Ubiquitous atmospheric production of organic acids mediated by cloud droplets.

Atmospheric acidity is increasingly determined by carbon dioxide and organic acids1-3. Among the latter, formic acid facilitates the nucleation of cloud droplets4 and contributes to the acidity of clouds and rainwater1,5. At present, chemistry-climate models greatly underestimate the atmospheric burden of formic acid, because key processes related to its sources and sinks remain poorly understood2,6-9. Here we present atmospheric chamber experiments that show that formaldehyde is efficiently converted to gaseous formic acid via a multiphase pathway that involves its hydrated form, methanediol. In warm cloud droplets, methanediol undergoes fast outgassing but slow dehydration. Using a chemistry-climate model, we estimate that the gas-phase oxidation of methanediol produces up to four times more formic acid than all other known chemical sources combined. Our findings reconcile model predictions and measurements of formic acid abundance. The additional formic acid burden increases atmospheric acidity by reducing the pH of clouds and rainwater by up to 0.3. The diol mechanism presented here probably applies to other aldehydes and may help to explain the high atmospheric levels of other organic acids that affect aerosol growth and cloud evolution.

Improvement of differential optical absorption spectroscopy with a multichannel scanning technique.

Differential optical absorption spectroscopy (DOAS) of atmospheric trace gases requires the detection of optical densities below 0.1%. Photodiode arrays are used more and more as detectors for DOAS because they allow one to record larger spectral intervals simultaneously. This type of optical multichannel analyzer (OMA), however, shows sensitivity differences among the individual photodiodes (pixels), which are of the order of 1%. To correct for this a sensitivity reference spectrum is usually recorded separately from the trace-gas measurements. Because of atmospheric turbulence the illumination of the detector while an atmospheric absorption spectrum is being recorded is different from the conditions during the reference measurement. As a result the sensitivity patterns do not exactly match, and the corrected spectra still show a residual structure that is due to the sensitivity difference. This effect usually limits the detection of optical densities to approximately 3 × 10(-4). A new method for the removal of the sensitivity pattern is presented in this paper: Scanning the spectrometer by small wavelength increments after each readout of the OMA allows one to separate the OMA-fixed pattern and the wavelength-fixed structures (absorption lines). The properties of the new method and its applicability are demonstrated with simulated spectra. Finally, first atmospheric measurements with a laser long-path instrument demonstrate a detection limit of 3 × 10(-5) of a DOAS experiment.

Simple Monte Carlo methods to estimate the spectra evaluation error in differential-optical-absorption spectroscopy.

Differential-optical-absorption spectroscopy (DOAS) permits the sensitive measurement of concentrations of trace gases in the atmosphere. DOAS is a technique of well-defined accuracy; however, the calculation of a statistically sound measurement precision is still an unsolved problem. Usually one evaluates DOAS spectra by performing least-squares fits of reference absorption spectra to the measured atmospheric absorption spectra. Inasmuch as the absorbance from atmospheric trace gases is usually very weak, with optical densities in the range from 10(-5) to 10(-3), interference caused by the occurrence of nonreproducible spectral artifacts often determines the detection limit and the measurement precision. These spectral artifacts bias the least-squares fitting result in two respects. First, spectral artifacts to some extent are falsely interpreted as real absorption, and second, spectral artifacts add nonstatistical noise to spectral residuals, which results in a significant misestimation of the least-squares fitting error. We introduce two new approaches to investigate the evaluation errors of DOAS spectra accurately. The first method, residual inspection by cyclic displacement, estimates the effect of false interpretation of the artifact structures. The second method applies a statistical bootstrap algorithm to estimate properly the error of fitting, even in cases when the condition of random and independent scatter of the residual signal is not fulfilled. Evaluation of simulated atmospheric measurement spectra shows that a combination of the results of both methods yields a good estimate of the spectra evaluation error to within an uncertainty of ~10%.