ArticleGeospatial health2014
Estimating the global abundance of ground level presence of particulate matter (PM2.5).
Article in Geospatial health, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of data mining and machine learning for air pollution epidemiology.BMC public health · 2017Pooled it
- miR-122-5p Promotes Cowshed Particulate Matter2.5-Induced Apoptosis in NR8383 by TargetingToxics · 2024Article
- Population exposure to multiple air pollutants and its compound episodes in Europe.Nature communications · 2024Article
- First close insight into global daily gapless 1 km PMNature communications · 2023Article
- BMAL1/p53 mediating bronchial epithelial cell autophagy contributes to PM2.5-aggravated asthma.Cell communication and signaling : CCS · 2023Article
- Applications of artificial intelligence in the field of air pollution: A bibliometric analysis.Frontiers in public health · 2022Article
- Prediction Tool on Fine Particle Pollutants and Air Quality for Environmental Engineering.SN computer science · 2022Article
- Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments.Sensors (Basel, Switzerland) · 2021Article
- Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning.Sensors (Basel, Switzerland) · 2021Article
- Population exposure across central India to PMScientific reports · 2021Article
- An association between air pollution and daily most frequently visits of eighteen outpatient diseases in an industrial city.Scientific reports · 2020Article
- Time-series analysis of satellite-derived fine particulate matter pollution and asthma morbidity in Jackson, MS.Environmental monitoring and assessment · 2019Article
- Using machine learning to understand the temporal morphology of the PMEnvironmental monitoring and assessment · 2019Article
- Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast AirborneInternational journal of environmental research and public health · 2019Article
- Estimating PMSensors (Basel, Switzerland) · 2019Article
- Applications of Space Technologies to Global Health: Scoping Review.Journal of medical Internet research · 2018Article
- Advanced Metrics for Assessing Holistic Care: The "Epidaurus 2" Project.Global advances in health and medicine · 2018Article
- Improving satellite-based PMScientific reports · 2017Article
- Insights Into the Morphology of the East Asia PMEnvironmental health insights · 2017Article
- Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies.Environmental health insights · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
With the increasing awareness of the health impacts of particulate matter, there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground level airborne particulate matter with a diameter of 2.5 microns or less (PM2.5). Here we use a suite of remote sensing and meteorological data products together with ground-based observations of particulate matter from 8,329 measurement sites in 55 countries taken 1997-2014 to train a machine-learning algorithm to estimate the daily distributions of PM2.5 from 1997 to the present. In this first paper of a series, we present the methodology and global average results from this period and demonstrate that the new PM2.5 data product can reliably represent global observations of PM2.5 for epidemiological studies.
Indexed as
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.