NO2 是主要的大气污染气体之一, 在大气光化学过程中起着重要作用。研究 NO2 浓度的时空演变, 预测其浓度变化趋势, 对政府出台改善环境措施具有重要意义。提出利用粒子群算法 (PSO) 的反向传播 (BP) 神经网络对大气 NO2 浓度进行预测。以合肥地区 2017 年 1 月 1 日至 2019 年 12 月 31 日的大气污染数据和气象数据为基础, 结合逐步回归方法筛选出与 NO2 浓度相关性较大的影响因子作为输入样本。构建 PSO-BP 神经网络预测模型, 利用 PSO 找出 BP 神经网络最优的初始权值和阈值。对比 BP 神经网络、遗传算法改进的 BP 神经网络和 PSO 改进的 BP 神经网络三种模型的预测结果, 发现 PSO-BP 模型能够较为准确地预测出 NO2 浓度的动态变化规律, 并且预测精度高、模式简单, 有望广泛应用于大气污染物浓度预测等方面的研究。

NO2 is one of the main atmospheric pollutants, which plays an important role in atmospheric photochemical process. It is of great significance to study the temporal and spatial variation law of NO2 concentration and predict the variation trend of NO2 concentration. The BP neural network based on particle swarm optimization (PSO) was proposed to predict atmospheric NO2 concentration. Based on the air pollution data and meteorologicaldata of Hefei area, China, from January 1, 2017 to December 31, 2019 and combined with the stepwise regression method, the influencing factors with high correlation with NO2 concentration were selected as the input samples. The PSO-BP neural networkprediction model was constructed, and then the optimal solution of the initial weight and threshold value of the BP neural network were found by using PSO algorithm. By comparing the prediction results of the traditional BP neural network, BP neural network improved by genetic algorithm and BP neural network improved by PSO, it was found that PSO-BP model can accurately predict the dynamic change of NO2 concentration with high prediction accuracy and simple model, which is expected to be widely usedin air pollutant concentration prediction in the future.