The stability of CuO nanoparticles (NPs) is expected to play a key role in the environmental risk assessment of nanotoxicity in aquatic systems. In this study, the effect of alginate (model polysaccharides) on the stability of CuO NPs in various environmentally relevant ionic strength conditions was investigated by using time-resolved dynamic light scattering. Significant aggregation of CuO NPs was observed in the presence of both monovalent and divalent cations. The critical coagulation concentrations (CCC) were 54.5 and 2.9 mM for NaNO3 and Ca(NO3)2, respectively. The presence of alginate slowed nano-CuO aggregation rates over the entire NaNO3 concentration range due to the combined electrostatic and steric effect. High concentrations of Ca2+ (>6 mM) resulted in stronger adsorption of alginate onto CuO NPs; however, enhanced aggregation of CuO NPs occurred simultaneously under the same conditions. Spectroscopic analysis revealed that the bridging interaction of alginate with Ca2+ might be an important mechanism for the enhanced aggregation. Furthermore, significant coagulation of the alginate molecules was observed in solutions of high Ca2+ concentrations, indicating a hetero-aggregation mechanism between the alginate-covered CuO NPs and the unabsorbed alginate. These results suggested a different aggregation mechanism of NPs might co-exist in aqueous systems enriched with natural organic matter, which should be taken into consideration in future studies.
The electro-Fenton (EF) process treatment of 0.1-M (rhodamine B) RhB solution was studied with different graphite cathode materials, and graphite felt (GF) was selected as a promising material in further investigation. Then, the degradation performances of gas diffusion electrode (GDE) and graphite felt (GF) were compared, and GDE was confirmed to be more efficient in RhB removal. The operational parameters such as Fe2+ dosage and current density were optimized, and comparison among different modified methods—polytetrafluoroethylene-carbon black (PTFE-CB), polytetrafluoroethylene-carbon nanotube (PTFE-CNT), electrodeposition-CB, and electrodeposition-CNT—showed 98.49 % RhB removal by PTFE-CB-modified cathode in 0.05 M Na2SO4 at a current density of 50 A/m2 and an air flow rate of 1 L/min after 20 min. Meanwhile, after cathode modified by PTFE-CB, the mineralization efficiency and mineralization current efficiency performed absolutely better than the pristine one. Cyclic voltammograms, SEM images, contact angles, and BET surface area were carried out to demonstrate stronger current responses and higher hydrophilicity of GF after modified. The value of biochemical oxygen demand/chemical oxygen demand (BOD5/COD) increased from 0.049 to 0.331 after 90-min treatment, suggesting the solution was biodegradable, and the modified cathode was confirmed to be stable after ten circle runs. Finally, a proposed degradation pathway of RhB was put forward. 相似文献
Microbe-assisted phytoremediation provides an effective approach to clean up heavy metal-contaminated soils. However, severe drought may affect the function of microbes in arid/semi-arid areas. Streptomyces pactum Act12 is a drought-tolerant soil actinomycete strain isolated from an extreme environment on the Qinghai-Tibet Plateau, China. In this study, pot experiments were conducted to assess the effect of Act12 on Cd tolerance, uptake, and accumulation in amaranth (Amaranthus hypochondriacus) under water deficit. Inoculated plants had higher Cd concentrations (root 8.7–33.9 %; shoot 53.2–102.1 %) and uptake (root 19.9–95.3 %; shoot 110.6–170.1 %) than non-inoculated controls in Cd-treated soil. The translocation factor of Cd from roots to shoots was increased by 14.2–75 % in inoculated plants, while the bioconcentration factor of Cd in roots and shoots was increased by 10.2–64.4 and 53.9–114.8 %, respectively. Moreover, inoculation with Act12 increased plant height, root length, and shoot biomass of amaranth in Cd-treated soil compared to non-inoculated controls. Physiochemical analysis revealed that Act12 enhanced Cd tolerance in the plants by increasing glutathione, elevating superoxide dismutase and catalase activities, as well as reducing malondialdehyde content in the leaves. The drought-tolerant actinomycete strain Act12 can enhance the phytoremediation efficiency of amaranth for Cd-contaminated soils under water deficit, exhibiting potential for application in arid and semi-arid areas. 相似文献
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 (R2?>?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. 相似文献
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.
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.
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. 相似文献
Environmental Science and Pollution Research - Improved understanding of the fractionation and geochemical characteristic of rare earth elements (REEs) from steel plant emissions is important due... 相似文献