The Utility of Continuous Monitoring in Detection and Prediction of “Worst Case” Ground-Gas Concentrations*CL:AIRE, 2011, Research Bulletin 13—The Utility of Continuous Monitoring in Detection and Prediction of “Worst-Case” Ground-Gas Concentration.
TL;DRAbstract
A primary requirement for understanding and predicting the behavior of complex systems is the ability to observe them. Observation contributes to understanding of processes and, therefore, the predictive power of physically based models but also provides calibration data vital for more empirical models. The heterogeneity in composition and large size of environmental systems makes their behavior complex, whilst the same factors make its representative observation difficult. Gas in the subsurface is a typical example of an environmental system, the predictability of which has been limited by insufficient observation. Traditionally due to the technology available these systems have been monitored using discrete measurements from which gas concentrations and migration potential are inferred. Unsurprisingly considering the nature of these systems this method of data collection frequently records highly variable regimes. Without understanding these variations large uncertainty on the data e
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A primary requirement for understanding and predicting the behavior of complex systems is the ability to observe them. Observation contributes to understanding of processes and, therefore, the predictive power of physically based models but also provides calibration data vital for more empirical models. The heterogeneity in composition and large size of environmental systems makes their behavior complex, whilst the same factors make its representative observation difficult. Gas in the subsurface is a typical example of an environmental system, the predictability of which has been limited by insufficient observation. Traditionally due to the technology available these systems have been monitored using discrete measurements from which gas concentrations and migration potential are inferred. Unsurprisingly considering the nature of these systems this method of data collection frequently records highly variable regimes. Without understanding these variations large uncertainty on the data e
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