China has experienced an upsurge in child abandonment since the late 1970s in parallel with its one-child policy (OCP) and market reforms. Due to the scarcity of individual-level data, the literature focuses on informal adoption and child trafficking. This study first demonstrates the spatial-temporal trends of child abandonment across over 100,000 self-reported cases spanning 40 years in China collected from an internet platform. We then examine how the OCP and the long-established clan culture influence the incidence of child abandonment at the provincial level. We further compare whether the influences vary across genders. The results indicate that a tougher OCP penalty increases child abandonment, particularly the abandonment of girls. The influence of the OCP on girl abandonment is weaker in provinces with a strong clan culture, where sex ratios at birth are more unbalanced due to an increased incidence of gender-selective abortions.
Accurately controlling catalytic activity and mechanism as well as identifying structure–activity–selectivity correlations in Fenton-like chemistry is essential for designing high-performance catalysts for sustainable water decontamination. Herein, active center size-dependent catalysts with single cobalt atoms (CoSA), atomic clusters (CoAC), and nanoparticles (CoNP) were fabricated to realize the changeover of catalytic activity and mechanism in peroxymonosulfate (PMS)-based Fenton-like chemistry. Catalytic activity and durability vary with the change in metal active center sizes. Besides, reducing the metal size from nanoparticles to single atoms significantly modulates contributions of radical and nonradical mechanisms, thus achieving selective/nonselective degradation. Density functional theory calculations reveal evolutions in catalytic mechanisms of size-dependent catalytic systems over different Gibbs free energies for reactive oxygen species generation. Single-atom site contact with PMS is preferred to induce nonradical mechanisms, while PMS dissociates and generates radicals on clusters and nanoparticles. Differences originating from reaction mechanisms endow developed systems with size-dependent selectivity and mineralization for treating actual hospital wastewater in column reactors. This work brings an in-depth understanding of metal size effects in Fenton-like chemistry and guides the design of intelligent catalysts to fulfill the demand of specific scenes for water purification.
A novel adaptive spectral method has been recently developed to numerically solve partial differential equations (PDEs) in unbounded domains. To achieve accuracy and improve efficiency, the method relies on the dynamic adjustment of three key tunable parameters: the scaling factor, a displacement of the basis functions, and the spectral expansion order. In this paper, we perform the first numerical analysis of the adaptive spectral method using generalized Hermite functions in both one- and multi-dimensional problems. Our analysis reveals why adaptive spectral methods work well when a “frequency indicator” of the numerical solution is controlled. We then investigate how the implementation of the adaptive spectral methods affects numerical results, thereby providing guidelines for the proper tuning of parameters. Finally, we further improve performance by extending the adaptive methods to allow bidirectional basis function translation, and the prospect of carrying out similar numerical analysis to solving PDEs arising from realistic difficult-to- solve unbounded models with adaptive spectral methods is also briefly discussed.
In the boundary element method (BEM), the sinh transformation method is an effective method for evaluating nearly singular integrals, but a relationship between the integration accuracy and the number of Gaussian points is needed to achieve adaptive computation. Based on deep learning, we propose a novel integration scheme, adaptive sinh transformation Gaussian quadrature (ASTGQ), which can determine the number of Gaussian points according to the required accuracy. First, a large number of integration data samples of the sinh transformation method are generated in different cases, and the neural network is trained to establish the relationship between the number of Gaussian points and the integration accuracy. Then, based on the improved loss function and evaluation index, a better network model is obtained to ensure that the actual integration accuracy is slightly higher than the requirement of using the minimum Gaussian points. In this way, when the trained neural network is used in the sinh transformation method, the higher accuracy requirement can be met at a lower cost. Numerical examples demonstrate that, compared to the adaptive Gaussian quadrature (AGQ) method, the proposed scheme can significantly improve the computational efficiency when evaluating the nearly singular integrals for very thin coatings and other structures.
This article investigates how environmental adversity affects competitive performance in cognitive-intensive settings. Using a comprehensive dataset of professional eSports tournaments and match-hour variation of fine particulate matters, we find robust evidence that pollution kills competition. Specifically, higher air pollution levels diminish the performance and winning odds of the weaker team in a matchup while boosting that of the stronger team, widening the gap between them. We document two operating channels: (i) pollution leads to heterogeneous performance-reducing effects contingent on a team’s relative strength against their opponent, rather than its absolute competitiveness; and (ii) a weaker team adjusts their strategic decision-making differently in a polluted environment compared to their stronger counterparts. Our findings elucidate the distributional impact of environmental adversity and underscore its influence on strategic decision-making.
Protected areas (PAs) are the major conservation tool for ecosystem conservation, but function unequally in mitigating human pressures in practice. Assessing PA vulnerability caused by human pressures and its association with socioeconomic and PA characteristic factors is vital for improving conservation effectiveness and the post-2020 PA expansion. Here, using a new framework integrating the intensity and temporal changes of human pressures in PAs and their matched unprotected areas, we categorize global terrestrial PAs into four anthropogenic vulnerability levels: high (11.7 %), moderate (18.6 %) and low (21.9 %) vulnerability and wilderness (47.8 %). We find significant variations in the anthropogenic vulnerability of PAs between countries, continents, and IUCN categories. Europe has the highest proportion of high-vulnerability PAs (ca. 19.7 % of protected areas in Europe), while South America and Oceania have the highest proportions of low-vulnerability PAs and wilderness PAs, respectively (33.2 % and 75.0 % respectively). The vulnerability of PAs is not significantly associated with socioeconomic factors at the country level, which might reflect the trade-offs between positive and negative outcomes of development. With a new framework that integrated four significant factors for anthropogenic vulnerability assessment, this study demonstrates that global PAs have different anthropogenic vulnerability levels and suggest that some PAs function effectively in mitigating human pressures despite currently intense human pressures within them. Our results also suggest that future evaluations on the conservation status should pay attention not only to PA coverage but also to the anthropogenic vulnerability levels within PAs to achieve higher conservation effectiveness.
Manganese oxides (MnOx) are recognized as a strongest oxidant and adsorbent, of which composites have been proved to be effective in the removal of contaminants from wastewater. This review provides a comprehensive analysis of Mn biochemistry in water environment including Mn oxidation and Mn reduction. The recent research on the application of MnOx in the wastewater treatment was summarized, including the involvement of organic micropollutant degradation, the transformation of nitrogen and phosphorus, the fate of sulfur and the methane mitigation. In addition to the adsorption capacity, the Mn cycling mediated by Mn(II) oxidizing bacteria and Mn(IV) reducing bacteria is the driving force for the MnOx utilization. The common category, characteristics and functions of Mn microorganisms in recent studies were also reviewed. Finally, the discussion on the influence factors, microbial response, reaction mechanism and potential risk of MnOx application in pollutants’ transformation were proposed, which might be the promising opportunities for the future investigation of MnOx application in wastewater treatment.
In this study, Mn-C composites using different MnO2 contents and solid carbon material were prepared to explore the synchronous removal performance of nutrients and SMX. Higher nitrate removal performance (97-98 %) with quickest nitrate removal rate (4.97 mg N L -1h- 1) was obtained in Mn\_20 systems. The increased Mn content and Mn-P compound were observed via surface characteristics, indicating the involvement of MnOx in pollutants removal, particularly for higher phosphorus removal (84-89 %) via Mn-P precipitation and BioMnOx adsorption. Nevertheless, compared to systems based on Mn\_0 composites (74 %), systems with Mn-C composites presented lower SMX reduction efficiency (34-51 %), which might be attributed to the large Mn(II) accumulation, impairing certain microbes and lower the MnOx function. Higher abundance of genera affiliated to Bacter-oidetes\_vadinHA17 and Rhodocyclaceae was observed in the Mn-C composites, as well as the gathering of Geo-bacter and Desulfovibrio as keystone taxa, responsible for the removal of nitrate and SMX and microbial interactions. Besides, the increase of sulfonamide ARGs was closely related to the predominant microbes in the Mn-C composites, which acted as the hosts of ARGs. This study broadens the knowledge of Mn-C composites in synergetic removal of nutrients and organics, and supports the potential application of manganese oxide in wastewater treatment.