Sources of PM <sub>2.5</sub> carbonaceous aerosol in Riyadh, Saudi Arabia
Bibliographic record
Abstract
Abstract. Knowledge of the sources of carbonaceous aerosol affecting air quality in Riyadh, Saudi Arabia, is limited but needed for the development of pollution control strategies. We conducted sampling of PM2.5 from April to September 2012 at various sites in the city and used a thermo-optical semi-continuous method to quantify the organic carbon (OC) and elemental carbon (EC) concentrations. The average OC and EC concentrations were 4.7 ± 4.4 and 2.1 ± 2.5 µg m−3, respectively, during this period. Both OC and EC concentrations had strong diurnal variations, with peaks at 06:00–08:00 LT and 20:00–22:00 LT, attributed to the combined effect of increased vehicle emissions during rush hour and the shallow boundary layer in the early morning and at night. This finding suggested a significant influence of local vehicular emissions on OC and EC. The OC ∕ EC ratio in primary emissions was estimated to be 1.01, close to documented values for diesel emissions. Estimated primary organic carbon (POC) and secondary organic carbon (SOC) concentrations were comparable, with average concentrations of 2.0 ± 2.4 and 2.8 ± 3.4 µg m−3, respectively. We also collected 24 h samples of PM10 onto quartz microfiber filters and analyzed these for an array of metals by inductively coupled plasma atomic emission spectroscopy (ICP-AES). Total OC was correlated with Ca (R2 of 0.63), suggesting that OC precursors and Ca may have similar sources, and the possibility that they underwent similar atmospheric processing. In addition to a ubiquitous dust source, Ca is emitted during desalting processes in the numerous refineries in the region and from cement kilns, suggesting these sources may also contribute to observed OC concentrations in Riyadh. Concentration weighted trajectory (CWT) analysis showed that high OC and EC concentrations were associated with air masses arriving from the Persian Gulf and the region around Baghdad, locations with high densities of oil fields and refineries as well as a large Saudi Arabian cement plant. We further applied positive matrix factorization to the aligned dataset of EC, OC, and metal concentrations (Al, Ca, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, and V). Three factors were derived and were proposed to be associated with oil combustion, industrial emissions (Pb based), and a combined source from oil fields, cement production, and local vehicular emissions. The dominant OC and EC source was the combined source, contributing 3.9 µg m−3 (80 %) to observed OC and 1.9 µg m−3 (92 %) to observed EC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".