Machine Learning-based Calibration Approach for Low-cost Air Pollution Sensors MQ-7 and MQ-131

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Dades bibliogràfiques
Publicat a:	Nature Environment and Pollution Technology vol. 23, no. 1 (Mar 2024), p. 401
Autor principal:	Rathnayake, L R S D
Altres autors:	Sakura, G B, Weerasekara, N A, Sandaruwan, P D
Publicat:	Technoscience Publications
Matèries:	Sri Lanka Outliers (statistics) Humidity Regression analysis Sensors Carbon monoxide Artificial neural networks Calibration Air quality Pollution monitoring Air pollution Machine learning Outdoor air quality Air monitoring Visualization Learning algorithms Neural networks Variables Algorithms Economic Environmental
Accés en línia:	Citation/Abstract Full Text Full Text - PDF
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Descripció
Resum:	Air quality is a vital concern globally, and Sri Lanka, according to WHO statistics, faces challenges in achieving optimal air quality levels. To address this, we introduced an innovative loT-based Air Pollution Monitoring (ARM) Box. This solution incorporates readily available Commercial Off-The-Shelf (COTS) sensors, specifically MQ-7 and MQ-131, for measuring concentrations of Carbon Monoxide (CO) and Ozone (03) ,Arduino and "ThingSpeak" platform. Yet, those COTS sensors are not factory-calibrated. Therefore, we implemented machine learning algorithms, including linear regression and deep neural network models, to enhance the accuracy of CO and 03 concentration measurements from these non-calibrated sensors. Our findings indicate promising correlations when dealing with MQ-7 and MQ-131 measurements after removing outliers.
ISSN:	0972-6268 2395-3454 0971-4871
DOI:	10.46488/NEPT.2024.v23i01.034
Font:	Engineering Database