Oxidative Potential and Inflammatory Impacts of Source Apportioned Ambient Air Pollution in Beijing
Bibliographic record
Abstract
Air pollution exposure is associated with a range of adverse health impacts. Knowledge of the chemical components and sources of air pollution most responsible for these health effects could lead to an improved understanding of the mechanisms of such effects and more targeted risk reduction strategies. We measured daily ambient fine particulate matter (<2.5 μm in aerodynamic diameter; PM2.5) for 2 months in peri-urban and central Beijing, and assessed the contribution of its chemical components to the oxidative potential of ambient air pollution using the dithiothreitol (DTT) assay. The composition data were applied to a multivariate source apportionment model to determine the PM contributions of six sources or factors: a zinc factor, an aluminum factor, a lead point factor, a secondary source (e.g., SO4(2-), NO3(2-)), an iron source, and a soil dust source. Finally, we assessed the relationship between reactive oxygen species (ROS) activity-related PM sources and inflammatory responses in human bronchial epithelial cells. In peri-urban Beijing, the soil dust source accounted for the largest fraction (47%) of measured ROS variability. In central Beijing, a secondary source explained the greatest fraction (29%) of measured ROS variability. The ROS activities of PM collected in central Beijing were exponentially associated with in vivo inflammatory responses in epithelial cells (R2=0.65-0.89). We also observed a high correlation between three ROS-related PM sources (a lead point factor, a zinc factor, and a secondary source) and expression of an inflammatory marker (r=0.45-0.80). Our results suggest large differences in the contribution of different PM sources to ROS variability at the central versus peri-urban study sites in Beijing and that secondary sources may play an important role in PM2.5-related oxidative potential and inflammatory health impacts.
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 | 0.001 | 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".