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The goal of this study is to determine the difference in CO2 emissions between 2019-2020 and 2020-2021, more specifically during lockdown periods during the COVID-19 pandemic. In the beginning of the pandemic, most countries were forced into lockdowns, and a countless number of people had to continue their daily work from home in isolation. Previously, people would go to an office or to school and leave their houses empty for eight hours, without having lights or any electronics on. Because of this, there should be a direct correlation between electricity usage before and during lockdowns, as a private residence should have higher electricity consumption during 2020-2021, when they are at home. Using machine learning, we will investigate to see if COVID-19 affected CO2 emissions as a result of more electricity usage in private residences. A model will be made to predict what the CO2 emissions would be for 2019-2020, based on electricity usage data from 2020-2021. Then, the real CO2 emissions from 2019-2020 will be compared with the model’s predicted values, and the difference will indicate if COVID-19 caused an inconsistency between actual and predicted CO2 emissions. Factors that were taken into account when making a model were independent variables relating to outdoor conditions, the number of people living in the house, and the temperature that the thermostat is set at, making the response variable CO2 emissions.

The goal of this study is to determine the difference in CO2 emissions between 2019-2020 and 2020-2021, more specifically during lockdown periods during the COVID-19 pandemic. In the beginning of the pandemic, most countries were forced into lockdowns, and a countless number of people had to continue their daily work from home in isolation. Previously, people would go to an office or to school and leave their houses empty for eight hours, without having lights or any electronics on. Because of this, there should be a direct correlation between electricity usage before and during lockdowns, as a private residence should have higher electricity consumption during 2020-2021, when they are at home. Using machine learning, we will investigate to see if COVID-19 affected CO2 emissions as a result of more electricity usage in private residences. A model will be made to predict what the CO2 emissions would be for 2019-2020, based on electricity usage data from 2020-2021. Then, the real CO2 emissions from 2019-2020 will be compared with the model’s predicted values, and the difference will indicate if COVID-19 caused an inconsistency between actual and predicted CO2 emissions. Factors that were taken into account when making a model were independent variables relating to outdoor conditions, the number of people living in the house, and the temperature that the thermostat is set at, making the response variable CO2 emissions.

The Neutralized Transport Experiment (NTX) at Lawrence Berkeley National Laboratory has been designed to study the final focus and neutralization of high perveance ion beams for applications in heavy ion fusion (HIF) and high energy density physics (HEDP) experiments. Pre-formed plasmas in the last meter before the target of the scaled experiment provide a source of electrons which neutralize the ion current and prevent the space-charge induced spreading of the beam spot. NTX physics issues are discussed and experimental data is analyzed and compared with 3D particle-in-cell simulations. Along with detailed target images, 4D phase-space data of the NTX at the entrance of the neutralization region has been acquired. This data is used to provide a more accurate beam distribution with which to initialize the simulation. Previous treatments have used various idealized beam distributions which lack the detailed features of the experimental ion beam images. Simulation results are compared with NTX experimental measurements for 250 keV K{sup +} ion beams with dimensionless perveance of 1-7 x 10{sup -4}. In both simulation and experiment, the deduced beam charge neutralization is close to the predicted maximum value.

The Neutralized Transport Experiment (NTX) at Lawrence Berkeley National Laboratory has been designed to study the final focus and neutralization of high perveance ion beams for applications in heavy ion fusion (HIF) and high energy density physics (HEDP) experiments. Pre-formed plasmas in the last meter before the target of the scaled experiment provide a source of electrons which neutralize the ion current and prevent the space-charge induced spreading of the beam spot. NTX physics issues are discussed and experimental data is analyzed and compared with 3D particle-in-cell simulations. Along with detailed target images, 4D phase-space data of the NTX at the entrance of the neutralization region has been acquired. This data is used to provide a more accurate beam distribution with which to initialize the simulation. Previous treatments have used various idealized beam distributions which lack the detailed features of the experimental ion beam images. Simulation results are compared with NTX experimental measurements for 250 keV K{sup +} ion beams with dimensionless perveance of 1-7 x 10{sup -4}. In both simulation and experiment, the deduced beam charge neutralization is close to the predicted maximum value.

The bromoform, and other volatile organics produced while chlorinating both the evaporator and condenser seawater during operation of the one megawatt (1 MW) OTEC-1 test facility are reported. Although many halogenated compounds might be produced as a result of chlorination, the quantitative analyses in this study focused on volatile EPA priority pollutants. Bromoform is the compound specifically recognized as a potential pollutant. Its concentration may be indicative of other halogenated species.

The bromoform, and other volatile organics produced while chlorinating both the evaporator and condenser seawater during operation of the one megawatt (1 MW) OTEC-1 test facility are reported. Although many halogenated compounds might be produced as a result of chlorination, the quantitative analyses in this study focused on volatile EPA priority pollutants. Bromoform is the compound specifically recognized as a potential pollutant. Its concentration may be indicative of other halogenated species.