\u27Green\u27 manufacturing: A life cycle inventory of the automotive paint process and protocols for industry application.

In response to growing interest in \u27green\u27 vehicles, Life Cycle Analysis (LCA) and Life Cycle Inventory (LCI) concepts can quantify the automobile\u27s environmental impacts. Potential benefits include Design-for-Environment (DfE) opportunities, increased manufacturing efficiencies, and future application to consumer-based eco-rating systems. Detailed and up-to-date LCI data for general application by LCI practitioners does not exist at this time for the majority of Manufacturing/Assembly processes in North America, including the automotive paint process. With an Original Equipment Manufacturer (OEM) industry partner\u27s commitment to a publicly available LCI database (i.e., NREL LCI Database), however, a representative vehicle assembly facility was selected for completion of a paint process LCI. A detailed LCI reference dataset was developed to include materials, energy, and emissions associated with the Pretreatment, E-coat, and Top Coat paint unit processes. The challenges and industry realities of completing the LCI enabled a detailed set of guidelines to be developed, adapting existing protocols to the specifics of the manufacturing paint process. (Abstract shortened by UMI.)Dept. of Civil and Environmental Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .A53. Source: Masters Abstracts International, Volume: 45-01, page: 0386. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

Predictors of Indoor Air Concentrations in Smoking and Non-Smoking Residences

Indoor concentrations of air pollutants (benzene, toluene, formaldehyde, acetaldehyde, acrolein, nitrogen dioxide, particulate matter, elemental carbon and ozone) were measured in residences in Regina, Saskatchewan, Canada. Data were collected in 106 homes in winter and 111 homes in summer of 2007, with 71 homes participating in both seasons. In addition, data for relative humidity, temperature, air exchange rates, housing characteristics and occupants’ activities during sampling were collected. Multiple linear regression analysis was used to construct season-specific models for the air pollutants. Where smoking was a major contributor to indoor concentrations, separate models were constructed for all homes and for those homes with no cigarette smoke exposure. The housing characteristics and occupants’ activities investigated in this study explained between 11% and 53% of the variability in indoor air pollutant concentrations, with ventilation, age of home and attached garage being important predictors for many pollutants

www.mdpi.com/journal/ijerph Article Predictors of Indoor Air Concentrations in Smoking and Non-Smoking Residences

Abstract: Indoor concentrations of air pollutants (benzene, toluene, formaldehyde, acetaldehyde, acrolein, nitrogen dioxide, particulate matter, elemental carbon and ozone

diffraction structural biology 968 doi:10.1107/S0909049513021596 J. Synchrotron Rad. (2013). 20, 968–973 Journal of Synchrotron Radiation

The Japan Aerospace Exploration Agency (JAXA) started a high-quality protein crystal growth project, now called JAXA PCG, on the International Space Station (ISS) in 2002. Using the counter-diffusion technique, 14 sessions of experiments have been performed as of 2012 with 580 proteins crystallized in total. Over the course of these experiments, a user-friendly interface framework for high accessibility has been constructed and crystallization techniques improved; devices to maximize the use of the microgravity environment have been designed, resulting in some high-resolution crystal growth. If crystal-lization conditions were carefully fixed in ground-based experiments, high-quality protein crystals grew in microgravity in many experiments on the ISS, especially when a highly homogeneous protein sample and a viscous crystal-lization solution were employed. In this article, the current status of JAXA PCG is discussed, and a rational approach to high-quality protein crystal growth in microgravity based on numerical analyses is explained