Health risk assessment posed by primary diesel particulate matter and vapor air toxics in Southeastern US

Air toxics concentrations and health effects that come from different sources emission scenarios by linking Models-3/CMAQ and cancer risk assessment were predicted. The year 1999 was used to emissions inventory and the year 2003 for meteorological data and modeling performance. To demonstrate the system's effectiveness, this study was performed on priority mobile sources air toxics; benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and diesel particulate matter (DPM). The analysis was applied mainly to Nashville in the Southeastern US. Ten emissions scenarios were selected to compare the principal results. DPM posed a cancer risk that was 4.2 times higher than the combined total cancer risk from all other four air toxics. Those high cancer risk levels were due mainly to non-road sources (57.9%). For the on-road diesel fueled sources, the principal reductions were due to the DPM generated by heavy duty diesel vehicles. The main on-road reductions were due to the air toxics generated by gasoline light duty vehicles, principally benzene and 1,3-butadiene. Reducing ambient DPM concentrations would lead to improvement in human health more than other air toxics, indicating that better technologies and regulations must be applied to the mobile diesel engines, principally, over non-road diesel sources. This is an abstract of a paper presented at the AWMA's 99th Annual Conference and Exhibition (New Orleans, LA 6/20-23/2006)

Modeling and source apportionment of diesel particulate matter

The fine and ultra fine sizes of diesel particulate matter (DPM) are of greatest health concern. The composition of these primary and secondary fine and ultra fine particles is principally elemental carbon (EC) with adsorbed organic compounds, sulfate, nitrate, ammonia, metals, and other trace elements. The purpose of this study was to use an advanced air quality modeling technique to predict and analyze the emissions and the primary and secondary aerosols concentrations that come from diesel-fueled sources (DFS). The National Emissions Inventory for 1999 and a severe southeast ozone episode that occurred between August and September 1999 were used as reference. Five urban areas and one rural area in the Southeastern US were selected to compare the main results. For urban emissions, results showed that DFS contributed (77.9% ± 8.0) of EC, (16.8% ± 8.2) of organic aerosols, (14.3% ± 6.2) of nitrate, and (8.3% ± 6.6) of sulfate during the selected episodes. For the rural site, these contributions were lower. The highest DFS contribution on EC emissions was allocated in Memphis, due mainly to diesel non-road sources (60.9%). For ambient concentrations, DFS contributed (69.5% ± 6.5) of EC and (10.8% ± 2.4) of primary anthropogenic organic aerosols, where the highest DFS contributions on EC were allocated in Nashville and Memphis on that episode. The DFS contributed (8.3% ± 1.2) of the total ambient PM2.5 at the analyzed sites. The maximum primary DPM concentration occurred in Atlanta (1.44 μg/m3), which was 3.8 times higher than that from the rural site. Non-linearity issues were encountered and recommendations were made for further research. The results indicated significant geographic variability in the EC contribution from DFS, and the main DPM sources in the Southeastern U.S. were the non-road DFS. The results of this work will be helpful in addressing policy issues targeted at designing control strategies on DFS in the Southeastern U.S

A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile

Box-Jenkins Time Series (ARIMA) and the multivariate linear models (MLM) have been important and popular linear tools in air quality forecasting during the past decade for urban areas. On the other hand, artificial neural networks (ANN) recently have been used successfully as a nonlinear tool in several research studies of pollution forecasting. A hybrid model that combines both ARIMA and ANN tools was proposed to improve the unique capabilities of ARIMA and ANN tools in linear and non linear modeling on particulate matter forecasting. To examine the effectiveness of the proposed hybrid model over real particulate matter data, the time series of PM10 and meteorological data observed in ambient air during 2000-2006 at a site in Temuco, Chile, was used In 2005, this city was declared a non-attainment area for PM10, whose pollution is the result of a great economic growth, a fast urban expansion, woodstoves, industrial sources, and a strong diesel vehicles growth. Experimental results with meteorological and PM10 data sets indicated that the hybrid model can be an effective tool to improve the forecasting accuracy obtained by either of the models used separately, and compared with a statistical multivariate linear regression. This is an abstract of a paper presented at the 100th Annual Conference and Exhibition of the Air and Waste Management Association 2007 (Pittsburgh, PA, 6/26-29/2007)

The effect of switching mobile sources to natural gas on the ozone in the great smoky mountains national park

Mobile sources are among the largest contributors of NOx in the Great Smoky Mountains National Park region (GSMNP). In 2001, these sources contributed 45% of NOx emissions. From 1990 to 2001, the growth of vehicle miles traveled (VMT) increased 60% and 55% in neighboring Sevier and Blount counties respectively. These emissions combined with the high volatile organic compounds (VOC) emissions in the Southeast area have caused the ozone ground level concentration to be as high as some major metropolitan areas in the summer season. In 2001, the maximum 8-hr ozone concentration inside the park was 103 parts per billion. In response to high ozone levels in other areas, federal, state, and local governments are promoting the use of alternative, clean, and reformulated fuel vehicles as a means to improve local air pollution. One of these fuels is compressed natural gas (CNG). The purpose of this project was to use USEPA's CMAQ system in order to model the air quality and compare the ozone ground level formation in the GSMNP from light duty vehicles (LDVs) operating with 100% CNG within 100 miles around GSMNP. A severe southeast ozone episode between August and September 1999 was used as a reference and 2004 was used as a future case. Results showed that LDVs fueled with 100% CNG in the domain could reduce ozone level by 10% and 8% for 1-hr and 8-hr ozone formation respectively in the GSMNP on the modeled time period. Scavenging occurred around the GSMNP in the morning time during the selected episode

International Journal of Fatigue, Volume 54:Evaluating surface deformation and near surface strain hardening resulting from shot peening a tempered martensitic steel and application to low cycle fatigue

The plastic deformation resulting from shot peening treatments applied to the ferritic heat resistant steelFV448 has been investigated. Two important effects have been quantified: surface roughness and strainhardening. 2D and 3D tactile and optical techniques for determining surface roughness amplitude parametershave been investigated; it was found that whilst Ra and Sa were consistent, Sz was generally higherthan Rz due to the increased probability of finding the worst case surface feature. Three different methodsfor evaluating the plastic strain profile have been evaluated with a view to establishing the variation inyield strength near the surface of a shot peened component. Microhardness, X-ray diffraction (XRD) linebroadening and electron backscatter diffraction (EBSD) local misorientation techniques were applied toboth uniaxially deformed calibration samples of known plastic strain and samples shot peened at intensitiesvarying from 4A to 18A to establish the variation in plastic strain and hence the variation in yieldstrength. The results from the three methods were compared; XRD and EBSD profiles were found to bethe most similar with microhardness profiles extending much deeper into the sample. Changes in themeasured plastic strain profile after exposure to low cycle fatigue and the correlation of these changeswith the cyclic stress–strain behaviour of the material are also discussed with a view to assessing theimportance of the dislocation profile in component life assessment procedures. 2013 Elsevier Ltd. All rights reserved

A U-235 confirmation method with a discriminating view

Reprocessed spent nuclear fuel that has gone through various stages of chemical processing is currently stored at the INEEL. The material consists of three categories: clean U-235 with radiation readings in the mR/h range, U-235 and fission products up to 900 mR/h, and U-235 with U-232 from 100--200 mR/h. The material is contained in plastic bottles and stored in steel structures consisting of seven vertically arranged individual compartments. A total of forty stacks reside in individual concrete wells. This material is considered hard to measure due to excessive radiation exposure to personnel involved with handling the material during mass and NaI U-235 confirmation measurements for Safeguards inventory purposes. A U-235 confirmation method was developed to assay the individual items in place with the ability to discriminate one item from the other items in the stack. Equipment used with this method includes a portable high-resolution gamma-ray detection system, an appropriate tungsten shield and collimator, and a laser-positioning device. A discrimination control test was incorporated to compare the gamma-ray signal of an item in place to the background signal when the item is removed. Total discrimination of the 186-keV gamma ray signal was achieved

A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile

Air quality time series consists of complex linear and non-linear patterns and are difficult to forecast. Box-Jenkins Time Series (ARIMA) and multilinear regression (MLR) models have been applied to air quality forecasting in urban areas, but they have limited accuracy owing to their inability to predict extreme events. Artificial neural networks (ANN) can recognize non-linear patterns that include extremes. A novel hybrid model combining ARIMA and ANN to improve forecast accuracy for an area with limited air quality and meteorological data was applied to Temuco, Chile, where residential wood burning is a major pollution source during cold winters, using surface meteorological and PM10 measurements. Experimental results indicated that the hybrid model can be an effective tool to improve the PM10 forecasting accuracy obtained by either of the models used separately, and compared with a deterministic MLR. The hybrid model was able to capture 100% and 80% of alert and pre-emergency episodes, respectively. This approach demonstrates the potential to be applied to air quality forecasting in other cities and countries

Electron-Phonon Interacation in Quantum Dots: A Solvable Model

The relaxation of electrons in quantum dots via phonon emission is hindered by the discrete nature of the dot levels (phonon bottleneck). In order to clarify the issue theoretically we consider a system of $N$ discrete fermionic states (dot levels) coupled to an unlimited number of bosonic modes with the same energy (dispersionless phonons). In analogy to the Gram-Schmidt orthogonalization procedure, we perform a unitary transformation into new bosonic modes. Since only $N(N+1)/2$ of them couple to the fermions, a numerically exact treatment is possible. The formalism is applied to a GaAs quantum dot with only two electronic levels. If close to resonance with the phonon energy, the electronic transition shows a splitting due to quantum mechanical level repulsion. This is driven mainly by one bosonic mode, whereas the other two provide further polaronic renormalizations. The numerically exact results for the electron spectral function compare favourably with an analytic solution based on degenerate perturbation theory in the basis of shifted oscillator states. In contrast, the widely used selfconsistent first-order Born approximation proves insufficient in describing the rich spectral features.Comment: 8 pages, 4 figure

Lifshitz Tails in Constant Magnetic Fields

We consider the 2D Landau Hamiltonian $H$ perturbed by a random alloy-type potential, and investigate the Lifshitz tails, i.e. the asymptotic behavior of the corresponding integrated density of states (IDS) near the edges in the spectrum of $H$. If a given edge coincides with a Landau level, we obtain different asymptotic formulae for power-like, exponential sub-Gaussian, and super-Gaussian decay of the one-site potential. If the edge is away from the Landau levels, we impose a rational-flux assumption on the magnetic field, consider compactly supported one-site potentials, and formulate a theorem which is analogous to a result obtained in the case of a vanishing magnetic field

Finite-temperature Fermi-edge singularity in tunneling studied using random telegraph signals

We show that random telegraph signals in metal-oxide-silicon transistors at millikelvin temperatures provide a powerful means of investigating tunneling between a two-dimensional electron gas and a single defect state. The tunneling rate shows a peak when the defect level lines up with the Fermi energy, in excellent agreement with theory of the Fermi-edge singularity at finite temperature. This theory also indicates that defect levels are the origin of the dissipative two-state systems observed previously in similar devices.Comment: 5 pages, REVTEX, 3 postscript figures included with epsfi