A quantitative structure- property relationship of gas chromatographic/mass spectrometric retention data of 85 volatile organic compounds as air pollutant materials by multivariate methods

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of retention times of volatile organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structure of compounds. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and artificial neural network (ANN). The stepwise regression was used for the selection of the variables which gives the best-fitted models. After variable selection ANN, MLR methods were used with leave-one-out cross validation for building the regression models. The prediction results are in very good agreement with the experimental values. MLR as the linear regression method shows good ability in the prediction of the retention times of the prediction set. This provided a new and effective method for predicting the chromatography retention index for the volatile organic compounds

The Economic Impact of the Value Chain of a Marcellus Shale Well

The Economic Impact of the Value Chain of a Marcellus Shale Well Site examines the direct economic impact of a Marcellus Shale well located in Southwestern Pennsylvania. This study seeks to fill a critical information gap on the impact of gas drilling and extraction from Marcellus Shale deposits deep underground: an assessment of the economic impacts – emphasizing the direct economic impact, rather than just focusing on the perceived benefits and impacts affecting the region. Our analysis is based on extensive field research, including a site visit and interviews with industry participants. It is further cross-validated by examining similar costs for development of Marcellus Wells by a vertically-integrated exploration and production firm