Numerical simulations to assess the tracer dilution method for measurement of landfill methane emissions

•Atmospheric dispersion modeling is used to evaluate landfill emissions measurements.•Sensitivity of tracer dilution method to different factors is assessed.•Tracer placement has significant impact on accuracy of estimated methane emissions.•Topography and wind direction have significant effects on accuracy of measurements.

Landfills are a significant contributor to anthropogenic methane emissions, but measuring these emissions can be challenging. This work uses numerical simulations to assess the accuracy of the tracer dilution method, which is used to estimate landfill emissions. Atmospheric dispersion simulations with the Weather Research and Forecast model (WRF) are run over Sandtown Landfill in Delaware, USA, using observation data to validate the meteorological model output. A steady landfill methane emissions rate is used in the model, and methane and tracer gas concentrations are collected along various transects downwind from the landfill for use in the tracer dilution method. The calculated methane emissions are compared to the methane emissions rate used in the model to find the percent error of the tracer dilution method for each simulation. The roles of different factors are examined: measurement distance from the landfill, transect angle relative to the wind direction, speed of the transect vehicle, tracer placement relative to the hot spot of methane emissions, complexity of topography, and wind direction. Results show that percent error generally decreases with distance from the landfill, where the tracer and methane plumes become well mixed. Tracer placement has the largest effect on percent error, and topography and wind direction both have significant effects, with measurement errors ranging from −12% to 42% over all simulations. Transect angle and transect speed have small to negligible effects on the accuracy of the tracer dilution method. These tracer dilution method simulations provide insight into measurement errors that might occur in the field, enhance understanding of the method’s limitations, and aid interpretation of field data.