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Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

文献类型：期刊论文

作者Guo, Qingchun3,4,5; He, Zhenfang2,4,5; Wang, Zhaosheng1
刊名TOXICS
出版日期2023
卷号11期号:1页码:19
关键词PM2 5 wavelet artificial neural network predicting DNN CNN LSTM COVID-19 epidemic
DOI10.3390/toxics11010051
通讯作者Guo, Qingchun(guoqingchun@lcu.edu.cn) ; He, Zhenfang(hezhenfang@lcu.edu.cn)
英文摘要Anthropogenic sources of fine particulate matter (PM2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM2.5 concentration in Shanghai. The PM2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM2.5 concentration in Shanghai. The PM2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM2.5 concentration predicting models were the PM2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM2.5 in the next day and the input influence factors, the PM2.5 showed the closest relation with the PM2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.
资助项目State Key Laboratory of Loess and Quaternary Geology[SKLLQG1907] ; Shandong Provincial Education Department
WOS研究方向Environmental Sciences & Ecology ; Toxicology
语种英语
出版者MDPI
WOS记录号WOS:000927177200001
资助机构State Key Laboratory of Loess and Quaternary Geology ; Shandong Provincial Education Department
源URL[http://ir.igsnrr.ac.cn/handle/311030/190087]  
专题中国科学院地理科学与资源研究所
通讯作者Guo, Qingchun; He, Zhenfang
作者单位1.Inst Geog Sci & Nat Resources Res, Chinese Acad Sci, Ecosyst Sci Data Ctr, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China
3.Inst Earth Environm, Chinese Acad Sci, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China
4.Liaocheng Univ, Inst Huanghe Studies, Liaocheng 252000, Peoples R China
5.Liaocheng Univ, Sch Geog & Environm, Liaocheng 252000, Peoples R China
推荐引用方式
GB/T 7714		 Guo, Qingchun,He, Zhenfang,Wang, Zhaosheng. Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China[J]. TOXICS,2023,11(1):19.
APA Guo, Qingchun,He, Zhenfang,&Wang, Zhaosheng.(2023).Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China.TOXICS,11(1),19.
MLA Guo, Qingchun,et al."Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China".TOXICS 11.1(2023):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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