ArticleEnvironmental science & technology2016
Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5.
Article in Environmental science & technology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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.
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Who cites it
25 citing papers in PubMed.
- Air pollution predicts healthcare spending among older adults in the United States.Environment international · 2026Article
- National PMEnvironmental pollution (Barking, Essex : 1987) · 2025Article
- Prenatal Exposure to Source-Specific Fine Particulate Matter and Autism Spectrum Disorder.Environmental science & technology · 2024Article
- Source-Specific Air Pollution and Loss of Independence in Older Adults Across the US.JAMA network open · 2024Article
- Ambient air pollution and rate of spontaneous abortion.Environmental research · 2024Article
- Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NOEnvironmental research letters : ERL [Web site] · 2024Article
- Structural and social determinants of health: The multi-ethnic study of atherosclerosis.PloS one · 2024Article
- Air pollution and fecundability in a North American preconception cohort study.Environment international · 2023Article
- Economic and racial disparities of the weather impact on air quality in Brazil.Scientific reports · 2023Article
- Does It Measure Up? A Comparison of Pollution Exposure Assessment Techniques Applied across Hospitals in England.International journal of environmental research and public health · 2023Article
- A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target.The Science of the total environment · 2023Article
- Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK.International journal of environmental research and public health · 2022Article
- A Deep Learning Approach for Meter-Scale Air Quality Estimation in Urban Environments Using Very High-Spatial-Resolution Satellite Imagery.Atmosphere · 2022Article
- Estimating 2013-2019 NOEnvironmental pollution (Barking, Essex : 1987) · 2022Article
- 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
- Estimating US Background Ozone Using Data Fusion.Environmental science & technology · 2021Article
- Improving Estimates of PMAtmospheric environment (Oxford, England : 1994) · 2021Article
- An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States.Environmental science & technology · 2020Article
- A comparison of statistical and machine learning methods for creating national daily maps of ambient PMAtmospheric environment (Oxford, England : 1994) · 2020Article
- Article
Corrections and comments
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Authors and funding
9 authors.
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Abstract
Assessments of long-term air pollution exposure in population studies have commonly employed land-use regression (LUR) or chemical transport modeling (CTM) techniques. Attempts to incorporate both approaches in one modeling framework are challenging. We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatiotemporal LUR model with spatial smoothing to estimate spatiotemporal variability of ozone (O3) and particulate matter with diameter less than 2.5 μm (PM2.5) from 2000 to 2008 in the Los Angeles Basin. The observations include over 9 years' data from more than 20 routine monitoring sites and specific monitoring data at over 100 locations to provide more comprehensive spatial coverage of air pollutants. Our composite modeling approach outperforms separate CTM and LUR models in terms of root-mean-square error (RMSE) assessed by 10-fold cross-validation in both temporal and spatial dimensions, with larger improvement in the accuracy of predictions for O3 (RMSE [ppb] for CTM, 6.6; LUR, 4.6; composite, 3.6) than for PM2.5 (RMSE [μg/m(3)] CTM: 13.7, LUR: 3.2, composite: 3.1). Our study highlights the opportunity for future exposure assessment to make use of readily available spatiotemporal modeling methods and auxiliary gridded data that takes chemical reaction processes into account to improve the accuracy of predictions in a single spatiotemporal modeling framework.
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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.