In order to characterize the features of particulate pollution in the Pearl River Delta (PRD) in the summer, continuous measurements of particle number size distributions and chemical compositions were simultaneously performed at Guangzhou urban site (GZ) and Back-garden downwind regional site (BG) in July 2006. Particle number concentration from 20 nm to 10 mu m at BG was (1.7 +/- 0.8) x 10(4) cm(-3), about 40% lower than that at GZ, (2.9 +/- 1.1) x 10(4) cm(-3). The total particle volume concentration at BG was 94 +/- 34 mu m(3) cm(-3), similar to that at GZ, 96 +/- 43 mu m(3) cm(-3). More 20-100 nm particles, significantly affected by the traffic emissions, were observed at GZ, while 100-660 nm particle number concentrations were similar at both sites as they are more regional. PM(2.5) values were similar at GZ (69 +/- 43 mu g m(-3)) and BG (69 +/- 58 mu g m(-3)) with R(2) of 0.71 for the daily average PM(2.5) at these two sites, indicating the fine particulate pollution in the PRD region to be regional. Two kinds of pollution episodes, the accumulation pollution episode and the regional transport pollution episode, were observed. Fine particles over 100 nm dominated both number and volume concentrations of total particles during the late periods of these pollution episodes. Accumulation and secondary transformation are the main reasons for the nighttime accumulation pollution episode. SO(4)(2-), NO(3)(-), and NH(4)(+) accounted for about 60% in 100-660 nm particle mass and PM(2.5) increase. When south or southeast wind prevailed in the PRD region, regional transport of pollutants took place. Regional transport contributed about 30% to fine particulate pollution at BG during a regional transport case. Secondary transformation played an important role during regional transport, causing higher increase rates of secondary ions in PM(1.0) than other species and shifting the peaks of sulfate and ammonium mass size distributions to larger sizes. SO(4)(2-), NO(3)(-), and NH(4)(+) accounted for about 70% and 40% of PM(1.0) and PM(2.5), respectively.
To study potential ecological impacts of CO2 leakage to shallow groundwater and soil/sediments from geologic CO2 sequestration (GCS) sites, this work investigated the viability and metal reduction of Shewanella oneidensis MR-1 under CO2 stress. While MR-1 could grow under high-pressure nitrogen gas (500 psi), the mix of 1% CO2 with N2 at total pressures of 15 or 150 psi significantly suppressed the growth of MR-1, compared to the N2 control. When CO2 partial pressures were over 15 psi, the growth of MR-1 stopped. The reduced bacterial viability was consistent with the pH decrease and cellular membrane damage under high pressure CO2. After exposure to 150 psi CO2 for 5 h, no viable cells survived, the cellular contents were released, and microscopy images confirmed significant cell structure deformation. However, after a relatively short exposure (25 min) to 150 psi CO2, MR-1 could fully recover their growth within 24 h after the stress was removed, and the reduction of MnO2 by MR-1 was observed right after the stress was removed. Furthermore, MR-1 survived better if the cells were aggregated rather than suspended, or if pH buffering minerals, such as calcite, were present. To predict the cell viability under different CO2 pressures and exposure times, a two-parameter mathematical model was developed.
Inspired by rich physics and functionalities of graphenes, scientists have taken an intensive interest in two-dimensional (2D) crystals of h-BN (analogue of graphite, so-called "white" graphite). Recent calculations have predicted the exciting potentials of BN nanoribbons in spintronics due to tunable magnetic and electrical properties; however no experimental evidence has been provided since fabrication of such ribbons remains a challenge. Here, we show that few- and single-layered BN nanoribbons, mostly terminated with zigzag edges, can be produced under unwrapping multiwalled BN nanotubes through plasma etching. The interesting stepwise unwrapping and intermediate states were observed and analyzed. Opposed to insulating primal tubes, the nanoribbons become semiconducting due to doping-like conducting edge states and vacancy defects, as revealed by structural analyses and ab initio simulations. This study paves the way for BN nanoribbon production and usage as functional semiconductors with a wide range of applications in optoelectronics and spintronics.
The Chinese financial aid system intends to increase the affordability of postsecondary education and provide access to college for disadvantaged students. However, the research base for access to aid in China is extremely thin. Using data from a large cross-sectional survey in Beijing, this study found that attending selective institutions with high-ability peers was positively correlated with the amount of aid awarded and the probability of receiving aid. Female students, students with college-educated fathers, and students from poorer households were expected to receive more aid. Junior and senior students along with more able individuals in science-related majors obtained significantly more financial assistance.
This paper proposes a Bayesian generalized linear multilevel model with a pth-order autoregressive error process to analyze unbalanced binary time-series cross-sectional (TSCS) data. The model specification is motivated by the generic TSCS data structure and is intended to handle the associated inefficiency and endogeneity problems. It accommodates heterogeneity across units and between time periods in the form of random intercepts and random-effect coefficients. At the same time, its pth-order autoregressive error process, employed either by itself or in concert with other dynamic methods, adequately corrects serial correlation and improves statistical inference and forecasting. With a stationarity restriction on the error process, the model can also be used as a residual-based cointegration test on discrete TSCS data. This is especially valuable because cointegration testing on discrete TSCS data is methodologically challenging and rarely conducted in practice. To handle the estimation difficulties, I developed an efficient Markov chain Monte Carlo (MCMC) algorithm by orthogonalizing the error term with the Cholesky decomposition and adding an auxiliary variable. The parameter expansion method, that is, partial group move–multigrid Monte Carlo updating (PGM-MGMC), is employed to further improve MCMC mixing and speed up convergence. The paper also provides a computational scheme to approximate the Bayes’s factor for the purposes of serial correlation diagnostics, lag order determination, and variable selection. Simulated and empirical examples are used to assess the model and techniques.