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

The detection and tracking of mine-water pollution from abandoned mines using electrical tomography

Increasing emphasis is being placed on the environmental and societal impact of mining, particularly in the EU, where the environmental impacts of abandoned mine sites (spoil heaps and tailings) are now subject to the legally binding Water Framework and Mine Waste Directives. Traditional sampling to monitor the impact of mining on surface waters and groundwater is laborious, expensive and often unrepresentative. In particular, sparse and infrequent borehole sampling may fail to capture the dynamic behaviour associated with important events such as flash flooding, mine-water break-out, and subsurface acid mine drainage. Current monitoring practice is therefore failing to provide the information needed to assess the socio-economic and environmental impact of mining on vulnerable eco-systems, or to give adequate early warning to allow preventative maintenance or containment. BGS has developed a tomographic imaging system known as ALERT ( Automated time-Lapse Electrical Resistivity Tomography) which allows the near real-time measurement of geoelectric properties "on demand", thereby giving early warning of potential threats to vulnerable water systems. Permanent in-situ geoelectric measurements are used to provide surrogate indicators of hydrochemical and hydrogeological properties. The ALERT survey concept uses electrode arrays, permanently buried in shallow trenches at the surface but these arrays could equally be deployed in mine entries or shafts or underground workings. This sensor network is then interrogated from the office by wireless telemetry (e.g: GSM, low-power radio, internet, and satellite) to provide volumetric images of the subsurface at regular intervals. Once installed, no manual intervention is required; data is transmitted automatically according to a pre-programmed schedule and for specific survey parameters, both of which may be varied remotely as conditions change (i.e: an adaptive sampling approach). The entire process from data capture to visualisation on the web-portal is seamless, with no manual intervention. Examples are given where ALERT has been installed and used to remotely monitor (i) seawater intrusion in a coastal aquifer (ii) domestic landfills and contaminated land and (iii) vulnerable earth embankments. The full potential of the ALERT concept for monitoring mine-waste has yet to be demonstrated. However we have used manual electrical tomography surveys to characterise mine-waste pollution at an abandoned metalliferous mine in the Central Wales orefield in the UK. Hydrogeochemical sampling confirms that electrical tomography can provide a reliable surrogate for the mapping and long-term monitoring of mine-water pollution

Landfill gas monitoring network - development of wireless sensor network platforms

A wireless sensor network has been developed for the application of landfill gas monitoring, specifically sensing methane, carbon dioxide and extraction pressure. This collaborative work with the Irish Environmental Protection Agency has been motivated by the need to reduce greenhouse gas emissions as well as aiming to improve landfill gas management and utilisation. This paper describes the preliminary findings of an ongoing trial deployment of multiple sensing platforms on an active landfill facility; data has been acquired for nine months to date. The platforms have operated successfully despite adverse on-site conditions, with validity demonstrated by reasonably strong correlation with independent on-site measurements. The increased temporal and spatial resolution provided by distributed sensor platforms is discussed with regard to improving landfill gas management practice

Analysis of landfill gas migration by use of autonomous gas monitoring platforms

Autonomous gas sensing platforms have been developed to facilitate the long-term continuous monitoring of landfill gas concentrations. The analysis of a municipal landfill site in Ireland forms part of an on-going collaboration with the Environmental Protection Agency in monitoring the migration of greenhouse gases, i.e. methane and carbon dioxide, emanating from the landfill site. Target gas concentrations were automatically recorded via infrared gas sensors calibrated for the respective gases, with this data being logged remotely every six hours to a central base-station. The autonomous platform with its web-based portal interface provides a flexible alternative to the existing labor-intensive, manual monitoring routines. Frequent occurrences of 70% v/v methane and 6% v/v carbon dioxide were substantially in breach of the regulatory limits of 1.5% v/v and 1.0% v/v, respectively. These excessive levels of gas migration were analyzed with respect to SCADA flare data, on-site measurements and meteorological data

Application of multi-scale assessment and modelling of landfill leachate migration: implications for risk-based contaminated land assessment, landfill remediation, and groundwater protection

There are a large number of unlined and historical landfill sites across Britain, contaminating groundwater and soil resources as well as posing a threat to human health and local communities. There is an essential requirement for robust methodology when carrying out risk-based site investigations prior to risk assessment and remediation of landfill sites. This research has focused upon the methods used during site investigations for two reasons. Firstly, the site investigation is often conducted using field instruments and methods that do not account for the heterogeneous conditions found at landfill sites. Interpreting geophysical conditions between sampled points is a common practise. Given the complex and heterogeneous conditions at landfill sites, such methodology introduces uncertainty into data sets. Secondly, risk estimation models that simulate groundwater flow and contaminant transport require extensive field information. The data used during model construction will significantly impact contaminant transport simulations. Modelling guidelines also need further development, ensuring that sound modelling practises are adhered toduring model construction.To address these concerns, four research objectives were identified: (1) Two new multi-spatial field assessment methods (remote sensing and ground penetrating radar), previously applied in other fields of science, were tested on landfill sites; (2) Kriging was used as a tool to improve landfill-sampling strategies; (3 & 4) Groundwater flow and contaminant transport models were used to evaluate whether different scales of field data and modelling practises influenced modelling assumptions and simulation.The utility of novel field- and airborne-based remote sensing methodologies in identifying the location and intensity of vegetation stress caused by leachate migration and inferring pathways of near surface contamination using patterns of vegetation stress was proven. The results from the kriging investigations demonstrated that additional insight into field conditions could be resolved to identify locations of additional sampling points, and provide information about variability in hydrological data sets. The Ground Penetrating Radar investigations provided three types of valuable near-surface information that could assist in determining landfill risks: buried landfill features, leachate plume locations and local hydrogeological conditions. These combined methods provided detailed synoptic geophysical and contaminant information that would otherwise be difficult to determine. Their application and acceptance as site assessment methods (used under certain landfill conditions) could increase the accuracy of assessing risks posed by landfill leachate.These applications also demonstrated that the most effective site assessments are achieved when integrated with other field data such as soil, vegetation, and groundwater quantity measurements, contaminant concentrations and aerial photographs, providing comprehensive information needed for risk estimation modelling.The modelling analyses found that close attention must be paid to site-specific and model-specific characteristics, as well as modelling practises. These factors influenced model results. By using additional data to infer model parameters, it was evident that the amount of data available will influence the way in which risk will be perceived. The more data that was available during model construction, the higher the risk prediction. This was the case for some seventy- percent of the models.By improving the accuracy of site investigation methodology, and by adhering to robust assessment and modelling practices, a higher level of quality assurance can be achieved in the risk assessment and remediation of contaminating landfill sites. If the improvements and recommendations presented in this research are considered, uncertainties inherent in the site investigation could be reduced, therefore enhancing the accuracy of landfill risk assessment and remedial decisions

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

Landfill elevated internal temperature detection and landfill fire index assessment for fire monitoring

Landfill fires are becoming a real threat to both people and environment due to lack of predictions and control methods. Processing of the infrared band from level-1 satellite images was employed and decades worth of archived data from USGS Earth Explorer databases were analyzed to obtain surface temperature values of Atlantic Waste Landfill, Virginia and Bridgeton Landfill, Missouri. Multitemporal thermal maps and frequency of maxima analysis maps of these two landfills showed the hotspots spreading through the waste site. A Landfill Fire Index (LFI) was created by investigating eight factors that give information about the hazardousness of the landfill conditions relative to the presence of a fire occurrence. The application of Analytical Hierarchy Method (AHP) resulted in the determination of the degree of importance of each Landfill Fire Index factor. Several monitoring well data sets were used to calculate the LFI for Bridgeton Landfill, Missouri, and Burlington County Landfill, New Jersey

Distributed environmental monitoring

With increasingly ubiquitous use of web-based technologies in society today, autonomous sensor networks represent the future in large-scale information acquisition for applications ranging from environmental monitoring to in vivo sensing. This chapter presents a range of on-going projects with an emphasis on environmental sensing; relevant literature pertaining to sensor networks is reviewed, validated sensing applications are described and the contribution of high-resolution temporal data to better decision-making is discussed

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%