This study conducted laboratory research on the nucleation mechanisms during the cooling process of motor vehicle exhaust, constructed mechanistic models, and empirically applied them in a chemical transport model. Although the authors have undertaken substantial work, the logical connections among the three parts of the manuscript remain somewhat unclear. Significant revisions are required before reconsideration for publication.

Major comments:

(1) In the introduction, the review of research progress on RIN is insufficient. It only briefly outlines various reasons that may lead to underestimation in particle size spectrum simulations, without introducing the mechanistic research advancements specifically related to RIN, which is the focus of this study.

(2)In section2, Is the use of SMPS alone sufficient to characterize the number concentration of nucleation-mode particles? Nucleated particles often exist in large quantities below 3 nm, while the detection limit of SMPS starts at approximately 11 nm. This likely leads to an underestimation of particle counts at 11 nm and below.

（3) In section3, the experiments in section2 were conducted using gasoline engines to quantify the nucleation process during exhaust cooling. Is this mechanism applicable to diesel vehicles? At the very least, this should be discussed. In real-world emissions, non-road mobile machinery, diesel vehicles, and even ships often use fuels with higher sulfur content. If the sulfuric acid-water binary nucleation mechanism can explain particle nucleation during cooling, these sources with higher fuel sulfur content might be more representative than gasoline vehicles. And what about the votile organic 

(4) Does the current mechanistic model lack consideration of the role of volatile organic compounds and semi-volatile organic compounds in nucleation during the condensation process? What impact would this have on the established mechanistic module and application of air quality model?

(5) The validation of the numerical simulation is limited. Firstly, the validation of meteorological simulation is missing, making it difficult to confirm whether the underestimation of simulated concentrations is due to biases in the meteorological simulation.

(6) Validation for gaseous precursors of PM2.5 such as SO2, NO2 and O3 is absent. Additionally, validation for related components is lacking, making it challenging to quantify the bias in simulated PM2.5 mass. This should be supplemented.

(7) It is recommended to supplement the time-series simulation of particle number concentration and particle size distribution. Presenting only statistical average results lacks persuasiveness.

Specific editorial issues:

- Line 276: "can be" is repeated.

- Line 310: The abbreviation PNC and its full form appears redundantly; similar issues occur with PSD and RIN.

The authors propose a new mechanism affecting particle number concentration and conduct extensive work from laboratory experiments to modeling, which may help improve the simulation of particle number concentrations. However, several statements and assumptions lack sufficient support. Major revisions are required before the manuscript can be reconsidered for publication.

(1)Lines 47–50: the authors state that “In urban areas, the underestimation of PNC is unlikely to be caused by weak/missing nucleation pathways, because the high concentration of existing aerosols will efficiently deplete precursors (e.g., sulfuric acid), leaving the nucleation process inconsequential.” This statement is insufficiently supported. Existing studies have shown that nucleation processes, particularly previously underappreciated acid–base nucleation, are a major source of particle number concentration, and that sulfuric acid concentrations in urban environments can remain high and are still highly sensitive in controlling nucleation rates and particle number concentrations.

(2) Line 73: the authors should provide supporting data, such as the proportion of motorcycles in the total vehicle fleet or their contribution to emissions, to justify the representativeness of motorcycles in this study.

(3) Line 77: extrapolating experimental results from a single motorcycle to all motorcycles and passenger vehicles may introduce significant uncertainty. An uncertainty analysis is needed to discuss how differences in vehicle type, engine technology, and operating conditions may affect the results, and the applicability range of the proposed parameterization should be clearly stated.

(4) Line 162: the authors state that “new particle formation is dominated by H₂SO₄–H₂O nucleation,” which is inconsistent with the widely accepted understanding that acid–base nucleation is the dominant nucleation mechanism in urban boundary-layer environments. Moreover, the two references cited in support of this statement do not appear to provide direct evidence for such a conclusion. The authors should provide clearer evidence to support this claim and explicitly discuss the uncertainties introduced by adopting only the H₂SO₄–H₂O nucleation framework while neglecting acid–base nucleation, as well as how the presence of ammonia or semi-volatile organic compounds in urban environments may modulate the efficiency of the proposed RIN mechanism.

(5) Lines 334–335: Although the simulations cover the period from 5 to 25 November 2020, the validation results shown in Figures 8 and 9 are limited to only two specific days (20 and 25 November). This limited validation makes it difficult to demonstrate the robustness of the modeling framework over a broader time period. It is recommended to provide validation results over longer time scales and to supplement the evaluation with quantitative statistical metrics such as RMSE.

(6) The title of the manuscript should be further constrained to explicitly indicate that the proposed recondensation-induced nucleation is related to traffic or vehicle exhaust emissions. The current wording is overly generalized. Although “cylinder” may implicitly refer to engine combustion processes, this terminology is too implicit and not sufficiently clear for an atmospheric science audience.