Search Results (459)

Battery-powered Internet of Things (IoT) sensor nodes for environmental monitoring face strict energy constraints, particularly when employing high-consumption sensors such as particulate matter sensors or gas analyzers. Extending operational lifetime without sacrificing measurement reliability remains a key challenge for large-scale air-quality monitoring deployments. This paper proposes a digital-twin-assisted adaptive sensing algorithm for reducing energy consumption by dynamically optimizing sensor usage for Indoor Air Quality (IAQ) monitoring system. The system consists of distributed sensing nodes and a central station that maintains digital twins to evaluate candidate sensing strategies based on historical data and environmental patterns. Strategies are assessed in terms of energy consumption and measurement fidelity and deployed only when a measurable improvement is achieved. The approach is evaluated across mobile and stationary sensor configurations used for monitoring indoor air quality in university laboratories while educational and research activities are carried out. For stationary nodes, clustering-based scheduling reduces the activation of high-power sensors, while for mobile nodes, variation-based triggering exploits correlations between equivalent and reference CO₂ measurements to limit energy-intensive sensing. Results demonstrate energy savings of up to approximately 70% while maintaining acceptable measurement fidelity. The findings show that reduced sensing can be used for system initialization, while digital twin evaluation enables reliable transition to adaptive sensing under suitable conditions. Full article

Poor indoor air quality (IAQ) in naturally ventilated school buildings remains a widespread problem, particularly during the heating season, when limited ventilation leads to elevated CO₂ concentrations. At the same time, increasing ventilation rates may significantly increase energy demand, creating a conflict between IAQ and energy efficiency. This study aims to evaluate whether CO₂-based demand-controlled mechanical ventilation, particularly with heat recovery (HRV), can improve IAQ while maintaining acceptable energy performance in existing school buildings. A previously validated CONTAM model of a Polish primary school classroom was used to simulate natural ventilation, mechanical exhaust ventilation, and balanced ventilation with heat recovery. In mechanical systems, CO₂-based demand-controlled ventilation (DCV) was applied. The resulting airflow rates were then used in EnergyPlus simulations to assess seasonal heating and primary energy demand under Kraków climatic conditions. Increasing the outdoor air supply rate significantly reduced indoor CO₂ concentration but led to higher heating demand in exhaust ventilation systems. In contrast, HRV reduced heating energy demand by more than 80% compared with exhaust ventilation while maintaining comparable indoor air quality. Although HRV required additional electricity for fan operation, the total primary energy consumption remained low. The results demonstrate that CO₂-based DCV systems with heat recovery provide an effective balance between indoor air quality and energy performance. These findings support the application of HRV as a practical retrofit solution for improving ventilation in existing school buildings. Full article

Nowadays, people spend over 80% of their lives indoors, which makes indoor air quality (IAQ) research important. The paper presents, firstly, a structured overview of publicly available IAQ datasets suitable for machine learning (ML) research, secondly, a comparative analysis of the reviewed datasets, thirdly, an ML-oriented mapping between tasks and algorithms, to outline the algorithmic families that are most appropriate given the dataset structure and the prediction target, and fourthly, an investigation on IAQ–ML using custom-made solutions that include sensors for data acquisition. The methodology included an analysis of 1162 papers from the Web of Science, 1536 from Scopus, and 756 from IEEE Xplore, between 1 January 2020 and 31 December 2025, to capture recent trends in ML-based IAQ research. The findings show that linear regression (132 articles), Logistic regression (91), random forest—RF (77), Long Short-Term Memory—LSTM (77), Principal Component Analysis (63), and Elastic Net are the most popular among researchers. Most studies report accuracy over 90%, with maximum values of 99.37% for LSTM and 99.20% for RF. In the case of regression, the R² values range between 82% and 98%, especially for CO₂ and PM_2.5 prediction. eXtreme Gradient Boosting or hybrid RF-LSTM architectures achieve R² values of up to 99%. The IAQ public and private datasets analyzed for this study provide a strong foundation for transfer learning, but differences require careful preprocessing to ensure consistent comparisons and reliable conclusions. The distribution of articles by sensor type for IAQ parameters shows that linear regression remains the most widely used ML method (26 studies), followed by LSTM (19) and RF (18). The research results confirm that there is no universal algorithm for IAQ, and the quality and structure of the data contribute to the success of ML models. This study aims to be a foundation for the development of future intelligent IAQ monitoring systems. Full article

This study evaluates simple exhaust, relative humidity-controlled and heat recovery ventilation systems in northern Spain (SEV, RHCV, HRV systems) through simulations of indoor air quality (IAQ), energy, and exergy performance. The IAQ analysis reveals poor performance of the RHCV system for indoor source pollutants such as formaldehyde (HCHO) and total volatile organic compounds (TVOC). The HRV system demonstrates superior energy efficiency, with 30% lower primary energy consumption than the SEV system, though it is necessary to evaluate whether the heat recovered compensates for the increased fan energy consumption. This condition is evaluated by defining an outdoor air temperature limit value. The exergy analysis shows the HRV system requires 30% less primary exergy than the SEV system despite higher system demand. While HRV emerges as the optimal solution for balancing IAQ and energy performance, the findings highlight that source control remains necessary to effectively manage HCHO and TVOC concentrations. The research provides guidance for selecting ventilation systems that minimize pollutant exposure while optimizing energy resources. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

Adolescent vaping has become a persistent health and behavioural challenge in schools, yet many institutions lack reliable tools to detect and respond to concealed e-cigarette use. This study addresses this problem by evaluating the real-world performance of a low-cost “Internet of Things” (IoT) vape detection system deployed across 37 high-risk restroom and change-room locations at a large Australian Independent school. The aim was to determine whether an IoT-based environmental monitoring platform could accurately identify vaping events, support timely staff intervention, and provide actionable insights into student behaviour patterns. A longitudinal case study design was used, collecting continuous particulate matter (PM_2.5 and PM₁₀) data at one-minute intervals over an 18-month period, where PM_2.5 and PM₁₀ refer to particulate matter with aerodynamic diameters ≤ 2.5 µm and ≤10 µm, respectively, reported in micrograms per cubic metre (µg/m³. Threshold-based alerting, cloud-based data processing, and school-led Closed-circuit television (CCTV) verification were combined to assess detection accuracy, temporal trends, and operational responses. The system recorded more than 300 vaping-related incidents, with clusters aligned to predictable times of day and higher prevalence among senior students. Operational detection performance was high, with alert events characterised by rapid, concurrent PM_2.5 and PM₁₀ excursions consistent with vaping-related aerosol profiles, although staff responsiveness declined over time due to alert fatigue and competing priorities. A major environmental smoke event demonstrated the need for context-aware logic to reduce false positives. The findings demonstrate that real-time aerosol monitoring is not only technically reliable but also highly effective in detecting vaping within school environments. These perspectives help explain why user engagement, alert fatigue, and institutional follow-through are as critical as sensor accuracy itself. Ultimately, the effectiveness of vape detection relies on strong organisational commitment, well-defined response workflows, and alignment with broader wellbeing and policy strategies. When these elements are in place, such systems can evolve from simple detection tools into intelligent, integrated components of school health governance. Full article

Indoor air quality (IAQ) is a critical determinant of environmental health, yet awareness among young adults in rapidly urbanizing regions remains unclear. This study assessed knowledge, awareness, and practices related to IAQ among university students in Ras Al Khaimah, United Arab Emirates, and compared outcomes between medical and non-medical disciplines, while examining associations between knowledge levels and IAQ-related behaviors. A cross-sectional survey was conducted among 386 undergraduate students from three universities using a pre-validated, self-administered questionnaire. Overall, 52.1% of participants had heard of IAQ. Appropriate knowledge (≥60%) was demonstrated by 26.9% of students, and only 3.4% achieved high knowledge (≥80%). Medical students were significantly more likely than non-medical students to demonstrate appropriate knowledge (38.1% vs. 18.3%; p = 0.001), and female students scored higher than males (32.8% vs. 20.3%; p = 0.006). Awareness of IAQ guidelines was limited (65.3% unaware). Although 85.2% reported engaging in at least one IAQ-improving behavior, practices were mainly limited to ventilation and avoidance of indoor smoking. Higher knowledge levels were significantly associated with protective behaviors (p < 0.001). These findings indicate limited objective knowledge despite moderate recognition of IAQ importance, underscoring the need for structured educational interventions to enhance environmental health literacy. Full article

This study investigates whether a university teaching complex equipped with CSA S600 continuous air purification and sanitation units can maintain indoor air quality (IAQ) within recommended thresholds under real occupancy conditions and evaluates the impact of renovation works on IAQ. The work provides the first real-world assessment of the CSA S600 integrated monitoring system in an academic environment. CO₂, PM_2.5, PM₁₀ and VOCs were continuously measured over three months; moreover, indoor PM₁₀ values were compared with outdoor data from the regional monitoring network. Indoor CO₂ generally remained below 800 ppm, with short peaks of 1000–1500 ppm during high occupancy. PM_2.5 and PM₁₀ consistently stayed below the latest WHO guidelines, showing uniform recurring temporal patterns overtime; furthermore, indoor PM₁₀ showed limited coupling with outdoor trends, indicating the predominance of internal sources and ventilation dynamics. After renovation of the main Lecture Hall, particulate levels remained low, while VOCs showed a modest increase attributable to new materials. Overall, the findings demonstrate that the CSA S600 system effectively supports healthy IAQ in educational settings and that continuous monitoring is essential for managing occupancy-driven fluctuations and assessing the effects of structural interventions. Full article

Indoor exposure to particulate matter (PM) is increasingly recognized as a major contributor to respiratory and cardiovascular risk, yet the relative contributions of outdoor pollution, building characteristics, and occupant behavior remain poorly resolved. PM₁ (aerodynamic diameter < 1 μm) warrants focus due to its higher alveolar deposition. “Evidence driven indoor air quality improvement” (EDIAQI) project aims to enhance indoor air quality guidelines and increase awareness by providing accessible data on exposure, pollution sources, and related risk factors. As part of the Zagreb pilot within the project, 103 paired indoor/outdoor PM₁ samples were analyzed. Seasonal analysis revealed substantial wintertime outdoor PM₁ spikes, while indoor medians remained stable. Chemometric analysis identified factors such as dwelling size, outdoor pollution, resuspension, building age/heating type, and urban context. Among the tested models, the validated gradient-boosted regressor (GBR) achieved the strongest performance, explaining ~65% variance in indoor PM₁ (test R² ≈ 0.65). Explainable machine learning analysis (SHAP) identified outdoor PM₁ levels, infiltration, and resuspension as the most influential predictors. Findings underscore wintertime outdoor emissions (e.g., residential heating and traffic) and dwelling-related and behavioral factors as key drivers, with the machine learning–environmental data integration enabling targeted residential IAQ management: optimized ventilation protocols, resuspension mitigation via behavior, and infiltration reduction through retrofits. Full article

Biomimetic strategies have increasingly informed adaptive environmental systems; however, biomimetic mechanism transfer into interior environmental comfort remains unevenly operationalized and weakly evidence-stratified. Despite rapid post-2020 expansion of nature-inspired strategies, cross-domain translation across thermal comfort, indoor air quality (IAQ), visual comfort, and acoustic performance remains fragmented. This study addresses this gap by systematically mapping biomimetic mechanism transfer pathways within interior environmental systems, using biophilic strategies as a comparative baseline. A systematic mapping review was conducted following PRISMA 2020 guidelines to examine biomimetic mechanism transfer across interior environmental comfort domains. Studies were coded according to comfort domain, intervention scale, evidence type, and empirical strength. Results indicate three recurrent imbalances in the screened corpus: biophilic strategies dominate the literature (71.8%), intervention activity is concentrated at system scale and within multi-domain configurations, and acoustic bio-inspired optimisation is absent as a primary research domain. At the same time, the evidence base remains weakly stratified: only 10.3% of studies report statistically validated empirical findings, whereas 50.0% remain review-based or concept-led. To address these imbalances, the study proposes the Biomimetic Mechanism Transfer Mapping Framework (CPMF), a six-layer model linking biological logic, physical process activation, measurable IEQ outputs, empirical robustness, and implementation feasibility. The framework advances biomimetics by structuring mechanism translation into operational interior environmental performance systems. Full article

Indoor air quality (IAQ) in educational buildings plays a critical role in the health, cognitive performance, and well-being of occupants. Aging university facilities often rely on outdated ventilation systems that are not designed to meet current demands or respond to dynamic occupancy levels. This study investigates the performance and feasibility of various advanced ventilation strategies in comparison to an existing balanced mechanical ventilation (BMV) system in a university classroom accommodating 100 students. Using a Dynamic Building Energy Simulation Program, simulations were conducted to evaluate IAQ (using CO₂ levels), energy consumption, and thermal comfort under three retrofitting scenarios: BMV, demand-controlled ventilation (DCV), and hybrid ventilation combining natural and mechanical airflow. The simulations indicate that DCV cuts annual HVAC energy use by 33% relative to the baseline, while the hybrid strategy achieves the greatest reduction of 42% and maintains CO₂ levels and thermal comfort within recommended limits. Although hybrid systems provide seasonal advantages, their complexity may limit applicability. In addition to technical analysis, this study also explores the financial and tax-related challenges associated with retrofitting ventilation systems in university buildings. Investment payback periods, operational costs, and potential tax incentives are discussed to evaluate economic viability. Overall, the endorse hybrid ventilation as the most cost-effective strategy where mixed-mode control is feasible, and DCV as a practical alternative for buildings unable to employ natural ventilation. Full article

Driven by global urbanization and increasing emphasis on sustainable building practices, indoor volatile organic compounds (VOCs) have emerged as a major environmental and health challenge. This review specifically focuses on room-temperature air-only catalytic oxidation of representative indoor VOCs under a recently matured and highly application-relevant research direction. Recent advances are systematically summarized, highlighting catalyst design strategies, air-phase reaction mechanisms, and performance of noble metal catalysts (NMCs), transition metal oxides (TMOs), bimetallic synergistic catalysts (BSCs), and single-atom catalysts (SACs). Emphasis is placed on thermodynamic feasibility, reaction kinetics, oxidation behavior of non-formaldehyde VOCs, and mechanistic insights associated with SACs interfacial synergy, which enable efficient O₂ activation, high selectivity, and operational stability without external oxidants even under high VOC concentrations. This review provides theoretical foundations and technical guidance for VOCs mitigation and supports the advancement of green, low-carbon, and safe indoor air purification strategies worldwide. Full article

Exposure to poor indoor air quality (IAQ) conditions represents a major public health concern, with adverse effects on human health and well-being. The adoption of innovative technological solutions can support timely risk awareness, enable informed decision-making, and ultimately mitigate this health burden. In this context, Large Language Models (LLMs) emerge as a promising technological avenue through the Retrieval-Augmented Generation (RAG) paradigm, which extends their inherent natural language understanding capabilities with explicit access to external knowledge bases, enabling evidence-grounded reasoning and informed recommendations. The present work introduces an integrated GraphRAG-based Question Answering (QA) system that couples a domain-specific knowledge graph encoding fundamental IAQ concepts and relationships with a RAG-based natural language interface, thereby enabling explainable, context-aware, and advanced analytical reasoning over IAQ data. The evaluation results demonstrate the effectiveness of the proposed QA system across both retrieval and generation stages. The retrieval mechanism achieved a context recall of 0.914 and a precision of 0.838, while the generation mechanism attained a faithfulness score of 0.906 and an answer relevancy score of 0.891. Full article

The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in educational buildings represents an emerging opportunity to enhance intelligent environmental monitoring, data analysis, and energy optimization. This article presents a systematic literature review focused on AI-based applications in IoT-enabled learning environments, with special attention to indoor air quality (IAQ) management. A total of 585 documents were initially retrieved from Web of Science, Scopus, and IEEE Xplore using two targeted search strings. After removing duplicates and applying successive relevance filters based on title, abstract, and pertinence, 128 final documents were selected for full-text analysis. This study addresses four research questions: (RQ1) Which AI techniques are applied to environmental data analysis in educational contexts? (RQ2) What methods are used to detect sensor anomalies in IoT-based monitoring systems? (RQ3) How is AI applied in real-time decision making based on air quality indicators? (RQ4) What AI-driven strategies support energy efficiency in classrooms? The results reveal a growing use of machine learning and deep learning models, such as convolutional neural networks, decision trees, and LSTM architectures, particularly in applications focused on air quality classification, fault detection, and predictive control. Supervised learning methods were the most frequently applied, with CNN-based models leading in air quality prediction tasks and decision trees being preferred for anomaly detection. Deep learning approaches showed higher accuracy but required greater computational resources, limiting their use in low-cost educational environments. However, the literature also shows a lack of contextualized implementations, especially in low-resource or Latin American environments, and a limited focus on user-centered and educationally integrable systems. In addition, the review identifies a research gap regarding the integration of environmental and educational data, suggesting the potential for future empirical studies that evaluate real classroom conditions using IoT devices to inform AI-driven energy optimization strategies in academic settings. Full article

Indoor climbing gyms are high-occupancy settings, yet integrated indoor air quality (IAQ) studies that analyze objective exposure and occupant perception remain scarce. The novelty consists of combining user perception with multi-zone, high-resolution IAQ measurements. We investigated a climbing gym in Romania to (i) quantify particulate matter (PM₁, PM_2.5, PM₁₀) and carbon dioxide (CO₂), (ii) compare natural and mechanical ventilation under real operating conditions with per capita normalization, (iii) relate exposure to occupancy and user perception, and (iv) coupling continuous optical monitoring with 24 h gravimetric and morphological/chemical analyses (scanning electron microscopy, confocal microscopy, X-ray fluorescence, and inductively coupled plasma mass spectrometry). The gravimetric 24 h reference measurements (EN 12341:2014) showed that daily means for PM_2.5 and PM₁₀ were 1.9–2.0× and 2.3–2.8× higher than the WHO guideline values, which confirms persistent daily particulate loads. Mechanical ventilation reduced coarse PM and CO₂, but absolute PM remained elevated and fine fractions persisted. CO₂ revealed a near-uniform vertical mixing, confirming dilution but indicating that CO₂ is not a surrogate for particulate exposure. Survey responses from occupants revealed a gap between perception and reality: most of the users rated IAQ as good despite high PM. This study is among the few integrations of perception of IAQ for climbing gyms and the first comprehensive assessment in Romania, providing evidence-based recommendations on ventilation and filtration upgrades, chalk use management, and dust-reservoir control, thus creating sparkling interest for IAQ researchers, building services engineers, sports facilities operators, and policymakers. Full article