Air Dispersion Model for Prediction Fugitive Landfill Gaseous Emission Impact in Ambient Atmosphere

This paper will explore formation of HCl aerosol at atmospheric boundary layers and encourages the uptake of environmental modeling systems (EMSs) as a practice evaluation of gaseous emissions (“framework measures”) from small and medium-sized enterprises (SMEs). The conceptual model predicts greenhouse gas emissions to ecological points beyond landfill site operations. It focuses on incorporation traditional knowledge into baseline information for both measurement data and the mathematical results, regarding parameters influence model variable inputs. The paper has simplified parameters of aerosol processes based on the more complex aerosol process computations. The simple model can be implemented to both Gaussian and Eulerian rural dispersion models. Aerosol processes considered in this study were (i) the coagulation of particles, (ii) the condensation and evaporation of organic vapors, and (iii) dry deposition. The chemical transformation of gas-phase compounds is taken into account photochemical formulation with exposure effects according to HCl concentrations as starting point of risk assessment. The discussion set out distinctly aspect of sustainability in reflection inputs, outputs, and modes of impact on the environment. Thereby, models incorporate abiotic and biotic species to broaden the scope of integration for both quantification impact and assessment risks. The later environmental obligations suggest either a recommendation or a decision of what is a legislative should be achieved for mitigation measures of landfill gas (LFG) ultimately.

Enhancing Landfill Gas Production by Methanogenic Sand Layer

Landfill gas, particularly methane is one of the greenhouse gases which contributes to global warming. This paper presents the findings of a study on methane gas production from simulated landfill reactor under saturated conditions. A reactor was constructed to represent a landfill cell of 2.5 m thickness on sandy soil. The reactor was 0.2 m in diameter and 4 m in height. One meter of sand and pebble layer was packed at the bottom of the reactor followed by 2.5 m of solid waste layer and 0.4 m of sand layer as the cover soil. Degradation of waste in the solid waste layer was at acidification stage as indicated by the leachate quality with COD as high as 55,511 mg/L and pH as low as 5.1. However, methanogenic environment was established at the bottom sand layer after one year of operation indicated by pH of 7.2 and methane gas generation. Leachate degradation took place as the leachate moved through the sand layer at an infiltration of rate 0.7 cm/day. This resulted in landfill gas production of 77 mL/day/kg containing 55 to 65% methane. The application of sand layer contributed to the gas production from landfill by an in-situ degradation of leachate in the sand at the bottom of the landfill.

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.

Combining Fuzzy Logic and Neural Networks in Modeling Landfill Gas Production

Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.