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Alternative carbon dioxide modelling approaches accounting for high residual gases in LandGEM

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Abstract

High Canadian waste disposal rates necessitate landfill gas monitoring and accurate forecasting. CO2 estimates in LandGEM version 3.02 currently rest on the assumptions that CO2 is a function of CH4, where the two gases make up nearly 100% of landfill gas content, leading to overestimated CO2 collection estimates. A total of 25 cases (five formulas, five approaches) compared annual CO2 collection at four western Canadian landfills. Despite common use in literature, the 1:1 ratio of CH4 to CO2 was not recommended to forecast landfill gas collection in cold climates. The existing modelling approach significantly overestimated CO2 production in three of four sites, resulting in the highest residual sum of squares. Optimization resulted in the most accurate results for all formulas and approaches, which had the greatest reduction in residual sums of squares (RSS) over the default approach (60.1 to 97.7%). The 1.4 Ratio approach for L o:L o-CO2 yielded the second most accurate results for CO2 flow (mean RSS reduction of 50.2% for all sites and subsection models). The annual k-modified LandGEM calculated k’s via two empirical formulas (based on precipitation) and yielded the lowest accuracy in 12 of 20 approaches. Unlike other studies, strong relationships between optimized annual k’s and precipitation were not observed.

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Acknowledgements

The research reported in this paper was supported by a grant (RGPIN-385815) from the Natural Sciences and Engineering Research Council of Canada. The authors are grateful for their support. Special acknowledgment goes to the cities’ landfill teams, who supported the data collection. The views expressed herein are those of the writers and not necessarily those of our research and funding partners.

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Correspondence to Kelvin Tsun Wai Ng.

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Bruce, N., Ng, K.T.W. & Richter, A. Alternative carbon dioxide modelling approaches accounting for high residual gases in LandGEM. Environ Sci Pollut Res 24, 14322–14336 (2017). https://doi.org/10.1007/s11356-017-8990-9

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