the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Jun 2025
Persisted PM2.5 pollution in the Pearl River Delta, South China, in the 2015–2017 cold seasons: The dominant role of meteorological changes during the El Niño-to-La Niña transition over emission reduction
Abstract. Effective air quality management requires a comprehensive understanding of how meteorological variability and emission changes shape multiannual changes in regional PM2.5 pollution. During the cold seasons of 2015–2017, persistent PM2.5 pollution occurred in the Pearl River Delta (PRD), South China, despite rapid emission reductions. This study systematically investigated the interconnections between climate variability, meteorology, PM2.5 levels, source contributions and budgets during these periods, aiming to uncover the detailed impacts of meteorological and emission changes on PM2.5 pollution. We found that drastic meteorological changes, closely linked to a transition from strong El Niño (2015) to weak/moderate La Niña (2017), were the main drivers of the three-year PM2.5 changes. Strengthened northerly winds and reduced humidity enhanced cross-regional PM2.5 transport into the PRD while concurrently suppressing local PM2.5 production and accumulation. WRF/CMAQ simulations indicate that transport (non-local) contributions to PM2.5 in the PRD increased from 70 % in 2015 to 74 % in 2016 and 78 % in 2017. While the transport of secondary inorganic PM2.5 components overall intensified, their responses to meteorological and emission changes varied: Variations in sulfate were more sensitive to emission reductions outside the PRD, whereas those for nitrate were primarily driven by meteorological shifts. Simulated PM2.5 mass budgets further support the increasing dominance of transport, especially via advections. Our findings underscore the potentially crucial role of meteorological variability in driving multiannual PM2.5 pollution changes in the PRD and other regions strongly impacted by cross-regional transport, emphasizing the necessity for regionally coordinated emission control strategies to effectively mitigate PM2.5 pollution.
Competing interests: One of the authors is a member of the editorial board of Atmospheric Chemistry and Physics. The authors declare no other conflict of interest.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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Status: open (until 10 Aug 2025)
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RC1: 'Comment on egusphere-2025-2404', Anonymous Referee #1, 07 Jul 2025
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This manuscript studied the impact of meteorological changes and emission reductions on PM2.5 pollution in the Pearl River Delta (PRD) during the cold seasons from 2015 to 2017. The authors aimed to explain why PM2.5 levels in the PRD remained high despite of significant emission reductions in PRD and its upwind regions in East China. By applying the regional models, they found that transport contributions to PM2.5 levels rose from 70% in 2015 to 78% in 2017, while local emissions declined. And they concluded that the meteorology change was the dominant driver of the multiannual variations of PM2.5 during the studied period. And the meteorological change was likely driven by large scale climate variability, namely the transition from a strong El Niño in 2015 to a weak/moderate La Niña in 2017. This study also pointed out that the meteorological impact should be taken into consideration when the emission control policies were assessed.
The manuscript is well organized and written clearly, the description is precise, and the discussion is fruitful. I recommend publishing it after minor revision. Below are my comments referring to lines (L), equations (Eqs.) and figures (Fig.).
L160: spinning -> spanning
L149: (1) Maybe it is better to specify the names of nine cities here when this concept is first mentioned.
(2) It seems that not all the nine cities are shown in Fig. 1.
Fig. 1: (1) The x tick labels and y tick labels need to show the unit, e.g., 112 °E.
(2) The orange line is not introduced in the caption.
Fig. 2: (1) The latitude and longitude labels are too small.
(2) "The black boxes are the simulation domains for WRF, while the nested areas indicate the simulation domains for CMAQ.": Does it mean that d01, d02, and the domain larger than d01 are all simulated by WRF? And CMAQ only simulates d01 and d02? Please specify it clearly.
L190: Oct and Dec are selected in the simulations, but in L159, the cold season is defined as the period from Oct to Jan. Will this affect the simulation results?
Eqs. 5 - 10: Why the contribution of S_Emis_O,15/16 is not calculated by C_L15O16M15 - C_Base15? And similar questions for S_Meteo,15/16, S_Emis_O,16/17, and S_Emis_L,16/17?
Fig. 4: The x tick labels and y tick labels need to show the unit, e.g., 110 °E.
Fig. 5: The legend of "Observations" is point instead of point+line.
L412: What does 'population-weighted mean' represent?
Fig. Graphic Abstract: Why the decrease of local emissions is marked with "Enhanced"?
Citation: https://doi.org/10.5194/egusphere-2025-2404-RC1
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