PROBABILISTIC METHODS IN STREAM QUALITY MANAGEMENT1

ABSTRACT. Recent advances in water quality modelling have pointed out the need for stochastic models to simulate the probabilistic nature of water quality. However, often all that is needed is an estimate of the uncertainty in predicting water quality variables. First order analysis is a simple method of providing an estimate in the uncertainty in a deterministic model due to uncertain parameters. The method is applied to the simplified Streeter-Phelps equations for DO and BOD; a more complete Monte Carlo simulation is used to check the accuracy of the results. The first order analysis is found to give accurate estimates of means and variances of DO and BOD up to travel times exceeding the critical time. Uncertainty in travel time and the BOD decay constant are found to be most important for small travel times; uncertainty in the reaeration coefficient dominates near the critical time. Uncertainty in temperature was found to be a negligible source of uncertainty in DO for all travel times.     

Computer software tool REALM for sustainable water allocation and management

REALM (REsource ALlocation Model) is a generalised computer simulation package that models harvesting and bulk distribution of water resources within a water supply system. It is a modeling tool, which can be applied to develop specific water allocation models. Like other water resource simulation software tools, REALM uses mass-balance accounting at nodes, while the movement of water within carriers is subject to capacity constraints. It uses a fast network linear programming algorithm to optimise the water allocation within the network during each simulation time step, in accordance with user-defined operating rules. This paper describes the main features of REALM and provides potential users with an appreciation of its capabilities. In particular, it describes two case studies covering major urban and rural water supply systems. These case studies illustrate REALM's capabilities in the use of stochastically generated data in water supply planning and management, modelling of environmental flows, and assessing security of supply issues.     

The effect of vent ducts on the reduced explosion pressures of vented dust explosions

An investigation into the effects of vent ducts on reduced explosion pressures is described. Experiments were made using an 18.5m³ explosion vessel and a modified 20 1 sphere, with dusts having K_st values ranging from 144 bar ms⁻¹ to 630 bar ms⁻¹. The vent area/vessel volume ratio bursting pressure of the vent cover, and the length to diameter ratio of the vent duct have been varied. Straight vent ducts, and ducts containing sharp 45° and 90° bends have been used.A simple model to describe the effect of vent ducts on the reduced explosion pressure has been derived and compared with the experimental results. Agreement is shown to be satisfactory in nearly all cases. A comparison between the experimental results and guidance on the effect of vent ducts already available in the literature is discussed.     

Monitoring and dispersion modelling of emissions from the fluidised bed combustion of poultry litter

Gaseous emissions from the fluidised bed combustion of chickenlitter were monitored and recorded. Emission data was used tocreate a dispersion model for a proposed site on a poultry farmin Limerick, Ireland. Variables within the combustion unit notonly influenced combustion but also emission levels ofpollutants such as SO₂ and NO_x. CO emissions decreased withuse of the correct ratio between fluidising and secondary air,the staging of secondary air and the degree of turbulence withwhich the secondary air is introduced. Dispersion modelling ofactual combustion at a proposed poultry unit predicted thatground level concentrations for the set of emissions data wouldbe below the limits and guidelines set by air quality standards.This was true for both and line source. Line sourceconcentration levels were less than those for point source.     

Integration of Case Studies on Global Change by Means of Qualitative Differential Equations

We present a novel methodology to integrate qualitative knowledge from different case studies on Global Change related issues into a single framework. The method is based on the concept of qualitative differential equations (QDEs) which represents a mathematically well-defined approach to investigate classes of ordinary differential equations (ODEs) used in conventional modeling exercises. These classes are defined by common qualitative features, e.g., monotonicity, signs, etc. Using the QSIM-algorithm it is possible to derive the set of possible solutions of all ODEs in the class. By this one can formulate a common, qualitatively specified cause–effect scheme valid for all case studies. The scheme is validated by testing it against the actually observed histories in the study regions with respect to their reconstruction by the corresponding QDE. The method is outlined theoretically and exemplary applied to the problem of land-use changes due to smallholder agriculture in developing countries. It is shown that the seven case-studies used can be described by a single cause–effect scheme which thus constitutes a pattern of Global Change. As a generally valid prerequisite for sustainability of this kind of land-use the presence of wage labor is shown to represent a decisive factor. This revised version was published online in July 2006 with corrections to the Cover Date.     

STATISTICAL AND GEOSTATISTICAL MODELLING OF PRELIMINARILY ADJUSTED DEFOLIATION ON AN EUROPEAN SCALE

Since 1987 tree crown condition is surveyed annually in large parts of Europe mainly in terms of defoliation. The plot-wise means of defoliation from 1994 to 2000 were evaluated by General Linear Models (GLM) in order to describe country-specific levels of defoliation and age-trends. Additional runs with estimates for influences of insects and fungi were performed. The amount of variance of defoliation explained by country, age and its interaction was between 35% and 59% for the main tree species, except for Quercus ilex. Additionally, up to 10% could be explained by the inclusion of estimates for infestations by insects and fungi. Residuals of the GLMs were taken as a measure of forest condition not biased by country or age effects and interpreted as preliminarily adjusted defoliation (PAD). PAD values were analysed using geostatistical methods. The modelled spatial autocorrelations were used for kriging. The resulting maps give an overview on regions with elevated defoliation, which may pinpoint regional causes of defoliation. The elimination of methodologically caused variance is a precondition of any cause–effect oriented analyses. The combination of explorative modelling and geostatistics will promote the choice of further promising predictors.     

Laboratory-scale chlorination to estimate the levels of halogenated DBPs in full-scale distribution systems

Occurrence of halogenated disinfection by-products (DBPs) (trihalomethanes –THMs– and haloacetic acids –HAAs–) in the waters of two utilities in Quebec City (Canada) was investigated using two approaches: experimental chlorination studies and full-scale sampling within distribution systems. Experimental studies were designed to reproduce treatment plant and distribution system conditions (chlorine dose, water temperature, pH and water residence time). Differences in halogenated DBPs in the two distribution systems under study were significant and comparable to those observed in experimental laboratory studies. For the waters of both utilities, chlorination studies better reproduced the occurrence of halogenated DBPs in points of the distribution system located near the treatment plant (low residence time of water) than in other points. Multivariate regression models for THMs, HAAs and their species were developed using the data from experimental studies in order to predict halogenated DBP levels measured in the distribution system. Models were all statistically significant, but showed low ability to predict full-scale halogenated DBPs, particularly in points located at distribution system extremities. Specifically, experimental chlorination-based models are not able to simulate the decrease of HAA levels. Results of this research suggest that the use of experimental data to predict halogenated DBP levels in full-scale distribution systems – for operational, regulatory and epidemiological purposes – must be done with caution.     

Application Of Artificial Neural Network Models To Analyse The Relationships Between Gammarus pulex L. (Crustacea, Amphipoda) And River Characteristics

This study aimed at analysing the relationship between river characteristics and abundance of Gammarus pulex. To this end, four methods which can identify the relative contribution and/or the contribution profile of the input variables in neural networks describing the habitat preferences of this species were compared: (i) the ‘PaD‘ (‘Partial Derivatives‘) method consists of a calculation of the partial derivatives of the output in relation to the input variables; (ii) the ‘Weights‘method is a computation using the connection weights of the backpropagation Artificial Neural Networks; (iii) the ‘Perturb‘method analyses the effect of a perturbation of the input variables on the output variable; (iv) the ‘Profile‘ method is a successive variation of one input variable while the others are kept constant at a fixed set of values. The dataset consisted of 179 samples, collected over a three-year period in the Zwalm river basin in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Gammarus pulex were used in this study. The different contribution methods gave similar results concerning the order of importance of the input variables. Moreover, the stability of the methods was confirmed by gradually removing variables. Only in a limited number of cases a shift in the relative importance of the remaining input variables could be observed. Nevertheless, differences in sensitivity and stability of the methods were detected, probably as a result of the different calculation procedures. In this respect, the ‘PaD‘method made a more severe discrimination between minor and major contributing environmental variables in comparison to the ‘Weights‘, ‘Profile‘ and ‘Perturb‘ methods. From an ecological point of view, the input variables ‘Ammonium‘ and to a smaller extent ‘COD‘, were selected by these methods as dominant river characteristics for the prediction of the abundance of Gammarus pulex in this study area.