Jeq50250 14..20

Estimates of enteric methane (CH4) emissions from ruminants are typically measured by confining animals in large chambers, using head hoods or masks, or by a ratiometric technique involving sampling respired air of the animal. These techniques are not appropriate to evaluate large-scale farm emissions and the variability between farms that may be partly attributed to different farm management. This study describes the application of an inverse-dispersion technique to calculate farm emissions in a controlled tracer-release experiment. Our study was conducted at a commercial dairy farm in southern Alberta, Canada (total of 321 cattle, including 152 lactating dairy cows). Sulfur hexafluoride (SF6) and CH4 were released from 10 outlet locations (barn and open pens) using mass-flow controllers. A Lagrangian stochastic (LS) dispersion model was then used to infer farm emissions from downwind gas concentrations. Concentrations of SF6 and CH4 were measured by gas chromatography analysis and open path lasers, respectively. Wind statistics were measured with a threedimensional sonic anemometer. Comparing the inferred emissions with the known release rate showed we recovered 86% of the released CH4 and 100% of the released SF6. The location of the concentration observations downwind of the farm was critically important to the success of this technique. THE GLOBAL RELEASE of methane (CH4) from agricultural sources accounts for two-thirds of the anthropogenic CH4 sources (Moss et al., 2000). These sources include rice growing, fermentation of feed by ruminants (enteric CH4), biomass burning, and animal wastes. Globally, ruminant livestock are responsible for about 85 Tg of the 550 Tg CH4 released annually. There is, however, considerable uncertainty associated with the estimates of CH4 from livestock due to the scant availability of data to document the variability that exists at the farm scale (e.g., due to the significant impact of diet on enteric CH4 production). Part of the problem is the difficulty of making emissions measurements from livestock facilities. A few studies have measured CH4 emissions using close approximations to real farms. Kinsman et al. (1995) calculated CH4 emitted from a barn housing dairy cows, by monitoring inflow and exhaust air concentration and airflows. However, this technique, aside from being technologically challenging, cannot be applied to the majority of more modern ‘‘open’’ dairy barns or beef feedlots where the airflow is passive. A more appropriate approach for open facilities is to release a tracer gas at a known rate (Marik and Levin, 1996; Kaharabata et al., 2000). This assumes the relationship of the tracer flux to the tracer concentration in the downwind plume is similar for that of the target gas (where the concentration is also measured). Although this ratiometric technique is theoretically straightforward, it relies on matching the tracer and target gas sources that can be difficult to achieve (because the true emission configuration is unknown) and requires extensive setup time. A more flexible technique for quantifying emissions is to model the dispersion of a target gas from the source (e.g., Kaharabata et al., 2000; Flesch et al., 2004), so that a downwind concentration of gas can establish the emission rate. This ‘‘inverse-dispersion’’ technique has the potential advantage of simplicity, as it requires only a single gas concentration measurement and basic wind information. However, most of the applications of this technique have been for emission sources over a uniform surface (e.g., Wilson et al., 1982). For more complex settings, like a farm with buildings and trees, further evaluation of the inverse-dispersion approach is needed. The objective of our study was to examine the application of an inverse-dispersion technique that could be easily deployed to determine farm-scale CH4 emissions (or any other trace gas). Such a technique would allow the quick evaluation of mitigation practices of entire farms and provide a means of understanding the variability that exists between different farm types. MATERIALS AND METHODS