Real-Time Air Quality Monitoring Model using Fuzzy Inference System

Air pollution, which is both environmental and social, is a serious issue that affects people's health as well as ecosystems and the environment. Air pollution currently poses a number of health problems to the ecosystem. The most important factor that has a direct impact on disease occurrence and decreases people's quality of life is city and metropolitan air quality. It is critical to establish real-time air quality monitoring in order to make timely decisions based on measurements and evaluations of environmental factors. Monitoring systems are influential in multiple smart city initiatives for keeping an eye on air quality and reducing pollutant concentrations in metropolitan areas. The Internet of Things (IoT) is becoming increasingly important in a variety of sectors, including air quality monitoring. In this research work, a real-time air quality monitoring model employing fuzzy inference is proposed for monitoring air pollution using multiple parameters such as Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Carbon Monoxide (CO), Ozone (O3) and Suspended Particulates (PM10). This proposed research presents a novel technique for improving air quality monitoring. This proposed fuzzy inference system also provides better results in terms of monitoring air quality in a more efficient and effective way.

@article{Saleem2024,
  title     = {Real-Time Air Quality Monitoring Model using Fuzzy Inference System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Saleem and Nitinkumar Shingari and Muhammad Sajid Farooq and Beenu Mago and Muhammad Adnan Khan},
  doi       = {10.14569/IJACSA.2024.0150684},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150684}
}