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Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the acceleration of urbanization, urban rail transit systems have become one of the most important modes of transportation in modern cities. Urban rail transit systems operate in enclosed or semi-enclosed environments, characterized by poor air circulation and high passenger flow. During the period of high incidence of respiratory infectious diseases in autumn and winter, air quality and pathogenic microorganism monitoring in public places have attracted much attention. This article provided a detailed introduction to air pollutants and their sources related to urban rail transit, including chemical pollutants, such as particulate matter and gaseous pollutants, and microbial aerosols. Based on identified air pollutants related to urban rail transit, this paper reviewed the research progress on air quality monitoring technologies for chemical pollution factors and microbial aerosols. Finally, a summary of the health effects of chemical pollutants and microbial aerosols was provided, along with suggestions for monitoring and improving air quality tailored to the characteristics of urban rail transit systems. Through a review of previous studies, carrying out research on air quality monitoring technologies and associated health effects related to urban rail transit is of great significance and values for air quality management, urban development, and public health.

With the rapid development of urbanization and industrialization, the problem of air pollution has become increas-ingly prominent, and air quality prediction is particularly important. Some representative studies currently monitor and forecast air quality in real time. For example, ZHOU Guangqiang et al. Used numerical prediction to analyze air quality in eastern China. However, experimental results show that this method is difficult to predict and is very important. SANKAR et al. Used multiple linear regression to predict air quality, but the experimental results showed that the linear model had low prediction accuracy and slow efficiency;PREZ et al. Used statistical methods to predict air quality, and the experimental results proved the prediction accuracy of the statistical method relatively low; WANG et al. Used an improved BP neural network to establish a prediction model for the air quality index, and their experiments verified that the BP neural network has a slow convergence rate and is prone to fall into the local optimal solution problem; YANG et al. Air quality concentration effect, a PM2.5 concentration prediction model based on random forests was established, and the empirical process proved that the meshing program weakened the quality and efficiency of subsequent air quality analysis; these methods are difficult to model from a time perspective, and the prediction accuracy is low is a more important issue. Because low prediction accuracy may lead to large errors in air quality prediction results. 河北科技大学学报 2020年 第1期 张冬雯，等:基于长短期记忆神经网络模型的空气质量预测 In this paper, a neural network model based on long -term memory (LSTM) is proposed to solve the problem of low prediction accuracy in air quality research.MAPE, RMSE, R, IA and MAE were used to test the predictive performance of LSTM neural network and the comparison model.Since Delhi and Houston are cities with high levels of air pollution, the experimental data sets used in this paper were from the air quality data of Punjabi Bagh monitoring station in Delhi from 2014 to 2016 and the air quality data of Harris County monitoring station in Houston from 2010 to 2016.By comparing LSTM neural network with multiple linear regression and regression model (SVR), the experimental results show that LSTM neural network is suitable for time series prediction with multiple variables or multiple inputs LSTM neural network has the advantages of high prediction accuracy, high speed and strong robustness.

Aiming to research the process of air flow remote control technology and best air distribution during disaster relief, by using mine ventilation, fluid dynamics and combustion theory, this paper analyzed the influence on air quantity regulation of fire spread speed, smoke back flow, and the phenomenon of leapfrog during fire disaster relief process. Combined with actual situation, analyzed the change of ventilation network before and after fire disaster relief, calculated fire area wind resistance and regulation wind resistance of air doors under four kinds of different openings. Based on ventilation network calculation technology, the best air distribution and fan operating conditions were obtained during disaster relief process. Remote air quantity regulation and control method were put forward, air quantity of smoke and non-smoke area monitored and real-time display, and adjustable opening of air doors structure and remote control program were designed. [ABSTRACT FROM AUTHOR]