预测沉积物-水微宇宙系统中化学品浓度变化的多介质模型

沉积物-水微宇宙系统是经济合作发展组织(Organisation for Economic Co-Operation and Development,OECD)颁布的化学品测试准则中推荐的试验系统之一,可用来测试化学品对底栖生物的慢性毒性。为了在试验前对化学品的浓度变化进行预测,进而确定试验方法,以摇蚊慢性毒性试验系统为例,采用环境多介质模型的建模方法,构建了一种可通过化学品理化性质和试验系统参数,对化学品在沉积物-水试验系统中浓度变化进行预测的模型。结合试验数据和文献资料,给出了模型中试验系统参数的推荐取值,并使用Matlab软件中的Simulink工具对模型进行编程和求解。以此模型为基础,给出了模型在3个方面的应用,即预测蓄积时间、预测平衡时间以及拟合试验数据。对80种已有或假想化学品的蓄积时间和平衡时间进行了计算,得出的范围分别为1~204 d和1~73 d。此外,适当修改模型结构和模型参数,也可将其应用于其他暴露场景中。但使用模型对化学品浓度进行预测时发现,模型仅对沉积物中化学品浓度的预测结果较为准确,而对水中化学品浓度的预测结果与实测值相差1~2个数量级。模型对浓度的预测精度未来仍需进一步提高。上述研究结果完善了沉积物-水微宇宙系统试验方法。     

检测细胞DNA断裂损伤效应的彗星实验法的改良

为了解决彗星实验过程中常出现的脱胶、细胞核分离操作繁琐、重复性低等问题,对彗星实验方法进行了改良,初步建立了彗星实验的快速操作流程。结果显示,通过对载玻片进行预处理,可确保凝胶悬挂均匀;采用改良机械法分离的细胞核浓度适中;以0.5%(w/v)涂层琼脂糖作为基层、以1.5%(w/v)低熔点包埋琼脂糖作为叠加层的"双层凝胶法",辅以"推片法"铺胶,操作便捷且不发生脱胶现象;细胞核膜经裂解处理后再进行电泳和荧光观察,彗星图像清晰,杂质少。应用改良后的彗星实验方法,操作简便,耗时更短,实验效果良好,可快速检测出细胞DNA损伤效应。     

表观遗传学机制与空气污染的健康效应

空气污染是一个全球性的问题,并且具有深远的环境影响。暴露于空气污染会对人体健康产生许多不同的影响,理解空气污染的健康效应又是一个复杂命题,既要考虑不同类型的污染物同时也要考虑相关疾病的复杂性。然而越来越多的研究表明,表观遗传学在空气污染相关疾病的发生、发展中发挥着重要的作用。空气污染物可引起DNA甲基化、组蛋白修饰和miRNA表达等表观遗传学改变,这种改变往往发生在疾病产生的早期,因此相关研究不仅可以了解疾病的发病机制,而且还为疾病早期诊断和预防筛选可能的标志物。本文综述了表观遗传学的几种修饰方式和空气污染物造成不良健康损伤机制的一些研究进展。     

铜-磺胺嘧啶复合胁迫对蔬菜种子发芽的急性毒性效应

研究了黄潮土中常用兽药磺胺嘧啶(SD)与重金属铜(Cu)单一及复合污染对小白菜和西红柿种子发芽(发芽率、根伸长、芽伸长)的影响,分析了土壤中药物浓度与作物生长抑制的剂量-效应关系及复合污染的毒性效应。结果表明,无论是在Cu或SD单一污染物作用下,根伸长和芽伸长抑制率与污染物浓度显著相关(P0.01),发芽抑制率与污染物浓度不相关(P0.05);污染物对根伸长及芽伸长的抑制高于对种子发芽的抑制;SD对2种作物的毒性效应明显强于Cu,SD对小白菜和西红柿根伸长的ID50(抑制率为50%时污染物浓度)分别为1.7和33.6 mg·kg~(-1),而Cu为273.6和457.7 mg·kg~(-1)。Cu-SD复合污染,对西红柿的根和芽伸长的拮抗作用显著(P0.01);但对小白菜来说,在低浓度Cu(100 mg·kg~(-1))作用下,二者的联合拮抗作用不显著(P0.05),随Cu的增加,拮抗作用显著(P0.01)。     

电沉积处理含镉废水的性能研究

电沉积含镉废水考察溶液的不同初始p H、镉的不同初始浓度、电流密度及金属配合物对电沉积效果的影响,利用SEM和XRD对沉积物的表面形貌和物相成分进行分析,研究沉积物金属的存在形式。实验结果表明,p H在1.5~3.5时,溶液中镉的去除效果达到99.3%;一级动力学常数随着初始镉溶液浓度增加而变大;当电流密度为4.46 m A/cm2进行90 min电沉积时反应速率较快且沉积量最大;氨水与镉形成的配合物可以促进电沉积镉的反应速率;对电沉积后的沉积物进行扫描电镜(SEM)和X-射线衍射(XRD)测试。SEM表明,沉积物呈现树枝状的纳米结构。XRD表明,沉积物主要成分为氢氧化镉和镉。     

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%).