Deconvolution of Partitioning Delays from Time-Resolved Trace Gas Measurements

Time-resolved measurements of low-volatility gas-phase compounds are limited by partitioning of the analyte to instrument surfaces, resulting in what are known as partitioning delays. These delays slow instrument responses and affect the accuracy of subsequent analyses. In this work, we introduce a deconvolution algorithm that corrects measurements affected by partitioning delays. We evaluate the performance of this algorithm using synthetic data and also demonstrate its utility in correcting partitioning delays in airborne nitric acid measurements. We compare the effectiveness of deconvolution to the current best practice for partitioning delays: frequent subtraction of instrument background. Frequent background measurements are outperformed by the deconvolution algorithm when sample concentrations are changing faster than the instrument response time. The deconvolution algorithm can be applied to time series that include frequent measurement of instrument backgrounds, enabling reanalysis of past data. Furthermore, the algorithm does not rely on any coincident data; it is effective without any external information about the true time series of an analyte. When applied to nitric acid measurements from a wildfire smoke plume, deconvolution increases the calculated normalized excess mixing ratio (ΔHNO3/ΔCO) by 72%. We conclude that the deconvolution algorithm is applicable to ground, airborne, and eddy covariance measurements of “sticky” compounds.