TY - JOUR
T1 - Prediction of optimum sampling rates of air quality monitoring stations using hierarchical fuzzy logic control system
AU - Abdul-Wahab, Sabah A.
AU - Charabi, Yassine
AU - Osman, Selma
AU - Yetilmezsoy, Kaan
AU - Osman, Isra Ibrahim
N1 - Publisher Copyright:
© 2019 Turkish National Committee for Air Pollution Research and Control
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
KW - Air quality
KW - Fuzzy logic
KW - Meteorological factors
KW - Monitoring stations
KW - Multi-pollutant system
KW - Sampling rate
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U2 - 10.1016/j.apr.2019.08.006
DO - 10.1016/j.apr.2019.08.006
M3 - Article
AN - SCOPUS:85071676951
SN - 1309-1042
VL - 10
SP - 1931
EP - 1943
JO - Atmospheric Pollution Research
JF - Atmospheric Pollution Research
IS - 6
ER -