Asset Details
Do you wish to reserve the book?
Assessment of Sensor Data from an Air Quality Monitoring Network - The Need for Machine Learning-Based Recalibration and Its Relevance in Health Impact Analysis of Local Pollution Events
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Assessment of Sensor Data from an Air Quality Monitoring Network - The Need for Machine Learning-Based Recalibration and Its Relevance in Health Impact Analysis of Local Pollution Events
Do you wish to request the book?
Assessment of Sensor Data from an Air Quality Monitoring Network - The Need for Machine Learning-Based Recalibration and Its Relevance in Health Impact Analysis of Local Pollution Events
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Assessment of Sensor Data from an Air Quality Monitoring Network - The Need for Machine Learning-Based Recalibration and Its Relevance in Health Impact Analysis of Local Pollution Events
2025
Overview
Accurate, high-resolution air quality data are crucial for understanding environmental health risks; however, the cost and complexity of maintaining dense, reference-grade monitoring networks remain a significant barrier. This study presents the first city-wide evaluation of next-generation air quality sensors in Zagreb, Croatia, involving 35 sensor locations, one local reference-grade station, and three national reference stations that measure PM[sub.10] and NO[sub.2]. Sensor performance was evaluated against reference data under various meteorological and temporal conditions. To better understand sensor drift and measurement bias, we developed machine learning (ML) calibration models (XGBoost) using spatiotemporal features, ERA5 meteorological variables, and traffic proxy indicators. The models significantly improved accuracy, reducing the root mean squared error (RMSE) by up to 82%, with the greatest improvements observed during pollution peaks. A rolling Root Mean Square Error (RMSE) approach was introduced to track model degradation over time, revealing that recalibration was typically needed within 1–6 months. Our findings demonstrate that, with proper calibration and maintenance, sensor networks can serve as reliable and scalable tools for urban air quality monitoring, capable of supporting both public health assessments and informed decision-making.
