With a rising attention for the issue of PM2.5 or PM0.3, particulate matters have become not only a potential threat to both the environment and human, but also a harming existence to instruments onboard International Space Station (ISS). Our team is aiming to relate various concentration of particulate matters to magnetic fields, humidity, acceleration, temperature, pressure and CO2 concentration. Our goal is to establish an early warning system (EWS), which is able to forecast the levels of particulate matters and provides ample reaction time for astronauts to protect their instruments in some experiments or increase the accuracy of the measurements; In addition, the constructed model can be further developed into a prototype of a remote-sensing smoke alarm for applications related to fires. In this article, we will implement the Bi-GRU (Bidirectional Gated Recurrent Unit) algorithms that collect data for past 90 minutes and predict the levels of particulates which over 2.5 micrometer per 0.1 liter for the next 1 minute, which is classified as an early warning

翻译：随着PM2.5和PM0.3等颗粒物问题日益受到关注，颗粒物不仅对环境和人类构成潜在威胁，也对国际空间站上的仪器设备造成危害。本团队致力于探究不同浓度颗粒物与磁场、湿度、加速度、温度、气压及二氧化碳浓度之间的关联性，旨在建立一套早期预警系统，该系统能够预测颗粒物浓度水平，为宇航员保护实验仪器或提高测量精度提供充足的反应时间；此外，所构建模型可进一步发展为面向火灾应用的遥感烟雾报警原型。本文采用双向门控循环单元（Bi-GRU）算法，收集过去90分钟的数据，预测未来1分钟内每0.1升空气中直径超过2.5微米的颗粒物浓度水平，该预测被视为一种早期预警机制。