低温短时水热预处理混合及初沉污泥的高温厌氧消化特性研究

以低温短时(90℃、30 min)水热预处理后的混合及初沉污泥为研究对象,分别进行了高温((55±1)℃)厌氧消化连续性试验.研究了该预处理条件下这两类污泥在水力停留时间(HRT)为20 d时的产气量、有机物分解率及物料平衡等,探讨了低温短时水热预处理对提高污泥厌氧消化性能的效果.结果表明:TS约为35 g·L-1的预处理混合污泥与初沉污泥经高温厌氧消化后,投加单位VS的产气量分别为(343.00±9.86)m L·g-1和(365.00±7.61)m L·g-1,VS去除率分别为38.9%和45.8%.COD物料平衡计算结果表明,混合和初沉污泥中分别有33.6%和43.9%的固体有机物被分解转化,生物气中CH4含量均在70%左右.     

污泥龄对低氧丝状菌活性污泥微膨胀系统的影响

为了研究污泥龄(SRT)对低氧丝状菌活性污泥微膨胀系统的影响,采用序批式间歇反应器(SBR)进行试验,分别按照厌氧/好氧和单级好氧的方式运行,考察了不同SRT下丝状菌污泥微膨胀系统的沉降性、脱氮除磷过程以及污泥特性的变化.结果表明,在好氧水力停留时间充分的条件下,低氧环境不但不会影响丝状菌微膨胀污泥的硝化进程,而且还有助于同步硝化反硝化(SND)、单级好氧除磷的发生.厌氧/好氧运行时,SRT与活性污泥的比硝化速率、比释磷速率和比吸磷速率成反比,与SND率和污泥的含磷量成正比.单级好氧运行时,减小SRT对硝化过程影响不大,但是有助于改善除磷效果.活性污泥的比耗氧速率(SOUR)、胞外聚合物(EPS)中多糖与蛋白质含量的比值、以及粘度都与SRT成反比.适当地减小SRT可以改善丝状菌微膨胀污泥的沉降性.厌氧/好氧运行时,厌氧段微氧环境易引发过度丝状菌污泥膨胀;单级好氧运行时,SRT过低会造成污泥黏性骤增而引发黏性污泥膨胀.     

土壤及铁锰结核中锰氧化菌的氧化特性分析

利用从土壤铁锰结核及其附近土壤中分离筛选得到的4株锰氧化菌,研究了不同pH、Mn(Ⅱ)初始浓度下菌株的锰氧化效率及生长情况,并用SEM-EDS及TEM对菌株WHS26、GY16形成的生物氧化锰及水羟锰矿进行了表征.结果表明,pH及Mn(Ⅱ)初始浓度对菌株的锰氧化效率均有影响,4株菌在pH 7~8、Mn(Ⅱ)初始浓度为10~20 mmol·L-1时,锰氧化效率最高;GY16、WHS26形成的生物氧化锰的形貌与化学合成的水羟锰矿存在明显差异,前者呈胶膜状附着在菌株表面,后者呈结晶态.该结果可为生物氧化锰应用于重金属污染修复提供技术支撑.     

四氯丙烯对稀有鮈鲫的急性毒性及安全浓度评价

采用半静态试验方式测试了四氯丙烯对稀有鮈鲫(Gobiocypris rarus)的急性毒性,试验浓度分别设定为0.25 mg·L~(-1)、0.30mg·L~(-1)、0.36 mg·L~(-1)、0.42 mg·L~(-1)、0.50 mg·L~(-1)和0.60 mg·L~(-1),同时进行空白对照(试验用水)和分散剂对照(体积分数为0.006%的吐温-80水溶液)试验。采用气相色谱法对试验溶液进行浓度分析,实测浓度分别为0.19 mg·L~(-1)、0.25 mg·L~(-1)、0.34 mg·L~(-1)、0.40 mg·L~(-1)、0.51 mg·L~(-1)和0.64 mg·L~(-1),试验结果以实测浓度表示和统计。结果表明:四氯丙烯致稀有鮈鲫无死亡发生(EC0)的最高浓度为0.25 mg·L~(-1),导致稀有鮈鲫100%死亡(EC100)的最低浓度为0.51 mg·L~(-1)。根据实测浓度,用SPSS(17.0)概率单位法求得四氯丙烯对稀有鮈鲫的半数致死浓度96 h-LC50为0.347 mg·L~(-1),95%置信区间为0.328 mg·L~(-1)~0.361 mg·L~(-1)。     

初始pH对厌氧环境下污泥中抗生素抗性基因行为特征的影响

污水处理厂产生大量的剩余污泥中含有丰富的抗性基因,给环境带来了潜在风险。以城市污水处理厂的剩余污泥为研究对象,在不同初始p H(对照组、初始p H=3、5、7、9、11)下观察厌氧条件下,8种抗生素浓度以及四环素类抗性基因(tet A、tet G、tet L、tet M、tet O、tet Q、tet W、tet X)、磺胺类抗性基因(sul I、sul II)和Ⅰ类整合子(int I 1)的行为特征。研究结果显示,初始p H对抗生素的降解影响较小,污泥中总抗生素的平均去除率为42%。对照组及初始p H为3、5、7、9、11下的总四环素类抗性基因分别削减0.65 log、0.96 log、0.75 log、0.62 log、0.86 log和0.98 log。不同四环素类抗性基因表现相似,在初始p H=3和初始p H=11下部分抗性基因削减较多,特别是tet A、tet G、tet L、tet O和tet X。2种磺胺类抗性基因均无削减,浓度平均上升0.18log。相关性分析显示,总抗性基因与TN、NH3-N、TP、SCOD(溶解性COD)均存在显著相关性(P0.05)。上述研究结果为污泥厌氧消化中抗生素抗性基因减量条件提供参考依据。     

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。     

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.     

Predicted no-effect concentrations for mercury species and ecological risk assessment for mercury pollution in aquatic environment

Mercury(Hg) exists in different chemical forms presenting varied toxic potentials. It is necessary to explore an ecological risk assessment method for different mercury species in aquatic environment. The predicted no-effect concentrations(PNECs) for Hg(Ⅱ) and methyl mercury(Me Hg) in the aqueous phase, calculated using the species sensitivity distribution method and the assessment factor method, were 0.39 and 6.5 × 10-3μg/L, respectively. The partition theory of Hg between sediment and aqueous phases was considered, along with PNECs for the aqueous phase to conduct an ecological risk assessment for Hg in the sediment phase. Two case studies, one in China and one in the Western Black Sea, were conducted using these PNECs. The toxicity of mercury is heavily dependent on their forms,and their potential ecological risk should be respectively evaluated on the basis of mercury species.     

Ozone concentrations, flux and potential effect on yield during wheat growth in the Northwest-Shandong Plain of China

Ozone (O₃) concentration and flux (F_o) were measured using the eddy covariance technique over a wheat field in the Northwest-Shandong Plain of China. The O₃-induced wheat yield loss was estimated by utilizing O₃ exposure-response models. The results showed that: (1) During the growing season (7 March to 7 June, 2012), the minimum (16.1 ppbV) and maximum (53.3 ppbV) mean O₃ concentrations occurred at approximately 6:30 and 16:00, respectively. The mean and maximum of all measured O₃ concentrations were 31.3 and 128.4 ppbV, respectively. The variation of O₃ concentration was mainly affected by solar radiation and temperature. (2) The mean diurnal variation of deposition velocity (V_d) can be divided into four phases, and the maximum occurred at noon (12:00). Averaged V_d during daytime (6:00–18:00) and nighttime (18:00–6:00) were 0.42 and 0.14 cm/sec, respectively. The maximum of measured V_d was about 1.5 cm/sec. The magnitude of V_d was influenced by the wheat growing stage, and its variation was significantly correlated with both global radiation and friction velocity. (3) The maximum mean F_o appeared at 14:00, and the maximum measured F_o was − 33.5 nmol/(m²·sec). Averaged F_o during daytime and nighttime were − 6.9 and − 1.5 nmol/(m²·sec), respectively. (4) Using O₃ exposure-response functions obtained from the USA, Europe, and China, the O₃-induced wheat yield reduction in the district was estimated as 12.9% on average (5.5%–23.3%). Large uncertainties were related to the statistical methods and environmental conditions involved in deriving the exposure-response functions.     

Effect of AlCl3 concentration on nanoparticle removal by coagulation

In recent years, engineered nanoparticles, as a new group of contaminants emerging in natural water, have been given more attention. In order to understand the behavior of nanoparticles in the conventional water treatment process, three kinds of nanoparticle suspensions, namely multi-walled carbon nanotube-humic acid (MWCNT-HA), multi-walled carbon nanotube-N,N-dimethylformamide (MWCNT-DMF) and nanoTiO₂-humic acid (TiO₂-HA) were employed to investigate their coagulation removal efficiencies with varying aluminum chloride (AlCl₃) concentrations. Results showed that nanoparticle removal rate curves had a reverse “U” shape with increasing concentration of aluminum ion (Al^3 +). More than 90% of nanoparticles could be effectively removed by an appropriate Al^3 + concentration. At higher Al^3 + concentration, nanoparticles would be restabilized. The hydrodynamic particle size of nanoparticles was found to be the crucial factor influencing the effective concentration range (ECR) of Al^3 + for nanoparticle removal. The ECR of Al^3 + followed the order MWCNT-DMF > MWCNT-HA > TiO₂-HA, which is the reverse of the nanoparticle size trend. At a given concentration, smaller nanoparticles carry more surface charges, and thus consume more coagulants for neutralization. Therefore, over-saturation occurred at relatively higher Al^3 + concentration and a wider ECR was obtained. The ECR became broader with increasing pH because of the smaller hydrodynamic particle size of nanoparticles at higher pH values. A high ionic strength of NaCl can also widen the ECR due to its strong potential to compress the electric double layer. It was concluded that it is important to adjust the dose of Al^3 + in the ECR for nanoparticle removal in water treatment.