An image-based sensor system for low-cost airborne particle detection in citizen science air quality monitoring

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dc.contributor.authorShah, Syed Mohsin Ali
dc.contributor.authorCasado Mansilla, Diego
dc.contributor.authorLópez de Ipiña González de Artaza, Diego
dc.date.accessioned2025-02-28T10:58:03Z
dc.date.available2025-02-28T10:58:03Z
dc.date.issued2024-10
dc.date.updated2025-02-28T10:58:03Z
dc.description.abstractAir pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people’s exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of (Formula presented.) particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for (Formula presented.) particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.en
dc.description.sponsorshipThis research is sponsored by the Internet of People project, funded by Spain’s Ministry of Science and Innovation (Grant No. PID 2020-119682RB-I00), the AmIAire project, funded by FECYT (FCT-23-19719) and the DEUSTOTECH (IT1582-22) grant from the A grade research team of the Basque University Systemen
dc.identifier.citationAli Shah, S. M., Casado-Mansilla, D., & López-de-Ipiña, D. (2024). An Image-Based Sensor System for Low-Cost Airborne Particle Detection in Citizen Science Air Quality Monitoring. Sensors, 24(19). https://doi.org/10.3390/S24196425
dc.identifier.doi10.3390/S24196425
dc.identifier.eissn1424-8220
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2406
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights© 2024 by the authors
dc.subject.otherAir pollution
dc.subject.otherCitizen science
dc.subject.otherData quantification
dc.subject.otherEnvironmental monitoring
dc.subject.otherImage processing
dc.subject.otherSynthetic data
dc.titleAn image-based sensor system for low-cost airborne particle detection in citizen science air quality monitoringen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue19
oaire.citation.titleSensors
oaire.citation.volume24
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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