Variations in Rural PM2.5 Sources and Composition in the Post Coal-to-Gas Period Based on a Three-Year Observation
Bibliographic record
Abstract
Abstract Introduction Various studies were conducted focused on the coal-to-gas (CTG) impacts on urban PM 2.5 during its implementation. However, the continuity of CTG effectiveness on PM 2.5 control in the post CTG remained unclear, especially in rural area, retarding the further emission-control policy optimization. To address this gap, we examined the wintertime rural PM 2.5 variations within the Beijing–Tianjin–Hebei during the non-epidemic-lockdown period of winter 2020–2022. Of which, 2020 holds the most stringent CTG enforcement, 2021 marks the conclusion of CTG, and 2022 represents the post CTG. Methods In this study, the PM 2.5 levels in rural areas of the Beijing–Tianjin–Hebei region were monitored during the winters of 2020, 2021, and 2022. Meanwhile, multiple chemical analysis methods were employed to determine its chemical components. The Positive Matrix Factorization (PMF) modeling and Potential source contribution function (PSCF) analysis were employed to analyze the contributions of different sources to PM 2.5 . Results and Discussion PM 2.5 exhibited an average decrease of 30.4%, and PMF modeling indicated the contributions of coal combustion (CC) to PM 2.5 fell from 22.4% in 2020 to 17.8% in 2021, and further to 10.8% by 2022, highlighting the enduring CTG effectiveness. The continuously decreasing CC-specific As, Pb, and SO 4 2– was another evidence for scattered coal prohibition. Reluctantly, the biomass burning (BB) contributions held higher increase of 17.2% in 2021–2022 than 8.86% in 2020–2021, and it has leapt to be the largest PM 2.5 contributor (25.4%) in winter 2022. The natural gas shortage and subsidy reduction in winter 2022, as well as the man, and forced demolition of coal-stoves in winter 2022 should be the main inducements. Contrary to the recent upward trend of secondary aerosols, SO 4 2– , NO 3 – , and NH 4 + showed a downward trend, with annual average dropped of 52.6%, 23.4%, and 53.8%, respectively. This should be ascribed to the enhanced primary emissions from BB and vehicle exhaust (VE). Increments of VE fraction might be related to the gradually unblocking of COVID-19. Correspondingly, the fractions of BB-dependent K + /Cl – and VE-specific Cu/Zn/NO 3 – obviously rose from 2020 to 2022. Conclusions This work highlighted that the priorities should be given to the emission control from BB, and guarantee of natural gas supply and certain financial CTG subsidies on the basis of retaining the original pollution control policies, for further rural air quality improvement in the post CTG period. Graphical abstract
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".