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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.

This study presents a hybrid digital twin system designed for real-time indoor air quality (IAQ) monitoring and filtration optimization within a residential environment. Using a network of low-cost sensors, physics-based simulations, and machine learning models, the system dynamically replicates the indoor environment to enable continuous assessment and optimization of key pollutants, including particulate matter, volatile organic compounds, and carbon dioxide. The system architecture integrates mass balance and decay models, computational fluid dynamics simulations, regression models, and neural network algorithms, all evaluated under both filtering and non-filtering conditions. A graphical user interface allows users to interact with the system, test air purifier placements, and visualize air quality dynamics in real time. The results demonstrate that, within this system, simpler models, such as linear regression, outperform more complex architectures under data-limited conditions, achieving test-set coefficients of determination ranging from 0.97 to 0.99 across multiple IAQ parameters. At the same time, the hybrid modelling approach enhances interpretability and robustness. Overall, this digital twin system contributes to smart building management by offering a scalable, interpretable, and cost-effective solution for proactive IAQ control and personalized decision-making.

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.

Understanding how traffic contributes to air pollution, especially in urban areas, is essential for designing effective strategies to reduce air pollution emissions. This study examines the hourly association between traffic volume and concentrations of two air pollution indicators (NO2 and PM10) using high-resolution data from two monitoring stations in Helsinki. A Prophet time series model was applied to forecast hourly traffic trends for 2024, which were then compared to yearly average NO2 and PM10 concentrations. Polynomial regression and cross-correlation analyses were used to capture temporal patterns and assess the strength and timing of the relationship. The results show a strong alignment between traffic and NO2 and PM10 concentrations, particularly at the traffic-heavy measuring site (Mäkelänkatu supersite), with minimal time lag observed. Root mean square error (RMSE) and polynomial fit comparisons confirmed the predictive value of traffic trends in estimating the behavior of NO2 and PM10 concentrations. These findings support the use of traffic-based proxy models as practical tools for real-time air pollution assessment and for informing targeted urban air quality interventions.

Understanding indoor black carbon (BC) dynamics is important for assessing human exposure and informing air quality management in residential settings. This study presents a high-resolution, multi-sensor dataset collected over 24 days in a semi-occupied home in Zagreb, Croatia, designed to characterize the temporal behavior and sources of indoor BC. Indoor BC concentrations were measured at 1 min resolution using a dual-spot aethalometer, with source apportionment into biomass burning and fossil fuel components. Complementary contextual data including motion detection, door and window states, and traffic activity were collected in parallel using smart sensors and annotated experimental logs. Across the monitoring period, daily mean BC concentrations ranged from 174.7 and 1053.1 ng/m3 for biomass burning BC and between 53.2 and 880.3 ng/m3 for fossil fuel component. Statistical analyses revealed significant increases in BC concentrations during direct combustion-related activities, including scented candle burning and gas burner use. Additional BC elevations were associated with mechanical heat sources and nearby vehicle traffic, particularly affecting the fossil fuel BC component. In contrast, non-combustion activities such as brief human presence exhibited minor or inconsistent effects on indoor BC levels. This study elucidates the primary role of combustion-based indoor activities in influencing short-term BC exposure and highlights the importance of synchronized, high-resolution datasets for indoor air quality research.

Indoor air quality (IAQ) significantly influences human health, as individuals spend up to 90% of their time indoors, where air pollutants can accumulate and interact dynamically. Despite advancements in monitoring technology, challenges remain in capturing the temporal and spatial variability of pollutants and understanding the interaction between indoor and outdoor environments. This study addresses these gaps by introducing a comprehensive dataset from a controlled experimental room in Croatia, leveraging a multi-instrumental approach to monitor IAQ across various real-life scenarios. The dataset integrates measurements from low-cost sensors, reference-grade devices, and auxiliary systems to track pollutants such as particulate matter (PM), black carbon (BC), volatile organic compounds (VOC), and indoor events deemed relevant for the assessment of pollutant levels. Key experiments simulated household activities, including cooking, cleaning, human presence, and ventilation, capturing their impacts on IAQ with high temporal resolution. The resulting dataset comprises over 19 subsets. This work contributes to the Horizon EDIAQI project, supporting the development of evidence-driven strategies to improve IAQ.

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 CO[sub.2] 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 CO[sub.2] concentrations between schools. In two-shift schools, the longer occupied period was associated with CO[sub.2] 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 CO[sub.2] 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.

Microglial cells are resident macrophages in the brain that have been implicated in the pathophysiology of schizophrenia. There is a lack of studies covering the effects of antipsychotics on microglial cells. The current literature points to a possible anti-inflammatory action without clear mechanisms of action. The aim of this study is to characterize the effects of haloperidol, risperidone and aripiprazole on BV-2 microglial cells in in vitro conditions. We have used immunofluorescence and flow cytometry to analyze the classical pro and anti-inflammatory markers, while a real-time metabolic assay (Seahorse) was used to assess metabolic function. We analyzed the expression of p70S6K to evaluate the mTOR pathway activity with Western blot. In this study, we demonstrate the varying effects of haloperidol, risperidone and aripiprazole administration in BV-2 microglial cells. All three tested antipsychotics were successful in reducing the pro-inflammatory action of microglial cells, although only aripiprazole increased the expression of anti-inflammatory markers. Most significant differences in the possible mechanisms of action were seen in the real-time metabolic assays and in the mTORC1 signaling pathway activity, with aripiprazole being the only antipsychotic to reduce the mTORC1 activity. Our results shed some new light on the effects of haloperidol, risperidone and aripiprazole action in microglial cells, and reveal a novel possible mechanism of action for aripiprazole.

Objective: The aim of this study was to explore the effectiveness and tolerability of long-acting paliperidone palmitate antipsychotic in adolescent first-episode schizophrenia patients while comparing the results with the oral antipsychotic risperidone. Methods: This study is a retrospective, noninterventional study to assess the effectiveness and tolerability of long-acting injectable antipsychotic paliperidone palmitate in first-episode adolescent patients during the first 12 months of treatment compared with the oral antipsychotic risperidone. The data include general sociodemographic characteristics, number of hospitalizations, side effects, and the following clinical scales: Positive and Negative Syndrome Scale (PANSS), Personal and Social Performance Scale (PSP), Clinical Global Impression Improvement and Severity (CGI-I and CGI-S), and Treatment Satisfaction Questionnaire for Medication (TSQM). Results: During the 12-month study period significant improvement was registered in patients receiving both paliperidone palmitate and risperidone in the following scales: PANSS, PSP, CGI-I, and CGI-S. Patients receiving paliperidone palmitate had significantly greater improvement in PANSS, CGI-S, and PSP compared with the risperidone group. Patients receiving risperidone had significantly higher number of hospitalizations than the patients receiving paliperidone palmitate. The TSQM revealed that the patients who were receiving paliperidone palmitate achieved significantly higher scores on the convenience scale, global satisfaction, and on the overall result, whereas no difference was observed on the effectiveness scale. There were several side effects reported for paliperidone (5.5% hyperprolactinemia, 5.5% weight gain) and risperidone (5.5% hyperprolactinemia, 16.7% weight gain). Conclusions: In conclusion, paliperidone palmitate seems to be safe and effective in adolescent patients. Furthermore, it compared favorably with risperidone in the clinical response, side effects, and hospitalizations.