Revolutionizing Satellite Real-Time Air Pollution Alerts through New On-Orbit System-on-Chip Technology.

Journal: Environmental science & technology
Published Date: Jul 29, 2025

Abstract

Exposure to abnormally high concentrations of particulate matter and ozone can cause severe harm to human health, highlighting the need for real-time satellite monitoring to enable rapid responses and timely warnings. However, the existing methods for on-orbit diagnostics under resource constraints are limited. This study presents SoC-POM (System-on-Chip for Particulate Matter and Ozone Monitoring), a real-time, on-orbit artificial intelligence algorithm embedded in satellites and designed to detect anomalous concentrations of PM, PM, and O. Based on tests with the Himawari-8 and Himawari-9 satellites, we demonstrate that SoC-POM achieves an average latency of 5.5 min, breaking through the hourly processing barrier while maintaining high accuracy, with correlation coefficients of 0.78, 0.76, and 0.81 for PM, PM, and O, respectively. This novel approach enables real-time monitoring of abnormal particulate matter and ozone levels and demonstrates the potential for the timely analysis of health exposure and its dynamic changes, marking a sustainable advancement in air pollution alerts and public health.

Authors

  • Jiayi Chen
    College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, China. dylee@zju.edu.cn.
  • Hang Lv
    Institute of Resources and Environment, Henan Polytechnic University, Jiaozuo, 454000, China.
  • Qiao Wang
    Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang R & D Centre for Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China; Department of Food Science and Nutrition, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, Zhejiang, China.
  • Guoqiang Wang
    School of Management, Hefei, Anhui, China.
  • Kun Jia
    State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
  • Chuanfeng Zhao
    State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China. Electronic address: czhao@bnu.edu.cn.
  • Wenzhong Shi
    Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Xing Yan
    State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.

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