Evidence map›Paper›PMID 42674653›Full record

ArticleEnvironmental science & technology2026

Health Risk of Fine Particulate Matter Across China with a Pollutant-Specific Index from 2013 to 2023.

Qian Li, Jiahao Duan, Huaiyue Xu, Shihan Zhen, Zhouxin Yin, Qian Li, Tao Tao, Fengchao Liang

Abstract read
In one paragraph

Article in Environmental science & technology, 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

8 authors.

Qian LiSchool of Public Health and Emergency Management, School of Medicine, Southern University of Science and Technology, Shenzhen518055, China.ORCID 0009-0008-5679-7158
Jiahao DuanSchool of Public Health and Emergency Management, School of Medicine, Southern University of Science and Technology, Shenzhen518055, China.
Huaiyue XuSchool of Public Health and Emergency Management, School of Medicine, Southern University of Science and Technology, Shenzhen518055, China.
Shihan ZhenSchool of Public Health and Emergency Management, School of Medicine, Southern University of Science and Technology, Shenzhen518055, China.
Zhouxin YinSchool of Public Health and Emergency Management, School of Medicine, Southern University of Science and Technology, Shenzhen518055, China.
Qian LiSchool of Public Health and Emergency Management, School of Medicine, Southern University of Science and Technology, Shenzhen518055, China.ORCID 0009-0002-7119-5143
Tao TaoSchool of Public Health and Emergency Management, School of Medicine, Southern University of Science and Technology, Shenzhen518055, China.
Fengchao LiangSchool of Public Health and Emergency Management, School of Medicine, Southern University of Science and Technology, Shenzhen518055, China.ORCID 0000-0003-2088-0722

Funding

Medical Research Innovation Project G030410001Ministry of Science and Technology of the People's Republic of China 2022YFC3702703National Natural Science Foundation of China-Guangdong Joint Fund 2023A1515011206National Natural Science Foundation of China (NSFC) 42107465National Natural Science Foundation of China (NSFC) 82422066National Natural Science Foundation of China (NSFC) 82574049Talents enlisted in major talent programs of Guangdong Province 2021QN020921
6 · The paper itself

Abstract

Fine particulate matter (PM2.5) remains a major public health challenge in China despite substantial improvements in air quality over the past decade. Assessments of PM2.5 distribution and its associated health risks are essential for pollution control and health protection. Thus, we developed a 1-km resolution daily PM2.5 estimation model using machine learning approaches from 2013 to 2023 across China. Meta-analyses were further conducted to estimate the short-term health effects of PM2.5 exposure on mortality and hospitalization outcomes. We integrated PM2.5 concentration and its health effect into a PM2.5-specific Air Quality Health Index (AQHIPM2.5) to quantify region-specific health risks. The model achieved high predictive accuracy (cross-validation R2 = 0.88 spatially and 0.82 temporally at daily level). PM2.5 concentrations declined substantially nationwide from 55.06 μg/m3 in 2013 to 26.50 μg/m3 in 2022, followed by a slight rebound to 27.46 μg/m3 in 2023. Each 10 μg/m3 increase in short-term PM2.5 exposure within 3 days was associated with increased risks of all-cause (0.42%), cardiovascular (0.61%), and respiratory (0.84%) mortality. AQHIPM2.5 indicated persistent moderate-to-high health risk days in northern and southwestern China, with some regions experiencing more than 80 medium- and high-risk days annually. Eliminating high-risk days identified by AQHIPM2.5 could avert an estimated 6,985 (95% CI: 6,419 to 7,588) deaths in 2023, accounting for 7.42% of PM2.5-attributable all-cause mortality burden. These findings indicate that substantial health risks from PM2.5 persist despite improved air quality and demonstrate that integrating high-resolution exposure estimation with health risk assessment can support more targeted pollution control and public health interventions.

Indexed as

Particulate MatterAir PollutantsChinaHumansAir PollutantsParticulate MatterAir Quality Health Indexfine particulate matterhealth risk assessmentPM2.5, machine learning

Identifiers

PMID42674653
PMCPMC13523694

What Socratic holds

Textmetadata
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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.