苯酚、苯胺及其衍生物对斜生栅列藻的急性毒性及QSAR研究

本文应用ＯＥＣＤ标准方法测定了１８种酚类胺类化合物对斜生栅列藻的４８ｈ－ＥＣ６０值,选择参数ＥＬＵＭＯ和ｌｇＫｏｗ进行了ＱＳＡＲ分析,得到下列回归方程：－１ｇＥＣ５０＝２．５４１－０．６４９ＥＬＵＭＯ＋０．２９９ＬｇＫｏｗ,ｎ＝１８,ｒ＝０．９０３,ＳＥ＝０．３１４应用所得方程计算了化合物的毒性,探讨了毒机理。     

湖库泥沙资源化多目标优化配置研究综述

合理利用泥沙的资源特性,实现泥沙资源优化配置是湖库生态环境治理迫切需要解决的关键问题之一。该文旨在对泥沙资源化及多目标优化配置方法的研究进行综述,总结泥沙资源化的研究现状和分析其发展趋势。首先对泥沙资源优化配置框架的相关研究进行了综述,主要集中于泥沙资源优化配置、泥沙资源化的途径、泥沙管理生态化及优化配置方法等方面,然后分析了目前对于泥沙资源优化配置的理论认识、模型构建及应用、方案评价及决策存在的主要问题,最后探讨了泥沙资源优化配置相关研究未来发展方向。     

Increasing the recovery of heavy metal ions using two microbial fuel cells operating in parallel with no power output

The present study aimed to improve the performance of microbial fuel cells (MFCs) by using an intermittent connection period without power output. Connecting two MFCs in parallel improved the voltage output of both MFCs until the voltage stabilized. Electric energy was accumulated in two MFCs containing heavy metal ions copper, zinc, and cadmium as electron acceptors by connection in parallel for several hours. The system was then switched to discharge mode with single MFCs with a 1000-Ω resistor connected between anode and cathode. This method successfully achieved highly efficient removal of heavy metal ions. Even when the anolyte was run in sequencing batch mode, the insufficient voltage and power needed to recover heavy metals from the cathode of MFCs can be complemented by the developed method. The average removal ratio of heavy metal ions in sequencing batch mode was 67 % after 10 h. When the discharge time was 20 h, the removal ratios of zinc, copper, and cadmium were 91.5, 86.7, and 83.57 %, respectively; the average removal ratio of these ions after 20 h was only 52.1 % for the control group. Therefore, the average removal efficiency of heavy metal ions increased by 1.75 times using the electrons stored from the bacteria under the open-circuit conditions in parallel mode. Electrochemical impedance data showed that the anode had lower solution resistance and polarization resistance in the parallel stage than as a single MFC, and capacitance increased with the length of time in parallel.

     

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。     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

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。