Evidence map›Paper›PMID 20049118›Full record

ArticleEnvironmental health perspectives2009

Spatial modeling of PM10 and NO2 in the continental United States, 1985-2000.

Jaime E Hart, Jeff D Yanosky, Robin C Puett, Louise Ryan, Douglas W Dockery, Thomas J Smith, Eric Garshick, Francine Laden

Abstract readComparative StudyValidation Study
In one paragraph

Article in Environmental health perspectives, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Integrating traffic pollution dispersion into spatiotemporal NOThe Science of the total environment · 2024
    Article
  6. Methodological Challenges in Spatial and Contextual Exposome-Health Studies.Critical reviews in environmental science and technology · 2023
    Article
  7. Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NOInternational journal of environmental research and public health · 2022
    Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Long-term exposure to PMRespiratory research · 2020
    Article
  13. Article
  14. Article
  15. Article
  16. Long-Term Coarse Particulate Matter Exposure Is Associated with Asthma among Children in Medicaid.American journal of respiratory and critical care medicine · 2018
    Article
  17. Use of mobile and passive badge air monitoring data for NOJournal of exposure science & environmental epidemiology · 2017
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jaime E HartExposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA. Jaime.hart@channing.harvard.edu
Jeff D Yanosky
Robin C Puett
Louise Ryan
Douglas W Dockery
Thomas J Smith
Eric Garshick
Francine Laden

Funding

Translational Research Support CoreP30ES000002 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI SUSAN A KORRICK · 1985 to 2026
$44.6M
Training Program in Environmental EpidemiologyT32ES007069 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI LADEN, FRANCINE · 1985 to 2024
$13.9M
DIESEL PARTICLE EXPOSURE AND LUNG CANCERR01CA090792 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI GARSHICK, ERIC · 2001 to 2005
$4.8M
NCI NIH HHS R01 CA090792NCI NIH HHS R01 CA90792NIEHS NIH HHS ES00002NIEHS NIH HHS P30 ES000002NIEHS NIH HHS T32 ES007069NIEHS NIH HHS T32 ES007069-29
6 · The paper itself

Abstract

backgroundEpidemiologic studies of air pollution have demonstrated a link between long-term air pollution exposures and mortality. However, many have been limited to city-specific average pollution measures or spatial or land-use regression exposure models in small geographic areas.

objectivesOur objective was to develop nationwide models of annual exposure to particulate matter < 10 microm in diameter (PM(10)) and nitrogen dioxide during 1985-2000.

methodsWe used generalized additive models (GAMs) to predict annual levels of the pollutants using smooth spatial surfaces of available monitoring data and geographic information system-derived covariates. Model performance was determined using a cross-validation (CV) procedure with 10% of the data. We also compared the results of these models with a commonly used spatial interpolation, inverse distance weighting.

resultsFor PM(10), distance to road, elevation, proportion of low-intensity residential, high-intensity residential, and industrial, commercial, or transportation land use within 1 km were all statistically significant predictors of measured PM(10) (model R(2) = 0.49, CV R(2) = 0.55). Distance to road, population density, elevation, land use, and distance to and emissions of the nearest nitrogen oxides-emitting power plant were all statistically significant predictors of measured NO(2) (model R(2) = 0.88, CV R(2) = 0.90). The GAMs performed better overall than the inverse distance models, with higher CV R(2) and higher precision.

conclusionsThese models provide reasonably accurate and unbiased estimates of annual exposures for PM(10) and NO(2). This approach provides the spatial and temporal variability necessary to describe exposure in studies assessing the health effects of chronic air pollution.

Indexed as

Models, StatisticalAir PollutantsAir PollutionEnvironmental MonitoringEpidemiological MonitoringHumansInhalation ExposureNitrogen DioxideParticle SizeParticulate MatterPower PlantsRegression AnalysisRetrospective StudiesTime FactorsUnited StatesVehicle EmissionsAir PollutantsNitrogen DioxideParticulate MatterVehicle EmissionsGISnitrogen dioxideoutdoor air pollutionparticulate matter

Identifiers

PMID20049118
PMCPMC2801201

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

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.