Evidence map›Paper›PMID 37680462›Full record

ArticleiScience2023

Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition.

Da Zhang, Qingyi Wang, Shaojie Song, Simiao Chen, Mingwei Li, Lu Shen, Siqi Zheng, Bofeng Cai, Shenhao Wang, Haotian Zheng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Da ZhangInstitute of Energy, Economy, and Environment, Tsinghua University, Beijing, China.
Qingyi WangDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Shaojie SongState Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
Simiao ChenHeidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.
Mingwei LiInstitute of Energy, Economy, and Environment, Tsinghua University, Beijing, China.
Lu ShenDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China.
Siqi ZhengDepartment of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA.
Bofeng CaiCenter for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, China.
Shenhao WangDepartment of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA.
Haotian ZhengState Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. Simulating pollution concentration is a crucial step of the estimation, but traditional approaches often rely on complicated chemical transport models that require extensive expertise and computational resources. In this study, we develop a machine learning framework that is able to provide precise and robust annual average fine particle (PM

Indexed as

Atmospheric chemistryAtmospheric scienceEnergy sustainabilityMachine learning

Identifiers

PMID37680462
PMCPMC10480617

What OpenQuestion holds

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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 OpenQuestion graph.