Characterization of PM2.5 Carbonaceous Components in a Typical Industrial City in China under Continuous Mitigation Measures
Bibliographic record
Abstract
The goals of the “dual carbon” program in China are to implement a series of air pollution policies to reduce the emission of carbon-bearing particulate matter (PM). Following improvements in the reduction in carbon emissions in Handan City, China, fine particulate matter (PM2.5) was collected in the winters from 2016 to 2020 to characterize the concentrations and sources of carbonaceous components in PM2.5. Trend analysis revealed that both organic carbon (OC) and elemental carbon (EC) concentrations significantly decreased. The proportion of total carbon aerosol (TCA) in PM2.5 decreased by 47.0%, highlighting the effective reduction in carbon emissions. Secondary organic carbon (SOC) concentrations increased from 2016 (12.86 ± 14.10 μg·m−3) to 2018 (36.76 ± 21.59 μg·m−3) and then declined gradually. SOC/OC was larger than 67.0% from 2018 to 2020, implying that more effective synergistic emission reduction measures for carbonaceous aerosol and volatile organic compounds (VOCs) were needed. In the winters from 2016 to 2020, primary organic carbon (POC) concentrations reduced by 76.1% and 87.6% under a light/moderate pollution period (LP) and heavy/severe pollution periods (HPs), respectively. The TCA/PM2.5 showed a decreasing trend under LP and HP conditions, decreasing by 42.1% and 54.7%, respectively. Source analysis revealed that carbonaceous components were mainly from biomass burning, coal combustion and automotive exhaust emissions in the winters of 2016 and 2020. OC/EC and K+/EC analysis pointed out that air pollutant reduction measurements should focus on rectification biomass fuels in the next stage. Compared with 2016, the contributions of automotive exhaust emissions decreased in 2020. OC and EC concentrations decreased due to control measures on automotive exhaust emissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".