General Assessment

Passenger vehicle emissions constitute a significant source of urban air pollution, and accurate quantification of their emission characteristics is fundamental for formulating effective control measures. This study integrates ride-hailing big data with traffic flow modeling to get hourly vehicle speed data gridded at a 0.01° resolution across the nation, and constructs a high spatiotemporal resolution passenger vehicle emission inventory (0.01°×0.01°; hourly). The research quantifies the distribution characteristics of multiple pollutants across different time scales (from hourly to annual), with comparative analyses conducted against conventional algorithms. Furthermore, the WRF-Chem model was employed to validate the inventory through simulation. The methodology employed in this study offers a novel approach for compiling motor vehicle emission inventories and provides enhanced data precision for urban traffic pollution management and air quality modeling. The manuscript is well-organized. Several additional comments are provided for further improvement as follows:

Major Comments:

1. The core of this paper is to establish the relationship between vehicle speed and emission factors (EF). However, the existing figures and tables do not intuitively present the EF-Speed It is recommended to add a new figure in Section 3.1 showing the EF-Speed curves for NOx, CO, and VOCs, with some explanation.
2. 207: Figures 1a and 1b present the national gridded vehicle speeds at 08:00 and 22:00. In my opinion, additional information should be included in this section, such as the differences in vehicle speeds across different road types (National Highways, Provincial Highways, and County Roads), and explain them properly in the text.
3. 231: In Section 3.2, the manuscript states that "Urban areas, despite occupying only 0.8% of the country’s total land area, accounted for a high 35.3% of total vehicle emissions." What is the definition criterion for "urban and rural areas"? It is necessary to clarify the criteria for defining urban boundaries, so as to avoid potential interpretation bias caused by ambiguous definitions.
4. Among different urban agglomerations, the emission density difference between urban and rural areas is pretty noticeable. What are the primary reasons for such differences?
5. The authors obtained national vehicle speeds using ride-hailing big data, but could this data introduce spatial bias into the results? Can it cover all roads? The authors need to conduct further discussions on these aspect to make sure the national-scale emission estimates are reliable.
6. L.279: The manuscript notes:"During the morning peak, hourly average emissions on workday exceeded those on weekend and holiday by 8% and 5%, respectively, increasing to 12% and 18% during the evening peak." Why does the phenomenon occur?
7. When comparing with the traditional algorithm, what vehicle speed is adopted in the traditional method? And what are the differences between it and the inventory developed in this study?
8. L.295: Section 3.4 only provides an overall emission comparison with the traditional method. It doesn’t show the differences in underestimation rates across different time periods—like seasons or holidays.

There are some minor comments:

1. In Section 2.4, The authors should provide the total number of monitoring stations, as well as the number of those located in regions with the top 20% road density.
2. L.208: It is recommended to revise the caption of Figure 1 from "Average speed in China" to "The average vehicle speed of passenger vehicles in China in 2019".
3. L.146 and L.158: Some symbols (such as K_j, v_f, L_p,i) lack complete unit annotations when they first appear.
4. L.228: Section 3.2 states " The annual amount of CO, VOCs, NOx, PM and NH₃ emitted from national passenger vehicles in 2019 were 4087.8, 1069.4, 211.7, 1.9, 77.5 kt, respectively" while Table 1 shows "CO: 408.78 ten thousand tons…" The unit conversions should be consistent throughout to avoid data ambiguity.
5. Check the citation format of all references in the full text.

General comments:

The article “An improved high-resolution passenger vehicle emission inventory for China using ride-hailing big data” by Li et al., provides information about emission inventory for pollutants emitted by passenger cars in China. The study highlights the importance of average vehicle speed on emission factors. These findings were established in comparison with traditional methods, both showing discrepancies between urban and rural vehicle emissions, weekend relative to workdays emissions and seasonal distribution of pollutants released from vehicular emissions.

Major comments:

The authors should provide more detailed information regarding the vehicular fleet, type of motorization, percentual distribution over the entire country, type of fuel, etc. Also, there is a need to discuss about the type of industrial vehicles and those used for agriculture, passenger car legislation related to the restrictions on pollution and how this apply to other nations.

The authors should expand their consideration to the traffic worldwide and not over the few cities in China and India, even if those cities are very polluted. The emission inventories steady state data should be presented not over the cities in China and India. Even if the present study construct emission inventory of atmospheric pollutants in China the introduction should include a wider view.

The data collected from Amap Ride-hailing Platform are representative for the entire fleet? All the vehicles on the road send data to the platform? It is possible that, systematically, old vehicles emit constantly more pollutants but are not equipped with the tracking system?

The sections 2.1 and 2.2 include many equations which are not well described and their parameters are not always clearly explained in terms of units and meaning (e.g. congestion delay index,  Kj, Ct, etc.).

Please include in the papers the problems which usually national monitoring stations have in the terms of trustable data provided. Which parameters are usually provided with huge errors and how these uncertainties are affecting the model?

Please describe in the paper the possible effect on the overall model of the average speed of (42.42 ±  5) km h-1 for example. This average speed should be considered with a range of uncertainties and to extend this uncertainty to the model output data.

Please include the uncertainty bars in the figure 2 for frequency on speed range. Three different days could have different frequency for the same speed range but there is an absolute number as average speed.

There is not convincingly that new model simulations with a better attribution of average speed parameter are the only responsible for the difference between the results in the model validation. The inventory optimization are actually not an important improvements since 0.36% in NMB and 0.02 for R2 are insignificant changes. Please discuss more in detail about the other advantages of the speed average model improvement.

Minor:

Line 95: please use consistent representation of the units „km per year, and g km-1”

Line 110: please include the year.

Line 135: congestion delay index (λ)