Phase objectives analysis for PM2.5 reduction using dynamics forecasting approach under different scenarios of PGDP decline

PM2.5 concentration prediction is one of the atmospheric environmental issues of great concern to the public, and specifically the long-term PM2.5 change prediction can provide scientific basis for the government’s energy conservation and emission reduction and industrial structure adjustment polici...

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Autores principales: Ping Wang, Hongyinping Feng, Xu Bi, Yongyong Fu, Xuran He, Guisheng Zhang, Jiawei Niu
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Lenguaje:EN
Publicado: Elsevier 2021
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Acceso en línea:https://doaj.org/article/a31256f708614c28ba8082561bcb1ca4
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spelling oai:doaj.org-article:a31256f708614c28ba8082561bcb1ca42021-12-01T04:57:45ZPhase objectives analysis for PM2.5 reduction using dynamics forecasting approach under different scenarios of PGDP decline1470-160X10.1016/j.ecolind.2021.108003https://doaj.org/article/a31256f708614c28ba8082561bcb1ca42021-10-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21006683https://doaj.org/toc/1470-160XPM2.5 concentration prediction is one of the atmospheric environmental issues of great concern to the public, and specifically the long-term PM2.5 change prediction can provide scientific basis for the government’s energy conservation and emission reduction and industrial structure adjustment policies in advance. This paper proposes a new dynamics forecasting approach suitable for small samples and the approach transforms the time series prediction into a dynamics system through the ordinary differential equation theory, which overcomes the limitation of traditional statistical methods on sample size. What is more important is that it not only includes the time series itself, but also uses prior information in the modeling process. Based on the dynamics forecasting approach, the phase objectives analysis model for PM2.5 reduction is constructed. The simulation experiment takes 11 prefecture level cities in Shanxi Province as research sites, and uses the annual PM2.5 concentration from 2014 to 2018 to verify whether the proposed model can significantly improve the fitting accuracy compared with the single SVM. The experimental results show that the MAE (mean absolute error) of Taiyuan site is reduced by 73.24% from 1.6030 of SVM model to 0.4290 of our proposed method. Similar conclusions can be obtained from other data sets, which considerably demonstrates the better generalization ability of the proposed model. In addition, this paper presents the forecast results of annual PM2.5 concentration in different PGDP (energy consumption per unit of GDP) scenarios from 2019 to 2023, and analyzes the impact of PGDP reduction on PM2.5 concentration. Among the three scenarios, the PM2.5 reduction is the most significant in the scenario of PGDP with 10% decrease. We would argue that a larger PGDP reduction might lead to a greater PM2.5 reduction. Therefore, PGDP can be used as the basis for the Chinese authorities to tailor strategies to reduce PM2.5 concentration.Ping WangHongyinping FengXu BiYongyong FuXuran HeGuisheng ZhangJiawei NiuElsevierarticleTime series forecastingPM2.5 concentration predictionDynamics forecasting approachPhase objectives analysis modelEnergy consumption per unit of GDP (PGDP)EcologyQH540-549.5ENEcological Indicators, Vol 129, Iss , Pp 108003- (2021)
institution DOAJ
collection DOAJ
language EN
topic Time series forecasting
PM2.5 concentration prediction
Dynamics forecasting approach
Phase objectives analysis model
Energy consumption per unit of GDP (PGDP)
Ecology
QH540-549.5
spellingShingle Time series forecasting
PM2.5 concentration prediction
Dynamics forecasting approach
Phase objectives analysis model
Energy consumption per unit of GDP (PGDP)
Ecology
QH540-549.5
Ping Wang
Hongyinping Feng
Xu Bi
Yongyong Fu
Xuran He
Guisheng Zhang
Jiawei Niu
Phase objectives analysis for PM2.5 reduction using dynamics forecasting approach under different scenarios of PGDP decline
description PM2.5 concentration prediction is one of the atmospheric environmental issues of great concern to the public, and specifically the long-term PM2.5 change prediction can provide scientific basis for the government’s energy conservation and emission reduction and industrial structure adjustment policies in advance. This paper proposes a new dynamics forecasting approach suitable for small samples and the approach transforms the time series prediction into a dynamics system through the ordinary differential equation theory, which overcomes the limitation of traditional statistical methods on sample size. What is more important is that it not only includes the time series itself, but also uses prior information in the modeling process. Based on the dynamics forecasting approach, the phase objectives analysis model for PM2.5 reduction is constructed. The simulation experiment takes 11 prefecture level cities in Shanxi Province as research sites, and uses the annual PM2.5 concentration from 2014 to 2018 to verify whether the proposed model can significantly improve the fitting accuracy compared with the single SVM. The experimental results show that the MAE (mean absolute error) of Taiyuan site is reduced by 73.24% from 1.6030 of SVM model to 0.4290 of our proposed method. Similar conclusions can be obtained from other data sets, which considerably demonstrates the better generalization ability of the proposed model. In addition, this paper presents the forecast results of annual PM2.5 concentration in different PGDP (energy consumption per unit of GDP) scenarios from 2019 to 2023, and analyzes the impact of PGDP reduction on PM2.5 concentration. Among the three scenarios, the PM2.5 reduction is the most significant in the scenario of PGDP with 10% decrease. We would argue that a larger PGDP reduction might lead to a greater PM2.5 reduction. Therefore, PGDP can be used as the basis for the Chinese authorities to tailor strategies to reduce PM2.5 concentration.
format article
author Ping Wang
Hongyinping Feng
Xu Bi
Yongyong Fu
Xuran He
Guisheng Zhang
Jiawei Niu
author_facet Ping Wang
Hongyinping Feng
Xu Bi
Yongyong Fu
Xuran He
Guisheng Zhang
Jiawei Niu
author_sort Ping Wang
title Phase objectives analysis for PM2.5 reduction using dynamics forecasting approach under different scenarios of PGDP decline
title_short Phase objectives analysis for PM2.5 reduction using dynamics forecasting approach under different scenarios of PGDP decline
title_full Phase objectives analysis for PM2.5 reduction using dynamics forecasting approach under different scenarios of PGDP decline
title_fullStr Phase objectives analysis for PM2.5 reduction using dynamics forecasting approach under different scenarios of PGDP decline
title_full_unstemmed Phase objectives analysis for PM2.5 reduction using dynamics forecasting approach under different scenarios of PGDP decline
title_sort phase objectives analysis for pm2.5 reduction using dynamics forecasting approach under different scenarios of pgdp decline
publisher Elsevier
publishDate 2021
url https://doaj.org/article/a31256f708614c28ba8082561bcb1ca4
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