This manuscript presents a significant study examining the impacts of meteorology and emission reductions on PM_2.5 levels during the COVID-19 lockdown in the North China Plain (NCP). The authors utilize the WRF-Chem model to investigate the complex interactions between anthropogenic emissions, meteorology, and air quality, revealing important regional disparities in PM_2.5 responses between the Northern and Southern NCP. The analysis of how adverse meteorological conditions in the Northern NCP negated the benefits of emission reductions is particularly noteworthy. This manuscript aligns with the scope of ACP, and the methodology is sound. However, there are several areas that require enhancement, particularly in clarifying the research objectives, providing more detail in the methodology, and including the rationale for the selected model and specific parameters. I will recommend acceptance of the manuscript after the following minor concerns are addressed. 

Major comments：

1. The relationship between air pollution and emission reduction during the COVID-19 lockdown in China is a notable case in air pollution control; however, there are several existing studies on this topic. It is recommended that the author further enhance the discussion by more robustly comparing the results of this study with prior research, thereby underscoring the distinctive contributions of this paper.
2. The authors have distinctly defined two regions of interest, namely the NNCP and the SNCP. Please elaborate on the specific reasons why these two regions were designated as depicted in Figure 1? What were the crucial factors that the authors took into account when defining the boundaries of the two regions?
3. The authors mainly discuss the spatial differences in the impact of emissions and meteorology on the total PM₅ concentrations, how about the chemical components within PM_2.5, particularly secondary inorganic and organic aerosols? Do these chemical components exhibit the same spatial variation characteristics?

Minor comments：

1. Provide a rationale for using the WRF-Chem model, highlighting its advantages for simulating meteorological and chemical interactions. Include specific parameters used in the WRF-Chem model simulations, such as resolution, boundary conditions, and initial conditions. This detail will help readers understand the modeling approach and assess its performance.
2. Present percentage reductions in emissions during the lockdown to contextualize the observed PM₅ changes, enhancing the understanding of emission effectiveness.
3. In section 3.1, the formulas from 1 to 3 are garbled, please correct them.
4. Please standardize the subscript for PM₅ in the manuscript.
5. Coloured or marked text in *.pdf manuscript file is not allowed. Please provide a clean version of *pdf manuscript file (with black text) with the next revision.

The paper employed WRF-Chem to simulate PM2.5 formation in the North China Plain (NCP) during a lockdown period (Jan 21 – Feb 16, 2020) under three scenarios: baseline, SEN_METEO, and SEN_EMIS. The SEN_METEO case replaced baseline meteorology with 2015-2019 mean climatology, while SEN_EMIS used baseline meteorology but substituted emissions with a no-lockdown scenario. By comparing their results, the study explores the impacts of meteorology and emission reductions on PM2.5 levels. Results indicate that, in the northern NCP, meteorological conditions had a stronger influence on PM2.5 levels than emission reductions, whereas, in the southern NCP, the benefits of emission reductions were more significant.

Major Comments

Overall, this study presents a solid approach with a well-evaluated model, but I have several concerns that need to be addressed before recommending this paper for publication:

1. The title used "insights from 6-year simulations," but the manuscript appears to focus on one-month simulations for Jan-Feb 2020. It would be helpful to clarify the source of this "6-year" claim.
2. In section 3.5, the discussion on "combined effects of meteorology and emission reduction" seems to involve a simple addition of the individual impacts of emissions and meteorology. This approach could be misleading. I suggest either comparing the magnitudes of these impacts separately or, if discussing combined effects, perform a simulation that perturbs both emissions and meteorology simultaneously. Alternatively, you could have a separate section discussing how emission impacts vary under different meteorological conditions (EP1 vs. EP2 vs. non-haze episodes), as this question inherently addresses the coupled effects of emissions and meteorology.
3. The study’s novelty feels somewhat limited, as numerous previous studies have explored the relationships between emission reductions, meteorology, and air quality during the COVID-19 lockdown, some of which are referenced in this manuscript. The approach and findings do not seem to offer significant new insights or contradictions compared to existing literature. It would be helpful if the authors could more explicitly highlight the innovative aspects of their approach and clarify the novelty of their findings.
4. The clarity and logical flow of the manuscript could be improved, especially given the multiple sets of comparisons (e.g., SEN_EMIS vs. baseline, SEN_METEO vs. baseline, haze vs. non-haze, NNCP vs. SNCP). At times, these discussions get mixed, making it difficult to follow. For example, section 3.4 compares SEN_EMIS vs. baseline (with the same meteorology) but mentions “decreased atmospheric transport” (line 329), which is confusing – perhaps this refers to EP2 vs. other episodes? If the aim is to explore how emission impacts vary under different meteorological conditions, this should be clearly stated and organized into a separate section/paragraph. This issue appears elsewhere as well, and it would be helpful to clearly signal when switching between comparison sets.

Specific comments

Page 5 line 96: How were the two regions of interest defined? Why are other parts of the NCP not included in your analysis or discussions?

Page 5 line 109: Please elaborate on the anthropogenic emissions dataset mentioned, “using a bottom-up approach based on near-real-time data.” What is the advantage of this dataset? Could you clarify its species and spatiotemporal resolution?

Page 6 line 120: Please specify the WRF-Chem version used.

Page 6 line 134: You mentioned “6-year simulations” in the title, but this section states the simulations were conducted from January 21 to February 16, 2020. Does this mean they are one-month simulations only?

Page 7 line 152: It would be useful to elaborate on how the climatology was averaged. Did you average all meteorological variables directly? If so, how did you ensure the averaged climatology remained physically coherent? Was interpolation done to match the WRF-Chem grid resolution?

Page 10 line 228: The exact time periods for EP1 and EP2 should be clearly stated here.

Page 10 line 233: Since Figures 5-7 show “non-haze times,” it would be helpful to explain the atmospheric conditions during those periods as well.

Page 12 line 303: Have you examined the impact of meteorological conditions on biogenic emissions? If so, what role does it play?

Figure 4: Clarify what “all time” refers to. Does it mean the one-month period (Jan 21 to Feb 16, 2020) or the 6-year period mentioned in the title?

Figure 5-8: Typically, anomaly values are calculated as [scenario X minus baseline]. If your figures show [baseline minus scenario X], it would be helpful to explicitly mention this in the legend to avoid confusion.

Figure 9: Refer to my major comment 2. The calculation of "combined effects" by simply adding meteorological and emission impacts is misleading.

Technical corrections

Page 3 line 67: “… haze above event” --> “… above haze event”

Page 4 line 74: Duplicate citations

Page 5 line 101: “PM2.5, O3, NO2, SO2 and CO” --> “PM_2.5, O₃, NO₂, SO₂ and CO”; check subscript formatting throughout the manuscript.

Page 6 line 136: Rephrase “consisted of a grid of 300 by 300 points, each spaced at a resolution of 6km” to “consisted of 300 × 300 horizontal grid cells with a 6 km resolution”

Page 6 line 139: Define the acronym “NCDP FNL” when first introduced