This study analyzed the pesticides in the gas and particle phase of air samples collected in the North China Plain and then investigated the gas-particle partitioning of pesticides by applying models, showing absorption as the main mechanism of gas-particle partitioning. Pesticides are a typical class of organic pollutants in the atmosphere, especially in the rural and agricultural production regions. Studying the abundance and gas-particle partitioning of atmospheric pesticides will enhance our understanding of organic pollutants in the atmosphere. The manuscript was well written and fits the scope of ACP. Therefore, I recommend the publication of this manuscript after the following questions are addressed.

Specific comment:

1.    Introduction: The author stated that a large amount of pesticides release to atmosphere. However, there is no data presented. I suggest to present the data of concentration of pesticides in the atmosphere.

2.    Line 97-106: The author showed the information of the geographical information of Quzhou County. But it did not tell us the amount of pesticides used in this county and the pesticide utilization rate, which is more relevant to this study. 

3.    Line 132: why these 38 pesticides were selected for the method development?

4.    Line 191: The proportions of organic matter used in the model simulations were set to 5%–30%. Although these values were not based on actual measurements, it would be better to provide supporting references. It is recommended to refer to the reported ranges of organic matter content in particulate matter from the literature to strengthen the relevant discussion.

5.    Line256-258: “… attributed to its high vapor pressure-second only to propamocarb-facilitating its volatilization…”, this sentence is not clear. Please rephrase it.

6.    Line 271-293: The concentration of atmospheric pesticides varied between different sampling dates. I suggest to present the sampling dates of previous studies for a better comparison. 

7.    Line 311: replace “soi” by “soil”.

8.    Figure 2, S1 and S2: Is it possible to mark the date of booting and heading stages of wheat in the figures? Then the readers can clearly see how the pesticides application affect the concentration of atmospheric pesticides.

9.    Figure S5: the color refers to the relative humidity during the sampling day? Is it an averaged relative humidity? Please clarify it in the figure caption.

10.    Figure 5: It looks busy. I suggest to clarify/improve the figure caption to make it more understandable.

11.     Limitation: I suggest to conduct the similar experiment in different seasons in the future, since the gas-particle partitioning of pesticides in the atmosphere is influenced by pesticide application and meteorology.

The study by Guo et al. simultaneously collected gas- and particle-phase pesticides in the atmosphere at a rural site in the North China Plain (NCP) at weekly intervals from February to May and analyzed the gas-particle partitioning of pesticides. Based on the data from continuously collected gas- and particle-phase samples, the authors explored the distribution profiles of 33 pesticides in the gas and particle phases and then assessed the effects of meteorological factors on pesticide concentrations, and compared the gas-particle partitioning behavior of pesticides using three models, explaining the underlying mechanisms. This study provides valuable new data on the atmospheric fate of pesticides and employs appropriate statistical and modeling tools. The manuscript is generally well organized and written in a professional tone. Below, I offer specific comments to strengthen the work. With these revisions, I believe the paper will be suitable for Atmospheric Chemistry and Physics.

Specific comments:

1. Line 63: The phrase “gas-particle partitioning” in the sentence “The conventional Junge-Pankow model attributes particle/gas partitioning” should be consistent with the term “gas-particle partitioning” used throughout the text. Please review all relevant instances in the manuscript.
2. Lines 109–115: In this study, a high-vol sampler fitted with PUF and filter was used to collect both gas- and aerosol-phase pesticides. I am wondering which component was positioned upstream in the sampling line—that is, was the PUF fitted first, followed by the filter, or vice versa? This should be clearly specified.
3. Lines 112-113: The seven-day integrated sampling period may allow for redistribution or chemical changes on the samplers. It would be helpful to elaborate on how this could bias the gas/particle ratio, perhaps by comparing the results with those from shorter-duration studies.
4. Line 169: It would be beneficial to first explain why the Junge-Pankow (J-P) adsorption model, Harner-Bidleman (H-B) K_oa absorption model, and L-M-Y model were selected to simulate the gas-particle phase distribution of pesticides, followed by a separate introduction of each model.
5. Lines 282-291: The formats of “48.00 pg/m³, 17.70 pg/m³, 7.20 pg/m³, and 7.95 pg/m³” and “21.66, 114.72, 96.19, and 2.17 pg/m³” are inconsistent, please ensure consistent unit formatting for all concentration values throughout the text.
6. Line 311: It contains a spelling error: “soi to the atmosphere” should be “soil to the atmosphere”.
7. Section 3.4: The authors mention that rising temperature promotes particle-phase concentration, which is counterintuitive since higher temperatures typically drive partitioning toward the gas phase. Although the authors provide some explanation, a more detailed and clearer clarification is needed.
8. Section 3.5: All models assume equilibrium; however, field conditions may not be at equilibrium, as the authors note. It would be helpful to clarify how this limitation affects the comparison. For example, the L-M-Y model (non-equilibrium steady state) underestimates φ. Do the authors conclude that equilibrium is more appropriate for this dataset? They suggest that equilibrium conditions were closer than steady state, which is reasonable, but they could further explain why short-range local sources might drive quasi-equilibrium partitioning.