Airborne Fungal Communities: Diversity, Health Impacts, and Potential AI Applications in Aeromycology

Abstract

International interests in bioaerosols have gained an increased attention to widen the knowledge pool of their identification, distribution, and quantification. Aeromycota signify a complex and diverse group of fungi dispersed through the atmosphere. Aeromycology is an important field of research due to its important role in human health. Aeromycoflora both indoors and outdoors, are responsible for many allergies and other respiratory diseases. The present review describes the diversity of the aeromycoflora, the techniques used for sampling, identification, and taxonomic classification, and the limitations of the traditional culture-based methods as they fail to detect unculturable species. Furthermore, the spatial and temporal variability in aeromycota complicate consistent monitoring. Both indoor and outdoor environments harbor airborne fungi. The diversity in indoor environments is greatly shaped by the moisture content, building design, and ventilation, which are further taken into consideration. Further, the health impacts of the indoor and outdoor fungi have been discussed and what control measures can be taken to reduce the exposure risks and management strategies that can be adopted. Artificial intelligence (AI) can bring revolution in this field of research and can help in improving detection, monitoring, and classification of airborne fungi. The review finally outlines the emerging role of AI in aeromycology.

1. Introduction

Over the past few decades, increasing attention has been paid to deep knowledge and gaining understanding of the dynamics, composition, and implications of air fungal spores, a field broadly given recognition as aeromycology. The field of aeromycology integrates different disciplines, such as environmental sciences, aerobiology, mycology, and public health. Airborne particles which are biological in origin are called bioaerosols and include viruses, bacteria, fungal spores, pollens, mites, and dead tissues. Bioaerosols are ubiquitously present in the biosphere with their sources being both natural and man-made. Soil, damp surfaces, water bodies, and living organisms are the major sources where both active and passive emission of microbes take place. Major development in aerobiology occurred in the 20th century. The work of Gregory is a landmark in aerobiology research and provides a clear picture of the presence and dispersal of microbes in the air [1]. From science to technology, evident progress has been made in the bioaerosol field around the globe [2]. The omnipresence of bioaerosols, their particle size, and their disease-causing abilities make them potentially hazardous and associated with a number of health issues over several decades [3]. Numerous sampling techniques have been developed for the monitoring and characterization of bioaerosols. Morphological characteristics have played a major role in mycological studies since they have been used as the basic criterion for species recognition.

A large number of species have been identified solely on the basis of morphological characters. In addition to taxonomy, a higher level of classification has been based on the morphological characters. Advancements in molecular techniques, next-generation sequencing, and metagenomics are gaining importance in environmental sequencing studies [4]. A diverse range of fungal spores (known as aeromycota) reflects the complex life cycle of fungi and their intersections with climatic and geographical factors [5]. Airborne fungal spores serve as agents of dispersal and propagation of fungi which influence ecological succession and biodiversity [6]. The temporal and spatial variability of fungal spores in the air is a reflection of the environmental conditions, such as wind patterns, temperature, and humidity, and is an important parameter in aeromycological studies. Investigations suggest that the airborne concentration of the spores shows an increase in the relative humidity, wind speed, and air temperature in a short time [7].

Airborne fungal spores originate from decaying vegetation, soil, aquatic systems, and building materials. The concentrations of the spores fluctuate with changes in geography, season, and human activities. Fungal spores are present in almost all types of environments. The composition of the aeromycoflora of a particular region plays an important role in the spreading of respiratory allergies. The indoor and outdoor airborne fungal spores may be different due to many factors but play a major role in public health as they are potent allergens for human populations and can be a causative agent of mycolic infections [8]. These fungal spores can survive for months in appropriate conditions [8]. It is well known that some fungal spore taxa are associated with a range of chronic respiratory conditions. For example, Alternaria, Aspergillus, Cladosporium, and Penicillium are associated with asthma exacerbations and asthma hospitalizations and hypersensitivity pneumonitis [9].

As per the report of the United States Environmental Protection Agency (EPA), the degree of indoor air pollution in the case of improper ventilation systems is two to five times that of outdoor air pollution [10]. In today’s era, as people spend most of their time indoors, so indoor air quality has a significant impact on their health [11]. Typically, newly constructed buildings are usually more airtight, leading to less ventilation and in turn damp and moldy conditions [12]. Outdoor fungal spores have ubiquitous distribution in the air and may contribute to asthma symptoms, though information about their effects on respiratory function is scarce.

The present review focuses on the current knowledge of the diversity of airborne fungi, their sampling techniques, characterization, and identification, as well as the health implications of aeromycoflora. The review has been well differentiated between the major health impacts of outdoor and indoor fungi. It has further taken into consideration the control measures, such as moisture control, building design, ventilation, air filtration, and cleaning, that can help in reducing the concentrations of the fungal spores to safe levels. The integration of artificial intelligence (AI) into aeromycology is a landmark in studies of airborne fungi. Machine learning algorithms and deep learning models are making an important contribution in analyzing the microscopic images, identifying fungal taxa, and forecasting spore dispersion patterns [13]. Further, the review discusses the emerging role of AI in aeromycology, how AI-integrated biosensors and smart air monitoring devices help in detecting outbreaks and issuing timely warnings. Further, how merging genomics and metagenomics with AI can be beneficial for studying aeromycoflora has been explained. The websites used for the compilation of the present critical review included Google Scholar, PubMed, ResearchGate, and many more. Figure 1 shows a schematic representation of methods used for writing the present article.

2. Diverse Fungal Spores in the Air

Biodiversity is one of the most fascinating aspects of biology which has attracted a lot of attention from the scientific community. Biodiversity can be of species which are genetic and ecological, and distributed in a number of different environments. A deeper study of biodiversity is still required for fully discovering the unknown species and their potential beneficial and harmful aspects of life. A huge diversity of fungal species is known with respect to nutritional, ecological, and morphological modes and the fungal group is the largest group after insects [14]. Fungi inhabit air, water, soil, plants, animals, and even survive in extreme environmental conditions [15].

The study of the diversity of aeromycota is as important as any other fungal habitat. Human exposure to aeromycota both indoors and outdoors has been known to impact health; for instance, they cause allergies, infections, and irritations. The American Industrial Hygiene Association recommends that the presence of some toxigenic fungal communities requires urgent risk management decisions [16]. It is important to study the association between airborne fungi and their adverse health impacts for which the fungal group and their relative frequencies in indoor as well as outdoor environments should be well known. The increased understanding and knowledge of aeromycota is as important as any other field of research to interpret results so that control measures can be taken.

Different investigations have been carried out to study the diversity and impact on health, causing allergies, infections, irritations, and serious health effects, of airborne fungi indoors and outdoors. It is important to study the association between airborne fungi as their adverse health impacts relate to indoor and outdoor exposure. Rainer, Peintner [17] studied airborne fungi in a hospital environment. The study identified a total of 98 fungal species, among which Aspergillus fumigatus and Aspergillus terreus and 48 others were reported as potential fungal pathogens. The Building Assessment Survey and Evaluation (BASE) study measured baseline concentrations of airborne fungi in 100 representative US office buildings. Diverse samples were collected from different sites, at different durations and times of the day. A comparative study was performed by exploring the fungal concentrations between different locations, sampling and analytical methods, and seasons. The study observed that more groups were seen in summer and aggregated concentrations tended to be higher than in winter except for culturable Aureobasidium spp. and Botrytis spp. outdoors and non-sporulating fungi in the studied locations. Overall, indoor and outdoor spore concentrations were observed to be approximately three and ten times higher, respectively, than culturable fungal concentrations. In a comparison of the number of different fungal groups and individual occurrence in buildings and samples, the study showed that the outdoor environment and summer season were more diverse. The proportional contributions of the groups were revealed to be very similar which suggested that the outdoor and indoor environments were related, as were summer and winter seasons for each location [18].

An investigation was carried out in the historic huts on Ross Island, Antarctica. The study revealed the dominance of Antarctomyces psychrotrophicus, Cladosporium cladosporioides, Geomyces sp., and Pseudeurotium desertorum in the air environment within the huts. Thebolus sp. and Cadophora sp., identified based on the morphology, were also reported from air samples [19]. Culture-independent characterization of fungi in a poultry bioaerosol using pyrosequencing identified the highest proportion of Sagenomella sclerotialis. The other identified species included Aspergillus ochraceus and Penicillium janthinellum. The total inhalable fungi concentration was estimated to be 1810 cells/m³ [20].

The Havana aeromycobiota diversity was studied. The reported species were Aspergillus flavus, Chaetomium globosum, Cladosporium cladosporioides, Penicillium citrinum, Rhizopus stolonifera, Rhizopus oryzae, and Syncephalastrum racemosum [21]. The aeromycoflora species belonging to Alternaria, Aspergillus, Cladopsorium, Colletotrichum, Curvularia, Fusarium, Penicillium, Pestalotia, Rhizopus, and Trichoderma have been reported from the air of Dhaka University campus [22]. The most abundant genus Cladosporium, followed by Penicillium, Alternaria, and Aspergillus, were reported in a study of Tikvesli et al., 2018 [23]. Airborne species of Alternaria, Cladosporium, and Epicoccum were reported by the eDNA metabarcoding approach [24]. The fungal aeroflora, including Aspergillus flavus, Curvularia australiensis, Curvularia spicifera, Fusarium oxysporum, Fusarium proliferatum, Kodmaea ohmeri, and Penicillium citrinum, were reported from a vegetable market [25]. Airborne fungi including, Aspergillus fumigatus, Aspergillus niger, Cladosporium herbarum, and Penicillium sp. were found in cultured and urban sites in Basra Province, Iraq [26]. Airborne culturable Aspergillus flavus, Aspergillus niger, and Cladosporium cladosporioides have been reported from both indoor and outdoor environments of shrines in Chennai [27]. The phylogenetic analysis of airborne fungal communities found that Ascomycota is the dominant phylum, followed by Zygomycota and Basidiomycota. Among Ascomycota, Aspergillus is the most dominant genera, followed by Penicillium (Figure 2).

3. Aeromycological Studies

3.1. Sampling Techniques of Airborne Fungi

Airborne fungal sampling can be performed using different methods, including impaction, electrostatic precipitation, filtration, liquid impingement, and gravitational settling [28]. The impaction method helps in the separation of particles from the air stream by means of inertia acting on the air particles to force deposition on a solid and semisolid collection surface which is basically an agar medium [29]. Liquid impingement is similar to the previously mentioned technique in the form of an inertial force acting on the air particle. In this collection medium, the liquid acts as a dilute buffer solution [30]. The filtration sampling technique is achieved by particle separation from the air stream by an air passage through a porous medium, including a membrane and fiber filter [31]. On the other hand, in electrostatic precipitation, biological samples with an air stream are drawn together using a sampler in which particles are electrically charged. Afterwards, particles are subjected to an electrostatic field that further deposits them onto a collection surface [32]. Another sampling technique, i.e., gravitational settling, also known as depositional sampling, is a non-quantitative collection in which agar medium is exposed to the environment’s air. This technique is inexpensive and easily performed and more sample is collected on the surface [29]. All these sampling techniques use various types of samplers, such as an impactor sampler, liquid-based sampler, filtration sampler, and electrostatic precipitation sampler [30]. Different studies have reported various sampling techniques and samplers for the isolation of airborne fungi. Airborne fungal sampling in an investigation was performed using a membrane filter and surface air system [33]. In another investigation, the sampling technique used was liquid sampling in which a liquid cyclonic impactor was used for the collection of samples [34]. In one investigation, the airborne fungal isolation was performed by the impact method and an Air Ideal 3P device was used as a sampling technique to collect the airborne fungal spores [35].

Yet, each sampling technique has its own advantages and disadvantages. Impaction allows direct culturing of fungi and fungal species to be differentiated. However, fragile spores may be damaged due to high impact velocity, and the method detects only viable fungi. Liquid impingement is suitable for both viable and non-viable particles and the liquid samples can be further analyzed by PCR or microscopy. However, the technique involves the use of more complex equipment, such as liquid cyclonic impactors. Filtration is simple, requires low maintenance, and is a portable technique, but the filters may dry out the fungal spores, thus reducing the viability. Electrostatic precipitation involves the minimum loss of the sample due to gentle collections but requires specific and expensive equipment with careful calibration to avoid inefficiencies. Gravitational settling is a simple, cost-effective technique and good for preliminary surveys and educational purposes, but is highly influenced by air currents and time [36,37,38].

3.2. Identification and Taxonomy of Aeromycota

Aeromycota identification and taxonomy can be performed through two different methods, i.e., traditional or culture-based identification and culture-independent identification. In culture-based identification of aeromycota, the air sample is collected using various sampling techniques and samplers followed by exposure of the air to various selective growth media. The isolated strains are further morphologically and molecularly identified. The molecular identification can be achieved through DNA isolation, PCR amplification, and DNA sequencing. For the culture-independent identification of aeromycota, omics techniques, including metagenomics approaches, can be used in which DNA is directly isolated from the sample [39]. In a report, airborne fungal genus Alternaria, Aspergillus, Davidiella, Didymella, Khuskia, Geosmitha, and Penicillium were identified through metagenomics approaches [40]. In another investigation, through metagenomics approaches, fungal species were identified from both the indoor and outdoor air of childcare facilities [41]. In one investigation, airborne fungal microbiota from indoor dust were identified through metagenomics approaches which found fungal species from phylum; Ascomycota, Basidiomycota, and Mucoromycota [42]. During a rainy season, airborne fungi Cladosporium and Penicillium were identified through metagenomic approaches from Mexico City [43].

3.3. Limitations of Culture-Based Methods and Emergence of Metagenomic Approaches

Fungal taxonomy has been widely explored during the past few decades by isolating and culturing fungal strains followed by identification by performing DNA sequencing. The taxonomic studies have been used to identify various fungal species, but a larger number of fungal species have not been yet identified as they cannot be cultured using synthetic media [39]. The major limitation of culture-based methods is that unculturable fungi, due to their specific nutritional requirements, cannot be cultured and studied. The utilized culture media favoring fast-growing, opportunistic fungi can obscure the slower-growing species. Culture-based methods are time-consuming and labor-intensive as they require isolation, sub-culturing, and monitoring [44]. Relying on culture-based methods can considerably impact the fungal diversity as a fraction of environmental fungi can be cultured, which became evident due to the emergence of metagenomic approaches. Metagenomic approaches have revolutionized the study of airborne fungi, moving beyond the limitations of culture-based methods. Metagenomics can be used for the identification of culturable, unculturable, rare, and dormant fungal diversity. Metagenomics involve air sampling, extraction of DNA, PCR amplification, high throughput sequencing, and data analysis using bioinformatic tools [45]. The approach helps in analyzing the species’ richness, diversity indices, and taxonomy.

3.4. Temporal and Spatial Variability in Airborne Fungal Populations

Airborne fungal populations are strongly dependent on the different meteorological conditions, including humidity and temperature. Sporulation in fungi is temperature-dependent [46]. Warmer temperatures favor high concentrations of fungal spores in the air. Relative humidity influences spore release, germination, and survival. A positive correlation between fungal abundance and high humidity has been observed. The spore concentrations can vary throughout the day, such as Alternaria and Cladosporium, which tend to peak in the afternoon [47]. Rain is another important factor influencing spore concentrations. Rain can suppress as well as promote the release of the fungal spores. Spatial variability refers to differences in airborne fungi across geographic regions, land use types, and microenvironments. Tropics and sub-tropics often have higher fungal diversity because of warmer and more humid climates. Wind patterns and geographic barriers affect spore dispersal, leading to localized variations in fungal concentrations [48,49]. Indoor air exhibits fungi from building materials and human activities whereas outdoor air fungal diversity is influenced by natural sources, including plants and soil [50,51].

Different studies have been published on the temporal and spatial variability in airborne fungal populations. In a study, temporal and spatial variability was considered in an airborne fungal population over an eight-day period and it was found that ascomycete fungi belonging to Hypocreales order was dominant in the air [52]. In another report, temporal and spatial variability was studied during the winter season in two different rooms and found Aspergillus, A. versicolor, and Penicillium as airborne fungi [53]. During dry and rainy seasons in Havana, fungal spores were variable and high levels of Cladosporium were found in the morning and Coprinus and Leptosphaeria peaks in the night [54]. A study reported airborne fungal variations with temperature, wind speed, and humidity at high altitudes. The study reported that temperature and wind tend to be in positive correlation and remove the dry spores of Cladosporium [7]. Another spatial variability in which samples of air were collected for a 14-day period found that with an increase in humidity, genera like Malassezia and Schizophyllum were abundant [55]. Airborne fungi, Alternaria, Cladosporium, and Epicoccum, from both indoor and outdoor environments, were positively correlated with the temperature and relative humidity [56]. Airborne fungi Cladosporium and Penicillium were found to be affected by height, relative humidity, temperature, and rainfall [57]. In an investigation of variations in airborne fungi, Cladosporium was found to be stable throughout the winter season [58]. Environmental factors, such as contact with particulate and gaseous air pollutants, have a strong impact on airborne fungal behavior. Recent research has demonstrated that particles of PM_2.5 and PM₁₀ serve as physical vectors of the fungal spores, increasing their atmospheric life cycle and making them susceptible to long-range transportation. Adherence of spores to the surfaces of particles alters the aerodynamics of these spores and affects their viability and allergenicity. In addition, oxidative stress in fungal cells can be caused by the chemical compositions of particulate matter, especially carbonaceous particles, heavy metals, and organic aerosols, which are capable of changing the integrity of membranes and the activity of enzymes, which subsequently affects the germination of the spores and the pathogenicity. Other gaseous pollutants of note are ozone (O₃), nitrogen oxides (NO_x), and sulfur dioxide (SO₂), which also have a great influence on fungal survival. These gases are capable of leading to oxidation of the spore walls and lowering the survivability of airborne fungi, but low to medium concentrations can induce sporulation in species that have evolved to survive in oxidative stresses. These interactions between pollutants and fungi are further tweaked by environmental parameters such as temperature, humidity, and the speed of wind. When humidity is high, spores are emitted and attached to PM, whereas spores and contaminated particulates can be dispersed into higher levels of the atmosphere, due to wind turbulence. Therefore, natural variability, together with anthropogenic pollution, contributes to the community, diversity, and seasonal abundance of airborne fungal populations in urban and rural settings.

4. Indoor and Outdoor Sources of Air Borne Fungi

Airborne fungi are a common and diverse component of bioaerosols found in both indoor and outdoor environments. These are primarily present in the form of spores and microscopic fragments that become suspended in the air due to physical or environmental disturbances [59]. There are primary colonizers which require low moisture activity and tend to colonize dry or slightly moist surfaces. Secondary colonizers require moderate moisture and release their spores into the air when materials remain wet and are found in damp areas. The third category includes the tertiary colonizers with the requirement of high-moisture activity which colonizes water-damaged materials [60]. Indoor sources of airborne fungi are closely linked to the presence of organic and moisture substrates that will promote fungal growth. Buildings with water damage, high humidity levels, and poor ventilation provide ideal conditions for the colonization of fungi on materials like wallpaper, drywall, carpets, ceiling tiles, and wooden structures [61]. These materials, once dumped, support the proliferation of fungal species, such as Penicillium, Aspergillus, Stachybotrys, and Cladosporium, which are commonly detected in indoor air [62].

HVAC systems, particularly when poorly maintained, can also act as significant sources by accumulating moisture and dust in filters and ducts, allowing fungi to grow and release spores throughout the indoor spaces [63]. The other common sources within indoor environments include potted plants and their soil, spoiled and stored food items, and damp furnishings. Human activities, such as walking, vacuuming, and opening doors and windows, can resuspend spores from settled dust into the breathable air [64]. Additionally, occupants and pets can carry outdoor fungal spores indoors on shoes, clothing, skin, or fur, creating a constant exchange between the two environments [63].

Outdoor sources of airborne fungi are primarily natural and include decaying leaves, soil, compost, plant surfaces, and wood [65]. The phylloplane, or the surface of plant leaves, support various fungal genera that are released into the atmosphere through mechanical agitation or natural spore dispersal mechanisms [66]. Agricultural activities, such as harvesting, composting, and plowing, disturb fungal reservoirs and significantly increase the concentration of airborne spores in rural and farming regions [67]. The common outdoor fungal genera include Fusarium, Alternaria, and Cladosporium [68]. The environmental factors like temperature, wind speed, rainfall, and humidity play an important role in the timing and extent of fungal spore releases. The seasonal variation influences fungal abundance, with concentrations typically peaking in warmer and more humid months [69]. The outdoor fungal spores can infiltrate buildings through open doors and windows or be drawn in via ventilation systems, contributing to indoor bioaerosol levels [70] (Figure 3).

In outdoor environments, the survivability of fungi is largely determined by meteorological conditions, including temperature, relative humidity, wind speed, precipitation, and sunlight. Germination of spores is likely to take place at high humidity and moderate temperature, but desiccation and exposure to UV radiation will diminish spores’ viability. The desiccated spores’ exported aerosols, produced by fungal metabolism, morph and modulate the fungi’s exported aerosol metas.

In indoor environments, key internal factors become the availability of moisture, relative humidity, construction materials, ventilation efficiency, and organic materials, such as wallpaper and carpets, and HVAC filters. Materials of high-water activity become focal points of fungal growth to the exclusion of air, and, ultimately, fungal colonization and sporulation will occur. Human activity, HVAC systems, and outdoor air infiltration will permit the introduction of cross-contaminated air and the interchange of fungal spores between the two environments. Ambient humidity, temperature, and the dynamics of particulate deposition remain common factors for both internal and external environments.

5. Health Impacts of Airborne Fungi

Airborne fungi play an important role in influencing human health, which has been discussed in this section.

Table 1. Airborne fungal limit values and guidelines all over the world as established by international agencies.

Country	Guideline Issued/Threshold for Airborne Fungi (CFU/m3)	Conditions	References
Brazil	~750	Indoor fungal contamination; recommended value	Belizario, Lopes [71]
Canada	~150	Limit value for indoor air in hospital critical areas	Belizario, Lopes [71]
European Commission	<500	Intermediate contamination level; above that is higher risk	Hazards, Ricci [72]
Malaysia	~1000	Mold, mixture of species	Er, Sunar [73]
South Korea	~500	For mold in indoor air; mixture of species	Shin, Yoon [74]
United Arab Emirates	~500	Mold guidelines	Semerjian, Al-Bardan [75]
WHO/Expert group	≤1000	For total microbial concentration indoors	Heseltine and Rosen [76]

shows the airborne fungal limit values and guidelines worldwide as established by international agencies.

5.1. Indoor Fungi

5.1.1. Allergic Reactions

Allergic Rhinitis

Indoor molds are a cause of perennial allergic rhinitis (AR) through low-level dysfunction in damp homes, schools, and workplaces. Epidemiology implicates dampness/mold indicators (particularly mold odor) as factors associated with an elevated risk of rhinitis and the symptom burden; up-to-date analyses and guidance workstreams have been addressing indoor fungi as triggers of action [77]. The pathophysiology focuses on barrier damage via fungal proteases, epithelial alarmin release (e.g., IL-33), and IgE-mediated reactions to mold antigens; concomitant sinus disease and involvement of lower airways comprise a united airways axis [78]. Management combines source control (moisture removal, dehumidification (<50 RH), enhanced ventilation/filtration, and contaminated porous material removal) with AR medication therapy. Intranasal medications are more effective than oral agents in reducing symptoms and the impact on quality of life; intranasal corticosteroids are first-line therapy, and fixed-dose intranasal corticosteroid–antihistamine combinations are superior treatment options when moderate–severe illness occurs or when the operation fails [79,80]. In refractory cases, consider the possibility of fungal sensitization and environmental factors (bedroom dampness, HVAC, post-flood repairs). Allergen immunotherapy has promise in targeting certain molds (especially Alternaria), but more general applicability is hampered by extract variability as well as cross-reactivity [81]. Future research should match standardized, health-relevant exposure measures with AR outcomes and examine whether building remediation in response to targeted exposures reduces the need for medication and the symptom scores.

Asthma Triggers and Exacerbation

Fungi are more exacerbators/triggers of asthma rather than unambiguous causes of de novo disease in people in the general population. In several recent literature reviews, researchers conclude that home mold/dampness contributes to a higher prevalence of asthma and, more strongly, exacerbation risk; a 2025 meta-analysis reported an estimated risk of 15–53 per cent higher risk of childhood asthma based on study design, whereas a 2025 pediatric study linked visible mold/odor with poorer asthma control and more adverse health outcomes [82,83]. Sensitization to Aspergillus, Penicillium, Cladosporium, and Alternaria causes severe phenotypes and acute attacks [84]. The mechanisms involve fungal protease damage to airway epithelial cells; innate immunostimulation by fungal β-glucans/chitin [85]; and trophic allergen-carrying fragments forming small-airways inflammatory pathology [86]. Treatment has two prongs: (i) guideline-based therapy (as-needed ICS-formoterol, daily ICS/LABA, and biologics in T2-high severe asthma, and (ii) environmental management. Public health and clinical recommendations underline the importance of removing the moisture sources and prompt drying after the event of water, ventilation/filtration improvements, and professional remediation in the case of large-scale growth [87]. Despite a limited number of randomized remediation trials with clinical endpoint data, there is convergent evidence and biological plausibility in incorporating home counseling and monitoring into asthma action plans, particularly in children with sensitization. Trials to quantify the effects on exacerbation rates after remediation and exposure measures which demonstrate fragments/extracellular vesicles, and not just spores, should be a priority.

Atopic Dermatitis (Eczema)

There is increasing evidence to support indoor mold as a cause of atopic dermatitis (AD). A birth cohort investigation performed using the Korean COCOA dataset reported that maternal exposure to fungi during pregnancy was related to increased risk of AD in children at the ages of 1 and 3 years [88]. Other longitudinal data substantiate that children living in water-damaged or damp housing are at higher risk of developing childhood eczema [89]. Mechanistically, 20 aeroallergens (e.g., 20 aeroallergens, fungal proteases) may establish residence when skin barriers are compromised and cause cytokine responses in keratinocytes, reinforcing Th2 pathogenic skin inflammation; low humidity and places with cold weather and wet surfaces further worsen the condition of the skin by altering the skin barriers and making the skin dry [90]. Clinical practice therefore focuses on environmental management (effective ventilation and moisture elimination) and guideline-driven dermatological management (emollients, topical corticosteroids, calcineurin inhibitors, and biologics in severe situations). Unvalidated mycotoxin testing is discouraged. The existing evidence and pediatric eczema cohort suggest that home dampness or musty odors should be enquired about when ADR is being evaluated by the physician. Randomized trials are required to find out whether local environment-based remedying would have any measurable consequences on the severity of AD.

5.1.2. Respiratory Infections

Hypersensitivity Pneumonitis

Hypersensitivity pneumonitis (HP) is an interstitial lung illness precipitated by recurrent exposure to environmental antigens, of which airborne fungi constitute notable causes. Aspergillus, Penicillium, Trichosporon, and Thermoactinomyces come under species that are inflicted by HP through the immune-mediated methods [91]. Pathogenesis is associated with type III and IV hypersensitivity reactions and an intense T-cell reaction and poorly formed granulomas, leading to alveolitis and fibrosis [92]. Clinically, HP is manifested as an acute, sub-acute, or chronic disease. HP causes acute manifestations, such as fever, dyspnea, and cough, and the late stages of chronic HP may cause a gradual fibrotic remodeling and respiratory disease. Ground-glass opacity, mosaic attenuation, and fibrosis, shown by high-resolution computed tomography with bronchoalveolar lavage and histopathology, help confirm the diagnosis. Exposure history is also an issue since moldy conditions, water-deteriorated shelter conditions, and HP have a heavy connection [93]. Strict antigen avoidance is mandatory in management, backed by corticosteroids in active inflammatory phases. Recent emerging antifibrotic treatment is under study in progressive fibrotic HP. Due to the similarities between chronic fibrotic HP and idiopathic pulmonary fibrosis, prognosis is largely influenced by timely identification and termination of exposure and can be quite poor once the process is established [91]. New guidelines have promoted uniformity in diagnostic scoring and the importance of multidisciplinary assessment. Airborne fungal exposure, in general, is an under-addressed driver of HP, which suggests that the environment must be remediated and that health vigilance in occupation must also be addressed.

Chronic Rhinosinusitis

Chronic rhinosinusitis (CRS) is a multidimensional mucosal inflammatory disease complex in which fungi may be pathogenic in both inflammatory and invasive diseases. Fungi like Aspergillus and dematiaceous molds play a special role in allergic fungal rhinosinusitis (AFRS) where there is eosinophilic mucin with fungal material [94]. The pathophysiology is based on fungal protease-mediated epithelial barrier breakdown, immune hypersensitivity, and development of biofilms that promote chronic inflammation. FRS commonly occurs in the atopic population, with nasal polyps, elevated serum IgE levels, and opacification of the sinuses on a radiograph.

Management treats with functional endoscopic sinus surgery along with corticosteroid therapy to diminish fungal antigen burden and inflammation. As compared to the invasive fungal rhinosinusitis, which is life-threatening and in a debilitated host will require urgent debridement and systemic antifungal therapy, non-invasive fungal sinusitis typically follows a more benign course and can often be managed conservatively [95]. Although initial speculations were that all CRS can be fungus-driven, studies on randomized trials of antifungal agents in non-AFRS CRS provided mixed or little benefit, and antifungal therapy is not suggested as a standard of care [96]. As of now, it seems that fungi are only a component of a larger microbial and immunologic interaction rather than a pathogen of their own. Recent developments in microbiome studies suggest that the fungi–bacterial interactions in the nasal mucosa are dynamic, and such interactions impact the severity of the disease as well as the treatment outcome. This has introduced a shift in research focus to targeted immunomodulation and biologics to treat eosinophilic CRS phenotypes, as well as surgery and corticosteroids. Fungal contributions are important to understand, especially when it comes to identifying various subtypes, including allergic, invasive, and biofilm, and then developing relevant treatment approaches accordingly.

5.1.3. Sick Building Syndrome

Sick building syndrome (SBS) describes a spectrum of diffuse symptoms (such as mucosal irritation, headache, fatigue, difficulties concentrating, skin irritation, and general malaise) experienced by occupants in specific buildings, which is often relieved upon leaving the building [97]. Poor indoor air quality because of damp and poorly ventilated buildings that serve as a home for airborne fungal agents is one of the major environmental contributors. Cladosporium, Penicillium, Aspergillus, and Alternaria are fungi that are highly likely to be found in humid indoor spaces and HVACs, and their growth results in spores, fragments, volatile organic compounds, 8-D-glucans, and mycotoxins [98,99]. Exposure to these bioaerosols can also result in mucous membrane irritation and immunologic/inflammatory responses, even with low and chronic exposures [100].

Penicillium species have been found to be significantly more prevalent in complaint areas in schools than in non-complaint areas or outdoor environments, associating their occurrence with SBS symptoms [101]. In addition, Stachybotrys chartarum and Penicillium chrysogenum produce mycotoxins that persist, being released into the air and having been found in the body fluid of exposed persons, lending biological plausibility to SBS [100]. Mechanically, these fungal agents can lead to neurotoxicity effects like headache, fatigue and poor concentration [102]. It has now been made clear that SBS is most probably the combination of fungal bioaerosols, chemical contaminants, lack of ventilation, and psychosocial factors—all acting in a synergistic relationship. To conclude, airborne fungi and the products of fungi are a potentially high contributor to SBS, which is biologically possible. To mitigate sick building syndrome (SBS), improving building ventilation, controlling moisture, maintaining clean HVAC systems, and remediating fungal contamination are essential to reduce the severity of occupant symptoms and enhance indoor environmental health.

5.1.4. Neurological and Cognitive Effects

There is an increasing interest in the role of airborne fungal exposure, especially to mycotoxin-producing molds, in neurological and cognitive dysfunction. Compounds like ochratoxin A (OTA), aflatoxin B₁ (AFB₁), fumonisin B₁ (FB₁), trichothecenes, deoxynivalenol, and zearalenone can enter the brain through the blood–brain barrier system, triggering oxidative stress and mitochondrial dysfunction, neuroinflammation, DNA damage, and targeted disturbed neurotransmission, which can all be involved in the pathogenesis of neurodegenerative disorders and cognitive alterations [103,104]. Biological plausibility is further supported by experimental evidence. However, in one controlled animal study, exposure to Stachybotrys chartarum spores—both toxic and non-toxic spores—suppressed the long-term spatial memory in mice during the Morris water maze testing with associated hippocampal neuroinflammation and elevated IL-1β levels [105]. At the molecular level, mycotoxins like T-2 toxin, deoxynivalenol, and others interfere with neurotransmitter systems, suppress protein synthesis, compromise mitochondrial energy production, and induce free radical-mediated neuronal damage, as covered in mechanistic reviews [106]. Taken together, these results suggest that airborne fungal toxins may be a contributing factor in cognitive deficits at work—cognitive deficits include memory loss, brain fog, and mood disturbances—and point to concerns about the long-term neurological effects of exposure, such as neurodegenerative diseases. A key next step is to mitigate exposure in indoor environments and to further research the development of neuroprotective interventions.

5.2. Outdoor Fungi

Outdoor fungal communities constitute a significant proportion of atmospheric bioaerosols. The composition of fungi and their dispersal dynamics are highly influenced by environmental conditions such as temperature, humidity, wind speed, and vegetation types [107,108]. The commonly existing outdoor fungi include Cladosporium, Alternaria, Aspergillus, Penicillium, and Fusarium [109,110]. These fungi work by releasing spores into the air that can travel long distances following inhalation by humans leading to a range of adverse health effects [111]. Populations most at risk include children, the elderly, and immunocompromised individuals, particularly during seasons with high spore counts in the environment. Agricultural workers also experience chronic exposure to thermophilic fungi linked to hypersensitivity pneumonitis [112,113].

5.2.1. Allergic Reactions

Fungi can cause many types of allergic reactions, especially to allergic-prone individuals. The level of allergy depends upon the fungus species and the host’s immunological sensitivity that affects IgE-mediated reactions in the host [114,115,116]. The fungal spores enter through the upper and lower respiratory systems. Fungal spores usually float in the air for extended periods of time. The most common genera that exist in the air are Cladosporium, Alternaria, Aspergillus, and Penicillium [117,118,119,120,121]. The spores of these species grow fast in late summer and fall. Their adverse health effects include chronic pulmonary problems, asthma exacerbations, and rhinitis. Climate change and urbanization impact fungal biodiversity and sporulation patterns affect exposure level and frequency of fungal allergies. Below, we explore specific allergic conditions associated with outdoor fungal exposure.

Allergic Rhinitis and Rhinosinusitis

Allergic rhinitis and rhinosinusitis are triggered by exposure to airborne fungal spores, leading to discomfort and nasal congestion, sneezing, runny nose, and itching [122]. Increased spore counts from Aspergillus, Alternaria alternata, and Cladosporium herbarum species are associated with seasonal and persistent rhinitis [123,124,125]. These spores behave as aeroallergens that trigger IgE-mediated immune responses. Longer exposure leads to fungal-induced rhinosinusitis that causes infection in the nasal passages and sinuses [126]. These infections can lead to nasal passage polyp formation and obstruction. The diagnosis of rhinitis involves skin prick testing or specific IgE assays [121]. The treatment of rhinitis requires antihistamines, corticosteroids, anti-leukotrienes, and avoidance strategies [126].

Asthma Exacerbation

Asthma development and severity is another health issue caused by outdoor fungal exposure. There are various epidemiological studies performed by researchers in this area [113,121]. Asthma individuals’ sensitivity is heightened by the Alternaria and Aspergillus spore inhalation [118,127,128]. In a study, the Alternaria alternata and Cladosporium herbarum sensitive individuals are at higher risk from asthma [129]. In asthmatic individuals, these spores also induce airway inflammation and bronchoconstriction. The spore concentrations peak in the summer and autumn resulting in asthma outbreaks, especially in children. Sensitization to these fungi is a significant risk factor for severe asthma and emergency hospitalizations. The mechanism involves both innate and adaptive immune responses leading to bronchial hyperreactivity and chronic inflammation. Environmental monitoring and allergen-reduction measures can play a preventive role in high-risk populations.

Allergic Bronchopulmonary Mycosis

Allergic bronchopulmonary mycosis (ABPM) is generally caused by Aspergillus fumigatus fungi and is a hypersensitivity reaction resulting in repeated asthma exacerbations, mucus plugging, and bronchiectasis [129]. Individuals with pre-existing asthma or cystic fibrosis are majorly affected. This allergy involves an immune response from Type I (IgE-mediated), Type IV (cell-mediated), and Type III (immune complex-mediated) hypersensitivity, resulting in bronchial wall damage [129]. Elevated serum IgE, eosinophilia, and radiographic in the diagnosis evidence the allergies due to bronchiectasis. Management involves corticosteroids and antifungal agents, such as itraconazole, to reduce fungal burden and inflammation [129].

5.2.2. Respiratory Infections

Outdoor airborne fungi are not only allergic in nature but are capable of causing a wide range of respiratory infections as well [130]. Although healthy individuals can tolerate low-level exposure to fungal spores, prolonged exposure can result in serious fungal diseases. Spores of Aspergillus, Fusarium, and Mucorales are very small in size, bypassing upper respiratory defense mechanisms to reach the alveoli. At the alveoli, they germinate and colonize pulmonary tissues. The respiratory issues range from mild sinusitis to life-threatening invasive aspergillosis. Climate change and antifungal resistance in fungal distribution complicate public health management. In this section, we are discussing a few fungal infections linked to outdoor exposure.

Fungal Sinusitis

Outdoor fungi also result in a variety of sinus diseases that range from invasive infections to the non-invasive forms of infections. The non-invasive form of sinus fungal diseases is usually caused by Aspergillus that commonly occurs in immunocompetent individuals [131,132]. On the other hand, invasive fungal sinusitis is a medical emergency that frequently affects immunocompromised people, including those who have health issues related to diabetes, cancer, or have received a transplant [133]. The infection can rapidly extend beyond the sinuses to the orbit and brain, requiring aggressive surgical debridement and antifungal therapy [134]. Environmental exposure, poor air quality, and high spore count in outdoor air contribute to disease risk.

Invasive Pulmonary Aspergillosis

Invasive pulmonary aspergillosis (IPA) is a life-threatening infection predominantly seen in severely immunocompromised individuals, such as those individuals that are undergoing intensive chemotherapy or stem cell transplantation [132]. Outdoor Aspergillus spores, especially A. fumigatus, are small enough (<3 µm) to penetrate deep into the alveoli, where they may evade innate immune clearance mechanisms [132,135]. If immune surveillance is impaired, spores germinate and invade pulmonary tissue. The symptoms of IPA include high fever, cough, hemoptysis, and pleuritic chest pain. Diagnosis requires imaging (CT scan), galactomannan assays, and culture, and treatment includes voriconazole or other systemic antifungals. Reducing spore exposure via high-efficiency particulate air (HEPA) filtration and face masks during high-risk periods may aid prevention.

Hypersensitivity Pneumonitis

Hypersensitivity pneumonitis (HP) is an immune-mediated inflammatory lung disease triggered by repeated inhalation of organic antigens that include fungal spores [136,137]. HP presents in both acute and chronic forms and is characterized by alveolitis, granuloma formation, and fibrosis. Outdoor fungi, such as Penicillium, Aspergillus, and Trichoderma, are known etiological agents [136]. HP is commonly found in agricultural and forestry settings. HP may manifest with cough, dyspnea, malaise, and weight loss [138]. CT scans reveal ground-glass opacities and centrilobular nodules. Management strategies include antigen avoidance, corticosteroids, and occupational safety protocols [139].

5.2.3. Dermal and Ocular Effects

Airborne fungal spores also affect the skin and eyes after prolonged outdoor fungi exposure. Dermal contact with fungal elements may lead to irritant or allergic contact dermatitis resulting in redness, itching, and inflammation [140]. Spores from Fusarium and Cladosporium may cause keratitis or conjunctivitis, especially in individuals with compromised ocular barriers or contact lens users [141]. Outdoor workers, athletes, and gardeners are more frequently affected. These symptoms often go underdiagnosed as fungal involvement in dermal and ocular allergies is less recognized compared to respiratory effects. Wearing protective clothes and eyeglasses in areas with a high concentration of spores is one preventive approach (Figure 3).

6. Control Measures for Outdoor Fungi and Indoor Fungi

6.1. Monitoring

Monitoring the outdoor fungal spores is a crucial step in managing exposure and reducing potential health risks. Continuous and systematic observation helps to generate reliable data on the types, concentrations, and seasonal variations in these airborne fungal spores [142]. Such information is especially valuable for those with respiratory illnesses, like allergies and asthma, as fungal spores can act as strong allergens in some cases of pathogens [143]. The process typically involves specialized tools, like air samplers, volumetric spore traps, and hirst-type devices, which collect air samples over time for qualifying and identifying the fungal species [144]. Meteorological factors, such as wind speed, temperature, and humidity, are recorded alongside the spore data, as they play a vital role in the growth of fungal and dispersion patterns. Advancements in the geographic information system (GIS) and remote sensing are now enabling more precise mapping and forecasting of fungal spore concentrations [145]. For accurate results, sampling equipment should be strategically positioned across urban, suburban, and rural areas to provide the representative profiles of fungal presence in the environment [146]. In cities, sampling near gardens, parks, and composting sites can provide valuable insights. Monitoring also plays a vital role in the identification of contaminants like poorly managed waste areas, decaying vegetation, and construction sites [147]. By tracking changes in the fungal counts over time, public health agencies can issue warnings during periods of high spore activity, especially during warm and humid months when fungal growth peaks [148]. Real-time monitoring also enables a quick response to unusual spikes in fungal counts that may result from environmental disturbances like wildfires, floods, and storms [149]. Data from monitoring systems can inform public policies like timing of outdoor activities, especially for sensitive groups of people. Moreover, long-term datasets contribute to fungal ecology research and the impact of climate change on spore distribution. Despite its importance, the monitoring efforts are often limited by a lack of awareness and funding [150].

6.2. Building Design

The design of buildings plays a vital role in controlling the infiltration and accumulation of outdoor fungal spores. Appropriate architectural planning and construction practices will significantly reduce the entry of fungi indoor environments by incorporating structural and material-based barriers [151]. One of the most effective design strategies is ensuring airtight constructions, which minimize the number of entry points for the airborne particles, including fungal spores [152]. Proper sealing of doors, windows, and ventilation systems helps to maintain the indoor air quality by preventing the unfiltered outdoor air from entering. The use of high-performance building envelopes, including moisture-proof membranes and weather-resistant barriers, further limit fungal growth and infiltration [153]. Another important design consideration is the selection of building materials. The materials that are resistant to moisture accumulation and microbial colonization, like antimicrobial coatings, treated wood, and non-porous surfaces, can help inhibit the growth of fungi on the exterior surfaces [154]. The roofs and facades should be designed to facilitate water drainage and avoid areas where moisture can deteriorate, as damp conditions are conducive to fungal development [155]. The landscaping around the building also impacts the fungal spread. For instance, avoiding the planting of dense vegetation close to structures reduces the transfer of spores through air currents and physical contact [156].

The design elements, like extended roof overhangs and proper gutter systems, can direct rainwater away from the building foundation, thereby preventing dampness in walls and basements. Moreover, the advanced air filtration systems installed within HVAC units can capture spores before spreading throughout the indoors. The integration of smart building technologies, including humidity sensors and automated ventilation controls, allow real-time adjustment of indoor environmental parameters to discourage fungal survival [157]. Public and commercial buildings, particularly schools and hospitals, should adhere to strict designs to safeguard vulnerable populations from fungal exposure [158]. Modern building codes and sustainability certificate programs like LEED and WELL increasingly highlight the importance of controlling microbial growth, reinforcing the value of fungi-aware design. Updating older buildings with improved sealing and advanced ventilation systems can help reduce fungal infiltration [159]. Integrating fungal control measures from the earliest stage of design enhances indoor air quality, supports energy efficiency, promotes occupant health, and reduces long-term maintenance costs. With careful planning and construction practices, buildings can serve as an effective barrier against outdoor fungal contamination [160].

6.3. Air Intake Management

The proper management of air intakes is vital for preventing outdoor fungal spores from entering indoor environments. As key components of ventilation, heating, and air conditioning (HVAC) systems, air intakes can become pathways for airborne contaminants, including fungi, which can infiltrate indoor spaces [161]. Without effective control measures, these systems may draw in elevated levels of spores, especially during peak seasons or from nearby sources like construction sites, stagnant water, compost heaps, or decaying vegetation [162]. The intake should be located away from pollutant sources, like water disposal areas, shaded and damp zones, water bodies and HVAC exhausts outlets, all of which encourage the growth of fungi. Elevating the intake above ground level reduces the risk of pulling in spores stirred up by traffic or foot activity [163]. Utilizing high-efficiency particulate air (HEPA) filters or filters with a high minimum efficiency reporting value (MERV) rating can capture spores as small as 0.3 microns. These filtration units should be routinely inspected, cleaned, and replaced to maintain optimal peak performance [164].

The installation of pre-filters can help trap larger particles, protecting the main filter and extending its lifespan. In addition, ultraviolet germicidal irradiation (UVGI) systems placed near intakes or within ductwork can neutralize fungal spores before entering occupied areas [165]. Additionally, sealing intake dampers tightly when not in use prevents passive airflow that may carry spores indoors. The seasonal intake management is also important during periods of high fungal activity, such as spring and autumn, when airflow rates may be reduced or temporarily adjusted to limit contamination [166]. Automated systems with real-time outdoor air quality monitoring can optimize these adjustments. In high-risk situations, such as hospitals or large facilities, installing redundant intake systems and maintaining positive indoor air pressure can further safeguard against contamination [167]. The maintenance staff should be trained to detect the early signs of fungal contamination around intake areas such as musty odors, discoloration, and biofilm growth. Regular cleaning of intake grilles and ducts reduces fungal colonization and reduces the risk of spores being reintroduced into the air supply [168].

6.4. Ventilation

Ventilation is an important factor when considering the control measures for indoor fungi, especially mold growth. Ventilation removes or dilutes the pollutants and control thermal environment as well as humidity in buildings. Ventilation in buildings must be either adequate for the removal of the pollutants and humidity, or their dilution must be to levels which are acceptable for health. Research shows an association between ventilation and improved human health. Prominent symptoms commonly observed in ventilation–health related studies comprise upper respiratory infections and allergies [169]. Ventilation impacts the air and moisture flow through the buildings, which may result in moisture problems that can result in structure degradation. Ventilation also brings changes in the pressure differences and may cause or prevent the infiltration of pollutants from building structures. Building ventilation, both natural and mechanical, serves as the primary means of exchanging indoor and outdoor air, significantly influencing human exposure to airborne particles, chemicals, and microbes.

Ventilation is strongly associated with perceived indoor air quality (IAQ), health, and productivity [170]. The preferred ventilation strategy plays a crucial role in shaping the composition and diversity of the indoor microbial community [171]. While CO₂ concentration remains the most recognized proxy indicator for indoor air quality (IAQ), indoor fungi can serve as complementary bioindicators reflecting, the biological component of IAQ [172]. Overall, for ventilation, windows and doors should be opened regularly for fresh air exchange. Exhaust fans can be used in homes to remove moist air. HVAC systems with HEPA filters can control humidity and filter spores. It is also common to find mechanical ventilation systems, such as heat recovery ventilation (HRV) and energy recovery ventilation (ERV) systems, in well-insulated buildings. These systems are useful to remove stale indoor air and replace them with fresh air from outdoors. In the case of polluted outdoor air, advanced methods and air purification systems are required [173]. The Welsh Housing Quality Standard (WHQS) 2023 mandates that modern kitchens must have proper ventilation systems to prevent dampness, mold growth, and harmful emissions [174].

6.5. Moisture Control

It is a well-known fact that fungi require water for their growth. This association should underlie any control strategy for indoor fungal growth, but it appears to be more complex than assumed. The water relations of fungal communities are studied under definite, stable water activity and temperature conditions. Time-dependent variations in humidity, temperature, and air movement introduce inertia effects in both the biological system of the growing fungus and the physical system of building materials and constructions. Considering the health of occupants, moisture control is thus essential for the proper functioning of any building [175]. The common moisture problems can be traced to poor decisions in design, construction, or maintenance. The American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) recognizes that varying perspectives from researchers across diverse professions contribute to challenges in achieving proper moisture control in buildings. Moisture problems can be effectively addressed by implementing key measures, such as preventing water intrusion and condensation in areas designed to remain dry, while accepting that certain building areas may remain wet due to their regular use, thereby necessitating appropriate design and maintenance strategies for those zones. These include kitchens, bathrooms, and many more [176].

6.6. Regular Cleaning

Cleaning and managing indoor air quality is important for human health and materials as well as the equipment in the indoor environment. It can be carried out by wiping down interior surfaces, vacuuming carpets, and cleaning drains and roof gutters [177]. Increased awareness of the health impacts associated with particulate matter, bacteria, and fungi on interior flooring led to more advanced vacuum cleaning technologies and systems. Skin irritations and respiratory problems have been revealed to be associated with airborne particulates and microbiological concentrations in indoor environments. A study was carried out to observe the effect of intensive, high-efficiency HEPA vacuuming on indoor fine particulates and fungal levels and composition in the residential environments of Australia. It showed that the regular maintenance of carpets with high-efficiency vacuuming can be helpful in decreasing the airborne fine particulates and maintaining a stable count of fungal spores indoors [178]. Vacuum cleaners with high-efficiency exhaust filtration are used for decreasing the exposure levels of the residential allergens. An investigation evaluated the efficiency of high-efficiency vacuuming to control airborne fungal spore levels. The assessment of the bioaerosols was continued for a period of ten months in homes. The analysis of air samples was performed for three fungal spore categories, representing taxa with predominantly outdoor sources and one representing taxa that commonly have indoor sources. The conclusions indicated that the high-efficiency filtered exhaust can be effective in controlling airborne fungal spore exposure when infiltration of outdoor air is very limited [179]. In-room air cleaners (ACs) and upper-room air ultraviolet germicidal irradiation (UVGI) are engineering interventions useful for reducing the concentrations of airborne bacterial and fungal spores in indoor environments [165]. Regular cleaning is important to remove the nutrients and conditions for fungal growth, thus supporting a cleaner and healthier indoor environment (Figure 3).

7. Integrating AI into Aeromycology

The integration of artificial intelligence (AI) into aeromycology is revolutionizing how scientists and public health professionals investigate, classify, and forecast the dynamics of airborne fungal communities. AI provides unprecedented capabilities for automating labor-intensive tasks, extracting complex patterns from large datasets, and developing adaptive models to inform public health action. This section explores four key domains of AI-driven innovation in aeromycology [180] (

Table 2. Common airborne fungi: traits, health impacts, and AI use cases.

Fungal Genus	Typical Morphology	Main Source	Health Effect	Traditional ID Challenge	AI Application Example
Alternaria	Large, club-shaped conidia	Plants/Soil	Severe allergy, asthma	Overlap with Ulocladium	CNN classification
Cladosporium	Dark, septate conidia	Decay/Leaves	Allergic rhinitis	High in air, small size	SVM with imaging
Aspergillus	Small, round conidia	Compost, dust	Asthma, aspergillosis	Mixed with Penicillium	Deep neural networks
Penicillium	Brush-shaped conidia	Indoor, food	Indoor allergies	Confused with Aspergillus	Random forest, SVM
Stachybotrys	Black, chain/cluster	Damp walls	Sick building syndrome	Rare, hard to isolate	Feature-based learning
Fusarium	Sickle-shaped, colored	Crops/Soil	Rare infection	Low abundance	Hybrid AI models

,

Table 3. AI techniques in aeromycology: purpose and performance.

Task Type	Input Data Type	AI/ML Approach	Output	Benefit	Key Limitation
Spore ID	Micrographs	CNN, SVM, RF	Species label, count	Automation, accuracy	Needs annotated data
Image Segmentation	Raw image stacks	U-Net, Mask R-CNN	Spore boundaries	Reliable quantitation	GPU needed, complexity
Abundance Forecast	Weather + spore ct.	RNN, Time Series	Daily spore levels	Real-time alerts	Model drift
Dispersion Mapping	Spatial data	Ensemble, Kriging	Risk heatmap	Spatial targeting	Data sparsity
Health Impact Correlation	Clinical + air data	Decision trees, Regression	Allergy event risk	Preventive action	Data privacy, lag
Explainability	Model decisions	SHAP, LIME	Feature attribution	Trust, regulatory	Interpretation skills

and Table 4).

7.1. AI for Fungal Spore Identification and Classification

Traditional fungal spore identification under the microscope is time-consuming and subject to observer bias. AI, particularly supervised machine learning algorithms, enables automated recognition and classification of fungal spores based on digital images or sensor data. Well-trained classifiers can distinguish subtle differences between morphologically similar spores, improving throughput and consistency in routine air quality or biodiversity monitoring. Techniques like random forest, support vector machines (SVM), and feature-based neural networks are commonly deployed for this task, reducing dependence on expert taxonomists and making identification scalable to large surveillance programs [181] (Figure 4).

7.2. Deep Learning for Microscopic Image Analysis

Deep learning, especially convolutional neural networks (CNNs), has dramatically improved the analysis of microscopic fungal images. Unlike classical approaches, CNNs can extract high-level features (shape, texture, arrangement) automatically and with higher discriminative power, resulting in robust identification even in noisy or mixed samples. Advances in transfer learning, data augmentation, and explainable AI further enhance deep learning’s utility, enabling its adaptation to rare species, novel morphotypes, and new imaging platforms. This automation accelerates environmental sampling and opens pathways for global, high-resolution fungal surveillance [182,183].

7.3. Predictive Modeling of Fungal Dispersion and Abundance

Accurate modeling of airborne fungal spore dispersion is crucial for both ecological studies and predicting exposure risks. AI-based predictive models employ weather, land use, and historical spore data to train algorithms, such as recurrent neural networks (RNNs), ensemble models, or Gaussian processes, for spatial and temporal forecasting of spore concentrations. These models can simulate the influence of meteorological factors (wind, humidity), geographic features, and ecological events, generating real-time risk maps that support pest control, urban planning, and public health interventions during high-risk periods (e.g., harvests, thunderstorms) [184].

7.4. AI in Allergenicity Forecasting and Public Health

Many fungal spores are potent allergens and fluctuations in their abundance can trigger asthma and allergic diseases. AI-driven systems integrate aerobiological, meteorological, and health data (such as emergency room visits or pharmacy sales) to forecast periods and areas of hazardous spore load. Ensemble learning, decision tree forecasting, and time-series analysis support personalized alerts, targeted interventions, and evidence-based policy for mitigating allergy and respiratory health burdens. As mentioned in Figure 5, emerging explainable AI techniques are also enabling transparency and trust in algorithm-driven health advisories, laying the groundwork for regulatory acceptance and public engagement [185].

Figure 5. AI-driven allergenicity forecasting system.

Figure 5. AI-driven allergenicity forecasting system.

Table 4. Advantages, disadvantages, and future scope of artificial intelligence in aeromycology.

Author and Citation	Advantages of the Study	Disadvantages of the Study	Potential Applications of the Study	Description of the Study	Future Scope of the Study
Clancy, Markey [180]	The study provides the first real-time pollen and fungal spore monitoring dataset for Dublin City, using advanced real-time fluorescence spectroscopy instruments. It demonstrates the strong potential of the WIBS-NEO for real-time bioaerosol detection, particularly for pollen and spores, and validates real-time sensors against traditional volumetric impactor methods. The methodology introduces novel comparative approaches and rigorous data filtering for cross-instrument validation.	Limitations include the restricted particle size bins of the IBAC-2 device, which result in weak correlations with conventional sampling data. The campaign duration was relatively short for long-term trend analysis. Discrepancies in particle fluorescence and size range between instruments can introduce measurement bias, and there is a need for further refinement in the instrumentation, especially for the IBAC-2.	The findings support the development of efficient real-time bioaerosol early warning systems for public health and allergy management in urban settings. The real-time tools can be integrated into environmental monitoring networks and used for epidemiological studies linking bioaerosol concentration trends with health outcomes. Urban air quality forecasting and mitigation strategies can benefit from higher temporal-resolution bioaerosol data.	This study compared the performance of WIBS-NEO and IBAC-2 real-time fluorescence-based spectroscopic instruments to the traditional Hirst volumetric impactor for detecting primary biological aerosol particles (PBAPs) in urban Dublin. Data were collected over a 41-day period during summer, measuring ambient pollen and fungal spore concentrations. Correlation analyses were performed between device outputs, revealing the effectiveness of WIBS-NEO and highlighting limitations of IBAC-2.	Future research should involve longer monitoring campaigns to validate and strengthen the findings. Enhancements in real-time instrumentation, including more particle sizing and advanced fluorescence markers, are recommended. Broader integration of meteorological and pollution data with bioaerosol monitoring is necessary for better source attribution and public health impact assessment. Further investigation into differentiating bioaerosols from anthropogenic fluorescent particles is also suggested.
Fidan, Çelik [183]	The study demonstrates the effective use of deep learning models—VGG16, EfficientNetB0, and MobileNetV3—for classifying microscopic images of dermatophyte fungi. The methodology includes a diverse, carefully curated dataset, and applies robust data augmentation and principal component analysis (PCA) for improved model generalization and visualization. The performance comparison highlights which architectures best suit the classification task. VGG16 achieved the most balanced performance, while MobileNetV3 achieved the highest overall accuracy.	Some classes, such as Candida albicans and Aspergillus niger, showed higher error rates in classification, signaling challenges in intra-class variability and image distinctiveness. The study primarily uses relatively small image segments (parcels), potentially missing contextual patterns. Training and validation rely heavily on a specific dataset (Defungi), which may limit immediate generalizability to other environments or acquisition protocols. Improvements in sample diversity and quality, as well as model ensemble methods, are needed for even better results.	These models can be integrated into diagnostic tools for rapid, automated, and accurate identification of dermatophyte infections in clinical laboratories. The approach can support medical professionals in both traditional and telemedicine settings, facilitating accelerated and more objective diagnostic workflows. Implementation in large-scale screening and public health surveillance of fungal infections becomes more feasible with AI-powered image analysis.	The research compares VGG16, EfficientNetB0, and MobileNetV3 deep learning models for classifying five different fungal types (Candida albicans, Aspergillus niger, Trichophyton rubrum, Trichophyton mentagrophytes, and Epidermophyton floccosum) from microscopic images. A manually labeled and balanced dataset of 5000 images was used, with performance evaluated using accuracy, precision, recall, and F1 score. Data augmentation was applied and PCA was used for feature visualization. Results indicate promising performance, especially with MobileNetV3 achieving 86.3% accuracy.	Future development will focus on increasing dataset diversity and re-labeling new image segments for improved granularity and model performance, especially for challenging classes like Candida albicans. The integration of other AI models, like CNN variants and Vision Transformers, is planned. Application in telediagnosis and wider clinical scenarios is envisioned, expanding usability for remote and non-specialized healthcare practitioners. Adoption of larger and more varied datasets will aid in improving both generalization and classification success.
Ren, Tan [182]	The dual-model deep learning framework combining YOLOX and MobileNet V2 achieves high precision, recall, and agreement with clinicians in identifying fungal forms. Diagnostic time and labor are significantly reduced using the AI-based approach.	Annotation of fungal images is time-consuming and requires considerable expertise. Generalization of the model may be limited by training data diversity and fungal species variability.	Can be integrated into clinical settings to automate fungal diagnosis from fluorescence images, assist clinicians, reduce workload, and support diagnostic decision-making.	The study presents an AI-driven system using YOLOX for detecting spores and hyphae, and MobileNet V2 for mycelium detection in fluorescence images from clinical samples. Data labeling and model evaluation were performed using multiple metrics and independent test sets.	Further optimization with alternative AI models (e.g., YOLOv5, Faster R-CNN), expanding and diversifying datasets, and advanced augmentation techniques could enhance accuracy and generalization for broader clinical and research applications.
Bruffaerts, Graf [181]	Establishes rigorous best practices for cultivating and collecting reference fungal material, greatly improving training data reliability for automated identification systems. Demonstrates enhanced classification accuracy using standardized samples and innovative protocols for fungal spore harvesting and aerosolization. Validates airflow cytometry and machine learning for better recognition rates than prior methods. Enables reproducibility, rapid data generation, and improved species range for research and clinical applications.	Dataset generalizability is limited by species selection and cultivation conditions. Variability in spore morphology and chemical characteristics due to laboratory vs. environmental growth may affect classification. Harvesting and aerosolization protocols still face challenges with certain fungi, and fluorescence-based identification remains sensitive to humidity, age, and environmental factors. Some instrument limitations persist, including noise and imaging constraints.	Facilitates high-throughput, real-time fungal spore monitoring for environmental, agricultural, allergenic, and plant pathology sectors. Allows AI-driven systems to assist in early warning for crop and health management. Can provide cleaner datasets for algorithm development, support standardization, and contribute to international aerobiological networks and climate modeling.	Presents a methodological advancement combining chamber-based protocols, dry cyclone harvesting, controlled Petri dish cultures, and machine learning algorithms trained on holographic and fluorescence spectra. Describes cultivation of 17 selected fungi, techniques for aerosolizing spores, and the use of advanced monitors (SwisensPoleno Jupiter, Plair Rapid-E) to build reference datasets. Reports improved recognition rates for airborne fungal spores and set out guidelines for data standardization.	Recommends expansion to more fungal taxa and datasets, cross-validation between monitors, and inclusion of naturally aged or environmentally sourced spores. Proposes multi-instrument integration, more chemical and morphological analysis, and adaptation for international use. Calls for future research on bioaerosol diversity, fluorescent signal variability, and automated systems capable of distinguishing between complex particle types in diverse atmospheric contexts.
Lee, Jeong [13]	The study introduces data-driven ML models, using qPCR, to predict fungal concentrations in diverse public facilities, increasing the accuracy and timeliness of air quality assessments. The Gradient Boosting model achieves strong predictive performance (R = 0.78 R = 0.78 R = 0.78), with interpretability enhanced through SHAP analysis. It leverages widely available environmental variables and uncovers influential factors, like facility type and precipitation. These strengths offer improved rapid monitoring compared to traditional culture or fluorescence methods.	The study is limited by geographic scope (South Korea) and facility types, which may restrict model generalizability. qPCR, while sensitive, entails longer sampling times and can be less practical for continuous real-time monitoring. Model accuracy depends on data labeling quality and environmental heterogeneity, with some instrumentation constraints remaining. The findings may not fully extrapolate to other climates or untested building types.	The models can be implemented to support routine indoor air management in large public spaces, aiding in early detection and response to bioaerosol threats. Their application could enhance health and safety protocols in day care centers, transit hubs, and retail environments. Potential exists for use in regulatory monitoring, epidemiological studies, and development of smart building solutions, benefiting public health and resource allocation.	The research quantifies fungal concentrations via qPCR in samples collected from various public facilities, then develops and validates seven ML models (including Gradient Boosting) linking these concentrations to environmental variables. It analyzes feature importance using SHAP, finding day care centers and precipitation to strongly affect indoor airborne fungi. Results show the Gradient Boosting model outperforms other approaches, offering valuable predictive insights.	Future research may expand geographic coverage and include additional building types to improve generalizability. Model development could incorporate more advanced algorithms, automated data integration, and real-time sensors for faster prediction. Validation across international settings and integration with broader air quality surveillance networks is recommended. There is potential for synergizing with health informatics and smart IoT monitoring platforms.

Table 4. Advantages, disadvantages, and future scope of artificial intelligence in aeromycology.

Author and Citation	Advantages of the Study	Disadvantages of the Study	Potential Applications of the Study	Description of the Study	Future Scope of the Study
Clancy, Markey [180]	The study provides the first real-time pollen and fungal spore monitoring dataset for Dublin City, using advanced real-time fluorescence spectroscopy instruments. It demonstrates the strong potential of the WIBS-NEO for real-time bioaerosol detection, particularly for pollen and spores, and validates real-time sensors against traditional volumetric impactor methods. The methodology introduces novel comparative approaches and rigorous data filtering for cross-instrument validation.	Limitations include the restricted particle size bins of the IBAC-2 device, which result in weak correlations with conventional sampling data. The campaign duration was relatively short for long-term trend analysis. Discrepancies in particle fluorescence and size range between instruments can introduce measurement bias, and there is a need for further refinement in the instrumentation, especially for the IBAC-2.	The findings support the development of efficient real-time bioaerosol early warning systems for public health and allergy management in urban settings. The real-time tools can be integrated into environmental monitoring networks and used for epidemiological studies linking bioaerosol concentration trends with health outcomes. Urban air quality forecasting and mitigation strategies can benefit from higher temporal-resolution bioaerosol data.	This study compared the performance of WIBS-NEO and IBAC-2 real-time fluorescence-based spectroscopic instruments to the traditional Hirst volumetric impactor for detecting primary biological aerosol particles (PBAPs) in urban Dublin. Data were collected over a 41-day period during summer, measuring ambient pollen and fungal spore concentrations. Correlation analyses were performed between device outputs, revealing the effectiveness of WIBS-NEO and highlighting limitations of IBAC-2.	Future research should involve longer monitoring campaigns to validate and strengthen the findings. Enhancements in real-time instrumentation, including more particle sizing and advanced fluorescence markers, are recommended. Broader integration of meteorological and pollution data with bioaerosol monitoring is necessary for better source attribution and public health impact assessment. Further investigation into differentiating bioaerosols from anthropogenic fluorescent particles is also suggested.
Fidan, Çelik [183]	The study demonstrates the effective use of deep learning models—VGG16, EfficientNetB0, and MobileNetV3—for classifying microscopic images of dermatophyte fungi. The methodology includes a diverse, carefully curated dataset, and applies robust data augmentation and principal component analysis (PCA) for improved model generalization and visualization. The performance comparison highlights which architectures best suit the classification task. VGG16 achieved the most balanced performance, while MobileNetV3 achieved the highest overall accuracy.	Some classes, such as Candida albicans and Aspergillus niger, showed higher error rates in classification, signaling challenges in intra-class variability and image distinctiveness. The study primarily uses relatively small image segments (parcels), potentially missing contextual patterns. Training and validation rely heavily on a specific dataset (Defungi), which may limit immediate generalizability to other environments or acquisition protocols. Improvements in sample diversity and quality, as well as model ensemble methods, are needed for even better results.	These models can be integrated into diagnostic tools for rapid, automated, and accurate identification of dermatophyte infections in clinical laboratories. The approach can support medical professionals in both traditional and telemedicine settings, facilitating accelerated and more objective diagnostic workflows. Implementation in large-scale screening and public health surveillance of fungal infections becomes more feasible with AI-powered image analysis.	The research compares VGG16, EfficientNetB0, and MobileNetV3 deep learning models for classifying five different fungal types (Candida albicans, Aspergillus niger, Trichophyton rubrum, Trichophyton mentagrophytes, and Epidermophyton floccosum) from microscopic images. A manually labeled and balanced dataset of 5000 images was used, with performance evaluated using accuracy, precision, recall, and F1 score. Data augmentation was applied and PCA was used for feature visualization. Results indicate promising performance, especially with MobileNetV3 achieving 86.3% accuracy.	Future development will focus on increasing dataset diversity and re-labeling new image segments for improved granularity and model performance, especially for challenging classes like Candida albicans. The integration of other AI models, like CNN variants and Vision Transformers, is planned. Application in telediagnosis and wider clinical scenarios is envisioned, expanding usability for remote and non-specialized healthcare practitioners. Adoption of larger and more varied datasets will aid in improving both generalization and classification success.
Ren, Tan [182]	The dual-model deep learning framework combining YOLOX and MobileNet V2 achieves high precision, recall, and agreement with clinicians in identifying fungal forms. Diagnostic time and labor are significantly reduced using the AI-based approach.	Annotation of fungal images is time-consuming and requires considerable expertise. Generalization of the model may be limited by training data diversity and fungal species variability.	Can be integrated into clinical settings to automate fungal diagnosis from fluorescence images, assist clinicians, reduce workload, and support diagnostic decision-making.	The study presents an AI-driven system using YOLOX for detecting spores and hyphae, and MobileNet V2 for mycelium detection in fluorescence images from clinical samples. Data labeling and model evaluation were performed using multiple metrics and independent test sets.	Further optimization with alternative AI models (e.g., YOLOv5, Faster R-CNN), expanding and diversifying datasets, and advanced augmentation techniques could enhance accuracy and generalization for broader clinical and research applications.
Bruffaerts, Graf [181]	Establishes rigorous best practices for cultivating and collecting reference fungal material, greatly improving training data reliability for automated identification systems. Demonstrates enhanced classification accuracy using standardized samples and innovative protocols for fungal spore harvesting and aerosolization. Validates airflow cytometry and machine learning for better recognition rates than prior methods. Enables reproducibility, rapid data generation, and improved species range for research and clinical applications.	Dataset generalizability is limited by species selection and cultivation conditions. Variability in spore morphology and chemical characteristics due to laboratory vs. environmental growth may affect classification. Harvesting and aerosolization protocols still face challenges with certain fungi, and fluorescence-based identification remains sensitive to humidity, age, and environmental factors. Some instrument limitations persist, including noise and imaging constraints.	Facilitates high-throughput, real-time fungal spore monitoring for environmental, agricultural, allergenic, and plant pathology sectors. Allows AI-driven systems to assist in early warning for crop and health management. Can provide cleaner datasets for algorithm development, support standardization, and contribute to international aerobiological networks and climate modeling.	Presents a methodological advancement combining chamber-based protocols, dry cyclone harvesting, controlled Petri dish cultures, and machine learning algorithms trained on holographic and fluorescence spectra. Describes cultivation of 17 selected fungi, techniques for aerosolizing spores, and the use of advanced monitors (SwisensPoleno Jupiter, Plair Rapid-E) to build reference datasets. Reports improved recognition rates for airborne fungal spores and set out guidelines for data standardization.	Recommends expansion to more fungal taxa and datasets, cross-validation between monitors, and inclusion of naturally aged or environmentally sourced spores. Proposes multi-instrument integration, more chemical and morphological analysis, and adaptation for international use. Calls for future research on bioaerosol diversity, fluorescent signal variability, and automated systems capable of distinguishing between complex particle types in diverse atmospheric contexts.
Lee, Jeong [13]	The study introduces data-driven ML models, using qPCR, to predict fungal concentrations in diverse public facilities, increasing the accuracy and timeliness of air quality assessments. The Gradient Boosting model achieves strong predictive performance (R = 0.78 R = 0.78 R = 0.78), with interpretability enhanced through SHAP analysis. It leverages widely available environmental variables and uncovers influential factors, like facility type and precipitation. These strengths offer improved rapid monitoring compared to traditional culture or fluorescence methods.	The study is limited by geographic scope (South Korea) and facility types, which may restrict model generalizability. qPCR, while sensitive, entails longer sampling times and can be less practical for continuous real-time monitoring. Model accuracy depends on data labeling quality and environmental heterogeneity, with some instrumentation constraints remaining. The findings may not fully extrapolate to other climates or untested building types.	The models can be implemented to support routine indoor air management in large public spaces, aiding in early detection and response to bioaerosol threats. Their application could enhance health and safety protocols in day care centers, transit hubs, and retail environments. Potential exists for use in regulatory monitoring, epidemiological studies, and development of smart building solutions, benefiting public health and resource allocation.	The research quantifies fungal concentrations via qPCR in samples collected from various public facilities, then develops and validates seven ML models (including Gradient Boosting) linking these concentrations to environmental variables. It analyzes feature importance using SHAP, finding day care centers and precipitation to strongly affect indoor airborne fungi. Results show the Gradient Boosting model outperforms other approaches, offering valuable predictive insights.	Future research may expand geographic coverage and include additional building types to improve generalizability. Model development could incorporate more advanced algorithms, automated data integration, and real-time sensors for faster prediction. Validation across international settings and integration with broader air quality surveillance networks is recommended. There is potential for synergizing with health informatics and smart IoT monitoring platforms.

8. Conclusions

Aeromycology needs far more focus and research due to its link with poor health outcomes. Airborne fungi are significant factors of pathogens that lead to allergies and other health problems, resulting in a huge socio-economic burden on health services. The present review has clearly detailed the sampling techniques for airborne fungi. It is clear that both traditional and DNA-based techniques have their own utility and role. Traditional methods, due to their low cost and ease of identification, are still used widely but modern methods are more accurate in the identification and characterization of fungi which cannot be cultured. Aeromycoflora are distributed in outdoor as well as indoor environments. A direct relationship has been observed between exposure to fungi and human health. In fact, exposure to indoor fungal allergens is more harmful to occupants than being exposed to outdoor pollutants. Thus, maintaining proper ventilation systems, cleaning carpets, controlling indoor moisture levels, and considering some aspects of building design are important mitigating factors. These are the major challenges for deeper understanding as they provide the basis for interventions which need to be considered for buildings in which there is more exposure to fungal spores. Further research is required for establishing causality and evaluating interventions for fungal- and dampness-related health effects. Integrating AI with aeromycology can greatly improve fungal identification and classification and will be useful in early warnings of adverse health outbreaks and supporting public health decision-making.