Ammonia emissions from a grazed field estimated by miniDOAS measurements and inverse dispersion modelling

Michael Bell, Chris Flechard, Yannick Fauvel, Christoph Häni, Jörg Sintermann, Markus Jocher, Harald Menzi, Arjan Hensen, Albrecht Neftel
2017 Atmospheric Measurement Techniques  
<p><strong>Abstract.</strong> Ammonia (NH<sub>3</sub>) fluxes were estimated from a field being grazed by dairy cattle during spring by applying a backward Lagrangian stochastic model (bLS) model combined with horizontal concentration gradients measured across the field. Continuous concentration measurements at field boundaries were made by open-path miniDOAS (differential optical absorption spectroscopy) instruments while the cattle were present and for 6 subsequent days. The deposition of
more » ... e deposition of emitted NH<sub>3</sub> to <q>clean</q> patches on the field was also simulated, allowing both <q>net</q> and <q>gross</q> emission estimates, where the dry deposition velocity (<i>v</i><sub>d</sub>) was predicted by a canopy resistance (<i>R</i><sub>c</sub>) model developed from local NH<sub>3</sub> flux and meteorological measurements. Estimated emissions peaked during grazing and decreased after the cattle had left the field, while control on emissions was observed from covariance with temperature, wind speed and humidity and wetness measurements made on the field, revealing a diurnal emission profile. Large concentration differences were observed between downwind receptors, due to spatially heterogeneous emission patterns. This was likely caused by uneven cattle distribution and a low grazing density, where <q>hotspots</q> of emissions would arise as the cattle grouped in certain areas, such as around the water trough. The spatial complexity was accounted for by separating the model source area into sub-sections and optimising individual source area coefficients to measured concentrations. The background concentration was the greatest source of uncertainty, and based on a sensitivity/uncertainty analysis the overall uncertainty associated with derived emission factors from this study is at least 30–40<span class="thinspace"></span>%.<br><br>Emission factors can be expressed as 6<span class="thinspace"></span>±<span class="thinspace"></span>2<span class="thinspace"></span>g<span class="thinspace"></span>NH<sub>3</sub><span class="thinspace"></span>cow<sup>−1</sup><span class="thinspace"></span>day<sup>−1</sup>, or 9<span class="thinspace"></span>±<span class="thinspace"></span>3<span class="thinspace"></span>% of excreted urine-N emitted as NH<sub>3</sub>, when deposition is not simulated and 7<span class="thinspace"></span>±<span class="thinspace"></span>2<span class="thinspace"></span>g<span class="thinspace"></span>NH<sub>3</sub><span class="thinspace"></span>cow<sup>−1</sup><span class="thinspace"></span>day<sup>−1</sup>, or 10<span class="thinspace"></span>±<span class="thinspace"></span>3<span class="thinspace"></span>% of excreted urine-N emitted as NH<sub>3</sub>, when deposition is included in the gross emission model. The results suggest that around 14<span class="thinspace"></span>±<span class="thinspace"></span>4<span class="thinspace"></span>% of emitted NH<sub>3</sub> was deposited to patches within the field that were not affected by urine or dung.</p>
doi:10.5194/amt-10-1875-2017 fatcat:7buj5ordprg5hc5ex3lj42vjsa