Online monitoring data of atmospheric environmental quality often deviate or are missing, causing a great impact on regional atmospheric quality analysis. In this study, a deep learning method to repair atmospheric environmental quality data based on Gaussian diffusion and gate recurrent unit (GD-GRU) was developed to improve repair accuracy. A multi-source Gaussian diffusion model was developed to estimate PM_2.5 based on the pollutant diffusion law and the data of 61 stations in Guilin. The root mean square error (RMSE) of the estimated and observed value was extracted as the error sequence. The error value was regarded as output of gate recurrent unit (GRU) with the inputs of weather and pollutant parameters. Missing data were calculated by Gaussian diffusion estimated value and the error predicted by GRU. The established GD-GRU model was applied to repair the long-sequence missing data. The analytical results indicated that the GD-GRU model had higher prediction accuracy of extreme values than Gaussian diffusion model and GRU model, because GD-GRU based on Gaussian diffusion can calculate the extreme value by simulating the diffusion and transmission mechanism. The established model predicted PM_2.5 concentration in the next hours with an RMSE of 12.561, which was approximately 21.02% better, on average, than methods like autoregressive integrated moving average model (ARIMA), support vector regression (SVR), recurrent neural network (RNN), long short-term memory model (LSTM), and GRU. The established GD-GRU model demonstrated good performance on extreme values prediction and air quality data repair, thus providing a new method for air quality long-sequence missing data repair.

大气环境质量在线监测数据经常出现偏差或缺失，对区域大气质量分析产生重大影响。在这项研究中，开发了一种基于高斯扩散和门递归单元（GD-GRU）修复大气环境质量数据的深度学习方法，以提高修复精度。开发了多源高斯扩散模型来估计PM _2.5基于污染物扩散规律和桂林市61个站点的数据。提取估计值和观测值的均方根误差（RMSE）作为误差序列。将该误差值作为天气和污染物参数输入的闸门循环单元（GRU）的输出。缺失数据通过高斯扩散估计值计算，而误差则通过GRU预测。建立的GD-GRU模型用于修复长期序列缺失数据。分析结果表明，GD-GRU模型具有比高斯扩散模型和GRU模型更高的极值预测精度，这是因为基于高斯扩散的GD-GRU可以通过模拟扩散和传输机制来计算极值。建立的模型预测PM _2.5在接下来的几个小时内，RMSE为12.561，平均比自回归综合移动平均模型（ARIMA），支持向量回归（SVR），递归神经网络（RNN），长短-术语记忆模型（LSTM）和GRU。建立的GD-GRU模型在极值预测和空气质量数据修复方面表现出良好的性能，从而为空气质量长期序列缺失数据修复提供了一种新方法。