The chemical composition and toxicity of particulate matter from household cooking and heating with solid fuel
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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".