Toward better understanding and feasibility of controlling greenhouse gas emissions from treatment of industrial wastewater with activated sludge

Wastewater treatment plants (WWTPs) have been recognized as important sources for anthropogenic greenhouse gas (GHG) emission. The objective of the study was to thoroughly investigate a typical industrial WWTP in southern Taiwan in winter and summer which possesses the emission factors close to those reported values, with the analyses of emission factors, mass fluxes, fugacity, lab-scale in situ experiments, and impact assessment. The activated sludge was the important source in winter and summer, and nitrous oxide (N₂O) was the main contributor (e.g., 57 to 91 % of total GHG emission in a unit of kg carbon dioxide-equivalent/kg chemical oxygen demand). Albeit important for the GHGs in the atmosphere, the fractional contribution of the GHG emission to the carbon or nitrogen removal in wastewater treatment was negligible (e.g., less than 1.5 %). In comparison with the sludge concentration or retention time, adjusting the aeration rate was more effective to diminish the GHG emission in the activated sludge without significantly affecting the treated water quality. When the aeration rate in the activated sludge simulation was reduced by 75 %, the mass flux of N₂O could be diminished by up to 53 % (from 9.6 to 4.5 mg/m²-day). The total emission in the WWTP (including carbon dioxide, methane, and N₂O) would decrease by 46 % (from 0.67 to 0.36 kg CO₂-equiv/kg COD). However, the more important benefit of changing the aeration rate was lowering the energy consumption in operation of the WWTP, as the fractional contribution of pumping to the total emission from the WWTP ranged from 46 to 93 % within the range of the aeration rate tested. Under the circumstance in which reducing the burden of climate change is a global campaign, the findings provide insight regarding the GHG emission from treatment of industrial wastewater and the associated impact on the treatment performance and possible mitigation strategies by operational modifications.

     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

Simultaneous determination of estrogenic odorant alkylphenols,chlorophenols, and their derivatives in water using online headspace solid phase microextraction coupled with gas chromatography-mass spectrometry

A simple online headspace solid-phase microextraction (HS-SPME) coupled with the gas chromatography-mass spectrometry (GC-MS) method was developed for simultaneous determination of trace amounts of nine estrogenic odorant alkylphenols and chlorophenols and their derivatives in water samples. The extraction conditions of HS-SPME were optimized including fiber selection, extraction temperature, extraction time, and salt concentration. Results showed that divinylbenzene/Carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber was the most appropriate one among the three selected commercial fibers, and the optimal extraction temperature, time, and salt concentration were 70 °C, 30 min, and 0.25 g/mL, respectively. The developed method was validated and showed good linearity (R ²?>?0.989), low limit of detection (LOD, 0.002–0.5 μg/L), and excellent recoveries (76–126 %) with low relative standard deviation (RSD, 0.7–12.9 %). The developed method was finally applied to two surface water samples and some of these target compounds were detected. All these detected compounds were below their odor thresholds, except for 2,4,6-TCAS and 2,4,6-TBAS wherein their concentrations were near their odor thresholds. However, in the two surface water samples, these detected compounds contributed to a certain amount of estrogenicity, which seemed to suggest that more attention should be paid to the issue of estrogenicity rather than to the odor problem.     

我国工业场地再利用的环境风险管理困境及对策

我国污染场地再利用的开发活动日渐增多,伴随场地的再利用对环境和人体健康造成不良影响的环境事件也愈加频繁。在土壤污染防治立法、相关的土壤环境质量标准与监管制度都欠缺的情况下,针对污染场地再利用环境风险管理的项目规划的失当、环境风险评估指标的缺失及监督管理制度不健全的障碍,应加强区域环评与单项建设项目环评的协调,落实场地监管主体、层级及各自的监管职责,形成环境损害赔偿机制,从而健全场地环境风险管理制度。     

Deodorization of pig manure using lignin peroxidase with different electron acceptors

Odor pollution is a big environmental problem caused by large-scale livestock production in China, and developing a practical way to reduce these odors is pressing. In this study, a combination of 0.2–1.0 U/mL lignin peroxidase (LiP) and one of three peroxides (H₂O₂, CaO₂, 2Na₃CO₃·3H₂O₂) was examined for its efficiency in reducing the release of eight chemicals (propionic acid, isobutyric acid, isocaproic acid, isovaleric acid, phenol, p-cresol, indole, and skatole), NH₃, H₂S, and odor intensity from pig manure. The results showed an approximately 90% reduction in p-cresol, 40–60% reduction in odor intensity, 16.5–40% reduction in indolic compounds, and 25–40% reduction in volatile fatty acids. Being the electron acceptors of LiP, 2Na₃CO₃·3H₂O₂ and CaO₂ performed better than H₂O₂ in reducing the concentration of eight chemicals, NH_3, H₂S, and odor intensity from pig manure. The effect of deodorization can last for up to 72 hr.

Implications: In China, one of the major environmental problems caused by confined feeding is odor pollution, which brings a major threat to the sustainability, profitability, and growth of the livestock industry. To couple the LiP with the electron acceptors, a low–cost, simple, and feasible method for odor removal was established in this study. Based on the study results, a practical treatment method was provided for odor pollution and supply the farm operators a more flexible time to dispose treated manure.     

印染RO浓水深度处理及回用

采用Fenton-石灰苏打法耦合工艺对某印染厂印染反渗透(RO)浓水进行深度处理。通过实验研究了不同H2O2和Fe2+投加量、p H和反应时间对废水COD去除率的影响,以及不同石灰和碳酸钠投加量对废水硬度的脱除效果,出水回用于染色工段进行染色实验。结果表明,在p H=3.0,Fe2+投加量为1.5 mmol/L,H2O2投加量为3.75 mmol/L,反应时间为45 min,石灰和碳酸钠投加量分别为450 mg/L和1 000 mg/L的条件下,出水COD和硬度的去除率可分别达到73.9%和85.0%,耦合工艺出水水质符合该厂回用染色水标准,且减少了盐的使用,可实现印染RO浓水回用。     

Fe~(3+)和Cu~(2+)对饮用水氯消毒副产物三卤甲烷形成的影响

为了研究水中存在Br-的情况下,Fe3+和Cu2+对三卤甲烷(THMs)的生成及CHCl3、CHBr Cl2、CHBr2Cl、CHBr34种消毒副产物相对分布的影响,以腐殖酸模拟氯消毒过程中的前体物进行实验。结果表明,在p H为6、7和9 3种条件下,Fe3+抑制了THMs的生成,p H=6时只有CHCl3生成量随着Fe3+浓度的增加逐渐减少,其余3种消毒副产物均在增加,p H=7时4种消毒副产物浓度均减小并在Fe3+浓度为2 mg/L时生成量最低,p H=9时的生成趋势与p H=6时类似。Cu2+能促进THMs的生成,在p H为6、7和9时,当加入0.5 mg/L Cu2+时,THMs总量分别增加了16.7%、22.6%和2.5%,随着p H增加,THMs总量增加。在3种p H环境中,Cu2+对THMs生成的影响大于Fe3+,在偏酸性环境中,Fe3+影响THMs生成,产生的致癌风险高,当金属离子浓度为2.5 mg/L时,致癌风险相差最高为15%,在中性和偏碱性环境中,Cu2+影响THMs生成,产生的致癌风险高。     

膜过滤-气相色谱法测定涂料中的苯系物、环己酮和甲苯二异氰酸酯

采用膜过滤-气相色谱法测定涂料中的苯系物、环己酮和甲苯二异氰酸酯（TD I）。用7种规格的石英毛细管色谱柱对混合标准溶液和涂料试样进行了分析实验,实验结果表明,不同属性的涂料试样添加固定量的正十四烷作内标物,经膜过滤净化后,苯系物、环己酮和TD I可采用DB-1-3或DB-1-2色谱柱同时分离并测定。测试数据经多级校准曲线处理后涂料试样中苯系物、环己酮和TD I的加标回收率为98.6%～101.8%,各组分分析结果的变异系数均小于2.0%,检出限均小于1.50×10^-5g。     

流动注射分光光度法测定水中铝的含量

以桑色素作显色剂,采用流动注射分光光度法测定水中铝的含量。在桑色素质量浓度为0．8g／L、聚氧乙烯辛基苯基醚体积分数为1．0％、进样环体积为400μL、检测波长420nm的条件下,采用流动注射分光光度法对水中铝的含量进行测定,本方法的线性范围为2～300μg／L,检出限（信噪比为3）为0．124μg／L,相对标准偏差为2．9％,加标回收率为96．4％～101．8％。     

密云水库微生物相变化对水质及嗅味的影响

研究北京市地表水源密云水库微生物相变化对水质及嗅味的影响.结果表明:密云水库细菌密度和种类相对稳定,对水质影响较小.2005—2006年,平均细菌密度约300 mL－1,多为革兰氏阴性菌,放线菌与霉菌较少.密云水库藻类生长与水质嗅味呈正相关性.年平均浮游微藻密度为655×104 L－1,全年有2个藻类生长高峰期,即9月和5月,藻密度分别为1　665×104和988×104 L－1.高峰期,水库中出现大量蓝绿藻(如颤藻、鱼腥藻、微囊藻等)以及颗粒直链藻,直接导致水体嗅味.2005—2006年,密云水库有机碳含量明显增加,总氮、总磷含量变化不大.