The rapid development of high-speed rail has markedly shortened the travel time from one city to another. However, the impact of space–time compression brought about by high-speed rail on city innovation has not received sufficient attention. This paper examines the space–time compression phenomenon produced by high-speed railway networks and its impact on city innovation from 2000 to 2019 using a sample of 279 Chinese prefecture-level cities. The empirical results show that there was a strong space–time compression during this period. The development of high-speed rail can promote city innovation. However, the construction of high-speed rail also produces a siphon effect, which accelerates the convergence of innovative elements in cities with stronger innovation capabilities. Nevertheless, it has a negative spillover effect on cities with weaker innovation capabilities. Finally, policy recommendations for promoting the balanced development of city innovation and recommendations for future research are presented.
Governmental investment in commercializing academic patents has spurred economic growth. This study explores the mechanism underlying the impact of academic patent commercialization on regional economic development based on holistic view of the integration of local and neighbourhood hierarchies. The empirical analysis of a sample of 31 Chinese provinces from 2010 to 2019 shows that fiscal expenditures on science and technology mediate the relationship between academic patent commercialization and economic development. Moreover, there is a positive spatial spill over effect between developing economies of neighbouring provinces. In the knowledge economy era, academic patent commercialization, supported by fiscal expenditure, plays an increasingly important role in regional economic growth.
Realizing efficient hydrogenation of N2 molecules in the electrocatalytic nitrogen reduction reaction (NRR) is crucial in achieving high activity at a low potential because it theoretically requires a higher equilibrium potential than other steps. Analogous to metal hydride complexes for N2 reduction, achieving this step by chemical hydrogenation can weaken the potential dependence of the initial hydrogenation process. However, this strategy is rarely reported in the electrocatalytic NRR, and the catalytic mechanism remains ambiguous and lacks experimental evidence. Here, we show a highly efficient electrocatalyst (ruthenium single atoms anchored on graphdiyne/graphene sandwich structures) with a hydrogen radical-transferring mechanism, in which graphdiyne (GDY) generates hydrogen radicals (H•), which can effectively activate N2 to generate NNH radicals (•NNH). A dual-active site is constructed to suppress competing hydrogen evolution, where hydrogen preferentially adsorbs on GDY and Ru single atoms serve as the adsorption site of •NNH to promote further hydrogenation of NH3 synthesis. As a result, high activity and selectivity are obtained simultaneously at −0.1 V versus a reversible hydrogen electrode. Our findings illustrate a novel hydrogen transfer mechanism that can greatly reduce the potential and maintain the high activity and selectivity in NRR and provide powerful guidelines for the design concept of electrocatalysts.
Sodium chloride is expected to be found on many of the surfaces of icy moons like Europa and Ganymede. However, spectral identification remains elusive as the known NaCl-bearing phases cannot match current observations, which require higher number of water of hydration. Working at relevant conditions for icy worlds, we report the characterization of three “hyperhydrated” sodium chloride (SC) hydrates, and refined two crystal structures [2NaCl·17H 2 O (SC8.5); NaCl·13H 2 O (SC13)]. We found that the dissociation of Na + and Cl − ions within these crystal lattices allows for the high incorporation of water molecules and thus explain their hyperhydration. This finding suggests that a great diversity of hyperhydrated crystalline phases of common salts might be found at similar conditions. Thermodynamic constraints indicate that SC8.5 is stable at room pressure below 235 K, and it could be the most abundant NaCl hydrate on icy moon surfaces like Europa, Titan, Ganymede, Callisto, Enceladus, or Ceres. The finding of these hyperhydrated structures represents a major update to the H 2 O–NaCl phase diagram. These hyperhydrated structures provide an explanation for the mismatch between the remote observations of the surface of Europa and Ganymede and previously available data on NaCl solids. It also underlines the urgent need for mineralogical exploration and spectral data on hyperhydrates at relevant conditions to help future icy world exploration by space missions.
Herein, electro-catalysis (EC) as the electron donor to accelerate the continuable Fe(III)/Fe(II) cycles in different inorganic peroxides (i.e., peroxymonosulfate (PMS), peroxydisulfate (PDS) and hydrogen peroxide (HP)) activation systems were established. These electro-cocatalytic Fenton-like systems exhibited an excellent degradation efficiency of sulfamethoxazole (SMX). A series of analytical and characterization methods including quenching experiments, probe experiments, and electron paramagnetic resonance spectrometry (EPR) were implemented to systematically sort out the source and yield of reactive oxygen species (ROS). A wide kind of ROS including hydroxyl radical (•OH), singlet oxygen (1O2), and sulfate radical (SO4•−), which contributed 38%, 37%, and 24% were produced in EC/Fe(III)/PMS system, respectively. •OH was the dominant ROS in both EC/Fe(III)/PDS and EC/Fe(III)/HP processes. According to the analysis of SMX degradation routes and biotoxicity, abundant degradation pathways were identified in EC/Fe(III)/PMS process and lower environmental impact was achieved in EC/Fe(III)/HP process. The diversiform ROS of EC/Fe(III)/PMS system makes it exhibit greater environmental adaptability in complex water matrixes and excellent low-energy consumption performance in many organic pollutants degradation. Continuous flow treatment experiments proved that the three systems have great sustainability and practical application prospect. This work provides a strong basis for constructing suitable systems to achieve different treatment requirements.
Hydrogen peroxide (H2O2), hydroxyl radicals (OH), hydroperoxyl (HO2), and superoxide (O2−) radicals interacting with aerosol particles significantly affect the atmospheric pollutant budgets. A multiphase chemical kinetic box model (PKU-MARK), including the multiphase processes of transition metal ions (TMI) and their organic complexes (TMI-OrC), was built to numerically drive H2O2 chemical behaviors in the aerosol particle liquid phase using observational data obtained from a field campaign in rural China. Instead of relying on fixed uptake coefficient values, a thorough simulation of multiphase H2O2 chemistry was performed. In the aerosol liquid phase, light-driven TMI-OrC reactions promote OH, HO2/O2−, and H2O2 recycling and spontaneous regenerations. The in-situ generated aerosol H2O2 would offset gas-phase H2O2 molecular transfer into the aerosol bulk phase and promote the gas-phase level. When combined with the multiphase loss and in-situ aerosol generation involving TMI-OrC mechanism, the HULIS-Mode significantly improves the consistency between modeled and measured gas-phase H2O2 levels. Aerosol liquid phase could be a pivotal potential source of aqueous H2O2 and influence the multiphase budgets. Our work highlights the intricate and significant effects of aerosol TMI and TMI-OrC interactions on the multiphase partitioning of H2O2 when assessing atmospheric oxidant capacity.
Antimicrobial resistance (AMR) has emerged as a significant challenge in human health. Wastewater treatment plants (WWTPs), acting as a link between human activities and the environment, create ideal conditions for the selection and spread of antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria (ARB). Unfortunately, current treatment processes are ineffective in removing ARGs, resulting in the release of large quantities of ARB and ARGs into the aquatic environment through WWTP effluents. This, in turn, leads to their dispersion and potential transmission to human through water and the food chain. To safeguard human and environmental health, it is crucial to comprehend the mechanisms by which WWTP effluent discharge influences the distribution and diffusion of ARGs in downstream waterbodies. In this study, we examine the latest researches on the antibiotic resistome in various waterbodies that have been exposed to WWTP effluent, highlighting the key influencing mechanisms. Furthermore, recommendations for future research and management strategies to control the dissemination of ARGs from WWTPs to the environment are provided, with the aim to achieve the “One Health” objective.
Declining levels of social welfare caused by climate warming and air pollution place increasing constraints on high-quality, sustainable global development. To achieve global climate-governance goals, it is essential to accelerate the process of peaking carbon emissions and meeting air-quality standards. Despite growing awareness of the impact of low-carbon city policies on the environment, few studies have focused on their impact on urban air quality. Based on panel data drawn from 275 cities between 2011 and 2017, the present study evaluates the effects of a low-carbon city policy on urban sulfur-dioxide emissions, using the low-carbon city policy as a quasi-natural experiment. The findings reveal that urban sulfur-dioxide emissions have obvious spatial-autocorrelation characteristics, showing obvious spatial clustering. The low-carbon city policy not only significantly reduced urban sulfur-dioxide emissions in pilot cities, but also suppressed sulfur-dioxide emissions in surrounding cities through an indirect rebound effect. This paper provides a theoretical reference for collaborative governance, which can help to achieve peak carbon emissions and air-quality standards. To reach those goals, nations must abandon territorial prevention-and-control methods based on administrative divisions and to fully activate cross-city regional joint prevention-and-control measures. This study proposes key policies, including promoting inter-city regional coordination mechanisms, strengthening the collaborative governance in relation to carbon-dioxide and sulfur-dioxide emissions, and promoting the construction of inner-city green facilities.
Summary Global changes over the past few decades have caused species distribution shifts and triggered population declines and local extinctions of many species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species (Red List) is regarded as the most comprehensive tool for assessing species extinction risk and has been used at regional, national, and global scales. However, most Red Lists rely on the past and current status of species populations and distributions but do not adequately reflect the risks induced by future global changes. Using distribution maps of >4,000 endemic woody species in China, combined with ensembled species distribution models, we assessed the species threat levels under future climate and land-cover changes using the projected changes in species’ suitable habitats and compared our updated Red List with China’s existing Red List. We discover an increased number of threatened species in the updated Red List and increased threat levels of >50% of the existing threatened species compared with the existing one. Over 50% of the newly identified threatened species are not adequately covered by protected areas. The Yunnan-Guizhou Plateau, rather than the Hengduan Mountains, is the distribution center of threatened species on the updated Red Lists, as opposed to the threatened species on the existing Red List. Our findings suggest that using Red Lists without considering the impacts of future global changes will underestimate the extinction risks and lead to a biased estimate of conservation priorities, potentially limiting the ability to meet the Kunming-Montreal global conservation targets.
Wastewater treatment plants (WWTPs) have been regarded as an important source of antibiotic resistance genes (ARGs) in environment, but out of municipal domestic WWTPs, few evidences show how environment is affected by industrial WWTPs. Here we chose Hangzhou Bay (HZB), China as our study area, where land-based municipal and industrial WWTPs discharged their effluent into the bay for decades. We adopted high-throughput metagenomic sequencing to examine the antibiotic resistome of the WWTP effluent and coastal sediment samples. And we proposed a conceptual framework for the assessment of antibiotic resistome risk, and a new bioinformatic pipeline for the evaluation of the potential horizontal gene transfer (HGT) frequency. Our results revealed that the diversity and abundance of ARGs in the WWTP’s effluent were significantly higher than those in the sediment. Furthermore, the antibiotic resistome in the effluent-receiving area (ERA) showed significant difference from that in HZB. For the first time, we identified that industrial WWTP effluent boosted antibiotic resistome risk in coastal sediment. The crucial evidences included: 1) the proportion of ARGs derived from WWTP activated sludge (WA) was higher (14.3 %) and two high-risky polymyxin resistance genes (mcr-4 and mcr-5) were enriched in the industrial effluent receiving area; 2) the HGT potential was higher between resistant microbiome of the industrial effluent and its ERA sediment; and 3) the highest resistome risk was determined in the industrial effluent, and some biocide resistance genes located on high-risky contigs were related to long-term stress of industrial chemicals. These findings highlight the important effects of industrial activities on the development of environmental antimicrobial resistance.
Forecast combination integrates information from various sources by consolidating multiple forecast results from the target time series. Instead of the need to select a single optimal forecasting model, this paper introduces a deep learning ensemble forecasting model based on the Dirichlet process. Initially, the learning rate is sampled with three basis distributions as hyperparameters to convert the infinite mixture into a finite one. All checkpoints are collected to establish a deep learning sub-model pool, and weight adjustment and diversity strategies are developed during the combination process. The main advantage of this method is its ability to generate the required base learners through a single training process, utilizing the decaying strategy to tackle the challenge posed by the stochastic nature of gradient descent in determining the optimal learning rate. To ensure the method’s generalizability and competitiveness, this paper conducts an empirical analysis using the weekly dataset from the M4 competition and explores sensitivity to the number of models to be combined. The results demonstrate that the ensemble model proposed offers substantial improvements in prediction accuracy and stability compared to a single benchmark model.