Continuous Landfill Gas Monitoring – Understanding the Risk


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


. The underlying cause of flaws is that whilst accurate quantification of risk should require accurate measurement of landfill gas con- centration and of fluxes, neither is directly measured, and both are likely to be temporally variable.


are implicit in the continuing evolution of landfill gas management guidance notes(2)


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.


Figure 1


, see figure 1, allows secure, unmanned collection of continuous ground-gas data. It is manufactured from stainless steel, is intrinsically safe with ingress protection rated IP-68. It is designed to fit in a 50 mm borehole and measures methane, carbon dioxide, oxygen, hydrogen sulphide, carbon monoxide and VOC concentrations, as well as atmospheric pressure, borehole pressure and temperature. Water level can also be measured with an optional pressure transducer. The device fits securely within a borehole whilst also allowing for controlled venting of the borehole.


GasClam® The Gasclam®


Benefits of time-series data Accounting for Temporal Variability


The current approach relies on discrete measurements of concentration from which representative ground gas concentrations and gas migration potential are inferred. However, as system data is poorly resolved temporally uncertainties in these inferences remain large. This is clearly demonstrated on a landfill where from traditional monthly spot sampling gas was only thought to migrate at Christmas. Continuous monitoring however, indicates the gas regime is highly variable, see figure 3. In this example if spot samples were taken on days represented by green dots the perceived risk would be very different to those taken on days represented by the black dots. With continuous data it is possible to overcome this mismatch in sampling frequency and variability in gas concentration revealing the true gas regime.


Summarising Time Series Data Currently the only summary of borehole gas concentration data is if a


trigger value has ever been recorded (e.g., 1 % v/v CH4) and will determine if action is necessary. By collecting more highly resolved time- series data it is possible to re-plot the data as a concentration duration curve allowing more direct interpretation of risk. In figure 4 time series data has been transformed in to a concentration duration curve, in this


example it is possible to see CH4 concentrations only exceed 1 % v/v for 1% of the time, rather than the trigger level had ever been exceeded. With this information it is easier to determine if and what level of action is required to prevent or mitigate migration.


Identifying Correlations


In addition to higher temporal resolution of gas concentration, other temporally variable environmental parameters can also be measured, allowing their inter-relationships to be more clearly defined. This in turn allows dominant controls on gas concentration to be recognised and for better prediction of gas concentration as these parameters change.


Figure 3: Continuous gas concentration data from “The Christmas Borehole”, a landfill perimeter borehole thought to indicate gas migration problems only at Christmas time. A period of continuous data collection has overcome the artefact arising from the sampling frequency (monthly) mismatching with the variability of concentration. The continuous data clearly showed that, although the CH4 concentration is variable, it is not only high at Christmas.


Atmospheric pressure is considered to be a strong driving force for gas migration and in general it is assumed that concentrations are higher when pressure is low and vice versa(1), this can be seen in data collected from a perimeter borehole in figure 5. Current UK guidance recommends that the spot sampling programme should be stratified and samples should be taken when atmospheric pressure is 1000 mBar and falling, as this will increase the chances of observing worst case. However, we can see the arbitrary nature of this condition, as concentration continues to vary depending on changes in atmospheric pressure, rather than displaying a clear dependency on the absolute pressure.


However, the widely reported relationship between pressure and concentration does not always exist; the inverse relationship is observed at a neighbouring borehole, figure 6a. Also sometimes another parameter is responsible for migration, Figure 6b.


Understanding Processes


The above examples demonstrate the variability of gas concentrations and some of the factors that influence them. From this it is clear spot sampling will often fail to identify the true gas regime and the dominant processes occurring at a landfill site. This is highlighted by a site where


Figure 4: Continuous data (left hand side) can be converted into concentration duration curve. Now the quantitative statement


IET November/December 2010 Overview


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