ArticleNature communications2025
Machine learning-enhanced high-resolution exposure assessment of ultrafine particles.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Practical considerations for characterization of the external exposome in Alzheimer's disease and related dementias cohorts.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- From Size-Resolved PAC Emissions to Age-Specific Respiratory Deposition: Toxicity Potential of Particulate PACs from Residential Coal and Biomass Combustion.Environment & health (Washington, D.C.) · 2026Article
- A Tutorial on Best Practices and Pitfalls in Applying Machine Learning to Environmental Research.ACS environmental Au · 2026Article
- Radiologic exposomics: imaging the environmental imprint on cancer for precision oncology.La Radiologia medica · 2026Review
- From Environmental Concentrations to Individual Inhalation: Analysis of Exposure Differences to PMToxics · 2026Article
- A Machine Learning Approach to Predicting Household Smoke Exposure Risk in Somalia: An Analysis With SHAP Explanations.Environmental health insights · 2026Article
- Multimodal Particulate Matter Prediction: Enabling Scalable and High-Precision Air Quality Monitoring Using Mobile Devices and Deep Learning Models.Sensors (Basel, Switzerland) · 2025Article
- Machine learning-enhanced high-resolution exposure assessment of ultrafine particles.Nature communications · 2025Article
- Artificial intelligence in maternal and child health: Current applications, translational gaps, and future research priorities.Women's health (London, England)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ultrafine particles (UFPs) under 100 nm pose significant health risks inadequately addressed by traditional mass-based metrics. The WHO emphasizes particle number concentration (PNC) for assessing UFP exposure, but large-scale evaluations remain scarce. In this study, we developed a stacking-based machine learning framework integrating data-driven and physical-chemical models for a national-scale UFP exposure assessment at 1 km spatial and 1-hour temporal resolutions, leveraging long-term standardized PNC measurements in Switzerland. Approximately 20% (1.7 million) of the Swiss population experiences high UFP exposure exceeding an annual mean of 10
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.