Measurement of representative landfill gas migration samples at landfill perimeters: a case study

This paper describes the development of a fully integrated autonomous system based on existing infrared sensing technology capable of monitoring landfill gas migration (specifically carbon dioxide and methane) at landfill sites. Sampling using the described system was validated against the industry standard, GA2000 Plus hand held device, manufactured by Geotechnical Instruments Inc. As a consequence of repeated sampling during validation experiments, fluctuations in the gas mixtures became apparent. This initiated a parallel study into what constitutes a representative sample of landfill gas migration as reported to the Environmental Protection Agency. The work described in this paper shows that gas mixture concentrations change with depth of extraction from the borehole well, but with evidence of a steady state after a time

Monitoring of gas emissions at landfill sites using autonomous gas sensors

Executive Summary This report details the work carried out during the Smart Plant project (2005-AIC-MS-43-M4). As part of this research, an autonomous platform for monitoring greenhouse gases (methane (CH4), carbon dioxide (CO2)) has been developed, prototyped and field validated. The modular design employed means that the platform can be readily adapted for a variety of applications involving these and other target gases such as hydrogen sulfide (H2S), ammonia (NH3) and carbon monoxide (CO) and the authors are in the process of completing several short demonstrator projects to illustrate the potential of the platform for some of these applications. The field validation for the greenhouse gas monitoring platform was carried out at two landfill sites in Ireland. The unit was used to monitor the concentration of CO2 and CH4 gas at perimeter borehole wells. The final prototype was deployed for over 4 months and successfully extracted samples from the assigned perimeter borehole well headspace, measured them and sent the data to a database via a global system for mobile (GSM) communications. The data were represented via an updating graph in a web interface. Sampling was carried out twice per day, giving a 60-fold increase on current monitoring procedures which provide one gas concentration measurement per month. From additional work described in this report, a number of conclusions were drawn regarding lateral landfill gas migration on a landfill site and the management of this migration to the site’s perimeter. To provide frequent, reliable monitoring of landfill gas migration to perimeter borehole wells, the unit needs to: • Be fully autonomous; • Be capable of extracting a gas sample from a borehole well independently of personnel; • Be able to relay the data in near real time to a base station; and • Have sensors with a range capable of adequately monitoring gas events accurately at all times. The authors believe that a unit capable of such monitoring has been developed and validated. This unit provides a powerful tool for effective management of landfill site gases. The effectiveness of this unit has been recognised by the site management team at the long-term deployment trial site, and the data gathered have been used to improve the day-to-day operations and gas management system on-site. The authors make the following recommendations: 1. The dynamics of the landfill gas management system cannot be captured by taking measurements once per month; thus, a minimum sampling rate of once per day is advised. 2. The sampling protocol should be changed: (i) Borehole well samples should not be taken from the top of the well but should be extracted at a depth within the headspace (0.5–1.0 m). The measurement depth will be dependent on the water table and headspace depth within the borehole well. (ii) The sampling time should be increased to 3 min to obtain a steady-state measurement from the headspace and to take a representative sample; and (iii) For continuous monitoring on-site, the extracted sample should be recycled back into the borehole well. However, for compliance monitoring, the sample should not be returned to the borehole well. 3. Devices should be placed at all borehole wells so the balance on the site can be maintained through the gas management system and extraction issues can be quickly recognised and addressed before there are events of high gas migration to the perimeter. 4. A pilot study should be carried out by the EPA using 10 of these autonomous devices over three to five sites to show the need and value for this type of sampling on Irish landfill sites

Development of an autonomous greenhouse gas monitoring system

This paper describes the designs of a first and second generation autonomous gas monitoring system and the successful field trial of the final system (2nd generation). Infrared sensing technology is used to detect and measure the greenhouse gases methane (CH4) and carbon dioxide (CO2) at point sources. The ability to monitor real-time events is further enhanced through the implementation of both GSM and Bluetooth technologies to communicate these data in real-time. These systems are robust,reliable and a necessary tool where the monitoring of gas events in real-time are needed

Landfill gas monitoring at borehole wells using an autonomous environmental monitoring system

An autonomous environmental monitoring system(Smart Landfill) has been constructed for the quantitative measurement of the components of landfill gas found at borehole wells at the perimeter of landfill sites. The main components of landfill gas are the greenhouse gases, methane and carbon dioxide and have been monitored in the range 0-5 % volume. This monitoring system has not only been tested in the laboratory but has been deployed in multiple field trials and the data collected successfully compared with on-site monitors. This success shows the potential of this system for application in environments where reliable gas monitoring is crucial

Autonomous greenhouse gas measurement system for analysis of gas migration on landfill sites

This paper describes the design, development and validation of an autonomous gas sensing platform prototype for monitoring of the greenhouse gases, methane (CH4) and carbon dioxide (CO2). The deployment undertaken for validation of the developed prototype monitored landfill gas migration to perimeter borehole wells on a landfill site. Target gas concentrations were captured via infrared gas sensors tuned for each target gas and data reported to an offsite data collection point at 12 hour intervals. This bespoke platform and the accompanying data recording and interface software provide a flexible alternative to the presently employed labor intensive, manual monitoring routines. This successful trial brought about a change in the management of the trial sites gas extraction system

Non-linear carbon dioxide determination using infrared gas sensors and neural networks with Bayesian regularization

Carbon dioxide gas concentration determination using infrared gas sensors combined with Bayesian regularizing neural networks is presented in this work. Infrared sensor with a measuring range of 0~5% was used to measure carbon dioxide gas concentration within the range 0~15000 ppm. Neural networks were employed to fulfill the nonlinear output of the sensor. The Bayesian strategy was used to regularize the training of the back propagation neural network with a Levenberg-Marquardt (LM) algorithm. By Bayesian regularization (BR), the design of the network was adaptively achieved according to the complexity of the application. Levenberg-Marquardt algorithm under Bayesian regularization has better generalization capability, and is more stable than the classical method. The results showed that the Bayesian regulating neural network was a powerful tool for dealing with the infrared gas sensor which has a large non-linear measuring range and provide precise determination of carbon dioxide gas concentration. In this example, the optimal architecture of the network was one neuron in the input and output layer and two neurons in the hidden layer. The network model gave a relationship coefficient of 0.9996 between targets and outputs. The prediction recoveries were within 99.9~100.0%

Chemical species concentration measurement via wireless sensors

This paper describes studies carried out to investigate the viability of using wireless cameras as a tool in monitoring changes in air quality. A camera is used to monitor the change in colour of a chemically responsive polymer within view of the camera as it is exposed to varying chemical species concentration levels. The camera captures this image and the colour change is analyzed by averaging the RGB values present. This novel chemical sensing approach is compared with an established chemical sensing method using the same chemically responsive polymer coated onto LEDs. In this way, the concentration levels of acetic acid in the air can be tracked using both approaches. These approaches to chemical plume tracking have many applications for air quality monitoring

Remote real-time monitoring of subsurface landfill gas migration

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

Remote real-time monitoring of subsurface landfill gas migration

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. Copyright 2011 by the authors; licensee MDPI, Basel, Switzerland