published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

The purpose was to evaluate airport impact on nearby residential property values (RPV). The hypothesis was that a relationship between noise levels, and nearby RPV could be identified and quantified. The objective was to model a relationship between housing location with respect to an airport and housing value, taking into consideration other relevant factors. Evaluation of aviation facilities impact on RPV was based on a sample of 587 single-family houses sold during 1984 and 1985. Selling price was used as the dependent variable. Independent variables included physical characteristics of a house, distance to the closest airport, accessibility of a house to the urban area, and supply and demand for houses. Distance to nearest airport was used as a surrogate for aircraft noise level. Study indicated that impact of aviation facilities on RPV could be quantified using selling price of a house as the dependent variable and distance from an airport as a primary independent variable along with physical characteristics of a house. Effect of an airport on RPV appears dependent not only on distance, but also on type of airport and selling price of a house. Davis-Monthan Airport (DM), a military airport, has a significantly greater impact than Tucson International Airport (TIA), a commercial airport. DM impact was greatest on houses priced above sample median. However, lower priced houses were effected more when the impact was expressed as a percent of selling price. DM impact area was approximately 2 miles from the airport, while TIA impact was about 1 mile from the facility. Distance from DM was the most significant variable in describing variation in selling price for houses closest to this facility. Living area of a house was also a very significant variable. Distance to TIA was not very significant in evaluating selling price variation. Living area and age of a house were generally the most significant variables describing selling price variation for TIA subset.

This thesis consists of two topics. The first topic is a performance assessment study of the flight operations in the North Atlantic Organized Track System. This study begins with the demand shortfall analysis of demand sets provided by the Federal Aviation Association (FAA). These sets were used to simulate OTS traffic for a number of scenarios that consider different separation minima. For this reason, algorithms were developed to modify the NAT OTS configuration applying reduced lateral separation between tracks and estimate the probability that any given flight that traverses the Atlantic will use the OTS. The preliminary results showed that the scenario with reduced lateral separation minimum (RLatSM) (25 nm) and the reduced longitudinal separation minimum (RLongSM) (8 nm) was the most optimal among all five that were simulated. The application of RLatSM also decrease the mean fuel consumption of flights that shift from traversing the OTS to flying random routes. The second topic is a noise study performed for the Chicago O'Hare International Airport. The contributions to this topic were three fold: 1) we analyzed data to understand the current operations at ORD airport 2) we verified the noise contours produced in 2002 by the FAA, Chicago Department of Aviation (CDA) and the engineering contractors 3) we produced noise contours for today's airport activity. / Master of Science

This research is concerned with the minimization of annoyance due to aircraft noise to airport communities. The approach developed is an interactive one which permits interaction with the Decision Maker. The immediate vicinity of an airport is divided into several areas based on population and proximity to the runways. The user can then minimize annoyance over any set of areas subject to the appropriate constraints. After any optimization, the results may be reviewed. The results provided include noise and annoyance in each area as well as the gradient of the objective and slackness associated with each constraint. This would assist the user in making a decision and aid in sensitivity analyses. At each stage of optimization, the problem solved has a nonlinear objective function and linear constraints. This problem is solved by solving successive surrogate linear problems. The method uses a first-order Taylor series expansion about the solution point to set up the surrogate linear problem. The first solution point is obtained by solving an approximate linear problem. The approach suggested is then applied to an example airport. Different scenarios are considered to illustrate the use of the model. Both the formulation of the objective as well as the constraints are illustrated. / Master of Science

Includes bibliographical references

The market becomes inefficient when externalities cause market failure. However, an externality does not entrain inefficiency if a market other than the one that generates it accounts for it in some way. Airports are a well-known source of the negative externality noise; and housing market are commonly thought to be affected by airport noise. A hedonic model was applied to airport noise and the housing market, together. It was found that the housing market of the West Island of Montreal did account implicitly for the noise annoyance from Dorval Airport, hence that the noise was a pecuniary externality. Moreover, each additional unit of noise annoyance (NEFdB) was found to cause an average depreciation in housing price (NDSI) of 0.76%. Finally, the linguistic predominance (French- or English-speaking) of a neighhourhood's residents may be an appropriate Canadian analogue for the racial variables that have been specified in some hedonic property models in the U.S.

No description available.

The major problem facing air transportation for the next decade is aircraft noise. The noise level due to the operation of large jet aircraft has created a very serious annoyance problem to the people living near of adjacent to jet airports. The noise problem has developed both for take-off and landing operations of these aircraft with take -off noise causing the greatest annoyance factor. A technique called Noise Exposure Forecast (NEF) has been developed to identify the annoyance factor of these noises to people and activities on the ground. With these NEF ratings or numbers, planners can better determine the type of buildings and activities to locate in the vicinity of airports. This paper presents a computer method for determining NEF areas or contours which eliminate the necessity of performing laborious hand calculations and iterations normally required to determine a given NEF locus about an airport. A land use compatibility table showing land use versus NEF numbers is given on page 3. A sample computer program is given on pages 21 through 25 of the appendix and a sample computer output page is given on page 26. The computer technique developed for this paper can be used for any airport.

An acoustic pattern recognition system has been developed to identify aerospace acoustic sources. The system is capable of classifying five different types of air and ground sources: jets, propeller planes, helicopters, trains, and wind turbines. The system consists of one microphone for data acquisition, a preprocessor, a feature selector, and a classifier. This thesis presents two new classifiers, one based on an associative memory and one on artificial neural networks, and compares their performance to that of the original classifier developed at VPI&SU (1,2). The acoustic patterns are classified using features that have been calculated from the time and frequency domains. Each of the classifiers undergoes a training period during which a set of known patterns is used to teach the classifier to classify unknown patterns correctly. Once training was completed each classifier is tested using a new set of unknown data. Two different classifier structures were tested, a single level structure and a tree structure. Results show that the single level associative memory and artificial neural network classifiers each identified 90.6 percent of the acoustic sources correctly. The original linear discriminant function single level classifier (1,2) identified 86.7 percent of the sources. The tree structure classifiers classified respectively 90.6 percent, 91.8 percent, and 90.1 percent of the sources correctly. / Master of Science

Most research on the urban sonic environment has been recent. One finding has been that physical noise exposure indices calibrate poorly with human noise response. The vagaries of human response to noise have given impetus to research to isolate the factors that differentiate human response to noise. The present thesis continues this research. The thesis specifies human noise response to occur on three levels: awareness, annoyance, and complaint. The factors that structure each level of noise response are identified in the published Iiterature. Noise awareness is a function of noise exposure. Noise annoyance is a function of noise exposure to a lesser degree. In addition annoyance is structured by attitudes toward the noise source, special interests in its economic benefits and personal susceptibility to noise irritation. Complaint concerning noise involves the previous factors plus an affluence or socio-economic component. The trend as one moves upward in the noise response hierarchy is for the structuring factors to become specific to individuals rather than location. Hence, noise awareness with respect to a stationary noise source will vary spatially, while complaint will be random in space. In testing these premises the author has made use of noise exposure indices for Portland International Airport and a social survey of response in the area surrounding the airport. The noise exposure indices were supplied by the consulting firm of Bolt, Beranek, and Newman Inc. and the social survey was conducted and tabulated by the Center for Population Research and Census at Portland State University. Multivariate methods were used for testing the premises concerning the hierarchical relationships between awareness, annoyance, and complaint.The variables representing awareness, annoyance, and complaint are quantified from the survey data through principle component and factor scores computer programs. The noise awareness measure is selected for greatest emphasis since it underlies to annoyance and complaint. The noise awareness measure is investigated and errors in its measurement are estimated through psychometric methods. Trend surface techniques are used to test the spatial regularity of awareness, annoyance, and complaint. As hypothesized, awareness is regular in space, annoyance less so, and complaint is random. Through multiple regression analysis noise awareness is tested against socio-economic measures, noise exposure measures, attitudes, and special interest. Noise exposure variables account for the largest part of the variation in noise awareness. A trend surface analysis is conducted on the values of awareness predicted by a large number of exposure, attitude, and other variables. The process is repeated for the residual values from the multiple regression. Predicted values are systematic in space but the residual values are largely random. The predicted values are mapped and compared with the actual smoothed awareness response surface. The two maps correspond well. It is concluded that noise exposure corrected for ambient noise levels adequately approximates the noise awareness response surface.