Did the widespread haze pollution over China increase during the last decade? A satellite view from space

Widespread haze layers usually cover China like low clouds, exerting marked influence on air quality and regional climate. With recent Collection 6 MODISDeep Blue aerosol data in 2000–2015, we analyzed the trends of regional haze pollution and the corresponding influence of atmospheric circulation in China. Satellite observations show that regional haze pollution is mainly concentrated in northern and central China. The annual frequency of regional haze in northern China nearly doubles between 2000 and 2006, increasing from30–50 to 80–90 days. Though there is amarked decrease in annual frequency during 2007–2009 due to both reduction of anthropogenic emissions and changes of meteorological conditions, regional pollution increases slowly but steadily after 2009, and maintains at a high level of 70–90 days except for the sudden decrease in 2015. Generally, there is a large increase in the number of regional-scale haze events during the last decade. Seasonal frequency of regional haze exhibits distinct spatial and temporal variations. The increasing winter haze events reach a peak in 2014, but decrease strongly in 2015 due partly to synoptic conditions that are favorable for dispersion. Trends of summer regional haze pollution aremore sensitive to changes of atmospheric circulation. Our results indicate that the frequency of regional haze events is associated not only with the strength of atmospheric circulation, but also with its direction and position, as well as variations in anthropogenic emissions

Did the widespread haze pollution over China increase during the last decade? A satellite view from space

Widespread haze layers usually cover China like low clouds, exerting marked influence on air quality and regional climate. With recent Collection 6 MODISDeep Blue aerosol data in 2000–2015, we analyzed the trends of regional haze pollution and the corresponding influence of atmospheric circulation in China. Satellite observations show that regional haze pollution is mainly concentrated in northern and central China. The annual frequency of regional haze in northern China nearly doubles between 2000 and 2006, increasing from30–50 to 80–90 days. Though there is amarked decrease in annual frequency during 2007–2009 due to both reduction of anthropogenic emissions and changes of meteorological conditions, regional pollution increases slowly but steadily after 2009, and maintains at a high level of 70–90 days except for the sudden decrease in 2015. Generally, there is a large increase in the number of regional-scale haze events during the last decade. Seasonal frequency of regional haze exhibits distinct spatial and temporal variations. The increasing winter haze events reach a peak in 2014, but decrease strongly in 2015 due partly to synoptic conditions that are favorable for dispersion. Trends of summer regional haze pollution aremore sensitive to changes of atmospheric circulation. Our results indicate that the frequency of regional haze events is associated not only with the strength of atmospheric circulation, but also with its direction and position, as well as variations in anthropogenic emissions

Decoding Voluntary Movement of Single Hand Based on Analysis of Brain Connectivity by Using EEG Signals

Research about decoding neurophysiological signals mainly aims to elucidate the details of human motion control from the perspective of neural activity. We performed brain connectivity analysis with EEG to propose a brain functional network (BFN) and used a feature extraction algorithm for decoding the voluntary hand movement of a subject. By analyzing the characteristic parameters obtained from the BFN, we extracted the most important electrode nodes and frequencies for identifying the direction of movement of a hand. The results demonstrated that the most sensitive EEG components were for frequencies delta, theta, and gamma1 from electrodes F4, F8, C3, Cz, C4, CP4, T3, and T4. Finally, we proposed a model for decoding voluntary movement of the right hand by using a hierarchical linear model (HLM). Through a voluntary hand movement experiment in a spiral trajectory, the Poisson coefficient between the measurement trajectory and the decoding trajectory was used as a test standard to compare the HLM with the traditional multiple linear regression model. It was found that the decoding model based on the HLM obtained superior results. This paper contributes a feature extraction method based on brain connectivity analysis that can mine more comprehensive feature information related to a specific mental state of a subject. The decoding model based on the HLM possesses a strong structure for data manipulation that facilitates precise decoding

Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim

With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node resources, short collection time, and lower collection efficiency. Moreover, public remote data sources have restrictions on user settings, such as access to IP, frequency, and bandwidth. In order to satisfy users’ demand for accessing public remote sensing data collection nodes and effectively increase the data collection speed, this paper proposes a TSCD-TSA dynamic task scheduling algorithm that combines the BP neural network prediction algorithm with PSO-based task scheduling algorithms. Comparative experiments were carried out using the proposed task scheduling algorithms on an acquisition task using data from Sentinel2. The experimental results show that the MAX-MAX-PSO dynamic task scheduling algorithm has a smaller fitness value and a faster convergence speed

High-fidelity quantitative differential phase contrast deconvolution using dark-field sparse prior

Differential phase contrast (DPC) imaging plays an important role in the family of quantitative phase measurement. However, the reconstruction algorithm for quantitative DPC (qDPC) imaging is not yet optimized, as it does not incorporate the inborn properties of qDPC imaging. In this research, we propose a simple but effective image prior, the dark-field sparse prior (DSP), to facilitate the phase reconstruction quality for all DPC-based phase reconstruction algorithms. The DSP is based on the key observation that most pixel values for an idea differential phase contrast image are zeros since the subtraction of two images under anti-symmetric illumination cancels all background components. With this DSP prior, we formed a new cost function in which L0-norm was used to represent the DSP. Further, we developed the algorithm based on the Half Quadratic Splitting to solve this NP-hard L0-norm problem. We tested our new model on both simulated and experimental data and compare it against state-of-The-Art (SOTA) methods including L2-norm and total variation regularizations. Results show that our proposed model is superior in terms of phase reconstruction quality and implementation efficiency, which significantly increases the experimental robustness, while maintaining the data fidelity. In general, the DSP supports high-fidelity qDPC reconstruction without any modification of the optical system, which simplifies the system complexity and benefit all qDPC applications

Retinex-qDPC: automatic background rectified quantitative differential phase contrast imaging

The quality of quantitative differential phase contrast reconstruction (qDPC) can be severely degenerated by the mismatch of the background of two oblique illuminated images, yielding problematic phase recovery results. These background mismatches may result from illumination patterns, inhomogeneous media distribution, or other defocusing layers. In previous reports, the background is manually calibrated which is time-consuming, and unstable, since new calibrations are needed if any modification to the optical system was made. It is also impossible to calibrate the background from the defocusing layers, or for high dynamic observation as the background changes over time. To tackle the mismatch of background and increases the experimental robustness, we propose the Retinex-qDPC in which we use the images edge features as data fidelity term yielding L2-Retinex-qDPC and L1-Retinex-qDPC for high background-robustness qDPC reconstruction. The split Bregman method is used to solve the L1-Retinex DPC. We compare both Retinex-qDPC models against state-of-the-art DPC reconstruction algorithms including total-variation regularized qDPC, and isotropic-qDPC using both simulated and experimental data. Results show that the Retinex qDPC can significantly improve the phase recovery quality by suppressing the impact of mismatch background. Within, the L1-Retinex-qDPC is better than L2-Retinex and other state-of-the-art DPC algorithms. In general, the Retinex-qDPC increases the experimental robustness against background illumination without any modification of the optical system, which will benefit all qDPC applications