壬基酚对鲫鱼(Carassius auratus)的雌激素效应研究

实验研究了环境雌激素4-壬基酚(NP)对鲫鱼(Carassiusauratus)的剂量-效应关系。雄性鲫鱼腹腔注射不同剂量的17β-雌二醇(E2)(质量分数分别为0 01,0 10,1 00mg kg(注射剂量 鱼体重,下同))和4-壬基酚(NP)(质量分数分别为1,10,50,100,200mg kg),用ELISA方法测定鲫鱼血浆中卵黄蛋白原含量。结果显示,与对照相比,在注射后14d内,0 01mg kgE2处理组和1 00mg kgNP处理组鲫鱼血浆卵黄蛋白原含量没有显著差异;其他各处理组在注射后8d,血浆卵黄蛋白原含量显著升高(P<0 05),且与暴露浓度呈依赖关系;注射后14d,10,50,100和200mg kgNP处理组的含量依次是对照组的10,59,340和435倍。研究表明,NP对鲫鱼具有明显的雌激素效应。      

污水处理厂中微塑料的去除效能与全流程分析——以北京某下沉式三级污水处理厂为例

污水处理厂排放是微塑料进入自然环境的重要途径之一,本研究对北京某下沉式三级污水处理厂八个工艺单元中微塑料的赋存特征和去除效能进行全流程分析.研究表明该厂赋存微塑料主要形态为纤维,主要成分为聚丙烯、聚酯和聚乙烯,尺寸≤500μm的微塑料影响最为显著.该厂对微塑料的整体去除率为91.7％,主要依靠沉淀和截留过滤去除.双层平...     

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 (R²), 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.     

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.     

Screening of agonistic activities against four nuclear receptors in wastewater treatment plants in Japan using a yeast two-hybrid assay

To assess the potential endocrine disruptive effects through multiple nuclear receptors(NRs),especially non-steroidal NRs,in municipal wastewater,we examined the agonistic activities on four NRs(estrogen receptor α,thyroid hormone receptor α,retinoic acid receptor α and retinoid X receptor α) of untreated and treated wastewater from municipal wastewater treatment plants(WWTPs) in Japan using a yeast two-hybrid assay.Investigation of the influent and effluent of seven WWTPs revealed that agonistic activities...     

城市污水处理厂各工艺阶段内分泌干扰物活性变化规律研究

使用固相萃取-酵母双杂交法研究了东北地区某污水厂内分泌干扰活性随季节的变化及不同处理单元的去除情况.结果表明,该法的EC50值为5.95×10-11mol·L-1,具有较高灵敏度.对污水水样检测得出,原污水及一沉池出水内分泌干扰活性雌二醇当量值分别为16.13～21.65和17.10～22.85ng·L-1,均以夏季最高、冬季最低;经过A/O工艺处理,二沉池出水的内分泌干扰活性降至3.37～6.76ng·L-1,以冬春较高、夏秋较低;整个污水处理工艺对内分泌干扰活性的去除率为58.1%～84.2%,平均为75.2%,也以夏季最高、冬季最低.研究还发现.二级生物处理工艺对内分泌干扰活性起主导去除作用,而一沉池对内分泌干扰物的去除能力非常有限,甚至较进水有升高现象发生.     

Assessing the anti-estrogenic activity of sodium pentachlorophenol in primary cultures of juvenile goldfish (Carassius auratus) hepatocytes using vitellogenin as a biomarker

Both pentachlorophenol and 2,3,7,8-tetrachlorodibenzo-p-dioxin （TCDD） had been studied widely because of their probable anti-estrogenic activity. Sodium pentachlorophenol （PCP-Na）, as a industrial product used in many fields, usually contains a trace of TCDD. The aim of this study was to assess the anti-estrogenic effect of PCP-Na in juvenile goldfish （Gurassius auratus） hepatocyte cultures using vitellogenin （VTG） as the biomarker. The ID50 of PCP-Na was investigated and then a series of concentrations （0.001 0.5 μg/ml） of PCP-Na were evaluated to estimate the anti-estrogenic activity. Results showed that PCP-Na was cytotoxic for hepatocytes even at very low concentration 〈1.21 μg/ml, and it could not induce VTG at any concentrations tested. Since it failed to stimulate VTG production, the possibility of its anti-estrogenic effect was tested, and a well-known anti-estrogenic compound-tamoxifen was used as positive control. PCP-Na caused a reduction in VTG synthesis in juvenile goldfish （Carassius auratus） hepatocytes at concentrations 〉0.1μg/ml when co-exposure with 1μg/ml 17β-estradiol （E2）, making its anti-estrogenic activity approximately as potent as tamoxifen. Our results indicate that PCP-Na can act as negative modulators of estrogenic function in juvenile goldfish （Carassius auratus） hepatocytes.     

污水处理厂污泥的综合利用

利用普通工业锅炉将消化污泥泥饼与煤制成混合燃料进行焚烧处理 ,提供了混合燃料组份分析及实际运行试验情况 ,得出了切实合理可行的泥煤最佳配比 ,并就产生的社会经济效益和环境效益进行了评估。     

厌氧-缺氧-好氧处理出水中溶解性有机物组分的特征分析

本研究利用XAD-8大孔树脂对某城市污水处理厂的厌氧-缺氧-好氧(A2O)工艺出水中的溶解性有机物进行了组分分离,分析了各组分的溶解性有机碳(Dissolved organic carbon,DOC)、紫外-可见光谱和遗传毒性.结果表明,污水中亲水性物质和疏水酸性物质的DOC含量分别占总DOC含量的65%和24%.亲水性物质组分中的芳香族化合物含量显著高于其它组分,但是单位质量的亲水性物质、疏水酸性物质和疏水碱性物质中的芳香族化合物含量相近.污水具有一定的遗传毒性,主要遗传毒性物质为亲水性物质和疏水酸性物质,而疏水碱性物质和疏水中性物质则未检出遗传毒性.     

含酚废水处理系统中苯酚降解菌的分离及分离物多样性分析

用富集培养和直接涂平板培养的方法,从含酚废水处理系统的悬浮污泥中分别分离了57株和55株苯酚降解菌.112株分离物经ERIC-PCR指纹图谱分析,共显示了15种差异明显的ERIC-PER指纹图谱,表明至少应有15种不同的菌株.其中直接涂平板法得到的分离物占有11种,富集培养法得到的分离物占4种.将直接涂平板得到的分离物,与来自不同种属苯酚降解菌的LmPH(the largest subunit of multi-component phenol hydroxylase)等位基因分子进行杂交显示,该11种分离物可分为3种以上不同的LmPH代谢类型;而富集培养获得的分离物巾只显示1种LmPH代谢类型.对显示不同ERIC-PCR指纹图的15个代表荫株的生长和苯酚降解效率进行研究发现,其中F-6菌株在苯酚浓度0.4～1.4g·L-1范围内,其苯酚降解效率达到了70%～100%.     

污水处理厂二级出水中四环素抗性菌的生长特性与耐药性

抗生素抗性菌的扩散已成为全世界面临的重要公共卫生安全隐患.本文从某城市污水处理厂二级出水中分离出21株四环素抗性菌,对其种属、生理生化特性、抗生素耐受性进行了研究.结果表明,21株四环素抗性菌均属于肠杆菌科,分别属于气单胞菌属(9株)、埃希氏菌属(5株)、肠杆菌属(3株)、克雷伯氏菌属(2株)、柠檬酸杆菌属(1株)和哈夫尼菌属(1株).这些菌大多属于条件致病菌,且有18株携带质粒.21株四环素抗性菌中,气单胞菌、埃希氏菌的平均最大生长量显著高于肠杆菌,大部分菌株的迟滞时间在2 h以内.抗生素耐受性试验结果表明,四环素抗性菌对氯霉素耐受能力最强,对β-内酰胺酶类抗生素(青霉素、氨苄青霉素、头孢噻吩)的耐受性次之,对利福平的耐受性最弱.75%以上的四环素抗性菌同时表现出对其它5种抗生素具有抗性.以上结果为我国污水处理厂抗生素抗性菌的风险分析与控制提供了参考数据.     

Removal of multicomponent VOCs in off-gases from an oil refining wastewater treatment plant by a compost-based biofilter system

Waste gases from oil refining wastewater treatment plants are often characterized by the presence of multicomponent and various concentrations of compounds. An evaluation of the performance and feasibility of removing multicomponent volatile organic compounds (VOCs) in off-gases from oil refining wastewater treatment plants was conducted in a pilot-scale compost-based biofilter system. This system consists of two identical biofilters packed with compost and polyethylene (PE). This paper investigates the effects of various concentrations of nonmethane hydrocarbon (NMHC) and empty bed residence time (EBRT) on the removal efficiency of NMHC. Based on the experimental results and practical applications, an EBRT of 66 s was applied to the biofilter system. The removal efficiencies of NMHC were within the range of 47%–100%. At an EBRT of 66 s, the average removal efficiency of benzene, toluene, and xylene were more than 99%, 99%, and 100%, respectively. The results demonstrated that multicomponent VOCs in off-gases from the oil refining wastewater treatment plant could be successfully removed in the biofilter system, which may provide useful information concerning the design criteria and operation of full-scale biofilters.     

An innovative approach to minimize excess sludge production in sewage treatment using integrated bioreactors

The present investigation deals with an application of integrated sequential oxic and anoxic bioreactor(SOABR) and fluidized immobilized cell carbon oxidation(FICCO) reactor for the treatment of domestic wastewater with minimum sludge generation. The performance of integrated SOABR-FICCO system was evaluated on treating the domestic wastewater at hydraulic retention time(HRT) of 3 hr and 6 hr for 120 days at organic loading rate(OLR)of 191 ± 31 mg/(L·hr). The influent wastewater was characterized by chemical oxygen demand(COD) 573 ± 93 mg/L; biochemical oxygen demand(BOD5) 197 ± 35 mg/L and total suspended solids(TSS) 450 ± 136 mg/L. The integrated SOABR-FICCO reactors have established a significant removal of COD by 94% ± 1%, BOD5 by 95% ± 0.6% and TSS by 95% ± 4% with treated domestic wastewater characteristics COD 33 ± 5 mg/L; BOD59 ± 0.8 mg/L and TSS 17 ± 9 mg/L under continuous mode of operation for 120 days. The mass of dry sludge generated from SOABR-FICCO system was 22.9 g/m~3. The sludge volume index of sludge formed in the SOABR reactor was 32 mL/g and in FICCO reactor it was 46 mL/g. The sludge formed in SOABR and FICCO reactor was characterized by TGA, DSC and SEM analysis. Overall, the results demonstrated that the integrated SOABR-FICCO reactors substantially removed the pollution parameters from domestic wastewater with minimum sludge production.     

Systematic analysis of microfauna indicator values for treatment performance in a full-scale municipal wastewater treatment plant

The indicator values of microfauna functional groups and species for treatment performancewere systematically evaluated based on the continuous monitoring of the entire microfauna communities including both protozoa and metazoa over a period of 14 months, in two parallel full-scale municipalwastewater treatment systems in a plant in Beijing, China. A total of 57 species of ciliates, 14 species (units) of amoebae, 14 species (units) of flagellates and4 classes of small metazoawere identified,with Arcella hemisphaerica, Vorticella striata, Vorticella convallaria, Epistylis plicatilis and small flagellates (e.g. Bodo spp.) as thedominant protozoa, and rotifers as thedominant metazoa. The abundance of the sessile ciliateswas correlatedwith the removals of BOD 5 (Pearson’s r = 0.410, p < 0.05) and COD Cr (r = 0.397, p < 0.05)while the testate amoebaewas significantly positively related to nitrification (r = 0.523, p < 0.01). At the same time, some other associationswere also identified: the abundances of the large flagellates (r = 0.447, p < 0.01), the metazoa (r = 0.718, p < 0.01) and species Aspidisca sulcata (r = 0.337, p < 0.05)were positively related to nitrification; the abundance of Aspidisca costatawas correlated to the TN (total nitrogen) removal (r = -0.374, p < 0.05 ); the abundances of the sessile species Carchesium polypinum (r = 0.458, p < 0.01) and E. plicatilis (r = 0.377, p < 0.05)were correlatedwith the removal of suspended solids.     

新型双介质气动雾化喷淋系统在城市污水厂恶臭气体处理中的应用

以城市污水厂格栅间为研究对象,在分析恶臭气体产生特性的基础上,研发出新型双介质气动雾化喷淋系统,并对系统运行参数进行了优化。将该系统应用于北京市某城市污水厂格栅间,考察了其对硫化氢和氨的去除效果。结果表明,该系统对污水厂格栅间的硫化氢和氨的去除率分别为90.54%~97.80%和93.75%~99.42%,取得了较好的除臭效果。该系统投资费用为95万元,运行费用为3.62万元/a,其中水费占运行费用的51.10%。新型双介质气动雾化喷淋系统较好适用于城市污水厂分散点源恶臭气体的原位处理。

     

滇池水质时间序列变化趋势识别及特征分析

应用STL(Seasonal-Trend Decomposition using LOESS)方法对1998—2010年滇池外海8个监测站点、10个水质指标的时间序列数据进行递归迭代,分解出低频率的趋势项、高频率的周期项及随机残差项.在此基础上计算稳态变化指数(RSI),对水质趋势的时、空变化特征进行识别和判定.结果表明,在分析的13年间,滇池外海8个监测站点各指标变化趋势总体一致;BOD5总体呈下降趋势,高锰酸盐指数(COD Mn)总体趋势显著上升,说明有机物可生化性下降;富营养化状态加剧,总氮(TN)总体趋势上升,并逐渐成为藻类的限制性营养盐,总磷(TP)浓度为劣V类,氨氮在氮元素形态中的主导地位正逐渐降低.研究结果表明,"十二五"期间滇池应重点控制氮磷、兼顾有机物污染.     

石化污水厂二级出水溶解性有机物分级解析研究

以某石化污水处理厂二级出水为研究对象,采用大孔吸附树脂将污水中溶解性有机物分离成亲水性物质、疏水酸性物质、疏水中性物质和疏水碱性物质4个组分,分析了各组分的有机物的组成特征、三维荧光光谱特征及红外光谱特征.结果表明,亲水性物质和疏水酸性物质分别占水样中溶解性有机碳的49%和29%,是该石化污水厂二级出水中的主要物质类别.亲水性物质中含有较多的生物源腐殖质类物质,疏水中性物质含有较多的芳香族蛋白质和溶解性微生物代谢产物类物质.4种组分的红外光谱图中600~1200cm-1波数的指纹区波峰数量最多,多为芳香类同分异构体化合物.     

南昌某城市污水处理厂进水浓度低成因分析研究

针对南昌某城市污水处理厂进水浓度低的现象,为明确造成污水处理厂进水浓度低的原因,通过对南昌市2018—2020年降雨特征对污水处理厂进水污染物浓度影响的统计分析,估算了降水对雨污合流制管网污水浓度的影响.考察了合流制管网4个污水提升泵站、污水处理厂的日处理水量,结合水量平衡三角法,核算地下水、河水、雨水的渗入.以及利用倒排查法对源居民小区内/外-污水提升泵站-污水处理厂的水质沿程采样分析,得出相关结论.研究结果表明：①2018—2019年上半年污水处理厂进水COD在降雨后2~5 h进水浓度明显降低;但2019年下半年—2020年这种相关性明显削弱.②污水处理厂日进水COD受泵站的日流量的影响,呈显著负相关.③水量三角平衡定量分析表明地下水、河水混入28%~38%,雨水混入11%~18%.④水质沿程的分析显示小区到某些泵站的浓度明显降低,雨天COD、氨氮浓度分别减少了69.78%、75.21%,晴天COD、氨氮浓度分别降低了66.78%、76.94%,表明相应的小区外-泵站段的市政管网是污水处理厂进水浓度降低的关键管段,亟需进一步重点排查和改造.⑤老旧小区排水浓度受天气的影响远比新小区大,雨天分别比晴天减少79.23%、67.87%和54.40%,可能发生了大量雨水混入.     

基于BP神经网络与马尔可夫链的污水处理厂脱氮效果模拟预测

在实际污水处理厂运行过程中,其最终出水水质会受多种因素影响制约,而基于生物反应机理的活性污泥数学模型(ASM)并未将这些生物反应以外的因素考虑在内,由此带来一些不足.对此,本文提出可通过基于数据挖掘技术的黑箱模型对污水厂处理效果进行模拟预测.结合具体实际分析,提出可将BP神经网络与马尔可夫链组合应用于污水处理脱氮效果预测中.首先,通过BP神经网络模型对北京某大型污水处理厂实际进出水数据和工艺参数进行粗略拟合;其次,利用马尔可夫链对拟合结果及误差进行状态划分以进一步提高预测精确度;最后,运用基于BP神经网络与马尔可夫链的组合模型预测分析了该厂的实际出水水质.试验结果表明,BP神经网络适用于污水处理脱氮过程的拟合计算,而通过与马尔可夫链组合,可以提高模拟预测的精度和可靠性.     

城市污水A~2/O处理系统好氧池N_2O和NO的释放特征及影响因素

以某污水处理厂好氧曝气池为研究对象,利用N_2O和NO在线检测仪,监测好氧池不同空间位置处N_2O和NO的释放量,考察了氮素浓度、溶解氧及p H值对N_2O和NO释放量的影响.该污水处理厂好氧池DO浓度在0.24~1.12mg/L之间,且大多处于0.6mg/L左右,较低的DO导致NO2--N浓度沿水流推流方向不断积累;相应的,N_2O和NO释放量沿水流推流方向不断升高,并于好氧池末段达到最高值.NO释放NO2--N量与浓度显著正相关,N_2O释放量与NO2--N浓度也有一定的相关性,但没有NO显著.基于空间检测数据估算获得该污水处理厂N_2O释放量占进水NH_4+-N比例为6.34%~8.83%,NO释放量占进水NH4+-N比例为0.033%~0.034%.