28 resultados para Natural Systems


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This paper describes the feasibility study on the application of passive and active stack systems to enhance natural ventilation in public housing in Singapore. About 86% of the population is staying in high-rise public housing, known as Housing and Development Board (HDB) flats, which is designed for natural ventilation. The primary objective of this work is to assess the status of natural ventilation in a typical four-room HDB flat using scaled model in the wind tunnel, and to develop an effective passive or active stack system to enhance natural ventilation in the flat. Four numbers of stacks with different sizes were tested at two locations in the flat. The study shows that the passive stack, incorporating the principle of airflow due to buoyancy, does not enhance air velocity in the flat. However, the active stack which operates based on the suction effect induced by a fan fixed at the top of the stack leads to substantial increase in the air velocity at the room and thus meeting the human’s thermal comfort condition. It was noted that the velocities increase along with the increase in the stack size.

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Nearly all drinking water distribution systems experience a "natural" reduction of disinfection residuals. The most frequently used disinfectant is chlorine, which can decay due to reactions with organic and inorganic compounds in the water and by liquid/solids reaction with the biofilm, pipe walls and sediments. Usually levels of 0.2-0.5 mg/L of free chlorine are required at the point of consumption to maintain bacteriological safety. Higher concentrations are not desirable as they present the problems of taste and odour and increase formation of disinfection by-products. It is usually a considerable concern for the operators of drinking water distribution systems to manage chlorine residuals at the "optimum level", considering all these issues. This paper describes how the chlorine profile in a drinking water distribution system can be modelled and optimised on the basis of readily and inexpensively available laboratory data. Methods are presented for deriving the laboratory data, fitting a chlorine decay model of bulk water to the data and applying the model, in conjunction with a simplified hydraulic model, to obtain the chlorine profile in a distribution system at steady flow conditions. Two case studies are used to demonstrate the utility of the technique. Melbourne's Greenvale-Sydenham distribution system is unfiltered and uses chlorination as its only treatment. The chlorine model developed from laboratory data was applied to the whole system and the chlorine profile was shown to be accurately simulated. Biofilm was not found to critically affect chlorine decay. In the other case study, Sydney Water's Nepean system was modelled from limited hydraulic data. Chlorine decay and trihalomethane (THM) formation in raw and treated water were measured in a laboratory, and a chlorine decay and THM model was derived on the basis of these data. Simulated chlorine and THM profiles agree well with the measured values available. Various applications of this modelling approach are also briefly discussed.

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Nearly all drinking water distribution systems experience a "natural" reduction of disinfection residuals. The most frequently used disinfectant is chlorine, which can decay due to reactions with organic and inorganic compounds in the water and by liquid/solids reaction with the biofilm, pipe walls and sediments. Usually levels of 0.2-0.5 mg/L of free chlorine are required at the point of consumption to maintain bacteriological safety. Higher concentrations are not desirable as they present the problems of taste and odour and increase formation of disinfection by-products. It is usually a considerable concern for the operators of drinking water distribution systems to manage chlorine residuals at the "optimum level", considering all these issues. This paper describes how the chlorine profile in a drinking water distribution system can be modelled and optimised on the basis of readily and inexpensively available laboratory data. Methods are presented for deriving the laboratory data, fitting a chlorine decay model of bulk water to the data and applying the model, in conjunction with a simplified hydraulic model, to obtain the chlorine profile in a distribution system at steady flow conditions. Two case studies are used to demonstrate the utility of the technique. Melbourne's Greenvale-Sydenham distribution system is unfiltered and uses chlorination as its only treatment. The chlorine model developed from laboratory data was applied to the whole system and the chlorine profile was shown to be accurately simulated. Biofilm was not found to critically affect chlorine decay. In the other case study, Sydney Water's Nepean system was modelled from limited hydraulic data. Chlorine decay and trihalomethane (THM) formation in raw and treated water were measured in a laboratory, and a chlorine decay and THM model was derived on the basis of these data. Simulated chlorine and THM profiles agree well with the measured values available. Various applications of this modelling approach are also briefly discussed.

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A mathematical model of a solar module is presented. This model takes into account solar model temperature and solar radiation. The experimental data of a solar module under natural environment condition (NEC) have been obtained to determine the model parameters. The experimental results are compared with those calculated by using a mathematical model. It shows that the mathematical model accurately simulates the current-voltage characteristics of the solar module under the NEC and therefore is suitable for photovoltaic system design and performance analysis.

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A case study is used to demonstrate the application of Geographical Information Systems (GIS) to inform sustainable development. The suitability of the landscape to support tourism accommodation in a Local Government Area (LGA) is modelled by integrating existing datasets, including conservation areas, residential zones, major roads and known locations of tourism operators into a logistic regression framework. By using a data-driven approach an indication of the relative importance of each explanatory variable can be accounted for, therefore informing planners of the importance of different assets. In a region where tourism is reliant upon natural features, this use of information systems in conjunction with quantitative statistical modelling can value-add to existing datasets. The provision of this kind of knowledge is important as it would otherwise not factor into the decision-making process had the datasets been considered independently of each other – a concept that applies to both the public and private sectors.

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An environmentally sustainable and thus green business process is one that delivers organizational value whilst also exerting a minimal impact on the natural environment. Recent works from the field of Information Systems (IS) have argued that information systems can contribute to the design and implementation of sustainable business processes. While prior research has investigated how information systems can be used in order to support sustainable business practices, there is still a void as to the actual changes that business processes have to undergo in order to become environmentally sustainable, and the specific role that information systems play in enabling this change. In this paper, we provide a conceptualization of environmentally sustainable business processes, and discuss the role of functional affordances of information systems in enabling both incremental and radical changes in order to make processes environmentally sustainable. Our conceptualization is based on (a) a fundamental definition of the concept of environmental sustainability, grounded in two basic components: the environmental source and sink functions of any project or activity, and (b) the concept of functional affordances, which describe the potential uses originating in the material properties of information systems in relation to their use context. In order to illustrate the application of our framework and provide a first evaluation, we analyse two examples from prior research where information systems impacted on the sustainability of business processes.

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The impact of salmon farming on the environment has been widely studied; there is, however, no consensus on the magnitude and quality of these effects and little information on their extent over large salmon farming areas. Forty-three salmon farm sites of which 29 are in full operation and grouped in nine locations in southern Chile were evaluated. Using statistical methods (two-way anova), no effects were found on water column variables such as nitrate, ammonia, orthophosphate and chlorophyll, whereas they were significant on sediment variables such as nitrogen (N), phosphorus (P) and organic carbon (OC), among others. Natural variability evaluation of some parameters revealed that P in sediments had the lowest coefficient of variation (23.2%) when compared with N (65.2%), particulate organic matter (POM) (139%) and OC (39%), and showed more marked salmon farming effects with no locality effects. Four of the nine locations showed stronger effects on sediments, with average P values ranging from 150 to 230 mmol that were six to nine times higher than in control sites (25 mmol). Nitrogen and carbon in sediments, while showing significant effects on salmon farming, also showed locality effects that revealed other biogenic processes influencing sediment composition. Thus, P in sediments was proposed as a promising indicator of impact on salmon farming, although the relationship with fauna in sediments was not linear and somewhat variable; more research is therefore needed to understand such connections. Considering entire geographical locations, no relationship was found between sediment conditions under salmon cages and the condition of the water column at a farm. This may indicate the possibility of high dilution rates and recycling processes, which so far preclude the detection of more global impacts beyond the cages shadow.

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Metabolomics may be defined as the comprehensive quantitative and/or qualitative analysis of all metabolites present in a bio-fluid, cell, tissue, or organism. It is essentially the study of biochemical phenotypes (or metabotypes). Metabolic profiles are context dependent, and vary in response to a variety of factors including environment and environmental stimuli, health status, disease and a myriad of other factors; as such, metabolomics has been applied to a wide range of fields and has been increasingly utilised to the study of the roles played by metals in a range of biological systems as well as, encouragingly, in understanding the underlying biochemical mechanisms. The role of metals (and metalloids) in biological organisms is complex and the majority of studies in this area have been performed in plants but the fields of natural product chemistry, human health and even bacterial corrosion of water distribution systems have been investigated using this technique. In this review some of the novel approaches in which the metabolomics toolbox has been used to unravel the roles of metals and metalloids in a range of biological systems are discussed and suggestions made for future research.

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Emergencies, including both natural and man - made disasters, increasingly pose an immediate threat to life, health, property, and environment. For example, Hurricane Katrina, the deadliest and most destructive Atlantic tropical cyclone of the 2005 Atlantic hurricane season, led to at least 1,883 people's death and an estimated loss of - 108 billion property. To reduce the damage by emergencies, a wide range of cutting-edge technologies on medicine and information are used in all phases of emergency management. This article proposes a cloud-based emergency management system for environmental and structural monitoring that utilizes the powerful computing and storage capability of datacenters to analyze the mass data collected by the wireless intelligent sensor network deployed in civil environment. The system also benefits from smartphone and social network platform to setup the spatial and population models, which enables faster evacuation and better resource allocation.

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Congestion pricing schemes have been implemented in cities worldwide as a means of addressing externalities associated with inefficient price signals in transport systems. Limited evidence exists however on the secondary impacts of these schemes, which may include both environmental and health benefits associated with a resultant reduction in motor vehicle usage. There is increasing recognition that transport behaviours may play a role as opportunistic population level targets to reduce physical inactivity. Yet limited evidence currently exists on the effectiveness of transport interventions, such as congestion pricing schemes, for improving physical activity levels.This study aims to examine the physical activity effects of congestion pricing, with the health benefits of physical activity well established. Congestion pricing schemes implemented internationally were considered as 'natural experiments' and evidence of modal shift from vehicle to active forms of transport or physical activity effect was reviewed. Twelve studies were included from a search of peer-reviewed and 'grey' literature, with overall evidence for a physical activity or modal shift effect considered weak. The quality of the available evidence was also considered to be low.This is not to say that congestion pricing schemes may not have important secondary physical activity related health benefits. Instead, this review highlights the paucity of evidence that has been collected from real-world implementation of congestion pricing schemes. Given the growing recognition of the importance of distal mediators and determinants of health and the need for an 'all-of-government' approach more and better quality evidence of effectiveness of transport interventions for a broad range of outcomes, including health, is required. Significant barriers to the collection of such evidence exist, with strategies for overcoming some of these barriers identified. Only with a better understanding of the full range of potential health impacts can transport policy be fully utilised as a tool for population health.

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Decisions affecting the management of natural resources in agricultural landscapes are influenced by both social and ecological factors. Models that integrate these factors are likely to better predict the outcomes of natural resource management decisions compared to those that do not take these factors into account. We demonstrate how Bayesian Networks can be used to integrate ecological and social data and expert opinion to model the cost-effectiveness of revegetation activities for restoring biodiversity in agricultural landscapes. We demonstrate our approach with a case-study in grassy woodlands of south-eastern Australia. In our case-study, cost-effectiveness is defined as the improvement in native reptile and beetle species richness achieved per dollar spent on a restoration action. Socio-ecological models predict that weed control, the planting of trees and shrubs, the addition of litter and timber, and the addition of rocks are likely to be the most cost-effective actions for improving reptile and beetle species richness. The cost-effectiveness of restoration actions is lower in remnant and revegetated areas than in cleared areas because of the higher marginal benefits arising from acting in degraded habitats. This result is contingent on having favourable landowner attitudes. Under the best-case landowner demographic scenarios the greatest biodiversity benefits are seen when cleared areas are restored. We find that current restoration investment practices may not be increasing faunal species richness in agricultural landscapes in the most cost-effective way, and that new restoration actions may be necessary. Integrated socio-ecological models support transparent and cost-effective conservation investment decisions. Application of these models highlights the importance of collecting both social and ecological data when attempting to understand and manage socio-ecological systems.