the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Feb 2024
Spatiotemporal Source Apportionment of Ozone Pollution over the Greater Bay Area
Abstract. It has been found that ozone (O3) pollution episodic case is prone to appear when the Greater Bay Area (GBA) is under the control of typhoons and sub-tropical high-pressure systems in summer. To prevent these pollutions effectively and efficiently, it’s essential to understand the contribution of O3 precursors emitted from different periods and areas under these unfavorable weather conditions. In this study, we further extended the Ozone Source Apportionment Technology (OSAT) from the Comprehensive Air Quality Model with Extensions (CAMx) model to include the function to track the emission periods of O3 precursors. Then the updated OSAT module was applied to investigate the spatial-temporal contribution of precursors emissions to the O3 concentration over the GBA in July and August 2016, when several O3 episodic cases appeared in this period. Overall, the emissions within GBA, from other regions of Guangdong province (GDo), and the neighbouring provinces are the three major contributors, which account for 23 %, 15 %, and 17 % of monthly average O3 concentration, respectively. More than 70 % of O3 in the current day is mainly formed from the pollutants emitted within 3 days and the same day’s emission contributed approximately 30 %. During the O3 episodes, when typhoon approached, more pollutants emitted 2–3 days ago from the GDo and adjacent provinces were transported to the GBA, leading to the increase of O3 in this region. Under the persistent influence of northerly wind, the pollutants originating from eastern China earlier than 2 days ago can also show an obvious impact on the O3 over the GBA in the present day, accounting for approximately 12 %. On the other hand, the O3 pollution is primarily attributed to the local emission within 2 days when the GBA is mainly under the influence of the sub-tropical high-pressure systems. These results indicated the necessity to consider the influence of meteorological conditions in implementing the control measures. Meanwhile, analogous relationships between source area/time and receptor were derived by the zero-out method, supporting the validity of the updated OSAT module. Our approach and findings could offer more spatial-temporal information about the sources of O3 pollutions, which could aid in the development of effective and timely control policies.
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RC1: 'Comment on egusphere-2023-3140', Anonymous Referee #2, 04 Mar 2024
The manuscript utilized a set of tools including the Temporal Source Apportionment Method and coupled WRF-CAMx model to quantify the spatial-temporal contribution of precursor emissions under these typical conditions (e.g., typhoons and sub-tropical high-pressure systems) over the Greater Bay Area (GBA) region. The results show that, when a typhoon approaches, O3 pollution is influenced by pollutants emitted 2-3 days earlier from both Guangdong province and its neighboring regions, as well as from eastern China before the 2-day mark. In contrast, local emissions within the 2-day timeframe play a predominant role in contributing to O3 pollution when the GBA is primarily affected by sub-tropical high-pressure systems. Overall, the topic is of interest to the audience and the manuscript is generally well organized. However, before I can only recommend it to be accepted by the EGUsphere journal, the manuscript needs some major revision.
- Major comments:
- Given that many quantitative results are given in this study, the primary concern the author needs to address is whether the results based on three cases are representative of typical weather conditions, especially considering that the results are highly sensitive to study region and episode. According to the research objectives and significance proposed by the authors, aimed at facilitating the development of effective and timely control policies, these quantitative results acquire persuasive and guiding significance only when they achieve statistical significance.
- Please elaborate on the criteria utilized in defining sub-regions within Guangdong Province.
- Please introduce the input data used in the TSA method including details such as sources, resolution, etc. Furthermore, please provide an overview of the fundamental workflow for the TSA method. Specifically, are 'Precursor Tracer Day-x' and 'O3 Tracer Day-x' calculated simultaneously or sequentially? It would be preferable to use numbers in Fig. 1 to indicate the logical sequence.
- In the discussion section, the authors suggested that the findings can provide more spatial and temporal information of O3 sources over the GBA, enabling local governments to design and implement targeted control measures more effectively and promptly. Further discussion is needed for more specific control strategies and policies, along with addressing the underlying potential problems and difficulties, such as the impact of the uncertainty in weather forecasts on final results, and obstacles in cross-regional governance.
- Specific comments:
- Line 111: “TSA” has already been defined in Line 105.
- Line 113: “Here, we further extend this method to track the temporal contribution of emissions to O3 and its precursors.” Please clarify the sentence for accurate expression given that O3 is not directly emitted from sources as a secondary air pollutant.
- Line 127: “add into” -> “be added into”
- Line 159: “Day3” -> “Day-3”
- Line 160: “Day-4 the total…”-> “Day-4 represents the total…”
- Line 167: The temporal resolution of the variables should be stated (hourly or daily).
- Please provide the definition and corresponding mathematical formula for evaluation metrics (e.g., index of agreement) in section 2.2.
- Line 181: “There were several O3 episodes occurred during the simulation period.” ->“There were several O3 episodes that occurred during the simulation period.”
- Line 181: “the 8-h maximum O3 concentration (MDA8)” -> “the maximum daily 8-h average (MDA8) O3 concentration”.
- Please elaborate on the standards for identifying ozone episodes.
- For clarity, please use a single dashed box to represent one O3 episode, while representing typhoons and high-pressure events with two distinctive colors in Fig. 3.
- Please plot the figures ( Fig. 3 & Fig. S3) with the reanalysis data (e.g. ERA5) with a satisfactory resolution, Additionally, clarify the source of O3 in the caption of Fig. 3 and include the results of model comparisons.
- Line 205: Please use “average MDA8 O3” instead of “average O3” if MDA8 O3 concentrations are used and maintain consistency throughout the entire text.
- Line 233: “because with” -> “because of”.
- Line 236: Please revise this sentence to ensure its semantic accuracy.
- Line 245: “accounting for” -> “accounted for”.
- Line 414: “…control area was set as the GBA, Guangdong province (GD), Guangdong…” Guangdong province has already been defined as GDo in Line 208. Please do not redefine variables and maintain consistency in their definitions. The same problem should be checked throughout the entire text.
- Line 434: “GD_neigh” should be used after it has been defined. The same problem should be checked throughout the entire text.
- If the same abbreviation is defined in the caption of one figure (in the first occurrence in the figure caption), there is no need for redefinition in subsequent figures (e.g., Fig. 6 & 7 & 8). The problem should be checked throughout the entire text.
- The language should be carefully refined before it is accepted.
Citation: https://doi.org/10.5194/egusphere-2023-3140-RC1 -
RC2: 'Comment on egusphere-2023-3140', Anonymous Referee #1, 11 Mar 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3140/egusphere-2023-3140-RC2-supplement.pdf
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