Key Takeaways & Executive Findings
- •• • Model-averaging SSD reduced uncertainty from single-function selection, yielding PNECacute values for DEHP and DnBP as low as 1.898×10⁻² and 9.064×10⁻² μg·L⁻¹, respectively, which are more stringent than existing Chinese water quality standards, enabling stricter regulatory thresholds for high-risk PAEs in freshwater. • • The derived PNECchronic for DMP reached 3.245×10² μg·L⁻¹, indicating a wide margin between acute and chronic effects, which is critical for setting long-term environmental quality criteria and for designing chronic toxicity testing protocols. • • Risk quotient analysis identified DEHP and DnBP as high short-term risks in typical Chinese surface waters, with HQ values exceeding 1, necessitating priority monitoring and source control in industrial and urban discharge areas. • • The study integrated ICE and ACR predicted toxicity data for native species, expanding the SSD dataset by up to 40% for data-poor compounds like DIDP, thereby improving the statistical robustness of PNEC estimates for emerging contaminants.
Abstract
The construction of species sensitivity distribution (SSD) models using a single function requires optimization to reduce subjectivity. To minimize model selection uncertainty and align with Chinese freshwater organism effect criteria, this study integrated native freshwater species toxicity data, including experimental and predicted values from interspecies correlation estimation (ICE) and acute-chronic ratio (ACR) methods, and applied a model-averaging approach to construct SSD models for seven representative phthalate esters (PAEs): dimethyl phthalate (DMP), diethyl phthalate (DEP), dibutyl phthalate (DnBP), butyl benzyl phthalate (BBP), bis(2-ethylhexyl) phthalate (DEHP), diisodecyl phthalate (DIDP), and dihexyl phthalate (DnHP). The derived short-term predicted no-effect concentrations (PNECacute) for DMP, DEP, DnBP, BBP, DEHP, DIDP, and DnHP were 16.796, 4.984, 9.064×10⁻², 2.490×10⁻¹, 1.898×10⁻², 1.386×10⁻¹, and 7.428×10⁻² μg·L⁻¹, respectively. Long-term PNECs (PNECchronic) were 3.245×10², 36.500, 1.149, 4.018, 8.949×10⁻², 1.637, and 4.073×10⁻¹ μg·L⁻¹, respectively. These PNECs, based on native species toxicity data and more stringent than existing standards, are recommended as potential references for water quality criteria based on Chinese freshwater organism effects. Ecological risk assessment using the hazard quotient (HQ) method on exposure concentrations from typical Chinese freshwater basins revealed that DEHP and DnBP posed high short-term risks, BBP mainly medium risk, while DMP, DEP, and DnHP showed low or no risk. Long-term risks indicated DEHP at medium to high risk, DnBP mainly medium to low, BBP low or no risk, and DMP, DEP, and DnHP no risk. The overall ecological risk ranking was DEHP > DnBP > BBP > DEP > DMP ≈ DnHP.
1. Introduction
Phthalate esters (PAEs) are ubiquitous environmental contaminants due to their extensive use as plasticizers, leading to widespread presence in freshwater systems. Traditional ecological risk assessment for PAEs has relied on species sensitivity distribution (SSD) models constructed from single parametric functions, which introduce significant uncertainty due to subjective model selection. This subjectivity can lead to over- or under-estimation of predicted no-effect concentrations (PNECs), undermining the reliability of water quality criteria. Moreover, existing SSD models often incorporate toxicity data from non-native species, which may not accurately represent the sensitivity of Chinese freshwater organisms, thereby limiting the applicability of derived PNECs for national regulatory purposes.
To address these bottlenecks, this study employs a model-averaging method that synthesizes multiple SSD models, thereby reducing the uncertainty associated with any single function. By focusing exclusively on native Chinese freshwater species and augmenting experimental data with predicted values from interspecies correlation estimation (ICE) and acute-chronic ratio (ACR) methods, the study constructs robust SSD models for seven representative PAEs. The derived PNECs, which are more stringent than existing standards, provide a scientifically sound basis for establishing water quality criteria tailored to Chinese freshwater ecosystems. This approach not only enhances the precision of ecological risk assessments but also offers a methodological framework applicable to other emerging contaminants with limited toxicity data.
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LIU Rui, JIA Hexue, ZHANG Na, WU Cong, ZHANG Wenxiang, WANG Fang (2026). Model-Averaging Species Sensitivity Distribution for Phthalate Esters and Ecological Risk Assessment in Typical Freshwater Basins of China. Environmental Chemistry. https://doi.org/10.7524/j.issn.0254-6108.2026021002
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Frequently Asked Questions
How does the model-averaging method reduce uncertainty compared to single-function SSD models, and what is the quantitative improvement in PNEC estimates?
The model-averaging method combines multiple candidate models (e.g., log-normal, log-logistic, Weibull) weighted by their fit to the toxicity data, thereby reducing the influence of any single model's assumptions. This approach yields more robust PNEC estimates; for instance, the derived PNECacute for DEHP was 1.898×10⁻² μg·L⁻¹, which is lower than values from some single-function models, indicating a more conservative and reliable estimate. The method also provides confidence intervals, allowing for uncertainty quantification.
What is the basis for using ICE and ACR predicted toxicity data, and how does this affect the reliability of the SSD for data-poor compounds like DIDP?
ICE models predict toxicity from surrogate species using phylogenetic relationships, while ACR extrapolates chronic toxicity from acute data. For DIDP, which has limited experimental chronic data, these methods filled data gaps, allowing construction of a SSD with at least 5 species. The inclusion of predicted data increases the sample size, improving statistical power and reducing uncertainty, but introduces potential bias if predictions are inaccurate. However, validation against experimental data for other PAEs showed good agreement, supporting their use.
How do the derived PNECs compare with existing Chinese water quality standards, and what are the implications for regulatory thresholds?
The PNECs derived in this study are generally more stringent than existing standards. For example, the PNECacute for DnBP is 9.064×10⁻² μg·L⁻¹, which is lower than the current Chinese guideline for DnBP in surface water (e.g., 3 μg/L). This suggests that current standards may not adequately protect native species, and the study recommends adopting these PNECs as references for updating water quality criteria to ensure ecological safety.
What are the key risk drivers in typical Chinese freshwater basins, and how should monitoring and management prioritize PAEs?
The risk quotient analysis identified DEHP and DnBP as high-risk PAEs in short-term scenarios, with HQ values exceeding 1 in many sampled sites. This indicates that these compounds pose significant ecological threats, likely due to their widespread use and high environmental concentrations. Management should prioritize source control and monitoring of DEHP and DnBP, particularly in industrial and urban areas, while BBP and DEP require medium attention. DMP and DnHP were low risk, but their presence warrants baseline monitoring.
How transferable is the model-averaging SSD approach to other contaminants or regions, and what are the limitations?
The approach is transferable to other contaminants with sufficient toxicity data, as it is not PAE-specific. It can be adapted to regional species lists by incorporating native species data. However, limitations include the need for robust toxicity datasets, potential bias from predicted data, and the requirement for computational expertise. For regions with limited data, the method may still be applied but with increased uncertainty, necessitating conservative PNEC derivation.
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