An image-based sensor system for low-cost airborne particle detection in citizen science air quality monitoring
Cargando...
Archivos
Fecha
2024-10
Título de la revista
ISSN de la revista
Título del volumen
Editor
Multidisciplinary Digital Publishing Institute (MDPI)
Resumen
Air 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.
Palabras clave
Air pollution
Citizen science
Data quantification
Environmental monitoring
Image processing
Synthetic data
Citizen science
Data quantification
Environmental monitoring
Image processing
Synthetic data
Descripción
Materias
Cita
Ali 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