Chemical substances of either anthropogenic or natural origin affect air quality and, as a consequence, also the health of the population. Therefore, there is a high demand for reliable air quality scenarios that can support possible management decisions. However, generating long term assessments of air quality assuming different emission scenarios is still a great challenge when using detailed atmospheric chemistry models. In this study, we test machine learning technique based on neural networks (NN) to emulate process-oriented modeling outcomes. A successfully calibrated NN might estimate concentrations of chemical substances in the air several orders faster than the original model and with reasonably small errors. We designed a simple recurrent 3-layer NN to reproduce daily mean concentrations of NO₂, SO₂ and C₂H₆ over Europe as simulated by the Community Multiscale Air Quality model (CMAQ). The general structure of the NN can be shown to approximate a continuity equation. Inputs of the network are daily mean meteorological state variables, taken from the climate model COSMO-CLM. The proposed NN emulates CMAQ outputs with an error not exceeding the difference between CMAQ and other known chemical transport models.

人为或自然来源的化学物质都会影响空气质量，进而影响人们的健康。因此，对可支持可能的管理决策的可靠空气质量情景有很高的要求。但是，在使用详细的大气化学模型时，假设不同的排放情景进行空气质量的长期评估仍然是一个巨大的挑战。在这项研究中，我们测试基于神经网络（NN）的机器学习技术，以模拟面向过程的建模结果。成功校准的NN可以估计空气中化学物质的浓度比原始模型快几个数量级，并且误差很小。我们设计了一个简单的循环3层NN，以再现NO ₂，SO的日平均浓度由欧洲共同体多尺度空气质量模型（CMAQ）模拟的欧洲₂和C ₂ H ₆。NN的一般结构可以显示为近似一个连续性方程。网络的输入是每日平均气象状态变量，取自气候模型COSMO-CLM。拟议的NN模拟CMAQ输出，其误差不超过CMAQ与其他已知化学传输模型之间的差。