Abstract：
Toxic heavy gas sulfur dioxide (SO2) is a specific life and environmental hazard. Predicting the diffusion of SO2 has become a research focus in fields such as environmental and safety studies. However, traditional methods, such as kinetic models, cannot balance precision and time. Thus, they do not meet the needs of emergency decision-making. Deep learning (DL) models are emerging as a highly regarded solution, providing faster and more accurate predictions of gas concentrations. To this end, this study proposes an innovative hybrid DL model, the parallel-connected convolutional neural network-gated recurrent unit (PC CNN-GRU). This model utilizes two CNNs connected in parallel to process gas release and meteorological datasets, enabling the automatic extraction of high-dimensional data features and handling of long-term temporal dependencies through the GRU. The proposed model demonstrates good performance (RMSE, MAE, and R2 of 20.1658, 10.9158, and 0.9288, respectively) with real data from the Project Prairie Grass (PPG) case. Meanwhile, to address the issue of limited availability of raw data, in this study, time series generative adversarial network (TimeGAN) are introduced for SO2 diffusion studies for the first time, and their effectiveness is verified. To enhance the practicality of the research, the contribution of drivers to SO2 diffusion is quantified through the utilization of the permutation importance (PIMP) and Sobol\' method. Additionally, the maximum safe distance downwind under various conditions is visualized based on the SO2 toxicity endpoint concentration. The results of the analyses can provide a scientific basis for relevant decisions and measures.
摘要：
有毒重气二氧化硫（SO2）是一种特定的生命和环境危害。预测SO2的扩散已成为环境和安全研究等领域的研究热点。然而,传统方法,比如动力学模型，无法平衡精度和时间。因此，他们不符合紧急决策的需要。深度学习(DL)模型正在成为一种备受推崇的解决方案，提供更快，更准确的气体浓度预测。为此,本研究提出了一种创新的混合DL模型，并行连接卷积神经网络门控递归单元(PCCNN-GRU)。该模型利用两个CNN并联连接到过程气体释放和气象数据集，通过GRU实现高维数据特征的自动提取和长期时间依赖的处理。所提出的模型表现出良好的性能(RMSE，MAE,和R2分别为20.1658、10.9158和0.9288），并具有来自Prairie草项目（PPG）案例的实际数据。同时,为了解决原始数据可用性有限的问题，在这项研究中，首次将时间序列生成对抗网络(TimeGAN)引入SO2扩散研究，并验证了其有效性。增强研究的实用性,驱动因素对SO2扩散的贡献通过利用排列重要性(PIMP)和Sobol方法进行量化。此外,根据SO2毒性终点浓度可视化各种条件下的最大顺风安全距离。分析结果可为相关决策和措施提供科学依据。
