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.     

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.     

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.     

A dynamic modelling approach for estimating critical loads of nitrogen based on plant community changes under a changing climate

A dynamic model of forest ecosystems was used to investigate the effects of climate change, atmospheric deposition and harvest intensity on 48 forest sites in Sweden (n = 16) and Switzerland (n = 32). The model was used to investigate the feasibility of deriving critical loads for nitrogen (N) deposition based on changes in plant community composition. The simulations show that climate and atmospheric deposition have comparably important effects on N mobilization in the soil, as climate triggers the release of organically bound nitrogen stored in the soil during the elevated deposition period. Climate has the most important effect on plant community composition, underlining the fact that this cannot be ignored in future simulations of vegetation dynamics. Harvest intensity has comparatively little effect on the plant community in the long term, while it may be detrimental in the short term following cutting. This study shows: that critical loads of N deposition can be estimated using the plant community as an indicator; that future climatic changes must be taken into account; and that the definition of the reference deposition is critical for the outcome of this estimate.     

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.     

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.     

High degree of polyandry in Apis dorsata queens detected by DNA microsatellite variability

Workers of six colonies of the giant honeybee Apis dorsata from Sabah, Malaysia (five colonies) and Java (one colony) were genotyped using single locus DNA fingerprinting. The colonies from Sabah nested in colony aggregations of 5 and 28 nests respectively on two trees. Three DNA microsatellite loci (A14, A76, A88) with a total of 27 alleles provided sufficient genetic variability to classify the workers into distinct sub-families revealing the degree of polyandry of the queens. Queens mated on average with 30.17 ± 5.98 drones with a range from 19 to 53. The average effective number of matings per queen was 25.56 ± 11.63. In the total sample of 192 workers, 22 individuals were found that were not offspring of the colony's queen. Three of these were potentially drifted offspring workers from genotyped queens of colonies nesting on the same tree.     

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.     

Developing a predictive understanding of landscape importance to the Punan-Pelancau of East Kalimantan, Borneo

In order for local community views to be incorporated into new development initiatives, their perceptions need to be clearly understood and documented in a format that is readily accessible to planners and developers. The current study sought to develop a predictive understanding of how the Punan Pelancau community, living in a forested landscape in East Kalimantan, assigns importance to its surrounding landscapes and to present these perceptions in the form of maps. The approach entailed the iterative use of a combination of participatory community evaluation methods and more formal modeling and geographic information system techniques. Results suggest that landscape importance is largely dictated by potential benefits, such as inputs to production, health, and houses. Neither land types nor distance were good predictors of landscape importance. The grid-cell method, developed as part of the study, appears to offer a simple technique to capture and present the knowledge of local communities, even where their relationship to the land is highly complex, as was the case for this particular community.     

Floating roof storage tank boilover

Storage tanks are important facilities for the major hazard installations (MHIs) to store large quantity of crude oil. There is several fire types can occur with large diameter open top floating roof storage tanks. Boilover is considered one of the most dangerous fires in large-scale oil tank. The world has witnessed many incidents due to boilover in floating roof storage tank. Boilover problem has been studied in experiments and by models to understand how to control the boilover phenomena. An experimental study has been carried out in Jebel Dhanna (JD) terminal area by Abu Dhabi Company for Onshore Oil Operations (ADCO) with support of Resource Protection International (RPI) consultant. 2.4 m diameter and 4.5 m diameters pans have been used to study the characteristics of the large oil-tank fires (i) to gain more knowledge of the boilover phenomenon of crude oil (ii) verify if the crude oil stored by ADCO would boilover (ii) estimation of rate of hot-zone growth and the period needed from ignition to boilover (iii) estimation of radiant heat and consequences of boilover. This paper presents an overview on the floating roof storage tank boilover. The paper also presents briefly boilover experimental research study carried out by ADCO.