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161.
Lan Q Cui K Zeng F Zhu F Liu H Chen H Ma Y Wen J Luan T Sun G Zeng Z 《Environmental monitoring and assessment》2012,184(8):4921-4929
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. 相似文献
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Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China 总被引:2,自引:0,他引:2
Xiaohu Wen Jing Fang Meina Diao Chuanqi Zhang 《Environmental monitoring and assessment》2013,185(5):4361-4371
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. 相似文献
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高效液相色谱-电感耦合等离子体质谱法测水中不同形态砷 总被引:1,自引:1,他引:0
采用高效液相色谱(HPLC)分离水中两种常见的砷形态As(III)、As(V),电感耦合等离子体质谱系统进行检测鉴定,利用C8色谱柱,探讨了甲醇含量、磷酸二氢钾浓度、四丁基氢氧化铵浓度、p H等测试条件,由此建立了水中砷形态的分析方法。结果表明,以1.5 mmol/L磷酸二氢钾、2 mmol/L四丁基氢氧化铵(TBAOH)、5%甲醇作为流动相,调节流动相为p H 5.5,流速为1.4 m L/min,上述两种不同形态的砷可在5.5 min内得以有效分离,As(III)、As(V)检出限分别为0.001、0.01μg/L,定量下限分别为0.005、0.03μg/L。该方法实现了对水中常见的不同形态砷(As(III)、As(V))的同时分析,具有灵敏度高、选择性好、检测速度快的特点,在水质分析领域具有重要意义与应用价值。 相似文献
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Mei-Ling Fang Ching-Yi Wu Ming-Shean Chou 《Journal of the Air & Waste Management Association (1995)》2018,68(7):755-762
Some metal etching operations emit limited flow rates of waste gases with reddish-brown NO2 fume, which may cause visual and acidic-odor complaints, as well as negative health effects. In this study, tests were performed by passing caustic-treated waste gases vented from Al-etching operations through columns packed either with virgin or regenerated granular activated carbon (GAC) to test their adsorptive conversion performance of NO2 in the gases. The gases contained 5–55 ppm NO2 and acetic and nitric acids of below 3 ppm. Exhausted carbon was regenerated by scrubbing it with caustic solution and water, and dried for further adsorption tests. Results indicate that with an (empty bed residence time (EBRT) of 0.15 sec for the gas through the GAC-packed space, around 60% of the influent NO2 of 54 ppm could be removed, and 47% of the removed NO2 was converted by and desorbed from the carbon as NO. GAC used in the present study could be regenerated at least twice to restore its capacity for NO2 adsorption. Within EBRTs of 0.076–0.18 sec, the adsorptive conversion capacity was linearly varied with EBRT. In practice, with an EBRT of 0.20 sec, a conversion capacity of 0.80 kg NO2 (kg GAC)?1 with an influent NO2 of 40 ppm can be used as a basis for system design.Implications: Some metal etching operations emit waste gases with reddish-brown (yellow when diluted) NO2 fume which may cause visual and acidic-odor complaints, as well as negative health effects. This study provides a simple process for the adsorptive conversion of NO2 in caustic-treated waste gases vented from metal-etching operations through a GAC column. With an EBRT of 0.20 sec, a conversion capacity of 0.80 kg NO2 (kg GAC)?1 with an influent NO2 of 40 ppm can be used as a basis for system design. Saturated GAC can be regenerated at least twice by simply scrubbing it with aqueous caustic solution. 相似文献