多巴胺非共价改性碳纳米管超滤膜的制备及抗污染性能

利用多巴胺(DA)的自聚合特性对多壁碳纳米管(MWCNT)进行非共价改性,制备了多巴胺改性碳纳米管超滤膜(PDA/MWCNT),并对其抗污染性能进行了系统研究.采用X射线光电子能谱、傅里叶红外光谱、接触角测量仪和扫描电镜对PDA/MWCNT膜进行亲水性和化学组成的表征.选取大肠杆菌菌液为目标污染物,研究了不同改性条件对PDA/MWCNT膜抗污染性能及抗蛋白黏附能力的影响.结果表明,DA成功引入到MWCNT表面.DA与MWCNT交联时间为5h、DA浓度1g/L、MWCNT负载量2.17mg/cm²时,MWCNT膜的接触角由改性前112.79°降至8.4°.改性后PDA/MWCNT膜表面的氧元素含量为改性前的2.9倍,膜的亲水性得到显著改善.膜通量较改性前提高1倍,膜的抗污染性能、抗菌性及抗蛋白黏附能力得到显著提高,错流冲洗后的膜通量恢复率从56.62%增加到88.74%.     

铁的环境地球化学综述

本文详细介绍了铁在表生地球化学环境中的存在状态,亚铁的氧化动力学,铁微生物地球化学及铁在表生带迁移转化的环境学意义,强调了铁环境地球化学研究的重要性     

有机生活垃圾微生物处理剂的研究

采用了多种培养基 ,对广泛收集的菌种、相关土样进行分离、筛选 ,得到了 8株有效微生物 ,其产生的蛋白酶、脂肪酶、淀粉酶和纤维素酶等酶系可使大分子物质分解成低分子物质 ,微生物摄取这些低分子物质后 ,将其转变成二氧化碳、水和少量氨气并释放能量 ,剩余的少量固体残渣可作为生物肥料。采用以上 8种微生物制成的有机生活垃圾处理剂 ,如配合一定的处理工艺和设备 ,可对日常有机生活垃圾进行减量化和资源化处理。     

亚铁活化过硫酸钠氧化预处理电镀废水

针对电镀生产过程产生的难降解、高浓度的有机废水,采用Fe~(2+)活化过硫酸钠产生硫酸根自由基的高级氧化技术对其进行预处理。重点探讨了S_2O_8~(2-)投加量、n(Fe~(2+))∶n(S_2O_8~(2-))、废水pH等因素对有机物去除及废水可生化性的影响。实验结果表明,常温下,在S_2O_8~(2-)投加量为4.0 g/L、n(Fe~(2+))∶n(S_2O_8~(2-))为1.00、废水pH为7.0的条件下,废水的处理效果最佳,反应20 min后COD去除率可达70%,BOD_5/COD从原水的0.21升至0.40,废水的可生化性大幅提高,能够满足深度生化处理的要求。     

哑铃湾网箱养殖水体中N的含量特征

根据2002年4月～2003年1月对哑铃湾网箱养殖海区水化学的现场调查,分析了网箱养殖水体中不同形态N的季节变化和含量特征,结果表明:网箱养殖使水体中各形态N都有不同程度的增加,尤其是DIN、NH4 N的增量更大;短期养殖对各形态N的影响不大,而长期养殖对水体中N的影响较显著。     

生态县评价体系及持续发展战略的研究

在生态县规划研究中,以复合生态系统理论为指南,探讨了生态县的内涵和评价标体系,并确定了以生态农业为基础,以生态型工业为主导,以社会调控为保障,依靠科技进步,寓环境建设于经济社会发展之中的建设生态县的战略,在近年实践中初见成效。     

亚铁活化及铁碳强化过硫酸钠氧化法修复模拟机油污染土壤

分别采用传统的Fe²⁺活化过硫酸钠（Na₂S₂O₈）氧化和铁碳强化Na₂S₂O₈氧化两种方法修复模拟机油污染土壤。实验结果表明：对于传统Fe²⁺-Na₂S₂O₈体系,在Na₂S₂O₈投加量为3.0%（w）、FeSO₄·7H₂O投加量为0.6%（w）的优化条件下,土壤中总石油烃（TPH）的去除率仅为33.12%;而对于Fe⁰-C-Na₂S₂O₈体系,在Na₂S₂O₈投加量为1.0%（w）、还原铁粉和活性炭的投加量均为0.1%（w）的优化条件下,土壤中TPH的去除率为42.99%;Fe⁰-C-Na₂S₂O₈体系较Fe²⁺-Na₂S₂O₈体系对土壤具有更好的修复效果,且Na₂S₂O₈的投加量减少了2/3。此外,Fe⁰-C-Na₂S₂O₈体系较Fe²⁺-Na₂S₂O₈体系对土壤pH的影响小,在实际应用中可适当提高铁粉的投加量来减小Na₂S₂O₈对土壤pH的影响。     

Comprehensive evaluation of spent mushroom substrate-chicken manure co-composting by garden waste improvement: physicochemical properties,humification process,and the spectral characteristics of dissolved organic matter

Environmental Science and Pollution Research - The performance of garden waste on spent mushroom substrate (SMS) and chicken manure (CM) co-composting efficiency and humification is unclear....     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.