Introduction of a spatiotemporal Life Cycle Inventory method using a wind energy example

This is the final version of the article. Available from the publisher via the DOI in this record.Life cycle assessment (LCA) is "primarily a steady-state-tool" and few studies to date have included dynamic temporal and spatial information in matrix-based LCA. Because of this many environmental impacts cannot be determined accurately in conventional LCA. We have integrated both temporal and spatial information in a novel dynamical life cycle inventory (LCI) framework that can produce detailed spatiotemporal results and thus offering more insights for sustainability assessment. This framework employs the existing Enhanced Structural Path Analysis (ESPA) method combined with spatial analysis to determine spatialised LCI over time. Previously we tested this new approach with a local spatial dispersion model using wheat production as an illustration. In this paper we demonstrate the new spatiotemporal LCI method over an entire life cycle, using wind energy as an example and a different approach to spatial analysis at a global scale.XY acknowledges financial support from the EU under Interreg project “ICE: Intelligent Community Energy”

Introducing a Localised Spatio-temporal LCI Method with wheat production as exploratory case study

The use of dynamical information, which is temporally and spatially explicit, to quantify environmental impacts is gaining importance in recent years. Life Cycle Assessment has been applied to identify environmental impacts of, for example, wheat production. However, conventional Life Cycle Assessment is typically limited by its static nature and cannot explicitly consider temporal and spatial variability in its matrix-based mathematical structure. To address this limitation, a novel dynamical Life Cycle Assessment framework that applies spatio-temporal mathematical models in Life Cycle Inventory is introduced. This framework employs the existing Enhanced Structural Path Analysis (ESPA) method paired with a spatial dispersion model to determine the localised emissions over time within the Life Cycle Inventory. The spatially explicit calculations consider emissions to the surrounding area of an origin. A case study was undertaken to demonstrate the developed framework using the production of wheat at the Helford area in Cornwall, UK. Results show the spatio-temporal dispersion for four example emissions atmosphere, soil, flowing and groundwater. These outcomes show that it is possible to implement both spatial and temporal information in matrix-based LCI. We believe this framework could potentially transform the way LCA is currently performed, i.e., in a static and spatially-generic way and will offer significantly improved understanding of life cycle environmental impacts and better inform management of processes such as agricultural production that have high spatial and temporal heterogeneity

Soybean Rust Makes it to Iowa

Soybean rust, Phakopsora pachyrhizi, was first reported in the United States in November 2004 and survived winters on kudzu in the south. Soybean rust can reduce soybean yields and/or significantly increase the cost of soybean production when the disease occurs during the growing season with high incidence and severity. During the 2005 and 2006 growing seasons, soybean rust was not a threat for Iowa soybean growers. This year was a different story, as soybean rust was established fairly early in the season in Texas and Louisiana creating the potential for soybean rust to get to Iowa during the growing season. Thankfully, soybean rust was not found while soybean plants were in a vulnerable stage; however, soybean rust was found in a field in Dallas County, Iowa, on Tuesday, September 25, 2007. Since the initial find, soybean rust was confirmed in 13 additional counties throughout Iowa (Figure 1)

Determination and optimization of mode matching into optical cavities by heterodyne detection

We report on a novel high-sensitivity method to characterize and improve mode matching into optical cavities. This method is based on heterodyne detection of cylindrical transverse cavity modes. A specially designed annular-segmented photodiode is used to measure the amplitude of nonresonant modes reflected by the cavity. Our measurements allow us to optimize cavity mode matching to nearly 99.98% and will play an important diagnostic role in gravitational-wave detectors

Dihadron fragmentation functions and high Pt hadron-hadron correlations

We propose the formulation of a dihadron fragmentation function in terms of parton matrix elements. Under the collinear factorization approximation and facilitated by the cut-vertex technique, the two hadron inclusive cross section at leading order (LO) in e+ e- annihilation is shown to factorize into a short distance parton cross section and the long distance dihadron fragmentation function. We also derive the DGLAP evolution equation of this function at leading log. The evolution equation for the non-singlet quark fragmentation function is solved numerically with a simple ansatz for the initial condition and results are presented for cases of physical interest.Comment: Latex, 4 pages, 4 figures, talk given at Quark Matter 2004, To appear in J. Phys.