No abstract

In this paper, a tunable metamaterials absorber with Dirac semimetals and VO2 strips is present in the Terahertz (THz) range. At room temperature, two absorption peaks are revealed at resonance frequencies 10.1 THz (76% amplitude) and 20.3 THz (60% amplitude), respectively. Based on the tunability of these two materials, three modulation strategies are proposed to control the resonant properties of the absorber. As the conductivity increase, three newly absorption peaks (62% amplitude at 19.9 THz, 65% amplitude at 27.7 THz, and 73% amplitude at 37.3 THz) are achieved due to four VO2 strips undergoing the insulation phase-metal phase changes. The resonant properties of this absorber can be converted due to the conversion function of four VO2 strips. Moreover, both original absorption peaks can be control in the continuous frequency range by increasing the Fermi energy. Finally, the magnetic field external is adopted to modulate the resonant properties of this absorber. Both of the original absorption peaks are increased in a continuous frequency range, and two newly absorption peaks (74% amplitude at 23.9 THz and 60% amplitude at 36 THz) are achieved.

In present China, continuing to control PM2.5 (particulate matter < 2.5 μm) and preventing the rise of O3 are the most urgent environmental tasks in its air clean actions. Considering that NO2 is an important precursor of PM2.5 and O3, a comprehensive analysis around this pollutant was conducted based on the real-time-monitoring-data from Jan 2018 to Mar 2019 in 11 prefecture-level cities in Shanxi Province of China. The results showed that the annual average concentration of NO2 in Shanxi prefecture-level cities is mainly distributed in the range of 28.84–48.93 μg/m3 with the values in five cities exceeding the Chinese Grade Ⅱ standard limit (40 μg/m3). The over-standard days were all concentrated in the heating season with a large pollution peak occurring in winter except in Lvliang, while four cities also had a small pollution peak in summer. High NO2 polluted areas were mainly concentrated in the central part of Shanxi, and trended on the whole from the southwest to the northeast (Lvliang/Linfen—Taiyuan/Jinzhong—Yangquan/Jinzhong), which was different from the spatial distribution of PM2.5 and O3. Lvliang was the hot spot of NO2 pollution in summer, while Taiyuan was the hot spot in winter. Concentration Weighted Trajectory (CWT) analysis indicated that central-north Shaanxi, central-south Shanxi, northern Henan, the south of Shijiazhuang and areas around Erdos in Inner Mongolia were important source areas of NO2 in Shanxi besides local emissions. Our findings are expected to provide valuable implications to policymakers in Shanxi of China to effectively abate the air pollution.

This paper studies quantile regression for spatial panel data models with varying coefficients, taking the time and location effects of the impacts of the covariates into account, i.e., the implications of covariates may change over time and location. Smoothing methods are employed for approximating varying coefficients, including B-spline and local polynomial approximation. A fixed-effects quantile regression (FEQR) estimator is typically biased in the presence of the spatial lag variable. The wild bootstrap method is employed to attenuate the estimation bias. Simulations are conducted to study the performance of the proposed method and show that the proposed methods are stable and efficient. Further, the estimators based on the B-spline method perform much better than those of the local polynomial approximation method, especially for location-varying coefficients. Real data about economic development in China are also analyzed to illustrate application of the proposed procedure.