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The Utility of Continuous Monitoring in Detection and Prediction of 'Worst Case' Ground-Gas Concentrations

机译:持续监测检测与预测“最差案例”地气浓度的效用

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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 exists which propagates through to a conservative risk assessment based on the unquantified 'worst' concentration/flux observed during monitoring. Variations in soil gas concentrations are related to environmental parameters such as atmospheric pressure, subsurface pressure, temperature and water table. The recent development of technology which enables continuous soil gas monitoring that also records these parameters not only allows the variability in concentrations to be quantified and accounted for but also allows these relationships to be identified. Only after these relationships are identified is it possible to predict how gas/vapor regimes will change in the future. Using data collected from sites with varying wastes, contaminations, geologies and hydrologies the benefits of continuous data will be illustrated and novel techniques for risk assessment including concentration duration curves will be introduced. This will demonstrate that the availability of new monitoring methodology will play a part in repositioning both the legislative requirements and the cost-benefits of a more proactive approach.
机译:理解和预测复杂系统行为的主要要求是观察它们的能力。观察有助于理解流程,因此,物理基础的模型的预测力,但也为更多实证模型提供校准数据。组成和大尺寸的环境系统中的异质性使其行为复杂,同时同样的因素使其代表性观察变得困难。地下的气体是环境系统的典型示例,其可预测性受到不充分的观察的限制。传统上由于技术的技术,这些系统已经使用离散测量监测,从中推断出气体浓度和迁移电位。考虑到这些系统的性质,这种数据收集方法经常记录高度可变的制度。不了解这些变化,存在对数据的巨大不确定性,其基于监测期间观察到的未受化的“最差”浓度/通量传播到保守风险评估。土壤气体浓度的变化与环境参数有关,如大气压,地下压力,温度和水位。最近的技术开发,使得连续的土壤气体监测也记录这些参数,不仅允许量化和算法的浓度的可变性,而且还允许识别这些关系。只有在确定这些关系之后,才能预测天然气/蒸汽制度如何将来会发生变化。使用从具有不同废物,污染物,地质和水文收集的网站收集的数据将被说明连续数据的益处,并将引入具有浓度持续时间曲线的风险评估的新技术。这将证明新的监测方法的可用性将在重新定位立法要求和更积极的方法的成本效益方面发挥作用。

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