坦桑尼亚水质和污染研究回顾

坦桑尼亚的海岸带(图1)有多种生物栖息地,其中包括珊瑚礁、红树林、海草床、沙坝、湿地和沙滩.除了承担海岸带所有功能中的基本连接作用外,这些海岸带栖息地还提供了生物和非生物资源.     

Conservation and development

Summary This, the Second World Conservation Lecture, was presented at the Royal Institution, London, UK on 18 March 1982. The lecture organized by World Wildlife Fund, UK, concerns itself with Conservation and Development from a developing country point of view. Published with the kind permission of World Wildlife Fund UK.     

Modelling Change in Ground Vegetation Response to Acid and Nitrogen Pollution, Climate Change and Forest Management at in Sweden 1500–2100 a.d.

The ForSAFE model, designed for modelling biogeochemical cycles (water, acidity, base cation, nitrogen and carbon) in terrestrial ecosystems, was modified with a vegetation response module (VEG), incorporating the effects of: nitrogen pollution, acidification, soil moisture, temperature, wind chill exposure, light and shading by trees, grazing by animals, competition between plants, above ground for light and below ground for water and nutrients. The model calculates the response of number ground vegetation plant groups. The integrated model was tested and validated at integrated level II forest monitoring sites across Sweden, four have been shown here, and used to assess the effect of acidification and nitrogen pollution in relation to factors such as climate change, forest management and changing grazing pressure. The response functions have been derived from single-factor experiments and integrated through the model structure for use on whole systems. The tests with the model suggest that the ground vegetation composition is reasonably well predicted, that much research remains before the model is fully tested and operational, and that the model may serve as a tool for assessing impacts of climate change, acid rain and forest management on plant biodiversity in forested areas.     

Ecology of sandy beaches in Oman

The benthic macrofauna and physical features of 10 sandy beaches along the coast of Oman were surveyed quantitatively. This is a mesotidal regime mostly subject to low to moderate wave energy but more exposed in the south. Five northern beaches are tide-dominated, with low wave energy, and their profiles consist of a berm, a steep, swash-dominated upper shore and a broad tide-dominated terrace from mid-shore downwards. They are composed of moderately sorted fine to medium sands. Southern beaches experience greater wave energy, particularly during the summer southwest monsoon, and exhibit smoother, concave profiles with fine, fairly well sorted carbonate sand. 58 species and species groups were recorded, with crustanceans, polychaetes and molluscs dominant. In general species richness was high, at least 19–25 species per beach, but dry biomass moderate to low at 26–90g/m shoreline, with one high value of 450g/m. Total abundance was moderate at 3–73×10³ organisms/m of beach. Some zonation was evident with ocypodid crabs andTylos in the supralittoral, cirolanid isopods on the upper shore and a variety of species on the lower shore. The coast of Oman appears to constitute a single zoogeographic region, but with some regional differentiation between north and south due to varying physical conditions. Thus, OmanÕs beaches are characterized by tide-dominated morphodynamics and exceptionally high species richness.     

Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station,USA

The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott’s index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).     

Strength and durability characterization of pelletized coal–biomass household briquettes

Increase in waste sludge disposal is always seen as a problem from the point of production industry. However, it is clear that the reuse and recycle of sewage sludge could be a serious economic input. The most important action should be to determine the sludge characterizations and direct the producers towards appropriate reuse and recycling opportunities. In this study, reuse method was examined to produce coal briquette, which will constitute an example for waste sludge. In order to make use of the waste sludge, five different coal briquette samples were produced by mixing powdered coal and bitumen together with waste sludge, at different ratios. The overall results indicated that the sample named CB3 having 70% powdered coal, 20% waste sludge, and 10% bitumen was found to be the optimum coal briquette among the other samples produced. The proximate analysis of the optimum briquette sample was carried out according to the Turkish standards and regulations and it was found out that the produced briquette coal can have commercial value with a gross calorific value of 30.03 MJ/kg and 7.30% ash content.     

A review on bisphenol A occurrences,health effects and treatment process via membrane technology for drinking water

Massive utilization of bisphenol A (BPA) in the industrial production of polycarbonate plastics has led to the occurrence of this compound (at μg/L to ng/L level) in the water treatment plant. Nowadays, the presence of BPA in drinking water sources is a major concern among society because BPA is one of the endocrine disruption compounds (EDCs) that can cause hazard to human health even at extremely low concentration level. Parallel to these issues, membrane technology has emerged as the most feasible treatment process to eliminate this recalcitrant contaminant via physical separation mechanism. This paper reviews the occurrences and effects of BPA toward living organisms as well as the application of membrane technology for their removal in water treatment plant. The potential applications of using polymeric membranes for BPA removal are also discussed. Literature revealed that modifying membrane surface using blending approach is the simple yet effective method to improve membrane properties with respect to BPA removal without compromising water permeability. The regeneration process helps in maintaining the performances of membrane at desired level. The application of large-scale membrane process in treatment plant shows the feasibility of the technology for removing BPA and possible future prospect in water treatment process.     

Ethnobotanical survey about medicinal plants used in traditional treatment of insomnia,asthenia, and oral and gum infections in the region Fez-Meknes,Morocco

Environmental Science and Pollution Research - An ethnobotanical survey was conducted among herbalists in the north-central region of Morocco using a questionnaire in order to highlight knowledge...     

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.     

An Examination of Governance Arrangements at Kisakasaka Mangrove Reserve in Zanzibar

This study employs insights largely derived from critical reflections on the common pool resources (CPR) theory to examine the current governance arrangements in place to manage the mangrove forest at Kisakasaka, in Zanzibar, Tanzania. Kisakasaka was used as a site for a community-based management pilot project of forest resources in Zanzibar. After some initial success in setting up a local management structure and regulating access to the mangrove for mainly charcoal production, there are now clear indications that forest conditions have deteriorated dramatically with concomitant ongoing resource use problems for local villagers who have relied heavily on forest resources as a source of cash income. Extra-local factors, such as urban population increases and associated market pressures for charcoal, are also conjectured to overlay and interact with the institutional problems at Kisakasaka. As a result, over concern about the deterioration in the condition of the mangrove forest, the responsible government authority decided not to renew the community-based governance arrangements after an initial five-year pilot period. While revealing the inadequacies of existing governance arrangements and of its relationship to deteriorating forest conditions at Kisakasaka, this study concludes by suggesting an approach to more fully understand forces driving local resource management and use.