Continuous Landfill Gas Monitoring – Understanding the Risk - Dr Peter Morris

The risks posed by Landfill gas migration are not always well understood. This is usually a result of monitoring programmes failing to identify the true subsurface gas regime and producing conceptual site models that cannot reliably predict how it may change in the future. As a consequence of this frequent monitoring is often required but even costly investigations with many site visits still result in large uncertainty in estimations of ground-gas concentration. As the upper bound of the uncertainty must be used in risk assessment the uncertainty in measurement ultimately results in expensive mitigation measures.

Data is currently collected as discrete periodic static measurements of gas concentrations from which the gas regime is inferred. Flaws in the current approach to quantifying and predicting risk arising from ground-gas are identified explicitly in the literature(1)and are implicit in the continuing evolution of landfill gas management guidance notes(2). The underlying cause of flaws is that whilst accurate quantification of risk should require accurate measurement of landfill gas concentration

and of fluxes, neither is directlymeasured, and both are likely to be temporally variable.

Measurement is indirect because soil-gas concentration is inferred from periodic sampling of gases that accumulate within a borehole; the flux is then inferred from these readings. The unit of flux is volume/time, therefore it cannot be directly measured without time series data.

Landfill industry regulators recognise the need for more representative data but cost has prevented the widespread collection of continuous records of landfill gas measurements. However, the availability of reliable miniature infra-red and photo-ionisation sensors has recently been combined with innovative engineering to produce a new instrument; GasClam, which will allow the collection of continuous data to become widely used. This article provides an overview of the technology, demonstrates the benefits of time-series data over traditional methods and introduces new risk assessment tools.