Heatwaves pose significant threats to urban environments, affecting both ecological systems and public health, primarily through the exacerbation of air pollution. Accurate prediction of air pollutant concentrations during heatwave periods is crucial for authorities to develop timely prevention and control strategies. Thus, we developed the 1D-CNN-BiLSTM-attention model, specifically designed to account for the unique data characteristics associated with heatwave conditions. Our model leverages an attention mechanism to enhance its ability to learn and predict air pollutant behavior during heatwaves. Across six scenario-based experiments, the model demonstrated high predictive accuracy, achieving a MAPE of 2.93 %. The model integrates meteorological indicators such as temperature, humidity, wind speed, cloud cover, and precipitation, extending its predictive capability across a spatial range of 150 km. In experiments testing the model's applicability to three typical city types in the Yangtze River Delta region, the results confirmed its effectiveness in predicting air pollutants. These findings highlight the model's usefulness for studying air pollution during urban heatwave periods on a regional scale, demonstrating its robustness and reliability under varying weather conditions.

中文翻译：

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基于热浪频发期间长三角地区注意力机制模型的空气污染物预测


热浪对城市环境构成重大威胁，主要通过加剧空气污染来影响生态系统和公共卫生。准确预测热浪期间空气污染物浓度对于当局及时制定防控策略至关重要。因此，我们开发了 1D-CNN-BiLSTM-attention 模型，专门用于解释与热浪条件相关的独特数据特征。我们的模型利用注意力机制来增强其学习和预测热浪期间空气污染物行为的能力。在六次基于情景的实验中，该模型表现出很高的预测准确性，实现了 2.93% 的 MAPE。该模型整合了温度、湿度、风速、云量和降水等气象指标，将其预测能力扩展到 150 公里的空间范围内。在测试该模型对长三角地区三种典型城市类型的适用性的实验中，结果证实了其在预测空气污染物方面的有效性。这些发现突出了该模型在区域范围内研究城市热浪期间空气污染的有用性，证明了其在不同天气条件下的稳健性和可靠性。