Abstract
Abstract. 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 NOx, NH3, SO2, volatile organic compounds (VOCs) and primary PM2.5 changing, on average, by −1.34 %, −2.65 %, −11.66 %, −19.19 % and 3.51 %, respectively, in China for 2015. Such ratios of NOx and PM2.5 are even higher (∼ 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 NO2, SO2, O3 and PM2.5 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.
Highlights
Clean air policies have been implemented by the Chinese government since 2010 and have been effectively reducing pollutant concentrations, such as sulfur dioxide (SO2) and nitrogen oxides (NOx) (Zheng et al, 2018)
We examined the performance of neural-network-based chemical transport model (NN-chemical transport model (CTM)) to check whether it had learned the relationship between emission and pollutant concentration
Results suggest that the neural networks (NNs)-CTM can reproduce the CTM well within an acceptable bias, and it can be used for emission adjustment
Summary
Clean air policies have been implemented by the Chinese government since 2010 and have been effectively reducing pollutant concentrations, such as sulfur dioxide (SO2) and nitrogen oxides (NOx) (Zheng et al, 2018). L. Huang et al.: Exploring deep learning for air pollutant emission estimation heavy-pollution events (Guo et al, 2014; Richter et al, 2005; Vesilind et al, 1988). Huang et al.: Exploring deep learning for air pollutant emission estimation heavy-pollution events (Guo et al, 2014; Richter et al, 2005; Vesilind et al, 1988) Such high pollutant concentrations may substantially affect human health given that air pollution has being ranked fifth among global risk factors with respect to mortality (Health Effects Institute, 2019)
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