The chemical composition and toxicity of particulate matter from household cooking and heating with solid fuel
Notice bibliographique
Résumé
Background: Particulate matter (PM) air pollution from the household combustion of solid fuel (e.g., coal, biomass) for cooking and heating is a widespread environmental exposure that causes an estimated 4 million yearly premature deaths and contributes to global and regional climate change. PM from different sources varies in its physicochemical properties, which may differentially impact its toxicity to humans and determine its net radiative forcing effect on the climate. Little is known about PM's composition or toxicity in different global contexts where solid fuels are burned for cooking and heating. Methods: I first conducted a literature review of studies that reported on the chemical composition and/or sources of PM in field settings of solid fuel combustion. I extracted a number of variables from each study (e.g., PM size fraction, chemical species concentrations) and calculated weighted mean daily household concentrations and 24-h personal exposures for select chemical components [black carbon, organic carbon, and benzo(a)pyrene]. PM sources as determined by formal source apportionment analyses were also compared across studies.I then performed an empirical analysis of the chemical composition and toxicity of 24-h fine particulate matter (PM2.5) exposures of 20 women in northern (n = 17) and southern (n = 3) China who cooked and heated their homes with solid fuel. PM2.5 samples were analyzed for mass, black carbon, water soluble organic carbon, ions, and select metals. Two different assays were used to measure the ability of PM2.5 exposures to generate reactive oxygen species (i.e., the "oxidative potential" of PM2.5 exposures). I performed a factor analysis with three factors to identify the primary indoor and outdoor sources of women's exposure to PM2.5 and their chemical markers. Linear regressions were used to determine the chemical species and, by extension, sources of exposure to PM2.5 that were most responsible for the oxidative potential of PM2.5. Results: My literature search identified 46 studies in 12 countries on the chemical speciation of PM. Weighted mean daily household concentrations of black carbon, organic carbon, and benzo(a)pyrene were 17.2 μg/m3, 61.9 μg/m3, and 156 ng/m3, respectively. In identified studies, solid fuel combustion was not always the major contributor to PM, explaining 29% to 48% of principal component / factor analysis variance and 41% to 87% of PM mass as determined by positive matrix factorization. In my empirical analysis, rural women's geometric mean exposures to PM2.5 were 248.6 μg/m3 and 83.9 μg/m3 in northern and southern Chinese field sites, respectively. The major source contributors to PM2.5 exposures were resuspended dust, biomass combustion, and coal combustion. Chemical markers for dust were associated with intrinsic oxidative potential in both univariate and multivariate linear regression models, whereas markers for coal and biomass combustion were not associated with redox activity. Conclusions: My literature review identified daily household concentrations and 24-h personal exposures to carbonaceous particles and benzo(a)pyrene that were high by global standards. The between-study differences in PM components were great, highlighting the importance of field setting (e.g., season, fuel and stove used) and measurement methods (e.g., monitor placement) on PM component concentrations. My review presented evidence that solid fuel combustion is not always the major contributor to PM indoor concentrations and exposures. In my empirical analysis, all women's 24-h exposures to PM2.5 exceeded the World Health Organization's interim target 1 annual guideline of 35 μg/m3. The null associations between markers for solid fuel combustion and intrinsic oxidative potential may result from myriad factors, including the existence of mechanisms other than oxidative stress that drive PM health relationships in these settings.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».