Search

Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for the scientific management of atmospheric pollution. This study develops a model that combines an attention mechanism, convolutional neural network (CNN), and long short-term memory (LSTM) unit to predict the O 3 and PM 2.5 levels in the atmosphere, as well as an air quality index (AQI). The prediction results given by the proposed model are compared with those from CNN-LSTM and LSTM models as well as random forest and support vector regression models. The proposed model achieves a correlation coefficient between the predicted and observed values of more than 0.90, outperforming the other four models. The model errors are also consistently lower when using the proposed approach. Sobol-based sensitivity analysis is applied to identify the variables that make the greatest contribution to the model prediction results. Taking the COVID-19 outbreak as the time boundary, we find some homology in the interactions among the pollutants and meteorological factors in the atmosphere during different periods. Solar irradiance is the most important factor for O 3 , CO is the most important factor for PM 2.5 , and particulate matter has the most significant effect on AQI. The key influencing factors are the same over the whole phase and before the COVID-19 outbreak, indicating that the impact of COVID-19 restrictions on AQI gradually stabilized. Removing variables that contribute the least to the prediction results without affecting the model prediction performance improves the modeling efficiency and reduces the computational costs. [Display omitted] • CNN coupled with LSTM and attention mechanism for atmospheric predictions. • Proposal outperforms other models, with R2 > 0.9 between predicted and observed data. • Sensitivity analysis identifies factors contributing most to pollutant levels. • Removing variables that contribute little to output improves modeling efficiency. • Key factors remain similar during the whole period and before COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

Modeling-based prediction methods enable rapid, reagent-free air pollution detection based on inexpensive multi-source data than traditional chemical reaction-based detection methods in order to quickly understand the air pollution situation. In this study, a convolutional neural network (CNN) and long and short-term memory (LSTM) neural networks are integrated to create a CNN-LSTM time series prediction model to predict the concentration of PM 2.5 and its chemical components (i.e., heavy metals, carbon component, and water-soluble ions) using meteorological data and air pollutants (PM 2.5 , SO 2 , NO 2 , CO, and O 3). In the integrated CNN-LSTM model, the CNN uses convolutional and pooling layers to extract features from the data, whereas the powerful nonlinear mapping and learning capabilities of LSTM enable the time series prediction of air pollution. The experimental results showed that the CNN-LSTM exhibited good generalization ability in the prediction of As, Cd, Cr, Cu, Ni, and Zn, with a mean R2 above 0.9. Mean R2 predicted for PM 2.5 , Pb, Ti, EC, OC, SO 4 2−, and NO 3 − ranged from 0.85 to 0.9. Shapley value showed that PM 2.5 , NO 2 , SO 2 , and CO had a greater influence on the predicted heavy metal results of the model. Regarding water-soluble ions, the predicted results were dominantly influenced by PM 2.5 , CO, and humidity. The prediction of the carbon fraction was affected mainly by the PM 2.5 concentration. Additionally, several input variables for various components were eliminated without affecting the prediction accuracy of the model, with R2 between 0.70 and 0.84, thereby maximizing modeling efficiency and lowering operational costs. The fully trained model prediction results showed that most predicted components of PM 2.5 were lower during January to March 2020 than those in 2018 and 2019. This study provides insight into improving the accuracy of modeling-based detection methods and promotes the development of integrated air pollution monitoring toward a more sustainable direction. [Display omitted] • CNN coupled with LSTM for atmospheric time series predictions. • Many elements had better simulation effects when atmospheric pollutants as inputs. • CNN-LSTM has superior prediction performance with more than 90% accuracy. • The Shapely value method identifies the key indicators for predicting pollutant. • Predicted pollutant contents were lower during COVID-19 outbreak than in 2019. [ABSTRACT FROM AUTHOR]