With the rapid development of nanotechnology in the past decades, AgNPs are widely used in various fields and have become one of the most widely used nanomaterials, which leads to the inevitable ...release of AgNPs to the aquatic environment through various pathways. It is important to understand the effects of AgNPs on aquatic plants and zooplankton, which are widely distributed and diverse, and are important components of the aquatic biota. This paper reviews the effects of AgNPs on aquatic plants and zooplankton at the individual, cellular and molecular levels. In addition, the internal and external factors affecting the toxicity of AgNPs to aquatic plants and zooplankton are discussed. In general, AgNPs can inhibit growth and development, cause tissue damage, induce oxidative stress, and produce genotoxicity and reproductive toxicity. Moreover, the toxicity of AgNPs is influenced by the size, concentration, and surface coating of AgNPs, environmental factors including pH, salinity, temperature, light and co-contaminants such as NaOCl, glyphosate, As(V), Cu and Cd, sensitivity of test organisms, experimental conditions and so on. In order to investigate the toxicity of AgNPs in the natural environment, it is recommended to conduct toxicity evaluation studies of AgNPs under the coexistence of multiple environmental factors and pollutants, especially at natural environmental concentrations.
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•Toxicity of silver nanoparticles to aquatic plants and zooplankton was reviewed.•Cytotoxicity and genotoxicity explained the adverse effects at individual level.•Environmental factors and co-contaminants affect the toxicity of AgNPs.
China plays a significant role in global sustainable development. The construction sector is not solely the backbone of China's economy, but also one of the most carbon-emitting industries. ...Consequently, it is exceedingly urgent to clarify the current status of carbon emissions of the construction sector (CECS) and seek useful strategies to reduce them. Based on the panel data from 30 Chinese provinces from 2005 to 2019, this study first used the Theil index, GIS techniques and the Moran's I index to depict the spatiotemporal evolution, and then utilized the Spatial Durbin Model (SDM) to analyze the effects of its influencing factors. The findings demonstrated that: (1) annual CECS generally grew at an average annual rate of 9.70%, but the growth rate slowed yearly, while carbon intensity declined at an average rate of 4.65% per year; (2) the CECS exhibited regional heterogeneity in both provinces and regions; (3) there was a significant positive spatial autocorrelation of CECS since 2008, but the spatial agglomeration it embodied diminished after 2016; (4) population size, economic level, technological innovation, government support, foreign trade, environmental regulation, and financial development may influence CECS, but different factors' direct and indirect effects differ. These results could provide empirical scientific evidence for local governments to formulate appropriate emission-reduction policies for the construction sector.
•We calculate the provincial CECS from 2005 to 2019.•The distribution of CECS shows remarkable spatial disparities.•The spatial autocorrelation of CECS has been significant since 2008.•We reveal the direct and indirect effects of different influencing factors.
The study area is situated in Shouguang City, Shandong Province, as the largest greenhouse vegetable production base in Northern China. Samples of facility agricultural soil, open-field agricultural ...soil, and agricultural plastic mulch film were collected to investigate the distribution characteristics, influencing factors, and discharging sources of microplastics (MPs). Microplastic abundance of three soil layers at all sampling sites ranged from 310 to 5698 items/kg, with an average value of 1444 ± 986 items/kg. The main size category of MPs was less than 0.5 mm, and the contribution of MPs with sizes <0.5 mm in the 10–25 cm layer of facility agricultural soils was significantly higher (p < 0.05) than that in the 0–5 cm soil layer, which indicated that small MPs tended to migrate to deeper soil layers. The prevailing shapes of MPs were fragment and film, while polypropylene, ethylene-propylene copolymer, and polyethylene dominated among chemical compositions. The fractions of silty and sandy particles were correlated with the abundance of MPs, and the microplastic abundance in sandy loam was significantly higher (p < 0.05) than that in silty loam or loam based on the international classification standard. Thus, the soil texture may affect the distribution of MPs in local agricultural soils. In addition, the planting age of facility agricultural soil was related to microplastic abundance, while there was no significant difference in the microplastic abundances of facility agricultural soils under different irrigation methods.
The microplastic abundance in sandy loam surpassed that in silty loam or loam, small size (<0.5 mm) MPs tended to migrate to deeper soil layers, and planting age affected microplastic abundance in facility agricultural soils.
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•Particles with sizes less than 0.5 mm dominated the size distribution of MPs in agricultural soils.•Smaller MPs (<0.5 mm) tended to migrate to deeper layers of agricultural soils.•The most frequent shapes of MPs in agricultural soils were fragment and film.•Soil texture and planting age of facility agriculture may affect the distribution of MPs in agricultural soils.
To identify factors and indicators that affect chronic pain and pain relief, and to develop predictive models using machine learning.
We analyzed the data of 67,028 outpatient cases and 11,310 valid ...samples with pain from a large retrospective cohort. We used decision tree, random forest, AdaBoost, neural network, and logistic regression to discover significant indicators and to predict pain and treatment relief.
The random forest model had the highest accuracy, F1 value, precision, and recall rates for predicting pain relief. The main factors affecting pain and treatment relief included body mass index, blood pressure, age, body temperature, heart rate, pulse, and neutrophil/lymphocyte × platelet ratio. The logistic regression model had high sensitivity and specificity for predicting pain occurrence.
Machine learning models can be used to analyze the risk factors and predictors of chronic pain and pain relief, and to provide personalized and evidence-based pain management.
Through the K-means clustering analysis, it divides the regions of China into four clusters according to the differences in high-tech industry development level between 2008 and 2016. Considering ...”environmental pollution” and ”innovation failure”, an improved SBM-DEA efficiency measurement model was constructed to measure the green technology innovation efficiency of China’s high-tech industry clusters. Lasso regression was used to screen out the factors affecting the green technology innovation efficiency of high-tech industry in each cluster area. On this basis, quantile regression method is used to study the influence degree and regional differences of various influencing factors on green innovation efficiency of high-tech industry at different quantile. Meanwhile, DEA-tobit model is used for robustness test. The research shows that in each cluster area, the factors that significantly affect the green innovation efficiency of high-tech industry are different, and the degree of influence of each factor on the innovation efficiency at different quantile is also different. Combining the empirical results with the reality of high-tech industries in various regions, the corresponding policy recommendations are put forward.
Microplastics (MPs) are emerging pollutants that act as a carrier of toxic pollutants, release toxic substances, and aggregate in biota. The adsorption behavior of MPs has recently become a research ...hot spot. The objective of this study was to summarize the main mechanisms by which MPs adsorb organic pollutants, introduce some mathematical models commonly used to study the adsorption behavior of MPs, and discuss the factors affecting the adsorption capacity from three perspectives, i.e., the properties of MPs and organic pollutants, and environmental factors. Adsorption kinetics and isothermal adsorption models are commonly used to study the adsorption of organic pollutants on MPs. We observed that hydrophobic interaction is the most common mechanism by which MPs adsorb organic pollutants, and also reportedly controls the portion of organic pollutants. Additionally, electrostatic interaction and other non-covalent forces, such as hydrogen bonds, halogen bonds, and π–π interactions, are also mechanisms of organic pollutant adsorption on MPs. The particle size, specific surface area, aging degree, crystallinity, and polarity of MPs, and organic pollutant properties (hydrophobicity and dissociated forms) are key factors affecting adsorption capacity. Changes in the pH, temperature, and ionic strength also affect the adsorption capacity. Current research on the adsorption behavior of MPs has mainly been conducted in laboratories, and in-depth studies on the adsorption mechanism and influencing factors are limited. Therefore, studies on the adsorption behavior of MPs in the environment are required, and this study will contribute to a better understanding of this topic.
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•This study reviews the mechanisms by which microplastics adsorb organic pollutants.•Factors affecting the adsorption capacity of microplastics are determined.•Microplastics adsorb organic pollutants mainly through hydrophobic interactions.•Microplastic and environmental properties affect adsorption of organic pollutants.
Purpose/Significance: Mobile health applications provide a convenient way for users to obtain health information and services. Studying the factors that influence users' acceptance and use of mobile ...health applications (apps or Apps) will help to improve users' actual usage behavior. Method/Process: Based on the literature review method and using the Web of Science core database as the data source, this paper summarizes the relevant research results regarding the influencing factors of the acceptance and use behavior of mobile health application users and makes a systematic review of the influencing factors from the perspectives of the individual, society, and application (app or App) design. Result/Conclusion: In terms of the individual dimension, the users' behavior is influenced by demographic characteristics and motivations. Social attributes, source credibility, and legal issues all affect user behavior in the social dimension. In the application design dimension, functionality, perceived ease of use and usefulness, security, and cost are the main factors. At the end of the paper, suggestions are given to improve the users' acceptability of mobile health applications and improve their use behavior.
Wilderness is of great value and conservation significance. An in-depth understanding of the evolution of the spatial distribution of wilderness conditions in China and the factors influencing them ...is crucial for biodiversity conservation and restoration. However, the evolution of its spatial and temporal patterns and the factors influencing them have been little explored in studies of wilderness. In this study, the Weighted Linear Combination model was utilized to assess the quality of wilderness in China over the past 20 years, and the geographic detector model assessed the key factors that have influenced wilderness. The study results indicate that (1) Over the past 20 years, the western wilderness has worsened, while the eastern and central regions have seen some improvement. (2) Changes in wilderness quality are mainly influenced by the temperature and the distribution of nature reserves. (3) High wilderness quality areas are mainly influenced by actual evapotranspiration and temperature, while population density and GDP play a dominant role in most medium and low wilderness quality areas. (4) Interacting factors are greater than any single factor affecting wilderness. The results of the study can provide a reference for the conservation and management of wilderness resources and biodiversity in China.
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•Wilderness quality improved in >60% of Chinese provinces, but declined in Xizang (45.3%) and Xinjiang (50.8%).•Changes in wilderness quality are mainly influenced by the temperature and nature reserves distribution.•The dominant factors affecting wilderness quality differ depending on the region.
With the increasing demand for efficient extraction of residual oil, enhanced oil recovery (EOR) offers prospects for producing more reservoirs’ original oil in place. As one of the most promising ...methods, chemical EOR (cEOR) is the process of injecting chemicals (polymers, alkalis, and surfactants) into reservoirs. However, the main issue that influences the recovery efficiency in surfactant flooding of cEOR is surfactant losses through adsorption to the reservoir rocks. This review focuses on the key issue of surfactant adsorption in cEOR and addresses major concerns regarding surfactant adsorption processes. We first describe the adsorption behavior of surfactants with particular emphasis on adsorption mechanisms, isotherms, kinetics, thermodynamics, and adsorption structures. Factors that affect surfactant adsorption such as surfactant characteristics, solution chemistry, rock mineralogy, and temperature were discussed systematically. To minimize surfactant adsorption, the chemical additives of alkalis, polymers, nanoparticles, co-solvents, and ionic liquids are highlighted as well as implementing with salinity gradient and low salinity water flooding strategies. Finally, current trends and future challenges related to the harsh conditions in surfactant based EOR are outlined. It is expected to provide solid knowledge to understand surfactant adsorption involved in cEOR and contribute to improved flooding strategies with reduced surfactant loss.
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•Up-to-date review of surfactant adsorption on mineral surfaces in cEOR is provided.•Adsorption behavior is a key to understand the adsorption loss of surfactants.•Influencing factors play a primary role in surfactant adsorption.•Strategies to reduce surfactant adsorption is given.
•A 3-level fragment division method is proposed for actual vehicle data processing.•The influencing factors of electric vehicle energy consumption are discussed.•A novel data-driven energy ...consumption prediction framework is proposed.•The driving conditions prediction is integrated in the proposed framework.
With increasing mass-adoption of electric vehicles, the energy consumption has become a key performance index to electric vehicle drivers, automakers and policy-makers. Accurate and real-time energy consumption prediction under real-world driving conditions is essential for alleviating the ‘range anxiety’ and can provide support for optimal battery sizing, energy-efficient route planning and charging infrastructures operation. In this paper, real-world driving data collected from fifty-five electric taxis in Beijing city are obtained and divided into three-level driving fragments. The influencing factors of energy consumption, including vehicle-, environment-, and driver-related factors, are extracted and studied. With the extracted key influencing factors, a novel machine learning-based energy consumption prediction framework integrated with driving condition prediction is proposed and used in actual energy consumption prediction. The real-world trip test results show that a root mean squared error of 0.159kWh (RMSE) and a mean absolute percentage error 12.68% (MAPE) are reached, the RMSE and the MAPE are respectively reduced by 32.05% and by 30.14% compared to the conventional method.
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