4,059 research outputs found

    Impact of the earthworm Lumbricus terrestris (L.) on As, Cu, Pb and Zn mobility and speciation in contaminated soils

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    To assess the risks that contaminated soils pose to the environment properly a greater understanding of how soil biota influence the mobility of metal(loid)s in soils is required. Lumbricus terrestris L. were incubated in three soils contaminated with As, Cu, Pb and Zn. The concentration and speciation of metal(loid)s in pore waters and the mobility and partitioning in casts were compared with earthworm-free soil. Generally the concentrations of water extractable metal(loid)s in earthworm casts were greater than in earthworm-free soil. The impact of the earthworms on concentration and speciation in pore waters was soil and metal specific and could be explained either by earthworm induced changes in soil pH or soluble organic carbon. The mobilisation of metal(loid)s in the environment by earthworm activity may allow for leaching or uptake into biota

    Managing urban socio-technical change? Comparing energy technology controversies in three European contexts

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    A {\em local graph partitioning algorithm} finds a set of vertices with small conductance (i.e. a sparse cut) by adaptively exploring part of a large graph GG, starting from a specified vertex. For the algorithm to be local, its complexity must be bounded in terms of the size of the set that it outputs, with at most a weak dependence on the number nn of vertices in GG. Previous local partitioning algorithms find sparse cuts using random walks and personalized PageRank. In this paper, we introduce a randomized local partitioning algorithm that finds a sparse cut by simulating the {\em volume-biased evolving set process}, which is a Markov chain on sets of vertices. We prove that for any set of vertices AA that has conductance at most ϕ\phi, for at least half of the starting vertices in AA our algorithm will output (with probability at least half), a set of conductance O(ϕ1/2log1/2n)O(\phi^{1/2} \log^{1/2} n). We prove that for a given run of the algorithm, the expected ratio between its computational complexity and the volume of the set that it outputs is O(ϕ1/2polylog(n))O(\phi^{-1/2} polylog(n)). In comparison, the best previous local partitioning algorithm, due to Andersen, Chung, and Lang, has the same approximation guarantee, but a larger ratio of O(ϕ1polylog(n))O(\phi^{-1} polylog(n)) between the complexity and output volume. Using our local partitioning algorithm as a subroutine, we construct a fast algorithm for finding balanced cuts. Given a fixed value of ϕ\phi, the resulting algorithm has complexity O((m+nϕ1/2)polylog(n))O((m+n\phi^{-1/2}) polylog(n)) and returns a cut with conductance O(ϕ1/2log1/2n)O(\phi^{1/2} \log^{1/2} n) and volume at least vϕ/2v_{\phi}/2, where vϕv_{\phi} is the largest volume of any set with conductance at most ϕ\phi.Comment: 20 pages, no figure
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