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 PM₁₀ and NO₂. 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.

This two-week pilot study within the Horizon Europe EDIAQI project evaluated the real-life performance of portable air filtration units in two office environments (a small office and a shared kitchen) under continuous device operation and daily filter replacement. Indoor particle concentrations were monitored continuously using low-cost sensors (LCS) from three providers and supported by gravimetric measurements, while daily activity logs documented occupancy patterns, printing, cooking, and other source events together with purifier ON/OFF status. Particulate matter (PM) mass concentrations showed no systematic improvement during purifier ON periods; instead, temporal variability was dominated by indoor activities and episodic emissions, with occasional short-term peaks around filter replacement suggestive of minor resuspension. Chemical analysis provided a clearer picture: polycyclic aromatic hydrocarbons (PAHs) responded differently across fractions and compositions. Across monitored locations, high-molecular-weight PAHs in the PM₁ fraction decreased during purifier ON periods (approximately 30% lower on average), whereas low-molecular-weight PAHs measured in total suspended particles (TSP) were higher during ON periods, indicating that semi-volatile fractions and activity/ventilation dynamics can outweigh simple filtration effects. Overall, the findings highlight a gap between laboratory-derived filtration performance metrics and outcomes in occupied, mixed-source indoor environments and emphasise the importance of device sizing, placement, airflow mixing, and complementary source control and ventilation strategies when deploying filtration-based IAQ interventions.

This study investigates indoor CO₂ levels in Croatian schools to identify environmental and temporal factors influencing classroom air quality. Using data from hundreds of low-cost sensors installed in 243 schools, we analyze seasonal patterns and differences in CO₂ concentrations between schools. In two-shift schools, the longer occupied period was associated with CO₂ remaining elevated later in the day. Time-series forecasting with the Prophet model accounts for seasonal variations, while statistical analyses quantify variability and identify key factors driving concentration differences. Additionally, Land Use Regression (LUR) models are developed and compared with direct sensor measurements at the school level to assess their association with CO₂ levels across different counties in the country. The results reveal consistent seasonal trends and notable local differences between schools, emphasizing the importance of detailed monitoring in environments with vulnerable populations. This research offers insights into the strengths and limitations of statistical and modeling methods for school-based air quality assessment and provides recommendations for enhancing monitoring strategies in similar large-scale networks.