环境气体标准样品量值的计算及不确定度分析

依据《气体分析校准气体混合物的制备重量法》（1SO6142—2001）和《气体分析气体标准样品组成的测定和校验比较法》（ISO6143—2001）,阐述了环境气体标准样品不同定值方法的量值计算及不确定度来源,并比较了同一气体标准样品采用不同定值方法得到的不同量值结果及不确定度：     

Behaviour of selected endocrine-disrupting chemicals in three sewage treatment plants of Beijing, China

Occurrence and fate of eight kinds of selected endocrine-disrupting compounds (EDCs) in three sewage treatment plants (STPs) of Beijing, China was investigated. These EDCs, composed of 4-octylphenol (4-OP), 4-n-nonylphenol (4-n-NP), bisphenol A (BPA), estrone (E1), 17α-estradiol (17α-E2), 17β-estradiol (E2), estriol (E3) and 17α-ethinylestradiol (EE2), in every step of STPs, were simultaneously analysed by gas chromatography/mass spectrometry after derivatisation. All the EDCs were detected in the influents of three STPs, and BPA was the most abundant compound. The concentrations of EDCs ranged from 36.6 ng/l of 17α-E2 (STP C) to 1342.3 ng/l of BPA (STP B) in the influent sewages and from below limits of detection of E2 and E3 (STP C) to 142.5 ng/l of E1 (STP B) in the effluent sewages. The STPs could not remove alkylphenols effectively from the aqueous phase with less than 40% reduction. BPA decreased over 90%, and steroid estrogens achieved considerable reductions from 64.8% of E2 to 94.9% of E3. Generally, biological treatment was more effective in removing alkylphenols, BPA and natural estrogens from the aqueous phase than primary treatment. However, the synthetic estrogen, EE2, was mostly removed by the primary treatment with about 63.5% reduction. It is the first time that the concentration of 17α-E2 in the sewage of China was reported in this paper. The compound might have a bearing with the waste effluents of dairy farms around urban area of Beijing.     

Mussel isotope signature as indicator of nutrient pollution in a freshwater eutrophic lake: species, spatial, and seasonal variability

Stable nitrogen isotope ratios of five mussel species from littoral and pelagic areas were investigated with different trophic states in the eutrophic Lake Taihu, the third largest lake in China. Interpopulation variability for these mussels was relatively small in foot tissues because of the slow turnover time. Seasonal and spatial variations among the δ ¹⁵N values of mussels might be due in part to the natural variation in δ ¹⁵N values of potential food sources and the variation in the amount of human pollutions discharged into various locations of the lake. Although the increase of mussel δ ¹⁵N values was accompanied by the increase of nutrient concentrations in most situations in this study, statistically significant correlations were only 22% of the total correlations in this survey, which might be attributed to the different time-scale variations in nutrient concentrations and isotope signatures and the unknown details of the trophic pathways and metabolism for incorporation of these nutrients.     

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.     

趋势分析法评价苯系物标准气体的时间稳定性

根据GB/T 15000.3—2008设计了苯系物标准气体的时间稳定性研究方案,制备了4瓶苯系物标准气体,随着时间的推移进行测定并将结果制图,结果显示量值随时间没有显著的变化趋势。以线性关系为模型,计算线性直线的斜率和斜率的标准偏差,用t检验判断斜率是否显著来评价标准气体的稳定性。计算结果显示,所有斜率均不显著,因此该研究制备的苯系物标准气体能够稳定保存22个月。根据时间稳定性数据计算由不稳定性引起的不确定度,并讨论了这种计算不稳定性引起不确定度的方法对结果有放大的可能性。     

Characteristics of atmospheric carbonyls and VOCs in Forest Park in South China

The diurnal variation of atmospheric carbonyls and VOCs in a forest in south China were studied in summer 2004. Twenty kinds of carbonyls and eight kinds of VOCs were identified and quantified. Formaldehyde and acetaldehyde were the two most abundant carbonyls, while the most abundant VOCs were isoprene, followed by o-xylene. Most C₃-C₁₀ carbonyls had higher concentrations from 09:00 to 15:00, and their levels were lower during night-time and often reached the lowest in early morning. Formaldehyde and acetaldehyde, however, showed two high levels in their diurnal patterns partly due to their different sources and sinks. The VOCs had different diurnal patterns compared to most carbonyls. The highest concentrations were observed from 03:00 to 06:00 for 1-butene, from 06:00 to 12:00 for isoprene, and from 12:00 to 15:00 for α-pinene. The highest levels for aromatic hydrocarbons occurred during midnight and the lowest in late afternoon. According to the study, emissions from vegetation and photo-oxidation of gas-phase hydrocarbons were the main sources for some carbonyls and VOCs in this region. Other compounds, such as formaldehyde, acetaldehyde and BTEX, showed anthropogenic sources.     

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.     

Biosorption properties of Cd(II), Pb(II), and Cu(II) of extracellular polymeric substances (EPS) extracted from Aspergillus fumigatus and determined by polarographic method

Extracellular polymeric substances (EPS) were extracted from Aspergillus fumigatus using cationic exchange resin technique. The EPS were mainly composed of polysaccharide and low quantities of protein and nucleic acid. Biosorption of Cd(II), Pb(II), and Cu(II) of EPS was investigated as a function of pH using differential pulse polarography and the Ruzic model. Results showed that the EPS biosorption capacity determined using either the direct titration curves i?=?f(C _M) or the method proposed by Ruzic (Analytica Chimica Acta 140:99–113, 1982) were coincident. Cu(II) had the highest affinity with EPS followed by Pb(II) and Cd(II). The total number of binding sites for Cu(II) and Cd(II) increased with pH in the range of 4.0–7.0. Similar trend was observed for Pb(II) at pH?4.0–5.0, while precipitates were observed at pH?6.0 and 7.0. The conditional binding constants of these three metals displayed low levels of fluctuation with pH and ranged from 4.02?±?0.02 to 5.54?±?0.05.     

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。