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Guor-Cheng Fang Yu-Chen Kuo Yuan-Jie Zhuang Yu-Cheng Chen 《Environmental monitoring and assessment》2014,186(7):4139-4151
This study characterized and discussed particulate ambient air particulate concentrations and seasonal variations for PM18, PM10, PM2.5, and PM1 during June 2013–July 2013 at this traffic sampling site. In addition, this study also characterized the ambient air particulates size distributions by using MOUDI-100S4 sampler to collect 1-day the ambient suspended particles (PM18, PM10, PM2.5, and PM1) at this sampling site. In addition, the study also showed that the main pollutants contributions were from traffic and residual areas. As for the pollutants seasonal concentrations variations, the results indicated that the average particle concentrations orders were all displayed as daytime?>?nighttime for PM18, PM10, PM2.5 and PM1 at this characteristic sampling site. The results further indicated that the mean highest of metal concentrations in this study indicated that the average metal concentration were all displayed as Mn?>?Cr?>?Ni?>?Pb?>?Cd for PM18, PM10, PM2.5 and PM1 on daytime and nighttime at this characteristic sampling site. 相似文献
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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