MétaCan
Menu
Back to cohort

Improving air pollution source apportionment in size-segregated PM using Pb isotope-based Bayesian mixing models in Tarragona (Spain)

2025· article· en· W4406683117 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAtmospheric Research · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversité du Québec à Montréal
FundersGeneralitat de CatalunyaMinisterio de Ciencia, Innovación y Universidades
KeywordsApportionmentEnvironmental scienceMixing (physics)PollutionBayesian probabilityAir pollutionAtmospheric sciencesMeteorologyStatisticsGeographyGeologyChemistryMathematicsPhysicsEcology

Abstract

fetched live from OpenAlex

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. • Extremely severe metal enrichment in PM 1 during the cold season. • Enrichments in metals suggest a road traffic and waste incinerator origin. • Bayesian mixing models identified both anthropic (∼90 %) and geogenic contributions. • Potential health impacts due to Pb exposure as high as for larger cities. • First Pb isotope-based Bayesian models applied for PM 1 .

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.353
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it