Variations in Rural PM2.5 Sources and Composition in the Post Coal-to-Gas Period Based on a Three-Year Observation
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».