Ozone production in four major cities of China: sensitivity to ozone precursors and heterogeneous processes

Abstract. Despite a large volume of research over a number of years, our understandings of the key precursors that control tropospheric ozone production and the impacts of heterogeneous processes remain incomplete. In this study, we analyze measurements of ozone and its precursors made at rural/suburban sites downwind of four large Chinese cities – Beijing, Shanghai, Guangzhou and Lanzhou. At each site the same measurement techniques were utilized and a photochemical box model based on the Master Chemical Mechanism (v3.2) was applied, to minimize uncertainties in comparison of the results due to differences in methodology. All four cities suffered from severe ozone pollution. At the rural site of Beijing, export of the well-processed urban plumes contributed to the extremely high ozone levels (up to an hourly value of 286 ppbv), while the pollution observed at the suburban sites of Shanghai, Guangzhou and Lanzhou was characterized by intense in-situ ozone production. The major anthropogenic hydrocarbons were alkenes and aromatics in Beijing and Shanghai, aromatics in Guangzhou, and alkenes in Lanzhou. The ozone production was found to be in a VOCs-limited regime in both Shanghai and Guangzhou, and a mixed regime in Lanzhou. In Shanghai, the ozone formation was most sensitive to aromatics and alkenes, while in Guangzhou aromatics were the predominant ozone precursors. In Lanzhou, either controlling NOx or reducing emissions of olefins from the petrochemical industry would mitigate the local ozone production. The potential impacts of several heterogeneous processes on the ozone formation were assessed. The hydrolysis of dinitrogen pentoxide (N2O5), uptake of the hydroperoxyl radical (HO2) on particles, and surface reactions of NO2 forming nitrous acid (HONO) present considerable sources of uncertainty in the current studies of ozone chemistry. Further efforts are urgently required to better understand these processes and refine atmospheric models

Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data

Training deep fully convolutional neural networks (F-CNNs) for semantic image segmentation requires access to abundant labeled data. While large datasets of unlabeled image data are available in medical applications, access to manually labeled data is very limited. We propose to automatically create auxiliary labels on initially unlabeled data with existing tools and to use them for pre-training. For the subsequent fine-tuning of the network with manually labeled data, we introduce error corrective boosting (ECB), which emphasizes parameter updates on classes with lower accuracy. Furthermore, we introduce SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that combines skip connections with the unpooling strategy for upsampling. The SD-Net addresses challenges of severe class imbalance and errors along boundaries. With application to whole-brain MRI T1 scan segmentation, we generate auxiliary labels on a large dataset with FreeSurfer and fine-tune on two datasets with manual annotations. Our results show that the inclusion of auxiliary labels and ECB yields significant improvements. SD-Net segments a 3D scan in 7 secs in comparison to 30 hours for the closest multi-atlas segmentation method, while reaching similar performance. It also outperforms the latest state-of-the-art F-CNN models.Comment: Accepted at MICCAI 201

Elastic imperfect tip-loaded cantilever cylinders of varying length

A number of recent publications have explored the crucial relationship between the length of a thin cylindrical shell and the influence of pre-buckling cross-sectional ovalisation on its nonlinear elastic buckling capacity under bending. However, the research thus far appears to have focused almost exclusively on uniform bending, with ovalisation under moment gradients largely neglected. This paper presents a comprehensive computational investigation into the nonlinear elastic buckling response of perfect and imperfect thin cantilever cylinders under global transverse shear. A complete range of practical lengths was investigated, from short cylinders which fail by shear buckling to very long ones which exhibit local meridional compression buckling with significant prior cross-section ovalisation. Two imperfection forms were applied depending on the length of the cylinder: the linear buckling eigenmode for short cylinders and a realistic weld depression imperfection for long cylinders. The weld depression imperfection was placed at the location where the cross-section of the perfect cylinder was found to undergo peak ovalisation under transverse shear, a location that approaches the base support with increasing length. Compact closed-form algebraic expressions are proposed to characterise the elastic buckling and ovalisation behaviour conservatively, suitable for direct application as design equations. This study contributes to complete the understanding of cylindrical structures of varying length where the dominant load case is global transverse shear, including multi-strake aerospace shells with short individual segments between stiffeners and long near-cylindrical wind-turbine support towers and chimneys under wind or seismic action

Multivariate dynamic kernels for financial time series forecasting

The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44781-0_40We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple time intervals. The analysis is done through multivariate dynamic kernels within support vector regression. We also propose two kernels for financial time series that are computationally efficient without a sacrifice on accuracy. The efficacy of the methodology is demonstrated by empirical experiments on forecasting the challenging S&P500 market.Peer ReviewedPostprint (author's final draft

Carbonyl sulfide, dimethyl sulfide and carbon disulfide in the Pearl River Delta of southern China: Impact of anthropogenic and biogenic sources

Reduced sulfur compounds (RSCs) such as carbonyl sulfide (OCS), dimethyl sulfide (DMS) and carbon disulfide (CS2) impact radiative forcing, ozone depletion, and acid rain. Although Asia is a large source of these compounds, until now a long-term study of their emission patterns has not been carried out. Here we analyze 16 months of RSC data measured at a polluted rural/coastal site in the greater Pearl River Delta (PRD) of southern China. A total of 188 canister air samples were collected from August 2001 to December 2002. The OCS and CS2 mixing ratios within these samples were higher in autumn/winter and lower in summer due to the influence of Asian monsoon circulations. Comparatively low DMS values observed in this coastal region suggest a relatively low biological productivity during summer months. The springtime OCS levels in the study region (574 ± 40 pptv) were 25% higher than those on other East Asia coasts such Japan, whereas the springtime CS2 and DMS mixing ratios in the PRD (47 ± 38 pptv and 22 ± 5 pptv, respectively) were 3-30 times lower than elevated values that have been measured elsewhere in East Asia (Japan and Korea) at this time of year. Poor correlations were found among the three RSCs in the whole group of 188 samples, suggesting their complex and variable sources in the region. By means of backward Lagrangian particle release simulations, air samples originating from the inner PRD, urban Hong Kong and South China Sea were identified. The mean mixing ratio of OCS in the inner PRD was significantly higher than that in Hong Kong urban air and South China Sea marine air (p < 0.001), whereas no statistical differences were found for DMS and CS2 among the three regions (p > 0.05). Using a linear regression method based on correlations with the urban tracer CO, the estimated OCS emission in inner PRD (49.6 ± 4.7 Gg yr-1) was much higher than that in Hong Kong (0.32 ± 0.05 Gg yr-1), whereas the estimated CS2 and DMS emissions in the study region accounted for a very few percentage of the total CS2 and DMS emission in China. These findings lay the foundation for better understanding sulfur chemistry in the greater PRD region of southern China. © 2010 Elsevier Ltd

Graphene-polyelectrolyte multilayer membranes with tunable structure and internal charge

One great advantage of graphene-polyelectrolyte multilayer (GPM) membranes is their tunable structure and internal charge for improved separation performance. In this study, we synthesized GO-dominant GPM membrane with internal negatively-charged domains, polyethyleneimine (PEI)-dominant GPM membrane with internal positively-charged domains and charge-balanced dense/loose GPM membranes by simply adjusting the ionic strength and pH of the GO and PEI solutions used in layer-by-layer membrane synthesis. A combined system of quartz crystal microbalance with dissipation (QCM-D) and ellipsometry was used to analyze the mass deposition, film thickness, and layer density of the GPM membranes. The performance of the GPM membranes were compared in terms of both permeability and selectivity to determine the optimal membrane structure and synthesis strategy. One effective strategy to improve the GPM membrane permeability-selectivity tradeoff is to assemble charge-balanced dense membranes under weak electrostatic interactions. This balanced membrane exhibits the highest MgCl2 selectivity (∼86%). Another effective strategy for improved cation removal is to create PEI-dominant membranes that provide internal positively-charged barrier to enhance cation selectivity without sacrificing water permeability. These findings shine lights on the development of a systematic approach to push the boundary of permeability-selectivity tradeoff for GPM membranes