Inverse-dispersion techniques allow inference of a gas emission rate Q from measured air concentration. In "ideal surface layer problems," where Monin-Obukhov similarity theory (MOST) describes the winds transporting the gas, the application of the technique can be straightforward. This study examines the accuracy of an ideal MOST-based inference, but in a nonideal setting. From a 6 m ϫ 6 m synthetic area source surrounded by a 20 m ϫ 20 m square border of a windbreak fence (1.25 m tall), Q is estimated. Open-path lasers gave line-averaged concentration C L at positions downwind of the source, and an idealized backward Lagrangian stochastic (bLS) dispersion model was used to infer Q bLS. Despite the disturbance of the mean wind and turbulence caused by the fence, the Q bLS estimates were accurate when ambient winds (measured upwind of the plot) were assumed in the bLS model. In the worst cases, with C L measured adjacent to a plot fence, Q bLS overestimated Q by an average of 50%. However, if these near-fence locations are eliminated, Q bLS averaged within 2% of the true Q over 61 fifteen-minute observations (with a standard deviation Q/Q ϭ 0.20). Poorer accuracy occurred when in-plot wind measurements were used in the bLS model. The results show that when an inverse-dispersion technique is applied to disturbed flows without accounting for the disturbance, the outcome may still be of acceptable accuracy if judgment is applied in the placement of the concentration detector.

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Inverse-dispersion calculations can be used to infer atmospheric emission rates through a combination of downwind gas concentrations and dispersion model predictions. With multiple concentration sensors downwind of a compound source (whose component positions are known) it is possible to calculate the component emissions. With this in mind, a field experiment was conducted to examine the feasibility of such multi-source inferences, using four synthetic area sources and eight concentration sensors arranged in different configurations. Multi-source problems tend to be mathematically ill-conditioned, as expressed by the condition number κ. In our most successful configuration (average κ = 4.2) the total emissions from all sources were deduced to within 10% on average, while component emissions were deduced to within 50%. In our least successful configuration (average κ = 91) the total emissions were calculated to within only 50%, and component calculations were highly inaccurate. Our study indicates that the most accurate multi-source inferences will occur if each sensor is influenced by only a single source. A "progressive" layout is the next best: one sensor is positioned to "see" only one source, the next sensor is placed to see the first source and another, a third sensor is placed to see the previous two plus a third, and so on. When it is not possible to isolate any sources κ is large and the accuracy of a multi-source inference is doubtful.

Livestock manure is a significant source of ammonia (NH3) emissions. In the atmosphere, NH3 is a precursor to the formation of fine aerosols that contribute to poor air quality associated with human health. Other environmental issues result when NH3 is deposited to land and water. Our study documented the quantity of NH3 emitted from a feedlot housing growing beef cattle. The study was conducted between June and October 2006 at a feedlot with a one-time capacity of 22,500 cattle located in southern Alberta, Canada. A backward Lagrangian stochastic (bLS) inverse-dispersion technique was used to calculate NH3 emissions, based on measurements of NH3 concentration (open-path laser) and wind (sonic anemometer) taken above the interior of the feedlot. There was an average of 3146 kg NH3 d(-1) lost from the entire feedlot, equivalent to 84 microg NH3 m(-2) s(-1) or 140 g NH3 head(-1) d(-1). The NH3 emissions correlated with sensible heat flux (r2 = 0.84) and to a lesser extent the wind speed (r2 = 0.56). There was also evidence that rain suppressed the NH3 emission. Quantifying NH3 emission and dispersion from farms is essential to show the impact of farm management on reducing NH3-related environmental issues.

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