Prediction of air pollutant concentration in Jinchang City

Hong Zhao; Pei Yang; Tengjiao Liu

doi:10.1117/12.2638676

23 May 2022 Prediction of air pollutant concentration in Jinchang City

Hong Zhao, Pei Yang, Tengjiao Liu

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 1225425 (2022) https://doi.org/10.1117/12.2638676
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

Air pollution has aroused widespread concern. Predicting air pollution can help people prepare for preventive responses. However, the existing air quality prediction methods are still in their infancy. In this paper, the LSTM (Long Short-Term Memory) sequence-to-sequence model based on the attention mechanism is applied to the prediction of air pollution. We used the meteorological and air pollutant observation data and pollutant discharge point monitoring data of Jinchang City, Gansu Province, and fused the meteorological and air pollution data of the neighboring areas to analyze the six basic air pollutants (PM2.5, PM10 , SO2, NO2, O3, CO), followed by model training and prediction evaluation. The results show that the prediction performance of the model for each air pollutant is different. For the prediction of short-term NO2, O3 and CO concentrations in the future, the A-LSTM-Seq2Seq model designed in this paper performs better; for PM2.5, PM10 and SO2, the prediction error of the model is larger than the previous three, because The observed values of these three are widely distributed and the data is discrete. In the future, it is necessary to improve the prediction performance from the perspective of improving the model. The model pays attention to all six basic air pollutants, and has certain practical value and application potential in the prediction of air pollution on the hourly time scale, and also has reference significance for the prediction of AQI.

Citation Download Citation

Hong Zhao, Pei Yang, and Tengjiao Liu "Prediction of air pollutant concentration in Jinchang City", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 1225425 (23 May 2022); https://doi.org/10.1117/12.2638676

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KEYWORDS

Data modeling

Performance modeling

Air contamination

Process modeling

Carbon monoxide

Computer programming

Neural networks

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