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• Tree areas of agroforestry systems (AFS) stored more C than adjacent herblands. • Surface organic horizon contained 10% of the C stored in the tree area of AFS. • C storage in surface organic horizon was positively related to tree age. • Establishing AFS can be an effective strategy for climate change mitigation. Agroforestry systems (AFS) are increasingly being recognized as sustainable land-use systems that can increase carbon (C) sequestration and improve food security. However, the relative contributions of various C pools to the overall C stock of AFS and their dependence on factors such as tree age, species diversity and density is still unclear in many regions. Using an equivalent soil mass approach, we investigated the effect of three different AFS (hedgerow, shelterbelt, and silvopasture) and their component land-use types (treed areas and adjacent cropland/grassland) on various C pools across 36 sites in central Alberta, Canada. On average, the treed area within AFS stored 178.5 t ha−1 more C relative to adjacent cropland or grassland, although the total C stock did not differ among the three AFS types. Most of the C in the treed area of AFS was stored in the mineral soil (61%), overstory trees (28%), and the LFH (litter, partially decomposed litter and humus, 10%) layer, while midstory trees, shrubs, and understory vegetation together accounted for the remaining 1% of the C storage. The C stock size in the treed area was closely associated with tree stand characteristics of the three AFS, with the C pool size in the LFH positively related to tree age. Our results highlight that the amount of C stored in the LFH layer is substantial and should be considered when assessing C storage within AFS and suggest that investing in AFS can be an effective strategy for meeting part of Canada's C reduction goals to mitigate climate change. [ABSTRACT FROM AUTHOR]

Abstract: This study presents a computational methodology to estimate the maximum probable leakage of CO2 along old wells in a geological sequestration operation. The methodology quantifies the maximum probable CO2 leakage as a function of the statistical characterization of existing wells. We use a Monte Carlo approach based on a computationally efficient simulator to run many thousands of realizations. Results from the Monte Carlo simulations are used to determine maximum leakage rates at 95% confidence. Uncertainty in the analysis is due to leaky well parameters, which are known to be highly uncertain. We consider a wide range of parameter values, with our focus on assignment of effective well permeability values and the correlation of those values along individual wells. We use a specific location in Alberta, Canada, to demonstrate the methodology using a hypothetical injection and an assumed probability structure for the well permeabilities. We show that for a wide range of parameter values, the amount of leakage is within the bounds suggested as acceptable for climate change mitigation. [Copyright &y& Elsevier]