湖泊富营养化模型的研究进展

湖泊的富营养化是全球普遍关注的环境问题之一.湖泊的富营养化模型是防治、修复和治理湖泊富营养化的重要决策工具.按研究的侧重点不同,将湖泊富营养化模型分为简单回归模型、水质模型、生态模型和生态-水动力水质模型,并分别回顾了四类模型的研究进展.最后指出湖泊富营养化模型的发展趋势,强调不确定理论、3S技术、耦合模型是今后湖泊富营养化模型研究的重点,应在此基础上建立通用的模拟、预测、评价和优化模型,为湖泊富营养化管理提供科学依据.     

Long-term estimation of plastic material resources from end-of-life vehicles in China: a scenario analysis considering multiple industry standards

Journal of Material Cycles and Waste Management - The recycling and treatment of plastic waste become an increasingly serious environmental degradation challenge. To promote the recycling of...     

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...     

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...     

环境因子和火干扰因子对喀纳斯泰加林柳兰分布的影响

喀纳斯泰加林植被的生长和分布受火干扰和环境因子的共同作用,为了解喀纳斯泰加林林下柳兰分布与生态因子的关系,促进种群扩繁,共设置了35个样地进行调查和分析。建立柳兰重要值与生态因子矩阵,利用CCA分析法对不同火烧林分中柳兰的生长分布状况与林火烈度、火后时间、海拔、坡度、坡向、郁闭度、土壤pH值、速效钾、土壤有机质、灌木层盖度、草本层盖度和草本层物种多样性指数等20个生态因子的关系进行了分析。结果显示,第一轴主要反映林火烈度、海拔、坡向,灌木层盖度、pH值、有机质、全钾和速效钾的变化;第二轴主要反映林火烈度、火后时间、草本层盖度、坡向、坡位、土壤容重、电导率、有机质、有效磷和全氮的变化。对柳兰分布有显著影响的生态因子是林火烈度、有机质、海拔、坡向、灌木层盖度、土壤容重、全钾和速效钾,前两轴的累计贡献率为72.48%。柳兰适合在土壤呈弱酸性、海拔低、阳坡、土壤容重小、土壤有机质、全钾和速效钾含量高的环境中生长。柳兰是阳性植物,是火烧后的先锋植物。对影响柳兰分布的生态因子进行定量分离,结果发现环境因子和火干扰因子对柳兰样地物种分布的解释能力为88.2%,显示出较好的排序效果。喀纳斯泰加林受人为干扰较少,柳兰所在植物群落与环境保持了良好的对应关系,生态因子与物种分布呈显著相关(P=0.003),表明CCA排序结果可以解释生态因子对物种分布的影响程度。     

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.