Prediction of optimum sampling rates of air quality monitoring stations using hierarchical fuzzy logic control system

Sabah A. Abdul-Wahab*, Yassine Charabi, Selma Osman, Kaan Yetilmezsoy, Isra Ibrahim Osman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Air quality monitoring systems are being developed and optimized continuously through different methods, and emerging technologies in computational software and simulations have provided efficient and promising results. The process of air quality monitoring requires taking air samples, and data sampling can consume energy, produce a redundant traffic load on the communication channel, and memory space in monitoring station electronic control units. Researches showed that adaptive sampling rate can be a solution to save energy and memory space by turning on sampling module during sensing different events. Therefore, the purpose of this paper was to predict the optimum sampling rates of air quality monitoring stations without compromising data accuracy. For this aim, a hierarchical fuzzy logic control system (simulated in MATLAB®/Simulink) was proposed based on contextual conditions including meteorological data of the country and measured concentrations of air pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). The analysis of the results showed that the sampling rate had a positive correlation with the concentration of the pollutants and the permissible thresholds. Also, the meteorological factors had a lower influence in determining the sampling rate compared to the concentrations of air pollutants.

Original languageEnglish
Pages (from-to)1931-1943
Number of pages13
JournalAtmospheric Pollution Research
Volume10
Issue number6
DOIs
Publication statusPublished - Nov 2019

Keywords

  • Air quality
  • Fuzzy logic
  • Meteorological factors
  • Monitoring stations
  • Multi-pollutant system
  • Sampling rate

ASJC Scopus subject areas

  • Waste Management and Disposal
  • Pollution
  • Atmospheric Science

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