To enhance the accuracy and robustness of PM$_{2.5}$ concentration forecasting, this paper introduces FALNet, a Frequency-Aware LSTM Network that integrates frequency-domain decomposition, temporal modeling, and attention-based refinement. The model first applies STL and FFT to extract trend, seasonal, and denoised residual components, effectively filtering out high-frequency noise. The filtered residuals are then fed into a stacked LSTM to capture long-term dependencies, followed by a multi-head attention mechanism that dynamically focuses on key time steps. Experiments conducted on real-world urban air quality datasets demonstrate that FALNet consistently outperforms conventional models across standard metrics such as MAE, RMSE, and $R^2$. The model shows strong adaptability in capturing sharp fluctuations during pollution peaks and non-stationary conditions. These results validate the effectiveness and generalizability of FALNet for real-time air pollution prediction, environmental risk assessment, and decision-making support.

翻译：为提高PM$_{2.5}$浓度预测的准确性与鲁棒性，本文提出FALNet（频率感知LSTM网络），该模型融合了频域分解、时序建模与注意力优化机制。首先通过STL与FFT分解提取趋势项、季节项及去噪残差分量，有效滤除高频噪声。随后将滤波后的残差输入堆叠LSTM以捕获长期依赖关系，并采用多头注意力机制动态聚焦关键时间步。在真实城市空气质量数据集上的实验表明，FALNet在MAE、RMSE及$R^2$等标准指标上均持续优于传统模型。该模型在捕捉污染峰值期间的剧烈波动及非平稳条件时表现出强适应性。实验结果验证了FALNet在实时空气污染预测、环境风险评估与决策支持方面的有效性与泛化能力。