Identifying pathways to visions of future land use in Europe

Regional Environmental Change - Plausible scenarios of future land use derived from model projections may differ substantially from what is actually desired by society, and identifying such...     

Evolving social influence in large populations

Darwinian studies of collective human behaviour, which deal fluently with change and are grounded in the details of social influence among individuals, have much to offer “social” models from the physical sciences which have elegant statistical regularities. Although Darwinian evolution is often associated with selection and adaptation, “neutral” models of drift are equally relevant. Building on established neutral models, we present a general, yet highly parsimonious, stochastic model, which generates an entire family of real-world, right-skew socio-economic distributions, including exponential, winner-take-all, power law tails of varying exponents, and power laws across the whole data. The widely used Barabási and Albert (1999) Science 286: 509-512 “B-A” model of preferential attachment is a special case of this general model. In addition, the model produces the continuous turnover observed empirically within these distributions. Previous preferential attachment models have generated specific distributions with turnover using arbitrary add-on rules, but turnover is an inherent feature of our model. The model also replicates an intriguing new relationship, observed across a range of empirical studies, between the power law exponent and the proportion of data represented in the distribution.     

Model-driven development of covariances for spatiotemporal environmental health assessment

Known conceptual and technical limitations of mainstream environmental health data analysis have directed research to new avenues. The goal is to deal more efficiently with the inherent uncertainty and composite space-time heterogeneity of key attributes, account for multi-sourced knowledge bases (health models, survey data, empirical relationships etc.), and generate more accurate predictions across space-time. Based on a versatile, knowledge synthesis methodological framework, we introduce new space-time covariance functions built by integrating epidemic propagation models and we apply them in the analysis of existing flu datasets. Within the knowledge synthesis framework, the Bayesian maximum entropy theory is our method of choice for the spatiotemporal prediction of the ratio of new infectives (RNI) for a case study of flu in France. The space-time analysis is based on observations during a period of 15 weeks in 1998–1999. We present general features of the proposed covariance functions, and use these functions to explore the composite space-time RNI dependency. We then implement the findings to generate sufficiently detailed and informative maps of the RNI patterns across space and time. The predicted distributions of RNI suggest substantive relationships in accordance with the typical physiographic and climatologic features of the country.     

A test of sequential extractions for determining metal speciation in sewage sludge-amended soils

Sequential extraction procedures are widely used to estimate the quantity of trace metals bound to different solid fractions in contaminated soils. However, reliability of speciation of trace metals by these procedures remains largely unexamined. In the present study, the selectivity of each extraction step was tested by observing the effect of reversing the extraction order in the procedure. Two different sequential extraction methods and their reversed modes were used for metal fractionation in sewage sludge-amended soils. Significantly increased amounts of extractable metals (Cd, Cu, Pb and Zn) were evident in the sludge-amended soils compared to control soil by all extraction schemes; however, the amounts of metals extracted by each step were strongly dependent on the order of extraction, the type of reagents and the nature of the individual metals. Caution is advised in deducing the forms of soil metals from sequential extraction results from metal-contaminated soils.