Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network
Abstract 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 inpu...
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| Format: | Article |
| Language: | English |
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Springer
2023-03-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.220448 |
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| author | Qingchun Guo Zhenfang He Zhaosheng Wang |
| author_facet | Qingchun Guo Zhenfang He Zhaosheng Wang |
| author_sort | Qingchun Guo |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-a06d85334ce145d7850dacc960678f13 |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-a06d85334ce145d7850dacc960678f132025-02-09T12:22:01ZengSpringerAerosol and Air Quality Research1680-85842071-14092023-03-0123611110.4209/aaqr.220448Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural NetworkQingchun Guo0Zhenfang He1Zhaosheng Wang2School of Geography and Environment, Liaocheng UniversitySchool of Geography and Environment, Liaocheng UniversityNational Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesAbstract 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.https://doi.org/10.4209/aaqr.220448Air pollutionArtificial neural networkMeteorological elementPredictPM2.5PM10 |
| spellingShingle | Qingchun Guo Zhenfang He Zhaosheng Wang Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network Aerosol and Air Quality Research Air pollution Artificial neural network Meteorological element Predict PM2.5 PM10 |
| title | Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network |
| title_full | Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network |
| title_fullStr | Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network |
| title_full_unstemmed | Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network |
| title_short | Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network |
| title_sort | prediction of hourly pm2 5 and pm10 concentrations in chongqing city in china based on artificial neural network |
| topic | Air pollution Artificial neural network Meteorological element Predict PM2.5 PM10 |
| url | https://doi.org/10.4209/aaqr.220448 |
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