ArticleAtmospheric environment (Oxford, England : 1994)2020
A comparison of statistical and machine learning methods for creating national daily maps of ambient PM
Article in Atmospheric environment (Oxford, England : 1994), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Scalable Bayesian Geostatistical Regression Model for Bias-Correcting Large-Scale Daily Satellite-Retrieved Aerosol Optical Depth and Chemical Transport Model Simulations.Atmospheric environment (Oxford, England : 1994) · 2026Article
- Comparison of High Spatial Resolution PMEnvironmental science & technology · 2026Article
- Investigation of road transport-based greenhouse gas prediction models and the use of ıntelligent transportation systems for emission reduction.Scientific reports · 2025Article
- PMScientific reports · 2025Article
- Application of the Lasso regularisation technique in mitigating overfitting in air quality prediction models.Scientific reports · 2025Article
- Assessing predictability of environmental time series with statistical and machine learning models.Environmetrics · 2025Article
- High-Resolution Estimation of Daily PMRemote sensing · 2025Article
- Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany.Sensors (Basel, Switzerland) · 2024Article
- Environmental Injustice, Tree Canopy Cover, and Academic Proficiency at Utah Public Primary Schools.Environmental justice (Print) · 2024Article
- Prediction and model evaluation for space-time data.Journal of applied statistics · 2024Article
- Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations.Environmental science & technology · 2022Article
- Multivariate Spatial Prediction of Air Pollutant Concentrations with INLA.Environmental research communications · 2021Article
- Short-term PMEnvironmental health : a global access science source · 2021Article
- Ambient fine particulate matter exposure and incident mild cognitive impairment and dementia.Journal of the American Geriatrics Society · 2021Article
- Furthering a partnership: Air quality modeling and improving public health.Journal of the Air & Waste Management Association (1995) · 2021Article
- Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air).Current environmental health reports · 2021Review
- Examining PMEnvironmental research · 2021Article
- SPATIAL DISTRIBUTED LAG DATA FUSION FOR ESTIMATING AMBIENT AIR POLLUTION.The annals of applied statistics · 2021Article
- The Impact of Individual Mobility on Long-Term Exposure to Ambient PMInternational journal of environmental research and public health · 2021Article
- Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment.Toxics · 2020Review
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
A typical challenge in air pollution epidemiology is to perform detailed exposure assessment for individuals for which health data are available. To address this problem, in the last few years, substantial research efforts have been placed in developing statistical methods or machine learning techniques to generate estimates of air pollution at fine spatial and temporal scales (daily, usually) with complete coverage. However, it is not clear how much the predicted exposures yielded by the various methods differ, and which method generates more reliable estimates. In this paper, we aim to address this gap by evaluating a variety of exposure modeling approaches, comparing their predictive performance. Using PM
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.