Remote real-time monitoring of subsurface landfill gas migration

Abstract

The cost of monitoring greenhouse gas emissions from landfill sites is of major concern for regulatory authorities. The current monitoring procedure is recognised as labour intensive, requiring agency inspectors to physically travel to perimeter borehole wells in rough terrain and manually measure gas concentration levels with expensive hand-held instrumentation. In this article we present a cost-effective and efficient system for remotely monitoring landfill subsurface migration of methane and carbon dioxide concentration levels. Based purely on an autonomous sensing architecture, the proposed sensing platform was capable of performing complex analytical measurements in situ and successfully communicating the data remotely to a cloud database. A web tool was developed to present the sensed data to relevant stakeholders. We report our experiences in deploying such an approach in the field over a period of approximately 16 months.

Keywords: carbon dioxide; chemistry; environmental monitoring; greenhouse gases; landfill; methane; sensor data management; sensor networks.

Figure 1.

Visual representation of the landfill gas sensing model from the device placed in the field to a web-based visualisation user interface. The model shows the progression of chemical sensed data from the physical world to the digital world by means of a gateway platform.

Figure 2.

Component layout of the gateway platform. (1) control system, (2) bluetooth module, (3) GSM module, (4) signal and actuation control lines, (5) power source, (6) extraction air pump, (7) gas chamber, (8) flow selection valves.

Figure 3.

Schematic flow diagram illustrating the gas flow control system. The flow control valves allow the system to be switched between sampling mode (from ‘Borehole Well Supply Supply’ to the ‘Borehole Well Exhaust’) and baseline and purge modes (from ‘Atmosphere Supply’ to the ‘Atmosphere Exhaust’)

Figure 4.

Block diagram showing the interactions between the GSM base station, the GSM database interface and the relational databases. The remotely reported data is received by the GSM base station where, through a number of programming stages, the data is stored on the primary landfill database.

Figure 5.

Overview of Data Storage, Backup and Presentation. Multiple landfill sensors can upload data to a single cell base-station. Thereafter these base-stations upload data to a central server, which is also backed up. Finally this data is available via the Internet for end users to access.

Figure 6.

Sensor Data Portal: Our web application which allows relevant stakeholders to easily view in real-time the air quality (CO₂ & CH₄) data from landfill sites. The website can be viewed at http://clarityapp.ucd.ie/~sensorportal/.

Figure 7.

Calibration of the system’s CO₂ infrared gas sensor. Points represent the average of the steady state response over circa 2 minutes. Error bars (present but difficult to see due to the high sensor accuracy) represent the standard deviation.

Figure 8.

Calibration of the system’s CH₄ infrared gas sensor. Points represent the average of the steady state response over circa 2 minutes. Error bars (present but difficult to see due to the high sensor accuracy) represent the standard deviation.

Figure 9.

Current analysis of the landfill system during a full monitoring routine (1) baseline procedure, (2) sampling procedure, (3) purge procedure, (4) communications and storage procedure.

Figure 10.

CO₂ and CH₄ readings from a 7 month field deployment at location C between March 2010 and October 2010. Note that CO₂ exceeds the recommended limit 96.6% of the time, while CH₄ never exceeds the recommended limit. The arrows on the graph illustrate significant CO₂ events that were recorded around the 17th of March, 28th April, and 25th of September. There were no CH₄ events.

Figure 11.

CO₂ readings over a 7 month field deployment at location C between March 2010 and October 2010. Note that a simulated human operator sampling the landfill emissions on a particular first day or each month would miss a lot of events of interest e.g., the middle of March-2010, the end of April-2010, and the middle of September. The average error across each of the 5 days (Mon–Fri) would have been 17% in our field deployment at location C.

Figure 12.

Profile of a typical 9 minutes baseline (A), sample (B), & purge (C) sampling stage, which is comprised of 180 CO₂ & CH₄ samples recorded every 3 seconds. This occurs in the order of 60× baseline, 60× sample, and 60× purge samples. Initially all 180 items were sampled, however after a close analysis of 10+ weeks of data, we have been able to minimise the length of this sampling procedure. This has a positive effect on battery power consumption.

Figure 13.

Profile of our “sample only” system, note the same signature as Figure 12, which possibly indicates that only the sample stage is needed to measure the air emissions at a given landfill site.