The effciency of sodium hydroxide treated rubber (Hevea brasiliensis) leaves powder (NHBL) for removing copper ions from aqueous solutions has been investigated. The e?ects of physicochemical parameters on biosorption capacities such as stirring speed, pH, biosorbent dose, initial concentrations of copper, and ionic strength were studied. The biosorption capacities of NHBL increased with increase in pH, stirring speed and copper concentration but decreased with increase in biosorbent dose and ionic strength... 相似文献
The unsustainable trade in wildlife is a key threat to Earth's biodiversity. Efforts to mitigate this threat have traditionally focused on regulation and enforcement, and there is a growing interest in campaigns to reduce consumer demand for wildlife products. We aimed to characterize these behavior-change campaigns and the evidence of their impacts. We searched peer-reviewed and grey literature repositories and over 200 institutional websites to retrieve information on demand-reduction campaigns. We found 236 campaigns, mainly in the grey literature. Since the 1970s, the number of campaigns increased, although for over 15% a start date could not be found. Asia was the primary focus, although at the national level the United States was where most campaigns took place. Campaigns most often focused on a single species of mammal; other vertebrates groups, with the exception of sharks, received limited attention. Many campaigns focused on broad themes, such as the wildlife trade in general or seafood. Thirty-seven percent of campaigns reported some information on their inputs, 98% on strategies, 70% on outputs, 37% on outcomes (i.e., changes in the target audience), and 9% on impacts (i.e., biological changes or threat reduction). Information on outcomes and impacts was largely anecdotal or based on research designs that are at a high risk of bias, such as pre- and postcampaign comparisons. It was unclear whether demand-reduction campaigns had direct behavioral or biological impacts. The lack of robust impact evaluation made it difficult to draw insights to inform future efforts, a crucial part of effectively addressing complex issues, such as the wildlife trade. If demand-reduction campaigns are to become a cornerstone of the efforts to mitigate the unsustainable trade in wildlife, conservationists need to adopt more rigorous impact evaluation and a more collaborative approach that fosters the sharing of data and insights. 相似文献
Conventional methods for water and wastewater treatment are energy-intensive, notably at the stage of coagulation–flocculation, calling for new strategies to predict pollutant reduction because the amount of energy consumed is related to how much of the pollutant is treated. Here we developed a model, named Bio-logic, inspired by ecosystems, where pollutants represent organisms, coagulants are food, and the wider environmental conditions are the living environment. Artificial intelligence was used to learn the biological behavior, which enabled an accurate prediction of the amount of pollutant reduction. Results show that pseudo-biological objects that have a strong affinity for biological food, such as turbidity, total phosphorus, ammonia nitrogen and the potassium permanganate index, induced a strong correlation, between measured pollutant consumption capacity and predicted values. For instance, R2 correlation coefficients are 0.97 for turbidity and 0.92 for the potassium permanganate index in the laboratory; and 0.99 for turbidity, 0.90 for total phosphorus, 0.75 for ammonia nitrogen and 0.63 for the potassium permanganate index in water treatment plants. Overall, our findings demonstrate that artificial intelligence can use the water Bio-logic model to predict the pollutant consumption capacity.
