Exploring deep learning for air pollutant emission estimation

Huang, Lin; Liu, Song; Yang, Zeyuan; Xing, Jia; Zhang, Jia; Bian, Jiang; Li, Siwei; Sahu, Shovan Kumar; Wang, Shuxiao; Liu, Tie-Yan

The inaccuracy of anthropogenic emission inventories on a high-resolution scale due to insufficient basic data is one of the major reasons for the deviation between air quality model and observation results. A bottom-up approach, which is a typical emission inventory estimation method, requires a lot of human labor and material resources, whereas a top-down approach focuses on individual pollutants that can be measured directly as well as relying heavily on traditional numerical modeling. Lately, the deep neural network approach has achieved rapid development due to its high efficiency and nonlinear expression ability. In this study, we proposed a novel method to model the dual relationship between an emission inventory and pollution concentrations for emission inventory estimation. Specifically, we utilized a neural-network-based comprehensive chemical transport model (NN-CTM) to explore the complex correlation between emission and air pollution. We further updated the emission inventory based on back-propagating the gradient of the loss function measuring the deviation between NN-CTM and observations from surface monitors. We first mimicked the CTM model with neural networks (NNs) and achieved a relatively good representation of the CTM, with similarity reaching 95 %. To reduce the gap between the CTM and observations, the NN model suggests updated emissions of NOinline-formulax, NHinline-formula3, SOinline-formula2, volatile organic compounds (VOCs) and primary PMinline-formula2.5 changing, on average, by inline-formula−1.34 %, inline-formula−2.65 %, inline-formula−11.66 %, inline-formula−19.19 % and 3.51 %, respectively, in China for 2015. Such ratios of NOinline-formulax and PMinline-formula2.5 are even higher (inline-formula∼ 10 %) in regions that suffer from large uncertainties in original emissions, such as Northwest China. The updated emission inventory can improve model performance and make it closer to observations. The mean absolute error for NOinline-formula2, SOinline-formula2, Oinline-formula3 and PMinline-formula2.5 concentrations are reduced significantly (by about 10 %–20 %), indicating the high feasibility of NN-CTM in terms of significantly improving both the accuracy of the emission inventory and the performance of the air quality model.

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Huang, Lin / Liu, Song / Yang, Zeyuan / et al: Exploring deep learning for air pollutant emission estimation. 2021. Copernicus Publications.

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