Understanding the Sources of Ambient Fine Particulate Matter (PM2.5) in Jeddah, Saudi Arabia
Bibliographic record
Abstract
Urban air pollution is rapidly becoming a major environmental problem of public concern in several developing countries of the world. Jeddah, the second-largest city in Saudi Arabia, is subject to high air pollution that has severe implications for the health of the exposed population. Fine particulate matter (PM2.5) samples were collected for 24 h daily, during a 1-year campaign from 2013 to 2014. This study presents a detailed investigation of PM2.5 mass, chemical composition, and sources covering all four seasons of the year. Samples were analyzed for black carbon (BC), trace elements (TEs), and water-soluble ionic species (IS). The chemical compositions were statistically examined, and the temporal and seasonal patterns were characterized using descriptive analysis, correlation matrices, and elemental enrichment factor (EF). Source apportionment and source locations were performed on PM2.5 samples using the positive matrix factorization (PMF) model, elemental enrichment factor, and air-mass back trajectory analysis. The 24-h mean PM2.5 and BC concentrations ranged from 33.9 ± 9.1–58.8 ± 25 µg/m3 and 1.8 ± 0.4–2.4 ± 0.6 µg/m3, respectively. Atmospheric PM2.5 concentrations were well above the 24-h WHO guideline of 15 µg/m3, with overall results showing significant temporal and seasonal variability. EF defined two broad categories of TEs: anthropogenic (Ni, V, Cu, Zn, Cl, Pb, S, Lu, and Br), and earth-crust derived (Al, Si, Mg, K, Ca, Ti, Cr, Mn, Fe, and Sr). The five identified factors resulting from PMF were (1) fossil-fuels/oil combustion (45.3%), (2) vehicular emissions (19.1%), (3) soil/dust resuspension (15.6%), (4) industrial mixed dust (13.5%), and (5) sea-spray (6.5%). This study highlights the importance of focusing control strategies, not only on reducing PM concentration but also on the reduction of components of the PM as well, to effectively protect human health and the environment.
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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 | 0.001 | 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) | 0.015 | 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".