4) is not only important but also a long-lived greenhouse gas. Scientists estimated that more than half of CH4 is released from the water column. Studies of methane from water column are almost focused on rice fields, wetlands and swamps in Taiwan. There are only limited studies of methane about rivers, lakes and coasts. So this study investigated CH4 distribution in natural waters on and around Taiwan. The average surface methane concentration in the South China Sea (SCS) is about 5.10¡Ó3.61 nM (n=103). The average surface methane concentration in the West Philippines Sea (WPS) is about 3.44¡Ó3.89 nM (n=56), lower than in the SCS. The average surface concentration in the Northern and Southern Taiwan Strait are, respectively, 4.72¡Ó3.19 nM (n=64) and 4.01¡Ó3.19 nM (n=51), and are between the average concentrations in the SCS and the WPS. The sea-to-air fluxes of methane in the SCS and the WPS are 0.38¡Ó0.99 £gmol/m2/h (n=103) and £gmol/m2/h (n=56), respectively. The sea-to-air fluxes of methane in the Northern and Southern Taiwan Strait are, respectively, 0.37¡Ó0.55 £gmol/m2/h (n=64) and 0.10¡Ó0.53 £gmol/m2/h (n=51). Although the sea-to-air fluxes for methane is much lower than the flux for carbon dioxide, methane emission in the SCS contributes nearly the same greenhouse effect as carbon dioxide does. In Taiwan, the average surface methane concentration in rivers is about 3221¡Ó12386 nM, and the emission is about 104¡Ó337 (£gmol/m2/h) (n=179). The average surface methane concentration and flux are, respectively, 2164¡Ó5432 nM and 265¡Ó1289 £gmol/m2/h (n=120) in the water column in China, including the coasts of Hong Kong , Pearl River and Yangtze River. The average surface methane concentration and flux in the natural water are higher than in Taiwan. In Asia, the average surface methane concentrations of the natural waters are, respectively, 8240¡Ó22753 nM (n=27) and 7639¡Ó24554 nM (n=50) in Thailand and Indonesia, twice the concentration in Taiwan. The average surface methane concentrations of the natural waters are, respectively, 2841¡Ó3358 nM (n=5) and 1939¡Ó3694 nM (n=15) in Malaysia and the Philippines, lower than in Taiwan. The emissions of methane in the natural waters are, respectively, 845¡Ó2622 £gmol/m2/h (n=50), 292¡Ó341 £gmol/m2/h (n=5) and 181¡Ó356 £gmol/m2/h (n=15) in Indonesia, Malaysia and the Philippines, also much higher than in Taiwan. The flux of methane in natural waters in Thailand (100¡Ó265 £gmol/m2/h, n=25) is as the same as in Taiwan.

With the increasing stringency on sulfur content in petrodiesel, there is a growing tendency of broader usage of ultra low sulfur diesel (ULSD) with sulfur content of 15 ppm. Refineries around the world should develop cost-effective and sustainable strategies to meet these requirements. The primary objective of this work is to analyze alternatives for producing ULSD. In addition to the conventional approach of revamping existing hydrotreating facilities, the option of blending petrodiesel with biodiesel is investigated. Blending petrodiesel with biodiesel is a potentially attractive option because it is naturally low in sulfur, enhances the lubricity of petrodiesel, and is a sustainable energy resource. In order to investigate alternatives for producing ULSD, several research tasks were undertaken in this work. Firstly, base-case designs of petrodiesel and biodiesel production processes were developed using computer-aided tools ASPEN Plus. The simulations were adjusted until the technical criteria and specifications of petrodiesel and biodiesel production were met. Next, process integration techniques were employed to optimize the synthesized processes. Heat integration for petrodiesel and biodiesel was carried out using algebraic, graphical and optimization methods to maximize the integrated heat exchange and minimize the heating and cooling utilities. Additionally, mass integration was applied to conserve material resources. Cost estimation was carried out for both processes. The capital investments were obtained from ASPEN ICARUS Process Evaluator, while operating costs were calculated based on the updated chemical market prices. The total operating costs before and after process integration were calculated and compared. Next, blending optimization was performed for three blending options with the optimum blend for each option identified. Economic comparison (total annualized cost, breakeven analysis, return on investment, and payback period) of the three options indicated that the blending of ULSD with chemical additives was the most profitable. However, the subsequent life-cycle greenhouse gas (GHG) emission and safety comparisons demonstrated that the blending of ULSD with biodiesel was superior.

We study timing effects of carbon mitigation and solar radiation management (SRM) policies for correlated pollutants, CO₂ and SO₂. We show that national levels of carbon and sulfur emissions quotas and SRM implementation are positively correlated with each other. First-mover advantages exist when deciding both carbon quotas and SRM levels. Moreover, we use an example to illustrate that if international equity is considered, governments would be willing to choose SRM levels before carbon quotas since it yields higher payoffs and less acid rain and droughts damages. This timing was neglected by all previous theoretical economic models on geoengineering.

Diss.--New Delhi Jawaharlal Nehru Univ.

Thesis (M.A.)--University of Canterbury, 2008. / Typescript (photocopy). Includes bibliographical references (leaves 79-91). Also available via the World Wide Web.

Thesis (Ph. D.)--Universität St. Gallen, 2001. / "Dissertation Nr. 2547." Includes bibliographical references (p. 231-270).

Activity-based travel demand model systems are increasingly being deployed to microsimulate daily activity-travel patterns of individuals. However, a critical dimension that is often missed in these models is that of vehicle type choice. The current dissertation addresses this issue head-on and contributes to the field of transportation planning in three major ways. First, this research develops a comprehensive vehicle micro-simulation framework that incorporates state-of-the-art household vehicle type choice, usage, and evolution models. The novelty of the framework developed is that it accommodates all the dimensions characterizing vehicle fleet/usage decisions, as well as accommodates all dimensions of vehicle transactions (i.e., fleet evolution) over time. The models estimated are multiple discrete-continuous models (vehicle type being the discrete component and vehicle mileage being the continuous component) and spatial discrete choice models that explicitly accommodate for multiple vehicle ownership and spatial interactions among households. More importantly, the vehicle fleet simulator developed in this study can be easily integrated within an activity-based microsimulation framework. Second, the vehicle fleet evolution and composition models developed in this dissertation are used to predict the vehicle fleet characteristics, annual mileage, and the associated fuel consumption and green-house gas (GHG) emissions for future years as a function of the built environment, demographics, fuel and related technology, and policy scenarios. This exercise contributes in substantial ways to the identification of promising strategies to increase the penetration of alternative-fuel vehicles and fuel-efficient vehicles, reduce energy consumption, and reduce greenhouse gas emissions. Lastly, this research captures several complex interactions between vehicle ownership, location, and activity-travel decisions of individuals by estimating 1) a joint tour-based model of tour complexity, passenger accompaniment, vehicle type choice, and tour length, and 2) an integrated model of residential location, work location, vehicle ownership, and commute tour characteristics. The methodology used for estimating these models allows the specification and estimation of multi-dimensional choice model systems covering a wide spectrum of dependent variable types (including multinomial, ordinal, count, and continuous) and may be viewed as a major advance with the potential to lead to redefine the way activity-based travel model systems are structured and implemented. / text