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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Mitigation of agricultural greenhouse gas emissions is a target area for the Australian Government and the pork industry. The present study measured methane (CH4), nitrous oxide (N2O) and ammonia (NH3) from a deep-litter piggery and litter stockpile over two trials in southern New South Wales, to compare emissions from housing pigs on deep litter with those of pigs from conventional housing with uncovered anaerobic effluent-treatment ponds. Emissions were measured using open-path Fourier transform infrared spectrometry, in conjunction with a backward Lagrangian stochastic model. Manure excretion was determined by mass balance and emission factors (EFs) were developed to report emissions relative to volatile solids and nitrogen (N) input. Nitrous oxide emissions per animal unit (1 AU = 500 kg liveweight) from deep-litter sheds were negligible in winter, and 8.4 g/AU.day in summer. Ammonia emissions were 39.1 in winter and 52.2 g/AU.day in summer, while CH4 emissions were 16.1 and 21.6 g/AU.day in winter and summer respectively. Emission factors averaged from summer and winter emissions showed a CH4 conversion factor of 3.6%, an NH3-N EF of 10% and a N2O-N EF of 0.01 kg N2O-N/kg N excreted. For the litter stockpile, the simple average of summer and winter showed an EF for NH3-N of 14%, and a N2O-N EF of 0.02 kg N2O-N/kg-N of spent litter added to the stockpile. We observed a 66% and 80% decrease in emissions from the manure excreted in litter-based housing with litter stockpiling or without litter stockpiling, compared with conventional housing with an uncovered anaerobic effluent-treatment pond. This provides a sound basis for mitigation strategies that utilise litter-based housing as an alternative to conventional housing with uncovered anaerobic effluent-treatment ponds.