Sustainability study of nanomaterials including societal and occupational implications

In recent years there has been a tremendous amount of research in the area of nanotechnology. History tells us that the commercialization of technologies will always be accompanied by both positive and negative effects for society and the environment. Products containing nanomaterials are already available in the market, and yet there is still not much information regarding the potential negative effects that these products may cause. The work presented in this dissertation describes a holistic approach to address different dimensions of nanotechnology sustainability. Life cycle analysis (LCA) was used to study the potential usage of polyethylene filled with nanomaterials to manufacture automobile body panels. Results showed that the nanocomposite does not provide an environmental benefit over traditional steel panels. A new methodology based on design of experiments (DOE) techniques, coupled with LCA, was implemented to investigate the impact of inventory uncertainties. Results showed that data variability does not have a significant effect on the prediction of the environmental impacts. Material profiles for input materials did have a highly significant effect on the overall impact. Energy consumption and material characterization were identified as two mainstreams where additional research is needed in order to predict the overall impact of nanomaterials more effectively. A study was undertaken to gain insights into the behavior of small particles in contact with a surface exposed to air flow to determine particle lift-off from the surface. A mapping strategy was implemented that allows for the identification of conditions for particle liftoff based on particle size and separation distance from the wall. Main results showed that particles smaller than 0:1mm will not become airborne under shear flow unless the separation distance is greater than 15 nm. Results may be used to minimize exposure to airborne materials. Societal implications that may occur in the workplace were researched. This research task explored different topics including health, ethics, and worker perception with the aim of identifying the base knowledge available in the literature. Recommendations are given for different scenarios to describe how workers and employers could minimize the unwanted effects of nanotechnology production.

PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.