This project involved demolition of existing structures on a site in London and remediation for residential development. During the site investigation phase, preliminary basic gas monitoring indicated a risk category CS1. During remediation, an old in-filled pond was discovered containing organic material, likely to cause a gas risk and thus likely to require additional gas protection measures. To fully investigate this, further gas monitoring was required. Doing this with spot monitoring would take between 6 – 8 weeks, slowing down the project with no guarantee that the data would prove the gas risk conclusively. Doing this with other forms of continuous monitoring equipment without the capability to measure flow-rate would not provide a full appreciation of the gas risk.
Three GasfluX™ units were installed around this part of the site, measuring bulk gases and flow-rates and data from a local meteorological station was integrated from Weather Underground to further contextualise the gas readings. The deployment ran for 1 month to cover a range of atmospheric pressure conditions to include a period of falling atmospheric pressures.
Upon installation, the client saw large fluctuations in CH4 which gradually reduced over time. These large fluctuations saw methane recorded at levels up to 15 – 20% & as low as 1%. If the same measurements had been taken with spot monitoring devices, the difference in potential results could have quite an impact on the project.
Throughout the project, very low flow-rates were evidenced. When coupled with the gas readings this allowed for much more accurate gas screening values to be calculated, given the higher density dataset available. In fact for most of the readings, the flow-rate recorded was negative, showing that the dominant meteorological conditions actually mitigated against potential gas migration. This provided huge confidence in terms of assessing the risk and recommending appropriate gas protection measures to be installed.
The live data also meant that by the time the project was completed, the consultant had a thorough understanding of the site and was in a position to advise their client accordingly. This sped up the site investigation process considerably, allowing the customer to provide an answer to their client much sooner than would be typically possible.
Fig.1 Client data display customised to their requirements
Since this case study Ambisense has launched Ambiliytics, environmental risk assessment software that integrates data from any IoT in-field sensor and combines it with contextual data such as satellite, weather and geophysical all on a single platform. Using machine learning it generates statistical models, giving users incredibly deep insights into their data and letting them make faster and better decisions.