A method for identifying VOCs aggregation areas based on the characteristics of air pollution
To achieve precise perception and recognition of VOCs aggregation areas, this paper proposes a VOCs aggregation area recognition method combining air pollution characteristics. First, the regional grid is partitioned and the IDW spatial interpolation method is employed to obtain the VOCs grid dataset. Second, HYSPLIT is used to calculate the trajectory of the backward air mass and VGG is introduced to extract trajectory features. The same dataset is input into the TCN-BiLSTM model to predict the VOCs concentration in each grid. Finally, the clustering area is identified based on the predicted results. In Beilin District in Xi'an, the concentration values of VOCs is predicted and the identification results of aggregation areas are visualized. Our results show the combined prediction model effectively improves the recognition accuracy. The MAE, MSE, RMSE, and R2 of the VOCs concentration prediction results are 6.657, 103.657, 10.181, and 0.976 respectively, which are superior to those of the comparison model. Through ablation experiments, it proves a consideration of the characteristics of air mass pollution effectively improves the accuracy of VOCs prediction and achieves accurate perception and recognition of VOCs aggregation areas.
aggregation of VOCsidentification of contaminated areasconcentration predictioncharacteristics of atmospheric pollutiondeep learning
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