Analysing the sensitivity behaviour of two hydrology models

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.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

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.     

Application of cryofocusing hydride generation and atomic fluorescence detection for dissolved mercury species determination in natural water samples

The concentration levels of mercury (Hg) species in natural water samples are usually low. Consequently, accurate analysis with low detection limits is still a major problem. In this work, a method was applied for the simultaneous direct determination of dissolved mercury species in water samples by on-line hydride generation (HG), cryogenic trapping (CT), gas chromatography (GC) and detection by atomic fluorescence spectrometry (AFS). The suitability of the method for real samples with different organic matter and chloride contents was evaluated by recovery experiments in synthetic and natural spiked water samples. The HG method was compared with other current available methods for mercury analysis with respect to the different fraction of mercury analysed, i.e. 'reactive', 'reducible' or total. HG derivatization and SnCl2 reduction (with and without previous oxidation with BrCl) were applied to synthetic and natural (spiked and non-spiked) water samples. The influence of chloride and dissolved organic matter concentrations was studied. The results suggest that the HG procedure is suitable for the simultaneous determination of Hg2+ and MeHg+ in surface water samples. Inorganic mercury analysed by HG (i.e. reducible) is close to the total inorganic mercury.     

Reduction of SO2 emissions by ammonia gas during unstaged combustion

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.     

High frequency monitoring of the coastal marine environment using the MAREL buoy

The MAREL Iroise data buoy provides physico-chemical measurements acquired in surface marine water in continuous and autonomous mode. The water is pumped 1.5 m from below the surface through a sampling pipe and flows through the measuring cell located in the floating structure. Technological innovations implemented inside the measuring cell atop the buoy allow a continuous cleaning of the sensor, while injection of chloride ions into the circuit prevents biological fouling. Specific sensors for temperature, salinity, oxygen and fluorescence investigated in this paper have been evaluated to guarantee measurement precision over a 3 month period. A bi-directional link under Internet TCP-IP protocols is used for data, alarms and remote-control transmissions with the land-based data centre. Herein, we present a 29 month record for 4 parameters measured using a MAREL buoy moored in a coastal environment (Iroise Sea, Brest, France). The accuracy of the data provided by the buoy is assessed by comparison with measurements of sea water weekly sampled at the same site as part of SOMLIT (Service d'Observation du Milieu LIToral), the French network for monitoring of the coastal environment. Some particular events (impact of intensive fresh water discharges, dynamics of a fast phytoplankton bloom) are also presented, demonstrating the worth of monitoring a highly variable environment with a high frequency continuous reliable system.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

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 NO_x 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, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x 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.     

Bioindicator organisms: Heavy metal pollution evaluation in the Ionian Sea (Mediterranean Sea—Italy)

Trace metal concentrations of mercury, cadmium, lead and chromium in Patella caerulea, and Mullus barbatus were investigated to provide information on pollution of Ionian Sea, since these metals have the highest toxic potential. High chromium levels (0.47–0.97 g g^–1 ww) were registered in limpet samples collected from two station near the Gulf of Taranto, while elevated concentration of mercury (0.31–1.50 g g^–1 ww) were found in mullet specimens from Sicily. The metal concentrations recorded at the clean stations may be considered as useful background levels to which to refer for comparison within the Mediterranean area. On the contrary, the high levels of chromium and mercury found respectively in the areas near the Gulf of Taranto and at Capo Passero being of concern in terms of environmental health need frequent monitoring.     

Environmental quality control and environmental games

Conclusion  In this paper we have considered a specific environmental game emphasizing both control-prevention efforts and the propensity to pollute by a firm which adopts a given pollution abatement technology. A random payoff game was constructed and solved under a risk neutral assumption and quadratic utilities for both the firm and the environmental controller. The game thus defined, provides a wide range of interpretations and potential approaches for selecting a control-inspection policies to prevent environmental risks. There are of course many facets to this problem, which could be considered and have not been considered in sufficient depth. For example, more complex control mechanisms and liabilities, the effects of insurance and risk sharing, the application of cooperative efforts and subvention of pollution abatement investments (through tax incentives and their like), etc. have not been considered [5,7]. These are topics for further research. The basic presumption of this paper is that it is very difficult to fully enforce pollution prevention by firms, as a result, some controls are needed to ensure that firms be controlled so that appropriate efforts are carried.