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.

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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%.

Solutions of benzenesulfonic acid (BSA) and monohydrated 4-toluenesulfonic acid (PTSA) in dimethylformamide (DMF) were studied by FT-Raman spectroscopy in the concentration range 1.0-5.0 mol dm −3 . Spectra in the region of the acid S -OH and benzenesulfonate anion SO 3 − stretching bands were analysed using band-fitting procedures in order to ascertain the degree of acid dissociation. In BSA solutions, this parameter changes from 0.34 (1.02 M solution) to 0.11 (5.01 M solution) despite the strong character of the acid. Interaction of DMF with undissociated BSA produces a new band in the n(C -N -C ) Raman spectral region near 866 cm −1 , displaced −11.0 cm −1 and assigned to DMF molecules hydrogen-bonded to BSA. In PTSA solutions, hydrogen bonds are formed with the oxonium ion (H 3 O + ) dissociated from the acid. In this case, the displacement observed is only −7.0 cm −1 , indicating a weaker interaction. From the concentration of hydrogen-bonded DMF, the mean number of hydrogen-bonds participating in bonding with each solvent molecule can be calculated. This number changes, in BSA solutions, from ca. 0.5 in the less concentrated solutions up to ca. 1.0 in the most concentrated. This result supports the conclusion that BSA-DMF complexes of fixed stoichiometry are not present in the range of concentrations studied here. On the contrary, the oxonium ions form a stable complex of stoichiometry (DMF) 2 ·H 3 O + which does not change with the acid concentration.