Investigating the origin of PM2.5 in Baltimore using highly time-resolved organic molecular markers measured during the Baltimore PM-supersite

The detailed organic composition of atmospheric fine particles with an aerodynamic diameter smaller than or equal to 2.5 micrometers (PM2.5) is an integral part of the knowledge needed in order to fully characterize its sources and transformation in the environment. For the study presented here, samples were collected at 3-hour intervals. This high time resolution allows gaining unique insights on the influence of short- and long-range transport phenomena, and dynamic atmospheric processes. A specially designed sequential sampler was deployed at the 2002-2003 Baltimore PM-Supersite to collect PM2.5 samples at a 3-hourly resolution for extended periods of consecutive days, during both summer and winter seasons. Established solvent-extraction and GC-MS techniques were used to extract and analyze the organic compounds in 119 samples from each season. Over 100 individual compounds were quantified in each sample. For primary organics, averaging the diurnal ambient concentrations over the sampled periods revealed ambient patterns that relate to diurnal emission patterns of major source classes. Several short-term releases of pollutants from local sources were detected, and local meteorological data was used to pinpoint possible source regions. Biogenic secondary organic compounds were detected as well, and possible mechanisms of formation were evaluated. The relationships between the observed continuous variations of the concentrations of selected organic markers and both the on-site meteorological measurements conducted parallel to the PM2.5 sampling, and the synoptic patterns of weather and wind conditions were also examined. Several one-to-two days episodes were identified from the sequential variation of the concentration observed for specific marker compounds and markers ratios. The influence of the meteorological events on the concentrations of the organic compounds during selected episodes was discussed. It was observed that during the summer, under conditions of pervasive influence of air masses originated from the west/northwest, some organic species displayed characteristics consistent with the measured PM2.5 being strongly influenced by the aged nature of these long-traveling background parcels. During the winter, intrusions from more regional air masses originating from the south and the southwest were more important.

Investigating the origin of PM2.5 in Baltimore using highly time-resolved organic molecular markers measured during the Baltimore PM-supersite

The detailed organic composition of atmospheric fine particles with an aerodynamic diameter smaller than or equal to 2.5 micrometers (PM 2.5) is an integral part of the knowledge needed in order to fully characterize its sources and transformation in the environment. For the study presented here, samples were collected at 3-hour intervals. This high time resolution allows gaining unique insights on the influence of short- and long-range transport phenomena, and dynamic atmospheric processes. A specially designed sequential sampler was deployed at the 2002-2003 Baltimore PM Supersite to collect PM2.5 samples at a 3-hourly resolution for extended periods of consecutive days, during both summer and winter seasons. Established solvent-extraction and GC-MS techniques were used to extract and analyze the organic compounds in 119 samples from each season. Over 100 individual compounds were quantified in each sample. For primary organics, averaging the diurnal ambient concentrations over the sampled periods revealed ambient patterns that relate to diurnal emission patterns of major source classes. Several short-term releases of pollutants from local sources were detected, and local meteorological data was used to pinpoint possible source regions. Biogenic secondary organic compounds were detected as well, and possible mechanisms of formation were evaluated. The relationships between the observed continuous variations of the concentrations of selected organic markers and both the on-site meteorological measurements conducted parallel to the PM2.5 sampling, and the synoptic patterns of weather and wind conditions were also examined. Several one-to-two days episodes were identified from the sequential variation of the concentration observed for specific marker compounds and markers ratios. The influence of the meteorological events on the concentrations of the organic compounds during selected episodes was discussed. It was observed that during the summer, under conditions of pervasive influence of air masses originated from the west/northwest, some organic species displayed characteristics consistent with the measured PM2.5 being strongly influenced by the aged nature of these long-traveling background parcels. During the winter, intrusions from more regional air masses originating from the south and the southwest were more important.

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Leakage temperature dependency aware real-time scheduling for power and thermal optimization

Catering to society's demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. ^ In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research. ^

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

Leakage Temperature Dependency Aware Real-Time Scheduling for Power and Thermal Optimization

Catering to society’s demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research.

Soot measurements in steady and pulsed ethylene/air diffusion flames using Laser-Induced Incandescence

Combustion-generated carbon black nano particles, or soot, have both positive and negative effects depending on the application. From a positive point of view, it is used as a reinforcing agent in tires, black pigment in inks, and surface coatings. From a negative point of view, it affects performance and durability of many combustion systems, it is a major contributor of global warming, and it is linked to respiratory illness and cancer. Laser-Induced Incandescence (LII) was used in this study to measure soot volume fractions in four steady and twenty-eight pulsed ethylene diffusion flames burning at atmospheric pressure. A laminar coflow diffusion burner combined with a very-high-speed solenoid valve and control circuit provided unsteady flows by forcing the fuel flow with frequencies between 10 Hz and 200 Hz. Periodic flame oscillations were captured by two-dimensional phase-locked LII images and broadband luminosity images for eight phases (0° – 360°) covering each period. A comparison between the steady and pulsed flames and the effect of the pulsation frequency on soot volume fraction in the flame region and the post flame region are presented. The most significant effect of pulsing frequency was observed at 10 Hz. At this frequency, the flame with the lowest mean flow rate had 1.77 times enhancement in peak soot volume fraction and 1.2 times enhancement in total soot volume fraction; whereas the flame with the highest mean flow rate had no significant change in the peak soot volume fraction and 1.4 times reduction in the total soot volume fraction. A correlation (fvRe-1 = a + b·Str) for the total soot volume fraction in the flame region for the unsteady laminar ethylene flames was obtained for the pulsation frequency between 10 Hz and 200 Hz, and the Reynolds number between 37 and 55. The soot primary particle size in steady and unsteady flames was measured using the Time-Resolved Laser-Induced Incandescence (TIRE-LII) and the double-exponential fit method. At maximum frequency (200 Hz), the soot particles were smaller in size by 15% compared to the steady case in the flame with the highest mean flow rate.

Hybrid approaches to estimating freeway travel times using point traffic detector data

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

Effect of water on olivine single crystals plasticity, deformed under high pressure and high temperature

The Earth's upper mantle, mainly composed of olivine, is seismically anisotropic. Seismic anisotropy attenuation has been observed at 220km depth. Karato et al. (1992) attributed this attenuation to a transition between two deformation mechanisms, from dislocation creep above 220km to diffusion creep below 220km, induced by a change in water content. Couvy (2005) and Mainprice et al. (2005) predicted a change in Lattice Preferred Orientation induced by pressure, which comes from a change of slip system, from [100] slip to [001] slip, and is responsible for the seismic anisotropy attenuation. Raterron et al. (2007) ran single crystal deformation experiments under anhydrous conditions and observed that the slip system transition occurs around 8GPa, which corresponds to a depth of 260Km. Experiments were done to quantify the effects of water on olivine single crystals deformed using D-DIA press and synchrotron beam. Deformations were carried out in uniaxial compression along [110]c, [011]c, and [101]c, crystallographic directions, at pressure ranging from 4 to 8GPa and temperature between 1373 and 1473K. Talc sleeves about the annulus of the single crystals were used as source of water in the assembly. Stress and specimen strain rates were calculated by in-situ X-ray diffraction and time resolved imaging, respectively. By direct comparison of single crystals strain rates, we observed that [110]c deforms faster than [011]c below 5GPa. However above 6GPa [011]c deforms faster than [110]c. This revealed that [100](010) is the dominant slip system below 5GPa, and above 6GPa [001](010) becomes dominant. According to our results, the slip system transition, which is induced by pressure, occurs at 6GPa. Water influences the pressure where the switch over occurs, by lowering the transition pressure. The pressure effect on the slip systems activity has been quantified and the hydrolytic weakening has also been estimated for both orientations. Data also shows that temperature affects the slip system activity. The regional variation of the depth for the seismic anisotropy attenuation, which would depend on local hydroxyl content and temperature variations and explains the seismic anisotropy attenuation occurring at about 220Km depth in the mantle, where the pressure is about 6GPa. Deformation of MgO single crystal oriented [100], [110] and [111] were also performed. The results predict a change in the slip system activity at 23GPa, again induced by pressure. This explains the seismic anisotropy observed in the lower mantle.

Soot Measurements in Steady and Pulsed Ethylene/Air Diffusion Flames Using Laser-Induced Incandescence

Combustion-generated carbon black nano particles, or soot, have both positive and negative effects depending on the application. From a positive point of view, it is used as a reinforcing agent in tires, black pigment in inks, and surface coatings. From a negative point of view, it affects performance and durability of many combustion systems, it is a major contributor of global warming, and it is linked to respiratory illness and cancer. Laser-Induced Incandescence (LII) was used in this study to measure soot volume fractions in four steady and twenty-eight pulsed ethylene diffusion flames burning at atmospheric pressure. A laminar coflow diffusion burner combined with a very-high-speed solenoid valve and control circuit provided unsteady flows by forcing the fuel flow with frequencies between 10 Hz and 200 Hz. Periodic flame oscillations were captured by two-dimensional phase-locked LII images and broadband luminosity images for eight phases (0°- 360°) covering each period. A comparison between the steady and pulsed flames and the effect of the pulsation frequency on soot volume fraction in the flame region and the post flame region are presented. The most significant effect of pulsing frequency was observed at 10 Hz. At this frequency, the flame with the lowest mean flow rate had 1.77 times enhancement in peak soot volume fraction and 1.2 times enhancement in total soot volume fraction; whereas the flame with the highest mean flow rate had no significant change in the peak soot volume fraction and 1.4 times reduction in the total soot volume fraction. A correlation (ƒv Reˉ1 = a+b· Str) for the total soot volume fraction in the flame region for the unsteady laminar ethylene flames was obtained for the pulsation frequency between 10 Hz and 200 Hz, and the Reynolds number between 37 and 55. The soot primary particle size in steady and unsteady flames was measured using the Time-Resolved Laser-Induced Incandescence (TIRE-LII) and the double-exponential fit method. At maximum frequency (200 Hz), the soot particles were smaller in size by 15% compared to the steady case in the flame with the highest mean flow rate.