用卷积LSTM预测南亚霾污染，五天预报误差小。
Forecasting Smog Events Using ConvLSTM: A Spatio-Temporal Approach for Aerosol Index Prediction in South Asia
- 用ConvLSTM捕捉污染的空间时间特征，提升预测能力。
- 五日预报误差均方根约0.0018，结构相似性达0.74。
- 适合关注空气质量预警与区域环境治理的研究者。
南亚霾是每年11月至2月在恒河平原地区反复出现的高污染事件，表现为污染物浓度升高、能见度下降，并带来显著社会经济影响。过去十年中，秸秆焚烧、机动车排放和气候模式变化使污染加剧。然而，区域尺度的颗粒物浓度实时预报系统仍不完善。气溶胶指数（Aerosol Index）与霾形成密切相关，是计算空气质量指数（AQI）的关键参数。本研究基于2019–2023年哨兵-5P卫星的空气成分数据，采用卷积长短期记忆网络（ConvLSTM）模型，以340–380 nm紫外波段气溶胶指数为输入，实现对气溶胶事件的时空预测。结果表明，模型可实现五天间隔预报，均方误差约为0.0018，损失值约0.3995，结构相似性指数约0.74。尽管效果良好，未来可通过融合更多数据并优化模型架构进一步提升性能。
原文摘要 · Abstract (English)
The South Asian Smog refers to the recurring annual air pollution events marked by high contaminant levels, reduced visibility, and significant socio-economic impacts, primarily affecting the Indo-Gangetic Plains (IGP) from November to February. Over the past decade, increased air pollution sources such as crop residue burning, motor vehicles, and changing weather patterns have intensified these smog events. However, real-time forecasting systems for increased particulate matter concentrations are still not established at regional scale. The Aerosol Index, closely tied to smog formation and a key component in calculating the Air Quality Index (AQI), reflects particulate matter concentrations. This study forecasts aerosol events using Sentinel-5P air constituent data (2019-2023) and a Convolutional Long-Short Term Memory (ConvLSTM) neural network, which captures spatial and temporal correlations more effectively than previous models. Using the Ultraviolet (UV) Aerosol Index at 340-380 nm as the predictor, results show the Aerosol Index can be forecasted at five-day intervals with a Mean Squared Error of ~0.0018, loss of ~0.3995, and Structural Similarity Index of ~0.74. While effective, the model can be improved by integrating additional data and refining its architecture.
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