饮用水源水库铁锰垂直分布规律及原因

经对水温分层型季节性缺氧水库铁、锰污染的研究表明,Fe、Mn分布随着季节变化、水库水温分层而变化,呈现明显的垂直分布规律:高浓度Fe、Mn主要出现在水温分层期水库中、下层,与库底缺氧、pH值下降、基岩溶蚀、沉积物高浓度Fe、Mn释放造成的二次污染等密切相关,从而为自来水厂实施优化分层取水和污染控制提供了依据.     

改良剂对根际土壤-水稻系统中镉运移的影响

稻田镉(Cd)污染治理是我国目前农田重金属污染治理的重点和难点.铁的氧化还原过程对Cd在土壤-水稻系统中的运移发挥着极其重要的作用.从铁循环调控入手,研发阻控稻米Cd累积的钝化技术及产品,势必能为农田重金属污染治理提供新的解决途径.本研究以自主研发的改良剂为对象,通过野外田间试验,研究改良剂对Cd从根际土壤向根表铁膜迁移及在水稻不同组织器官间转运的影响,探索铁循环影响Cd在根际土壤-水稻系统中运移的机制.结果表明,改良剂施用促进了根际土壤中铁硫化物的形成,其与Cd共沉淀显著减少了分蘖期、拔节期和灌浆期根际土壤中乙酸铵浸提态Cd(NH4AcCd)的含量;成熟期排水烤田致使铁硫化物被氧化,其对Cd的释放显著增大了根际土壤中NH4Ac-Cd的含量;铁硫化物形成减弱了Fe(Ⅱ)从根际土壤向根表的迁移能力,铁膜中铁(DCB-Fe)的含量随之减小,铁膜对Cd的吸附能力减弱导致分蘖期和灌浆期铁膜中Cd(DCB-Cd)的含量显著减小;改良剂施用有效减弱了根系对Cd的转运能力,增大了拔节期、灌浆期和成熟期Cd在根系的分布比例,而减少了拔节期、灌浆期和成熟期Cd在茎叶、以及成熟期Cd在糙米中的分布比例.本研究成果将为后期改良剂的研发及应用提供理论依据,对解决稻田Cd污染治理难题具有重要意义.     

基于最佳范化能力的BP网络隐节点数反比关系式的环境预测模型

网络结构和样本集复杂性是影响BP网络泛化能力的两个最重要因素.对一个给定的训练样本集,为了构造一个与样本集复杂性相匹配的网络结构,使BP网络具有最佳泛化能力,在分析BP网络的泛化能力(用检测误差E2表示)与网络结构和样本集复杂性之间关系的基础上,建立了含参数的BP网络检测误差E2与网络隐节点数H、样本的因子数n、样本数...     

新型MIL-100(Fe)基MOFs材料的制备及其对Sr2+的吸附

采用水热法和溶剂热法制备了金属有机骨架材料——MIL-100(Fe)和MIL-100(Fe)-DMA.利用FTIR、XRD、SEM、XPS和N₂吸附/脱附对材料结构进行了表征.通过静态吸附实验,探究了初始Sr²⁺浓度、吸附时间、溶液pH值和不同共存阳离子对材料吸附性能的影响,研究了两种材料对Sr²⁺吸附过程的吸附动力学和吸附等温线模型.表征结果表明:两种材料基本骨架一致,吸附Sr²⁺对材料骨架影响均可忽略;虽然MIL-100(Fe)-DMA比表面积小于MIL-100(Fe),但由于晶粒尺寸小,堆积形成的介孔结构更有利于Sr²⁺的扩散.吸附实验结果表明:随着反应时间和初始Sr²⁺浓度的增加,两种材料对Sr²⁺的吸附量先迅速增大再逐渐达到平衡;随着pH值的增大,两种材料对Sr²⁺的吸附量增大,且碱性条件下吸附效果好;两种材料吸附过程均符合准二级动力学模型、颗粒内扩散模型和Langmuir吸附等温模型;采用三价铁盐作为铁源以及DMA作为有机溶剂可提高MIL-100(Fe)材料对Sr²⁺的吸附效果.     

Fe3O4纳米磁性微粒对全氟辛烷磺酸盐的吸附

采用共沉淀法合成Fe3O4纳米磁性颗粒,用透射电子显微镜(TEM)、X射线衍射仪(XRD)以及振动样品磁强计(VSM)对Fe3O4纳米磁性颗粒的粒径、形貌和磁性进行表征并研究Fe3O4纳米磁性微粒对全氟辛磺酸盐的吸附。结果表明:在PFOS初始浓度4 mg/L,pH为3,反应时间24 h,Fe3O4纳米磁性微粒投加量1.25 g/L,对全氟辛磺酸盐去除率达到90%。Fe3O4纳米磁性微粒对PFOS的吸附符合Freundlich吸附方程。     

农业生态与土壤环境中铁元素的关系

论述了铁元素的地球化学特征及其分布 ,与铁元素有关的主要作物分类、作物与铁元素的关系以及影响铁元素有效态的因素 ,铁肥的种类和施用方法与效果     

Removal of Alachlor from Water by Catalyzed Ozonation in the Presence of Fe2+, Mn2+, and Humic Substances

Abstract

The effects of Fe(II), Mn(II) and humic substances on the catalyzed ozonation of alachlor, an endocrine disruptor were investigated. Results revealed that small amounts of Fe(II), Mn(II), and humic substances could enhance the ozonation of alachlor, but larger amounts of them would retard the oxidation. These results were successfully identified by an electron paramagnetic resonance (EPR) spectroscopy/spin-trapping method that could quantify hydroxyl radicals. The production of hydroxyl radicals was obviously increased with the increasing of Fe(II) concentration, which contributed to enhance ozonation at low concentrations. But the excess Fe(II) consumed some of the radicals when it was added at a higher concentration (1.5 mg/L). However, no obvious radicals were observed when a different amount of Mn(II) was used, and the catalytic ozonation of alachlor by Mn(II) mainly followed the mechanism of “active sites created on the surface of MnO₂.” The radical pathway was followed when alachlor was ozonated with different concentrations of humic substances because of its radical initiating, promoting, and inhibiting effects.     

Effects of nitrate concentration in interstitial water on the bioremediation of simulated oil-polluted shorelines

Nutrient addition has been proved to be an effective strategy to enhance oil biodegradation in marine shorelines.To determine the optimal range of nutrient concentrations in the bioremediation of oil-polluted beaches,nitrate was added to the simulated shoreline models in the initial concentration of 1,5 and 10 mg/L.Whenever the NO3-N concentration declined to 70% of its original value, additional nutrients were supplemented to maintain a certain range.Results showed adding nutrients increased the oil biodegradation level,the counts of petroleum degrading bacteria(PDB)and heterotrophic bacteria (HB),and the promoted efficiency varied depending on the concentration of nitrate.Oil degradation level in 5 mg/L(NO_3-N)group reached as much as 84.3% accompanied with the consistently highest counts of PDB;while in 1 mg/L group oil removal efficiency was only 35.2%,and the numbers of PDB and HB were relatively low compared to the other groups supplemented with nutrients.Although counts of HB in the 10 mg/L group were remarkable,lower counts of PDB resulted in poorer oil removal efficiency (70.5%) compared to 5 mg/L group.Furthermore,it would need more NO_3-N(0.371 mg)to degrade 1 mg diesel oil in the 10 mg/L group than in the 5 mg/L group(0.197 mg).In conclusion, Nitrate concentration in 5 mg/L is superior to 1 and 10 mg/L in the enhancement of diesel oil biodegradation in simulated shorelines.     

铁铜双金属有机骨架MIL-101(Fe,Cu)活化双氧水降解染料性能

针对非均相芬顿传质效率低和Fe(Ⅲ)→Fe(Ⅱ)转化慢导致活性低等问题,采用溶剂热法制备铁铜双金属有机骨架材料[MIL-101(Fe,Cu)],并研究了材料界面性质、催化降解染料(亚甲基蓝)性能以及活化催化机制.结果表明,MIL-101(Fe,Cu)晶体结构完整且呈三维八面体形状;比表面积和平均孔径分别为667.2 m²·g^-1和1.9 nm,可充分暴露反应活性位点.MIL-101(Fe,Cu)在广谱p H范围可活化H₂O₂高效降解亚甲基蓝.当p H=5、反应20 min,MIL-101(Fe,Cu)/H₂O₂对20mg·L^-1亚甲基蓝的去除率为100%,较MIL-101(Fe)/H₂O₂和单独H₂O₂分别提高43.1%和88.9%.自由基猝灭实验与反应前后铁和铜价态变化结果表明,羟基自由基(·OH)是MIL-101(Fe,Cu)/H_2<...     

Scaling range sizes to threats for robust predictions of risks to biodiversity

Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range‐size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red‐list assessments for decades, appropriate spatial scales of AOO for predicting risks of species’ extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale‐sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1–1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer‐scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid‐measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape‐scale threats to species and ecosystems.