Modeling landfill CH4 emissions

Abstract

The current IPCC landfill methane (CH4) methodology excludes critical process drivers now known to control emissions. These include site-specific (1) operational factors (i.e., thickness and composition of various cover soils; physical extent of engineered biogas recovery) and (2) temporal climate effects on soil moisture/temperature profiles in each cover which, in turn, drive gaseous transport, microbial methanotrophic oxidation, and temporally variable “net” CH4 emissions over an annual cycle. Herein, we address the international field validation and application of a process-based model CAlifornia Landfill Methane Inventory Model (CALMIM) which encompasses site-specific climate, cover soils, engineered biogas recovery, and other site-specific strategies. Using embedded soil microclimate models with (a) default 30-year climate data, (b) site-specific annual weather data, or (c) future climate predictions (i.e., CMIP5), the transient soil moisture and temperature effects on bidirectional diffusive CH4/oxygen transport and microbial oxidation can be estimated for any cover soil at any global location. We focus on site-specific field data comparisons to CALMIM-predicted annual and monthly CH4 emissions both without and without methanotrophic oxidation. Overall, 74% of 168 individual surface CH4 emission measurements across 34 international sites were consistent with CALMIM-modeled annual predictions with oxidation (+ or – SD). Notably, the model overpredicted 30 comparisons and underpredicted 13 comparisons. In addition to improving site-specific landfill CH4 inventories, we address how this freely available tool can be used to (a) recommend site-specific cover soil modifications to minimize emissions; (b) systematically compare the spatial and temporal variability of emissions for diverse global locations, latitudinal gradients, extreme climates, and future climate scenarios; (c) assist scheduling of field campaigns to capture seasonal variability; and (d) provide a 12-month annual framework with average monthly CH4 emission statistics for comparison to periodic temporal results from diverse bottom-up and top-down field techniques with variable uncertainties. Importantly, CALMIM does not require intensive site-specific model calibrations.