Governments worldwide have taken unprecedented social distancing and community lockdown measures to halt the COVID-19 epidemic, leaving millions of people restrained in locked-down communities and their mental well-being at risk. This study examines Chinese rural residents' mental health risk under emergency lockdown during the COVID-19 pandemic. It investigates how the environmental, socioeconomic, and behavioral dimensions of community support affect mental health in this emergency context. We also explore whether community support's effectiveness depends on the strictness of lockdown measures implemented and the level of individual perceived COVID-19 infection risk. We collect self-reported mental health risk, community support, and demographics information through a cross-sectional survey of 3892 Chinese rural residents living in small towns and villages. Ordinary least square regressions are employed to estimate the psychological effects of community support. The results suggest that the COVID-19 epidemic and lockdown policies negatively affect psychological well-being, especially for rural females. The capacity for community production has the largest impact on reducing mental health risks, followed by the stability of basic medical services, community cohesion, housing condition, the stability of communications and transportation supply, and the eco-environment. The effectiveness of different community support dimensions depends on the level of lockdown policy implemented and the levels of one's perceived risk of COVID-19 infection. Our study stresses the psychological significance of a healthy living environment, resilient infrastructure and public service system, and community production capacity during the lockdown in rural towns and villages.
Mobile edge computing (MEC) has been an effective paradigm to support real-time computation-intensive vehicular applications. However, due to highly dynamic vehicular topology, these existing centralized-based or distributed-based scheduling algorithms requiring high communication overhead, are not suitable for task offloading in vehicular networks. Therefore, we investigate a novel service scenario of MEC-based vehicular crowdsourcing, where each MEC server is an independent agent and responsible for making scheduling of processing traffic data sensed by crowdsourcing vehicles. On this basis, we formulate a data-driven task offloading problem by jointly optimizing offloading decision and bandwidth/computation resource allocation, and renting cost of heterogeneous servers, such as powerful vehicles, MEC servers and cloud, which is a mixed-integer programming problem and NP-hard. To reduce high time-complexity, we propose the solution in two stages. First, we design an asynchronous deep Q-learning to determine offloading decision, which achieves fast convergence by training the local DQN model at each agent in parallel and uploading for global model update asynchronously. Second, we decompose the remaining resource allocation problem into several independent subproblems and derive optimal analytic formula based on convex theory. Lastly, we build a simulation model and conduct comprehensive simulation, which demonstrates the superiority of the proposed algorithm.
Due to the characteristics of ozone-depleting and high global warming potential, chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs) and hydrofluorocarbons (HFCs) have been restricted by the Montreal Pro- tocol and its amendments over the world. Considering that China is one of the main contributors to the emission of halocarbons, a long-term atmospheric observation on major substances including CFC-11 (CCl3F), CFC-12 (CCl2F2), HCFC-22 (CHClF2), HCFC-141b (CH3CCl2F), HCFC-142b (CH3CClF2) and HFC-134a (CH2FCF3) was conducted in five cities (Beijing, Hangzhou, Guangzhou, Lanzhou and Chengdu) of China during 2009–2019. The atmospheric concentrations of CFC-11, CFC-12, HCFC-141b and HCFC-142b all showed declining trends on the whole while those of HCFC-22 and HFC-134a were opposite. A paired sample t-test showed that the ambient mixing ratios of HCFC-22 and HFC-134a in cities were 41.9% and 25.7% higher on average than those in sub- urban areas, respectively, while the other substances did not show significant regional differences. The annual emissions of halocarbons were calculated using an interspecies correlation method and the results were generally consistent with the published estimates. Discrepancies between bottom-up inventories and the estimates in this study for CFCs emissions were found. Among the most consumed ozone depleting substances (ODSs) in China, CFCs accounted for 75.1% of the ozone depletion potential (ODP)-weighted emissions while HCFCs contributed a larger proportion (58.6%) of CO2-equivalent emissions in 2019. China’s emissions of HCFC-141b and HCFC-142b contributed the most to the global emission (17.8%–48.0%). The elimination of HCFCs in China will have a crucial impact on the HCFCs phase-out in the world.
Abstract We investigated the ice nucleation activities of humic-like substances (HULIS), an important component of organic aerosol (OA), derived from atmospheric and biomass burning aerosols, and produced from aqueous-phase chemical reactions. Respective HULIS can effectively trigger heterogeneous IN under mixed-phase cloud conditions. HULIS ice active entities (IAE) were aggregates in size between 0.02 and 0.10 μm. At −20°C, the IAE numbers per unit HULIS mass varied from 213 to 8.7 × 104 mg−1. Such results were different than those detected in aquatic humic substances (HS) from previous studies, implying using HS as surrogates may not robustly estimate the IAE concentrations in the real atmosphere. Combining the abundance of atmospheric HULIS with the present results suggests that HULIS could be an important IAE contributor in the atmosphere where other ice nucleating particle species, such as dust and biological particles, are either low in concentration or absent.
The dataset was firstly proposed in Fu et al. (2019) and further used in Fu and Chen (2020). It contains audiovisual (AV) stimuli and the corresponding EEG data collected from 16 normal-hearing subjects, for an auditory attention decoding (AAD) task. The link provides you the location of the dataset at the PKU netdisk. To download it, a password is required. The access of the dataset is generally permitted for non-commercial use by contacting the corresponding author Prof. Jing Chen (chenj@cis.pku.edu.cn). If the link was found invalid, please contact us to modify it.
Bifunctional Bi12O17Cl2/MIL-100(Fe) composite (BMx) was firstly constructed via facile ball-milling method. The optimal BM200 was highly efficient for Cr(VI) sequestration and activation of persulfate (PS) for bisphenol A (BPA) decomposition under white light illumination, which was much more remarkable than the pristine MIL-100(Fe) and Bi12O17Cl2, respectively. Furthermore, the photocatalytic reduction efficiency can be significantly improved via the addition of some green small organic acids (SOAs). As well, the BPA degradation can be achieved over an extensive initial pH range of 3.0–11.0. When the PS concentration increased to more than 2.0 mM, the BPA degradation efficiency decreased due to the SO4−• self-scavenging effect. It was also found that the co-existence of inorganic anions like H2PO4−, HCO3−, SO42−, Cl− and NO3− could decelerate the BPA degradation. The excellent photocatalytic Cr(VI) reduction and persulfate activation performances originated from both MIL-100(Fe) with excellent PS activation ability and Bi12O17Cl2 with a favorable band position, which not only enabled the efficient separation of charges but also accelerated the formation of SO4−• radicals. The BM200 displayed prominent stability and recyclability. More importantly, the credible degradation pathway was proposed based on UHPLC-MS analysis and DFT calculation. This research revealed that the Fe-based MOFs/bismuth-rich bismuth oxyhalides (BixOyXz, X = Cl, Br and I) composites possessed great potential in wastewater remediation.