Document Type

Article

Publication Date

10-2016

Abstract

Landfill methane emissions (LME) vary in short periods depending upon the meteorological and atmospheric conditions. In this paper, coupling the Atmospheric InfraRed Sounder (AIRS) with the tracer dilution method (TDM) is proposed during unmeasured emission days to have a better annual estimation of the LME. Some assumptions were made to develop this proposed model. The atmospheric model Advanced Regional Prediction System (ARPS) was employed to evaluate assumptions made during emission estimation using the proposed technique. Methane emissions of a landfill for 13 days during 2011–2013 were measured by the TDM and filtered to remove unreliable data. Then, the filtered data was employed to train the proposed linear regression model to estimate methane emissions. Daytime methane vertical profile concentrations (DMVPC) and nighttime methane vertical profile concentrations (NMVPC) were utilized to study correlations between ground field and satellite measurements for model training. Because field measurements were carried out around noon times, the DMVPC data showed a stronger correlation. Finally, both the TDM interpolation, which is the (normal approach for annual emission estimation) and a coupled of remote sensing (RS) and the TDM technique were utilized to estimate annual LME. The results revealed that interpolating TDM measurements with wide gaps underestimates the LME by about 13% compared to this new RS- field technique, which produces a higher estimation of LME.

Comments

NOTICE: this is the author’s version of a work that was accepted for publication in Remote Sensing Applications: Society and Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing Applications: Society and Environment, volume 4, in 2016. DOI: 10.1016/j.rsase.2016.04.004

The Creative Commons license below applies only to this version of the article.

Peer Reviewed

1

Copyright

Elsevier

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Sunday, April 29, 2018

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