Low digestibility of phytate phosphorus,their impacts on the environment,and phytase opportunity in the poultry industry

Environmental Science and Pollution Research - Phosphorus is an essential macro-mineral nutrient for poultry, needed for the body growth, development of bones, genomic function, good...     

Correction to: Preparation of GO/MIL-101(Fe,cu) composite and its adsorption mechanisms for phosphate in aqueous solution

Environmental Science and Pollution Research - A Correction to this paper has been published: https://doi.org/10.1007/s11356-021-15526-6     

Comprehensive utilization of residues of Magnolia officinalis based on fiber characteristics

Journal of Material Cycles and Waste Management - The traditional Chinese medicinal plant Magnolia officinalis has a wide range of applications; including more than 200 kinds of patented Chinese...     

Fast and efficient aqueous arsenic removal by functionalized MIL-100(Fe)/rGO/δ-MnO2 ternary composites: Adsorption performance and mechanism

Because of its significant toxicological effects on the environment and human health,arsenic(As) is a major global issue.In this study,an Fe-based metal-organic framework(MOF)(Materials of Institut Lavoisier:MIL-100(Fe)) which was impregnated with reduced graphene oxide(rGO) by using a simple hydrothermal method and coated with birnessitetype manganese oxide(δ-MnO_2) using the one-pot reaction process(MIL-100(Fe)/rGO/δ-MnO_2 nanocomposites) was synthesized and applied successfully in As removal.The removal efficiency was rapid,the equilibrium was achieved in 40 min and 120 min for As(Ⅲ) and As(Ⅴ),respectively,at a level of 5 mg/L.The maximum adsorption capacities of As(Ⅲ) and As(Ⅴ) at pH 2 were 192.67 mg/g and 162.07 mg/g,respectively.The adsorbent revealed high stability in pH range 2-9 and saturated adsorbent can be fully regenerated at least five runs.The adsorption process can be described by the pseudo-second-order kinetic model and Langmuir monolayer adsorption.The adsorption mechanisms consisted of electrostatic interaction,oxidation and inner sphere surface complexation.     

Investigating the bacterial community and amoebae population in rural domestic wastewater reclamation for irrigation

Reclamation of domestic wastewater for agricultural irrigation is viewed as a sustainable option to create an alternative water source and address water scarcity. Free-living amoebae(FLA), which are amphizoic protozoa, are widely distributed in various environmental sources. The FLA could cause considerable environmental and health risks. However, little information is available on the risk of these protozoa. In this study, we evaluated the feasibility using rural domestic wastewater for agricultural irrigation, and analyzed dynamic changes of the microbial community structure and FLA populations in raw and treated wastewater, as well as the phyllosphere and rhizosphere of lettuce production sites that were irrigated with different water sources. The bacterial community dynamics were analyzed by terminal restriction fragment length polymorphism(T-RFLP). The bacterial community structures in the influent were similar to that in the effluent, while in some cases relative abundances varied significantly. The populations of Acanthamoeba spp. and Hartmannella vermiformis in the anaerobically treated wastewater were significantly higher than in the raw wastewater. The vegetables could harbor diverse amoebae, and the abundances of Acanthamoeba spp. and H. vermiformis in the rhizosphere were significantly higher than in the phyllosphere. Accordingly, our studies show insight into the distribution and dissemination of amoebae in wastewater treatment and irrigation practices.     

雾霾环境天气下对户外体育运动人群影响研究

构建雾霾环境天气下户外体育运动生理健康指标参数,以最大摄氧量(VO2)、最大通气量(VEmax)、氧脉搏O2P、心率(HR)等参量为约束指标,构建户外体育运动人群身体健康指标的约束体系,采用心电图的负荷试验和代谢分析实验方法测试雾霾环境天气下对户外体育运动人群健康影响,根据受测运动员的气体代谢和心肺功能指标进行雾霾环境天气下运动人群的心肺机能特征分析。雾霾环境天气下,应当减少户外运动,在空气质量好的环境下进行一定的有氧运动,避免吸入污染空气,导致肺部疾病。     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Reduction of SO2 emissions by ammonia gas during unstaged combustion

The reduction of SO₂ by the addition of ammonia gas has been studied in a 2 m high fluidized bed combustor having a 30 cm static bed height and a freeboard height of 170 cm. Ammonia gas was injected at 52 cm above the distributor where the temperature is ca. 700° C by an uncooled stainless steel tube injector. Experiments were carried out to investigate the effects of amminia gas injection on sulphur dioxide emissions at unstaged conditions of: (i) excess air level, (ii) NH₃:SO₂ molar ratio, (iii) fluidizing velocity and (iv) bed height.A maximum reduction of 75% in SO₂ emissions was found at 40% excess air, at an NH₃:SO₂ molar ratio of 5.4. The onset of SO₂ reduction occurred at an NH₃:SO₂ ratio of 1.5 However, the most effective ratio was found to be between 3 and 5. Fluidizing velocity and bed height were also found to have significant influence on SO₂ reduction.It is difficult to determine how the SO₂ reduction varied with operating conditions. When ammonia is added in the main combustor zone, the temperature is much higher than that required for the occurrence of sulphur dioxide-ammonia and sulphur trioxide-ammonia reactions. However, this paper points out the significance of ammonia addition in the reduction of sulphur dioxide.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

博斯腾湖富营养化状态水平调查

通过水质特征和生物学特征的一个水文年规范化实地调查,采用Scirgesew参数分类标准和相关加权综合营养状态指数,对博斯腾湖水体富营养化状态水平进行了调查和评价。