VARIABILITY OF NO AND NO2 ABOVE THE SNOWPACK AT SUMMIT, GREENLAND

Nitric oxide (NO) and nitrogen dioxide (NO2) were measured at levels of approximately 7.5 m, 3 m, and 0.5 m above the surface snowpack at Summit, Greenland from July 2008 to July 2010, respectively, with two sets of instrument systems. Instrument I measured NO and NO2 at levels of 3 m and 0.5 m with two inlets, and instrument II measured NO and NO2 at a level of 7.5 m plus total reactive nitrogen oxides (NOy) at the same level with one inlet. Compared to previous measurements, the data provided the first year-round simultaneous record of NO and NO2 at different levels above the snowpack at a high latitude Arctic site. Apparent seasonal and diurnal cycles were observed for both NO and NO2 at different levels. NO reached high levels when solar radiation was high from late spring to summer in a seasonal scale and at around noon in a diurnal scale, while NO2 reached high levels from early spring to early fall in a seasonal scale and from afternoon to night in a diurnal scale. The vertical gradients of NO and NO2 between 3 m and 0.5 m above the snowpack suggested the emission of NO from the surface snowpack. An improved mechanism for snowpack photochemistry at Summit is proposed to explain the seasonal variability of NO and NO2. Furthermore, a pollution event study showed that FLEXPART retroplume simulations were in agreement with the measurements. During polar night season, NO2 exactly followed FLEXPART simulation. Nitrate accumulation through snowpack deposition was proposed to attribute NO2 increase in early spring. In sunlight season, nitrate deposition was proposed to occur during the pollution events and was re-emitted from the snowpack via photolysis after the event, resulting in subsequent NO2 increase

RESIDENTIAL ENERGY DEMAND AND ENERGY EFFICIENCY

The first essay investigates the relatively higher energy efficiency (EE) investment rates in housing units of homeowners versus those of renters. In the empirical analysis, discrete choice models are employed to explore households\u27 EE investment behavior. After testing three groups of implications derived from the initial analysis, the paper suggests that due to the existence of contracting costs, landlords/renters make efficient decisions to invest less in EE than homeowners due to renters\u27 increased mobility and the characteristics of typical EE investments. The second essay analyzes households\u27 choices of energy efficient dishwashers and the potential influence from those choices on dish washing behavior. An ordered Probit model is developed to investigate households\u27 demand for dish washing services. Two-stage residual inclusion (2SRI) is used to deal with the endogeneity problem, caused by households choosing energy efficient dishwashers because of higher expected usage frequency. Households using energy efficient dish washers compared with households using standing dishwashers display approximately 7.7% more frequent usage behavior. The final essay examines U.S. residential consumption of four main fuels. Double-log demand models are applied and two-stage residual inclusion is used to address price endogeneity. Besides various elasticity estimates, the paper further explores causes of the rising per capital electricity consumption over time despite the efficiency progress. Historical survey data reveal that households increase electricity consumption by increasing the quantity of electronics and/or purchasing electronics with extra energy-consuming attributes

Variational Quantum Singular Value Decomposition

Singular value decomposition is central to many problems in engineering and scientific fields. Several quantum algorithms have been proposed to determine the singular values and their associated singular vectors of a given matrix. Although these algorithms are promising, the required quantum subroutines and resources are too costly on near-term quantum devices. In this work, we propose a variational quantum algorithm for singular value decomposition (VQSVD). By exploiting the variational principles for singular values and the Ky Fan Theorem, we design a novel loss function such that two quantum neural networks (or parameterized quantum circuits) could be trained to learn the singular vectors and output the corresponding singular values. Furthermore, we conduct numerical simulations of VQSVD for random matrices as well as its applications in image compression of handwritten digits. Finally, we discuss the applications of our algorithm in recommendation systems and polar decomposition. Our work explores new avenues for quantum information processing beyond the conventional protocols that only works for Hermitian data, and reveals the capability of matrix decomposition on near-term quantum devices.Comment: 23 pages, v3 accepted by Quantu