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A system intended to detect, classify, and track radioactive sources in moving vehicle traffic is under development at Lawrence Livermore National Laboratory (LLNL). This system will fuse information from a network of sensor suites to provide real time tracking of the location of vehicles emitting gamma and/or neutron radiation. This work examined aspects of the source terms of interest and applicable gamma detection technologies for passive detection of emitted gamma radiation. The severe restriction placed on the length of count due to motion of the source is presented. Legitimate gamma sources expected in traffic are discussed. The requirement to accurately classify and discriminate against these "nuisance" sources and cost restraints dictate the choice of NaI(Tl) detectors for this application. The development of a capability to automatically analyze short duration, low signal-to-noise NaI(Tl) spectra collected from vehicles passing a large, stationary detector is reported. The analysis must reliably discriminate between sources commonly transported in motor vehicles and alert on the presence of sources of interest. A library of NaI(Tl) spectra for 33 gamma emitting sources was generated with MCNP4B Monte Carlo modeling. These simulated spectra were used as parent distributions, from which multiple realizations of short duration spectra were sampled. Principal component analysis (PCA) of this data set yielded eigenvectors that enable the conversion of unknown spectra into principal component space (PCS). An algorithm using least squares fitting of the positions of library sources in PCS as basis functions, capable of identifying library nuclides in unidentified spectra, is reported. Analysis results for experimental spectra are compared against those achieved using simulated spectra. A valuable characteristic of this method is its ability to identify sources despite unknown shielding geometries. The successful identification of radionuclides and false identification rates found were excellent for the signal levels involved. For many of the sources, identification performance against experimental spectra was somewhat poorer than found using simulated spectra. The results demonstrate that the PCA-based algorithm is capable of high success rates in identifying sources in short duration, low signal-to-noise NaI(Tl) spectra. / Graduation date: 2001

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

On-road remote sensing has the ability to operate in real-time, and under real world conditions, making it an ideal candidate for detecting gross polluters on major freeways and thoroughfares. In this study, remote sensing was employed to detect carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxide (NO). On-road remote sensing data taken from measurements performed in six states, (Arizona, California, Colorado, Illinois, Texas, and Utah) were cleaned and analyzed. Data mining and exploration were first undertaken in order to search for relationships among variables such as make, year, engine type, vehicle weight, and location. Descriptive statistics were obtained for the three pollutants of interest. The data were found to have non-normal distributions. Applied transformations were ineffective, and nonparametric tests were applied. Due to the extremely large sample size of the dataset (508,617 records), nonparametric tests resulted in "p" values that demonstrated "significance." The general linear model was selected due to its ability to handle data with non-normal distributions. The general linear model was run on each pollutant with output producing descriptive statistics, profile plots, between-subjects effects, and estimated marginal means. Due to insufficient data within certain cells, results were not obtained for gross vehicle weight and engine type. The "year" variable was not directly analyzed in the GLM because "year" was employed in a weighted least squares transformation. "Year" was found to be a source of heteroscedasticity; and therefore, the basis of a least-squares transformation. Grouped-years were analyzed using medians, and the results were displayed graphically. Based on the GLM results and descriptives, Japanese vehicles typically had the lowest CO, HC, and NO emissions, while American vehicles ranked high for the three. Illinois, ranked lowest for CO, while Texas ranked highest. Illinois and Colorado were lowest for HC emissions, while Utah and California were highest. For NO, Colorado ranked highest with Texas and Arizona, lowest.

Thesis (PhD (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2006. / The research described in this dissertation examined the effects that fuel formulation can have on the regulated exhaust emissions produced by spark ignition engines in a South African context. Typical South African engine technology, and fuels representative of available fuels were investigated. To broaden the scope and provide information on as many fuel parameters as possible, fuel formulations other than typical retail fuels were also investigated. In order to gain insight into the mechanisms taking place, combustion analysis was performed on measured cylinder pressure traces

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Seyed Hossein Mansouri. / Ph.D.