Thesis title: Control of airborne biocontamination in HVAC systems through UV-C irradiation. An experimental and numerical study of the UV-C field.
Indoor air quality is one of the most urgent and cross-disciplinary challenges in public health and environmental safety. It involves many sectors, from building design to systems engineering, from occupational medicine to public health. It is especially important in enclosed spaces with high occupancy or health risks, such as hospitals, offices, schools, residential areas, and public transit.
What has made this issue even more urgent in recent years is the global experience of the COVID-19 pandemic, which dramatically highlighted the importance of ventilation, natural aeration, and the control of biological pollutants in indoor spaces.
At the same time, the increasing adoption of remote work has significantly changed the purpose of home environments, turning them into both living and working spaces. This shift has led to more time spent indoors, increasing the risk of exposure to invisible pollutants such as fine particulate matter, VOCs (volatile organic compounds), chemicals from building materials or furniture, and microbiological agents. As a result, there is an urgent need to focus on indoor air quality as a health concern in both public and private spaces.
One phenomenon that highlights the complexity and significance of indoor environments is the socalled Sick Building Syndrome (SBS). Introduced by the World Health Organization in the 1980s, SBS describes a range of nonspecific symptoms, such as headaches, eye and mucous membrane irritation, dry skin, respiratory problems, fatigue, decreased concentration, or general discomfort, that occur after spending time in certain buildings. What characterizes this syndrome is that, in most cases, there is no clearly defined illness, and the symptoms tend to disappear or significantly lessen once the person leaves the environment. This shows a direct link between building features and occupant health, emphasizing a subtle but important interaction between the built environment and human response.
In recent years, numerous studies have explored the possible causes and mechanisms behind SBS. Two recent systematic reviews [1, 2] confirm that the syndrome cannot be linked to a single cause but results from an interaction of physical, chemical, biological, and psychosocial factors. Among the most involved chemical contaminants are volatile organic compounds (VOCs) emitted by construction materials, furnishings, adhesives, and paints; formaldehyde; ozone; carbon monoxide and carbon dioxide; and fine particulate matter (PM2.5 and PM10), which can penetrate the respiratory tract and cause inflammatory responses.
Besides these, physical factors such as extreme temperatures, excessive or insufficient humidity, poor ventilation, distracting ambient noise, and inadequate lighting can all harm perceived comfort and lead to symptoms.
Special attention is now focused on the microbiological contamination of indoor air, a phenomenon often overlooked but increasingly recognized as crucial, especially in sensitive environments like healthcare and social care facilities. Molds, bacteria, and bioaerosols flourish in humid, poorly ventilated spaces or areas with structural problems such as leaks and condensation, and they can cause allergic reactions, respiratory conditions, and ongoing discomfort. The reviews mentioned earlier highlight a growing link between biological contaminants and the occurrence of SBS-related symptoms, particularly in cases where HVAC systems are poorly maintained or material quality is insufficient.
Beyond the chemical composition of the air, the literature increasingly emphasizes the importance of subjective and psychological factors, such as perceived comfort, stress levels, or the quality of workplace organization. These elements not only influence individual responses to environmental stimuli but can also amplify or diminish the appearance of symptoms. In other words, SBS is a syndrome that exists at the intersection of the physical environment and the individual's psychobehavioral interpretation.
In such a complex and multidimensional context, the importance of developing integrated monitoring and prevention strategies becomes more evident. These strategies should not be limited to assessing the chemical-physical aspects of the air, but should also include microbiological analysis and monitoring of the occupants’ physical and psychological health. Only a multidisciplinary approach that combines expertise in environmental science, engineering, healthcare, and psychology can ensure a true understanding of the phenomenon and enable effective intervention on risk factors.
In recent years, mainly due to the significant impact of the pandemic caused by the SARS-CoV-2 virus, a large number of air purifiers have entered the market. These devices operate using various physical and chemical mechanisms, some of which are combined. However, their great commercial success is often not supported by rigorous scientific research, particularly with regard to the dose of the agent supplied to biological contaminants. Some of these new devices claim to use UV-C irradiation.
The germicidal effectiveness of UV-C sources has become a major focus of research, using both experimental and numerical methods. In experimental studies, germicidal performance is most often evaluated through microbiological culture techniques, comparing the colony-forming units (CFUs) of samples exposed to UV-C radiation with those of unexposed controls. The experimental literature often provides incomplete details, often mentioning only the exposure time or the nominal radiant flux of the UV-C source. As a result, the actual dose received by microorganisms is rarely reported, making it difficult to directly compare different studies and limiting the reproducibility of the results. Additionally, factors such as spatial nonuniformity in irradiance, self-shading within samples, and the optical properties of surrounding materials can significantly affect measured inactivation but are rarely quantified.
On the numerical side, assessing UV-C effectiveness usually involves radiometric measurements and computational fluid dynamics (CFD) simulations. These methods help predict the spatial distribution of irradiance, radiant fluence, and the resulting local dose within a specific volume. When combined with models of microbial transport, they can also estimate the effective exposure of microorganisms in moving air streams. However, many numerical studies overlook the interaction between radiative transfer and fluid dynamics. In particular, airflow velocity and turbulence significantly influence how long microorganisms remain within the irradiated area, thus affecting the actual inactivation process. If the flow velocity is too high, microorganisms may pass through the UV field too quickly to receive the necessary dose for deactivation, even if the average fluence rate appears sufficient in static conditions.
Therefore, accurately evaluating UV-C germicidal effectiveness in indoor environments requires a multi-physics approach that combines radiative, fluid-dynamics, and biological-kinetics models. Such a framework should also consider the optical properties of materials, scattering effects, and potential re-emission phenomena that can influence the local fluence rate. Only by standardizing the definition of dose, carefully measuring irradiance patterns, and realistically modeling air movement can experimental and computational analyses be meaningfully compared and used to develop effective UV-C disinfection systems for indoor air quality improvement [3].
This research's novelty lies in combining experimental and numerical methods to assess the actual UV-C irradiation field in HVAC systems. Unlike most existing studies, which typically examine optical or microbiological aspects separately, this work aim to construct a unified framework that links irradiance distribution, air flow, dose, and microbial inactivation under real-world operating conditions.
In the present work, a unique contribution involves the optical modeling of UV-C LED sources using ray-tracing simulations, an area that is still rarely explored in current research. Additionally, the study aims to develop a robust and versatile modeling framework that can be easily reproduced and adapted to various UV-C source types, geometrical configurations, and application scenarios.
The experimental phase of the research was conducted in collaboration with Sagicofim S.p.A., a company specializing in air filtration and cleanroom technologies. A full-scale prototype of a galvanized steel duct was constructed and equipped with modular UV-C LED sources. Each module comprised thirty-two diodes arranged in an “80–110” configuration, designed to maximize uniform irradiation across the surface of a downstream HEPA filter. The LEDs emitted at three characteristic wavelengths within the germicidal range (254, 265, and 275 nm), allowing for a comparative evaluation of spectral efficiency and optical behavior.
Irradiance measurements were conducted using a Newport 918D photodiode coupled with a 1919-R precision optical power meter [4]. Before testing, the instruments were calibrated against certified standards to ensure traceability and repeatability. Measurements were performed on a grid of 494 points distributed across several planes orthogonal to the airflow, enabling the reconstruction of detailed irradiance maps that represent the spatial distribution of light within the duct.
The experimental data revealed a clear dependence of irradiance on both wavelength and geometry. The configuration operating at 265 nm exhibited the highest radiant output, consistent with the absorption peak of nucleic acids [5-7], whereas the 275 nm LEDs showed reduced power and less uniform coverage. Pronounced non-uniformities were observed near the duct corners and walls, attributed to reflection losses and shadowing effects caused by structural components. These findings underscore the necessity of numerical modeling to complement experimental analysis and guide design optimization.
To interpret these results, a detailed three-dimensional model was developed using the Ray Optics Module of COMSOL Multiphysics [8]. The model simulated the LED emission profile, accounting for angular dispersion, multiple reflections, and surface absorption. The optical properties of the duct materials were determined experimentally and implemented into the simulation to improve accuracy. The comparison between simulated and measured irradiance maps demonstrated a strong correlation, validating the model as a reliable predictive tool for UV-C system design.
Once validated, the model was used to conduct a parametric study exploring the effects of geometric and material variables. Adjusting the LED orientation, spacing, and wall reflectivity optimized light distribution. Simulations showed that coatings with reflectivity above 85% significantly improved the uniformity of the fluence rate and reduced low-irradiance zones. Even small changes in LED tilt angles or inter-module distances yielded measurable improvements in optical efficiency.
In addition to the optical analysis, the research defined repeatable procedures for measurement calibration, LED stabilization, and data processing. Each step, from voltage control to temperature monitoring, was standardized to ensure reproducibility. The combined experimental–numerical approach provided a comprehensive framework for identifying sources of non-uniformity and for predicting system performance under different configurations.
Although microbial inactivation tests and energy performance assessments were beyond the scope of this work, the study provides a solid foundation for future investigations that will extend the optical findings to biological validation. The irradiance maps and validated numerical models developed here form a database for estimating germicidal dose and for designing UV-C modules optimized for safety, efficiency, and long-term reliability.
From an engineering perspective, the study highlights the influence of wavelength, angular emission, material reflectivity, and system geometry on UV-C performance. The proposed methodology enables a quantitative assessment of each parameter, offering design guidelines for integrating LEDbased disinfection technologies into HVAC systems. The approach also promotes standardization and comparability among studies, supporting the development of regulatory frameworks for UV-C systems.
In a broader context, this research contributes to the scientific effort to harmonize testing and measurement procedures for UV-C disinfection. The experimental–numerical methodology proposed here can serve as a reference for future standards, enabling reproducible, traceable, and verifiable evaluations of UV-C systems in real-world conditions.
In conclusion, the doctoral research presented in this thesis establishes an integrated framework combining experimental characterization and optical modeling for the analysis and optimization of UV-C LED systems applied to air disinfection. The results provide engineers and researchers with validated tools for designing efficient, safe, and environmentally sustainable solutions. Future developments will focus on coupling optical and thermal modeling, evaluating long-term LED stability, and integrating real-time control systems that adapt UV-C intensity to air quality variations. Ultimately, this work aims to contribute to the creation of healthier, safer indoor environments through the responsible, innovative use of UV-C technology.
1. Subri, M.S.M.; Yahya, E.; Mohd Yunus, N.A.; Daar, M.; Abu Hassan, F.A. The Parameter of the Sick Building Syndrome: A Systematic Literature Review. Heliyon 2024, 10(12), e32431. https://doi.org/10.1016/j.heliyon.2024.e32431
2. Aziz, N.; Abdul Shukor, S.F.; Hassan, N. Indoor Air Quality (IAQ) and Related Risk Factors for Sick Building Syndrome (SBS) at the Office and Home: A Systematic Review. [Journal information pending] 2023.
3. Cattai, M.; D’Orazio, A.; Sbardella, F. Application of UV-C radiation in HVAC systems: A review of current challenges and standardization needs. Build. Environ. 2023, 237, 110391. https://doi.org/10.1016/j.buildenv.2023.110391
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