Two-Stream Networks for COPERT Correction Model with Time-Frequency Features Fusion

• Published in: Atmosphere Vol. 14; no. 12; p. 1766
• Main Authors: Xu, Zhenyi, Wang, Ruibin, Pan, Kai, Li, Jiaren, Wu, Qilai
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2023
• Subjects: Accuracy; Air pollution; Artificial intelligence; Carbon dioxide; Comparative analysis; Computer software; Continuous wavelet transform; COPERT; Correlation analysis; Diesel motor exhaust gas; Distribution; Emission analysis; emission factors; Emissions; Energy consumption; Environmental aspects; Environmental monitoring; Environmental protection; Modelling; Neural networks; Nitrogen compounds; Nitrogen oxides; OBD; Outdoor air quality; Pollutants; Rivers; Road transportation; Roads & highways; Time series; Time-frequency analysis; time-frequency features; two-stream network; Vehicles; China; United States > US; California
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos14121766

Emission factors serve as a valuable tool for quantifying the release of pollutants from road vehicles and predicting emissions within a specific time or area. In order to overcome the limitation of the computer program to calculate emissions from the road transport (COPERT) model in directly obtaining precise emission factors from on-board diagnostic (OBD) data, we propose a novel two-stream network that combines time-series features and time-frequency features to enhance the accuracy of the COPERT model. Firstly, for the instantaneous emission factors of NOx from multiple driving segments provided by heavy-duty diesel vehicles in actual driving, we select the monitored attributes with a high correlation to the emission factor of NOx considering the data scale and employing Spearman rank correlation analysis to obtain the final dataset composed of them and emission factors. Subsequently, we construct an information matrix to capture the impact of past data on emission factors. Each attribute of the time series is then converted into a time-frequency matrix using the continuous wavelet transform. These individual time-frequency matrices are combined to create a multi-channel time-frequency matrix, which represents the historical information. Finally, the historical information matrix and the time-frequency matrix are inputted into a two-stream parallel model that consists of ResNet50 and a convolutional block attention module. This model effectively integrates time-series features and time-frequency features, thereby enhancing the representation of emission characteristics. The reliability and accuracy of the proposed method were validated through a comparative analysis with existing mainstream models.