电化学氧化PFOA阳极材料筛选及其机制研究

全氟辛酸(PFOA)具有环境持久性及内分泌干扰性,传统的生物降解及高级氧化法对PFOA的去除效果微弱.本研究采用电化学氧化法降解PFOA,选取BDD(掺硼金刚石电极)、Pt、Ti、Ti/RuO2、Ti/RuO2-IrO2、Ti/In2O3、Ti/SnO2-Sb2O5-IrO2、Ti/SnO2-Sb2O5-RhO2、Ti/SnO2-Sb2O5、Ti/SnO2-Sb2O5-CeO2和Ti/SnO2-Sb2O5-Bi2O3这11种阳极材料为备选电极.采用线性扫描伏安法测试析氧电位(OEP),评价11种电极对PFOA的降解效果和脱氟效果,并采用超声-电化学协同方法间接证明PFOA分子在电极表面的直接电子转移是降解反应的第一步.Ti/SnO2-Sb2O5-Bi2O3、Ti/SnO2-Sb2O5-CeO2、Ti/SnO2-Sb2O5和BDD对PFOA有较好的电氧化效果,降解率分别是89.8%、89.8%、93.3%和98.0%.Pt、Ti/SnO2-Sb2O5-RhO2、Ti/SnO2-Sb2O5-IrO2和Ti/In2O3对PFOA的电氧化效果微弱,降解率分别是2.1%、2.3%、12.5%和3.1%.而Ti、Ti/RuO2和Ti/RuO2-IrO2对PFOA没有电氧化能力.PFOA分子经过电极表面的直接电化学氧化发生脱羧反应,后经过逐步脱掉CF2单元的循环生成短链的全氟羧酸类化合物C6F13COO-、C5F11COO-、C4F9COO-和C3F7COO-.     

NO在分子筛ZSM-5催化剂上催化氧化动力学研究

研究了常温下NO在疏水型高硅分子筛ZSM-5上的氧化反应. 结果发现,NO在分子筛ZSM-5表面氧化的同时,伴随着明显的吸附过程,待吸附饱和后释放出NO2.相比于活性炭,高硅分子筛ZSM-5上NO氧化受水汽影响较小,303K饱和湿气下,NO稳态转化率只比干气下降低6%.在排除内、外扩散影响的条件下,于等温积分反应器中研究了稳定阶段NO氧化的本征动力学,根据不同温度下X~W/FA0及NO分压数据,计算了反应速率,建立简化的动力学模型并获得了反应速率方程,结果表明其拟合复相关系数较高.     

TSP-PM10-PM2.5-2型中流量大气颗粒物采集系统的开发和应用

自行开发并研制了TSP-PM10-PM2.5-2型中流量TSP、PM10、PM2.5大气颗粒物采集系统,是目前中国唯一可以采集TSP、PM10、PM2.5样品并提供足够的样品量进行大气颗粒物化学成分分析的中流量大气颗粒物采集器.该系统精心设计和加工的限流孔可以保持完全固定的流量,保证切割粒径的稳定,减小采样的误差并方便操作.该系统已经成功地应用于20多个城市和地区大气颗粒物的监测和研究中,为研究大气颗粒物的污染状况和来源提供了有效的技术手段和支持.     

差压式BOD5测定法的评价研究

对BOD5的测压法测定进行了评价,探讨了该法对接种量的特殊要求;比较了含毒物水样不同稀释比BOD5测定的区别,从而验证了该测定法可用以表征毒性物质对微生物活性的影响程度.通过模拟含毒印染废水活性污泥的处理,指出其不同稀释比BOD5值可作为该处理法的设计和运行管理的重要参数.     

广州主要工业源排放的气态污染物扩散模拟研究

以广州省控工业污染源排放的气态污染物(SO₂、NO_x为主要研究对象,通过中尺度气象模式MM5与空气质量模式CALPUFF耦合,模拟11月典型气象条件下, SO₂和NO_x的扩散传输过程,研究其时空分布特征,并分析省控工业污染源排放对特定区域(主要针对2010年亚运场馆)空气质量的影响。结果表明,主要受典型风速的影响,SO₂和NO_x浓度具有明显的时空分布不均匀性。浓度高峰值主要出现在晚间至凌晨时段,而浓度低峰值主要出现在白天至中午时段。受污染源分布、排放高度和风向的影响,荔湾区和越秀区污染物浓度较高,且在广州西南部形成较明显的污染带;且这些省控污染源对南沙体育馆空气质量有较大影响。 研究结果对广州空气污染来源分析具有一定参考意义。     

Ti/Ru/SnO2+Sb2O5电极的制备及其对垃圾渗滤液的电催化氧化

通过实验探究了不同Ru掺杂量对 Ti/Ru/SnO2+Sb2O5电极电化学性能及表面结构的影响。结果表明：Sb含量10%,溶胶涂覆6层,煅烧温度600 ℃,煅烧时间1 h条件下,Ru掺杂量为10%的涂层电极的加速寿命最长,为50 min;该条件下制备的电极涂层表面金属氧化物 SnO2的特征峰强度较大,峰形窄;涂层表面光滑,无裂缝。同时用Ti/Ru/SnO2+Sb2O5电极处理垃圾渗滤液MBR出水,Ru掺杂为10%的电极对废水COD去除率最高,为93.33%,电解出水芳香化程度大幅降低,对腐殖质和芳香族化合物的去除效果良好。研究对Ti/Ru/SnO2+Sb2O5电极在实际工业生产中的应用具有指导意义。     

公交车燃用B5生物柴油的非常规污染物排放特性及对区域大气污染物减排的作用

为推行区域大气污染联合防治并推广使用B5生物柴油,基于重型底盘测功机,采用C-WTVC循环,对比研究满足国Ⅳ、国Ⅴ排放标准的柴油公交车分别燃用国Ⅴ柴油、京Ⅵ柴油和B5生物柴油时的非常规污染物排放特性。结果表明：相比京Ⅵ柴油在采用B5生物柴油后,在不同耐久里程下的所有样车整体上的1,3-C4H6排放因子平均降低20.53%;C6H6排放因子平均降低7.67%;C7H8排放因子平均降低11.22%;HCHO排放因子平均降低14.92%;SO2排放因子平均降低6.09%。实验证明城市公交车在燃用B5生物柴油后的非常规污染物排放均降低。城市公交车推广使用B5生物柴油可有效净化大气质量,对区域大气污染物减排有着重要意义。     

Synergistic effects of Cu species and acidity of Cu-ZSM-5 on catalytic performance for selective catalytic oxidation of n-butylamine

In this work, a series of Cu-ZSM-5 catalysts with different SiO₂/Al₂O₃ ratios(25, 50, 100 and₂00) were synthesized and investigated in n-butylamine catalytic degradation. The n-butylamine can be completely catalytic degradation at 350 °C over all Cu-ZSM-5 catalysts. Moreover, Cu-ZSM-5(25) exhibited the highest selectivity to N₂, exceeding 90% at 350 °C. These samples were investigated in detail by several characterizations to illuminate the ...     

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.     

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.