Forecasting PM2.5 and NO2 Concentrations in Patras Using Low-Cost Sensors and Machine Learning Abstract: We present a machine learning methodology for forecasting next day’s PM2.5 and NO2 concentrations in Patras, Greece,…
An IoT integrated Air Quality Monitoring device based on microcomputer technology and leading industry low-cost sensor solutions Abstract Indoor and outdoor air quality monitoring is essential for the prevention of undesired exposure to air pollutants,…
Improving Real-time High-Resolution Estimates of PM2.5 Concentration Fields in Urban Areas by the SmartAQ+ System with Data Fusion and Machine Learning Abstract Monitoring PM2.5 (mass of particles with diameter less than 2.5 μm) concentrations is challenging due to the limited number…
Monitoring of Indoor Air Quality in a Classroom Combining a Low-Cost Sensor System and Machine Learning Abstract Monitoring indoor air quality in schools is essential, particularly as children are highly vulnerable to air pollution.…
Evaluation of air quality in a primary school classroom during wintertime Abstract The levels of gas and particulate pollutants were measured inside and outside of a primary school classroom…
Prediction of the concentration and source contributions of PM2.5 and gas-phase pollutants in an urban area with the SmartAQ forecasting system
Calibration and inter-unit consistency assessment of an electrochemical sensor system using machine learning
Field calibration of a low-cost Air Quality monitoring device in a southeastern European site using Machine Learning models
Improving Real-time High-Resolution Estimations of PM2.5 Concentration Fields for the present time in Urban Areas by the SmartAQ+ System with Data Fusion and Machine Learning
Development and calibration of an Air Quality Monitoring (AQM) appliance based on low-cost electrochemical and laser sensors
