School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China; Institute of Future Cities, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
Institute of Future Cities, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
Sci Total Environ. 2021 Mar 10;759:143513. doi: 10.1016/j.scitotenv.2020.143513. Epub 2020 Nov 14.
Air pollution exerts serious impacts on human health and sustainable development. The accurate forecasting of air quality can guide the formulation of mitigation strategies and reduce exposure to air pollution. It is beneficial to explicitly consider both spatial and temporal information of multiple factors, e.g., the meteorological data, in the forecasting of air pollutant concentrations. The temporal information of relevant factors collected at a location should be considered for forecasting. In addition, these factors recorded at other locations may also provide useful information. Existing methods utilizing the spatio-temporal information of these relevant factors are usually based on some very complicated frameworks. In this study, we propose a novel and simple spatial attention-based long short-term memory (SA-LSTM) that combines LSTM and a spatial attention mechanism to adaptively utilize the spatio-temporal information of multiple factors for forecasting air pollutant concentrations. Specifically, the SA-LSTM employs gated recurrent connections to extract temporal information of multiple factors at individual locations, and the spatial attention mechanism to spatially fuse the temporal information extracted at these locations. This method is effective and applicable to forecast any air pollutant concentrations when spatio-temporal information of relevant factors has to be utilized. To validate the effectiveness of the proposed SA-LSTM, we apply it to forecast the daily air quality in Hong Kong, a high density city with peculiar cityscapes, by using the air quality and meteorological data. Empirical results demonstrate that the proposed SA-LSTM outperforms the conventional models with respect to one-day forecast accuracy, especially for extreme values. Moreover, the attention weights learned by the SA-LSTM can identify hotspots of the air pollution process for reducing computational complexity of forecasting and provide a better understanding of the underlying mechanism of air pollution.
空气污染对人类健康和可持续发展造成严重影响。空气质量的准确预测可以指导减排策略的制定，减少人们对空气污染的暴露。明确考虑多个因素（如气象数据）的时空信息，有利于对空气污染物浓度进行预测。应考虑在预测时在相关因素的时空信息。此外，其他地点记录的这些因素也可能提供有用的信息。现有的利用这些相关因素的时空信息的方法通常基于一些非常复杂的框架。在这项研究中，我们提出了一种新颖而简单的基于空间注意力的长短期记忆模型（SA-LSTM），该模型结合了 LSTM 和空间注意力机制，用于自适应地利用多个因素的时空信息来预测空气污染物浓度。具体来说，SA-LSTM 使用门控循环连接来提取各个位置的多个因素的时间信息，以及空间注意力机制来对这些位置提取的时间信息进行空间融合。该方法在需要利用相关因素的时空信息时是有效且适用的，可以预测任何空气污染物浓度。为了验证所提出的 SA-LSTM 的有效性，我们应用它来预测香港的日常空气质量，香港是一个高密度城市，拥有独特的城市景观，使用空气质量和气象数据。实证结果表明，所提出的 SA-LSTM 在一天的预测精度方面优于传统模型，特别是对于极端值。此外，SA-LSTM 学习的注意力权重可以识别空气污染过程的热点，以降低预测的计算复杂度，并更好地理解空气污染的潜在机制。
