Qingchun Guo This email address is being protected from spambots. You need JavaScript enabled to view it.1,2,3, Zhenfang He This email address is being protected from spambots. You need JavaScript enabled to view it.1,2,4, Zhaosheng Wang5 1 School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
2 Institute of Huanghe Studies, Liaocheng University, Liaocheng 252000, China
3 State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
4 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
5 National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Received:
December 11, 2022
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Revised:
March 12, 2023
Accepted:
March 27, 2023
Download Citation:
||https://doi.org/10.4209/aaqr.220448
Guo, Q., He, Z., Wang, Z. (2023). Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network. Aerosol Air Qual. Res. 23, 220448. https://doi.org/10.4209/aaqr.220448
Cite this article:
Accurate prediction of air pollution is a difficult problem to be solved in atmospheric environment research. An Artificial Neural Network (ANN) is exploited to predict hourly PM2.5 and PM10 concentrations in Chongqing City. We take PM2.5 (PM10), time and meteorological elements as the input of the ANN, and the PM2.5 (PM10) of the next hour as the output to build an ANN model. Thirteen kinds of training functions are compared to obtain the optimal function. The research results display that the ANN model exhibits good performance in predicting hourly PM2.5 and PM10 concentrations. Trainbr is the best function for predicting PM2.5 concentrations compared to other training functions with R value (0.9783), RMSE (1.2271), and MAE (0.9050). Trainlm is the second best with R value (0.9495), RMSE (1.8845), and MAE (1.3902). Similarly, trainbr is also the best in forecasting PM10 concentrations with R value (0.9773), RMSE value (1.8270), and MAE value (1.4341). Trainlm is the second best with R value (0.9522), RMSE (2.6708), and MAE (1.8554). These two training functions have good generalization ability and can meet the needs of hourly PM2.5 and PM10 prediction. The forecast results can support fine management and help improve the ability to prevent and control air pollution in advance, accurately and scientifically.HIGHLIGHTS
ABSTRACT
Keywords:
Air pollution, Artificial neural network, Meteorological element, Predict, PM2.5, PM10, Chongqing, Training function
