The implementation of sustainable development may seem a simple concept when written on paper. However to carry-out long term
actions put forward by the Agenda 21 (AG21) at the local level represents one of the main challenges as municipal governments
in general do not have the capacity to effectively implement the process.
Regional environmental assessment (REA) has shown to be effective in supporting decision-making not only to correct environmental
problems due to past unsustainable social-economic developments but also help local governments to implement sustainable actions.
However this requires long-term investments of AG21 plans and projects. The allocation of regular and consistent financial
resources is one of the main ingredients for the sustainable development process. But traditional plans and projects financed
by national and/or international funds may not be sustainable in the long-term because they become dependent on external funding.
Research demonstrate that innovative economic instruments such as ecotaxes represent a feasible alternative to sponsor local
sustainability because taxes are collected permanently by the government and could be invested in continuous actions. Ecotaxes
experiences have provided important reference to structure a municipal incentive model (MIM) to sponsor AG21’s environmental
plans and projects on a long-term (permanent) basis.
However sustainable development cannot be solely through economic investments. A comprehensive municipal environmental management
scheme (MEMS) has been established to support the incentive model. The scheme seeks not only to improve local institutional
framework but also incentive continuous participation of local stakeholders at all levels of society. Participatory events
and the provision of incentives (educational and financial) are key to motivate society to protect the environment and support
actively the sustainable development process as emphasised in the RIO-92 Conference. 相似文献
Bombardier beetles (Coleoptera, Carabidae, Brachininae) possess a remarkable defense mechanism where a hot chemical spray is released from the tip of their abdomen, with an audible explosive sound. To date, the repellent properties of these chemicals have been tested against a limited number of taxa, such as amphibians and insects. To investigate the impact of bombardier beetle defenses on avian predators, feeding trials were conducted using the bombardier beetle (Pheropsophus jessoensis) and the Japanese quail (Coturnix japonica), a sympatric and generalist predator. All naïve, hand-reared quail attacked live beetles, indicating the absence of an innate aversion to them. However, most of the quail rejected consuming the beetles whether or not the beetles sprayed them with chemicals. Naïve quail also rejected dead P. jessoensis individuals. These results support the recent hypothesis that it is not essential for P. jessoensis to spray noxious chemicals to deter predators. We also found that some of the quail exposed to live P. jessoensis remembered to avoid them for up to 5 weeks. Our results provide the first evidence of the repelling effects of bombardier beetle defense mechanisms on avian predators.
Recently, the New Morris Method has been presented as an effective sensitivity analysis tool for mathematical models. The
New Morris Method estimates the sensitivity of an output parameter to a given set of input parameters (first-order effects)
and the extent these parameters interact with each other (second-order effects). This method requires the specification of
two parameters (runs and resolution) that control the sampling of the output parameter to determine its sensitivity to various
inputs. The criteria for these parameters have been set on the analysis of a well-behaved analytical function (see Cropp and
Braddock, Reliab. Eng. Syst. Saf. 78:77–83, 2002), which may not be applicable to other physical models that describe complex
processes. This paper will investigate the appropriateness of the criteria from (Cropp and Braddock, 2002) and hence the effectiveness
of the New Morris Method to determine the sensitivity behaviour of two hydrologic models: the Soil Erosion and Deposition
System and Griffith University Representation of Urban Hydrology. In the first case, this paper will separately analyse the
sensitivity of an output parameter on a set of input parameters (first- and second-order effects) for each model and discuss
the physical meaning of these sensitivities. This will be followed by an investigation into the sampling criteria by exploring
the convergence of the sensitivity behaviour for each model as the sampling of the parameter space is increased. By comparing
these trends to the convergence behaviour from Cropp and Braddock (2002), we will determine how well the New Morris Method
estimates the sensitivity for each model and whether the sampling criteria are appropriate for these models. It will be shown
that the New Morris Method can provide additional insight into the functioning of these models, and that, under a different
metric, the sensitivity behaviour of these models does converge confirming the sampling criteria set by Cropp and Braddock. 相似文献
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. 相似文献
The analysis for arsenic in hair is commonly used in epidemiological studies to assess exposure to this toxic element. However, poor correlation between total arsenic concentration in hair and water sources have been found in previous studies. Exclusive determination of endogenous arsenic in the hair, excluding external contamination has become an analytical challenge. Arsenic speciation in hair appears as a new possibility for analytical assessing in As-exposure studies. This study applied a relative simple method for arsenic speciation in human hair based on water extraction and HPLC-HG-ICP-MS. The concentration of arsenic species in human hair was assessed in chronically As(V)-exposed populations from two villages (Esqui?a and Illapata) of the Atacama Desert, Chile. The arsenic concentrations in drinking water are 0.075 and 1.25 mg L(-1), respectively, where As(V) represented between 92 and 99.5% of the total arsenic of the consumed waters. On average, the total arsenic concentrations in hair from individuals of Esqui?a and Illapata were 0.7 and 6.1 microg g(-1), respectively. Four arsenic species, As(III), DMA(V), MMA(V) and As(V), were detected and quantified in the hair extracts. Assuming the found species in extracts represent the species in hair, more than 98% of the total arsenic in hair corresponded to inorganic As. On average, As(III) concentrations in hair were 0.25 and 3.75 microg g(-1) in Esqui?a and Illapata, respectively; while, the As(V) average concentrations were 0.15 and 0.45 microg g(-1) in Esqui?a and Illapata, respectively. Methylated species represent less than 2% of the extracted As (DMA(V)+ MMA(V)) in both populations. As(III) in hair shows the best correlation with chronic exposure to As(V) in comparison to other species and total arsenic. In fact, concentrations of As(total), As(III) and As(V) in hair samples are correlated with the age of the exposed individuals from Illapata (R= 0.65, 0.69, 0.57, respectively) and with the time of residence in this village (R= 0.54, 0.71 and 0.58, respectively). 相似文献
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. 相似文献
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 NOx 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, NOx and NO2 concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NOx 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. 相似文献