Accounting for Field-Scale Dry Deposition in Backward Lagrangian Stochastic Dispersion Modelling of NH3 Emissions

Abstract

Abstract:A controlled ammonia (NH 3 ) release experiment was performed at a grassland site. The aim was to quantify the effect of dry deposition between the source and the receptors (NH 3 measurement locations) on emission rate estimates by means of inverse dispersion modelling. NH 3 was released for three hours at a constant rate of Q = 6.29 mg s −1 from a grid of 36 orifices spread over an area of 250 m 2 . The increase in line-integrated NH 3 concentration was measured with open-path optical miniDOAS devices at different locations downwind of the artificial source. Using a backward Lagrangian stochastic (bLS) dispersion model (bLSmodelR), the fraction of the modelled release rate to the emitted NH 3 (Q bLS /Q) was calculated from the measurements of the individual instruments. Q bLS /Q was found to be systematically lower than 1, on average between 0.69 and 0.91, depending on the location of the receptor. We hypothesized that NH 3 dry deposition to grass and soil surfaces was the main factor responsible for the observed depletion of NH 3 between source and receptor. A dry deposition algorithm based on a deposition velocity approach was included in the bLS modelling. Model deposition velocities were evaluated from a 'big-leaf' canopy resistance analogy. Canopy resistances (generally termed R c ) that provided Q bLS /Q = 1 ranged from 75 to 290 s m −1 , showing that surface removal of NH 3 by dry deposition can plausibly explain the original underestimation of Q bLS /Q. The inclusion of a dry deposition process in dispersion modelling is crucial for emission estimates, which are based on concentration measurements of depositing tracers downwind of homogeneous area sources or heterogeneously-distributed hot spots, such as, e.g., urine patches on pastures in the case of NH 3 .

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“…The overall uncertainty of the emissions estimated with the bLS model is complex to evaluate due to the uncertainties of the model itself, the concentration measurements, and the wind statistics. However, the bLS model has been proved to be an acceptable method with relatively small uncertainty (∼10%) (Harper et al and Lemes et al) and good recovery in controlled release experiments. ,− …”

“…The bLS model was implemented with six million trajectories into an R package (bLSmodelR, ; version 4.3) as described in ref . A computer cluster programming rig for modern engineering (PRIME, Aarhus University) was used to execute the bLS model in R. A relatively high number of trajectories was used to reduce the uncertainty of the model.…”

Ammonia emissions from a dairy housing and a wastewater treatment plant quantified with an inverse dispersion method accounting for deposition loss

“…The bLS model modified by (Häni et al 2018) (R package bLSmodelR available at https://www.agrammon.ch/documents-to-download/blsmodelr/) was used to calculate dry deposition by multiplying the measured concentration increase with a proportionality constant called the deposition velocity (Wesely and Hicks 2000). The deposition velocity was approximated by a resistances approach (Sutton et al 1995b), whereby the velocity is the inverse of the sum of several resistances to deposition that must be overcome.…”

“…This method allows a simple, yet reasonable approximation of the likely deposition and corrected emissions therefore represent an average of the possible lower and upper flux magnitudes. The deposition flux Fd was modelled following Häni et al (2018) by…”

“…Likewise, modelling studies have estimated that in total about 44% of emitted NH3 is lost in the vicinity of the source area through dry deposition of gaseous NH3, while NH4 + aerosols travel longer distances (Asman et al 1998). Although line-integrating instruments are located 100-150 m from the source, the deposition loss is often not accounted for in emission measurements, which leads to a systematic underestimation when determining total emissions from defined hot spots or source areas (Häni et al 2018).…”