Calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system
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 i...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Multidisciplinary Digital Publishing Institute (MDPI)
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/73321/ http://psasir.upm.edu.my/id/eprint/73321/1/SENSOR.pdf |
| _version_ | 1848857260948717568 |
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| author | Ezani, Nur Eliani alhasa, kemal maulana Mohd Nadzir, Mohd Shahrul Latif, Mohd Talib Olalekan, Popoola Yusup, Yusri Faruque, Mohammad Rashed Iqbal Ahamad, Fatimah Abd. Hamid, Haris Hafizal Aiyub, Kadaruddin Md Ali, Sawal Hamid Khan, Md Firoz Abu Samah, Azizan Yusuff, Imran Othman, Murnira Tengku Hassim, Tengku Mohd Farid |
| author_facet | Ezani, Nur Eliani alhasa, kemal maulana Mohd Nadzir, Mohd Shahrul Latif, Mohd Talib Olalekan, Popoola Yusup, Yusri Faruque, Mohammad Rashed Iqbal Ahamad, Fatimah Abd. Hamid, Haris Hafizal Aiyub, Kadaruddin Md Ali, Sawal Hamid Khan, Md Firoz Abu Samah, Azizan Yusuff, Imran Othman, Murnira Tengku Hassim, Tengku Mohd Farid |
| author_sort | Ezani, Nur Eliani |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T11:54:44Z |
| format | Article |
| id | upm-73321 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:54:44Z |
| publishDate | 2018 |
| publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-733212020-11-30T06:49:23Z http://psasir.upm.edu.my/id/eprint/73321/ Calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system Ezani, Nur Eliani alhasa, kemal maulana Mohd Nadzir, Mohd Shahrul Latif, Mohd Talib Olalekan, Popoola Yusup, Yusri Faruque, Mohammad Rashed Iqbal Ahamad, Fatimah Abd. Hamid, Haris Hafizal Aiyub, Kadaruddin Md Ali, Sawal Hamid Khan, Md Firoz Abu Samah, Azizan Yusuff, Imran Othman, Murnira Tengku Hassim, Tengku Mohd Farid 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. Multidisciplinary Digital Publishing Institute (MDPI) 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/73321/1/SENSOR.pdf Ezani, Nur Eliani and alhasa, kemal maulana and Mohd Nadzir, Mohd Shahrul and Latif, Mohd Talib and Olalekan, Popoola and Yusup, Yusri and Faruque, Mohammad Rashed Iqbal and Ahamad, Fatimah and Abd. Hamid, Haris Hafizal and Aiyub, Kadaruddin and Md Ali, Sawal Hamid and Khan, Md Firoz and Abu Samah, Azizan and Yusuff, Imran and Othman, Murnira and Tengku Hassim, Tengku Mohd Farid (2018) Calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system. Sensors, 18 (12). art. no. 4380. pp. 1-21. ISSN 1424-3210 https://www.mdpi.com/1424-8220/18/12/4380/htm 10.3390/s18124380 |
| spellingShingle | Ezani, Nur Eliani alhasa, kemal maulana Mohd Nadzir, Mohd Shahrul Latif, Mohd Talib Olalekan, Popoola Yusup, Yusri Faruque, Mohammad Rashed Iqbal Ahamad, Fatimah Abd. Hamid, Haris Hafizal Aiyub, Kadaruddin Md Ali, Sawal Hamid Khan, Md Firoz Abu Samah, Azizan Yusuff, Imran Othman, Murnira Tengku Hassim, Tengku Mohd Farid Calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system |
| title | Calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system |
| title_full | Calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system |
| title_fullStr | Calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system |
| title_full_unstemmed | Calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system |
| title_short | Calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system |
| title_sort | calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system |
| url | http://psasir.upm.edu.my/id/eprint/73321/ http://psasir.upm.edu.my/id/eprint/73321/ http://psasir.upm.edu.my/id/eprint/73321/ http://psasir.upm.edu.my/id/eprint/73321/1/SENSOR.pdf |