Fabrication and characterization of lightweight aggregate prepared from steel mill sludge in one step

Journal of Material Cycles and Waste Management - The qualified green lightweight aggregate (LWA) was successfully prepared from steel mill sludge (SMS) and fly ash (FAS) in one step using the...     

我国海洋经济高质量发展指标体系构建及时空分析——基于海洋强国战略背景

通过对海洋强国战略背景下我国海洋经济高质量发展内涵的分析,构建评价指标体系,运用全局熵权法对2013—2019年沿海11省份指标数据进行研究。结果发现:①时间上,我国沿海省份海洋经济发展质量整体呈上升趋势,广东、上海稳定处于高水平梯度,河北、广西、海南持续处于低水平,辽宁、福建、天津略有变动。②空间上,从海洋经济平均发展质量看,长三角位列第一;但在增长率方面珠三角高于长三角,第三是环渤海海洋经济区。③影响因素上,环境保护力度、科技创新能力、对外开放规模,海洋新兴产业等有待提高和发展。最后针对我国海洋经济高质量发展,提出相应建议。     

郑州与福州降水同位素特征及水汽来源对比分析

研究基于郑州与福州两地区GNIP(1985—1992年)大气降水同位素资料,对其大气降水同位素的季节变化以及环境因子进行比较分析。结果表明,郑州地区较福州地区季节变化明显,且两地区与温度和降水量均呈现负相关关系;根据两地区大气降水线方程得出,福州地区大气降水线方程斜率和截距大于郑州地区;两地区的d-excess值夏季高,冬季低;福州地区受台风影响,两地区降水量差别较大导致降水量在决定两地区月加权平均d-excess值时,福州地区整体比郑州地区偏大;采用MeteoInfo软件,并利用由美国国家大气研究中心所提供的气象资料,对两地区气团轨迹进行后向模拟,比较分析得出:郑州地区在夏季大部分水汽来自南海,春季、秋季和冬季的水汽均来自北方大陆;福州地区在夏季的水汽全部水汽来自低纬度的海洋,而春季、秋季和冬季的水汽仅有少部份来自北方大陆。     

The interception effect of heavy metals in different types of Chinese fir plantations

Environmental Geochemistry and Health - The aim of this research is to investigate the interception effect of heavy metals, such as zinc (Zn), copper (Cu), lead (Pb), arsenic (As), and cadmium (Cd)...     

Improving protected area effectiveness through consideration of different human-pressure baselines

Previous assessments of the effectiveness of protected areas (PAs) focused primarily on changes in human pressure over time and did not consider the different human-pressure baselines of PAs, thereby potentially over- or underestimating PA effectiveness. We developed a framework that considers both human-pressure baseline and change in human pressure over time and assessed the effectiveness of 338 PAs in China from 2010 to 2020. The initial state of human pressure on PAs was taken as the baseline, and changes in human pressure index (HPI) were further analyzed under different baselines. We used the random forest models to identify the management measures that most improved effectiveness in resisting human pressure for the PAs with different baselines. Finally, the relationships between the changes in the HPI and the changes in natural ecosystems in PAs were analyzed with different baselines. Of PAs with low HPI baselines, medium HPI baselines, and high HPI baselines, 76.92% (n=150), 11.11% (n=12), and 22.86% (n=8) , respectively, showed positive effects in resisting human pressure. Overall, ignoring human-pressure baselines somewhat underestimated the positive effects of PAs, especially for those with low initial human pressure. For PAs with different initial human pressures, different management measures should be taken to improve effectiveness and reduce threats to natural ecosystems. We believe our framework is useful for assessing the effectiveness of PAs globally, and we recommend it be included in the Convention on Biological Diversity Post-2020 Strategy.     

Effects of bedform migration on nutrient fluxes at the sediment–water interface: a theoretical analysis

Environmental Fluid Mechanics - Nutrient fluxes at the sediment–water interface are essential for water quality and aquatic ecosystems. In this study, a unified expression for the sediment...     

Hydraulic jumps with low inflow Froude numbers: air–water surface patterns and transverse distributions of two-phase flow properties

Environmental Fluid Mechanics - Hydraulic jumps are commonly employed as energy dissipators to guarantee long-term operation of hydraulic structures. A comprehensive and in-depth understanding of...     

Characteristics of plankton Hg bioaccumulations based on a global data set and the implications for aquatic systems with aggravating nutrient imbalance

• Hg bioaccumulation by phytoplankton varies among aquatic ecosystems. • Active Hg uptake may exist for the phytoplankton in aquatic ecosystems. • Impacts of nutrient imbalance on food chain Hg transfer should be addressed. The bioaccumulation of mercury (Hg) in aquatic ecosystem poses a potential health risk to human being and aquatic organism. Bioaccumulations by plankton represent a crucial process of Hg transfer from water to aquatic food chain. However, the current understanding of major factors affecting Hg accumulation by plankton is inadequate. In this study, a data set of 89 aquatic ecosystems worldwide, including inland water, nearshore water and open sea, was established. Key factors influencing plankton Hg bioaccumulation (i.e., plankton species, cell sizes and biomasses) were discussed. The results indicated that total Hg (THg) and methylmercury (MeHg) concentrations in plankton in inland waters were significantly higher than those in nearshore waters and open seas. Bioaccumulation factors for the logarithm of THg and MeHg of phytoplankton were 2.4–6.0 and 2.6–6.7 L/kg, respectively, in all aquatic ecosystems. They could be further biomagnified by a factor of 2.1–15.1 and 5.3–28.2 from phytoplankton to zooplankton. Higher MeHg concentrations were observed with the increases of cell size for both phyto- and zooplankton. A contrasting trend was observed between the plankton biomasses and BAF_MeHg, with a positive relationship for zooplankton and a negative relationship for phytoplankton. Plankton physiologic traits impose constraints on the rates of nutrients and contaminants obtaining process from water. Nowadays, many aquatic ecosystems are facing rapid shifts in nutrient compositions. We suggested that these potential influences on the growth and composition of plankton should be incorporated in future aquatic Hg modeling and ecological risk assessments.     

Understanding the role of nano-TiO2 on the toxicity of Pb on C. dubia through modeling–Is it additive or synergistic?

• A two-compartment model is able to quantify the effect of nano-TiO₂ on Pb toxicity. • Nano-TiO₂ reduces Pb tolerance level and increased the killing rate for C. dubia. • Thus, nano-TiO₂ synergistically enhances Pb toxicity. • Algae reduce Pb transfer rate to the body tissue and the killing rate. Nano-TiO₂ can remarkably increase lead (Pb) toxicity in aquatic organisms. However, the mechanism of this toxicity, additive or synergistic, is not well understood. To explore this mechanism, we inspected the role of nano-TiO₂ in the toxicity of Pb on Ceriodaphnia dubia (C. dubia), a model water flea species typically used for ecotoxicity studies. The effect of algae, a diet for aquatic organisms, on the effect of this binary mixture was also investigated. A two-compartment toxicokinetic (TK)-toxicodynamic (TD) modeling approach was used to quantify the Pb toxicity under these complex conditions and to develop critical parameters for understanding the mechanism of toxicity. This two-compartment modeling approach adequately described the Pb accumulation in the gut and in the rest of the body tissue under different nano-TiO₂ concentrations, with and without algae, and predicted the toxicity response of C. dubia. It indicated that increasing the nano-TiO₂ concentration reduced the Pb tolerance level and concurrently increased the killing rate constant of C. dubia. Therefore, nano-TiO₂ synergistically enhanced Pb toxicity. Algae remarkably reduced the toxicity of this binary mixture through reducing the Pb transfer rate to the body tissue and the killing rate, although it did not affect the Pb tolerance level. This two-compartment modeling approach is useful in understanding the role of nanoparticles when assessing the overall toxicity of nanoparticles and other toxic elements in the environment.     

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.