Ecological risk assessment for organophosphate esters in the surface water from the Bohai Sea of China using multimodal species sensitivity distributions.

Organophosphate esters (OPEs) as the foremost substitutes of brominated flame retardants have been ubiquitously found in the aquatic environment around the world. However, the information on the community-level risks induced by OPEs to the marine ecosystem remains scarce. This study adopted ten commonly used species sensitivity distribution (SSD) parametric statistical approaches coupled with the acute-to-chronic transformation for the toxicity data to fit the sensitivity distributions of different species to four major OPE congeners including triethyl phosphate (TEP), tri-n-butyl phosphate (TnBP), tri(2-chloroethyl) phosphate (TCEP), and tris(1-chloro-2-propyl) phosphate (TCPP) in the surface water of the Bohai Sea. All SSD models except Exponential for TnBP, TCEP, and TCPP fitted well the chronic toxicity data for the four OPE congeners. Discrepancies appeared among the best fitting models for different congeners, which also happened to the fitting results from the multiple SSD models for each congener. Based on the best fitting models, the hazard concentrations corresponding to the cumulative probability of 5% were 3.58 mg/L, 0.116 mg/L, 1.30 mg/L, and 1.44 mg/L for TEP, TnBP, TCEP, and TCPP, respectively. The risks induced by the four OPE congeners to the Bohai Sea ecosystem were negligible during the monitoring period because of both the risk quotients and the hazard indexes far <0.1. This study drew a clear picture of the joint ecological risks of TEP, TnBP, TCEP, and TCPP to the Bohai Sea environment. The application of multimodal SSD statistical methods will benefit the accurate derivation of water quality criteria and the community-level ecological risk assessment for pollutants.

Comparison of receptor models for source identification of organophosphate esters in major inflow rivers to the Bohai Sea, China.

A better understanding of the sources of organophosphate esters (OPEs) is a prerequisite for OPE control and the establishment of related environmental policies. Sources of OPEs in 35 major inflow rivers to the Bohai Sea of China were quantitatively analyzed using three effective receptor models (principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and Unmix) in this paper. The similarities and differences in results from PCA-MLR, PMF, and Unmix were discussed in depth. All three models well predicted the spatial variability of the total concentrations of nine OPEs (triethyl phosphate, tri-n-butyl phosphate, triisobutyl phosphate, tri (2-ethylhexyl) phosphate, tri (2-chloroethyl) phosphate, tris(1-chloro-2-propyl) phosphate, tris(1,3-dichloro-2-propyl) phosphate, triphenyl phosphate, and triphenylphosphine oxide) (∑9OPEs) (r2 = 0.90-0.96, p = 0.000) and explained 98.4%-101.2% of the observed ∑9OPEs. The predicted ∑9OPEs values from each pairwise model were significantly correlated (r2 = 0.88-0.91, p = 0.000). Three OPE sources were extracted by all three models: rigid and flexible polyurethane foam/coating, cellulosic/acrylic/vinyl polymer/unsaturated polyester, and polyvinyl chloride, contributing 49.9%, 29.7%, and 20.5% by PCA-MLR, 57.9%, 28.6%, and 13.5% by PMF, and 47.9%, 30.8%, and 22.4% by Unmix to the ∑9OPEs, respectively. PMF was recommended as the preferred receptor model for analyzing OPE sources in water during the monitoring period because of its optimal performance.