Joint estimation of PM2.5 and O3 over China using a knowledge-informed neural network |
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| Institution: | 1. School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai, China;2. Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou, China;3. School of Geodesy and Geomatics, Wuhan University, Wuhan, China |
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| Abstract: | China has currently entered a critical stage of coordinated control of fine particulate matter (PM2.5) and ozone (O3), it is thus of tremendous value to accurately acquire high-resolution PM2.5 and O3 data. In contrast to traditional studies that usually separately estimate PM2.5 and O3, this study proposes a knowledge-informed neural network model for their joint estimation, in which satellite observations, reanalysis data, and ground station measurements are used. The neural network architecture is designed with the shared and specific inputs, the PM2.5-O3 interaction module, and the weighted loss function, which introduce the prior knowledge of PM2.5 and O3 into neural network modeling. Cross-validation (CV) results indicate that the inclusion of prior knowledge can improve the estimation accuracy, with R2 increasing from 0.872 to 0.911 and from 0.906 to 0.937 for PM2.5 and O3 estimation under sample-based CV, respectively. In addition, the proposed joint estimation model achieves comparable performance with the separate estimation model, but with higher efficiency. Mapping results of PM2.5 and O3 derived by the proposed model have demonstrated interesting findings in the spatial and temporal trends and variations over China. |
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| Keywords: | Joint estimation Knowledge-informed neural network |
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