Effects of Environmental Factors on Ozone Flux over a Wheat Field Modeled with an Artificial Neural Network

Abstract

Ozone (O3) flux-based indices are considered better than O3 concentration-based indices in assessing the effects of ground O3 on ecosystem and crop yields. However, O3 flux (Fo) measurements are often lacking due to technical reasons and environmental conditions. This hampers the calculation of flux-based indices. In this paper, an artificial neural network (ANN) method was attempted to simulate the relationships between Fo and environmental factors measured over a wheat field in Yucheng, China. The results show that the ANN-modeled Fo values were in good agreement with the measured Fo values. The R2 of an ANN model with 6 routine independent environmental variables exceeded 0.8 for training datasets, and the RMSE and MAE were 3.074 nmol·m−2·s and 2.276 nmol·m−2·s for test dataset, respectively. CO2 flux and water vapor flux have strong correlations with Fo and could improve the fitness of ANN models. Besides the combinations of included variables and selection of training data, the number of neurons is also a source of uncertainties in an ANN model. The fitness of the modeled Fo was sensitive to the neuron number when it ranged from 1 to 10. The ANN model consists of complex arithmetic expressions between Fo and independent variables, and the response analysis shows that the model can reflect their basic physical relationships and importance. O3 concentration, global radiation, and wind speed are the important factors affecting O3 deposition. ANN methods exhibit significant value for filling the gaps of Fo measured with micrometeorological methods.