移动式空气质量传感器的研究与应用进展

移动式空气质量传感器作为一种成本低、机动性和灵活性高、使用场景丰富的测量工具，近年来在大气环境监测和污染溯源领域得到广泛应用. 本研究通过文献调研方式对国内外移动式空气质量传感器的研究和应用进行综述. 移动式空气质量传感器的研究主要有数据质量评估及数据校准，本研究总结了移动式空气质量传感器数据质量的评价指标和校准方法. 根据移动式空气质量传感器搭载平台的移动路径可将其应用分为固定路线移动监测、随机路线移动监测以及可操控移动监测三大类，总结其在环境空气质量监测、污染源监控、个人健康暴露研究、排放源清单动态识别等领域的优势和不足，并展望其发展方向和应用前景. 移动式空气质量传感器的应用主要存在以下几方面问题:①与固定式传感器相比，移动式空气质量传感器除受环境影响较大外，还受搭载平台的运行速度及振荡幅度影响，且校准困难；②移动式空气质量传感器的不足限制了其监测能力，因此应用场景有一定局限性；③移动式空气质量传感器能产生大量高时空分辨率的数据，但目前利用率较低且与其他数据的融合度较差. 因此，应通过硬件或算法升级，以提高移动式空气质量传感器的稳定性及可靠性，适应更复杂的走航环境，保证数据质量，并与多种数据进行融合，丰富应用场景. 

Advances in Research and Application of Mobile Air Quality Sensors

Mobile air quality sensors have been widely used in the field of atmospheric pollution monitoring and pollution source tracking in recent years due to their low cost, high mobility and flexibility, and versatile application scenarios. This study provided an overview of the research and applications of mobile air quality sensors through an extensive literature research. The research on mobile air quality sensors focused on data quality assessment and data calibration, and summarized the data quality evaluation indexes and calibration methods of mobile air quality sensors. By analyzing the research and application status in the fields of ambient air quality monitoring, pollution source identification, personal health exposure research, and the development of emission source inventory, the advantages and existing problems of mobile air quality sensor technology were summarized. Then we looked forward to its development direction and application prospects. The main shortcomings of the mobile sensor technology are summarized as follows: (1) Mobile sensor is not only affected by the surrounding environment factors, but also by the driving speed and the vibration amplitude, which makes calibration difficult. (2) The shortcomings of mobile sensors limit their monitoring capabilities, the application scenarios have certain limitations. (3) Mobile sensors can generate large amounts of data with high spatial and temporal resolution, which brings great challenges to data mining and integration analysis with other data. Therefore, hardware or algorithm upgrades should be carried out to improve the stability and reliability of mobile sensors, adapt them to more complex navigation environments, ensure data quality, improve integration with other types of data, and enrich the application scenarios.