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Prediction of Particulate Concentration Based on Correlation Analysis and a Bi-GRU Model

In recent decades, particulate pollution in the air has caused severe health problems. Therefore, it has become a hot research topic to accurately predict particulate concentrations. Particle concentration has a strong spatial–temporal correlation due to pollution transportation between regions, mak...

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Detalles Bibliográficos
Autores principales: Xu, He, Zhang, Aosheng, Xu, Xin, Li, Peng, Ji, Yimu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9603264/
https://www.ncbi.nlm.nih.gov/pubmed/36293843
http://dx.doi.org/10.3390/ijerph192013266
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author Xu, He
Zhang, Aosheng
Xu, Xin
Li, Peng
Ji, Yimu
author_facet Xu, He
Zhang, Aosheng
Xu, Xin
Li, Peng
Ji, Yimu
author_sort Xu, He
collection PubMed
description In recent decades, particulate pollution in the air has caused severe health problems. Therefore, it has become a hot research topic to accurately predict particulate concentrations. Particle concentration has a strong spatial–temporal correlation due to pollution transportation between regions, making it important to understand how to utilize these features to predict particulate concentration. In this paper, Pearson Correlation Coefficients (PCCs) are used to compare the particle concentrations at the target site with those at other locations. The models based on bi-directional gated recurrent units (Bi-GRUs) and PCCs are proposed to predict particle concentrations. The proposed model has the advantage of requiring fewer samples and can forecast particulate concentrations in real time within the next six hours. As a final step, several Beijing air quality monitoring stations are tested for pollutant concentrations hourly. Based on the correlation analysis and the proposed prediction model, the prediction error within the first six hours is smaller than those of the other three models. The model can help environmental researchers improve the prediction accuracy of fine particle concentrations and help environmental policymakers implement relevant pollution control policies by providing tools. With the correlation analysis between the target site and adjacent sites, an accurate pollution control decision can be made based on the internal relationship.
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spelling pubmed-96032642022-10-27 Prediction of Particulate Concentration Based on Correlation Analysis and a Bi-GRU Model Xu, He Zhang, Aosheng Xu, Xin Li, Peng Ji, Yimu Int J Environ Res Public Health Article In recent decades, particulate pollution in the air has caused severe health problems. Therefore, it has become a hot research topic to accurately predict particulate concentrations. Particle concentration has a strong spatial–temporal correlation due to pollution transportation between regions, making it important to understand how to utilize these features to predict particulate concentration. In this paper, Pearson Correlation Coefficients (PCCs) are used to compare the particle concentrations at the target site with those at other locations. The models based on bi-directional gated recurrent units (Bi-GRUs) and PCCs are proposed to predict particle concentrations. The proposed model has the advantage of requiring fewer samples and can forecast particulate concentrations in real time within the next six hours. As a final step, several Beijing air quality monitoring stations are tested for pollutant concentrations hourly. Based on the correlation analysis and the proposed prediction model, the prediction error within the first six hours is smaller than those of the other three models. The model can help environmental researchers improve the prediction accuracy of fine particle concentrations and help environmental policymakers implement relevant pollution control policies by providing tools. With the correlation analysis between the target site and adjacent sites, an accurate pollution control decision can be made based on the internal relationship. MDPI 2022-10-14 /pmc/articles/PMC9603264/ /pubmed/36293843 http://dx.doi.org/10.3390/ijerph192013266 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, He
Zhang, Aosheng
Xu, Xin
Li, Peng
Ji, Yimu
Prediction of Particulate Concentration Based on Correlation Analysis and a Bi-GRU Model
title Prediction of Particulate Concentration Based on Correlation Analysis and a Bi-GRU Model
title_full Prediction of Particulate Concentration Based on Correlation Analysis and a Bi-GRU Model
title_fullStr Prediction of Particulate Concentration Based on Correlation Analysis and a Bi-GRU Model
title_full_unstemmed Prediction of Particulate Concentration Based on Correlation Analysis and a Bi-GRU Model
title_short Prediction of Particulate Concentration Based on Correlation Analysis and a Bi-GRU Model
title_sort prediction of particulate concentration based on correlation analysis and a bi-gru model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9603264/
https://www.ncbi.nlm.nih.gov/pubmed/36293843
http://dx.doi.org/10.3390/ijerph192013266
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