Physical and economic potential of geological CO2 storage in saline aquifers.

Carbon sequestration in sandstone saline reservoirs holds great potential for mitigating climate change, but its storage potential and cost per ton of avoided CO2 emissions are uncertain. We develop a general model to determine the maximum theoretical constraints on both storage potential and injection rate and use it to characterize the economic viability of geosequestration in sandstone saline aquifers. When applied to a representative set of aquifer characteristics, the model yields results that compare favorably with pilot projects currently underway. Over a range of reservoir properties, maximum effective storage peaks at an optimal depth of 1600 m, at which point 0.18-0.31 metric tons can be stored per cubic meter of bulk volume of reservoir. Maximum modeled injection rates predict minima for storage costs in a typical basin in the range of $2-7/ ton CO2 (2005 U.S.$) depending on depth and basin characteristics in our base-case scenario. Because the properties of natural reservoirs in the United States vary substantially, storage costs could in some cases be lower or higher by orders of magnitude. We conclude that available geosequestration capacity exhibits a wide range of technological and economic attractiveness. Like traditional projects in the extractive industries, geosequestration capacity should be exploited starting with the low-cost storage options first then moving gradually up the supply curve.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.