the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Jan 2026
Rapid assessment of drivers and air quality effects of regional daily changes in air pollutant emissions based on near-real-time techniques: A case in Jiangsu Province, China
Abstract. Fast and timely estimation of changing air pollutant emissions is critical for understanding the complex sources of air pollution and supporting air quality improvement, while current regional emission inventory was commonly reported with time lag or coarse temporal resolution. Here we developed a near-real-time approach that calculates the daily emissions of anthropogenic air pollutants, and applied this approach for Jiangsu province, a typical developed region in eastern China. We estimated that the annual total anthropogenic emissions of SO2, NOX, primary fine particles (PM2.5), non-methane volatile organic compounds (NMVOCs), and NH3 were 246, 727, 298, 1186, and 377 Gg, respectively, for Jiangsu in 2022. Compared to the national emission inventory, application of the provincial-level daily emission estimates provided better model performance of PM2.5 and ozone (O3) simulation for all the involved months. The NOX, SO2, PM2.5, and NMVOCs emissions in Jiangsu during April–May 2022 (the period of COVID-19 lockdown in Shanghai) were respectively 8 %, 6 %, 6 %, and 10 % smaller than those in the same period of 2023. Transportation and Industry respectively contributed 89 % of NOX emission reduction and 93 % NMVOCs reduction. Combining with machine learning algorithms, moreover, we revealed that the changing agricultural NH3 emissions dominated the variability of daily PM2.5 concentration, and that off-road transportation contributed substantially to variabilities of both PM2.5 and O3 levels. The study proved advantages of incorporation of near-real-time data and machine learning techniques on tracking the fast-changing emissions and detecting the sources of varying air quality.
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- RC1: 'Comment on egusphere-2025-5605', Anonymous Referee #2, 01 Mar 2026 reply
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RC2: 'Comment on egusphere-2025-5605', Anonymous Referee #1, 04 Apr 2026
reply
The manuscript presents a valuable and timely contribution to the field of atmospheric chemistry and environmental management. The authors developed a framework of making a near-real-time anthropogenic emission inventory for Jiangsu Province, by integrating multi-source dynamic activity data. Furthermore, the inclusion of machine learning techniques (XGBoost-SHAP) offered a rapid and efficient tool of decoupling the influences of meteorological conditions and anthropogenic emission changes on air quality Application of such tool in Jiangsu proved impressive and scientifically rigorous.
In general, the methodology of this current work is robust, and the results of emissions with high temporal resolution could potentially help the short-term air quality forecasting and policy design of emergent emission controls. The manuscript is logically structured and clearly written, and the conclusions were basically supported by the data presented. I recommend the manuscript to be accepted for publication after revisions.
- The methodology for updating the temporal profiles using high-frequency monitoring data (e.g., CEMS and traffic index) is prominent. However, the spatial allocation of these near-real-time emissions is somewhat ambiguous. Given that the manuscript aims to provide a “high-resolution spatial and temporal distribution”, does the framework utilize static spatial proxies (e.g., POI data or fixed road networks) for these dynamic emissions, or are the spatial distributions also updated with a relatively high frequency (e.g., daily for some sectors)? Please clarify this in Section 2.1.
- The near-real-time emission framework represents a valuable tool for Jiangsu Province in China. However, the system relies heavily on region-specific, high-quality data streams (e.g., extensive CEMS coverage and provincial traffic monitors). What are the limitations in transferring this methodology to less developed regions in China or other developing countries where such high-frequency ground data might be sparse? Adding a discussion on the general interest of this framework would broaden the paper’s impact for the scientific community.
- Besides the emission estimation, the application of XGBoost algorithm to explore the relationship between PM2.5/O3 concentrations and precursor emissions is an useful addition, highlighting the benefit of coupling machine learning with traditional modeling. However, tree-based models were often considered as “black boxes” without sufficient capability of interpreting the results. Could the authors briefly elaborate on how they interpreted the XGBoost outputs to draw meaningful conclusions?
- In the Methodology, the authors mention using the traffic congestion index for mobile sources. Please clarify if the same scaling factor was applied to all vehicle types (e.g., heavy-duty trucks, passenger cars, and others). If not, how was the difference in temporal patterns of the fleet emissions between those types recorded or reflected in the daily emission updates?
- The framework primarily scales “Activity Levels” using near-real-time data. However, Emission Factors (EF) for vehicles could be highly dependent on vehicle speed and engine load, and might vary greatly during heavy traffic or lockdowns. Did the authors consider these complicated factors for EFs in Equation 7, or were they treated as constants for the 2022 period?
- Section 3.2 provides a case study of COVID-19 lockdown for Shanghai in 2023. While the result proved reasonable for the whole province, could more anlaysis be conducted for cities so as to explore the different impacts of the lockdown for various regions?
- The section of evaluation of WRF-CMAQ modeling performance different emission inventories (Section 3.3) is quite descriptive. If possible, the manuscript would benefit from some more discussion or analysis, to interprate why the model performed better during specific periods with the near-real-time emission inventory developed in this work.
- Line 704. The NOx emission from certain sources? Lines 710-712: The description needs to be revised. Although VOCs emission indeed declined in winter, the NOX emissions were not “enhanced” in winter. As shown in Figure 4, instead, the NOX emissions in winter were relatively low.
- The format of References should be thoroughly checked and improved. For example, there is an inconsistency in the capitalization style of article titles (e.g, Lines 1086-1088: Multi-Scale Dynamics and Spatial Consistency...)
The formatting of Digital Object Identifiers (DOI) varies across the reference list. (e.g., Line 1094: doi: 10.1007/s11430-023-1230-3; Line 1097: https://doi.org/10.5194/acp-15-2105-2015).
Citation: https://doi.org/10.5194/egusphere-2025-5605-RC2 -
RC3: 'Comment on egusphere-2025-5605', Anonymous Referee #3, 16 Apr 2026
reply
This study develops a “near-real-time” emission inventory for Jiangsu Province, China. Its performance and advantages are evaluated using air quality simulations and machine learning techniques. The topic is interesting, and the manuscript is generally well written. I was particularly impressed that recent social data enable the development of such a near-real-time emission inventory. However, please address the following comments.
1. First, it is necessary to define what is meant by “near-real-time” emissions. I assume that this concept has two aspects. One refers to the most recent emission estimates available before official statistical data are released; however, a delay of at least one month is required. The other refers to daily emissions, which are generally not available in conventional emission inventories.
2. I did not understand the rationale for estimating vehicle emissions using Equation (7). Is the emission factor (EF) defined on a daily basis? What is the traffic congestion index? Please explain the basis and justification of Equation (7).
3. NH3 emission factors are affected by various factors, including meteorological conditions, as stated in Lines 279–285. Could you also clarify the activity data used in the estimation? How were these data obtained?
4. Emissions estimated for 2022 were compared with those for 2015 and 2019 in the beginning of Section 3.1.1. Were these emissions estimated using consistent methodologies? If not, differences in estimation methodologies may influence the differences in emission estimates across years.
5. The influences of activity changes on NH3 emissions are discussed in Lines 464–465, whereas their treatment is not described in Lines 279–285. How about influences of changes in emission factors?
6. What types of restrictions were imposed on coal-fired boilers and industrial plants in September, as described in Lines 466–477? Please clarify the nature and scope of these restrictions.
7. (Lines 517–534) It is difficult for me to judge that SO2 and PM2.5 emissions demonstrate a close agreement between the monthly variations in emissions and those in observed concentrations in Figure 4. In addition, although possible reasons for the poor agreement in NO2 variations are discussed, some of these reasons including meteorological conditions also apply to SO2 and PM2.5. A fraction of NO2 and PM2.5 is secondarily formed in the atmosphere. Therefore, I believe it is inherently difficult to achieve close agreement between emission variations and observed concentration variations.
8. (Line 532-534) It is not possible to assess the importance of controlling NOx emissions for reducing PM2.5 pollution solely on the basis of a similar correlation between PM2.5 and NO2 in their monthly trends.
9. Does “12,877 metric tons” represent an annual total? (Line 550) Please clarify this point in other similar instances in the manuscript as well.
10. The model performance using the emissions obtained in this study was compared with that using MEIC emissions in Section 3.3. Although the results indicate that the emissions from this study perform better than those from MEIC, this does not necessarily imply that “near-real-time” emissions are superior to conventional emission inventories. Other modeling experiments are required to show their advantages.
11. Please clearly indicate the connections among the three boxes in Figure 1.
Citation: https://doi.org/10.5194/egusphere-2025-5605-RC3
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General Comments: The fast-changing emissions are important factors driving the variability of air quality, and substantial challenges exist in tracking the emissions by sector and species, attributed to data access limitation and methodology. The manuscript develops a near-real-time emission accounting framework and combines it with machine learning to assess driving factors of air quality. They applied the methodology in Jiangsu Province, a hotspot of industrial and traffic activities, energy consumption, and anthropogenic emissions in Yangtze River Delta region, China. The efforts advanced the regional emission estimation and its application for the scientific community. Generally, the manuscript is well organized and easy to follow, and provides credible scientific evidence for regional air quality management. I recommend acceptance after addressing the following comments to further improve the manuscript.
Specific comments: