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Enregistrement W7077050762 · doi:10.5281/zenodo.16099070

Code and data from: The risks to human health of air toxics, PM2.5, and ozone from the 2023 Canadian wildfires

2025· dataset· en· W7077050762 sur OpenAlexaboutno aff

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Langueen
DomainePhysics and Astronomy
ThématiqueTheoretical and Computational Physics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNetCDFHuman healthCMAQMetadataPopulationDocumentationLatitude

Résumé

récupéré en direct d'OpenAlex

Supporting data for calculations of the risks to human health of air toxics, PM2.5, and ozone from the 2023 Canadian wildfires Data archive created by Havala Pye 0000-0002-2014-2140 For use of the data, users are encouraged to cite both this data archive (https://doi.org/10.5281/zenodo.16099070) for transparency and the following article for methods documentation: H. O. T. Pye, W. T. Hutzell, N. L. Fann, T. N. Skipper, M. Pye, J. Beidler, C. Allen, B. N. Murphy, E. L. D’Ambro, S. Lin, K. Talgo, L. Reynolds, D. Kang, J. Bash, K. M. Seltzer, S. L. Farrell, K. W. Appel, K. Brehme, R. C. Gilliam, B. H. Henderson, and A. W. H. Chan: The risks to human health of air toxics, PM2.5, and ozone from the 2023 Canadian wildfires, ChemRxiv, , 2025. This content is a preprint and has not been peer-reviewed. Please update to the final journal article when available. Additional model-ready data to run CMAQ (emissions, meteorology, and other input files) from this work are available at: US EPA, 2025, "CMAQ 2023 12US4 CRACMM2 Inputs and Meteorology", https://doi.org/10.15139/S3/GL41QC, UNC Dataverse, V1. Please see documentation on the CMAQ github repository for standard CMAQ conventions. Contents 12US4_files: netcdf and shape files with information for the 12US4 domain including shapefiles: shape files and other domain specification information population: population data for the U.S. and Canada on 12US4 grid as a netcdf file (acs_2023_5yr_bg_pop_12US4.nc, can_2021_cenus_subd_pop_12US4.nc) masks: mask for Canada used to remove fires for that region in CMAQ analysis/inputs: inputs used in analysis of CMAQ output cities.txt: Latitude and longitude of locations for data extraction in Figure 2. Formatted as a writesite (https://github.com/USEPA/CMAQ/tree/main/POST/writesite) file. 20250512Dose_Response_Library_Oct2024.xlsx: HEM toxicity values file from October 2024 and mapping to CRACMM species; CMAQCONCCOMBINE_UGM3 indicates combine species name (without units), CMAQHAP is the CMAQ-CRACMM species name, CMAQEMIS is the emitted species name (without T_ prepending); CMAQ names should not be cross-walked by line; See Pye et al. (2025) supplemental information Table S7 for more details and a readable version with crosswalk. degradelog.txt: excerpt from CMAQ simulation processor log file documenting active hazardous air pollutant (HAP) degradation reactions. TableS12_pye2024_erbymass.xlsx: Table S12 from Pye et al. 2024 (https://doi.org/10.1021/acs.est.4c06187) and supplemented with Gkatzelis et al. 2024 (https://doi.org/10.5194/acp-24-929-2024). 2023hc_CRACMM2_task4_final_sector_reports_withTSpecies_09apr2025.csv: Emissions totals in tons/yr by sector in CRACMM species names. Target_Organ_resp_Oct2024.xlsx: subset of HEM target organ file indicating all species with a respiratory target organ impact. Target_Organ_neuro_Oct2024.xlsx: subset of HEM target organ file indicating all species with a neurological target organ impact. analysis/scripts: scripts used to analyze concentrations and determine risk in the work of Pye et al. (2025) in python notebook (ipynb) and html. FigS19_NAPS_HAPS_Data_processing.ipynb/html: Creates Figure S17 evaluating CMAQ vs NAPS VOCs. Pye2025_allsrc_hapemiss_FigS1.ipnyb/html: Creates Figure S1 comparing HAP emissions across sectors and studies. Pye2025_FigS14S16S17_aqs_fire_analysis.ipnyb/html: Creates Figures S14,S16,S17 comparing CMAQ to AQS observations. Pye2025_Stackedbar_Fig2S6.ipynb/html: Creates Figures 2 and S6 with risk and photochemical age. Pye2025_SpeciesSIfigs_HAPs_HItotal_Fig1S5S20.ipynb/html: Creates Figure 1, S5, S20 including concentrations of HAPs. Pye2025_SpeciesSIfigs_PMox_S3S4S21S22.ipynmb/html: Creates PM and oxidant figures (S3, S4, S21, S22). Pye2025_TOC.ipynb/html: Creates Table of Contents (TOC) art. CMAQcode BLD: CMAQv5.5 fortran code with additional updates resulting in CRACMM3HAPs as described by Pye et al. (2025) base_config: build script, run scripts, control files, and example log files (text files) for the base simulation in Pye et al. (2025). Known issues: - while the control file indicates acrylonitrile emissions were scaled from CO for Canadian fires, the Canadian fire emissions of acrylonitrile were not implemented due to a bug in CMAQv5.5 DESID. - emissions of HAPs from biogenic sources were accidentally doubled in these scripts - lightning NOx was omitted nofire_config: build script, run script, control files, and example log files (text files) for the simulation without Canadian fires. post: species definitions files used to prepare ONLYHAPS and PMOXIDANTS netcdf output. CMAQoutput: netcdf gridded CMAQ output for base simulation and simulation without Canadian fires (nocanfire) Annual average HAP concentrations (and select other species such as CO): ANNUALAVG_20250502cracmm3hap_base_ONLYHAPS_2023_12US4.nc ANNUALAVG_20250502cracmm3hap_nocanfire_ONLYHAPS_2023_12US4.nc Annual average OH, O3, NO3, and PM2.5: ANNUALAVG_20250502cracmm3haps_base_PMOXIDANTS_2023_12US4.nc ANNUALAVG_20250502cracmm3haps_nocanfire_PMOXIDANTS_2023_12US4.nc Seasonal (Apr-Sept) average of max daily 8hr avg ozone: SEASAVG_APR2SEP_20250502cracmm3haps_base_2023_12US4.nc SEASAVG_APR2SEP_20250502cracmm3haps_nocanfire_2023_12US4.nc Seasonal (Apr-Sept) average of max daily 1 hour ozone: SEASAVG_1hrmaxo3_APR2SEP_20250502cracmm3haps_base_2023_12US4.nc SEASAVG_1hrmaxo3_APR2SEP_20250502cracmm3haps_nocanfire_2023_12US4.nc evaluation: output from CMAQ site compare utility by month baseaqs.tar: base simulation results by month nocanfireaqs.tar: no Canadian fire simulation results by month For each month: Ozone, PM, and major HAPS: AQS_Daily_2023_12US4_* files Select additional VOCs: AQS_Daily_VOC_2023_12US4_* files Canada_NAPS_VOC: intermediate files used in the evaluation of CMAQ predictions of select VOCs with NAPS observations boundaryconditions: sample scripts for how to download and map GEOSCF to CMAQ-CRACMM for boundary conditions python_batchfall.csh: cshell batch script that calls the following file to obtain GEOSCF files in original GEOSCF species (step 1) 2024fall_12US4_getgeoscfonly.py: gets GEOSCF files for fall (run in 2024) *expr: files used by aqmbc to map GEOSCF variables to CRACMM2 (also available in AQMBC on github) 2024_12US4_geoscfBCONbyhour.ipynb: creates BCON files along 12US4 boundary in CRACMM2 species on original GEOSCF time stamps (step 2) 2024_12US4_geoscfBCONplots.ipynb: QA check on BCON files (step 3) 2024_12US4_geoscf_modelready.ipynb: Concatenate and time shift the BCON files so they are CMAQ-ready (step 4) GRIDDESC: CMAQ input and grid description used by aqmbc benefits: BenMAP and AQBAT input files, outputs, and additional documentation. Individual files detailed in the Readme.docx. Public tools, data, and repositories related to this work: CMAQ: https://doi.org/10.5281/zenodo.1079878 and https://github.com/USEPA/CMAQ CRACMM: https://github.com/USEPA/CRACMM AMET: https://github.com/USEPA/AMET AQS data: https://www.epa.gov/aqs NAPS data: https://www.canada.ca/en/environment-climate-change/services/air-pollution/monitoring-networks-data/national-air-pollution-program.html BenMAP: https://www.epa.gov/benmap/benmap-downloads AQBAT: https://health-infobase.canada.ca/aqbat/ AQMBC: https://barronh.github.io/aqmbc/index.html

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,019
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,446
Score d'incertitude au seuil0,922

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,019
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0040,008
Études des sciences et des technologies0,0020,001
Communication savante0,0060,003
Science ouverte0,0040,003
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,3530,175

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,052
Tête enseignante GPT0,306
Écart entre enseignants0,254 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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