Evidence map›Paper›PMID 36153982›Full record

ArticleEnvironmental science & technology2022

National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data.

Provat K Saha, Albert A Presto, Steve Hankey, Benjamin N Murphy, Chris Allen, Wenwen Zhang, Julian D Marshall, Allen L Robinson

Open access · greenAbstract read
In one paragraph

Article in Environmental science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 26% of its field
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

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Review
  3. Hyper-local to regional exposure contrast of source-resolved PMJournal of exposure science & environmental epidemiology · 2024
    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 at 6 institutions in 1 country.

Provat K SahaCenter for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.ORCID 0000-0002-4044-9350
Albert A PrestoCenter for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.ORCID 0000-0002-9156-1094
Steve HankeySchool of Public and International Affairs, Virginia Tech, Blacksburg, Virginia 24061, United States.ORCID 0000-0002-7530-6077
Benjamin N MurphyCenter for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, Durham, North Carolina 27709, United States.ORCID 0000-0003-3542-5378
Chris AllenGeneral Dynamics Information Technology, Research Triangle Park, Durham, North Carolina 27711, United States.
Wenwen ZhangDepartment of Public Informatics, Rutgers University, New Brunswick, New Jersey 08901, United States.
Julian D MarshallDepartment of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States.ORCID 0000-0003-4087-1209
Allen L RobinsonCenter for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.ORCID 0000-0002-1819-083X
Carnegie Mellon University · USEnvironmental Protection Agency · USResearch Triangle Park Foundation · USRutgers, The State University of New Jersey · USUniversity of Washington · USVirginia Tech · US

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

This paper investigates the feasibility of developing national empirical models to predict ambient concentrations of sparsely monitored air pollutants at high spatial resolution. We used a data set of cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA; traffic primary organic PM) measured using aerosol mass spectrometry across the continental United States. The monitoring locations were selected to span the national distribution of land-use and source-activity variables commonly used for land-use regression modeling (e.g., road length, restaurant count, etc.). The models explain about 60% of the spatial variability of the measured data (

Indexed as

Air PollutantsAir PollutionAerosolsEnvironmental MonitoringHydrocarbonsMass SpectrometryParticulate MatterUnited StatesAerosolsAir PollutantsHydrocarbonsParticulate Matteraerosol mass spectrometryfine particulate matterspatial modeling

Identifiers

PMID36153982
PMCPMC11809489
OpenAlexW4297240708

What OpenQuestion holds

Textmetadata
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.