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

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.

The Impact of Movement Control Order (MCO) during Pandemic COVID-19 on Local Air Quality in an Urban Area of Klang Valley, Malaysia

“…Although the 65 reference monitoring stations provide a good overview of air quality in the country, they are generally located away from emission sources and are unable to provide observations at high spatial resolutions. Therefore, low-cost air quality sensors for air quality measurements are deployed by local authorities and researchers globally to fill in the research gap (Alavi-Shoshtari et al, 2013;Austin et al, 2015;Alhasa et al, 2018). One potential solution for air pollution monitoring in urban areas is the use of 'Low-cost Air Quality Sensors' or LAQS.…”

Particulate matter (PM2.5) as a potential SARS-CoV-2 carrier

“…Indoor PM 2.5 : We measuredPM 2.5 using an in-house air qualitysensor 12,26 , AiRBOXSense (see SI forspeci cations). This was in tandem with sampling ofPM 2.5 on lter papers using a Low Volume Sampler (LVS)(see SI for details).…”

Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

“…The ANFIS model shares both numerical and linguistic knowledge. It has been used in numerous applications and presentations of various fields (like mechanics, physics, economics, biology, industry and others) (e.g., [95][96][97][98][99][100][101][102][103][104][105], to name just a few recent). It has also been studied by other authors or compared with other neural networks (e.g., [103,106,107]).…”