Evidence map›Paper›PMID 42528658›Full record

ArticleNPJ climate and atmospheric science2026

High-resolution aerosol liquid water content in the contiguous United States using machine learning.

Bingqing Zhang, Lifei Yin, Yuhan Yang, Hongyu Guo, Lu Xu, Qian Di, Yaguang Wei, Jing Wei, Da Pan, Joel Schwartz and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ climate and atmospheric science, 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

13 authors.

Bingqing ZhangSchool of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA USA.
Lifei YinSchool of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA USA.
Yuhan YangSchool of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA USA.
Hongyu GuoSchool of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.
Lu XuDepartment of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO USA.
Qian DiVanke School of Public Health, Tsinghua University, Beijing, China.
Yaguang WeiDepartment of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, New York, NY USA.
Jing WeiMEEKL-AERM, College of Environmental Sciences and Engineering, Institute of Tibetan Plateau, and Center for Environment and Health, Peking University, Beijing, China.
Da PanSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA USA.
Joel SchwartzDepartment of Environmental Health, Harvard TH Chan School of Public Health, Boston, Massachusetts, MA USA.
Nga L NgSchool of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA USA.
Rodney J WeberSchool of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA USA.
Pengfei LiuSchool of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aerosol liquid water content (ALWC) plays an important role in climate and public health by influencing aerosol formation, chemical composition, and toxicity. However, ALWC remains sparsely measured and poorly constrained across space and time, despite its large variability. In this study, we derived a high-resolution (1 km × 1 km, daily) ALWC dataset for the contiguous US from 2000 to 2019. The dataset was generated by training machine learning (ML) models on outputs from a chemical transport model (GEOS-Chem) to capture the thermodynamic relationships between ALWC and relevant predictors, then applying these relationships to high-resolution, biased-corrected input datasets. Compared with GEOS-Chem simulations, the ML-based dataset better captures daily variations and spatial heterogeneity in ALWC. The predicted ALWC levels are highest in the Midwest US and lowest in the Western US, largely driven by regional differences in PM

Indexed as

ChemistryClimate sciencesEnvironmental sciencesMathematics and computing

Identifiers

PMID42528658
PMCPMC13414564

What Socratic holds

Textmetadata
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 Socratic graph.