MoO3掺杂WO3催化剂的制备及其光催化性能

采用低温水热法制备掺杂MoO3的WO3粉体,利用XRD,XPS,DRS和PL光谱对产物进行表征,以太阳光为光源对番红花红T溶液进行光催化降解研究.结果表明:MoO3掺杂含量为10%的WO3粉体对番红花红T溶液的脱色率为89.65%,COD去除率为88.9%.借助DRS和PL光谱分析结果,初步探讨了MoO3掺杂WO3粉体对番红花红T光催化降解的过程与机理.     

镉对鸡垂体Fas和caspase-3 mRNA表达的影响

探讨氯化镉(CdCl2)诱导鸡脑垂体细胞凋亡的发生情况及对凋亡相关基因Fas和caspase-3表达的影响.选择健康50日龄海兰白蛋鸡90只,分为3组,每组30只,以CdCl2含量为0、140、210 mg·kg -1的拌料饲喂,分别于染毒20d、40d和60d时取垂体,用原位末端标记(Tunel)和RT-PCR方法检测细胞凋亡和基因表达情况.实验结果表明,CdCl2作用后可致鸡腺垂体细胞呈现明显的凋亡征象,并可诱导垂体细胞Fas和caspase-3 mRNA表达的增加;在整个试验期的各个时间点2个剂量组的凋亡率和基因表达量与对照组相比差异显著(p<0.01),且随着染镉时间的延长,低剂量组的凋亡率以及Fas、caspase-3 mRNA表达增加较明显.这表明,一定剂量的CdCl2可诱导鸡垂体细胞凋亡,在此过程中Fas和caspase-3 mRNA表达呈现出与凋亡较为一致的趋势.     

全耦合活性污泥模型(FCASM3) Ⅰ:建模机理及数学表征

在充分分析活性污泥系统中生物反应机理的基础上,建立了活性污泥系统生物去除营养物质的细观机理模型--全耦合活性污泥模型(Fully Coupled Activated Sludge Model No.3,简称FCASM3).FCASM3将系统中微生物划分为8类菌群,包含31种组分、72个子过程;该模型的主要特点是将活性污泥系统中的微生物进一步细化,充分考虑了系统中微生物间的相互作用.FCASM3引入了硝化-反硝化过程中的中间产物亚硝酸盐.实现了对两步硝化-反硝化过程的模拟;FCASM3不仅包含聚糖菌的有关生物反应过程,而且还考虑了聚磷菌(非反硝化聚磷菌和反硝化聚磷菌)以及聚糖菌的厌氧维持过程,为直接体现温度对生物反应的影响,FCASM3将温度作为一个变量直接耦合到生物反应速率方程中.     

ERT技术在无机酸污染场地调查中的应用

利用高密度电阻率法对硫酸废液污染的场地进行污染调查,由于硫酸废液侵入土壤,使其介质的溶质浓度发生了改变,导致SO₄2-与地表潜水作用,电传导性提高,被污染土壤含盐量增加,但是电阻率降低与未被硫酸废液侵入的土层形成了一定的电性差异. 通过二维和三维电阻率层析成像(ERT)技术,圈定硫酸废液侵入土壤中的分布范围、侵蚀深度以及扩散羽流等,并对ERT结果进行土工试验异常验证. 结果表明:硫酸废液污染土壤的视电阻率小于6.0 Ω·m,污染区域长度达25.0 m,深度约4.0 m;地表土污染长度为12.0 m,表面宽度约4.0 m,污染土壤的总体积约为222.0 m3.      

稀土镧(La)及La(POA)3对镉污染绿豆幼苗生理影响

以水培的方式研究了稀土元素镧及其配合物La(POA)3(HPOA=苯氧乙酸)对绿豆幼苗的生理生态影响。结果表明,单一镉(10mg/L)胁迫时,NR、CAT及POD活性下降,而MDA、Pro含量上升;较低浓度(5～30mg/L)的镧及其配合物能明显地促进绿豆幼苗生长,并且可以缓解镉对绿豆幼苗的胁迫作用,经镧及其配合物作用后,上述生理指标皆比单一镉胁迫时有明显的改善,而高浓度(100mg/L)的镧及其配合物对镉污染的缓解作用都减弱;另外,较低浓度(10、15、30mg/L)的La(POA)3对镉胁迫的缓解作用较镧更明显;随着作用时间的延长,镧及其配合物对绿豆幼苗镉污染的缓解作用均逐渐下降。     

磁性纳米Fe3O4@C对SBR脱氮除磷性能及其微生物种群组成的影响

为探究磁性纳米Fe₃O₄@C对序批式活性污泥反应器(SBR)污水处理系统脱氮除磷性能的影响,建立了2个相同的SBR(编号分别为0号和1号),其中,0号反应器未投加任何磁性材料(对照组),1号反应器中投加0.5 g·L^-1的Fe₃O₄@C(实验组),并采用高通量测序和实时定量PCR(qPCR)技术对2个反应器内生物种群结构及关键脱氮除磷功能菌群进行分析.结果表明:(1)Fe₃O₄@C对SBR除污性能有显著影响,其中,1号反应器化学需氧量(COD)的去除性能得到增强,24 d后去除率稳定在90%左右;0号和1号反应器总氮(TN)平均去除率分别为35.83%和52.18%;从第43 d起,1号反应器除磷性能明显高于0号反应器,运行70 d后,0号和1号反应器对总磷(TP)的平均去除率分别为47.52%和56.33%.(2)添加Fe₃O₄@C材料后,反应器内变形菌门(Proteobacteria)的...     

郑州市大气氨排放清单及驱动力分析

采用排放因子法建立郑州市分县区2017年大气氨排放清单,并实现1 km×1 km空间网格分配,同时进行2007～2017年氨排放趋势及1989～2017年氨排放驱动力相关性分析.结果表明,郑州市2017年氨排放量为18 143.3 t,排放强度为2.4t·km^-2,农业源为主要排放源(63.4%),逸散源次之(11.3%);农业源中畜禽养殖氨排放主要来自蛋禽、肉猪和奶牛养殖;排放量前三的区县为登封市、荥阳市和新密市,分别占总量的19.3%、16.5%和15.6%;空间上郑州市南部及中西部地区排放量较高,东北部地区排放量较小; 2007～2017年各区县氨排放整体呈下降趋势,1989～2017年郑州市氨排放呈类似环境库兹涅茨曲线趋势,即氨排放整体上随着人均GDP和城镇化率上升而先增加再下降.     

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.     

多因素影响下巷道变形特性数值模拟研究

基于FLAC3D有限差分软件对开挖深度、侧压力系数、断面形状等多因素影响下的巷道变形参数进行正交试验，根据正交试验分析结果，分别选取同一深度条件下的圆形、马蹄形、直墙拱形、矩形巷道进行模拟试验并以圆形巷道作为典型断面，对不同深度下的巷道变形特征进行模拟试验。研究结果表明:开挖深度对巷道开挖后的应力差变化影响明显，断面形状对巷道围岩内的塑性区深度和具有显著影响；巷道开挖后的塑性区深度和随形状指数增加而呈线性增长趋势，圆形巷道的塑性区深度和最小，矩形最大；应力差随着巷道开挖深度的增加呈线性增加，深部开采应重视巷道内的应力分布情况。     

Efficacy of extracting indices from large‐scale acoustic recordings to monitor biodiversity

Passive acoustic monitoring could be a powerful way to assess biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices (i.e., a mathematical summary of acoustic energy) offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examined the relationship between acoustic indices and the diversity and abundance of biological sounds in recordings. We reviewed the acoustic‐index literature and found that over 60 indices have been applied to a range of objectives with varying success. We used 36 of the most indicative indices to develop a predictive model of the diversity of animal sounds in recordings. Acoustic data were collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental United States. For terrestrial recordings, random‐forest models with a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R² ≥ 0.94, mean squared error [MSE] ≤170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively affected by insect, weather, and anthropogenic sounds. For marine recordings, random‐forest models poorly predicted Shannon diversity, richness, and total number of biological sounds (R² ≤ 0.40, MSE ≥ 195). Our results suggest that using a combination of relevant acoustic indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats.