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.     

Validation of two air quality models for Indian mining conditions

All major mining activity particularly opencast mining contributes to the problem of suspended particulate matter (SPM)directly or indirectly. Therefore, assessment and prediction are required to prevent and minimize the deterioration of SPM due tovarious opencast mining operations. Determination of emission rate of SPM for these activities and validation of air quality models are the first and foremost concern. In view of the above, the study was taken up for determination of emission rate for SPMto calculate emission rate of various opencast mining activitiesand validation of commonly used two air quality models for Indianmining conditions. To achieve the objectives, eight coal and three iron ore mining sites were selected to generate site specific emission data by considering type of mining, method of working, geographical location, accessibility and above all resource availability. The study covers various mining activitiesand locations including drilling, overburden loading and unloading, coal/mineral loading and unloading, coal handling orscreening plant, exposed overburden dump, stock yard, workshop, exposed pit surface, transport road and haul road. Validation of the study was carried out through Fugitive Dust Model (FDM) and Point, Area and Line sources model (PAL2) by assigning the measured emission rate for each mining activity, meteorologicaldata and other details of the respective mine as an input to the models. Both the models were run separately for the same set ofinput data for each mine to get the predicted SPM concentrationat three receptor locations for each mine. The receptor locationswere selected such a way that at the same places the actual filedmeasurement were carried out for SPM concentration. Statisticalanalysis was carried out to assess the performance of the modelsbased on a set measured and predicted SPM concentration data. The value of coefficient of correlation for PAL2 and FDM was calculated to be 0.990-0.994 and 0.966-0.997, respectively, which shows a fairly good agreement between measured and predicted values of SPM concentration. The average index of agreement values for PAL2 and FDM was found to be 0.665 and0.752, respectively, which represents that the prediction by PAL2 and FDM models are accurate by 66.5 and 75.2%, respectively. These indicate that FDM model is more suited for Indian mining conditions.     

Spatial based compromise programming for multiple criteria decision making in land use planning

Today, competing land use is continuing to occur in many developed regions. In the Agricultural Development Zone of Western Sydney Region, which is characterised by complex landscape patterns, land use competition is widespread. From a land use planning perspective, identification of suitable locations for a given type of land use is necessary for decision makers to formulate land use alternatives in different locations, based on existing land potential and constraints. For such a region, use of a simple method that implements a categorical system and considers only inherent land characteristics in the analysis is often inadequate to arrive at an optimal spatial decision. The primary aim of this paper is to develop spatial modelling procedures for agricultural land suitability analysis using compromise programming (CoPr) and fuzzy set approach within a geographical information systems (GIS) environment. Five main sets of spatial data for use as decision criteria were developed by using fuzzy set methodology: a land suitability index (LSI) for maximising the land productivity objective; an erosion tolerance index (ETI) for minimising the erosion risk objective; a runoff curve number (CN) for maximising the water discharge regulation objective; an accessibility (RP) measure for maximising the land accessibility objective; and the proximity to water body (WP) for minimising the water pollution objective. An L _p -metric was used in the analysis utilising different strategies with representative indices ranging from a situation where full tradeoff among criteria occurs to a noncompensatory condition. Different weighting combinations were also applied, and decision analysis was carried out by using values ranging from 0 to 1.0, where 1.0 is considered as an ideal point. The CoPr model demonstrated in this paper yielded a promising result, as several different techniques of sensitivity analysis show reasonably good results. Likewise, an overlay of that result with the present land use/land cover indicates a good corresponding spatial matching between existing land use (orchard and cultivated land), and the cells (land parcels) classified as the best in CoPr. The results are amenable to various map display techniques, either using continuous values or by defining different cut off points in the data space within a raster GIS environment.     

On the Performance and Applicability of Nonlinear Two-Equation Turbulence Models for Urban Air Quality Modelling

It is well known that the commonly used k- turbulence models yield inaccurate predictions for complex flow fields. One reason for this inaccuracy is the misrepresentation of Reynolds stress differences. Nonlinear turbulence models are capable to overcome this weakness while being not considerably more complex. However no comprehensive studies are known which analyze the performance of nonlinear turbulence models for three-dimensional flows around building-shaped structures. In the present study the predictions of the flow around a surface-mounted cube using three nonlinear two-equation turbulence models are discussed. The results are compared with predictions of the standard k- turbulence model and wind tunnel measurements. It is shown that the use of nonlinear turbulence models can be beneficial in predicting wind flows around buildings.     

The Relative Influences of Primary and Secondary Particulates to Urban Air Quality in the United Kingdom

This study uses a combination of data from U.K. monitoringstations and from modelling undertaken with the U.K.Meteorological Office's NAME Model to investigate therelative influences of primary and secondary particulateson total PM₁₀ levels at sites in the United Kingdom. Co-located PM₁₀ and sulphate aerosol measurementsindicate that sulphate has a disproportionately largeinfluence on the variation of PM₁₀ levels incomparison to its contribution to their total mass.Comparisons of measured PM₁₀ at urban centre, roadsideand rural sites suggest that local primary sources havevery little influence on daily mean levels. NAME has beenused to model both primary particles and sulphate aerosolfrom sources across the whole of Europe. The discrepanciesbetween modelled and observed PM₁₀ suggest that coarseparticles, such as windblown dust and resuspended roaddust,may comprise a very large, if not dominant, proportion ofobserved PM₁₀ levels. The apparently minor role ofprimary particles (especially locally-sourced ones) raisesa number of issues regarding the suitability of current U.K.and European legislation to addressing the particle problem.     

Spatial Variability and Source-Receptor Relations at a Street Intersection

A wind tunnel study of dispersion at a simple urban intersection comprising two perpendicular streets is described. Concentration and flow field measurement were undertaken to determine the importance of the exchange of pollutants between the streets and to investigate source-receptor relationships at the intersection. The results showed that only in a symmetrical situation were exchanges negligible and that small departures from symmetry, brought about in the experiments through an off-set in the street alignment or a change of orientation relative to the wind, were sufficient to establish significant exchanges. The results also showed that significant structure appeared in the concentration fields in the streets as a result. Examples are shown where concentrations on one side of a street are entirely due to emissions from the perpendicular street, whereas on the opposite side concentrations depend on emission upwind in the same street as the receptor. The results imply that exchanges between street systems are likely to be the norm in practice and that the consequences of such exchanges are not confined to the immediate vicinity of the intersection.     

Improvements in Air Quality Modelling by Using Surface Boundary Layer Parameters derived from Satellite Land Cover Data

Many atmospheric dispersion models include only simpletreatment of surface features to estimate the wind profilesand stability parameters. Detailed characterisation of theland cover, particularly in large and complex urbanconurbations, is especially important, as the surfacefeatures can vary significantly over the area. This paperdiscusses the use of satellite land cover data to derivespatially resolved surface boundary layer (SBL) parameters.These parameters have been used in an air quality model,PEARL (Prediction Air Quality in Urban and RegionalLocations) for estimating monthly and annual COconcentrations. Land cover data, derived from LANDSATThematic Mapper Imagery, has been used to estimate SBLparameters (surface roughness length, albeedo, Bowen ratioand anthropogenic heat flux) for a study area of 10000km² encompassing Greater London and the surroundingcounties. The SBL parameters have been assigned according tomajor land cover types for the whole area at a spatialresolution of 1 × 1 km. Predictions from two versions of the PEARL model (one with land cover data and one without)have been compared with each other and with measured data forannual and monthly CO concentrations from seven London airquality monitoring sites. This comparison shows thatdifferences between predicted and observed values can bereduced by up to a factor of three. The use of SBLparameters derived from land cover data also yields moredetailed predicted annual CO spatial patterns especially inand around suburban areas. The performance of both versionsof the model for monthly CO concentrations has been comparedwith a range of statistical measures. This comparisonconfirms that improved agreement is observed betweenmodelled and measured monthly CO concentrations when use ismade of spatially resolved SBL parameters.