Partitioning of dioxins (PCDDs/Fs) in ambient air at urban residential locations

In this work, 17-polychlorinated dibenzo-p-dioxin/furan (PCDD/Fs) isomers were measured in ambient air at four urban sites in Seoul, Korea (from February to June 2009). The concentrations of their summed values (ΣPCDD/Fs) across all four sites ranged from 1,947 (271 WHO₀₅ TEQ) (Jong Ro) to 2,600 (349 WHO₀₅ TEQ) fg/m³ (Yang Jae) with a mean of 2,125 (± 317) fg/m³ (292 WHO₀₅ TEQ fg/m³). The sum values for the two isomer groups of ΣPCDD and ΣPCDF were 527 (30 WHO₀₅ TEQ) and 1,598 (263 WHO₀₅ TEQ) fg/m³, respectively. The concentration profile of individual species was dominated by the 2,3,4,7,8-PeCDF isomer, which contributed approximately 36 % of the ΣPCDD/Fs value. The observed temporal trends in PCDD/F concentrations were characterized by relative enhancement in the winter and spring. The relative contribution of different sources, when assessed by principal component analysis, is explained by the dominance of vehicular emissions along with coal (or gas) burning as the key source of ambient PCDD/Fs in the residential areas studied.     

Cloud computing and virtualization within the regional climate model and evaluation system

The Regional Climate Model Evaluation System (RCMES) facilitates the rapid, flexible inclusion of NASA observations into climate model evaluations. RCMES provides two fundamental components. A database (RCMED) is a scalable point-oriented cloud database used to elastically store remote sensing observations and to make them available using a space time query interface. The analysis toolkit (RCMET) is a Python-based toolkit that can be delivered as a cloud virtual machine, or as an installer package deployed using Python Buildout to users in order to allow for temporal and spatial regridding, metrics calculation (RMSE, bias, PDFs, etc.) and end-user visualization. RCMET is available to users in an “offline”, lone scientist mode based on a virtual machine dynamically constructed with model outputs and observations to evaluate; or on an institution’s computational cluster seated close to the observations and model outputs. We have leveraged RCMES within the content of the Coordinated Regional Downscaling Experiment (CORDEX) project, working with the University of Cape Town and other institutions to compare the model output to NASA remote sensing data; in addition we are also working with the North American Regional Climate Change Assessment Program (NARCCAP). In this paper we explain the contribution of cloud computing to RCMES’s specifically describing studies of various cloud databases we evaluated for RCMED, and virtualization toolkits for RCMET, and their potential strengths in delivering user-created dynamic regional climate model evaluation virtual machines for our users.     

Performance evaluation of ultrafine cement as a grout for reservoir impermeability improvement

Acta Geotechnica - Although Ordinary Portland Cement (OPC) has been widely used as one of the significant grouting materials, there are many drawbacks to its application in the field. In this...     

Color of the Corona Continuum of July 11, 1991

Geomagnetism and Aeronomy - The color of the coronal continuum was determined from observations in order to search for manifestations of coronal expansion. A brief review of studies determining the...     

Impact of sensor measurement error on sensor positioning in water quality monitoring networks

This paper studies the impact of sensor measurement error on designing a water quality monitoring network for a river system, and shows that robust sensor locations can be obtained when an optimization algorithm is combined with a statistical process control (SPC) method. Specifically, we develop a possible probabilistic model of sensor measurement error and the measurement error model is embedded into a simulation model of a river system. An optimization algorithm is used to find the optimal sensor locations that minimize the expected time until a spill detection in the presence of a constraint on the probability of detecting a spill. The experimental results show that the optimal sensor locations are highly sensitive to the variability of measurement error and false alarm rates are often unacceptably high. An SPC method is useful in finding thresholds that guarantee a false alarm rate no more than a pre-specified target level, and an optimization algorithm combined with the thresholds finds a robust sensor network.     

Data Analysis and Modeling to Optimize Thermal Treatment Cost and Performance

The objective of in situ thermal treatment is typically to reduce the contaminant mass or average soil concentration below a specified value. Evaluation of whether the objective has been met is usually made by averaging soil concentrations from a limited number of soil samples. Results from several field sites indicate large performance uncertainty using this approach, even when the number of samples is large. We propose a method to estimate average soil concentration by fitting a log normal probability model to thermal mass recovery data. A statistical approach is presented for making termination decisions from mass recovery data, soil sample data, or both for an entire treatment volume or for subregions that explicitly considers estimation uncertainty which is coupled to a stochastic optimization algorithm to identify monitoring strategies to meet objectives with minimum expected cost. Early termination of heating in regions that reach cleanup targets sooner enables operating costs to be reduced while ensuring a high likelihood of meeting remediation objectives. Results for an example problem demonstrate that significant performance improvement and cost reductions can be achieved using this approach.     

The evaluation of the probability of rock wedge failure using the point estimate method

In a probabilistic analysis of rock slope stability, the Monte Carlo simulation technique has been widely used to evaluate the probability of slope failure. While the Monte Carlo simulation technique has many advantages, the technique requires complete information of the random variables in stability analysis; however, in practice, it is difficult to obtain complete information from a field investigation. The information on random variables is usually limited due to the restraints of sampling numbers. This is why approximation methods have been proposed for reliability analyses. Approximation methods, such as the first-order second-moment method and the point estimate method, require only the mean and standard deviation of the random variable; therefore, it is easy to utilize when the information is limited. Usually, a single closed form of the formula for the evaluation of the factor of safety is needed for an approximation method. However, the commonly used stability analysis method of wedge failure is complicated and cumbersome and does not provide a simple equation for the evaluation of the factor of safety. Consequently, the approximation method is not appropriate for wedge failure. In order to overcome this limitation, a simple equation, which is obtained from the maximum likelihood estimation method for wedge failure, is utilized to calculate the probability of failure. A simple equation for the direct estimation of the safety factors for wedge failure has been empirically derived from failed and stable cases of slope, using the maximum likelihood estimation method. The developed technique has been applied to a practical example, and the results from the developed technique were compared to the results from the Monte Carlo simulation technique.     

Web-based application for estimating water quality impacts due to environmental dredging

A Web-based dredge plume simulation software has been developed for estimating sediment transport and contaminant release from dredging operations. This application software comprises a near-field source model and far-field sediment transport model, combined with the settling analysis of flocculent particles. It is designed to run on all computing platforms with a standard Web browser and is accessible over the Internet. This paper describes its internal software structures and the major features of the application software. In addition, an example application of sediment transport in the Savannah River, GA, is included to illustrate typical input and output screens and to demonstrate the accuracy of the model. The water quality assessment software developed herein will greatly enhance the ability of the predictive tool for estimating sediment transport and resuspension during dredging operations.     

Investigation of CO2 behavior and study on design of optimal injection into Gorae-V aquifer

This study presents the Bottom-hole pressure (BHP) behavior with different wettabilities and the optimal design scheme to effectively inject CO₂ into the Gorae-V aquifer. As a result, the injection rate and injectivity were increased as the wettability condition became more water-wet. However, the more wettability condition becomes water-wet, the more the ultimate CO₂ injection volume decreases. When the injectivity was 346 ton/day/Mpa at the Gorae-V aquifer, the aquifer can sustain CO₂ injection at a rate of 2,425 tons per day over this time period. A design for a complete CCS system was developed based on the existing off-shore pipeline in combination with new on-shore CO₂ transport infrastructure, and a pressure of 12.8 MPa is required at the CO₂ source to maintain this injection rate.