Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control

Chao Tung Yang, Ho Wen Chen, En Jui Chang, Endah Kristiani, Kieu Lan Phuong Nguyen, Jo Shu Chang

研究成果: Article同行評審


Air pollution is at the center of pollution-control discussion due to the significant adverse health effects on individuals and the environment. Research has shown the association between unsafe environments and different sizes of particulate matter (PM), highlighting the importance of pollutant monitoring to mitigate its detrimental effect. By monitoring air quality with low-cost monitoring devices that collect massive observations, such as Air Box, a comprehensive collection of ground-level PM concentration is plausible due to the simplicity and low-cost, propelling applications in agriculture, aquaculture, and air quality, water resources, and disaster prevention. This paper aims to view IoT-based systems with low-cost microsensors at the sensor, network, and application levels, along with machine learning algorithms that improve sensor networks’ precision, providing better resolution. From the analysis at the three levels, we analyze current PM monitoring methods, including the use of sensors when collecting PM concentrations, demonstrate the use of IoT-based systems in PM monitoring and its challenges, and finally present the integration of AI and IoT (AIoT) in PM monitoring, indoor air quality control, and future directions. In addition, the inclusion of Taiwan as a site analysis was illustrated to show an example of AIoT in PM-control policy-making potential directions.

期刊Journal of Hazardous Materials
出版狀態Published - 2021 十月 5

All Science Journal Classification (ASJC) codes

  • 環境工程
  • 環境化學
  • 廢物管理和處置
  • 污染
  • 健康、毒理學和誘變


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