The emission rate of minority atmospheric gases is inferred by a new approach
based on neural networks. The neural network applied is the multi-layer
perceptron with backpropagation algorithm for learning. The identification of
these surface fluxes is an inverse problem. A comparison between the new
neural-inversion and regularized inverse solution id performed. The results
obtained from the neural networks are significantly better. In addition, the
inversion with the neural netwroks is fster than regularized approaches, after
training