为了解决门控循环单元（gated recurrent unit, GRU）超参数选取困难的问题，提出一种基于卷积神经网络（convolutional neural networks, CNN）、门控循环单元和麻雀搜索算法（sparrow search algorithm, SSA）的组合模型（CNN-GRU-SSA）。首先利用CNN对输入的多维数据集进行特征提取；然后将CNN提取到的特征输入GRU模型；最后使用SSA算法优化GRU模型的超参数，并将其应用于PM_2.5浓度预测。选取西部城市成都与东部城市杭州作为研究区域，使用2021年12月1日—2022年2月13日的大气污染物、气象因素、边界层高度（boundary layer height, BLH）以及大气可降水量（precipitable water vapor, PWV）的小时数据进行建模，分别预测两市2022年2月14日—2月28日PM_2.5浓度变化。实验结果表明，CNN-GRU-SSA模型预测精度与其他模型相比有明显提高，其中成都的预测值最接近实际值。

To address the challenge of difficulty in selecting hyperparameters for gated recurrent units (GRU), a combined model (CNN-GRU-SSA) was proposed, integrating convolutional neural networks (CNN), gated recurrent units, and the sparrow search algorithm (SSA). Firstly, CNN for feature extraction from the multidimensional input dataset was utilized. Subsequently, the features extracted by CNN into the GRU model are inputted. Lastly, the hyperparameters of the GRU model using the SSA algorithm were optimized and were applied to the model to predict PM_2.5 concentrations. Chengdu in the western region and Hangzhou in the eastern region was selected as research areas. Hourly data of air pollutants, meteorological factors, boundary layer height (BLH), and precipitable water vapor (PWV) from December 1, 2021, to February 13, 2022, were utilized for modeling. The aim was to predict the variation in PM_2.5 concentrations in both cities from February 14 to February 28, 2022. Experimental results indicate that the CNN-GRU-SSA model demonstrates a noticeable improvement in prediction accuracy compared to other models, with Chengdu exhibiting the closest proximity between predicted and actual values.