Ozone (O3) flux-based indices are considered better than O3 concentration-based indices in assessing the effects of ground O3 on\necosystem and crop yields. However, O3 flux (Fo) measurements are often lacking due to technical reasons and environmental\nconditions. (is hampers the calculation of flux-based indices. In this paper, an artificial neural network (ANN) method was\nattempted to simulate the relationships between Fo and environmental factors measured over a wheat field in Yucheng, China. (e\nresults show that the ANN-modeled Fo values were in good agreement with the measured Fo values. The R2 of an ANN model with\n6 routine independent environmental variables exceeded 0.8 for training datasets, and the RMSE and MAE were 3.074 nmol.m-2.s\nand 2.276 nmol.m-2.s for test dataset, respectively. CO2 flux and water vapor flux have strong correlations with Fo and could\nimprove the fitness of ANN models. Besides the combinations of included variables and selection of training data, the number of\nneurons is also a source of uncertainties in an ANN model. (e fitness of the modeled Fo was sensitive to the neuron number when\nit ranged from 1 to 10. (e ANN model consists of complex arithmetic expressions between Fo and independent variables, and the\nresponse analysis shows that the model can reflect their basic physical relationships and importance. O3 concentration, global\nradiation, and wind speed are the important factors affecting O3 deposition. ANN methods exhibit significant value for filling the\ngaps of Fo measured with micrometeorological methods.
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