ArticleEnvironmental health perspectives2022
Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.
Article in Environmental health perspectives, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Optimizing Air Pollution Exposure Assessment with Application to Cognitive Function.Research report (Health Effects Institute) · 2025Article
- Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.Journal of exposure science & environmental epidemiology · 2025Article
- Integrating traffic pollution dispersion into spatiotemporal NOThe Science of the total environment · 2024Article
- Evaluating low-cost monitoring designs for PMEnvironmental pollution (Barking, Essex : 1987) · 2024Article
- Associations between Short-Term Exposure to Ambient Air Pollution and Influenza: An Individual-Level Case-Crossover Study in Guangzhou, China.Environmental health perspectives · 2023Article
- Acute Effects of Ambient Air Pollution on Asthma Emergency Department Visits in Ten U.S. States.Environmental health perspectives · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundBased on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations.
objectivesTo develop a daily, high-resolution ambient CO exposure prediction model at the city scale.
methodsWe developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (
resultsThe CO model had spatial cross-validation (CV) DISCUSSION: The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889.
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Registered trials
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