Evidence map›Paper›PMID 42305386›Full record

ArticleJournal of geophysical research. Atmospheres : JGR2026

Application and Evaluation of a NOAA GFS-Driven Air Quality Model Using CMAQv5.4 and High-Resolution Emissions: FIREX-AQ 2019.

Kiarash Farzad, Yang Zhang, Daniel Tong, Siqi Ma, Youhua Tang, Jianping Huang, Patrick C Campbell, Havala O T Pye, Benjamin Murphy, Fanglin Yang and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of geophysical research. Atmospheres : JGR, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Kiarash FarzadDepartment of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA.ORCID 0009-0000-8305-6736
Yang ZhangDepartment of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA.ORCID 0000-0002-8591-9598
Daniel TongNOAA Air Resources Laboratory, College Park, MD, USA.ORCID 0000-0002-4255-4568
Siqi MaDepartment of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA, USA.ORCID 0000-0002-3796-2911
Youhua TangNOAA Air Resources Laboratory, College Park, MD, USA.ORCID 0000-0001-7089-7915
Jianping HuangNOAA National Centers for Environmental Prediction, College Park, MD, USA.
Patrick C CampbellNOAA Air Resources Laboratory, College Park, MD, USA.
Havala O T PyeOffice of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA.ORCID 0000-0002-2014-2140
Benjamin MurphyOffice of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA.ORCID 0000-0003-3542-5378
Fanglin YangNOAA National Centers for Environmental Prediction, College Park, MD, USA.ORCID 0000-0003-1042-7983
Barry BakerNOAA Air Resources Laboratory, College Park, MD, USA.ORCID 0000-0002-6431-2391
Daiwen KangOffice of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA.ORCID 0000-0002-4952-1021
Bill HutzellOffice of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA.
Golam SarwarOffice of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA.

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

The National Air Quality Forecast Capability (NAQFC) provides numerical forecasting guidance of air quality up to 72 hr ahead over the United States. In this study, we evaluate the changes in its prediction skills by updating the Community Multiscale Air Quality (CMAQ) modeling system from version 5.3.1 to 5.4 and utilizing a high-resolution 1-km anthropogenic emission data set. The baseline and several sensitivity simulations with emission and science updates in CMAQv5.4 were conducted and evaluated. The results show improved prediction performance among model configurations over the contiguous United States (CONUS) in terms of correlation coefficient, normalized mean bias (NMB), and normalized mean error for fine particulate matter (PM

Identifiers

PMID42305386
PMCPMC13266822

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.