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Regionally distinct threats from polycyclic aromatic hydrocarbons in global reservoirs

Nature Geoscience volume 19, pages 68–74 (2026)Cite this article

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Abstract

Polycyclic aromatic hydrocarbons pose inconsistent yet increasing threats to freshwater reservoirs worldwide, with implications for ecosystem health and water security. Although local-scale contamination has been widely documented, a comprehensive global synthesis of polycyclic aromatic hydrocarbon occurrence and drivers in reservoirs remains lacking. Here we developed a framework of data compilation, arrangement and statistics to integrate existing data to determine the geographical distribution and potential sources of polycyclic aromatic hydrocarbon pollution in reservoirs globally. Statistical analyses revealed spatial heterogeneity in dominant components and pollution levels across continents. Almost 38% of water samples exceeded an ecologically relevant threshold (0.20 μg l−1), and 42% of sediment samples surpassed the threshold effect concentration, indicating widespread ecological risks. Cluster analysis and source apportionment of the reservoir-level data identified three distinct polycyclic aromatic hydrocarbon patterns, each shaped by region-specific land-use practices, combustion sources and climatic factors. These findings emphasize and inform the need for region-specific monitoring and management strategies, such as expanding monitoring in subtropical and temperate regions, with a focus on polycyclic aromatic hydrocarbon accumulation in aquatic organisms.

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Fig. 1: Global occurrence and components of PAHs in reservoirs.
Fig. 2: Components’ similarity and source attribution of PAHs in reservoir sediments.
Fig. 3: Spatio-temporal patterns of PAH pollution in three clusters.
Fig. 4: CAS framework for assessing PAH pollution in freshwater reservoirs.

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Data availability

The datasets that support the findings of this study are available via figshare at https://doi.org/10.6084/m9.figshare.30626969 (ref. 51). Source data are provided with this paper.

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Acknowledgements

This work was financially supported by the Young Scientists Program of the National Natural Science Foundation of China (grant number 42307066 to Z.-F.G.), the National Key Research and Development Program of China (grant number 2025YFE0111301 to Y.-Y.X. and Z.-F.G.) administered by the Ministry of Science and Technology, the Open Competition Mechanism to Select the Best Candidates Program (grant number IUE-JBGS-202203 to Y.-Y.X.) from the Institute of Urban Environment, Chinese Academy of Sciences (IUE, CAS) and the Ningbo S&T project (2021-DST-004 to Y.-Y.X.) at the Ningbo Science and Technology Bureau.

Author information

Authors and Affiliations

  1. State Key Laboratory of Regional and Urban Ecology, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, People’s Republic of China

    Zhao-Feng Guo, Yao-Yang Xu, Dong Liu & Xiao-Ru Yang

  2. Zhejiang Key Laboratory of Pollution Control for Port-Petrochemical Industry, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, People’s Republic of China

    Zhao-Feng Guo, Yao-Yang Xu, Dong Liu & Xiao-Ru Yang

  3. Department of Fish, Wildlife and Conservation Ecology, New Mexico State University, Las Cruces, NM, USA

    Wiebke J. Boeing

  4. Department of Engineering, University of Cambridge, Cambridge, UK

    Edoardo Borgomeo

  5. Water and Environment Centre, Royal Scientific Society, Amman, Jordan

    Othman A. Al-Mashaqbeh

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  1. Zhao-Feng Guo
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Contributions

Z.-F.G. and Y.-Y.X. conceptualized the research. Z.-F.G., Y.-Y.X. and D.L. collected and analysed the data. Z.-F.G., W.J.B., Y.-Y.X., E.B., O.A.A.-M., D.L. and X.-R.Y. contributed to the interpretation of the results and paper writing. Z.-F.G. and Y.-Y.X. acquired funding. All authors contributed to the final manuscript and approved its submission.

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Correspondence to Yao-Yang Xu.

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Nature Geoscience thanks Yonghong Bi, Belén González-Gaya and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Alison Hunt, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Relationship between ∑16PAH concentration and distance from the dam.

Correlations were evaluated using two-sided Pearson’s tests (df = n-2). For each fitted relationship, coefficient of determination (R²) and p-values are reported in the corresponding panels. Linear regressions were fitted only when |Pearson’s r | ≥ 0.3 and R² ≥ 0.15. Shaded areas denoted 95% confidence intervals. All statistical analyses were performed using two-sided tests without adjustment for multiple comparisons.

Source Data

Extended Data Fig. 2 ∑PAH concentrations at different sampling depths within an individual reservoir.

Different colors represent different sampling sites of sediments: Vossoroca (VS, Cluster α), Osaka Castle (OC, Cluster β), Echo (EC, Cluster γ), Fosdic (FS, Cluster γ), Como (CO, Cluster γ).

Source Data

Extended Data Fig. 3 Heterogeneity of PAH composition in different environmental media.

Analysis of similarities (ANOSIM) was used to test the null hypothesis that PAH compositions do not differ signidicantly among environmental media. Correlations were evaluated using a two-sided, permutation-based test (999 permutations). The r statistic (effect size) and p-values are shown in the corresponding panels. y-axis indicates the degree of dissimilarity between and within groups. Boxplots display the distribution of rank dissimilarities: centre line = median, box = 25th–75th percentiles, whiskers = min/max within 1.5 times interquartile range (IQR), points beyond whiskers = outliers. Statistical significance is denoted as “*” for p < 0.05 and “**” for p < 0.01. For Loskop Reservoir, sediment (n = 18, N = 9) and water (n = 16, N = 9) samples were collected from nine sites during flood and storage seasons; for Blachownia, sediment (n = 5, N = 4) and water (n = 20, N = 4), and for Cedara, sediment (n = 4, N = 4) and water (n = 4, N = 4) samples were collected across seasons. Each n denotes an independent field sample (biological replicate). PAH concentrations were meansured in triplicate (technical replicates), and mean values were used for analyses.

Source Data

Extended Data Fig. 4 Interannual and seasonal variation in PAH concentrations from reservoir water and sediment.

The blue represents water and the green is sediment. Seasonal comparisons were evaluated using two-sided Mann–Whitney U tests, and interannual variations using two-sided Kruskal–Wallis tests with Dunn’s post-hoc tests (Bonferroni adjustment). Statistical significance is denoted as “*” for p < 0.05. Boxplots show the distribution of PAH concentrations: centre line = median, box = 25th–75th percentiles, whiskers = min/max within 1.5 times interquartile range (IQR), points beyond whiskers = outliers. Sampling sites included the Three Gorges (water N = 59; sediment N = 13), Shuikou (N = 10), and Loskop (water N = 24; sediment N = 14) reservoirs across multiple years and hydrological seasons (see Supplementary Data 1). Each n denotes an independent field sample (biological replicate).

Source Data

Extended Data Table 1 Median concentrations of PAHs in different environmental media and corresponding sample sizes across continents
Full size table

Supplementary information

Supplementary Information (download PDF )

Supplementary Fig. 1 and Texts 1 and 2.

Supplementary Data 1 (download XLSX )

Source data for supplementary figures.

Source data

Source Data Fig. 1 (download XLSX )

Source data for Figs. 1–4, Extended Data Figs. 1–4 and Extended Data Table 1.

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Guo, ZF., Boeing, W.J., Xu, YY. et al. Regionally distinct threats from polycyclic aromatic hydrocarbons in global reservoirs. Nat. Geosci. 19, 68–74 (2026). https://doi.org/10.1038/s41561-025-01872-4

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