Improving air pollution source apportionment in size-segregated PM using Pb isotope-based Bayesian mixing models in Tarragona (Spain)
Bibliographic record
Abstract
A total of 75 outdoor PM 10 , PM 2.5 , and PM 1 samples from 14 schools, and 9 samples from potential local emission sources, were collected and analysed for their metallic content and lead (Pb) isotope ratios in 2 seasonal campaigns in Tarragona (Catalonia, Spain) to identify and apportion contamination sources and to assess associated health risks. Lead was predominantly found in PM 1 , and although its levels were below air quality standards, its Enrichment Factors (EF), along with those of other potentially toxic elements (Cd, Cr, Cu and Sb), indicated extremely severe enrichment in all PM sizes. Seasonal differentiation in Pb enrichment was particularly significant in PM 1 during the cold campaign. This suggests an anthropogenic origin, mainly from combustion processes such as road traffic and a municipal solid waste incinerator , as supported by profiles of other metals (Cu, V and Zn) and the spatial distribution of the EF Pb , respectively. Non-radiogenic Pb isotope ratios ( 208 Pb/ 204 Pb and 206 Pb/ 204 Pb) indicated a geogenic origin in some PM 10 samples, based on their similarity to the geochronology of specific Spanish ore samples. However, radiogenic ratios ( 208 Pb/ 207 Pb and 206 Pb/ 207 Pb) pointed to coal-fired electrical plants (EGUs) and road traffic as the sources of the majority of the samples. These findings were corroborated by EF spatial distribution maps and by our previous study coupling air masses back trajectories with C and N isotopes in the same PM samples. Bayesian mixing models using both 204 Pb- and 207 Pb-normalised Pb isotope ratios estimated sources' contributions as follows: i) municipal solid waste incinerator (at least 10 % in PM 10 and up to 60 % in both PM 2.5 and PM 1 ); ii) road traffic (up to 40 % for all size fractions); iii) coal-fired EGUs (around 20 % for all size fractions); and iv) geogenic particles (<10 % for all size fractions). Despite this strong contribution of anthropogenic sources , the potential health impacts of potentially toxic elements exposure were low, i.e., 3 additional cancer cases for adults per million of people due to Pb exposure, which nonetheless is comparable to levels observed in cities with populations 30 or more times larger than that of Tarragona.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".