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Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System

Alhasa KM 1 , Mohd Nadzir MS 2 , Olalekan P 3 , Latif MT 4 , Yusup Y 5 , Iqbal Faruque MR 6 Show all authors , Ahamad F 7 , Abd Hamid HH 8 , Aiyub K 9 , Md Ali SH 10 , Khan MF 11 , Abu Samah A 12 , Yusuff I 13 , Othman M 14 , Tengku Hassim TMF 15 , Ezani NE 16

Affiliations 

  • 1 Space Science Centre (ANGKASA), Institute of Climate Change, Level 5, Research Complex Building, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia. kemalalhasa@gmail.com
  • 2 School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia. shahrulnadzir@ukm.edu.my
  • 3 Centre of Atmospheric Sciences, Chemistry Department, University of Cambridge, Cambridge CB2 1EW, UK. oamp2@cam.ac.uk
  • 4 School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia. talib@ukm.edu.my
  • 5 Environmental Technology, School of Industrial Technology, Universiti Sains Malaysia, Pulau 11800, Pinang, Malaysia. yusriy@usm.my
  • 6 Space Science Centre (ANGKASA), Institute of Climate Change, Level 5, Research Complex Building, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia. rashed@ukm.edu.my
  • 7 Centre for Tropical System and Climate Change (IKLIM), Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia. fatimah.a@ukm.edu.my
  • 8 School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia. haris@ukm.edu.my
  • 9 School of Social, Development and Environmental Studies, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia. kada@ukm.edu.my
  • 10 Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia. sawal@ukm.edu.my
  • 11 Centre for Tropical System and Climate Change (IKLIM), Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia. mdfiroz.khan@ukm.edu.my
  • 12 National Antarctic Research Centre, IPS Building, University Malaya, Kuala Lumpur 50603, Malaysia. azizans@um.edu.my
  • 13 Nuclear Science Programme, School of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia. imranyusuff@ukm.edu.my
  • 14 School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia. murnira@ukm.edu.my
  • 15 TXMR Sdn Bhd., No.1, Jalan TS 6/10, Taman Industri Subang, Subang Jaya 47500, Selangor, Malaysia. faridtxmr@gmail.com
  • 16 Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor Darul Ehsan, Malaysia. elianiezani@upm.edu.my
Sensors (Basel), 2018 Dec 11;18(12).
PMID: 30544953 DOI: 10.3390/s18124380

Abstract

Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O₃), nitrogen dioxide (NO₂), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O₃ measurements due to the lack of a reference instrument for CO and NO₂. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO₂) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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