水力停留时间对反应沉淀一体化反应器中半亚硝化反应的影响

由于反应沉淀一体化反应器的HRT与SRT不同,因此HRT是否会影响反应器中氮的存在状态,亚硝态氮积累是否能实现尚无明确结论。针对以上问题,研究不同水力停留时间对反应沉淀一体化反应器中半亚硝化反应的影响,研究结果表明：反应器运行虽然运行过程中无污泥流失,但仍可实现亚硝酸盐的积累,出水亚硝态氮和氨氮的浓度比例受水力停留时间的影响。HRT为24 h时,亚硝酸盐积累率可达到70%,但出水氨氮接近于0,很难满足ANAMMOX 的进水要求;HRT为16 h和12 h时,亚硝酸盐积累率均可超过80%,出水氨氮和亚硝态氮的比例分别达到1.39：1和1.46：1,可为后续ANAMMOX反应提供良好进水条件。水力停留时间对污泥亚硝化潜力的影响为12 h>16 h>24 h,对硝化潜力的影响为24 h>16 h>12 h。不同水力停留时间下氨氧化速率和亚硝酸盐氧化速率均为24 h>16 h>12 h。     

慢滤池去除污水中邻苯二甲酸二(2-乙基己基)酯的效能

将慢滤池用于污水二级出水的深度处理,并利用小试装置研究了慢滤池去除邻苯二甲酸二(2-乙基己基)酯(DEHP)的效能。慢滤池采用粒径为0.4～0.6 mm,厚度为800 mm的石英砂做滤料并以0.1 m/h的恒定滤速运行。实验结果表明,慢滤池出水浊度、COD和色度远低于现行《城市污水再生利用城市杂用水水质标准》(GB/T18920-2002)的相应数值。污水二级处理出水中DEHP浓度为6.1～62.8μg/L时,经过慢滤池过滤后,DEHP浓度降为1.7～7.3μg/L。当接纳282.8μg/L的DEHP冲击负荷时,慢滤池出水DEHP仍能低于10μg/L。实验证明,慢滤池内的生物膜吸附过滤和生物降解共同保证了慢滤池去除DEHP的良好性能。     

Solidification/stabilization of municipal solid waste incineration fly ash using uncalcined coal gangue–based alkali-activated cementitious materials

Environmental Science and Pollution Research - The proper disposal of municipal solid waste incineration fly ash (MSWI FA) is necessary due to the presence of hazardous metals (Cu2+, Zn2+, Pb2+ and...     

厌氧硫酸盐还原-氨氧化的研究

采用厌氧序批式反应器,在无机营养条件下培养历时354 d,成功实现了SO2-4和NH+4的同步生物去除。结果表明,提高进水的TN负荷有利于促进硫酸盐还原-氨氧化的发生,当进水TN负荷提高到120 mg/(L·d)时,对TN的平均去除速率和硫酸盐硫的平均去除速率达到了最大,分别为64.43 mg/(L·d)和44.82 mg/(L·d);在同步生物脱氮除硫前期生成了大量的NO-3-N,平均浓度为53.88 mg/L,远大于由Anammox反应生成的量,推测部分NO-3是直接由NH+4和SO2-4发生氧化还原反应生成。该体系中存在单质硫的自养反硝化,可以解释反应后期硫酸盐重生成的现象。     

基于玄武岩纤维载体的生物膜法净化污染河道水体

为考察不同水体环境状况下,所构建的基于玄武岩纤维的污染河道水体净化技术的净化效能,通过模拟不同p H、温度及DO的污染河道水体环境,研究了水体中的COD、氨氮及TP指标变化规律,得到所构建的净化技术对COD、氨氮及TP的削减速率。研究结果表明,不同温度环境下COD、氨氮及TP的净化效率会随着时间增加而增强;不同DO环境下COD、氨氮及TP的净化效率随时间增加而增强;不同p H条件下COD净化效率随着时间增加而持续增强,氨氮净化效率随时间增加至某一极值后趋于平稳,而TP的净化效率会随着时间增加表现先升高后降低的趋势。此外,实验期间不同水体环境下COD、氨氮与TP皆在温度20~25℃,p H为7,DO为2~4 mg/L时分别获得93%、90%和36%的最大净化效率;根据实验结果得出的最适宜水体环境为温度20~25℃,p H为7,DO为2~4 mg/L;COD、氨氮及TP削减速率分别为0.63~0.84、3.6×10-3~4.0×10-2和7.1×10-4~2.9×10-3kg/(m3·d)。     

The accuracy of two- and three-way positive matrix factorization models: Applying simulated multisite data sets

The application of three-way data sets (combined multisite data sets) for source apportionment has become common, but its influence on the performance of receptor modeling techniques has not yet been explored systematically. To study the influence of site-to-site correlations of source contributions and the spatial variability of source profiles on two- and three-way positive matrix factorization (PMF), simulated three-way data sets were constructed and modeled by different applications of PMF (PMF2 for each site individually, PMF2 for data sets combining all sites together, and PMF3 for all sites). In addition, the performance of PMF was evaluated under conditions of collinearity and different source categories at two sites. The results indicated that if the sites were contributed by sources with identical profiles, the site-to-site correlations of source contributions would not influence the PMF2, and the three-way blocks could be used by PMF2. However, the PMF2 using three-way data sets was sensitive to the spatial variability of source profiles. For the three-way model, PMF3 could perform well only when all of the sources exhibited strong site-to-site associations among all sites, and at the same time, the spatial variability of source profiles were sufficiently small. It might due to the algorithm that, for each source, PMF3 produces the same source profile and the same temporal variation in daily contributions among all sites.

Implications:?The application of multisite data sets for source apportionment has become common. However, limited work investigated the accuracy of two- and three-way PMFs when using multisite data sets. If the application of PMFs using multisite data sets were not appropriate, the results would be unreasonable. The unreasonable results would supply confused information for PM control strategies. In this work, simulated multisite data sets were modeled by different applications of PMFs. The effort to assess and compare the performance of two- and three-way PMFs using multisite data sets is very limited. The findings could provide information for multisite source apportionment.     

餐厨垃圾和绿化废弃物换向通风好氧堆肥

以某高校餐厨垃圾和绿化废弃物为原料,利用一种换向通风的堆肥降解装置对其进行好氧堆肥处理,研究了2个进气温度(35℃、45℃)和3个通风速率(1.5、2.5和4 m3/h)6个处理组对堆肥原料理化性质(堆体温度、含水率、pH值、有机质含量、C/N、种子发芽指数)的影响。餐厨垃圾和绿化废弃物进行15 d好氧堆肥后,pH值上升到7左右,有机质含量达到20%~80%,C/N都降到20以下,种子发芽指数均大于50%。经过1个月腐熟,其含水率≤30%。上述指标符合有机肥料标准(NY525-2011)的要求,该堆肥产物可用于园林绿地,具有显著的经济效益和生态效益。本研究结果表明,将城市餐厨垃圾和绿化废弃物混合后进行好氧堆肥是废弃物处理的高效工程,具有良好的发展前景。     

底物浓度影响微生物燃料电池性能和生物生长的模拟研究

建立了含有悬浮微生物、电极上生物量、可溶性化学底物和中介体的微生物燃料电池(MFC)数学模型。通过底物降解、生物增长和电流产生过程的模拟,考察了生物量和底物随时间的变化规律,底物质量浓度对生物量、底物降解和电流的影响。结果表明,当溶液中初始微生物量很少时,随着MFC反应的进行,生物主要富集在电极上,溶液中生物生长缓慢;MFC中的生物生长经历延滞期、对数期和平稳期,底物分解经历缓慢、快速和消耗殆尽3个阶段。底物质量浓度小于等于250 mg/L时,生物延滞期时间、底物缓慢分解阶段时间、生物生长到达平稳时间、底物消耗殆尽的时间和电流到达最大值所需的时间随着底物质量浓度的增加而缩短。底物质量浓度大于250 mg/L时,生物延滞期时间、底物缓慢分解阶段时间、生物生长到达平稳时间、底物消耗殆尽的时间和电流到达最大值所需的时间随着底物质量浓度的增加而增加。     

酸析与超声波-超重力-臭氧氧化法联合预处理二硝基甲苯废水

将二硝基甲苯废水(简称废水)酸析后进行超声波-超重力-臭氧氧化处理,考察了酸的种类及废水pH对酸析效果的影响.实验结果表明:加入质量分数98％的H2SO4溶液调节废水pH为1.0时,酸析效果较好,酸析后废水COD去除率为38.50％,硝基化合物的去除率为45.26％,酸析析出物为一硝基甲苯磺酸;酸析后废水经超声波-超重...     

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

● Used a double-stage attention mechanism model to predict ozone. ● The model can autonomously select the appropriate time series for forecasting. ● The model outperforms other machine learning models and WRF-CMAQ. ● We used the model to analyze the driving factors of VOCs that cause ozone pollution. Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.