Enhanced short-term prediction of urban PM

Yin Zhou, Yaping Lyu, Xiuli Dang, Roland Bol, Peng Zhang, Na Yu, Yuling Zhang

Abstract read

In one paragraph

Article in Journal of environmental health science & engineering, 2025. 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

7 authors.

Yin Zhou *College of Land and Environment, National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Northeast Key Laboratory of Conservation and Improvement of Cultivated Land, Shenyang Agricultural University, Shenyang, 110866 P. R. China.

Yaping Lyu *Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei, 230026 P.R. China.

Xiuli DangCollege of Land and Environment, National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Northeast Key Laboratory of Conservation and Improvement of Cultivated Land, Shenyang Agricultural University, Shenyang, 110866 P. R. China.ORCID 0000-0002-1702-9745

Roland BolInstitute of Bio- and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.

Peng ZhangDepartment of Environmental Science and Engineering, University of Science and Technology of China, Hefei, 230026 P.R. China.

Na YuCollege of Land and Environment, National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Northeast Key Laboratory of Conservation and Improvement of Cultivated Land, Shenyang Agricultural University, Shenyang, 110866 P. R. China.

Yuling ZhangCollege of Land and Environment, National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Northeast Key Laboratory of Conservation and Improvement of Cultivated Land, Shenyang Agricultural University, Shenyang, 110866 P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The aim of this study was to investigate the impact of improved deep learning model on the predictive performance of PM Methods: We developed a new model combining one-dimensional convolutional neural network and bidirectional long short-term memory neural network to predict PM Results: The root mean square error, mean absolute error, mean absolute percentage error of the new method were reduced by 49%, 51%, 44% and the R Conclusions: In this study, the proposed model improvement method can significantly reduce the error of the model in predicting PM Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-025-00954-0.

Indexed as

Air pollutionBidirectional long short-term memory neural networkConvolutional neural networksDeep learningPM2.5 concentration prediction

Identifiers

PMID40873743

DOI10.1007/s40201-025-00954-0

PMCPMC12378881

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.