EDTA-2Na/Fe(Ⅱ)对自养生物体系下硝酸盐还原过程的影响

采用厌氧序批式生物膜反应器(ASBBR),以固定浓度的硝酸盐和硫酸亚铁为基质,按不同梯度条件添加EDTA-2Na,进行长时间的培养驯化,研究铁盐脱氮的启动过程,同时探究不同EDTA-2Na/Fe(Ⅱ)对铁自养反硝化过程以及硝酸盐异化还原为铵(DNRA)的影响.结果表明:经过65d的培养驯化,反应器成功稳定运行.当EDTA-2Na/Fe(Ⅱ)<1.50时,反应器只进行铁自养反硝化过程,NO₃^--N去除率最高仅为71.70%;当EDTA-2Na/Fe(Ⅱ)≥1.50时,反应器同时进行铁自养反硝化与DNRA过程,NO₃^--N去除率最高为99.70%.值得注意的是,在EDTA-2Na/Fe(Ⅱ)=1.50时,铁自养反硝化速率达到最大值1.63mg/(L·h)的同时,DNRA的产氨量也达到最大值9.75mg/L.Visual MINTEQ模拟结果表明:EDTA-2Na与Fe(Ⅱ)的摩尔比会影响进水中EDTA-2Na与Fe(Ⅱ)的存在形态,物质的量比越大,FeEDTA^2-度越高,Fe...     

天津市基于新标准的空气质量预报模型效果评估

利用2015年环境空气质量监测数据,对天津市OPAQ空气质量统计预报模型预测效果进行验证评估。结果表明,模型对天津市AQI和PM_(2.5)、PM_(10)、O_3、NO——2的预测结果与实测结果具有较好的趋势一致性,且预测时间越临近,拟合度越好,24 h预报的相关系数r全部达到0.8以上。对PM_(2.5)的预报性能明显优于PM_(10)、O_3和NO_2,PM_(2.5)平均值预测略呈正偏差,但重污染预测值偏低约15%;O_3和NO_2预测值呈明显负偏差,O_3峰值预测不足,NO_2预测值整体偏低,均以24 h预报趋势性最好,但负偏差最为突出。     

污染源排气自动监测氮氧化物的不确定度分析

使用烟气排放连续监测系统自动监测仪器测定污染源排气中氮氧化物,按照最新的《测量不确定度评定与表示》(JJF1059.1—2012)的要求,测量不确定度评定的要求,首次对其氮氧化物自动监测的不确定度进行分析,得出不确定度报告。     

Effect of heavy metals (Cu, Pb, and As) on the ultrastructure of Sargassum pallidum in Daya Bay, China

Concentrations of Cu, Pb, and As were determined in seawater, surface sediment, Sargassum pallidum collected from the Daya Bay, China. The influence of metal contamination on the marine alga was investigated at chemical and ultrastructural level. Mean concentrations of Cu (19.44 mg kg^?1) and Pb (33.99 mg kg^?1) were found to be high in sediment, whereas concentration of As (122.29 mg kg^?1) in S. pallidum was higher than that in water and sediment. The ultrastructure of S. pallidum cells was anomalous and aberrant. Energy-dispersive x-ray spectroscopic analysis revealed that the nanometal particles in the form of comparatively high-electron density substance diffused in the cell structures constituted by Cu, Pb, As, etc. There is a remarkable similarity or correspondence in the anomalous elements between the geochemistry and the botanic cell, and the heavy metals have potential hazardous effect on the ocean ecology system in Daya Bay.     

Characteristics and assessment of phthalate esters in urban dusts in Guangzhou city, China

Phthalate esters (PAEs) were examined in indoor and outdoor dust samples from the subtropical city of Guangzhou, China. The ∑(16)PAEs concentrations ranged from 121 to 3,223 μg g(-1) dust, with the median concentration of 840 μg g(-1) dust. Significantly higher concentrations of PAEs in dust samples were found in offices where electrical and electronic devices, carpet pads, and office furniture were widely used. Of the 16 PAEs, diisobutyl phthalate (DiBP), di-n-butyl phthalate (DnBP), and di(2-ethylhexyl) phthalate (DEHP) dominated the PAEs in indoor and outdoor dust samples, and accounted for >96.8% and >93.1% of the ∑(16)PAEs concentrations, respectively. The median daily inhalation exposure of ∑(16)PAEs were 3.53 and 0.247 μg kg(-1) body weight day(-1), and at the 95(th) percentile were 7.62 and 0.530 μg kg(-1) body weight day(-1), up on the measured concentrations and estimated dust ingestion rates, respectively, for toddles and adults. The ubiquitous distribution of PAEs as noted in this study suggests the need for detailed assessment of PAEs concentrations using more sites and to further investigate the factors influencing PAEs exposure in China.     

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl^?), calcium (Ca²⁺), total alkalinity, total hardness, nitrate nitrogen (NO₃-N), and ammonical nitrogen (NH₄-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO₃-N, NH₄-N, and Ca²⁺. Cl^? was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.     

重点流域水系沉积物中多环芳烃标准样品制备

环境标准样品是监测过程中重要的质量控制手段,中国受天然基体标准样品制备技术的制约,尚未拥有自己的沉积物中多环芳烃标准样品。为制备符合中国重点流域沉积物类型及多环芳烃浓度水平的沉积物标准样品,对中国6个重点流域的沉积物进行了采样,获得了制备多环芳烃标准样品的原料。分析常温及冷冻2种干燥方式对多环芳烃的影响,为制备大批量多环芳烃标准样品提供数据支持。对11个点位样品采用气相色谱质谱法测定16种优控多环芳烃化合物浓度,结果表明,样品均匀性良好,样品中多环芳烃检出率在99%以上,江河沉积物样品中多环芳烃浓度区间为664~2.91×103μg/kg,湖泊样品最高值达到1.25×105μg/kg,其主要污染源是化学燃料的燃烧。为中国制备水系沉积物中多环芳烃标准样品积累了技术基础。     

气相色谱-质谱法测定大气中98种挥发性有机物

通过色谱-质谱联用法(简称GC-MS),利用低温冷阱技术对大气中的VOCs进行浓缩富集,然后经过加热解吸分别至毛细管色谱柱和FID检测器及MS检测器,对大气中98种挥发性有机化合物(VOCs)进行分离、定性、定量测定。方法检出限为0.008×10-9~0.100×10-9(V/V);线性相关系数的平方值为0.992 7~1,相对标准偏差为4.0%~20.2%,总体标准偏差为0.154 2~0.952 1。