Scaling air quality effects from alternative jet fuel in aircraft and ground support equipment

Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 74-78).Many of the nation's largest airports, including Los Angeles International Airport, the Hartsfield-Jackson Atlanta International Airport, Chicago O'Hare International Airport and Washington Dulles International Airport are located within areas designated by the EPA as having ambient particulate matter concentrations that exceed National Ambient Air Quality standards. When inhaled, fine particulate matter can enter the blood stream from the lungs and increase the risk of illness and premature mortality. This thesis examines the potential of two jet fuel types, ultra low sulfur jet fuel and synthetic paraffinic kerosene, to reduce aviation's contribution to ambient particulate matter concentrations. Scaling factors were developed for airport criteria pollutant emissions to model alternative jet fuels in aircraft and ground support equipment. These linear scaling factors were based on currently published studies comparing standard diesel and jet fuels with alternative jet fuels. It was found that alternative jet fuels lower or maintain all air pollutant emissions considered (primary particulate matter, sulfur oxides, nitrous oxides, unburned hydrocarbons and carbon monoxide) for both aircraft and ground support equipment. To quantify the potential benefits of changing fuel composition on ambient particulate matter concentrations, a study of the Atlanta Hartsfield Jackson International Airport was completed using both emissions inventory analysis and atmospheric modeling. The atmospheric modeling captures both primary particulate matter and other emissions that react in the atmosphere to form secondary particulate matter. It was found that the use of an ultra low sulfur jet fuel in aircraft gas turbines could reduce the primary particulate matter inventory by 37% and synthetic paraffinic kerosene could reduce the primary particulate matter inventory by 64%. The atmospheric modeling predicts that an ultra low sulfur jet fuel in aircraft could reduce ambient particulate matter concentrations due to aircraft by up to 57% and synthetic paraffinic kerosene could reduce particulate matter concentrations due to aircraft by up to 67%. Thus, this study indicates that the majority of air quality benefits at Atlanta Hartsfield Jackson International Airport that could be derived from the two fuels considered can be captured by removing the sulfur from jet fuel through the use of an ultra low sulfur jet fuel.by Pearl Donohoo.S.M.in Technology and Polic

Design of wide-area electric transmission networks under uncertainty : methods for dimensionality reduction

Thesis: Ph. D. in Technology, Management, and Policy, Massachusetts Institute of Technology, Engineering Systems Division, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 141-148).The growth of location-constrained renewable generators and the integration of electricity markets in the United States and Europe are forcing transmission planners to consider the design of interconnection-wide systems. In this context, planners are analyzing major topological changes to the electric transmission system rather than more traditional questions of system reinforcement. Unlike a regional reinforcement problem where a planner may study tens of investments, the wide-area planning problem may consider thousands of investments. Complicating this already challenging problem is uncertainty with respect to future renewable-generation location. Transmission access, however, is imperative for these resources, which are often located distant from electrical demand. This dissertation frames the strategic planning problem and develops dimensionality reduction methods to solve this otherwise computationally intractable problem. This work demonstrates three complementary methods to tractably solve multi-stage stochastic transmission network expansion planning. The first method, the St. Clair Screening Model, limits the number of investments which must be. The model iteratively uses a linear relaxation of the multi-period deterministic transmission expansion planning model to identify transmission corridors and specific investments of interest. The second approach is to develop a reduced-order model of the problem. Creating a reduced order transformation of the problem is difficult due to the binary investment variables, categorical data, and networked nature of the problem. The approach presented here explores two alternative techniques from image recognition, the Method of Moments and Principal Component Analysis, to reduce the dimensionality. Interpolation is then performed in the lower dimensional space. Finally, the third method embeds the reduced order representation within an Approximate Dynamic Programming framework. Approximate Dynamic Programming is a heuristic methodology which combines Monte Carlo methods with a reduced order model of the value function to solve high dimensionality optimization problems. All three approaches are demonstrated on an illustrative interconnection-wide case study problem considering the Western Electric Coordinating Council.by Pearl Elizabeth Donohoo-Vallett.Ph. D. in Technology, Management, and Polic