黄姜皂素废酸液在均相离子膜中渗析规律的研究

为了研究皂素水解废液在离子交换膜中的渗析规律,实验考察了废液中电导率、含盐量、氢离子浓度、硫酸盐浓度、Ca离子浓度、Mg离子浓度和有机物浓度随渗析时间的关系。研究结果表明:渗析时间进行100 h后,处理效果达到稳定状态;废液与渗析液的电导率几乎相等;含盐量、H+浓度、SO2-4浓度的分离效率分别为71%、62%、78%;Ca去除效果优于Mg去除效果;大部分有机物无法通过离子交换膜。     

Applying a new method for direct collection, volume quantification and determination of N2 emission from water

The emission of N₂ is important to remove excess N from lakes, ponds, and wetlands. To investigate the gas emission from water, Gao et al. (2013) developed a new method using a bubble trap device to collect gas samples from waters. However, the determination accuracy of sampling volume and gas component concentration was still debatable. In this study, the method was optimized for in situ sampling, accurate volume measurement and direct injection to a gas chromatograph for the analysis of N₂ and other gases. By the optimized new method, the recovery rate for N₂ was 100.28% on average; the mean coefficient of determination (R²) was 0.9997; the limit of detection was 0.02%. We further assessed the effects of the new method, bottle full of water, vs. vacuum bag and vacuum vial methods, on variations of N₂ concentration as influenced by sample storage times of 1, 2, 3, 5, and 7 days at constant temperature of 15°C, using indices of averaged relative peak area (%) in comparison with the averaged relative peak area of each method at 0 day. The indices of the bottle full of water method were the lowest (99.5%-108.5%) compared to the indices of vacuum bag and vacuum vial methods (119%-217%). Meanwhile, the gas chromatograph determination of other gas components (O₂, CH₄, and N₂O) was also accurate. The new method was an alternative way to investigate N₂ released from various kinds of aquatic ecosystems.     

Pollution levels and characteristics of phthalate esters in indoor air of offices

The pollution status and characteristics of PAEs (phthalate esters) were investigated in indoor air of offices, and PAEs of both gas-phase and particulate-phase were detected in all the samples. The concentration (sum of the gas phase and the particulate phase) was 4748.24 ng/m³, ranging between 3070.09 and 6700.14 ng/m³. Diethyl phthalate, dibutyl phthalate, and di(2-ethylhexyl) phthalate were the most abundant compounds, together accounting for 70% of the Σ6PAEs. Dividing the particulate-phase PAEs into four size ranges (<2.5, 2.5-5, 5-10, >10 μm), the result indicated that PAEs in PM_2.5 were the most abundant, with the proportion of 72.64%. In addition, the PAE concentration in PM_2.5 correlated significantly with the total particulate-phase PAEs (R² = 0.85). Thus, the amount of PAEs in PM_2.5 can be estimated from the total amount of particulate-phase PAEs using this proportion. In a comparison between the offices and a newly decorated study room, it was found that pollution characteristics were similar between these two places. Thus, it is implied that the PAE concentration decreased by 50% 2 yr after decorating.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Composition and source apportionment of dust fall around a natural lake

The aim of this study was to determine the source apportionment of dust fall around Lake Chini, Malaysia. Samples were collected monthly between December 2012 and March2013 at seven sampling stations located around Lake Chini. The samples were filtered to separate the dissolved and undissolved solids. The ionic compositions(NO-3, SO2-4, Cl-and NH+4) were determined using ion chromatography(IC) while major elements(K, Na, Ca and Mg) and trace metals(Zn, Fe, Al, Ni, Mn, Cr, Pb and Cd) were determined using inductively coupled plasma mass spectrometry(ICP-MS). The results showed that the average concentration of total solids around Lake Chini was 93.49 ± 16.16 mg/(m2·day). SO2-4, Na and Zn dominated the dissolved portion of the dust fall. The enrichment factors(EF) revealed that the source of the trace metals and major elements in the rain water was anthropogenic, except for Fe. Hierarchical agglomerative cluster analysis(HACA) classified the seven monitoring stations and 16 variables into five groups and three groups respectively. A coupled receptor model, principal component analysis multiple linear regression(PCA-MLR), revealed that the sources of dust fall in Lake Chini were dominated by agricultural and biomass burning(42%),followed by the earth's crust(28%), sea spray(16%) and a mixture of soil dust and vehicle emissions(14%).     

Mercury adsorption characteristics of HBr-modified fly ash in an entrained-flow reactor

In this study, the mercury adsorption characteristics of HBr-modified fly ash in an entrained-flow reactor were investigated through thermal decomposition methods. The results show that the mercury adsorption performance of the HBr-modified fly ash was enhanced significantly. The mercury species adsorbed by unmodified fly ash were HgCl₂, HgS and HgO. The mercury adsorbed by HBr-modified fly ash, in the entrained-flow reactor, existed in two forms, HgBr₂ and HgO, and the HBr was the dominant factor promoting oxidation of elemental mercury in the entrained-flow reactor. In the current study, the concentration of HgBr₂ and HgO in ash from the fine ash vessel was 4.6 times greater than for ash from the coarse ash vessel. The fine ash had better mercury adsorption performance than coarse ash, which is most likely due to the higher specific surface area and longer residence time.     

Pollution levels and characteristics of phthalate esters in indoor air of offices

The pollution status and characteristics of PAEs(phthalate esters) were investigated in indoor air of offices, and PAEs of both gas-phase and particulate-phase were detected in all the samples. The concentration(sum of the gas phase and the particulate phase) was4748.24 ng/m3, ranging between 3070.09 and 6700.14 ng/m3. Diethyl phthalate, dibutyl phthalate, and di(2-ethylhexyl) phthalate were the most abundant compounds, together accounting for 70% of the ∑ 6PAEs. Dividing the particulate-phase PAEs into four size ranges( 2.5, 2.5–5, 5–10, 10 μm), the result indicated that PAEs in PM2.5were the most abundant,with the proportion of 72.64%. In addition, the PAE concentration in PM2.5correlated significantly with the total particulate-phase PAEs(R2= 0.85). Thus, the amount of PAEs in PM2.5can be estimated from the total amount of particulate-phase PAEs using this proportion. In a comparison between the offices and a newly decorated study room, it was found that pollution characteristics were similar between these two places. Thus, it is implied that the PAE concentration decreased by 50% 2 yr after decorating.     

Preparation of steel slag porous sound-absorbing material using coal powder as pore former

The aim of the study was to prepare a porous sound-absorbing material using steel slag and fly ash as the main raw material, with coal powder and sodium silicate used as a pore former and binder respectively. The influence of the experimental conditions such as the ratio of fly ash, sintering temperature, sintering time, and porosity regulation on the performance of the porous sound-absorbing material was investigated. The results showed that the specimens prepared by this method had high sound absorption performance and good mechanical properties, and the noise reduction coefficient and compressive strength could reach 0.50 and 6.5 MPa, respectively. The compressive strength increased when the dosage of fly ash and sintering temperature were raised. The noise reduction coefficient decreased with increasing ratio of fly ash and reducing pore former, and first increased and then decreased with the increase of sintering temperature and time. The optimum preparation conditions for the porous sound-absorbing material were a proportion of fly ash of 50% (wt.%), percentage of coal powder of 30% (wt.%), sintering temperature of 1130°C, and sintering time of 6.0 hr, which were determined by analyzing the properties of the sound-absorbing material.     

Pollution levels and characteristics of phthalate esters in indoor air in hospitals

The concentrations of phthalate esters(PAEs) in Chinese hospitals were investigated by simultaneously determining concentrations of gas- and particle-phase PAEs. PAEs were detected in two third-class first-grade hospitals, two second-class first-grade hospitals, and a community health service center. Hospital drugstores had the highest concentration(24.19 μg/m3), which was 1.54 times that of newly decorated houses. The second highest concentration was found in the transfusion rooms, averaging 21.89 μg/m3; this was followed by the concentrations of PAEs in the nurse's workstations, the wards, and the doctor's offices, with mean concentrations of 20.66, 20.0, and 16.92 μg/m3, respectively. The lowest concentrations were found in the hallways(16.30 μg/m3). Of the six different kinds of PAEs found, major pollutants included diethyl phthalates, dibutyl phthalates, butylbenzyl phthalates and di(2-ethylhexyl) phthalates, comprising more than 80% of all PAEs present.Meanwhile, a comparison between different wards showed that PAE concentrations in the maternity wards were 1.63 times higher than in the main wards. Based on known health hazards, our results suggest that the PAEs seriously influence the health of the pregnant women and babies; therefore, it is of great importance to take the phthalate concentrations in hospitals into consideration. In addition, hospital indoor air was more seriously contaminated than the air of newly decorated houses.