In Silico Characterization of Molecular Interactions of Aviation-Derived Pollutants with Human Proteins: Implications for Occupational and Public Health

Abstract

Combustion of aviation jet fuel emits a complex mixture of pollutants linked to adverse health outcomes among airport personnel and nearby communities. While epidemiological studies showed the detrimental effects of aviation-derived air pollutants on human health, the molecular mechanisms of the interactions of these pollutants with cellular biomolecules like proteins that drive the adverse health effects remain poorly understood. In this study, we performed molecular docking simulations of 272 pollutant–protein complexes using AutoDock Vina 1.2.7 to characterize the binding strength of the pollutants with the selected proteins. We selected 34 aviation-derived pollutants that constitute three chemical categories of pollutants: volatile organic compounds (VOCs), polyaromatic hydrocarbons (PAHs), and organophosphate esters (OPEs). Each pollutant was docked to eight proteins that play critical roles in endocrine, metabolic, transport, and neurophysiological functions, where functional disruption is implicated in disease. The effect of binding of multiple pollutants was analyzed. Our results indicate that aliphatic and monoaromatic VOCs display low (<6 kcal/mol) binding affinities while PAHs and organophosphate esters exhibit strong (>7 kcal/mol) binding affinities. Furthermore, the binding strength of PAHs exhibits a positive correlation with the increasing number of aromatic rings in the pollutants, ranging from nearly 7 kcal/mol for two aromatic rings to more than 15 kcal/mol for five aromatic rings. Analysis of intermolecular interactions showed that these interactions are predominantly stabilized by hydrophobic, pi-stacking, and hydrogen bonding interactions. Simultaneous docking of multiple pollutants revealed the increased binding strength of the resulting complexes, highlighting the detrimental effect of exposure to pollutant mixtures found in ambient air near airports. We provide a priority list of pollutants that regulatory authorities can use to further develop targeted mitigation strategies to protect the vulnerable personnel and communities near airports.

1. Introduction

Combustion of aviation fuels produces a diverse array of pollutants, including inorganic gases such as nitrogen oxides (NOx), sulfur oxides (SOx), and volatile and semivolatile organic compounds (VOCs/SVOCs), and particulate matter containing polyaromatic hydrocarbons (PAHs). Vapors from jet engine lubricating oils add organophosphate esters (OPEs) [1]. Epidemiological and meta-analysis studies have established correlations between short- and long-term exposure to these pollutants and cardiovascular [2], respiratory [3], and neurodegenerative diseases [4], including stroke, chronic obstructive pulmonary disease (COPD), ischemic heart disease, aggravation of asthma, and increased risk of lung cancer [5].

Exposure to aviation emissions is particularly relevant to ground crew and maintenance personnel who service aircraft on tarmacs and in hangars, as well as residents in communities residing in the vicinity of airports. Ground crew and aircraft maintenance personnel may experience heightened exposure levels due to direct contact with exhaust plumes and spillages, often in confined or poorly ventilated areas [1]. Analysis of indoor and outdoor air pollutants in residential areas in the vicinity of airports revealed up to 4.8-fold higher concentrations of all gaseous compounds and particulate matter when residences were downwind of the airport, and 7.5-fold higher particle number concentrations from overhead landing operations [6].

Adverse health effects of exposure to aviation-related emissions have been demonstrated in numerous studies [1]. Mechanistic studies are limited. In particular, knowledge of the molecular-level interactions between aviation-derived pollutants and proteins that are involved in critical physiological functions that may be impacted by aviation-derived air pollutant exposure is lacking. Increasing evidence suggests that interactions with persistent organic pollutants trigger alterations in the structure and function of proteins [7]. For example, PAHs are metabolically activated by cytochrome P450 to produce reactive oxygen species that lead to inflammation associated with asthma and COPD [8]. Pollutants such as VOCs, PAHs, and organophosphate esters have been shown to have endocrine disrupting effects leading to reduced fertility, negatively impacting reproductive health [9]. These studies provide important insights into the negative effects of these pollutants on health. However, the underlying molecular mechanisms, including structural changes due to interactions between aviation-derived pollutants and cellular proteins, that drive these detrimental health effects, remain largely uncharacterized.

One approach to address this knowledge gap is to use in silico molecular docking as in prior studies that have already been used to characterize the effect of binding of some environmental pollutants with selected proteins [10,11,12,13]. Docking studies showed the strong binding affinity of benzo-a-pyrene, released from cigarette smoke and fuel combustion, with selected proteins, highlighting the potential health effects of these interactions [11]. Integration of molecular docking and in vitro analyses to characterize the molecular interactions between phenolic pollutants and cytochrome p450 demonstrated the reliability of docking simulations based on the consistency between docking and experimental observations [13]. Organophosphates, widely recognized for their toxicity to the nervous system, can inhibit acetylcholinesterase, potentially influencing neurological and developmental disorders [14].

In this study, we identified air pollutants derived from aviation jet fuel combustion belonging to discrete chemical categories: VOCs, PAHs, and OPEs. We characterized the interaction of each of these three chemical categories of aviation-derived pollutants with target proteins whose functional disruption is implicated in disease. By examining a diverse range of protein–pollutant interactions and quantifying their binding affinities, we identified which pollutant–protein interactions pose the highest risk for function disruption. This study demonstrates how aviation-derived pollutants bind to cellular proteins and potentially disrupt the normal function of proteins, resulting in their potency to aggravate health. We present a list of pollutants that have a strong interaction effect with critical human proteins, which should prompt regulatory authorities to investigate the need to develop targeted mitigation strategies to address this health concern.

2. Methods

2.1. Identification of Target Proteins and Aviation-Derived Air Pollutants

We performed a systematic analysis of the scientific literature to create a list of proteins which have been reported to interact with air pollutants such as VOCs, PAHs, particulate matter, and OPEs. We compiled >40 candidate proteins from peer-reviewed toxicology literature and databases linked to combustion derived VOCs, PAHs, or OPEs. We selected a subset of proteins from this list to identify proteins that perform discrete critical cellular and physiological functions, disruption of which is associated with disease states such as cancer, endocrine, cardiopulmonary diseases, and neurotoxicity, related to air pollution. Based on these criteria, we selected the final set of eight target proteins for investigation in this study (Table 1).

Pollutants derived from aviation fuel combustion were selected manually from a comprehensive search of scientific publications and government documents. We selected pollutant molecules in the following categories: VOCs, PAHs and organophosphates. The VOCs were further categorized into light chain hydrocarbons and included short-chain aliphatic (<C₇), long-chain aliphatic (C₈–C₁₄), and monoaromatic pollutants (Figure 1). The heavy VOCs were selected due to their exclusive presence in aircraft-related emissions and absence in gasoline emissions [15]. The selected PAHs include an increasing number of aromatic rings, from one to five aromatic rings, enabling characterization of the effect of the number of rings on the strength of interactions with proteins [16].

Table 1. Target proteins selected for analysis of the effect of binding of jet fuel emission pollutants.

Protein Name	Protein Structure	PDB ID	Normal Function in Cells	Key Health Effects
Estrogen receptor (ER) α ligand-binding domain		3ERT	Nuclear hormone receptor; ligand-activated transcription factor for estrogen-responsive genes.	Endocrine disruption linked to breast cancer and fertility disorders in exposed populations [17].
Androgen receptor (AR) ligand-binding domain		2AM9	Ligand-activated transcription factor controlling male sexual differentiation and reproductive function.	Endocrine disruption affects pubertal development and spermatogenesis [18].
Thyroid hormone receptor β ligand-binding domain		1N46	Nuclear receptor for T3; regulates basal metabolism and development via gene transcription.	Exposure to air pollutants is associated with increased risk of thyroid cancer and hypothyroidism [19].
Vitamin D3 receptor ligand-binding domain		1IE9	Ligand-activated transcription factor controlling calcium/phosphate homeostasis.	Vitamin D deficiency exacerbates asthma and chronic obstructive pulmonary disorder (COPD) in chronically exposed cohorts [20].
Acetylcholinesterase		4M0E	Hydrolyzes acetylcholine to terminate neurotransmission at cholinergic synapses.	Structural alterations observed in different types of tumors from the brain, lung, breast, renal, and colon cancers [21].
Human serum albumin (HSA)		1H9Z	Main plasma carrier for fatty acids, hormones, and drugs; maintains oncotic pressure.	Forms adducts with pollutants like PAHs and is used as a biomarker for pollutant exposure [22].
Hemoglobin (α-chain)		2DN1	Transports O2 from lungs to tissues and CO2 back to lungs.	Exposure to air pollutants has been associated with reduced hemoglobin levels and anemia [23].
Cytochrome P450 1A1 (CYP1A1)		4I8V	Monooxygenase metabolizing xenobiotics (PAHs; drugs) and endogenous lipids.	Impacts pulmonary inflammation due to PAH-induced activity of CYP1A1 [24].

Table 1. Target proteins selected for analysis of the effect of binding of jet fuel emission pollutants.

Protein Name	Protein Structure	PDB ID	Normal Function in Cells	Key Health Effects
Estrogen receptor (ER) α ligand-binding domain		3ERT	Nuclear hormone receptor; ligand-activated transcription factor for estrogen-responsive genes.	Endocrine disruption linked to breast cancer and fertility disorders in exposed populations [17].
Androgen receptor (AR) ligand-binding domain		2AM9	Ligand-activated transcription factor controlling male sexual differentiation and reproductive function.	Endocrine disruption affects pubertal development and spermatogenesis [18].
Thyroid hormone receptor β ligand-binding domain		1N46	Nuclear receptor for T3; regulates basal metabolism and development via gene transcription.	Exposure to air pollutants is associated with increased risk of thyroid cancer and hypothyroidism [19].
Vitamin D3 receptor ligand-binding domain		1IE9	Ligand-activated transcription factor controlling calcium/phosphate homeostasis.	Vitamin D deficiency exacerbates asthma and chronic obstructive pulmonary disorder (COPD) in chronically exposed cohorts [20].
Acetylcholinesterase		4M0E	Hydrolyzes acetylcholine to terminate neurotransmission at cholinergic synapses.	Structural alterations observed in different types of tumors from the brain, lung, breast, renal, and colon cancers [21].
Human serum albumin (HSA)		1H9Z	Main plasma carrier for fatty acids, hormones, and drugs; maintains oncotic pressure.	Forms adducts with pollutants like PAHs and is used as a biomarker for pollutant exposure [22].
Hemoglobin (α-chain)		2DN1	Transports O2 from lungs to tissues and CO2 back to lungs.	Exposure to air pollutants has been associated with reduced hemoglobin levels and anemia [23].
Cytochrome P450 1A1 (CYP1A1)		4I8V	Monooxygenase metabolizing xenobiotics (PAHs; drugs) and endogenous lipids.	Impacts pulmonary inflammation due to PAH-induced activity of CYP1A1 [24].

2.2. Molecular Docking Simulations

The three-dimensional structures of the selected pollutant compounds (Figure 1) were downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/, accessed on 30 March 2025) in the SDF format. The ligands were converted to PDB format using PyMol [25]. The selected proteins (Table 1) were downloaded from the Protein Data Bank (PDB) database (https://www.rcsb.org/, accessed on 31 March 2025) in PDB format. The structures were processed to retain a single chain for further analyses. All waters were removed from the proteins, and polar hydrogens were added to all proteins. The heme prosthetic groups of Cytochrome P450 1A1 and the hemoglobin alpha chain were retained as part of the receptors in the docking simulations. All ligand and protein structures were processed using AutoDockTools-1.5.7, and the output pdbqt files were used for molecular docking. Each of the 34 ligands were docked with the eight proteins using AutoDock Vina 1.2.7 [26]. The grid box dimensions for each protein were set to 126 Å × 126 Å × 126 Å, with a spacing of 0.375 Å except CYP1A1, which was set to 40 Å × 40 Å × 40 Å. Three independent molecular docking simulations were performed for each protein–ligand complex. The lowest binding energy structures of each protein–ligand docking were used to calculate the averages of the triplicate simulations. Multiple-ligand docking simulations were performed by docking the androgen receptor, human serum albumin, and acetylcholinesterase with the following pairs of ligands: p-xylene and benzo-a-pyrene; tricresyl phosphate and benzo-a-pyrene; and p-xylene and tricresyl phosphate.

2.3. Determination of Protein–Pollutant Interactions

Intermolecular interactions between the selected protein–ligand complexes were determined using the protein–ligand interaction profiler (PLIP) [27]. Complexes with the highest binding affinity scores of the triplicate simulations were used for this analysis. Inter-molecular interactions between protein and ligand were visualized using PyMol [25] and ChimeraX 1.8 [28]. Intermolecular interactions were analyzed using in-house Python 3.0 and Bash scripts.

3. Results and Discussion

3.1. Selection of Pollutants and Proteins for Molecular Docking Analysis

Jet fuel combustion produces CO, CO₂, NOx, SOx, and a variety of (S)VOCs, and particulate matter (PM), with particles containing PAHs, and organophosphates derived from lubricating oils. The chemical identities and concentration of pollutants vary based on fuel type, engine operating conditions [1], and seasons [29]. Table S1 shows the list of aviation-derived pollutants categorized based on their type, sampling location, and emission trends, where applicable, that were selected for further analysis. The pollutants include six light chain aliphatic VOCs, four heavy chain aliphatic VOCs, six monoaromatic VOCs, fifteen PAHs, and three OPEs produced during different stages of engine operation and have been observed at airports and in the vicinity of the airport environment [15,29,30,31].

A total of eight target proteins were selected for this analysis. The selected proteins are involved in diverse physiological functions in humans such as metabolism, transport, endocrine signaling, and neurological function. Changes to the structure or interaction with pollutants have been linked to a variety of cancers, asthma, chronic obstructive pulmonary disease (COPD), and cardiovascular diseases (last column of Table 1). In this study, we selected these proteins to characterize the effect of pollutant binding on endocrine and reproductive disorders, cancer, cardiovascular, pulmonary, and neurological disorders.

3.2. Binding Affinities of Pollutant–Protein Complexes

We performed molecular docking simulations to determine the binding strength of the 272 protein–ligand pairs (34 pollutants × 8 proteins) and characterize the molecular-level interactions that promote favorable binding.

Table 2. Average binding affinity (kcal/mol) of the pollutant–protein complexes categorized into five defined pollutant categories (first column). The color spectrum indicates weak (<5 kcal/mol), medium (5–7 kcal/mol), and strong (>7 kcal/mol) binding represented in the gray, orange-green, and green-blue colors of the gray–orange–blue color spectrum.

Pollutant Category	Chemical Pollutant	Estrogen Receptor	Androgen Receptor	Thyroid Receptor	Nuclear Receptor Vit D	Acetylcholinesterase	Human Serum Albumin	Hemoglobin Alpha	Cytochrome P450 1A1	Average Affinity Across Proteins	Average Affinity for Pollutant Category
Light chain aliphatic VOCs	Formaldehyde	−1.8	−1.7	−1.9	−2.3	−2.0	−2.0	−1.7	−1.7	−1.9	−3.1
	Acetaldehyde	−2.6	−2.6	−2.6	−3.0	−3.0	−2.6	−2.3	−2.5	−2.6
	Acrolein	−2.8	−3.3	−3.2	−3.2	−3.1	−3.3	−2.8	−3.3	−3.1
	Propionaldehyde	−2.9	−3.2	−3.3	−2.8	−3.0	−3.4	−2.9	−3.4	−3.1
	1,3-Butadiene	−3.1	−3.6	−3.3	−2.9	−3.3	−3.7	−3.2	−4.0	−3.4
	Hexane	−3.8	−4.0	−4.2	−3.4	−4.1	−4.5	−4.1	−5.0	−4.1
Heavy chain aliphatic VOCs	Nonane	−4.4	−3.8	−4.8	−3.8	−5.0	−5.6	−4.5	−5.9	−4.7	−4.7
	Decane	−4.1	−3.9	−3.5	−3.9	−5.1	−5.7	−4.7	−6.2	−4.7
	Nonanal	−4.3	−4.0	−4.6	−3.8	−5.0	−5.4	−3.7	−5.9	−4.6
	Decanal	−4.3	−4.2	−4.1	−4.1	−5.0	−5.7	−5.3	−6.2	−4.9
Monoaromatic VOCs	Benzene	−4.2	−5.0	−4.5	−3.7	−4.9	−5.2	−4.4	−5.6	−4.7	−5.5
	Toluene	−5.0	−5.0	−5.2	−4.3	−5.5	−6.1	−5.3	−6.4	−5.3
	Ethylbenzene	−5.0	−5.0	−5.2	−4.5	−5.7	−6.5	−5.5	−6.8	−5.5
	o-Xylene	−5.5	−5.3	−5.7	−4.6	−6.1	−6.8	−5.8	−7.1	−5.9
	p-Xylene	−5.3	−5.3	−5.8	−4.7	−5.9	−6.8	−5.5	−7.1	−5.8
	Styrene	−4.9	−5.3	−5.5	−4.5	−5.7	−6.5	−5.6	−6.8	−5.6
PAHs - two (top) to five (bottom) aromatic rings	Naphthalene	−6.3	−5.9	−6.4	−5.2	−7.2	−8.3	−7.0	−8.5	−6.9	−9.0
	2-hydroxynaphthalene	−6.2	−6.1	−5.6	−5.3	−7.0	−8.3	−5.7	−8.8	−6.6
	Acenaphthene	−6.9	−7.4	−7.3	−5.7	−8.4	−9.6	−8.3	−9.9	−7.9
	Fluorene	−7.5	−7.6	−6.6	−5.8	−8.9	−10.0	−7.8	−10.5	−8.1
	Acenaphthylene	−6.9	−7.4	−8.0	−5.8	−8.4	−9.8	−8.3	−9.9	−8.1
	Phenanthrene	−7.9	−8.2	−8.2	−6.1	−9.6	−10.7	−8.3	−11.2	−8.8
	Anthracene	−7.6	−6.7	−8.1	−6.1	−9.4	−10.7	−7.9	−11.2	−8.5
	Fluoranthene	−8.4	−9.0	−8.0	−6.8	−10.2	−12.0	−8.6	−12.6	−9.5
	Pyrene	−8.2	−7.6	−6.9	−6.7	−10.5	−11.8	−10.4	−12.3	−9.3
	Cyclopenta-cd-pyrene	−8.4	−8.3	−8.2	−7.1	−11.2	−13.2	−7.6	−13.9	−9.7
	Benzo-a-pyrene	−8.6	−9.5	−8.0	−7.1	−12.2	−13.3	−7.3	−15.3	−10.2
	Benzo-g-chrysene	−9.3	−7.8	−8.2	−7.7	−10.9	−14.5	−8.1	−16.3	−10.3
	Dibenz-ah-anthracene	−8.8	−8.3	−8.3	−7.8	−12.6	−14.0	−8.0	−16.3	−10.5
	Dibenzo-ae-pyrene	−10.1	−8.6	−9.4	−8.0	−12.2	−15.0	−8.4	−17.6	−11.1
	Benzo-ghi-perylene	−8.9	−8.1	−8.2	−7.4	−10.8	−14.6	−7.4	−16.1	−10.2
Organophosphate esters	TCP	−7.5	−7.4	−7.1	−6.6	−8.9	−9.8	−6.5	−9.5	−7.9	−6.7
	TDCIPP	−4.6	−4.3	−5.0	−4.5	−5.4	−5.4	−4.0	−6.6	−5.0
	TPHP	−6.9	−6.5	−6.5	−6.5	−7.3	−8.7	−6.3	−9.5	−7.3
Average affinity of protein for all pollutants		-6.0	−5.9	−5.9	−5.2	−7.2	−8.2	−6.0	−8.8

shows the binding affinities calculated by averaging triplicate molecular docking simulations for each protein–ligand pair. The small standard deviations for the triplicate docking runs indicate the convergence of the simulations (Table S2). A comparison of the binding affinities in this study with the binding energies reported for some pollutant-protein pairs in previous studies showed similar affinities (Table S3), further validating the results. A comparison of the average binding affinities for each pollutant category (last column of Table 2) highlights the distinct binding characteristics of the different categories of the aviation-derived air pollutants. The light chain aliphatic VOCs displayed weak interactions, suggesting non-specific or transient interactions. The longer chain VOCs and the monoaromatic VOCs showed weak to moderate affinities, with the highest affinities observed for binding to human serum albumin (HSA) and cytochrome p450 1A1 (CYP1A1). These findings suggest that increased hydrocarbon chain length and aromatic character contribute to the enhanced interactions with target proteins. In contrast to the VOCs, all PAHs and OPEs displayed strong binding affinities with all target proteins.

The highest binding affinities were observed for high-molecular-weight PAHs, ranging from −8 kcal/mol to around −15 kcal/mol. Strong binding affinities were observed for all target proteins, with acetylcholinesterase, HSA, and CYP1A1 displaying the strongest effects. These strong interactions are consistent with the metabolic activation of PAHs by CYP1A1 [32] and the endocrine-disrupting potential observed for PAHs [16]. Our results demonstrate increased binding affinities with increasing number of aromatic rings in the PAHs, consistent with previous observations [33]. The organophosphate esters showed moderate to strong binding. Specifically, tricresyl phosphate and triphenyl phosphate (TPHP) displayed high affinity for all target proteins, while Tris(1,3-dichloro-2-propyl)phosphate (TDCIPP) showed weaker interactions. The binding characteristics of the OPEs are consistent with previous studies of organophosphate compounds from jet engine lubricants and pesticides with these functional groups [1,34,35]. These results underscore the disruptive properties of these interactions on the normal function of these proteins, contributing to adverse health effects.

The ubiquitous strong interactions of PAHs with all analyzed proteins demonstrate the profound effect of these pollutants on diverse physiological functions, leading to numerous harmful health effects. These include carcinogenicity due to PAHs interactions with HSA and CYP1A1, endocrine disruption due to interaction of PAHs and organophosphates with estrogen, androgen, and thyroid receptors, and neurotoxicity due to strong interactions of PAHs and organophosphates with the acetylcholinesterase enzyme. Strong interactions displayed by HSA for the diverse organic pollutants suggest that these compounds can be transported efficiently in the bloodstream and sequestered by these serum proteins. The favorable interactions of PAHs like benzo-a-pyrene with CYP1A1 further illustrate their potential metabolic transformation into intermediates that increase cancer risk. This is consistent with the observation of elevated lung cancer risk in individuals with occupational exposure to these pollutants [36].

3.3. Structural Interactions of High-Affinity Protein–Pollutant Complexes

We performed the structural analysis of the aviation-derived pollutant–protein complexes displaying the highest binding affinities to characterize the types of intermolecular interactions that stabilize these complexes. Figure 2 shows the atomic-level interactions for CYP1A1 and HSA with dibenzo-ae-pyrene and tricresyl phosphate, respectively, which showed the strongest binding affinities for these pollutants. The pollutant-protein interactions were stabilized predominantly by hydrophobic interactions, as expected. Analysis of intermolecular interactions revealed that the complexes with the highest binding affinities are stabilized by hydrophobic, pi-stacking, and hydrogen bonding interactions (

Table 3. Inter-molecular interactions between selected pollutant–protein complexes that display strong binding affinities toward all target proteins.

Protein	Ligand	Interaction	Residues
Estrogen receptor	Benzo-a-pyrene	Hydrophobic interactions	Ala350, Leu354, Trp383, Leu525, Leu536
		Pi-stacking	Trp383
	Dibenzo-ae-pyrene	Hydrophobic interactions	Thr347, Leu354, Trp383, Leu525, Leu536
		Pi-stacking	Trp383
	Tricresyl phosphate	Hydrophobic interactions	Leu354, Trp383, Leu525, Tyr526, Lys529, Val533, Leu536
		Pi-stacking	Trp383
Androgen receptor	Benzo-a-pyrene	Hydrophobic interactions	Leu707, Gln711, Met745, Val746, Leu873
		Pi-stacking	Phe764
	Dibenzo-ae-pyrene	Hydrophobic interactions	Ile816, Val818, Lys912, Ile914, Tyr915
	Tricresyl phosphate	Hydrogen bonds	Trp751
		Hydrophobic interactions	Glu681, Pro682, Val684, Ala748, Arg752, Pro801, Phe804, Leu805
		Pi-cation interactions	Arg752
Thyroid receptor	Benzo-a-pyrene	Hydrophobic interactions	Ile303, Lys306, Ala436, Phe459
	Dibenzo-ae-pyrene	Hydrophobic interactions	Ile303, Lys306, Ala436, Phe459
		Pi-cation interactions	Arg383
	Tricresyl phosphate	Hydrogen bonds	Lys411, His412, Phe417, Trp418
		Salt bridges	Lys211
		Hydrophobic interactions	Ile407, Lys411, Trp418
		Pi-cation interactions	Lys211
Nuclear receptor Vit D	Benzo-a-pyrene	Hydrophobic interactions	Gln364, Arg368, Leu378, Tyr380
	Dibenzo-ae-pyrene	Hydrophobic interactions	Gln152, Phe153, Tyr236, Thr415
	Tricresyl phosphate	Hydrogen bonds	Arg343, Asn394
		Hydrophobic interactions	Pro344, Arg391, Asn394, Glu395
Acetylcholinesterase	Benzo-a-pyrene	Hydrophobic interactions	Tyr72, Trp286, Val294, Phe338, Tyr341
		Pi-stacking	Trp286, Tyr341
	Dibenzo-ae-pyrene	Hydrophobic interactions	Tyr72, Trp286, Leu289, Phe338, Tyr341
		Pi-stacking	Trp286
	Tricresyl phosphate	Hydrophobic interactions	Trp286, Leu289, Glu292, Tyr337, Phe338, Tyr341
		Pi-stacking	Tyr341
Human Serum Albumin (HSA)	Benzo-a-pyrene	Hydrophobic interactions	Arg117, Phe134, Tyr138, Ile142, Ala158, Tyr161, Phe165
		Pi-stacking	Tyr138, Tyr161
	Dibenzo-ae-pyrene	Hydrophobic interactions	Tyr138, Ile142, Leu154, Tyr161
		Pi-stacking	Tyr161
	Tricresyl phosphate	Hydrogen bonds	Tyr161
		Hydrophobic interactions	Arg117, Phe134, Tyr138, Ile142, Phe157, Tyr161, Phe165
		Pi-stacking	Tyr138, Tyr161
Hemoglobin alpha	Benzo-a-pyrene	Hydrophobic interactions	Glu23, Glu27, Glu30, Val55
	Dibenzo-ae-pyrene	Hydrophobic interactions	Glu23, Ala26, Glu27, Glu30, Val55, Lys56
	Tricresyl phosphate	Hydrophobic interactions	Glu23, Ala26, Val55, Lys56
Cytochrome P450 1A1 (CYP 1A1)	Benzo-a-pyrene	Hydrophobic interactions	Phe123, Phe224, Phe258, Ala317, Phe319, Asp320, Thr321, Ile386, Leu496
		Pi-stacking	Phe224
	Dibenzo-ae-pyrene	Hydrophobic interactions	Ile115, Phe123, Phe224, Phe258, Leu312, Asp313, Ala317, Phe319, Asp320, Thr321, Ile386, Leu496
		Pi-stacking	Phe224
	Tricresyl phosphate	Hydrophobic interactions	Ile115, Phe224, Leu254, Phe258, Leu312, Ala317, Phe319, Asp320, Ile386, Leu496
		Pi-stacking	Phe123, Phe224
		Hydrogen bonds	Ala317

). These observations are consistent with the chemical properties of the ligand and highlight the significant impact these stable interactions can have on the normal function and downstream effect on the physiological functions of these proteins.

Our docking simulation results demonstrate strong binding characteristics of pollutants commonly released during aviation fuel combustion with proteins involved in critical physiological functions in humans. In the ambient environment, the presence of a mixture of pollutants released in the emissions may influence the binding of more than one ligand to these critical proteins [37]. To assess the effect of the simultaneous binding of multiple pollutants to the target proteins, we performed molecular docking simulations of pairs of ligands, representing VOC-PAH, PAH-OPE, and VOC-OPE interactions, with three proteins—androgen receptor, acetylcholinesterase, and human serum albumin. The ligands representing the three pollutant categories were p-xylene (VOC), benzo-a-pyrene (PAH), and tricresyl phosphate (OPE). For all three pollutant pairs, docking the two pollutants simultaneously produced significantly higher calculated binding affinities than docking each pollutant individually (

Table 4. Comparison of the binding affinities of single vs. multi-pollutant binding for the androgen receptor, human serum albumin, and acetylcholinesterase. The pollutants are representative of the three pollutant categories—volatile organic compounds (VOCs), polyaromatic hydrocarbons (PAHs), and organophosphate esters (OPEs). The ligand identity is identified within parentheses.

	Individual Pollutant Binding Affinity			Multi-Pollutant Binding Affinity
Protein	VOC (p-xylene)	PAH (Benzo-a-pyrene)	OPE (Tricresyl phosphate)	VOC-PAH	PAH-OPE	VOC-OPE
Androgen receptor	−5.4	−10.5	−7.4	−16	−13	−9.9
Human serum albumin	−5.9	−12.2	−9	−19.3	−18.5	−14.6
Acetylcholinesterase	−6.7	−13.3	−10	−18	−17.6	−12.5

). The binding affinity of the two-ligand interactions showed significantly higher binding affinity (for example, −18 kcal/mol for androgen receptor binding to p-xylene and benzo-a-pyrene) relative to the individual pollutant docking (−6.7 and −13.3 kcal/mol for p-xylene and benzo-a-pyrene, respectively). These observations illustrate the potential health effects of exposure to mixtures of these pollutants. These results further highlight the need to perform toxicological studies to characterize the effect of multi-pollutant interactions. By correlating in silico binding affinities with in vivo toxicity profiles, regulatory authorities and public health practitioners can better identify which pollutants present the greatest risk and develop targeted mitigation or monitoring strategies to protect airport ground crews and nearby populations.

4. Conclusions

Exposure to air pollutants derived from aviation jet fuel emissions poses a significant health risk for airport ground personnel, flight crew, passengers, and neighborhood communities. In this study, we performed a comprehensive characterization of the intermolecular interactions between eight proteins that perform critical physiological functions and a broad set of pollutants derived from aviation jet engine emissions. Among all pollutant categories analyzed, PAHs and organophosphate esters showed the strongest binding affinities, with the binding scores of PAHs displaying a positive correlation with the number of aromatic rings. These high-affinity complexes were dominated by hydrophobic and π–π stacking interactions, with polar groups like the phosphate moieties in OPEs stabilized by hydrogen bonding interactions. In the real world, individuals are exposed to mixtures of these pollutants, leading to health risks associated with simultaneous interactions of human proteins with multiple pollutants. Analysis of multiple pollutant interactions showed stronger interactions for the ligand pairs relative to individual ligands. Further experimental analysis of the toxicological effects is necessary for regulators and policymakers to make data-driven informed decisions to establish emission and exposure regulations for these pollutant classes.

The findings from this study suggest that emission standards should be stratified by pollutant class, particularly targeting high-affinity binders such as benzo-a-pyrene and PAHs with a larger number of aromatic rings such as fluoranthene, pyrene, cyclopenta-cd-pyrene, benzo-a-pyrene, benzo-g-chrysene, dibenz-ah-anthracene, dibenzo-ae-pyrene, and benzo-ghi-perylene, and organophosphate esters such as TCP and TPHP. A revision of occupational exposure limits (OELs) for ground personnel must be considered for the identified priority compounds. The aviation industry is increasing the use of sustainable/synthetic aviation fuels (SAFs). The increase in the use of SAFs will be significant at a number of airports in certain jurisdictions. In this context, the results from this and further experimental analyses can be used to inform the formulation and selection of SAFs that will minimize the production of especially hazardous combustion-derived pollutants released from the gradual replacement of conventional jet fuels with SAFs. Finally, this study underscores the urgent need for targeted toxicological and epidemiological studies to quantify the real-world risk of exposure to toxic and carcinogenic compounds in aircraft engine exhaust and to develop effective interventions to mitigate the health risks associated with chronic exposure to these hazardous pollutants.