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. 2025 Jul 28;3(12):1501–1509. doi: 10.1021/envhealth.5c00193

Constructing Petroleum-Derived Aryl Hydrocarbon Receptor Related Toxic Source Profiles via Effect-Directed Analysis

Zikang Li †,‡, Yong He †,*, Xiaoxi Yang †, Yi Wang †,§, Yunhe Guo †,∥, Yaquan Liu †,‡, Shunhao Wang †,⊥, Jinfeng Peng #, Guangbo Qu †,‡,§,∥, Guibin Jiang †,‡,§,∥
PMCID: PMC12723536  PMID: 41446767

Abstract

Aryl hydrocarbon receptor (AhR)-related toxicity caused by AhR agonists has threatened ecological health, and toxic source apportionment plays an important role in pollution management. However, there are no AhR-related toxic source profiles to date. This study aims to construct petroleum-derived AhR-related toxic source profiles by utilizing the polluted soils after a petroleum leakage accident. Three polluted soils and two unpolluted soils were designed for toxic source profile construction, and we use n-alkanes and their proxy indicators to verify the rationality of classification. Then, the key AhR agonists were identified by effect-directed analysis. We discovered that the key AhR agonists in petroleum-polluted soils are constituted by chrysene, methylated benzo­[a]­anthracene (MeBaA) isomers, and methylated chrysene (MeChr) isomers, where 2-MeChr and 9-MeBaA/3-MeChr contribute the most toxicity. Meanwhile, we also confirmed the existence of synergistic effect. Moreover, the constructed toxic source profile explained most of the toxicity of crude oil, and its specificity was also validated by principal coordinate analysis. In conclusion, this study constructed new petroleum-derived AhR-related toxic source profiles for toxic source apportionment and pollution management, and also provided a new framework for other toxic source profile construction.

Keywords: toxic source profile, aryl hydrocarbon receptor (AhR), petroleum, effect-directed analysis (EDA), synergistic effect

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1. Introduction

With the rapid development of social economy and industry, a large number of chemicals have entered into the environment , and caused adverse ecological effects. − Among them, aryl hydrocarbon receptor (AhR)-mediated toxicity caused by AhR agonists is ubiquitously investigated. , It was illustrated that long-term exposure of AhR agonists had altered the evolution process of species in ecosystem, − which implies that the potential toxicity caused by AhR agonists requires to be highly concerned and effectively managed. To this end, the source apportionment of toxicity is firstly required, which plays an important role in pollution management.

However, current source apportionment approaches are based on the known chemicals and cannot directly apportion the source of toxicity. , Actually, the adverse ecological effects are ascribed to the toxicity caused by the key toxicants. , Therefore, only apportioning the source of key toxicants can realize the source apportionment of toxicity. Currently, two strategies regarding source apportionment are conducted. One is based on the known source profiles such as chemical mass balance (CMB), and this strategy is highly dependent on accurate source profiles. , The other one is based on statistical inference such as positive matrix factorization (PMF), − multilinear engine 2 (ME2), , principal component analysis-multivariate linear regression (PCA-MLR), , and Unmix. − This strategy requires rationally explaining the decomposed profiles via multiple ways including known source profiles matching and specific molecular marker annotation. In any case, source apportionment relies on the source profiles. Consequently, constructing source profiles of key toxicants from typical pollution samples is a prerequisite for source apportionment of toxicity in soils.

Petroleum leakage is one of the main sources of AhR agonists in the environment. − Current studies about petroleum leakage mainly focused on identifying the key AhR agonists from the Hebei Spirit oil spill event in South Korea. − However, there are no AhR-related toxic source profiles to date, causing a gap for efficient environmental management. In August 2020, a petroleum leakage accident occurred at a petroleum exploitation region in central China, causing a petroleum pollution event. It provides us typical petroleum polluted samples and a great opportunity to construct petroleum-derived AhR-related toxic source profiles. Accordingly, this study aims to identify the key AhR agonists of petroleum-polluted soils and then construct petroleum-derived AhR-related toxic source profiles for toxic source apportionment. It is expected that this study could give new insights into toxic source apportionment application and pollution management, and also provide a pilot study for toxic source profile construction.

2. Materials and Methods

2.1. Experiment Design

In this study, a source-driven strategy was utilized to construct the petroleum-derived AhR-related toxic source profiles (Figure ).

1.

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Framework of the construction of petroleum-derived AhR-related toxic source profiles.

Firstly, five soil samples were collected after an emergency of petroleum leakage occurred in a petroleum exploitation region in August 2020 (Figure S1). Among them, S0 (petroleum leakage area), S1 (petroleum exploitation area), and S2 (leakage surrounding area) were designed as source group because they were affected by the petroleum pollution. S3 (pollution remediation area) and S4 (residential area) are almost free from the petroleum pollution so that they were designed as control group. Soil samples were packaged in aluminum foil after collection and send back to laboratory at −20 °C. In the laboratory, soil samples were freeze–dried and homogenized. To check the rationality of group classification, n-alkanes and their related proxy indicators were utilized because alkanes are primary components in petroleum and n-alkanes can be employed as molecular markers to distinguish biogenic and anthropogenic sources. , Please see Text S3 for details of the analysis of n-alkanes.

Then, the key AhR agonists for each group were identified by applying effect-directed analysis (EDA). The EDA processes including sample extraction (Text S1), fraction separation (Text S2), toxic bioassay (Text S4), and key toxicant identification (Text S5) are summarized in the Supporting Information.

Approximately 5 L crude oil was also gathered to validate if the constructed toxic source profile could explain most toxicity of crude oil. The EDA procedures for crude oils were the same as those for soil samples. Moreover, principal coordinate analysis (PCoA) was employed to validate the specificity of the toxic source profiles. The analysis was conducted on R platform (version 4.4.2) using “vegan” package, and the dissimilarity matrix is calculated using Bray–Curtis distance.

2.2. Quality Assurance/Quality Control

In chemical analysis, all reagents were high-performance liquid chromatograph-grade. Two procedural blanks were set. The R-squares of each internal calibration curve all exceed 0.95. For bioassay, 1% dimethyl sulfoxide (DMSO) and 1 nmol L–1 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), respectively, in three repetitions per 96-well plate were designed to calibrate the luminescent differences among 96-well plates (Text S2). Before serial dilution, cell counting kit-8 (CCK-8, Abbkine Scientific Co., Ltd., China) was applied to evaluate the cell viability under exposure concentrations in three repetitions, and cell viability >80% was regarded as no obvious cytotoxicity. , The serial dilution of exposure was initiated from the no cytotoxic concentration, and three repetitions were conducted for the dose–response curves. In EDA, the reconstitution solution was applied to evaluate the effect recovery of fraction separation. The TEQbio ratio of reconstitution solution to sample extracts within 80% to 120% was considered as acceptable effect recovery.

For the source-driven strategy, a successful and applicable construction of toxic source profile should meet two criteria: (1) most toxicities in source samples and crude oil could be properly explained; (2) the constructed toxic source profile is specific so that it can be used in source apportionment models.

3. Results and Discussion

3.1. Rationality Verification of Classification by n-Alkanes Analysis

The classification rationality of petroleum-polluted samples and the negative control was verified by n-alkanes and their related proxy indicators (Table ). Firstly, the concentrations of Σ29 n-alkanes in the petroleum leakage area (S0) are an order of magnitude higher than those in petroleum-polluted area (S1 and S2), while the concentrations of Σ29 n-alkanes in petroleum-polluted area are an order of magnitude higher than those unpolluted area (S3 and S4). Secondly, the carbon preference indexes with different carbon-chain ranges (CPI10–38 and CPI10–20) are all close to 1 or lower than 1 in all sites, indicating typically anthropogenic input. , The higher CPI10–38 values in S2 and S4 indicate a certain contribution of terrestrial plants. , The anthropogenic contributions in S0, S1, and S2 are all over 95% while the anthropogenic contributions in S3 and S4 are both lower. Thirdly, the carbon-chain distributions also present significant patterns in different sites (Figure ). The carbon-chain distributions in S0, S1, and S2 are single-peak. The carbon-chain distributions in S0 and S1 are more similar to that of crude oil (Figure S2) except for the short-chain homologues (carbon-chain length below 18). The anthropogenic average chain lengths (ACLanthro) are 24.6, 27.0, and 21.5 for S0, S1, and crude oil, respectively. It implies that soils in S0 and S1 are polluted by crude oil after light petroleum volatilized. , For S2, despite the same single-peak distribution, the ACLanthro is 17.5 for S2 with the carbon number maximum (C max) at C15. It indicates the pollution of light petroleum such as refined gasoline and diesel. , Different from the polluted area and crude oil, the carbon-chain distributions in S3 and S4 are dual-peak, indicating a mixed source with low residual level. Summarily, these results above all demonstrate the rationality of sample classification that S0–S2 are petroleum-polluted samples, while S3 and S4 are negative control.

1. Molecular Markers of n-Alkanes.

sites Σ29 n-alkanes (ng g–1) CPI10–38 CPI10–20 CPI21–38 % anthro ACLanthro
S0 1.32 × 106 1.26 0.87 1.30 95.1% 24.6
S1 1.19 × 105 1.14 0.70 1.14 95.0% 27.0
S2 5.50 × 104 1.20 1.20 1.70 97.2% 17.5
S3 9.59 × 102 0.79 0.50 1.44 91.2% 19.8
S4 3.94 × 103 0.92 0.76 2.59 89.4% 16.4
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Occurrence and carbon-chain distribution of n-alkanes in soils.

3.2. Identification of Key Toxicants via EDA

In this study, the soil extracts are separated into three fractions, entitled F1, F2, and F3. The TEQbio values of extraction, reconstruction, and the three fractions are listed in Table . The TEQbio values from S0 to S2 are measured as 438, 110, and 44.1 ng TCDD g–1, respectively. S3 posed no AhR activity, while S4 posed weak AhR activity with a TEQbio of 0.06 ng TCDD g–1. The TEQbio values in the petroleum-polluted area are obviously higher than those in the unpolluted area. Reconstructions generated from F1, F2, and F3 are designed to monitor the loss of effect. According to Table , TEQbio ratios of reconstruction-to-extraction, or effect recoveries, range from 92% to 99%, indicating an acceptable result for a loss of effect during separation.

2. Bioassay-Based Toxic Equivalence of Each Sample and Their Effect Recoveries.

  TEQbio (ng TCDD g–1)
  
site extraction reconstruction F1 F2 F3 effect recovery
S0 438 407 59.4 289 66.5 93%
S1 110 109 6.13 46.2 4.38 99%
S2 44.1 40.8 3.27 15.9 4.13 92%
S3 inactive inactive inactive inactive inactive NA
S4 0.06 inactive inactive inactive inactive NA
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a

Relative luciferase activity at the highest exposure without cell toxicity is below 20%.

b

Not available.

Among the three fractions, F2 is the major fraction contributing the highest AhR activities in the petroleum-polluted samples. Therefore, F2 was filtered out for AhR agonist candidate screening. Notably, the TEQbio values of reconstruction solutions in S1 and S2 are significantly higher than those of the sum of the three fractions, implying the exist of synergetic effect (Table ).

Nine parent PAHs were confirmed by standard reference, and their chemical information, retention indexes, and relative potencies are listed in Table . Particularly, the isomer pairs of benzo­[b]­fluorene-benzo­[c]­fluorene (BbFlu-BcFlu) and benzo­[b]­fluoranthene-benzo­[j]­fluoranthene (BbFla-BjFla) cannot be separated in gas chromatograph triple quadruple mass spectrometer (GC-TQ, 8890–7000E, Agilent Technologies Inc., USA) because of the highly similar retention time and the same m/z value. Due to these isomers exhibiting approximately the same responses in quantitative analysis, their total concentrations are calibrated, and ReP ranges of these isomers are estimated to calculate their TEQchem values. Moreover, a compound with feature m/z of 242 and retention index of 2622 is identified by Agilent MassHunter Unknown Analysis as the best hint of 1-methylchrysene (1-MeChr). Therefore, the standard reference of 1-MeChr is purchased and its ReP is measured at 2.78 × 10–3, which is at a similar level with some parent PAHs. However, its retention index is 2634 that is significantly different from the result of unknown analysis, implying that the identified compound should be other methylated isomer(s) with molecular weight of 228 including chrysene (Chr), benzo­[a]­anthracene (BaA), and benzo­[c]­phenanthrene (BcP). Due to the diversity of methylated position and the high ReP of 1-MeChr, the GC-TQ system is operated in the selective ion monitor mode to further confirm the accurate chromatogram of m/z 242, and multiple peaks are found (Figure A). Comparing with the known reference standards, four methylbenzo­[a]­anthracenes (MeBaAs) and three methylchrylenes (MeChrs) are confirmed, where three isomer pairs (9-MeBaA/3-MeChr, 5-MeBaA/6-MeChr, and 7-MeBaA/10-MeBaA) posed same retention index (Figure ). Accordingly, the ReP ranges of these isomer pairs are also estimated to calculate their TEQchem values. Notably, only one chromatographic peak is not confirmed (Figure A). For the unconfirmed peak, it was labeled as Un-MeBaA (possibly methylated at 3, 8, 11, or 12 carbon position by exclusion), and its ReP is estimated by the range of known isomers of MeBaAs.

3. Identified Key Toxicants in Soils.

3.2.

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3.

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Extracted ion chromatogram at m/z = 242. (A) Peak identification for sample S0, fraction F2 with peak deconvolution. (B–D) Retention times for individual compounds.

According to formula S4, TEQchem of each sampling sites are calculated with ranges from 104 to 124, 13.2 to 19.3, 8.89 to 10.3, 6.41 × 10–5 to 7.99 × 10–5, and 2.50 × 10–4 to 2.65 × 10–4 ng TCDD g–1 for S0, S1, S2, S3, and S4, respectively. The TEQchem in polluted area was predominated by the identified 2-MeChr, 9-MeBaA/3-MeChr, and the unknown MeBaA with total contribution of over 98%, while it mainly consists of Chr, BbjFla, BkFla, and BaP in unpolluted area (Figure ). However, only 36.1% to 43.0%, 28.5% to 41.9%, and 55.9% to 64.7% of the TEQbio of F2 are explained by the additive effect of individual toxicants in S0, S1, and S2, respectively. The percentages of explained toxicities in our study are higher than those in crude oil and contaminated coastal sediments in Korea (approximately 20% on average). However, our results do not meet the requirement of the mostly explained toxicity. Usually, it was considered that there still exists unknown key toxicant. , However, assuming that there exist ten unknown key AhR agonists with average ReP of 10–2, their average concentrations should be within the 102 to 104 ng g–1 that is similar to or higher than the identified AhR agonists (Figure S3). If the ReP was lower, then the concentration would be higher. Under this condition, these unknown AhR agonists must pose significantly strong chromatographic peaks in the scan mode and be able to be identified after peak deconvolution because of the high enough signal-to-noise. Therefore, the unexplained toxicities should not be primarily ascribed to unknown key AhR agonists.

4.

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Contribution of main identified AhR agonists to the ΣTEQchem. For chemicals without definite RePs, their TEQchem values are all calculated by (A) minimal RePs and (B) maximal RePs, respectively.

Actually, the toxic effects of toxicants are intricate. However, current studies consider only the additive effect and ignore the synergistic effect. Here, we hypothesize that the key AhR agonists have been mainly identified and that the unexplained toxicities are basically derived from the synergistic effect. To test this hypothesis, two main AhR agonists including 2-MeChr and 9-MeBaA, as well as two representative AhR agonists containing BaP and BkFla, were coexposed with F2 (Text S6). Here, the synergistic index is introduced to quantify the potential synergistic effect (formula S6), and the results are illustrated in Figure A. Our results illustrated obvious synergistic effects between F2 and selected AhR agonists in S0 to S3 with synergistic indexes ranging from 1.50 to 15.59. For S4, the synergistic effect was measured only between the BkFla and F2. No synergistic effects were observed for blank control. After correction, the explained toxicities nearly doubled with the percentages of 84.0%, 68.0%, and 123.6% on average for S0, S1, and S2, respectively (Figure B). Obviously, the explained toxicities in S1 and S2 are not within 80% to 120%. However, such correction is merely conducted between F2 and single AhR agonist, and the potential synergism among various agonists is not well concerned. Nevertheless, our results in Figure are sufficient to validate the hypothesis of synergism. Therefore, it implies that the key AhR agonists are identified completely and most toxicities are explained properly.

5.

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Synergistic effects between F2 and main AhR agonists. (A) Synergistic index between different AhR agonists and fraction F2. Synergistic index >1.2 indicates the existence of synergism. (B) Explained toxicities of F2 in S0, S1, and S2 before and after synergistic correction.

3.3. Construction of Petroleum-Derived AhR Toxic Profiles

To test if the toxicity in crude oil could be properly explained by the identified AhR agonists, the TEQbio for crude oil was also measured (Figure S4). The TEQbio values of extraction, reconstruction, F1, F2, and F3 are 19.3, 21.8, 0.36, 4.49, and 1.49 ng TCDD μL–1, respectively, with the effect recovery of 113%. Totally, the identified AhR agonists explain 77.7% to 88.6% of the TEQbio of F2, implying a satisfied validation result.

In this study, the toxic source profile is illustrated by a polar coordinate system, reflecting both concentration and toxicity information. The polar angle represents the contribution of AhR agonist in the concentration, while the polar radius indicates its relative potency in log10-unit. The petroleum-derived toxic source profiles are generated from crude oil and petroleum-polluted soils (Figure A). Our results illustrated that the toxic source profiles are mainly constituted by Un-MeBaA and are followed by 9-MeBaA/3-MeChr, 2-MeChr, 7-MeBaA/10-MeBaA, 1-MeChr, and Chr, respectively. Among them, 2-MeChr and 9-MeBaA/3-MeChr contribute the most AhR toxicities. PCoA illustrated that the AhR agonist composition of crude oil is highly similar to that of petroleum-polluted sites and disparate to that of unpolluted sites (Figure B). It indicates that the constructed toxic profiles have certain specificity, and 2-MeChr, 9-MeBaA/3-MeChr, and Un-MeBaA are capable of molecular markers of petroleum for source apportionment.

6.

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(A) Toxic source profiles generated from crude oil and petroleum-polluted soils. Toxic source profile is illustrated by a polar coordinate system. The polar angle represents the contribution of AhR agonist in concentration, while the polar radius indicates its relative potency in log10-unit. (B) Principal coordinates analysis (PCoA) of Bray–Curtis distances of crude oil and soils.

3.4. Implications, Limitations, and Perspectives

This study constructed the petroleum-derived AhR toxic profiles for the first time, and the results provide a reference for other studies regarding AhR toxic source apportionment. For example, the constructed toxic profiles could be directly applied in the CMB model. Moreover, they can also be employed to identify the source factor by alignment in the PCA-MLR and PMF models. This study also provides a pilot study for the top-down construction of other sources. However, our conclusion exhibits that certain imperfections due to the limitation of samples. Firstly, the toxic source profiles are constructed only based on several polluted soils in a petroleum exploitation region, and their applicable spatial ranges in source apportionment require further demonstration. In the future, it will be essential to systematically validate and correct the universality of conducted toxic source profiles by analyzing more petroleum-polluted samples from other regions. Secondly, the specificity of toxic source profiles is declared just by the comparison between polluted and unpolluted samples. Whether other AhR toxic source profiles such as coal combustion, vehicle exhaust, and straw burning pose similar patterns is still unknown and may result in potential bias during source identification. Hence, a meta-analysis based on multiple sources is indispensable to clarify the cross-source specificity.

Moreover, this study discovered the interaction within fractions, implying the existence of possible AhR potentiator and synergist. However, this study aims to propose the concept of the toxic source profile and provide a case regarding the pollution source of petroleum. It mainly highlights the key AhR agonists and a proper explanation of toxicities. Future studies could further reveal the toxic synergistic mechanism and incorporate the identified AhR potentiators and synergists into the toxic source profile to expand its scope. Nevertheless, this study proposed a new framework for constructing the toxic source profile and provided valuable guidance for effectively controlling adverse effects caused by key toxicants in the environment.

4. Conclusion

This study constructed petroleum-derived AhR-related toxic source profiles for the first time, providing a pilot study for constructing toxic source profiles and valuable insights for new pollutant management. We found that 2-MeChr, 9-MeBaA/3-MeChr, and Un-MeBaA were the key AhR agonists in petroleum-polluted soils. Moreover, the synergistic effect was confirmed and quantified, and the synergistic index was proposed, expanding the scope of EDA and providing new insights into the common scenario of a lower toxicity explanation by key toxicants in EDA.

Supplementary Material

eh5c00193_si_001.pdf (359.4KB, pdf)

Acknowledgments

This work was supported by the China National Postdoctoral Program for Innovative Talents (No. BX20230401), the China Postdoctoral Science Foundation (No. 2023M743705), the National Science Fund for Distinguished Young Scholars (No. 22325606), the National Key R&D Program of China (No. 2023YFA0915100), and the National Natural Science Foundation of China (No. 21527901).

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/envhealth.5c00193.

  • Sample extraction; fraction separation; analysis of n-alkanes; toxic bioassay; key toxicant identification; synergistic effect test and synergistic index calculation; monitoring ions of n-alkanes; retention time calibration; monitoring ions of target compounds in F2; toxic potencies of selected AhR agonists and F2 only and their mixtures; information of sampling sites (S0, petroleum leakage area; S1, petroleum exploitation area; S2, leakage surrounding area; S3, pollution remediation area; S4, residential area); carbon-chain distribution and n-alkane proxies of crude oil; occurrence of identified AhR agonists and estimated average concentration of unknown key toxicants based on the precondition of additive effect and assumption of ten unknown key toxicants with average RePs of 10–2; and TEQbio of extraction, reconstruction, and all fractions for crude oil, and the explainable toxicity of F2 by identified AhR agonists from soils (PDF)

Zikang Li: conceptualization, data curation, formal analysis, investigationexperiment, methodology, validation, visualization, and writingoriginal draft; Yong He: conceptualization, data curation, formal analysis, funding acquisition, methodology, project administration, software, supervision, validation, visualization, writingoriginal draft, and writingreview and editing; Xiaoxi Yang: methodology; Yi Wang: investigationsampling; Yunhe Guo: investigationsampling; Yaquan Liu: investigationsampling; Shunhao Wang: investigationsampling; Jinfeng Peng: investigationsampling; Guangbo Qu: funding acquisition, resources, and supervision; and Guibin Jiang: funding acquisition, resources, and supervision.

The authors declare no competing financial interest.

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