This manuscript presents measurements of secondary organic aerosol formation potential (SOA-FP) in the Los Angeles basin during summer 2022 using a novel dual oxidation flow reactor (OFR) system. By employing an electrically conductive perfluoroalkoxy alkane inlet upstream of one OFR, the authors separate ambient precursors according to volatility and experimentally quantify the relative contributions of higher- and lower-volatility compounds to SOA formation. The measurements are further compared with predictions from two box models.

Overall, this is a well-written, carefully executed, and comprehensive study. The experimental approach is novel and represents an important advance in the direct characterization of SOA precursor volatility classes in ambient air. The manuscript is clearly structured, the methodology is described in sufficient detail, and the interpretation is generally well supported by the observations. I believe that this work is suitable for publication after the authors address the comments below.

1. The manuscript discusses SOA formation primarily in terms of volatility classes. However, recent laboratory and field studies have demonstrated that rapid autoxidation and HOM formation can substantially enhance SOA yields even for relatively volatile precursors, especially monoterpenes and aromatic compounds. Given that OFRs can efficiently generate HOMs under low-NO conditions, could the authors comment on how autoxidation chemistry may influence the inferred SOA-FP distribution across volatility classes? This may be particularly relevant when comparing the measurements with mechanisms such as CRACMM that contain simplified autoxidation treatments.
2. The manuscript attributes the lower SOA formation potential observed in 2022 relative to CalNex 2010 to a combination of reduced precursor concentrations and enhanced ambient OH exposure, which presumably leads to greater consumption of SOA precursors prior to sampling. However, increased atmospheric oxidation can also generate lower-volatility products that remain available for further SOA formation in the OFR. Could the authors discuss how multigenerational oxidation influences the interpretation of SOA-FP? Specifically, does increased photochemical age necessarily imply lower SOA-FP, or could aging shift the precursor distribution toward more oxygenated, lower-volatility species that are not fully captured by the present framework?
3. One important aspect of the experimental design that may influence the interpretation of the results is the performance of the c-PFA inlet as a volatility filter. While the authors characterize the transmission efficiency, it remains unclear whether multifunctional compounds with moderate volatility and strong polarity may experience enhanced wall interactions beyond those predicted solely from volatility. Previous studies have shown substantial wall losses of oxygenated organics in sampling lines (e.g., Pagonis et al., 2017; Deming et al., 2019). Could the authors discuss whether differential losses of highly functionalized species could bias the inferred partitioning between higher and lower-volatility precursor classes?
4. The comparison between model performance and observations provides useful insight. This study shows that CRACMM reproduces ambient OA reasonably well but substantially underpredicts SOA-FP, while the Caltech model exhibits the opposite behavior (underestimates OA and overestimates SOA-FP). Although the model–measurement discrepancies are clearly presented, it remains somewhat unclear which processes are primarily responsible for these biases. Could the authors further discuss whether the disagreement mainly arises from uncertainties in emissions, gas-phase oxidation chemistry, volatility distributions, or gas-particle partitioning or some other factors? Such discussion would really strengthen the implications for future model development.
5. The present study attributes only a modest fraction of SOA-FP to terpenoids. However, because biogenic oxidation products can undergo extensive multigenerational oxidation and partition into lower-volatility bins, is it possible that some SOA-FP attributed to lower-volatility precursors originated from earlier terpenoid oxidation? How does the volatility-based framework distinguish between primary terpenoid precursors and their aged oxidation products? Also, the OFRs were operated in OFR185 mode at approximately two equivalent days of OH exposure under low-NO conditions. Since terpenoid oxidation chemistry and SOA yields are strongly dependent on NOx, it would be useful to discuss in more detail how representative the inferred terpenoid SOA-FP is of summertime atmospheric conditions in Los Angeles, where oxidation frequently occurs under moderate-to-high NOx conditions. Could the OFR operating conditions systematically bias the estimated contribution of terpenoids?

Publisher’s note: a supplement was added to this comment on 26 June 2026.