the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Calibrating Interdependent Photochemistry, Nucleation, and Aerosol Microphysics in Chamber Experiments
Abstract. Laboratory experiments addressing complex phenomena such as atmospheric new-particle formation and growth typically involve numerous instruments measuring a range of key coupled variables. In addition to independent calibration, the combined dataset provides not just constraints on the parameters of interest but also on the critical instrument calibrations. Here we find good agreement between production and loss rates of sulfuric acid (H2SO4) in an experiment performed at the CERN CLOUD chamber involving oxidation of sulfur dioxide (SO2) in the presence of ammonia (NH3) at 58 % relative humidity, driving new-particle formation and growth of particles by H2SO4 + NH3 nucleation initiated by O3 photolysis via several light sources. This closure requires consistency across numerous parameters, including: the particle number and size distribution; their condensation sink for H2SO4; the particle growth rates; the concentration of H2SO4; and the nucleation coefficients for both neutral and ion-induced pathways. Our study shows that accurate agreement can be achieved between production and loss of condensable vapors in laboratory chambers under atmospheric conditions, with accuracy ultimately tied to particle number measurement (i.e. a condensation particle counter). This, in turn implies parameters such as the H2SO4 concentration and particle size distributions can be determined to a comparable precision.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques.
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- RC1: 'Comment on egusphere-2025-2412', Anonymous Referee #1, 28 Sep 2025 reply
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The study by Donahue et al. presents an interesting approach to constraining H₂SO₄ production and loss based on particle number measurements, effectively through an optimization exercise across a multiparameter space coupled with modeling, ultimately claiming to minimize systematic and random calibration biases. Although lengthy, the paper is very well written, and the CLOUD team has made significant contributions in this area. I believe the paper could be publishable in AMT after the authors address some considerations, which I detail below.
Major comments:
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