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Background, aim, and scope Life cycle analyses (LCA) approaches require adaptation to reflect the increasing delocalization of production to emerging countries. This work addresses this challenge by establishing a countrylevel, spatially explicit life cycle inventory (LCI). This study comprises three separate dimensions. The first dimension is spatial: processes and emissions are allocated to the country in which they take place and modeled to take into account local factors. Emerging economies China and India are the location of production, the consumption occurs in Germany, an Organisation for Economic Cooperation and Development country. The second dimension is the product level: we consider two distinct textile garments, a cotton T-shirt and a polyester jacket, in order to highlight potential differences in the production and use phases. The third dimension is the inventory composition: we track CO 2 , SO 2 , NO x , and particulates, four major atmospheric pollutants, as well as energy use. This third dimension enriches the analysis of the spatial differentiation (first dimension) and distinct products (second dimension). Materials and methods We describe the textile production and use processes and define a functional unit for a garment. We then model important processes using a hierarchy of preferential data sources. We place special emphasis on the modeling of the principal local energy processes: electricity and transport in emerging countries. Results The spatially explicit inventory is disaggregated by country of location of the emissions and analyzed according to the dimensions of the study: location, product, and pollutant. The inventory shows striking differences between the two products considered as well as between the different pollutants considered. For the T-shirt, over 70% of the energy use and CO 2 emissions occur in the consuming country, whereas for the jacket, more than 70% occur in the producing country. This reversal of proportions is due to differences in the use phase of the garments. For SO 2 , in contrast, over two thirds of the emissions occur in the country of production for both T-shirt and jacket. The difference in emission patterns between CO 2 and SO 2 is due to local electricity processes, justifying our emphasis on local energy infrastructure.chain. The inclusion of two different products in the LCI highlights the importance of the definition of a product's functional unit in the analysis and implications of results. Several use-phase scenarios demonstrate the importance of consumer behavior over equipment efficiency. The spatial emission patterns of the different pollutants allow us to understand the role of various energy infrastructure elements. The emission patterns furthermore inform the debate on the Environmental Kuznets Curve, which applies only to pollutants which can be easily filtered and does not take into account the effects of production displacement. We also discuss the appropriateness and limitations of applying the LCA methodology in a global context, especial...

The Close-Up Imager (CLUPI) onboard the ESA ExoMars Rover is a powerful high-resolution color camera specifically designed for close-up observations. Its accommodation on the movable drill allows multiple positioning. The science objectives of the instrument are geological characterization of rocks in terms of texture, structure, and color and the search for potential morphological biosignatures. We present the CLUPI science objectives, performance, and technical description, followed by a description of the instrument's planned operations strategy during the mission on Mars. CLUPI will contribute to the rover mission by surveying the geological environment, acquiring close-up images of outcrops, observing the drilling area, inspecting the top portion of the drill borehole (and deposited fines), monitoring drilling operations, and imaging samples collected by the drill. A status of the current development and planned science validation activities is also given. Key Words: Mars-Biosignatures-Planetary Instrumentation. Astrobiology 17, 595-611.

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