The main purpose of this article is to link the environment, economy, electricity, and society and put forward a new point of view. The current research mainly explores the relationship between the environment, economy, and society and lacks a discussion on electricity. Using a new research framework, this article examines the relationship between energy intensity, energy consumption structure, population density, urbanization rate, and carbon intensity based on relevant data from 2000 to 2017 in China. In the empirical research, according to the cluster analysis, China’s 30 provinces are divided into three regions according to the electrification rate standard. The cross-sectional dependence test method is used to verify the cross-sectional dependence of the data, and the second-generation panel unit root test method is used. Exploring the relationship between the variables, this article finally uses the convergence analysis method to explore the degree of influence of each variable on the carbon intensity. The empirical results show that there are both short-term effects and long-term relationships in various regions, and the influencing factors of each region are different. It further shows that the carbon intensity of the four panels shows convergence, β absolute convergence, and β conditional convergence, but the main influencing factors in different regions are different. Finally, based on the results of empirical research, policy recommendations for reducing carbon intensity in different regions are put forward.

Environmental problems not only relate to residents’ happiness but also challenge the innovation development of industries. This study first measures the innovation efficiency of China’s high-tech industries using the super-efficiency data envelopment analysis model and portrays its spatial characteristics through the Moran’s I index and the local indicators of spatial association map. Second, we use the entropy weight method to construct the local living environment index from both natural and social environments. Finally, we utilize spatial econometric models to analyze the impact of local living environment on high-tech industries’ innovation efficiency. The results reveal that, first, the spatial variation of innovation efficiency in China’s high-tech industries is significant, with efficiency being higher in the east than the Midwest, and higher in the south than the north. Second, innovation efficiency has a positive adjacent and geographical spatial autocorrelation, and low-low agglomeration and low-high agglomeration dominate the types of spatial correlation. Finally, the contribution of the local living environment to the innovation efficiency of high-tech industries is positive and significant. This contribution has an obvious spatial spillover effect and regional heterogeneity. This study can help regional governments to improve local living environments and promote industrial innovation and development.

A Fe₃O₄ hybrid material (Fe₃O₄@SiO₂-S₂) modified by Schiff-base was prepared by grafting methyl acrylate (MA), ethylenediamine (EDA), and salicylaldehyde (SA) to the prepared magnetic hybrid material (Fe₃O₄@SiO₂-NH₂) successively; what’s more, the structure was characterized by FTIR, XRD, SEM, TEM, and VSM. The results showed that the Fe₃O₄@SiO₂-S₂ has an obvious core–shell structure and a saturation magnetization of 45.9 emu/g. Study on the adsorption of aqueous heavy metal ions showed that Fe₃O₄@SiO₂-S₂ posed selective adsorption for Hg²⁺ with the saturated adsorption capacity of 362 mg/g (1.12 mmol/g), which was superior to Fe₃O₄@SiO₂-NH₂, Fe₃O₄@SiO₂-HO-S, and other adsorbents, at the condition of pH = 6, 45℃, the adsorption capacity remained 89% after 5 cycles of adsorption–desorption; what is more, adsorption equilibrium was reached at about 300 min, and the adsorption isotherm conformed to the Langmuir isotherm adsorption model; in addition, pseudo-second-order model could be well described the adsorption kinetic process of Fe₃O₄@SiO₂-S₂ to Hg²⁺. The adsorption mechanism demonstrated that the N atoms of Schiff-base were mainly contributed to the adsorption of Hg²⁺; what is more, the N atom of tertiary amine and the O atoms of hydroxy and carbonyl also help to the adsorption of Hg²⁺.

Due to the reduction of fossil fuels’ resources and their contribution to environmental problems, biodiesel fuels have attracted significant attention as substitutes for diesel fuels. However, since their NOₓ emissions are higher than that of diesel fuels in most cases and also because of their higher viscosity than diesel, fuel additives are used to enhance their properties and reduce emissions. In this study, the effect of n-hexane and n-hexadecane addition to biodiesel and diesel fuels on exhaust emissions and performance of a single-cylinder diesel engine was investigated by using grey-based Taguchi method. Fuel additive, the additive amount, and fuel type were considered as the operating parameters. Three fuel types including diesel, rapeseed oil biodiesel, and cottonseed oil biodiesel were used in this investigation, while n-hexane and n-hexadecane were considered as the two fuel additives. As well as, three levels were assigned to the additive amount which were 4, 8, and 12%. Based on the operating parameters and their levels, the plan of experiments was generated according to L₁₈ orthogonal array. Using grey relational analysis, this multi-response optimization problem was first transformed into a single response optimization. Then, this single system response, which is known as grey relational grade, was utilized in Taguchi approach for statistical evaluations. The results demonstrated that rapeseed was the best selection for fuel type compared to cottonseed and diesel in order to have the optimum system responses and hexadecane gave better results for system optimization in comparison with hexane additive. As well as, the analysis of variance showed that fuel type was the predominant operating factor influencing the grey relational grade which means fuel type was the most important parameter in the simultaneous optimization of exhaust emissions and engine performance. The Taguchi results also revealed that the optimum condition of engine performance and exhaust emissions happened when engine was fueled with rapeseed biodiesel containing 12% hexadecane as an additive. The confirmation test result validated the reliability of Taguchi approach in this investigation.

Pollution with trace metals (TM) has been shown to affect diversity and/or composition of plant and animal communities. While ecotoxicological studies have estimated the impact of TM contamination on plant and animal communities separately, ecological studies have widely demonstrated that vegetation is an important factor shaping invertebrate communities. It is supposed that changes in invertebrate communities under TM contamination would be explained by both direct impact of TM on invertebrate organisms and indirect effects due to changes in plant communities. However, no study has clearly investigated which would more importantly shape invertebrate communities under TM contamination. Here, we hypothesized that invertebrate communities under TM contamination would be affected more importantly by plant communities which constitute their habitat and/or food than by direct impact of TM. Our analysis showed that diversity and community identity of flying invertebrates were explained only by plant diversity which was not affected by TM contamination. Diversity of ground-dwelling (GD) invertebrates in spring was explained more importantly by plant diversity (27% of variation) than by soil characteristics including TM concentrations (8%), whereas their community identity was evenly explained by plant diversity and soil characteristics (2–7%). In autumn, diversity of GD invertebrates was only explained by plant diversity (12%), and their identity was only explained by soil characteristics (8%). We conclude that vegetation shapes invertebrate communities more importantly than direct effects of TM on invertebrates. Vegetation should be taken into account when addressing the impacts of environmental contamination on animal communities.

Co-doped magnetic Mn₃O₄ was synthesized by the solvothermal method and adopted as an effective catalyst for the degradation of oxytetracycline (OTC) in water. Synergistic interactions between Co-Mn₃O₄ and Fe₃O₄ not only resulted in the enhanced catalytic activity through the activation of peroxymonosulfate (PMS) to degrade OTC but also made Fe₃O₄/Co-Mn₃O₄ easy to be separated and recovered from aqueous solution. 94.2% of OTC could be degraded within 60 min at an initial OTC concentration of 10 mg L⁻¹, catalyst dosage of 0.2 g L⁻¹, and PMS concentration of 10 mM. The high efficiency of OTC removal was achieved in a wider pH range of 3.0–10.0. Co (II), Co (III), Fe (II), Fe (III), Mn (II), Mn (III), and Mn (IV) on Fe₃O₄/Co-Mn₃O₄ were identified as catalytic sites based on XPS analysis. The free radical quenching experiments showed that O₂•⁻ radicals and ¹O₂ played the main role in the degradation process and the catalytic degradation of OTC involved both free radical and non-free radical reactions. Eventually, the intermediates of OTC degradation were examined, and the possible decomposition pathways were proposed. The excellent catalytic performances of Fe₃O₄/Co-Mn₃O₄ came from the fact that the large specific surface area could provide abundant active sites for the activation of PMS and the redistribution of inter-atomic charges accelerated the redox reactions of metal ions. The high degradation efficiency and rate constant of OTC in actual water samples indicated that Fe₃O₄/Co-Mn₃O₄ had a good practical application potential.

This review aims to understand the impacts of plasticizers on the thyroid system of animals and humans. The thyroid gland is one of the earliest endocrine glands that appear during embryogenesis. The thyroid gland synthesizes thyroid hormones (TH), triiodothyronine (T3), and thyroxine (T4) that are important in the regulation of body homeostasis. TH plays critical roles in regulating different physiological functions, including metabolism, cell growth, circadian rhythm, and nervous system development. Alteration in thyroid function can lead to different medical problems. In recent years, thyroid-related medical problems have increased and this could be due to rising environmental pollutants. Plasticizers are one such group of a pollutant that impacts thyroid function. Plasticizers are man-made chemicals used in a wide range of products, such as children’s toys, food packaging items, building materials, medical devices, cosmetics, and ink. The increased use of plasticizers has resulted in their detection in the environment, animals, and humans. Studies indicated that plasticizers could alter thyroid function in both animals and humans at different levels. Several studies demonstrated a positive and/or negative correlation between plasticizers and serum T4 and T3 levels. Plasticizers could also change the expression of various TH-related genes and proteins, including thyroid-stimulating hormone (TSH), thyrotropin-releasing hormone (TRH), and transporters. Histological analyses demonstrated thyroid follicular cell hypertrophy and hyperplasia in response to several plasticizers. In conclusion, plasticizers could disrupt TH homeostasis and the mechanisms of toxicity could be diverse.

An increasing amount of electronic waste (e-waste) is not a new concern. It has been causing trouble globally. This waste comprises valuable metals and harmful compounds that lead to detrimental environmental conditions. Managing this kind of waste in developing economies is difficult due to different barriers hindering the process. Therefore, the goal of this research work is to determine the barriers while taking expert opinions and through available literature, and subsequently prioritize them to address the challenges in e-waste management. Moreover, this study utilizes an integrated Fuzzy Decision-Making Trail and Evaluation Laboratory (F-DEMATEL) and Fuzzy Interpretive Structural Modeling (F-ISM) approaches to determine the interrelationship between these identified barriers. Performance data obtained from this combined approach is applied to determine an overall rank for 15 identified barriers. The F-DEMATEL technique facilitates in obtaining the influence of barriers on each other and categorizes them into causal or effect groups. In addition, a Fuzzy Matrice d’impacts Croisés Multiplication Appliquée an un Classeement (F-MICMAC) analysis is exercised to sort them into dependent or driving factor. The findings suggest that the underlying cause barriers include “lack of customer awareness about return,” “less policies addressing e-waste problem,” “lack of long-term planning,” and “insensitiveness of public towards environmental issues.” The methodology is integrated with fuzzy logic to take uncertainty in the data gathered into consideration. This approach aids policymakers and decision-makers in determining the barriers’ mutual relationships and interconnections.

Heavy metal pollution has increasingly affected human life, and the treatment of heavy metal pollution, especially chromium pollution, is still a major problem in the field of environmental governance. As a commonly used industrial metal, chromium can easily enter the environment with improperly treated industrial waste or wastewater, then pollute soil and water sources, and eventually accumulate in the human body through the food chain. Many countries and regions in the world are threatened by soil chromium pollution, resulting in the occurrence of cancer and a variety of metabolic diseases. However, as a serious threat to agriculture, food, and human health. Notwithstanding, there are limited latest and systematic review on the removal methods, mechanisms, and effects of soil chromium pollution in recent years. Hence, this article outlines some of the methods and mechanisms for the removal of chromium in soil, including physical, chemical, biological, and biochar methods, which provide a reference for the treatment and research on soil chromium pollution drawn from existing publications.

Dam safety assessment is important to implement the appropriate measures to avoid a dam break disaster as part of the water reservoirs management process. Prediction-based approaches are valuable to compare the actual measurements with the simulated values to proactively detect anomalies. However, the application of the conventional hydrostatic seasonal time (HST) has some limitations related to an instantaneous response of the dam to environmental factors, which can lead to inaccurate prediction and interpretation, especially for daily measurements. Besides, the generalization ability (GA) of these models is not analyzed enough despite its crucial importance in selecting the appropriate models. In this study, the multiple linear regression (MLR), artificial neural network (ANN), support vector regression (SVR), and adaptive boosting (AdaBoost) models with nonlinear autoregressive exogenous (NARX) inputs are proposed to incorporate the response delay of the dam to the hydraulic load. Thus, these models were evaluated and compared with the HST model for predicting the daily pore water pressure in an embankment dam. Moreover, we proposed a classification method of the models into four categories, namely perfect, excellent, good, and poor according to the GA. Results show that, except for the AdaBoost, the other ML models outperformed the traditional statistical approach (HST) in terms of prediction accuracy as well as the GA. Overall, the study results provide new insights in enhancing the monitoring processes and dam safeties by detecting the anomalies early through the comparison of the measurements and simulated results produced by the best-fitted models from the confidence interval (CI) perspective.