With air quality issues becoming ever greater global concerns, many countries are facing numerous air pollution problems. Among all the particulate matter of air pollution, PM2.5 (whose aerodynamic diameter is 2.5 μm or less) is of particular concern. Long-term exposure to high concentrations of PM2.5 can negatively affect human health. The negative effects of PM2.5 make forecast its concentration an urgent need. In this paper, we propose a novel Factory-aware Attentional LSTM Model (FAA-LSTM) for PM2.5 air pollution predictions. The proposed model collects air pollution data from both the monitor stations and micro air quality sensors, in which a spatial transformation is designed to obtain the local area data of PM2.5 grids. Next, a novel factory-aware attention mechanism over a long short-term memory (LSTM) neural network is proposed to extract the hidden features of industrial factors and derive their factory attention weights on the influences of PM2.5 concentrations. The influence of neighboring factory data over local PM2.5 grids can be weighted to discover the importance of PM2.5 concentrations of neighboring areas. Moreover, the model combines these heterogeneous data and the global station data to forecast PM2.5 concentrations. The experiment’s evaluation is conducted using both air pollution data and industrial data. The results show that the factory-aware attention mechanism helps to improve the prediction performance by exploring the effect of the factory distribution on PM2.5 pollutants in the local areas. While monitoring stations have been established to collect air quality information and forecast air quality, few studies have taken the different monitoring areas and industrial features into account. The proposed novel model considering PM2.5 concentrations from local neighboring areas and global station, and industrial data as features can effectively indicate the impact of PM2.5 pollution with industrial emissions and spatial relationships. Our research work can improve the prediction accuracy of PM2.5 and contribute to increasing the practical value for air quality management.

中文翻译：

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基于工厂感知注意力LSTM神经网络的空气污染预测

随着空气质量问题日益成为全球关注的焦点，许多国家正面临着众多的空气污染问题。在空气污染的所有颗粒物中，PM2.5（其空气动力学直径为2.5μm或更小）尤其受到关注。长期接触高浓度 PM2.5 会对人体健康产生负面影响。PM2.5 的负面影响使得预测其浓度成为当务之急。在本文中，我们提出了一种用于 PM2.5 空气污染预测的新型工厂感知注意力 LSTM 模型（FAA-LSTM）。所提出的模型从监测站和微型空气质量传感器收集空气污染数据，其中设计了空间变换以获得 PM2.5 网格的局部区域数据。下一个，提出了一种基于长短期记忆 (LSTM) 神经网络的新型工厂感知注意力机制，以提取工业因素的隐藏特征，并推导出其工厂注意力对 PM2.5 浓度影响的权重。可以加权邻近工厂数据对当地 PM2.5 网格的影响，以发现邻近地​​区 PM2.5 浓度的重要性。此外，该模型结合这些异构数据和全球站数据来预测 PM2.5 浓度。该实验的评估是使用空气污染数据和工业数据进行的。结果表明，工厂感知注意力机制通过探索工厂分布对局部地区PM2.5污染物的影响，有助于提高预测性能。虽然已经建立了监测站来收集空气质量信息和预测空气质量，但很少有研究考虑到不同的监测区域和行业特征。所提出的以局部邻近地区和全球站点的 PM2.5 浓度和工业数据为特征的新模型可以有效地表明 PM2.5 污染对工业排放和空间关系的影响。我们的研究工作可以提高PM2.5的预测精度，有助于提高空气质量管理的实用价值。和工业数据作为特征可以有效地表明 PM2.5 污染与工业排放和空间关系的影响。我们的研究工作可以提高PM2.5的预测精度，有助于提高空气质量管理的实用价值。和工业数据作为特征可以有效地表明 PM2.5 污染与工业排放和空间关系的影响。我们的研究工作可以提高PM2.5的预测精度，有助于提高空气质量管理的实用价值。