Estimation of total landfill surface methane emissions using geospatial approach combined with measured surface ambient air methane concentrations

ABSTRACT

The concentration of surface air methane (CH₄) measured in parts per million by volume (ppmv) near the soil/atmosphere interface should, in theory, have a positive correlation with surface methane emissions fluxes, measured in grams per square meter per day (gm⁻²d⁻¹). Some researchers suggest that CH₄ flux can be reasonably inferred from simple measurements of CH₄ concentrations near the landfill surface. Ground-based and drone-based surface emissions monitoring (SEMs) were performed at several municipal solid waste landfills as tracer correlation method (TCM) testing was being used to measure total methane emissions from the same landfills. The TCM data and SEM data were used to establish a new simple correlation to convert surface methane concentrations in ppmv to localized surface methane emission flux in gm⁻²d⁻¹.

The SEM data obtained from ten ground and drone monitoring campaigns were log-transformed and geospatially treated using inverse distance weighting to the power of 2 to predict methane surface concentrations in the entire footprint of the SEM measurements area. The developed new correlation equation was then used to convert every predicted surface methane concentration to an emissions flux. The total estimate of surface emissions from the entire landfill was obtained by integrating the predicted fluxes over the area of the footprint of the SEM measurement area. The use of the new developed correlation resulted in higher total emissions estimates than other correlations reported in the literature and should be considered more conservative. Not including other factors, the proposed approach provides estimate of total methane emissions with a coefficient of variation of 20%. This study introduces a novel approach that utilizes a developed correlation between surface methane concentrations and surface emissions fluxes to estimate total methane emissions from municipal solid waste landfills or from a specified area. This study provides an additional use of the quarterly SEM data.

Implications: The proposed approach provides an occasion for additional use of the easily obtainable quarterly SEMs data that can be performed by most landfills. The SEMs data are the most abundant landfill methane concentrations data. This approach gives them more benefit for the user. It is intended to convert ambient air concentrations to some estimates of surface emissions that can help landfill owners with decision making such as remediation activities or adjustments of their gas collection a systems.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

Additional information

Funding

The work was supported by the Environmental Research and Education Foundation.

Notes on contributors

Tarek Abichou

Tarek Abichou is Professor of Civil and Environmental Engineering. His research focuses on sustainable solid waste and geo-environmental engineering.

Jorge M. Del’Angel

Jorge M. Del’Angel is a Graduate Student of Civil and Environmental Engineering. His master’s thesis focus on characterization of greenhouse gas emissions from landfills.

Mohammadreza Koloushani

Mohammadreza Koloushani is a PhD Candidate in Civil and Environmental Engineering. His research focus on using remote sensing and GIS for civil engineering applications.

Kostas Stamatiou

Kostas Stamatiou is a Chief Technology Officer of BlueSky Resources and a subject matter expert in artificial intelligence.

Nizar Belhadj Ali

Nizar Belhadj Ali is a Professor of civil engineering focusing on optimization applications in civil and environmental engineering.

Roger Green

Roger Green is a Director Engineering Science at Waste Management leading the landfill emissions program.