Response of particle number concentrations to Clean Air Action: Lessons from the first long-term aerosol measurements in a typical urban valley, West China
Bibliographic record
Abstract
Abstract. The strictest ever Clean Air Action (CAA) has been implemented by Chinese government since 2013 to alleviate the severe haze pollution. The PM2.5 mass concentration was found to be largely reduced in response to emission mitigation policies, but response of particle number concentrations (PNCs) to CAA was less evaluated in the previous studies, which may be largely different from PM2.5 mass due to newly formed particle impacts. In this work, the first in-situ observation of particle number size distributions (PNSDs) during 2012–2019 in urban Lanzhou was used to analyze long-term PNCs variations and CAA impacts. The average number of particles in nucleation (N13–25, particle number in the size range of 13–25 nm), Aitken (N25–100, particle number in the size range of 25–100 nm) and accumulation (N100–800, particle number in the size range of 100–800 nm) modes were respectively 2514.0 cm−3, 10768.7 cm−3, and 3258.4 cm−3, and N25–100 accounted for about 65.1 % of total PNCs during the campaign. K-means clustering technique was used to classify the hourly mean PNSDs into six clusters, and each cluster corresponded to a specific source and influencing factor. The polluted clusters governed the winter PNCs before 2016, and their occurrence was less and less frequent after 2016, which was largely dominated by reduction in primary emissions. However, the contribution of new particle formation (NPF) events to summer N13–25 decreased from 50 % to about 10 % during 2013 to 2015, and then increased to reach around 60 % in 2019. The trends of size-resolved PNCs for each cluster were quantified by Theil-Sen regression. The size-segregated PNCs exhibited downward trends for all clusters during 2012–2015, especially in spring. The annual relative slopes of spring PNCs varied from −54.7 % to −17.2 %, −42.6 % to −14.1 %, and −40.7 % to −17.5 % per year for 13–25, 25–100, and 100–800 nm size ranges, and the reduction in the polluted clusters was much larger than NPF clusters. The ultrafine particle number was increased and the amplitude was much greater during 2016–2019. The annual relative slopes of N13–25 varied between 8.0 % in fall and 135.5 % in spring for NPF cluster. In response to CAA, the increased daytime net radiation, higher ambient temperature and lower relative humidity at noon for NPF events also could partly explain the higher N13–25 induced by the more frequent nucleation events after 2016, especially in spring. The air mass were mainly from the adjacent regions of urban Lanzhou and less affected by long-range transport for NPF events, and the thus particles were not easily grown by coagulation during transport processes, which was helpful for occurrence of NPF events. Therefore, some effective control measures cooperatively controlled particle number and mass should be took for the Chinese megacities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".