离心模型试验技术在环境岩土工程中的应用现状与展望

首先简单介绍了离心模型试验技术的发展状况,然后探讨了其在环境岩土工程中的应用,研究了模型和原型的相似规律,提出了环境岩土工程离心模型试验的发展方向.     

Endocrine and metabolic changes in Anguilla anguilla L. following exposure to beta-naphthoflavone--a microsomal enzyme inducer

Anguilla anguilla L. were exposed during 24 and 48 h to 2.7 muM beta-naphthoflavone (BNF), a known microsomal enzyme inducer. The BNF effects on thyroid-stimulating hormone (TSH), free triiodothyronine (T3), free thyroxine (T4) and cortisol plasma levels were investigated. Alterations on plasma glucose and lactate levels were also measured as an indication of energy-mobilizing hormones alterations. BNF showed to be able to decrease significantly A. anguilla plasma T4 levels, whereas TSH, T3 and cortisol plasma remained constant. However, plasma glucose levels were significantly increased, demonstrating that intermediary metabolism has been affected. These results demonstrate that BNF a PAH-like compound alters the normal functioning of the hypothalamo-pituitary-thyroid (HPT) axis in A. anguilla.     

利用剩余活性污泥的生物吸附降低城市污水污泥重金属含量

对香港沙田和广州大坦沙污水处理厂的污水、污泥作了初步的生物吸附试验，结果表明：少量活性的污泥能够显著降低污水中Ｃｕ、Ｚｎ、Ｎｉ的浓度，污泥浓度为０。．０６ｇ／Ｌ时，广州污水上述３种重金属的去除率分别为８２％，６９％和５１％，因此回流活性污泥到一沉池可有效降低上述金属进入生物反应池。     

Sustainability measurement of product-service systems: Brazilian case studies about electric car-sharing

Modifying the traditional production system and the culture of consumption is necessary to achieve a sustainable society. The product-service system (PSS) business model allows companies to progressively add services to physical products, thus promoting more pro-sustainable business outcomes. The purpose of this work is to verify how a PSS model can contribute to sustainability by analyzing two electric car-sharing projects using a set of five factors. As a methodological strategy, a Systematic Literature Review and a case study of the two projects (VAMO and EMOTIVE) were performed. Findings show benefits generated by both projects as to environmental aspects, mainly related to gases emission. It was also possible to observe social benefits due to income development and environmental awareness. This paper thus contributes to the discussion of the potential of the PSS models and how they can leverage business models toward a more sustainable society. Although the results are applicable only in this case, this article can motivate new projects and contribute to enriching the debate about sustainable solutions, as the literature in this field still provides few case studies showing these types of applications.     

Time Series Forecasting of Cyanobacteria Blooms in the Crestuma Reservoir (Douro River, Portugal) Using Artificial Neural Networks

In this work, time series neural networks were used to predict the occurrence of toxic cyanobacterial blooms in Crestuma Reservoir, which is an important potable water supply for the Porto region, located in the north of Portugal. These models can potentially be used to provide water treatment plant operators with an early warning for developing cyanobacteria blooms. Physical, chemical, and biological parameters were collected at Crestuma Reservoir from 1999 to 2002. The data set was then divided into three independent time series, each with a fortnightly periodicity. One training series was used to “teach” the neural networks to predict results. Another series was used to verify the results, and to avoid over-fitting of the data. An additional independently collected data series was then used to test the efficacy of the model for predicting the abundance of cyanobacteria. All of the models tested in this study incorporated a prediction time (look-ahead parameter) equal to the sampling interval (two weeks). Various lag periods, from 2 to 52 weeks, were also investigated. The best model produced in this study provided the following correlations between the target and forecast values in the training, verification, and validation series: 1.000 (P = 0.000), 0.802 (P = 0.000), and 0.773 (P = 0.001), respectively. By applying this model to the three-year data set, we were able to predict fluctuations in cyanobacteria abundance in the Crestuma Reservoir, with a high level of precision. By incorporating a lag-period of eight weeks, we were able to detect secondary fluctuations in cyanobacterial abundance over the annual cycle.     

Microwave Pretreatment for Enhancement of Phosphorus Release from Dairy Manure

Both the advanced oxidation process (AOP) using a combination of hydrogen peroxide addition and microwave heating (H₂O₂/microwave), and the microwave heating process were used for solubilization of phosphorus from liquid dairy manure. About 80% of total phosphate was released into the solution at a microwave heating time of 5 min at 170°C. With an addition of H₂O₂, more than 81% of total phosphate could be released over a reaction period of 49 h at ambient temperature. The AOP process could achieve up to 85% of total phosphate release at 120°C. The results indicated that both the microwave, and the AOP processes could effectively release phosphate from liquid dairy manure. These processes could serve as pretreatments for phosphorus recovery from animal wastes, and could be combined with the struvite crystallization process to provide a new approach in treating animal wastes.     

Biotransformation, stress and genotoxic effects of 17beta-estradiol in juvenile sea bass (Dicentrarchus labrax L.)

The effects of 17beta-estradiol (E(2)) on fish became a matter of concern, since significant levels of this hormone were detected in the aquatic environment released mainly by domestic sewage treatment plants. In this perspective, the current study was focused on E(2) effects upon biotransformation, stress and genotoxic responses of juvenile Dicentrarchus labrax L. (sea bass). Fish were exposed to E(2) during 10 days in two different ways: water diluted (200 ng/L or 2,000 ng/L) and i.p. injected (0.5 mg/kg or 5 mg/kg). A battery of biological responses was evaluated: liver ethoxyresorufin-O-deethylase (EROD) and alanine transaminase (ALT) activities, liver somatic index (LSI), plasma cortisol, glucose and lactate concentrations, as well as erythrocytic nuclear abnormalities (ENA). All the exposure conditions induced endocrine disruption, measured as plasma cortisol decrease, and genotoxicity, measured as ENA increase. Thus, no differences were detected either between different exposure routes or tested concentrations. Concerning liver EROD and ALT activities, as well as plasma glucose and lactate concentrations no differences were found between treated and control groups. LSI was the only parameter to respond differently in the two exposure routes, as only E(2) water diluted induced a significant increase in this hepatic indicator.     

Biotransformation and Genotoxic Biomarkers in Mullet Species (LIZA SP.) From a Contaminated Coastal Lagoon (Ria De Aveiro, Portugal)

The aim of the present study was to assess liver ethoxyresorufin-O-deethylase (EROD) activity and erythrocytic nuclear abnormalities (ENA) frequency in three mullet species, as biotransformation and genotoxicity biomarkers, respectively, for monitoring aquatic ecosystems. Specimens of Liza aurata, Liza ramada and Liza saliens were caught bimonthly, between May 2000 and March 2001, at nine stations in the Ria de Aveiro (Portugal), a polluted coastal lagoon. The survey revealed two critical areas (Vagos and Carregal) where it was observed elevated EROD activity and ENA frequency, comparing to the reference sites. The results often reflect a contamination by a mixture of pollutants, from point and non-point sources. Nevertheless, the responses observed at Vagos are probably attributable to high polycyclic aromatic hydrocarbon (PAH) water levels measured at that station. All species were able to detect inter-site differences; however L. aurata seems to be the most appropriate bio-indicator due to its larger geographical distribution and abundance during the whole year. The adopted biomarkers, applied to mullet species, can be successfully employed for biomonitoring anthropogenic contamination in both Atlantic and Mediterranean European coastal waters.     

Forecasting of cyanobacterial density in Torrão reservoir using artificial neural networks

The ability of general regression neural networks (GRNN) to forecast the density of cyanobacteria in the Torr?o reservoir (Tamega river, Portugal), in a period of 15 days, based on three years of collected physical and chemical data, was assessed. Several models were developed and 176 were selected based on their correlation values for the verification series. A time lag of 11 was used, equivalent to one sample (periods of 15 days in the summer and 30 days in the winter). Several combinations of the series were used. Input and output data collected from three depths of the reservoir were applied (surface, euphotic zone limit and bottom). The model that presented a higher average correlation value presented the correlations 0.991; 0.843; 0.978 for training, verification and test series. This model had the three series independent in time: first test series, then verification series and, finally, training series. Only six input variables were considered significant to the performance of this model: ammonia, phosphates, dissolved oxygen, water temperature, pH and water evaporation, physical and chemical parameters referring to the three depths of the reservoir. These variables are common to the next four best models produced and, although these included other input variables, their performance was not better than the selected best model.