Performance and methodological evaluation of a quantum-cascade-laser photoacoustic aerodynamic gradient system for field-scale NH ₃ flux measurements

Abstract. Reliable quantification of ammonia (NH3) surface–atmosphere exchange remains challenging due to the compound's reactivity, inlet interactions, and the sensitivity of gradient-based flux estimates to instrumental response characteristics. We present the field evaluation and methodological assessment of a quantum-cascade-laser (QCL) based photoacoustic (PA) aerodynamic gradient system for half-hourly NH3 flux measurements under agricultural conditions. The campaign covered a 54 d post-fertilization period under predominantly dry soil conditions, including the transition from bare soil to a developing winter rapeseed canopy. Instrumental performance was assessed through co-located inlet comparison experiments, yielding a random uncertainty of ±2 ppb (1σ) and demonstrating negligible systematic bias between sampling channels. The system operated continuously under field conditions with active thermal stabilization and humidity management. Fluxes were calculated using Monin–Obukhov similarity theory (MOST) across an ensemble of universal stability functions to evaluate methodological sensitivity. Sensitivity analysis indicated that variability attributable solely to stability-function selection remained small relative to observed diurnal flux amplitudes. The mean NH3 loss over the investigated period was 1.85 nmol m−2 s−1, corresponding to a cumulative loss of 1.21 kg N ha−1 or 4.0 % of the applied fertilizer nitrogen. Based on instrumental uncertainty (±2 ppb) and literature-reported uncertainty ranges of MOST-based aerodynamic gradient flux calculations, the overall uncertainty is estimated to be approximately ±25 %, corresponding to ±0.30 kg N ha−1. A pronounced diurnal asymmetry was observed, with daytime emissions approximately 1 order of magnitude higher than nighttime values, reflecting strong coupling between turbulent exchange and radiation-driven surface processes. Complementary machine-learning analysis indicated that incorporating short-term temporal memory substantially improved the representation of NH3 flux dynamics and revealed distinct daytime and nighttime exchange regimes. The combined QCL–photoacoustic gradient system demonstrated robust field performance and low instrumental bias, supporting its applicability for long-term field-scale studies of agricultural NH3 exchange under post-fertilization conditions.