# Investigating the Atmospheric Microbial Ecosystem Through Theory, Bioenergetics, and Numerical Modeling: A Breath of Fresh Air for Aeromicrobiology

Eloi Martinez-rabert, Laura Molares Moncayo, Elizabeth Trembath-reichert, Rachael Lappan, Chris Greening, Jacqueline Goordial, James A Bradley

<figure><img src="image_1.png" /></figure>

## To cite this version:

Eloi Martinez-rabert, Laura Molares Moncayo, Elizabeth Trembath-reichert, Rachael Lappan, Chris Greening, et al.. Investigating the Atmospheric Microbial Ecosystem Through Theory, Bioenergetics, and Numerical Modeling: A Breath of Fresh Air for Aeromicrobiology. Journal of Geophysical Research: Biogeosciences, 2025, 130 (8), 10.1029/2025jg009071. hal-05389237

HAL Id: hal-05389237

https://hal.science/hal-05389237v1

Submitted on 29 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

# JGR Biogeosciences

## REVIEW ARTICLE

10.1029/2025JG009071

**Special Collection:**

Microbial Geochemistry,
Chemical Geobiology, and
Geobiochemistry: Integrated
Approaches to Understanding the
World Around Us - a tribute to
Jan Amend

**Key Points:**

* Aerobiology research is hindered by several technical challenges facing experimental studies, sampling, and monitoring strategies

* Theoretical approaches and numerical modeling tools provide powerful avenues to investigate the atmospheric microbial ecosystem

* Anticipated theoretical advances include evaluating the survival and growth of atmospheric microbial cohorts on different metabolites

**Correspondence to:**

E. Martinez-Rabert and J. A. Bradley,
eloy.MARTINEZ-RABERT@univ-amu.fr;
james. Bradley@nio.osupytheas.fr

**Citation:**

Martinez-Rabert, E., Molares Moncayo, L., Trembath-Reichert, E., Lappan, R., Greening, C., Goordial, J., & Bradley, J. A. (2025). Investigating the atmospheric microbial ecosystem through theory, bioenergetics, and numerical modeling: A breath of fresh air for aeromicrobiology. *Journal of Geophysical Research: Biogeosciences*, 130, e2025JG009071. https://doi.org/10.1029/2025JG009071

Received 6 MAY 2025

Accepted 7 JUL 2025

**Author Contributions:**

**Conceptualization:** Eloi Martinez-Rabert, Laura Molares Moncayo, Elizabeth Trembath-Reichert, Rachael Lappan, Chris Greening, Jacqueline Goordial, James A. Bradley
**Formal analysis:** Eloi Martinez-Rabert, Laura Molares Moncayo, James A. Bradley

© 2025. The Author(s).
This is an open access article under the
terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.

<figure><img src="image_5.png" /></figure>

## Investigating the Atmospheric Microbial Ecosystem Through Theory, Bioenergetics, and Numerical Modeling: A Breath of Fresh Air for Aeromicrobiology

Eloi Martinez-Rabert¹, Laura Molares Moncayo¹,²,³, Elizabeth Trembath-Reichert⁴ ID, Rachael Lappan⁵,⁶,⁷ ID, Chris Greening⁵,⁷, Jacqueline Goordial⁸ ID, and James A. Bradley¹,⁹ ID

¹Aix Marseille Université, Université de Toulon, CNRS, IRD, MIO, Marseille, France, ²School of Geography, Queen Mary University of London, London, UK, ³Natural History Museum, London, UK, ⁴School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA, ⁵Department of Microbiology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia, ⁶School of Earth, Atmosphere & Environment, Monash University, Clayton, VIC, Australia, ⁷Securing Antarctica's Environmental Future, Monash University, Clayton, VIC, Australia, ⁸School of Environmental Sciences, University of Guelph, Guelph, Ontario, Canada, ⁹School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK

**Abstract** The atmosphere may constitute the Earth's largest microbial ecosystem, yet it remains the least understood. While microorganisms can persist and may even thrive in the polyextremes of the Earth's atmosphere, it is still unknown whether the atmosphere sustains an active microbial community. Despite growing awareness of the role of the aeromicrobiome in shaping global biogeography, epidemiology, and climate, fundamental questions about its metabolic activity and ecological significance remain unanswered. Here, we outline how theoretical approaches and numerical modeling tools provide powerful avenues to investigate the atmospheric microbial ecosystem, offering unique insights that complement experimental and observational-based studies and can overcome many of the challenges they face. We consider frameworks that integrate (a) theoretical considerations for microbial metabolism across a range of catabolic and anabolic processes, (b) microbial physiology and metabolic states, (c) thermodynamics and bioenergetics, (d) the chemical and physical conditions of the atmosphere and bioaerosols, (e) transport and residence time of microorganisms, and (f) bottom-up and top-down approaches. Theory and modeling-based investigations into the aeromicrobiome can generate and test theory and model-informed hypotheses, formulate mechanistic explanations of biological processes and observations, and inform targeted sampling strategies and experimentation. Together, these approaches bring us closer to determining whether the Earth's atmosphere is a true ecosystem—that is, a metabolically active community of organisms interacting with each other and with the environment. Advances in aeromicrobiology research brought about by theory and modeling can reveal significant insights into global biogeography, biogeochemical cycles, climate processes, and the limits for life.

**Plain Language Summary** The atmosphere presents numerous challenges to microorganisms including low temperatures, aridity, UV radiation, and low nutrient availability. Despite research demonstrating that microorganisms can tolerate and even grow under these conditions, we still do not have a clear picture of whether the Earth's atmosphere constitutes an active microbial ecosystem. Here, we outline how theoretical and numerical modeling tools provide new avenues to investigate microorganisms in the atmosphere. We evaluate theoretical bases of microbial metabolism (the set of processes used by microorganisms to obtain energy from nutrients), thermodynamics, and bioenergetics (how microbes transform energy), in context with the chemical, physical, and biological composition of the atmosphere, to outline how theory and modeling-based investigations can generate and refine hypotheses, sampling strategies, and experimentation; evaluate the capacity of the atmosphere to sustain life and support a microbial ecosystem; and assess how biological processes influence the Earth's atmosphere. Advances in aeromicrobiology research brought about by theory and modeling can reveal significant insights into global biogeography, biogeochemical cycles, climate processes, and the limits for life in planetary atmospheres.

## 1. Introduction

The investigation of the atmosphere's ability to not only passively transport biological material through the air but also to potentially sustain life residing in the air, is an emerging concept in aerobiology. This avenue has the
**Funding acquisition:** Eloi Martinez-Rabert, Elizabeth Trembath-Reichert, Rachael Lappan, Chris Greening, Jacqueline Goordial, James A. Bradley

**Investigation:** Eloi Martinez-Rabert, Laura Molares Moncayo, Elizabeth Trembath-Reichert, Rachael Lappan, Chris Greening, Jacqueline Goordial, James A. Bradley

**Methodology:** Eloi Martinez-Rabert, Laura Molares Moncayo, Elizabeth Trembath-Reichert, James A. Bradley

**Project administration:** Eloi Martinez-Rabert, Jacqueline Goordial, James A. Bradley

**Resources:** Eloi Martinez-Rabert, James A. Bradley

**Supervision:** Eloi Martinez-Rabert, James A. Bradley

**Visualization:** Eloi Martinez-Rabert, Laura Molares Moncayo

**Writing – original draft:** Eloi Martinez-Rabert, Laura Molares Moncayo

**Writing – review & editing:**

Eloi Martinez-Rabert, Laura Molares Moncayo, Elizabeth Trembath-Reichert, Rachael Lappan, Chris Greening, Jacqueline Goordial, James A. Bradley

potential to advance understanding of microbial biogeography, biodiversity, ecosystem dynamics, atmosphere processes, climate, biogeochemistry, epidemiology, and the capability of microbial life to inhabit extreme environments. With recent advances, including the identification of viable microorganisms in the atmosphere (Amato et al., 2005, 2007; Imshenetsky et al., 1978; Sattler et al., 2001; Vaïtilingom et al., 2010; Wainwright et al., 2003), estimations of the cell abundance in the atmosphere (~$10^4$ cells/m$^3$ in the troposphere) (Flemming & Wuertz, 2019), the capabilities of microorganisms to cope under a range of extreme and polyextreme conditions (Matthews et al., 2023; Merino et al., 2019), and physiological characterizations of microbes capable of oxidizing atmospheric trace gases (Greening & Grinter, 2022; Ortiz et al., 2021; Schmider et al., 2024), we are closer to answering a fundamental question at the frontier of aerobiology research: Is the atmosphere solely a transport medium for microbial cells, or is it also an active microbial ecosystem?

Progress in aerobiology research is hindered by several challenges facing experimental studies and sampling and monitoring strategies. These include the low biomass of the atmosphere, the prevalence and ease of contamination, the lack of standardized sampling and cataloging methods, the difficulty in replicating atmospheric conditions in a laboratory or experiment, and other limitations and shortcomings of both culture-dependent and culture-independent studies (Lappan et al., 2024). Moreover, recent advances and efforts in aeromicrobiology research have prioritized the description of microbial community composition in the atmosphere, with limited exploration of microbial traits and metabolic activities (Lappan et al., 2024). As such, our ability to evaluate the Earth's atmosphere as an active ecosystem through experimental and observational approaches, and, importantly, to deepen our understanding of its presumed biological and ecological dynamics, remain limited (Prosser, 2020). Theoretical approaches and numerical models offer insights and tools that complement and overcome many of the challenges facing experimental and observational-based studies. Modeling-based investigations can generate and test hypotheses (*theory and model-informed hypotheses*), as well as formulate mechanistic explanations of biological processes and dynamics in the atmosphere. Moreover, the theory, hypotheses, and knowledge generated through modeling can serve to accelerate experimentation and direct sampling campaigns (Martinez-Rabert, Sloan, et al., 2023). For the atmosphere, this could include using thermodynamic and bioenergetic principles to identify plausible microbial metabolisms and rates of activity, and where to detect these microorganisms. Theory advancement and modeling have played crucial roles in the biological sciences (Guna-wardena, 2013). Examples include the development of enzymatic kinetics, where the enzyme-substrate complex was first mathematically described (Michaelis & Menten, 1913) and experimentally confirmed 30 years later (Chance, 1943); the biochemical receptor, hypothesized (Ahlquist, 1948; Dresel, 1967; Hill, 1909; Lang-ley, 1905) decades before it was confirmed (Ahlquist, 1973); the complete ammonia oxidation process (comammox), first theorized based on kinetic theory of optimal pathway length (Costa et al., 2006) and experimentally isolated 9 years later (Daimis et al., 2015; van Kessel et al., 2015); and recently, the speculation of sulfur comproportionation as a novel metabolism, predicted using Gibbs free energy calculations (Amend et al., 2020) and subsequently experimentally supported by the isolation of a putative sulfur comproportionating microorganism (Aronson et al., 2025).

Theoretical and modeling studies in aeromicrobiology research will play a critical role in advancing our understanding of the likelihood and limits of biological dynamics and habitability of the atmosphere on Earth. Similarly, modeling approaches can enable intellectual advances despite sampling limitations due to technological and logistical constraints. These advances can be made not only for the Earth's atmosphere but also the atmospheres of other planetary bodies. By integrating modeling approaches with ongoing observational and experimental efforts, we can generate, test, and validate hypotheses, while also guiding new research directions and the development of new scientific tools. An interdisciplinary effort is crucial for addressing persistent challenges in aerobiology, enhancing our understanding of microbial characteristics, ecological functions, and the limits of atmospheric microbial life.

## 2. The Earth's Atmosphere Is a Complex Biophysicochemical System

The Earth's atmosphere is a structured layer of gases and suspended particles (aerosols) that extend over the Earth's surface and dynamically interfaces with the hydrosphere, lithosphere, phyllosphere, and cryosphere (Fowler et al., 2009). The atmosphere serves not only as a medium for physical and chemical processes but also as a potential microbial habitat, linking underlying ecosystems through the transport of biological materials (Womack et al., 2010). The distinct layers of the Earth's atmosphere are classically defined by their thermal properties, chemical compositions, and densities, and not (yet) by their biological characteristics. The troposphere
<figure><img src="image_6.png" /><figcaption>A) Monophasic bioaerosol</figcaption></figure>

Arrow legend

<figure><img src="image_7.png" /><figcaption>B) Biphasic bioaerosol</figcaption></figure>

<figure><img src="image_8.png" /></figure>

Process legend

Chemical processes

① Acid-base reactions

② Chemical reactions

③ Particle degradation

Physical processes

① Solubility/volatility

② Diffusion

③ Particle degradation

④ Gravitational settling

<figure><img src="image_9.png" /><figcaption>C) Triphasic bioaerosol</figcaption></figure>

**Figure 1.** Schematic representation of the atmospheric microbial environment and key elements and processes of biological aerosols (bioaerosols). Panel (a) represents a monophasic bioaerosol (only gas) considering free-floating cells or aggregates outside of liquid droplets (blue shapes). Panel (b) represents a biphasic bioaerosol (gas + liquid) considering individual cells or aggregates in liquid droplets. Panel (c) represents a triphasic bioaerosol (gas + liquid + solid) incorporating solid particles (brown shapes) in the liquid droplet. Symbol legends are as follows: *T* is absolute temperature; *P*<sub>T</sub> represents total pressure; UV denotes ultraviolet radiation; *P*<sub>i</sub> is partial pressure of compound *i*; and *C*<sub>i</sub> represents molar concentration. The subscript legend is as follows: *In* represents an inhibitor; (*i*, *i* + 1, *i* + 2, *i* + 3) represent different noninhibiting compounds. The superscript legend is as follows: *G* is the gas phase; *L* represents the liquid phase. Gray arrows represent the potential of microbial activity to influence the composition of the Earth's atmosphere. Ecological interactions represented in the biphasic bioaerosol include substrate competition (*C*<sub>i</sub><sup>L</sup>), amensalism (production of inhibitor *C*<sub>i</sub><sup>L</sup><sub>in</sub>), and commensalism (*C*<sub>i+1</sub><sup>L</sup> produced by a metabolic predecessor from *C*<sub>i</sub><sup>L</sup> feeds a second microbial cohort).
and the lower stratosphere, which contain 75% of the atmosphere's molecular mass and most of its water vapor and particulate material (Barry & Chorley, 2009), are considered to be the potential bioactive zone of the Earth's atmosphere, where microbes are most likely to survive and remain metabolically active. The ozone-rich layer in the lower stratosphere (~20–25 km ASL) has been proposed as the upper limit for microbial survival due to extremely high levels of ultraviolet (UV) radiation, oxidative stress, and low temperatures above this range (Šantl-Temkiv et al., 2022). However, some microorganisms may withstand these conditions (e.g., *Deinococcus radiodurans* or *Planococcus halocryophilus*) (Mattimore & Battista, 1996; Mykytczuk et al., 2013) or may enter dormancy or a spore state to withstand these extremes (Lennon & Jones, 2011; Womack et al., 2010), given evidence of microbial transport into higher altitudes (such as during volcanic eruptions or strong convective storms) suggests that microbes may occasionally bypass the lower stratosphere and enter these upper layers (Bryan et al., 2019; DasSarma & DasSarma, 2018).

Aerosols, suspensions of liquid, solid or mixed particles, are classified according to their origin, such as sea spray, freshwater spray, mineral dust, biomass burning, volcanic dust, and biogenic aerosols derived from living things (Myhre et al., 2009; Yang et al., 2024). Their global composition exhibits significant spatiotemporal variability, influenced by environmental factors such as climate, weather patterns, and anthropogenic activity (Manavi et al., 2025; Zhu et al., 2019). This variability significantly influences the Earth's climate and atmospheric chemistry. Aerosols affect the radiative balance through processes such as scattering and absorption of solar radiation (Wang et al., 2023), altering cloud formation properties and contributing to phenomena like biological ice nucleation (Failor et al., 2017; Patade et al., 2022). Aerosols also affect the chemical composition of the atmosphere by serving as reactive surfaces for heterogeneous chemical reactions, contributing to the formation of secondary organic aerosols (SOAs), and driving processes like the acidification of atmospheric droplets due to acidity-dependent oxidation of organic compounds, halogen activation, metal solubilization, hydration processes, and solubilization of atmospheric acids such as CO₂, SO₂, or H₂S (Pye et al., 2020; Tilgner et al., 2021).

In addition to their effects on climate and atmospheric chemistry, aerosol variety may also influence the composition, viability, and activity of aeromicrobiomes, by altering the concentration of nutrients and reactive oxygen species (ROS), and their physicochemical microenvironments (e.g., pH) (Ervens et al., 2025). Solid particles, such as mineral dust, can act as a substratum for the formation of microbial biofilms and serve as a potential source of nutrients through biophysicochemical degradation (Flemming et al., 2016). Attached microorganisms create microniches that enhance nutrient availability, protect against environmental stressors, and support microbial survival and activity in the atmosphere (Iwasaka et al., 2009). Outside of cloud droplets or fine solid particles, microorganisms may also be present in the air as free-floating entities or multicell aggregates separated by considerable distances and surrounded by at least a thin layer of water (about 20% of their own water mass) (Ervens et al., 2025). The spatial distance can benefit microorganisms by minimizing the effect of antagonistic interactions between individuals (competition, amensalism, and exploitation), with antagonistic interactions being more frequent than beneficial ones (Palmer & Foster, 2022). While the specific impacts of these physical conditions on microbial survival and activity are not yet fully understood, we expect that the habitat form in which atmosphere-dwelling microorganisms are present could significantly influence the physicochemical and metabolic processes that determine their activity and survival. We identified the main physicochemical processes of free-floating microorganisms surrounded by a thin layer of water (monophasic bioaerosol, Figure 1a); bioaerosols generated by aerosolization of water bodies such as oceans, lakes, or rivers (biphasic bioaerosol, Figure 1b); and suspended particulate matter surrounded by a layer of liquid (triphasic bioaerosol, Figure 1c).

Biogeochemical cycling of essential nutrients (e.g., nitrogen, inorganic carbon, and sulfur) and trace elements (e.g., iron and nonmetal chlorine) are among the most important factors influencing atmosphere chemistry and, potentially, the habitability of the aeromicrobiome. These cycles could provide both energy and constituent elements (e.g., CHNOPS) for microorganisms. For example, we hypothesize that the biogeochemical cycling of dimethyl sulfide (DMS) and its emission from marine environments could contribute to microbial habitability of the atmosphere by supplying a potential energy and a nutrient source in the atmosphere (Boden et al., 2011), and by generating new carbon and sulfur sources (e.g., dimethyl sulfoxide (Cruz et al., 2023)) through reactions with strong atmospheric oxidants like ozone (O₃) and hydroxyl radicals (·OH) (Fung et al., 2022; Teng et al., 2021). These reactions may also promote microbial survival by reducing the concentration of life-inhibiting oxidants and thereby limiting their effects. Furthermore, atmospheric microorganisms may metabolize greenhouse gases such as CH₄ and N₂O, and other trace gases including volatile organic compounds (VOCs) (Greening & Grinter, 2022; Yoshikawa et al., 2017), potentially contributing to global biogeochemical cycles and mitigating anthropogenic
<figure><img src="image_10.png" /></figure>

$$P_{\text{Cell}} = P_{\text{Ana}} + P_{\text{Maint}}$$

$$P_{\text{Net}} = P_{\text{Cat}} - P_{\text{Req}}$$

**High activity/growth state**

$$P_{\text{Net}}^{G} = P_{\text{Cat}} - P_{\text{Cell}} > 0$$

**Maintenance-only state**

$$P_{\text{Net}}^{M} = P_{\text{Cat}} - P_{\text{Maint}} > 0$$

$$P_{\text{Net}}^{M} = P_{\text{Cat}} - P_{\text{S}} > 0$$

**Death state**

$$P_{\text{Net}}^{S} = P_{\text{Cat}} - P_{\text{S}} < 0$$

**Figure 2.** Schematic representation of microbial metabolism. Symbol legends are as follows: $P_{\text{Cat}}$ represents the power produced in catabolism; $P_{\text{E}}$ is the power produced in endogenous catabolism; $P_{\text{Ana}}$ denotes the power required for the synthesis of cellular components; $P_{\text{Maint}}$ is the power required for basal maintenance needs; $P_{\text{Cell}}$ represents the power required for growth (including maintenance requirements); $P_{\text{S}}$ is the required minimal energy flux; $P_{\text{Net}}$ represents the net power of metabolism ($P_{\text{Net}}^{G}$, net power of high activity/growth state; $P_{\text{Net}}^{M}$ denotes the net power of the maintenance-only state; and $P_{\text{Net}}^{S}$, refers to the net power of the death state); and $P_{\text{Req}}$ represents the required power to maintain metabolic states (the high activity/ growth state is $P_{\text{Cell}}$; the maintenance-only state is $P_{\text{Maint}}$; and the death state is $P_{\text{S}}$). Net cell-specific power equations (right green box) can be also based on endogenous power ($P_{\text{E}}$ instead of $P_{\text{Cat}}$). The units of metabolic powers are in energy per unit of time per cell.

climate warming. Nonetheless, the broader implications of these processes remain speculative, highlighting the need for further research and integration of theoretical and numerical modeling to evaluate and predict the interactions between atmospheric microorganisms, biogeochemical cycles, and atmospheric chemical processes.

### 3. Definition of the Problem to Be Modeled

Any aspect of the model (in this case, the conceptual representation of the Earth's atmosphere) must be accurately defined, starting with the definition of the ecosystem, especially being in a situation of “modeling before experimentation.” A true ecosystem is a community of metabolically active organisms interacting with each other and with the environment. The Earth's atmosphere is a hostile environment where microorganisms would be exposed to low temperature, high UV radiation, free radicals, desiccation, hypobaric pressure, low pH, and limited nutrient availability. Microorganisms may be unable to complete the life cycle under these conditions (i.e., the generation of a new cell), especially if their atmospheric residence times are brief. Therefore, the distinct metabolic states of microorganisms must also be considered in order to evaluate the capacity of the Earth's atmosphere to sustain life. The metabolic state of microorganisms depends on their resilience to harsh conditions and the availability of nutrients to generate energy for cell maintenance and synthesis of cellular components.

In a simplified framework (Figure 2), any microbial metabolism can be regarded as a network of catabolism and anabolism coupled by redox cofactors (e.g., NAD) and energy carriers (principally adenosine triphosphate, ATP). Catabolism includes the set of redox reactions by which organic or inorganic compounds are oxidized by, for example, hydrolysis and breakdown of cellular macromolecules, through which free energy is usually released (i.e., exergonic reactions). In anabolism, complex biomolecules are constructed from simpler ones requiring energy (i.e., endergonic reactions) through a set of metabolic pathways, carbon assimilation, or fixation. Part of the catabolic energy is used for cell maintenance (understood as nongrowth-related metabolic processes, Maint). When the available catabolic energy is not sufficient to fulfil cell maintenance requirements, microorganisms may start utilizing their own intracellular substances to provide additional catabolic energy to support maintenance demands; this is known as endogenous catabolism (Van Loosdrecht & Henze, 1999). The amount of metabolic energy produced or consumed per unit of time per cell is known as metabolic power ($P_i$).

Active life can only be sustained if catabolic activities (catabolic power, $P_{\text{Cat}}$) or endogenous catabolism (endogenous power, $P_{\text{E}}$) produce enough energy to meet cellular maintenance needs (basal maintenance power, $P_{\text{Maint}}$), that is, the net power of metabolism is greater than zero ($P_{\text{Net}} > 0$). Maintenance demands are those energetic costs of the cell to remain viable in a given environment, such as nutrient transport, stress response, internal turnover of macromolecular compounds (anabolic processes), or preservation of ionic gradients
(Bodegom, 2007; Kempes et al., 2017). For the completion of the life cycle (requiring power for growth; $P_{Cell}$), additional energy (over the maintenance requirement) is needed in order to synthesize all of the cellular components for growth (anabolic power; $P_{Ana}$). Microorganisms require a minimal energy flux (survival power; $P_{S}$) for the preservation of membrane integrity and key macromolecules (e.g., enzymes), as well as other maintenance costs, such as maintaining energized membranes or the conservation of catabolic energy. Dormancy and endogenous catabolism—along with stress resistance traits such as reactive oxygen species tolerance and UV damage repair—can be viewed as a positively selected adaptation, enabling microorganisms to endure atmospheric transport and persist until deposition in more favorable environments (assuming that the atmosphere itself is an unfavorable environment for the cell). Based on the environmental conditions and metabolic powers, we define the following metabolic states:

*   **High activity or growth state:** Microorganisms are growing (i.e., carrying out cellular division) or are in a highly active state (i.e., energetically expensive nonessential cellular activities as well as essential maintenance activities). The environment provides sufficient power to synthesize new cellular components and meet cellular maintenance needs ($P_{Cat} \ge P_{Cell}$, where $P_{Cell}$ is the sum of $P_{Ana}$ and $P_{Maint}$).

*   **Maintenance-only state:** Microorganisms are only carrying out essential maintenance activities. The environment provides the power necessary for microorganisms to remain viable and metabolizing, but not to complete their life cycle (i.e., to grow) or to remain in a highly active state. This state comprises from the basal functioning of microorganisms ($P_{Cell} > P_{Cat} \ge P_{Maint}$) to the survival situation ($P_{Maint} > P_{Cat} \ge P_{S}$).

*   **Death state:** Microorganisms are dead (irreversible loss of all functioning of the organism). The environmental conditions are so unfavorable that microorganisms cannot remain viable after environmental exposure ($P_{Cat} < P_{S}$).

In the context of the theoretical framework, the aeromicrobiome would be shaped by biological constraints (polyextreme adaptations and metabolic capabilities), ecological dynamics, and physicochemical constraints (such as transport, radiation, wind shear, or water freezing). The null hypothesis would thus assume that the atmosphere does not function as a true microbial ecosystem of metabolically active organisms interacting with each other and with the environment. If metabolic activity cannot be sustained at sufficient levels under atmospheric conditions, then the dominant process shaping microbial diversity in the atmosphere may be selection through the exposure to an unsustainable-living environment, with the fraction of viable microbial aerosols decreasing with increasing transport time, altitude, and distance from emission sources, leading to shifts in community composition (Amato et al., 2023). For example, studies over the Southern Ocean suggest that while the atmosphere does not serve as an absolute dispersal barrier, it may be selectively filtering microorganisms entering Antarctica, allowing only those with traits suited for atmospheric stress to survive transit (Malard et al., 2022). Long-range atmospheric dispersal can thus act as a physicochemical filter, filtering out less resilient taxa while allowing both highly specialized and ubiquitous groups to persist (Menéndez-Serra et al., 2024). The atmospheric transport and selection of microorganisms may benefit emerging or disturbed ecosystems (such as glacier forefields or postfire landscapes) by reseeding them with microbes from other regions, potentially accelerating recovery through natural bioaugmentation. Even if the model predicts that the atmosphere does not support a metabolically active microbial ecosystem, the selective filtering of microbial communities would still be a fundamental and important process to be considered in studies of biogeography, as it will determine which microbes ultimately reach and colonize new environments.

It is important to consider that microorganisms can also use intracellular energy sources (i.e., endogenous catabolism; $P_{E}$) when the environment does not provide enough power to meet metabolic needs for growth, maintenance, or survival, or microorganisms are not able to metabolize the atmospheric constituents. In this situation, atmospheric residence time, and the storage and metabolic capacity of microorganisms play a crucial role in the viability of aeromicrobiomes.

## 4. Evaluation and Modeling of the Earth's Atmosphere as a True Ecosystem

Theoretical and mathematical descriptions of biological processes usually require some degree of abstraction, generalization, and simplification. Nevertheless, models can be extremely powerful tools for testing hypotheses and scenarios, identifying and articulating gaps in knowledge and understanding; disentangling interoperating processes, complex data sets, and unclear observations; quantifying processes and fluxes; understanding interactions; testing sensitivities; and making predictions. These advances not only move knowledge forward but
may also inform efficient and effective laboratory and field studies, and shape the direction of future research. Here, we outline and discuss how a range of theory and model-based approaches might be used and applied to advance and address some of the most pressing questions in aeromicrobiology research.

Given the complexity of the Earth's atmosphere, efforts to theoretically and numerically describe and model the aeromicrobiome must implicitly or explicitly consider and account for a wide range of physical, chemical, and biological parameters and processes, including (but not limited to) transport processes, atmospheric physics, thermal properties, chemical compositions, ecological dynamics, and metabolic processes (Figure 1).

A top-down approach relies largely on using available data to assess and evaluate patterns, propose mechanisms that generate those patterns, and then test those mechanisms. However, the field of aeromicrobiology has historically been limited by a lack of adequate experimental tools, methodological processes, and technological advancements (Lappan et al., 2024), necessary to generate sufficient data for top-down approaches to address whether atmospheric microorganisms are active in the Earth's atmosphere.

A bottom-up approach, on the other hand, involves the translation of hypothetical and known mechanisms to theory, and the application of that theory to produce predictions that can be evaluated alongside observations, measurements, and other data. A bottom-up approach will typically start with the simplest and most realistic model or theoretical framework possible, and gradually increase its complexity (in silico *bottom-up methodology*) (Martinez-Rabert, Sloan, et al., 2023). The in silico *bottom-up methodology* allows us to independently evaluate each element and process of the model, and its influence on the system as a whole. By adding, removing, or modifying the elements and processes of the model (in this case, the implicit or explicit descriptions of various aspects of the Earth's atmosphere), it is possible to make advancements on the understanding of biological and ecological dynamics of aeromicrobiomes by proposing and testing scenarios and mechanisms, and ruling out results and outputs outside what is considered plausible. Bottom-up theoretical tools are thus well suited to addressing major knowledge gaps in aeromicrobiology, including whether atmospheric microbes are better suited to be free-living or in a community; how the origin, type and characteristics of an aerosol influences the viability of aeromicrobiomes; and the longevity of bioaerosols and their microbial inhabitants.

We propose that a bottom-up approach to modeling the Earth's aeromicrobiome must consider a range of assumptions and complexities to span and address the various physical, chemical, and biological characteristics of the atmosphere, as well as the temporal and spatial scales that are relevant to individual applications. In Table 1, we provide nonexhaustive examples of factors that could be considered and how they may be captured in a numerical framework.

One of the advantages of modeling over experimentation is that the researcher has complete control over the simulated system, so long as there are no discrepancies between the constructed model and the real domain, or that all modeling decisions are justified (e.g., if one were to overlook a key element or process to understand its influence on the system). Because of the complexity of the Earth's atmosphere and the distinct phases and shapes in which aeromicrobiomes could be found in it (Figure 1), several modeling choices and assumptions can be made to define the conceptual representation of the Earth's atmosphere. The data, knowledge of processes, and mathematical descriptions that are required for modeling, will depend on these choices (Table 1). The model can then be constructed depending on the need for the complexity with which the Earth's atmosphere is defined, and, at the same time, this will establish the boundaries of the model (i.e., the range of application of the model). Having evaluated a range of potential questions and model applications, we contemplate some primary modeling considerations as follows:

1. *The source of energy and constituent elements for life in the atmosphere.* As in the hydrosphere, lithosphere, phyllosphere, and cryosphere, the Earth's atmosphere could also exhibit different ecological niches depending on the origin and chemical composition of bioaerosols, and on local anthropogenic or natural emissions.

2. *The physical phases of the atmosphere.* The various physical phases of the atmosphere expand the range of ecological niches to be considered. For example, only individuals that synthesize metabolic water will be able to thrive in the gas phase of the atmosphere; the acidification of bioaerosols (Pye et al., 2020; Tilgner et al., 2021) will end up selecting acidophiles and acid-tolerant (liquid phase); and the presence of solid particles promotes the formation of biofilms and can provide additional sources of energy and constituent elements for life.
<table><caption>**Table 1**

Overview of Modeling Choices (or Assumptions) for the Definition of the Earth's Atmosphere</caption><thead><tr><th>Assumption</th><th>Required data and process</th><th>Available material</th></tr></thead><tbody><tr><td colspan="3"><strong>Source of energy and essential elements for life</strong></td></tr><tr><td>Only from the atmosphere</td><td><ul><li>General composition of atmosphere (if ideal atmosphere is considered)</li><li>Vertical profiles of component concentrations and temperature throughout atmosphere altitude</li></ul></td><td><ul><li>International Standard Atmosphere (ISO 2533:1975)</li><li>Databases (UK AIR, EDGAR, and Global Monitoring Lab from NOAA)</li><li>Global climate and atmospheric models (CAMS, NRLMSIS, AM4VR, CAMS-GLOB-ANT, and GEFS)</li></ul></td></tr><tr><td>Atmosphere + in situ<sup>a</sup></td><td><ul><li>Component concentrations in the place of origin of bioaerosols and/or analysis of collected bioaerosols</li><li>Endogenous compounds and cell debris</li></ul></td><td><ul><li>In situ measurements of water</li><li>Databases (EMODnet, Ocean chemistry from NOAA, RTOMS Ocean Chemistry, and MarChem)</li><li>Aeromicrobiology studies related to community structure and cell abundance</li></ul></td></tr><tr><td colspan="3"><strong>Considered physical phases of the Earth's atmosphere</strong></td></tr><tr><td>Only gas phase</td><td><ul><li>(Re)hydration process of cells with metabolic water</li></ul></td><td><ul><li>Hydration calculations, for example, from Ortiz et al. (2021)</li></ul></td></tr><tr><td>Gas and liquid</td><td><ul><li>Solubility and volatility processes</li></ul></td><td><ul><li>Described by Henry's law</li><li>Compilation of Henry's law constants for freshwater and seawater (Sander, 2023)</li></ul></td></tr><tr><td>Gas, liquid, and solid</td><td><ul><li>Adsorption/desorption processes</li><li>Physicochemical degradation of particles and solubilization of their components</li></ul></td><td><ul><li>Studies focusing on adsorption and desorption on airborne particles (Hyttinen et al., 2001; Kondo et al., 2018; Pan et al., 2002)</li></ul></td></tr><tr><td colspan="3"><strong>Considered physicochemical complexity of the Earth's atmosphere</strong></td></tr><tr><td>Climate consideration</td><td><ul><li>Climate data and chemical composition (temperature, humidity, irradiance, reactive oxygen species, etc.)</li></ul></td><td><ul><li>Databases (Meteostat, Weather Underground, Solcast, and CDR from NOAA)</li><li>Global climate and atmospheric models (CAMS, NRLMSIS, AM4VR, CAMS-GLOB-ANT, and GEFS)</li></ul></td></tr><tr><td colspan="3"><strong>Habitat form of aeromicrobiome</strong></td></tr><tr><td>Free-living in gas phase</td><td><ul><li>(Re)hydration process of cells with metabolic water</li></ul></td><td><ul><li>Hydration calculations, for example, from Ortiz et al.'s (2021) study</li></ul></td></tr><tr><td>Self-attached aggregate</td><td><ul><li>Diffusion of compounds through self-attached aggregate</li><li>Synthesis of extracellular polymers (EPS)</li></ul></td><td><ul><li>Diffusion described by Fick's law</li><li>Relative diffusivities in microbial aggregates (van den Berg et al., 2021)</li></ul></td></tr><tr><td>Attached to solid particle (biofilm)</td><td><ul><li>Diffusion of compounds through biofilm</li><li>Synthesis of extracellular polymers (EPS)</li><li>Biodegradation of solid particle</li></ul></td><td><ul><li>Fick's law and relative diffusivities in microbial aggregates</li><li>Biodeterioration of mineral material extensively studied (Gaylardé & Morton, 2003)</li></ul></td></tr><tr><td colspan="3"><strong>Acidity of bioaerosols (pH)</strong></td></tr><tr><td>pH consideration</td><td><ul><li>Chemical (or ion) speciation</li><li>pH resolution (Newton-Raphson method)</li></ul></td><td><ul><li>Acid-base reactions in aerosols are well described</li><li>Estimation of acid-base constants by Gibbs free energy calculations</li></ul></td></tr><tr><td colspan="3"><strong>Transport and residence time of bioaerosols in the Earth's atmosphere</strong></td></tr><tr><td>Bioaerosol transport and residence time</td><td><ul><li>Air mass trajectories and dispersion of pollutants and solid particles in the Earth's atmosphere</li></ul></td><td><ul><li>Atmospheric dispersion and Lagrangian trajectory models (FLEXPART, LAGRANTO, ADMS3, and DISPERSION)</li><li>Light detection and ranging (LIDAR) measurements</li></ul></td></tr></tbody></table>
<caption>**Table 1**</caption>

*Continued*

<table><thead><tr><th>Assumption</th><th>Required data and process</th><th>Available material</th></tr></thead><tbody><tr><td><strong>Spatial dimension</strong><br>The system does change through space:<br>2D: Lat. + Long<br>3D: Lat. + Long. + Alt</td><td><ul><li>Spatial discretization of domain (2D or 3D)</li><li>Sampling data from distinct sites distributed around the Earth</li></ul></td><td><ul><li>Numerical methods (finite difference, finite element, and finite volume)</li><li>Databases (UK AIR, EDGAR, and Global Monitoring Lab from NOAA)</li></ul></td></tr><tr><td><strong>Temporal dimension</strong><br>The system does change over time</td><td><ul><li>Temporal discretization of processes, described by ODE or PDE</li><li>Multiscale modeling</li><li>Seasonal sampling data</li></ul></td><td><ul><li>Numerical methods (Euler, Crank-Nicolson, and Runge-Kutta)</li><li>Databases (UK AIR, EDGAR, and Global Monitoring Lab from NOAA)</li></ul></td></tr></tbody></table>

3. *The habitat form of aeromicrobiomes.* One of the mechanisms used by microorganisms to cope with desiccation is the synthesis of extracellular polymeric substance (EPS), promoting the retention of water and the formation of microbial aggregates (biofilms, granules, and flocs) (Costa et al., 2018; Flemming et al., 2016). However, the synthesis of EPS supposes an additional energetic cost, providing an energetic advantage to those microbes capable of surviving as free-living individuals in the Earth's atmosphere by generating metabolic water.

4. *The acidity (pH) of bioaerosols.* The pH of bioaerosols varies significantly with the type, composition, and life cycle of atmospheric droplets. The pH affects the metabolic activity and growth of microorganisms and alters the Gibbs free energy of redox reactions and is therefore an important factor for estimating the metabolic power supply. Furthermore, metabolic activities could contribute to the acidification, alkalinization, or buffering of aqueous aerosols (Ratzke & Gore, 2018).

5. *The transport and residence time of bioaerosols.* Bioaerosols can travel long distances (Cáliz et al., 2025; Jacob et al., 2017), facilitating microbial dispersal and inoculation of new environments if microorganisms remain viable. At the same time, the viability of microorganisms is not only determined by their resilience but also by the time they remain in unfavorable conditions (i.e., in the case of an ecologically unfavorable atmosphere, the atmospheric residence time). Factors such as cloud droplet nucleation, ice phase scavenging, wind speed, particulate matter distribution, or the habitat forms of aeromicrobiomes influence transport and atmospheric residence time of microbes (Burrows et al., 2009; Ervens et al., 2025), and delimit the influence of the Earth's atmosphere on microbes in a short or a long term.

## 4.1. Physical and Chemical Complexity of the Earth's Atmosphere

The way we define the Earth's atmosphere determines the set of microbial cohorts and metabolic activities that can be considered in our model, as it establishes the energy and constituent elements available to microorganisms. The International Standard Atmosphere (ISO 2533:1975; ISA) can be considered as the simplest representation of the physical and chemical complexity of the Earth's atmosphere under “ideal” simplified conditions, with a composition of 78.0840%vol N₂, 20.9476%vol O₂, 0.9340%vol Ar, and 0.0417%vol CO₂ (updated from NOAA) for its major constituents. To assess the plausible microbial metabolisms that could meet the energetic demands of atmospheric microorganisms, organic compounds and trace gases such as H₂, CH₄, and CO must also be considered. These trace compounds may be used both as energy sources (by organotrophic and lithotrophic microorganisms) and carbon sources (by heterotrophic and autotrophic microorganisms). Given the high abundance of O₂ in Earth's atmosphere (~21%vol), we consider that the atmosphere will be dominated by aerobic microorganisms, and the probability of active obligate anaerobes in the Earth's atmosphere is low. Thermodynamic calculations (nonstandard Gibbs free energy calculations) can be used to determine which metabolic reactions are feasible as energy sources (i.e., exergonic reactions) under the conditions of the Earth's atmosphere, as well as to estimate the metabolic power that microorganisms can obtain from these reactions (Bradley et al., 2020; LaRowe & Amend, 2015a, 2015b).

<footnote>ª"In situ" refers to components coming from the place of origin of bioaerosol(s).</footnote>
## 4.2. Thermodynamics and Bioenergetics Applied to the Earth's Atmosphere

The fact that a given reaction is exergonic under certain environmental conditions does not necessarily mean that microorganisms are able to catalyze that reaction or obtain sufficient energy to survive under those conditions. Moreover, the overall metabolic reaction is generally used to calculate the Gibbs free energy, overlooking the actual metabolic pathway catalyzed by the microorganisms and the inherent inefficiencies of biological processes (e.g., where some part of the free energy generated by an exergonic redox reaction is lost to heat or catabolic pathways that do not harvest energy per se) (Hoehler et al., 2001). This means that the survivability of microorganisms may be overestimated when only nonstandard Gibbs free energy calculations are used. To address this issue, bioenergetic calculations can be applied to more accurately estimate the actual energy that microorganisms can harness under the conditions of the Earth's atmosphere, by counting the formation of ATP from ADP and inorganic phosphate coupled to redox reactions (ATP-balancing method), by considering the potential difference of membrane invested in the energy conversion (membrane potential method), including the metabolic efficiency in Gibbs free energy calculations (dissipation method), computing the electron balance of catabolism and anabolism using electron equivalent units (TEEM2 method), including a multicompartmental genome-scale description of microorganisms (multicompartmental method), or by a thermodynamic reinterpretation of the Monod model (TIME and GEDYM methods), to name just a few (Haraldsdóttir et al., 2012; Kleerebezem & Van Loosdrecht, 2010; Liu et al., 2003; Martinez-Rabert, Smith, et al., 2023; McCarty, 2007; Smeaton & Van Cappellen, 2018).

## 5. Bridging Observation, Experimentation, and Modeling

Most current research on aeromicrobiomes has focused on cloud droplets, though bioaerosol particles outside of clouds should also be considered when evaluating the hypothetical activity and ecological interactions of atmosphere-dwelling microorganisms (Amato et al., 2023). Despite the complexity of bioaerosols in the Earth's atmosphere (Figure 1), a wealth of knowledge, databases, and mathematical models are available to simulate the physicochemical processes in the Earth's atmosphere and bioaerosols (Table 1). The main bottleneck in the modeling of aeromicrobiomes lies in the lack of physiological characterization, metabolic characterization, and activity measurements of atmospheric microorganisms under atmospheric conditions (Lappan et al., 2024). The metabolic and growth parameters of extremophiles and candidate atmosphere-dwelling microorganisms from other environments with similar conditions and energy sources to those of the Earth's atmosphere (such as atmospheric methane-oxidizing bacteria (Schmider et al., 2024), atmospheric hydrogen-oxidizing bacteria (Greening et al., 2014; Ortiz et al., 2021) or atmospheric CO-oxidizing bacteria (Cordero et al., 2019)) may be used to estimate potential activity and activity-based parameters on biochemical principles (González-Cabaleiro et al., 2021; McCarty, 2007), or to inform theoretical frameworks such as the metabolic theory of ecology (MTE) and dynamic energy budget (DEB) theory (van der Meer, 2006).

Modeling is not an end in itself but must be carried out in line with biological, physical, and chemical developments in experimentation and observation. Thus, the effective integration of experimental and modeling methodologies is a two-way effort. Challenges facing biological modeling must be addressed, including, notably, a lack of observations, descriptions, and characterization of atmospheric microorganisms (compared to other habitats). Additional concerns facing model design and execution include Levins' 3-way trade-off (between precision, generality, and realism) (Levins, 1966), the inherent limitations of biological and ecological models due to their underlying assumptions (van den Berg et al., 2022), the transferability of existing models to the Earth's atmosphere (Yates et al., 2018), the absence of a unified definition and interpretation of microbial dormancy (McDonald et al., 2024), the accuracy of the description of key mechanisms and elements (especially for an in silico bottom-up methodology) (Martinez-Rabert, Sloan, et al., 2023), and the uncertainty in climate and weather projections (Cael et al., 2022). For this reason, modeling studies should be conducted closely with experimental and technical advances. For the atmosphere, this may include the development of novel techniques for microbial bioaerosol generation (Fernandez et al., 2019; Pottage et al., 2022), the development and use of atmospheric simulation chambers (Kiendler-Scharr et al., 2023), and the measurement of metabolic activity in bioaerosols (Dillon et al., 2022; Dimmick et al., 1975).

## 6. Outlook

The understanding of the Earth's atmosphere as a true ecosystem remains limited by the challenges facing experimental and observational practices. However, theoretical modeling offers a powerful framework to
accelerate aerobiology research by (a) evaluating the capacity of the Earth's atmosphere to sustain life, (b) generating theory and model-informed hypotheses to guide experimental and sampling efforts, (c) assessing microbial cohorts for their potential survival in the atmosphere, (d) assessing general and specific regions and ranges of the Earth's atmosphere more likely to harbor active microorganisms, (e) evaluating the contribution of atmospheric microorganisms to regulating the chemical composition of the atmosphere, and (f) evaluating the Earth's atmosphere not only as a true ecosystem but also as a natural selector of microbial communities, and an analogue for life in other planetary atmospheres. While the physicochemical processes of the Earth's atmosphere, although complex, can be well described by various climate/atmospheric models, public databases, and physical rules, the incorporation of thermodynamic and bioenergetic principles for the aeromicrobiome is in its infancy and must be systematically and rigorously employed. Applications of theoretical and modeling frameworks from other environments, combined with knowledge of extremophiles and candidate atmosphere-dwelling microorganisms, will also inform the creation of a robust theoretical framework for the aeromicrobiome study. The theoretical tools necessary to evaluate the habitability and ecological dynamics of the Earth's atmosphere are not "up in the air" but already at our fingertips.

## Data Availability Statement

Data were neither used nor created for this research.

## Acknowledgments

This work was supported by the Human Frontier Science Program (HFSP) (RGY0058/2022: https://doi.org/10.52044/HFSP.RGY00582022.pc.gr.153592, to JG, CG, ETR, and JAB), the Agence Nationale de la Recherche (ANR23-CPJ1-0172-01, to JAB), and the European Research Council (ERC) under the European Union's Horizon Europe Research and Innovation programme (Grant agreement No. 101115755, acronym SIESTA, to JAB). RL is supported by an Australian Research Council Discovery Early Career Award (DE230100542) and acknowledges support from the ARC SRIEAS Grant SR200100005 Securing Antarctica's Environmental Future. JG is supported by a Canadian Institute for Advanced Research (CIFAR) Fellowship.

## References

Ahlquist, R. P. (1948). A study of the adrenotropic receptors. *American Journal of Physiology*, **153**(3), 586–600. https://doi.org/10.1152/ajplegacy.1948.153.3.586

Ahlquist, R. P. (1973). Adrenergic receptors: A personal and practical view. *Perspectives in Biology and Medicine*, **17**(1), 119–122. https://doi.org/10.1353/pbm.1973.0047

Amato, P., Demeer, F., Melaouhi, A., Fontanella, S., Martin-Biesse, A. S., Sancelme, M., et al. (2007). A fate for organic acids, formaldehyde and methanol in cloud water: Their biotransformation by micro-organisms. *Atmospheric Chemistry and Physics*, **7**(15), 4159–4169. https://doi.org/10.5194/acp-7-4159-2007

Amato, P., Mathonat, F., Nuñez Lopez, L., Péguilhan, R., Bourhane, Z., Rossi, F., et al. (2023). The aeromicrobiome: The selective and dynamic outer-layer of the Earth's microbiome. *Frontiers in Microbiology*, **14**, 1186847. https://doi.org/10.3389/fmicb.2023.1186847

Amato, P., Ménager, M., Sancelme, M., Laj, P., Mailhot, G., & Delort, A. M. (2005). Microbial population in cloud water at the Puy de Dôme: Implications for the chemistry of clouds. *Atmospheric Environment*, **39**(22), 4143–4153. https://doi.org/10.1016/j.atmosenv.2005.04.002

Amend, J. P., Aronson, H. S., Macalady, J., & LaRowe, D. E. (2020). Another chemolithotrophic metabolism missing in nature: Sulfur com-proportionation. *Environmental Microbiology*, **22**(6), 1971–1976. https://doi.org/10.1111/1462-2920.14982

Aronson, H. S., LaRowe, D. E., Macalady, J. L., & Amend, J. P. (2025). Isolation of a putative sulfur comproportionating microorganism. *Scientific Reports*, **15**, 17999. https://doi.org/10.1038/s41598-025-01009-y

Barry, R. G., & Chorley, R. J. (2009). Atmosphere, weather and climate (1st ed. ed.). https://doi.org/10.4324/9780203871027

Bodegom, P. V. (2007). Microbial maintenance: A critical review on its quantification. *Microbial Ecology*, **53**(4), 513–523. https://doi.org/10.1007/s00248-006-9049-5

Boden, R., Murrell, J. C., & Schäfer, H. (2011). Dimethylsulfide is an energy source for the heterotrophic marine bacterium Sagittula stellata. *FEMS Microbiology Letters*, **322**(2), 188–193. https://doi.org/10.1111/j.1574-6968.2011.02349.x

Bradley, J. A., Arndt, S., Amend, J. P., Burwicz, E., Dale, A. W., Egger, M., & LaRowe, D. E. (2020). Widespread energy limitation to life in global subseafloor sediments. *Science Advances*, **6**(32), eaba0697. https://doi.org/10.1126/sciadv.aba0697

Bryan, N. C., Christner, B. C., Guzik, T. G., Granger, D. J., & Stewart, M. F. (2019). Abundance and survival of microbial aerosols in the troposphere and stratosphere. *The ISME Journal*, **13**(11), 2789–2799. https://doi.org/10.1038/s41396-019-0474-0

Burrows, S. M., Butler, T., Jöckel, P., Tost, H., Kerkweg, A., Pöschl, U., & Lawrence, M. G. (2009). Bacteria in the global atmosphere—Part 2: Modeling of emissions and transport between different ecosystems. *Atmospheric Chemistry and Physics*, **9**(23), 9281–9297. https://doi.org/10.5194/acp-9-9281-2009

Cael, B. B., Britten, G. L., Mir Calafat, F., Bloch-Johnson, J., Stainforth, D., & Goodwin, P. (2022). Climate nonlinearities: Selection, uncertainty, projections, and damages. *Environmental Research Letters*, **17**(8), 084025. https://doi.org/10.1088/1748-9326/ac8238

Cáliz, J., Menéndez-Serra, M., Triadó-Margarit, X., Avila, A., & Casamayor, E. O. (2025). Persistent Desert microbiota in the Southern European Sky. *Environmental Microbiology*, **27**(2), e70046. https://doi.org/10.1111/1462-2920.70046

Chance, B. (1943). The kinetics of the enzyme-substrate compound of peroxidase. *Journal of Biological Chemistry*, **151**(2), 553–577. https://doi.org/10.1016/s0021-9258(18)44929-0

Cordero, P. R. F., Bayly, K., Man Leung, P., Huang, C., Islam, Z. F., Schittenhelm, R. B., et al. (2019). Atmospheric carbon monoxide oxidation is a widespread mechanism supporting microbial survival. *The ISME Journal*, **13**(11), 2868–2881. https://doi.org/10.1038/s41396-019-0479-8

Costa, E., Pérez, J., & Kreft, J. U. (2006). Why is metabolic labour divided in nitrification? *Trends in Microbiology*, **14**(5), 213–219. https://doi.org/10.1016/j.tim.2006.03.006

Costa, O. Y. A., Raaijmakers, J. M., & Kuramae, E. E. (2018). Microbial extracellular polymeric substances: Ecological function and impact on soil aggregation. *Frontiers in Microbiology*, **9**, 1636. https://doi.org/10.3389/fmicb.2018.01636

Cruz, E., Haeberle, A. L., Westerman, T. L., Durham, M. E., Suyemoto, M. M., Knodler, L. A., & Elfenbein, J. R. (2023). Nonredundant dimethyl sulfoxide reductases influence Salmonella enterica serotype typhimurium anaerobic growth and virulence. *Infection and Immunity*, **91**(2), e0057822. https://doi.org/10.1128/iai.00578-22

Daimis, H., Lebedeva, E. V., Pjevac, P., Han, P., Herbold, C., Albertsen, M., et al. (2015). Complete nitrification by Nitrospira bacteria. *Nature*, **528**(7583), 504–509. https://doi.org/10.1038/nature16461
DasSarma, P., & DasSarma, S. (2018). Survival of microbes in Earth's stratosphere. *Current Opinion in Microbiology*, **43**, 24–30. https://doi.org/10.1016/j.mib.2017.11.002

Dillon, K. P., Krumins, V., Deshpande, A., Kerkhof, L. J., Mainelis, G., & Fennell, D. E. (2022). Characterization and DNA stable-isotope probing of methanotrophic bioaerosols. *Microbiology Spectrum*, **10**(6), e03421–e03422. https://doi.org/10.1128/spectrum.03421-22

Dimmick, R. L., Straat, P. A., Wolochow, H., Levin, G., Chatigny, M., & Schrot, J. (1975). Evidence for metabolic activity of airborne bacteria. *Journal of Aerosol Science*, **6**(6), 387–393. https://doi.org/10.1016/0021-8502(75)90054-3

Dresel, P. E. (1967). Report on the discussion of session. I. General concepts of receptors. *Annals of the New York Academy of Sciences*, **139**(3), 645–648. https://doi.org/10.1111/j.1749-6632.1967.tb41234.x

Ervens, B., Amato, P., Aregahegn, K., Joly, M., Khaled, A., Labed-Veydert, T., et al. (2025). Ideas and perspectives: Microorganisms in the air through the lenses of atmospheric chemistry and microphysics. *Biogeosciences*, **22**(1), 243–256. https://doi.org/10.5194/bg-22-243-2025

Failor, K. C., Schmale, D. G., III., Vinatzer, B. A., & Monteil, C. L. (2017). Ice nucleation active bacteria in precipitation are genetically diverse and nucleate ice by employing different mechanisms. *The ISME Journal*, **11**(12), 2740–2753. https://doi.org/10.1038/ismej.2017.124

Fernandez, M. O., Thomas, R. J., Garton, N. J., Hudson, A., Haddrell, A., & Reid, J. P. (2019). Assessing the airborne survival of bacteria in populations of aerosol droplets with a novel technology. *Journal of the Royal Society Interface*, **16**(150), 20180779. https://doi.org/10.1098/rsif.2018.0779

Flemming, H.-C., Wingender, J., Szewzyk, U., Steinberg, P., Rice, S. A., & Kjelleberg, S. (2016). Biofilms: An emergent form of bacterial life. *Nature Reviews Microbiology*, **14**(9), 563–575. https://doi.org/10.1038/nrmicro.2016.94

Flemming, H.-C., & Wuertz, S. (2019). Bacteria and archaea on Earth and their abundance in biofilms. *Nature Reviews Microbiology*, **17**(4), 247–260. https://doi.org/10.1038/s41579-019-0158-9

Fowler, D., Pilegaard, K., Sutton, M., Ambus, P., Raivonen, M., Duyzer, J., et al. (2009). Atmospheric composition change: Ecosystems–Atmosphere interactions. *Atmospheric Environment*, **43**(33), 5193–5267. https://doi.org/10.1016/j.atmosenv.2009.07.068

Fung, K. M., Heald, C. L., Kroll, J. H., Wang, S., Jo, D. S., Gettelman, A., et al. (2022). Exploring dimethyl sulfide (DMS) oxidation and implications for global aerosol radiative forcing. *Atmospheric Chemistry and Physics*, **22**(2), 1549–1573. https://doi.org/10.5194/acp-22-1549-2022

Gaylarde, C., & Morton, G. (2003). Biodeterioration of mineral materials. In *Encyclopedia of Environmental Microbiology*. https://doi.org/10.1002/0471263397.env135

González-Cabaleiro, R., Martinez-Rabert, E., Argiz, L., van Kessel, M. A., & Smith, C. J. (2021). A framework based on fundamental biochemical principles to engineer microbial community dynamics. *Current Opinion in Biotechnology*, **67**, 111–118. https://doi.org/10.1016/j.copbio.2021.01.001

Greening, C., Berney, M., Hards, K., Cook, G. M., & Conrad, R. (2014). A soil actinobacterium scavenges atmospheric H2 using two membrane-associated, oxygen-dependent [NiFe] hydrogenases. *Proceedings of the National Academy of Sciences*, **111**(11), 4257–4261. https://doi.org/10.1073/pnas.1320586111

Greening, C., & Grinter, R. (2022). Microbial oxidation of atmospheric trace gases. *Nature Reviews Microbiology*, **20**(9), 513–528. https://doi.org/10.1038/s41579-022-00724-x

Gunawardena, J. (2013). Biology is more theoretical than physics. *Molecular Biology of the Cell*, **24**(12), 1827–1829. https://doi.org/10.1091/mbc.e12-03-0227

Haraldsdóttir, H. S., Thiele, I., & Fleming, R. (2012). Quantitative assignment of reaction directionality in a multicompartmental human metabolic reconstruction. *Biophysical Journal*, **102**(8), 1703–1711. https://doi.org/10.1016/j.bpj.2012.02.032

Hill, A. V. (1909). The mode of action of nicotine and curari, determined by the form of the contraction curve and the method of temperature coefficients. *The Journal of Physiology*, **39**(5), 361–373. https://doi.org/10.1113/jphysiol.1909.sp001344

Hoehler, T. M., Alperin, M. J., Albert, D. B., & Martens, C. S. (2001). Apparent minimum free energy requirements for methanogenic Archaea and sulfate-reducing bacteria in an anoxic marine sediment. *FEMS Microbiology Ecology*, **38**(1), 33–41. https://doi.org/10.1111/j.1574-6941.2001.tb00879.x

Hyttinen, M., Pasanen, P., & Kalliokoski, P. (2001). Adsorption and desorption of selected VOCs in dust collected on air filters. *Atmospheric Environment*, **35**(33), 5709–5716. https://doi.org/10.1016/S1352-2310(01)00376-4

Imshenetsky, A. A., Lysenko, S. V., & Kazakov, G. A. (1978). Upper boundary of the biosphere. *Applied and Environmental Microbiology*, **35**(1), 1–5. https://doi.org/10.1128/aem.35.1.1-5.1978

Iwasaka, Y., Shi, G. Y., Yamada, M., Kobayashi, F., Kakikawa, M., Maki, T., et al. (2009). Mixture of Kosa (Asian dust) and bioaerosols detected in the atmosphere over the Kosa particles source regions with balloon-borne measurements: Possibility of long-range transport. *Air Quality, Atmosphere & Health*, **2**(1), 29–38. https://doi.org/10.1007/s11869-009-0031-5

Jacob, J., Nowka, B., Merten, V., Sanders, T., Spieck, E., & Dähnke, K. (2017). Oxidation kinetics and inverse isotope effect of marine nitrite-oxidizing isolates. *Aquatic Microbial Ecology*, **80**(3), 289–300. https://doi.org/10.3354/ame01859

Kempes, C. P., van Bodegom, P. M., Wolpert, D., Libby, E., Amend, J., & Hoehler, T. (2017). Drivers of bacterial maintenance and minimal energy requirements. *Frontiers in Microbiology*, **8**, 31. https://doi.org/10.3389/fmicb.2017.00031

Kiendler-Scharr, A., Becker, K., Doussin, J., Fuchs, H., Seakins, P., Wenger, J., et al. (2023). Introduction to atmospheric simulation chambers and their applications. In J.-F. Doussin, H. Fuchs, A. Kiendler-Scharr, P. Seakins, & J. Wenger (Eds.), *A Practical Guide to Atmospheric Simulation Chambers* (pp. 1–72). Springer International Publishing. https://doi.org/10.1007/978-3-031-22277-1_1

Kleerebezem, R., & Van Loosdrecht, M. C. M. (2010). A generalized method for thermodynamic state analysis of environmental systems. *Critical Reviews in Environmental Science and Technology*, **40**(1), 1–54. https://doi.org/10.1080/10643380802000974

Kondo, K., Kagi, N., & Namiki, N. (2018). Study on the mechanism of SVOC adsorption onto airborne particles in indoor air. *Japan Architectural Review*, **1**(4), 528–537. https://doi.org/10.1002/2475-8876.12052

Langley, J. N. (1905). On the reaction of cells and of nerve-endings to certain poisons, chiefly as regards the reaction of striated muscle to nicotine and to curari. *The Journal of Physiology*, **33**(4–5), 374–413. https://doi.org/10.1113/jphysiol.1905.sp001128

Lappan, R., Thakar, J., Molares Moncayo, L., Besser, A., Bradley, J. A., Goordial, J., et al. (2024). The atmosphere: A transport medium or an active microbial ecosystem? *The ISME Journal*, **18**(1), wrae092. https://doi.org/10.1093/ismejo/wrae092

LaRowe, D. E., & Amend, J. P. (2015a). Catabolic rates, population sizes and doubling/replacement times of microorganisms in natural settings. *American Journal of Science*, **315**(3), 167–203. https://doi.org/10.2475/03.2015.01

LaRowe, D. E., & Amend, J. P. (2015b). Power limits for microbial life [Original Research]. *Frontiers in Microbiology*, **6**, 718. https://doi.org/10.3389/fmicb.2015.00718

Lennon, J. T., & Jones, S. E. (2011). Microbial seed banks: The ecological and evolutionary implications of dormancy. *Nature Reviews Microbiology*, **9**(2), 119–130. https://doi.org/10.1038/nrmicro2504
Levins, R. (1966). The strategy of model building in population biology. *American Scientist*, **54**(4), 421–431. Retrieved from http://www.jstor.org/stable/27836590

Liu, Y., Lin, Y. M., & Yang, S. F. (2003). A thermodynamic interpretation of the Monod equation. *Current Microbiology*, **46**(3), 0233–0234. https://doi.org/10.1007/s00284-002-3934-z

Malard, L. A., Avila-Jimenez, M. L., Schmale, J., Cuthbertson, L., Cockerton, L., & Pearce, D. A. (2022). Aerobiology over the Southern Ocean—Implications for bacterial colonization of Antarctica. *Environment International*, **169**, 107492. https://doi.org/10.1016/j.envint.2022.107492

Manavi, S. E. I., Aktypis, A., Siouti, E., Skyllakou, K., Myriokefalitakis, S., Kanakidou, M., & Pandis, S. N. (2025). Atmospheric aerosol spatial variability: Impacts on air quality and climate change. *One Earth*, **8**(3), 101237. https://doi.org/10.1016/j.oneear.2025.101237

Martinez-Rabert, E., Sloan, W. T., & Gonzalez-Cabaleiro, R. (2023). Multiscale models driving hypothesis and theory-based research in microbial ecology. *Interface Focus*, **13**(4), 2023008. https://doi.org/10.1098/rsfs.2023.0008

Martinez-Rabert, E., Smith, C. J., Sloan, W. T., & Gonzalez-Cabaleiro, R. (2023). Competitive and substrate limited environments drive metabolic heterogeneity for comammox *Nitrospira*. *ISME Communications*, **3**(1), 91. https://doi.org/10.1038/s43705-023-00288-8

Matthews, A., Lima-Zaloumis, J., Debes, R. V., II, Boyer, G., & Trembath-Reichert, E. (2023). Heterotrophic growth dominates in the most extremotolerant extremophile cultures. *Astrobiology*, **23**(4), 446–459. https://doi.org/10.1089/ast.2022.0100

Mattimore, V., & Battista, J. R. (1996). Radioresistance of Deinococcus radiodurans: Functions necessary to survive ionizing radiation are also necessary to survive prolonged desiccation. *Journal of Bacteriology*, **178**(3), 633–637. https://doi.org/10.1128/jb.178.3.633-637.1996

McCarty, P. L. (2007). Thermodynamic electron equivalents model for bacterial yield prediction: Modifications and comparative evaluations. *Biotechnology and Bioengineering*, **97**(2), 377–388. https://doi.org/10.1002/bit.21250

McDonald, M. D., Owusu-Ansah, C., Ellenbogen, J. B., Malone, Z. D., Ricketts, M. P., Frolking, S. E., et al. (2024). What is microbial dormancy? *Trends in Microbiology*, **32**(2), 142–150. https://doi.org/10.1016/j.tim.2023.08.006

Menéndez-Serra, M., Cáliz, J., Triadó-Margarit, X., Alonso, D., & Casamayor, E. O. (2024). Selective pressure influences inter-biome dispersal in the assembly of saline microbial communities. *Environmental Microbiology*, **26**(12), e70019. https://doi.org/10.1111/1462-2920.70019

Merino, N., Aronson, H. S., Bojanova, D. P., Feyhl-Buska, J., Wong, M. L., Zhang, S., & Giovannelli, D. (2019). Living at the extremes: Extremophiles and the limits of life in a planetary context. *Frontiers in Microbiology*, **10**, 780. https://doi.org/10.3389/fmicb.2019.00780

Michaelis, L., & Menten, M. L. (1913). Die kinetik der invertinwirkung. *Biochemische Zeitschrift*, **49**(333–369), 352.

Myhre, G., Berglen, T. F., Johnsrud, M., Hoyle, C. R., Berntsen, T. K., Christopher, S. A., et al. (2009). Modelled radiative forcing of the direct aerosol effect with multi-observation evaluation. *Atmospheric Chemistry and Physics*, **9**(4), 1365–1392. https://doi.org/10.5194/acp-9-1365-2009

Mykytczuk, N. C. S., Foote, S. J., Omelon, C. R., Southam, G., Greer, C. W., & Whyte, L. G. (2013). Bacterial growth at -15°C; molecular insights from the permafrost bacterium Planococcus halocryophilus Or1. *The ISME Journal*, **7**(6), 1211–1226. https://doi.org/10.1038/ismej.2013.8

Ortiz, M., Leung, P. M., Shelley, G., Jirapanjawat, T., Nauer, P. A., Van Goethem, M. W., et al. (2021). Multiple energy sources and metabolic strategies sustain microbial diversity in Antarctic desert soils. *Proceedings of the National Academy of Sciences*, **118**(45), e2025322118. https://doi.org/10.1073/pnas.2025322118

Palmer, J. D., & Foster, K. R. (2022). Bacterial species rarely work together. *Science*, **376**(6593), 581–582. https://doi.org/10.1126/science.abn5093

Pan, G., Krom, M. D., & Herut, B. (2002). Adsorption–Desorption of phosphate on airborne dust and riverborne particulates in East Mediterranean seawater. *Environmental Science & Technology*, **36**(16), 3519–3524. https://doi.org/10.1021/es020516d

Patade, S., Waman, D., Deshmukh, A., Gupta, A. K., Jadav, A., Phillips, V. T. J., et al. (2022). The influence of multiple groups of biological ice nucleating particles on microphysical properties of mixed-phase clouds observed during MC3E. *Atmospheric Chemistry and Physics*, **22**(18), 12055–12075. https://doi.org/10.5194/acp-22-12055-2022

Pottage, T., Ngabo, D., Parks, S., Hookway, H., Verlander, N. Q., Kojima, K., & Bennett, A. M. (2022). Microbial aerosols generated from standard microbiological laboratory procedures. *Appl Biosaf*, **27**(2), 92–99. https://doi.org/10.1089/apb.2021.0038

Prosser, J. I. (2020). Putting science back into microbial ecology: A question of approach. *Philosophical Transactions of the Royal Society B: Biological Sciences*, **375**(1798), 20190240. https://doi.org/10.1098/rstb.2019.0240

Pye, H. O. T., Nenes, A., Alexander, B., Ault, A. P., Barth, M. C., Clegg, S. L., et al. (2020). The acidity of atmospheric particles and clouds. *Atmospheric Chemistry and Physics*, **20**(8), 4809–4888. https://doi.org/10.5194/acp-20-4809-2020

Ratzke, C., & Gore, J. (2018). Modifying and reacting to the environmental pH can drive bacterial interactions. *PLoS Biology*, **16**(3), e2004248. https://doi.org/10.1371/journal.pbio.2004248

Sander, R. (2023). Compilation of Henry's law constants (version 5.0.0) for water as solvent. *Atmospheric Chemistry and Physics*, **23**(19), 10901–12440. https://doi.org/10.5194/acp-23-10901-2023

Šant-Temkiv, T., Amato, P., Casamayor, E. O., Lee, P. K. H., & Pointing, S. B. (2022). Microbial ecology of the atmosphere. *FEMS Microbiology Reviews*, **46**(4), fuac009. https://doi.org/10.1093/femsre/fuac009

Sattler, B., Puxbaum, H., & Psenner, R. (2001). Bacterial growth in supercooled cloud droplets. *Geophysical Research Letters*, **28**(2), 239–242. https://doi.org/10.1029/2000GL011684

Schmider, T., Hestnes, A. G., Brzykcy, J., Schmidt, H., Schintlmeister, A., Roller, B. R. K., et al. (2024). Physiological basis for atmospheric methane oxidation and methanotrophic growth on air. *Nature Communications*, **15**(1), 4151. https://doi.org/10.1038/s41467-024-48197-1

Smeaton, C. M., & Van Cappellen, P. (2018). Gibbs Energy Dynamic Yield Method (GEDYM): Predicting microbial growth yields under energy-limiting conditions. *Geochimica et Cosmochimica Acta*, **241**, 1–16. https://doi.org/10.1016/j.gca.2018.08.023

Teng, Z.-J., Qin, Q. L., Zhang, W., Li, J., Fu, H. H., Wang, P., et al. (2021). Biogeographic traits of dimethyl sulfide and dimethylsulfonium-propionate cycling in polar oceans. *Microbiome*, **9**(1), 207. https://doi.org/10.1186/s40168-021-01153-3

Tilgner, A., Schaefer, T., Alexander, B., Barth, M., Collett, J. L., Jr., Fahey, K. M., et al. (2021). Acidity and the multiphase chemistry of atmospheric aqueous particles and clouds. *Atmospheric Chemistry and Physics*, **21**(17), 13483–13536. https://doi.org/10.5194/acp-21-13483-2021

Vaïtilingom, M., Amato, P., Sancelme, M., Laj, P., Leriche, M., & Delort, A. M. (2010). Contribution of microbial activity to carbon chemistry in clouds. *Applied and Environmental Microbiology*, **76**(1), 23–29. https://doi.org/10.1128/AEM.01127-09

van den Berg, L., van Loosdedrecht, M. C. M., & de Kreuk, M. K. (2021). How to measure diffusion coefficients in biofilms: A critical analysis. *Biotechnology and Bioengineering*, **118**(3), 1273–1285. https://doi.org/10.1002/bit.27650

van den Berg, N. I., Machado, D., Santos, S., Rocha, I., Chacón, J., Harcombe, W., et al. (2022). Ecological modelling approaches for predicting emergent properties in microbial communities. *Nature Ecology & Evolution*, **6**(7), 855–865. https://doi.org/10.1038/s41559-022-01746-7

van der Meer, J. (2006). Metabolic theories in ecology. *Trends in Ecology & Evolution*, **21**(3), 136–140. https://doi.org/10.1016/j.tree.2005.11.004
van Kessel, M. A. H. J., Speth, D. R., Albertsen, M., Nielsen, P. H., Op den Camp, H. J. M., Kartal, B., et al. (2015). Complete nitrification by a single microorganism. *Nature*, **528**(7583), 555–559. https://doi.org/10.1038/nature16459

Van Loosdrecht, M. C. M., & Henze, M. (1999). Maintenance, endogeneous respiration, lysis, decay and predation. *Water Science and Technology*, **39**(1), 107–117. https://doi.org/10.1016/S0273-1223(98)00780-X

Wainwright, M., Wickramasinghe, N., Narlikar, J., & Rajaratnam, P. (2003). Microorganisms cultured from stratospheric air samples obtained at 41 km. *FEMS Microbiology Letters*, **218**(1), 161–165. https://doi.org/10.1111/j.1574-6968.2003.tb11513.x

Wang, P., Yang, Y., Xue, D., Ren, L., Tang, J., Leung, L. R., & Liao, H. (2023). Aerosols overtake greenhouse gases causing a warmer climate and more weather extremes toward carbon neutrality. *Nature Communications*, **14**(1), 7257. https://doi.org/10.1038/s41467-023-42891-2

Womack, A. M., Bohannan, B. J. M., & Green, J. L. (2010). Biodiversity and biogeography of the atmosphere. *Philosophical Transactions of the Royal Society B: Biological Sciences*, **365**(1558), 3645–3653. https://doi.org/10.1098/rstb.2010.0283

Yang, Y., Mou, S., Wang, H., Wang, P., Li, B., & Liao, H. (2024). Global source apportionment of aerosols into major emission regions and sectors over 1850–2017. *Atmospheric Chemistry and Physics*, **24**(11), 6509–6523. https://doi.org/10.5194/acp-24-6509-2024

Yates, K. L., Bouchet, P. J., Caley, M. J., Mengersen, K., Randin, C. F., Parnell, S., et al. (2018). Outstanding challenges in the transferability of ecological models. *Trends in Ecology & Evolution*, **33**(10), 790–802. https://doi.org/10.1016/j.tree.2018.08.001

Yoshikawa, M., Zhang, M., & Toyota, K. (2017). Biodegradation of volatile organic compounds and their effects on biodegradability under co-existing conditions. *Microbes and Environments*, **32**(3), 188–200. https://doi.org/10.1264/jsme2.ME16188

Zhu, J., Xia, X., Che, H., Wang, J., Cong, Z., Zhao, T., et al. (2019). Spatiotemporal variation of aerosol and potential long-range transport impact over the Tibetan Plateau, China. *Atmospheric Chemistry and Physics*, **19**(23), 14637–14656. https://doi.org/10.5194/acp-19-14637-2019