Legacy and Emerging Organophosphate Esters (OPEs) in a Rural–Urban Transition Watershed: Spatiotemporal Distribution, Sources, and Toxicity Screening

Abstract

Agricultural watersheds are undergoing rapid rural–urban transitions, yet the relative contributions of diffuse agricultural runoff versus rural domestic and point sources to organophosphate esters (OPEs) pollution remain poorly understood. This study investigated the occurrence, spatiotemporal distribution, and potential risks of 17 legacy and emerging OPEs in the Dalongdong River, China. Combined non-target and target analyses revealed mean OPE concentrations of 111.94 ng/L in water and 8.76 ng/g in sediments. Spatially, total OPE concentrations increased progressively from upstream to downstream, with pronounced hotspots downstream of townships and near wastewater treatment facilities, indicating that rural domestic effluents and urban runoff, alongside agricultural activities, are critical contributors to OPE pollution in this watershed. Seasonally, concentrations of six legacy OPEs were significantly higher during the wet season. Furthermore, high-throughput phenotypic screening using Caenorhabditis elegans, combined with toxicological priority index analysis, showed that emerging OPEs generally pose higher integrated health and ecological risks, although certain legacy compounds, such as triphenyl phosphate, still display substantial toxic potential. These findings clarify the potential biological hazards of these compounds and provide baseline data on the fate of OPEs in riverine systems influenced by mixed agricultural and rural–urban anthropogenic activities.

1. Introduction

Organophosphate esters (OPEs) belong to a category of organophosphorus compounds that can be subdivided into halogenated OPEs, alkyl OPEs, and aryl OPEs [1]. In 2022, worldwide usage of phosphorus-based flame retardants totaled 756,000 tons, with projections for ongoing expansion [2]. However, owing to their rapid growth, global production and gradual emergence of toxic effects, several traditional OPEs (known as legacy OPEs; L-OPEs), including tris (2-chloroethyl) phosphate (TCEP), tris (1-chloro-2-propyl) phosphate (TCPP) and tris (1,3-dichloro-2-propyl) phosphate (TDCPP), are being regulated or even banned in many regions and countries [3,4,5,6]. This trend has driven the market to seek emerging OPEs (E-OPEs) as alternatives to traditional OPEs. However, several E-OPEs have been identified in consumer products, as well as in the environment [7,8,9]. Therefore, the historical cumulative contamination of L-OPEs and the emergence of newer OPEs have made the current OPE pollution problem more severe and complex.

Because OPEs are added to products in a physically combined manner, OPEs may be continuously released into the environment as the product is produced, transported, used, and processed. Both L- and E-OPEs have been found in multiple environmental media, and their pollution has spread across the globe, even to remote Antarctica and the deepest Mariana Trench on Earth [10,11,12,13]. OPEs are commonly identified in diverse aquatic environments, including sewage [14], surface water [15], seawater [16], drinking water [17], etc. The contamination level and composition of OPEs in the aquatic environments are heavily influenced by human activities [12]. While the occurrence and sources of OPEs in highly urbanized metropolises and industrial zones have been well documented, the pollution characteristics in river systems undergoing rapid rural–urban transitions remain under-characterized [18,19].

Traditionally, OPE inputs in agricultural basins have been attributed primarily to diffuse non-point sources, including the use of OPE-containing pesticide adjuvants, leaching from agricultural plastic mulches, and the mobilization of soil residues through irrigation return flows or storm-driven runoff [20,21,22]. However, this conventional agricultural-focused perspective does not fully capture the evolving emission dynamics associated with rapid urbanization. Many transitional watersheds now contain dense rural settlements, expanding but uneven wastewater infrastructure, and dispersed small-scale industries. In such settings, untreated domestic sewage, effluents from decentralized treatment units, and discharges from small workshops can serve as major point sources of OPEs. In rapidly developing regions, wastewater treatment capacity often fails to keep pace with population growth, leading to outdated or overloaded systems that release elevated levels of emerging contaminants [23,24]. Despite this shift, the resulting spatial gradients and localized hotspots of riverine OPE contamination driven by these emerging point sources remain poorly understood.

OPEs are known to exert significant adverse effects on aquatic organisms and human health [1,12]. However, the extensive structural diversity of OPE congeners, coupled with the lack of well-defined ecological benchmark data for many E-OPEs (e.g., environmental quality standards, predicted no-effect concentrations, or risk thresholds), substantially constrains the applicability of conventional risk assessment approaches [13]. Caenorhabditis elegans (C. elegans) serves as a robust and versatile model organism in environmental toxicology for the high-throughput assessment of multiple toxicity endpoints, such as lethality, growth, reproduction, locomotion, and metabolism [25,26]. Integrating C. elegans phenotypic screening with the Toxicological Priority Index (ToxPi) provides a multidimensional framework for comparing ecological and potential human health risks in situations where traditional toxicological thresholds are unavailable.

To address these knowledge gaps, this study selected the Dalongdong River (DLDR), an agricultural watershed undergoing rapid rural–urban transition, as a representative system. The study systematically characterized the occurrence and spatiotemporal distribution of L- and E-OPEs in surface water and sediments, clarified phase-specific distribution patterns and source contributions along the river continuum, and integrated C. elegans phenotypic screening with ToxPi analysis to evaluate ecological and health risks. These efforts provide a robust scientific basis for targeted pollution control and risk-management strategies in transitional agricultural watersheds affected by complex rural–urban interactions.

2. Materials and Methods

2.1. Study Area and Sample Collection

The DLDR is the largest river system in Taishan City, South China, extending 58.17 km from the protected Dalongdong Reservoir to Guanghai Bay. The basin drains 709 km² and includes three major tributaries. Although the upstream reservoir is designated as a drinking-water protection zone, it cannot be used as a uniform background reference because downstream reaches are influenced by mixed human activities. Land use within the watershed is characterized by intensive agriculture and aquaculture. Cultivated land occupies 131.33 km², primarily for rice and vegetable production, while aquaculture covers 59.6 km². Forests are distributed mainly in the upper catchment, whereas rural settlements and small township-level industries are concentrated midstream and downstream [27]. These spatially heterogeneous land-use patterns create a complex mixture of diffuse agricultural runoff and localized point-source discharges.

Water and sediment samples were gathered throughout the DLDR watershed (Table S1). The spatial layout of sampling sites is shown in Figure 1. A total of 82 samples were obtained from 21 locations (including 40 surface water samples, 40 sediment samples, and 2 seawater samples; each sample was obtained in triplicate) in August (wet season) and December (dry season) of 2023. Text S1 describes the representativeness of sampling points in detail. The watershed was divided into upstream, midstream, and downstream sections based on terrain, population distribution, and tributary inputs. The upstream area is mountainous with low population density; the midstream contains scattered villages, farmland, and aquaculture; and the downstream is flatter with denser settlements and industrial activity. Sampling near tributary confluences enables evaluation of tributary-derived OPE inputs. These factors together justify the three-zone division. More classification details are shown in Text S2. Water samples (2 L) were filtered through glass fiber membranes and preserved in amber glass bottles at temperatures below 4 °C for subsequent analysis, using a stainless-steel grab sampler to collect sediment samples, and subsequently sealed in aluminum containers for preservation at −20 °C.

2.2. Chemicals and Reagents

Seventeen target OPE standards were used. Tributyl phosphate (TNBP), triisobutyl phosphate (TIBP), phenyl phosphate (TPHP), tetraphenyl 1,3-phenylene bis (phosphate) (RDP), bisphenol A bis (diphenyl phosphate) (BDP), tris (methylphenyl) phosphate (TMPP), TCEP, TCPP, and tetrakis (2-chloroethyl) dichloroisopentyl diphosphate (V6) were purchased from Toronto Research Chemicals Inc. (Toronto, ON, Canada). Dimethyl ethyl phosphate (DEEP) and Bis (2-chloroethyl) hydrogen phosphate (BCEP) were purchased from Cato Inc. (Charlotte, NC, USA), and TDCPP was purchased from AccuStandard Inc (Connecticut, USA). Tris (2-butoxyethyl) phosphate (TBOEP), Tris (2,4-di-tert-butylphenyl) phosphate (AO168=O), and Tris (4-tert-butylphenyl) phosphate (T4tBPPP) were purchased from Dr. Ehrenstorfer, GmbH (Augsburg, Germany). Triphenylphosphine oxide (TPPO) and Diphenyl phosphate (DPHP) were purchased from ANPEL (Shanghai, China). Table S2 outlines the physicochemical attributes of the specified compounds. Among them, DEEP, BDP, RDP, and V6 are considered E-OPEs, whereas TNBP, TIBP, TBOEP, TPHP, TMPP, TCEP, TCPP, and TDCPP are considered L-OPEs [28]. The purity of the standards was better than 98%.

2.3. Non-Target Screening and Identification of OPEs

The collected samples were pretreated within 1 week. Water samples were passed using a filter with a defined pore size, then purified and enriched via an Oasis HLB cartridge (500 mg, 6 mL; Waters, Milford, MA, USA), and finally concentrated under a mild nitrogen flow. Sediment aliquots were measured and extracted using acetonitrile. After multiple ultrasonic centrifugation cycles, an Oasis HLB column (60 mg, 3 cm³; Waters, USA) was used for extraction. OPEs’s suspect and non-target screening is in line with previous studies, with some modification [29]. The extracted samples were analyzed using an ultra-high-performance liquid chromatograph coupled with a quadrupole-Orbitrap high-resolution mass spectrometer (UHPLC-Q-Orbitrap HRMS, Thermo Fisher Scientific, Waltham, MA, USA). Raw datasets were analyzed via Compound Discoverer 3.2 and Xcalibur 4.1. Key steps included peak extraction, retention-time alignment, and background subtraction. Suspect screening was carried out using a list of OPEs from our in-house OPEs database, which were collected from the core literature on OPEs reported in Web of Science, the NORMAN Suspect List Exchange, and the US EPA CompTox Chemistry Dashboard. The sample pretreatment and non-target screening method are available in Texts S3 and S4.

2.4. Quantifying the OPEs in the Water and Sediment from the DLDR

Quantification of OPEs in environmental samples was detected using a triple quadrupole liquid chromatography–tandem mass spectrometry (LC–MS/MS) system. Chromatographic separation was achieved on a Hypersil GOLD C18 column (50 × 2.1 mm, 1.9 μm) with high sensitivity and reproducibility. Sediment samples are freeze-dried, homogenized, and ultrasonically extracted prior to analysis. Aqueous samples were enriched via solid-phase extraction cartridges (CNW, Wiesbaden, Germany), followed by nitrogen blow-down concentration with an ANPEL EFAA-DC24 system. Instrumental and procedural details are shown in Text S5 and Table S3. Additionally, water samples were measured for salinity (SAL), pH, dissolved oxygen (DO), and temperature (T) using amulti-parameter water quality analyzer (YSI, Springs, OH, USA).

2.5. Quality Assurance and Quality Control (QA/QC)

An OPE mixed standard solution, ranging from 0.10 to 200 μg/L, was prepared using the internal standard quantitative method to generate standard curves for the target compounds, achieving correlation coefficients R² > 0.99. Glassware was utilized throughout sampling, transportation, storage, and concentration purification to maintain result accuracy. In the experiment, an experimental blank and a matrix blank were measured, and the matrix blank was deducted from all results. The method detection limit (MDL) and the method quantitation limit (MQL) were defined as three and ten times the signal-to-noise ratio, respectively. Linear relationships were strong, with R² > 0.992. The water and sediment recovery rates were 63.5–106.8%. Tables S4 and S5 provide more detailed information on quality assurance.

2.6. Partitioning Coefficients of OPEs

The distribution of OPEs between water and sediment was characterized using field-derived organic carbon–normalized partition coefficients (LogKoc) and octanol–water partition coefficient (LogKow), calculated from paired concentration data as follows:

$${LogK}_{oc}=\log \left({\displaystyle \frac{{\mathrm{C}}_s}{C_w}}\right)-\mathrm{l}\mathrm{o}\mathrm{g}\left({\displaystyle \frac{TOC}{100}}\right)$$

$$Log{K}_{ow}=\mathrm{l}\mathrm{o}\mathrm{g}\left({\displaystyle \frac{C_o}{C_{water}}}\right)$$

where C_S (ng/g dry weight) and C_W (ng/L) represent OPE concentrations in sediment and water, respectively, and TOC denotes the total organic carbon content of sediments (%). C_O (mol/L, corresponding to C_W in mol/L) refers to the OPE concentration in the octanol phase. This calculation reflects how hydrophobicity and site-specific environmental factors influence the OPE distribution and potential remobilization from sediments, which may act as secondary contamination sources.

2.7. C. elegans High-Throughput Phenotypic Screening of OPEs and Toxicological Prioritization Index Analysis

C. elegans was grown on Nematode Growth Medium (NGM) culture dishes coated with E. coli at an incubation temperature of 20 °C [30]. Three exposure groups were set up: control group (Group C), low-dose group (Group L, 200 μM) and high-dose group (Group H, 600 μM). The L4 stage nematodes were randomly divided into 60 nematodes in each group and 30 nematodes in each plate. All nematodes were placed in biochemical incubators at 20 °C for 96 h to observe the long-term effects of OPEs on C. elegans. During the exposure period, 1 mg of Escherichia coli (OP50) was added as food every day to maintain normal activity of the nematodes.

A total of 90 nematodes were obtained and scanned using an Epson perfectv850 Pro scanner (Seiko Epson Corporation, Nagano Prefecture, Japan) and silverfast8 software. A 16-bit grayscale image-processing mode with a resolution of 6400 dpi was used, which was sufficient to observe nematodes at different stages, obtain clear images, and perform two scans within 10 min. A standard ruler placed on the scanner was used to verify the size of the generated image. The image capture and statistical methods used to obtain the C. elegans data were followed by previous procedures with minor modifications [29]. Comprehensive details of the high-throughput phenotypic screening can be found in Text S6. Details of the statistical analysis are shown in Text S7.

Based on the results of the C. elegans high-throughput phenotypic screening of OPEs, the toxicological priority index (ToxPi) model was further used to evaluate and rank the potential toxicity of OPEs detected in the DLDR watershed. The model is as follows:

$$D={\displaystyle \frac{C_i-C_0}{C_0}}\times 100\%$$

$$ToxPi=\sum {\omega }_{1}P+\sum {\omega }_{2}B+\sum {\omega }_{3}M+\sum {\omega }_{4}T+\sum {\omega }_{5}F$$

Among them, $D$ is the deviation value of high-throughput phenotypic screening; $C_i$ is the average index of the high-throughput phenotypic screening of OPEs; $C_0$ is the average index of high-throughput phenotypic screening of the control group; $ToxPi$ is the ToxPi score; $\omega$ is the weighting factor, ${\omega }_1={\omega }_2={\omega }_3={\omega }_4={\omega }_5=0.2$; P is the persistence parameter; B is the bioaccumulation parameter; M is the mobility parameter; T is the toxicity parameter; F is the metabolic transformation parameter. The result is retained to 2 decimal places, reflecting the degree of deviation between the experimental group and the control group.

3. Results and Discussion

3.1. Contamination Profiles of OPEs

For suspect screening, we compiled and established an in-house OPE compound database (CD) containing 291 species collected from the Web of Science, Chemical Toxicology Information Dashboard, and the NORMAN Suspect List Exchange. Using a non-targeted HPLC-MS workflow, the MS/MS spectra of candidate OPEs were matched to CD libraries and fragment ion patterns analyzed for structure confirmation. Ultimately, 17 compounds were identified with a confidence level of ≥2 (Figures S1–S17) (Table S6).

The occurrence and distribution of the screened OPEs across different environmental media from the DLDR were further examined using targeted analysis. Detection frequencies (DFs) are summarized in Tables S8 and S9. The 17 organophosphate esters (OPEs) were universally identified in the water samples, displaying detection frequencies (DFs) that varied between 47.62% and 100%. Among the legacy OPEs (L-OPEs), TNBP, TBOEP, TPHP, TCEP, and TCPP showed 100% detection across all samples. For emerging OPEs (E-OPEs), the mean DF in water exceeded 50% across both seasons. In sediments, DFs ranged from 10.00% to 100%, with TCEP and BDP showing the highest occurrence (100%). The concurrent detection of both L-OPEs and E-OPEs in water and sediments highlights the persistence of historically used compounds alongside the ongoing introduction of emerging substitutes in the DLDR system. The average concentrations of the total OPEs were 111.94 ng/L in surface water (range: 21.02–377.69 ng/L) and 8.76 ng/g in sediment (range: 0.50–39.05 ng/g) (Tables S7 and S8). L-OPEs predominated in water, averaging 97.98 ng/L, far surpassing the 13.96 ng/L for E-OPEs (Figure 2; Table S7). A similar trend was observed in sediment, where L-OPEs averaged 7.64 ng/g compared with 1.12 ng/g for E-OPEs (Figure 3) (Table S8). This pattern suggests that legacy compounds remain the primary contributors to OPE burdens despite recent regulatory shifts toward emerging alternatives.

Globally, OPE contamination levels vary greatly with anthropogenic activity intensity. The average OPE concentration in surface water from this study was lower than levels in densely populated or urbanized rivers, including the Yangtze River (330.88 ng/L) [31], Pearl River (361.8 ng/L) [32], Yellow River (1187.7 ng/L) [33], and the e-waste-impacted Lian River (1200 ng/L) [34]. However, they exceeded levels observed in purely agricultural rivers in Chongqing (52.6 ng/L) and were comparable to those reported in mariculture-influenced waters of the Beibu Gulf (122 ng/L) [35,36]. In sediments, total OPE concentrations were markedly lower than those in intensively impacted systems, such as the Yellow River Delta aquaculture farms and Jiaozhou Bay-affected industrial rivers, but comparable to aquaculture-dominated bays, including Xiangshan Bay and Beibu Gulf mariculture zones [37,38,39]. These comparisons indicate a moderate contamination level characteristic of watersheds subject to mixed agricultural activities and rural–urban anthropogenic inputs rather than intensive industrial pollution.

In surface water, TCEP predominated among individual OPEs, averaging 27.22 ng/L and representing 23.27% and 26.11% of total OPEs across the two seasons. Next were TCPP (24.95 ng/L; 19.35% and 27.30%) and TNBP (13.62 ng/L; 12.64% and 11.38%) (Figure 2; Table S8). Their high usage and environmental persistence further explain their dominance [40]. Furthermore, due to higher usage rates and longer environmental persistence, the latter three OPEs have been found at elevated concentrations and compositions in aquatic environments. In sediment, TCPP (2.73 ng/g, 42.16% and 26.15% in both seasons), TCEP (average 1.94 ng/g, 6.00% and 29.39% the two sampling seasons), and TDCPP (average 1.01 ng/g, 15.53% and 9.78% the two sampling seasons) were the major OPEs (Figure 3). In contrast, E-OPEs showed relatively low concentrations in both media, likely reflecting a lag between regulatory shifts toward E-OPEs and the phase-out of legacy compounds. Among E-OPEs, V6 was most abundant in water (6.32 ng/L), though lower than levels reported near airports in New York State (median 55.5 ng/L) [41]. Overall, these contamination profiles underscore the need to consider both historical usage and contemporary rural point sources when interpreting OPE occurrence in agriculturally dominated regions undergoing urbanization.

3.2. Spatiotemporal Distribution of OPEs

3.2.1. Distribution of Dry and Wet Seasons

The OPE concentrations in water varied markedly between dry and wet seasons (Figure 4). All L-OPEs exhibited markedly elevated levels in the wet season, with mean concentrations reaching 141.11 ng/L—approximately 70.5% greater than that in the dry season (Table S8). Although increased runoff during the rainy period can dilute pollutants, it simultaneously enhances soil erosion, surface mobilization, and hydrological transport of OPEs from surrounding urban and agricultural sources. These competing processes likely result in a net elevation of OPE concentrations in the wet season [33,39,42]. Runoff in South China river systems is typically strongly regulated by monsoon rainfall; therefore, the elevated OPE concentrations observed in the DLDR during the rainy season are likely driven by increased runoff and associated surface inputs [34]. In contrast, sediment-associated OPE concentrations were higher in the dry season (12.11 ng/g) than in the wet season (5.42 ng/g). This pattern is likely attributable to the substantially lower runoff in the DLDR during the dry period, as reduced hydrodynamic disturbance and increased sediment stability under low-flow conditions favor the deposition and retention of particle-bound OPEs [43]. Similar accumulation–remobilization dynamics have been observed for other classes of emerging contaminants [44,45]. Overall, these results highlight the importance of runoff changes brought about by wet and dry seasons in influencing the occurrence and migration of OPE in urban–rural transitional river systems.

3.2.2. Spatial Distribution Along the Rural–Urban Gradient

The spatial distribution of OPEs along the DLDR shows an obvious urban–rural gradient, reflecting the impact of gradual changes in land use, population density, and human activity intensity throughout the basin on the concentration and composition of river OPEs (Figure 4). This study divides the DLDR watershed into upper, middle, and lower reaches based on the degree of population agglomeration and the influence of tributaries (Text S2). OPE concentrations were lowest in the upstream reservoir control area, increased slightly in the predominantly rural midstream area, and peaked in the downstream urban area, indicating the cumulative impact of mixing area and point sources.

The lowest total OPE concentrations were detected at site DU2 within the Dalongdong Reservoir (mean: 2.15 ng/L; Figure S18), representing a rural background level under strict drinking-water-source protection. This concentration was substantially lower than those reported for other protected source-water systems in China, suggesting that limited human activity and effective management practices strongly constrained OPE inputs at this site [27,46]. Moving downstream into predominantly rural midstream areas, total OPE concentrations increased modestly, suggesting the possible presence of diffuse background inputs associated with rural settlements, aquaculture, and agricultural activities.

A pronounced elevation in OPE concentrations was observed at DM4, T3, and DL1–DL5 (mean: 14.27 ng/L; SD: 4.92 ng/L), all located within township (urbanized) areas along the DLDR. These sites consistently showed higher OPE levels than adjacent rural reaches (p < 0.05, Mann–Whitney U test comparing rural vs. urban sites), suggesting that rural domestic wastewater, urban runoff, and intensified human activities may be the main factors affecting OPE pollution patterns in urban–rural transition areas. In particular, the sharp increase at DL5 (peak: 377.69 ng/L), located immediately downstream of the wastewater treatment plant (WWTP), suggests that treated effluents may constitute a major point-source contribution in the urban-affected river reach, potentially exceeding agricultural inputs. Similar patterns observed in other transitional catchments lend support to this interpretation [23,24,47]. By contrast, total OPE concentrations decreased in Guanghai Bay (mean: 50.23 ng/L; SD: 12.45 ng/L), likely due to seawater dilution and enhanced hydrodynamic dispersion [48]. Overall, although the limited sampling frequency may not fully capture the overall spatial variability, the results consistently show a gradual increase in total OPE concentrations from upstream rural zones to downstream urbanized reaches, forming a clear longitudinal enrichment pattern along the river continuum. This spatial trend aligns with observations reported in other watersheds undergoing rapid rural-to-urban transition [49,50].

Hierarchical cluster analysis in water further corroborated this spatial heterogeneity, categorizing sites into three groups that mirror the watershed’s urbanization gradient (Figure 5). The profiles shifted from low-concentration background patterns in upstream and rural areas (Cluster 1) to moderately elevated levels with increased chemical diversity (e.g., TBOEP) in transitional zones (Cluster 2). Notably, the highly urbanized sites (Cluster 3) formed a distinct group characterized by the highest total concentrations and a dominance of chlorinated L-OPEs (TCEP and TCPP) [36,51]. Although sediment OPEs were not included in the clustering analysis due to their low concentrations and the resulting uncertainty in aggregation patterns, the clustering of water samples still provides meaningful insights. The observed grouping patterns indicate that point source discharges, particularly urban sewage, become a major controlling factor in the composition of OPE as rivers flow into urbanized reaches.

3.3. Partitioning of OPEs Between Water and Sediment

OPEs adsorbed onto sediments can be remobilized into the overlying water through environmental disturbances, indicating that sediments may act as both sinks and secondary sources of OPEs in aquatic systems. For OPEs detected in over 60% of samples, LogK_OC correlated strongly with LogK_OW (R² = 0.70), highlighting that hydrophobicity may be the key driver of water–sediment partitioning behavior (Figure 6). These findings reveal that the OPE distribution is greatly influenced by their LogK_OW values. The LogK_OC values of the seven target compounds ranged from 3.46 to 4.07. The value obtained for BDP was comparable to that in the Haihe River [52], whereas TCPP and TCEP exhibited slightly higher LogK_OC values than those reported for the Haihe River, Lake Taihu, and the Jiaozhou Bay (Table S9). It should be noted that field-derived partition coefficients represent dynamic processes. The transformation, sorption, and migration of OPEs are jointly modulated by multiple environmental parameters—such as organic carbon content, pH, DO, and salinity—which, together, govern their spatial distribution patterns (Table S10). As depicted in Figure S19, the relationships between LogK_OC and the physicochemical characteristics of OPEs were multifaceted, indicating diverse environmental controls. A negative correlation was observed between temperature and LogK_OC values for several OPE congeners, implying that elevated temperatures facilitate the release of OPEs from sediment particles into the aqueous phase [53], whereas DO showed positive correlations with TCPP and TCEP. The relationships between LogK_OC and other environmental parameters demonstrated more intricate patterns. Collectively, these findings indicate that water–sediment partitioning of OPEs in the DLDR may be influenced by the combined effects of compound-specific hydrophobicity and site-specific environmental controls, with important implications for their transport, persistence, and ecological risk in watersheds undergoing rural–urban transition.

3.4. High-Throughput Phenotypic Toxicity Screening and ToxPi Toxicity Assessment of OPEs in the DLDR Watershed

The C. elegans high-throughput phenotypic screening platform enables rapid and comprehensive evaluation of multidimensional toxic effects across numerous pollutants [29]. Using this approach, we quantified and ranked the relative toxicological profiles of 17 OPEs detected in the DLDR watershed. E-OPEs exhibited markedly higher toxicity than L-OPEs (Table S11). In Group H, the mean toxicity score of E-OPEs was −7.73, substantially lower than that of L-OPEs (−2.17), indicating stronger toxic effects (lower screening scores denote higher health risk) (Table S11). A similar pattern was observed in Group L, where E-OPEs yielded an average score of −2.62, compared with a markedly higher score for L-OPEs (8.88). These results collectively suggest that E-OPEs pose a greater overall potential health risk in the DLDR watershed than their legacy counterparts. Nevertheless, certain L-OPEs, such as TPHP, still exhibited significant risk and should not be overlooked. In group L, 9 out of 17 OPEs were significantly toxic to C. elegans, and most were E-OPEs, while all tested OPEs induced significant toxicity in the Group H (Figure 7A,B). Compared with L-OPEs, most E-OPEs caused broader and more severe adverse effects across multiple phenotypic endpoints. Notably, RDP disrupted all three assessed phenotypes—growth, survival, and locomotion—highlighting its strong integrated toxicity. This observation is consistent with previous studies reporting that RDP induces developmental impairment and neurotoxicity in aquatic organisms, including zebrafish larvae [54,55].

To further integrate biological toxicity with physicochemical hazard characteristics, a ToxPi-based assessment was conducted by incorporating parameters related to persistence, bioaccumulation, mobility, toxicity, and metabolic transformation (Tables S12–S15). The results showed that E-OPEs had a higher mean comprehensive risk score (0.64) than L-OPEs (0.47), with higher scores indicating greater combined ecological and health risks. Among all compounds, AO168=O exhibited the highest ToxPi score, identifying it as the most concerning OPE in the DLDR watershed (Figure 7C). Previous studies have reported that AO168=O and structurally related emerging OPEs display stronger biological activity and more pronounced developmental or neurotoxic effects than L-OPEs under comparable exposure conditions, and have identified AO168=O as a major contributor to toxicological risk in urban and wastewater-impacted river systems [56]. In addition, certain L-OPEs, such as TPHP and TMPP, also exhibited elevated integrated risk scores, indicating that both L- and E-OPEs present notable ecological and potential health risks within the DLDR watershed. Overall, the high-throughput phenotypic screening and ToxPi assessment consistently showed that E-OPEs exhibit higher integrated toxicity and ecological-health risks than L-OPEs in the DLDR watershed. Among all compounds, AO168=O emerged as a dominant risk driver, while certain legacy OPEs (e.g., TPHP) also contributed substantially to the overall toxicity burden.

4. Conclusions

This study advances the understanding of L- and E-OPEs in a rural–urban transitional watershed by integrating occurrence monitoring, source characterization, and phenotype-based risk evaluation. Beyond confirming the widespread presence of OPEs, the findings reveal several management-relevant implications. The pronounced downstream accumulation and rainy-season intensification of OPEs suggest that mixed-point sources linked to rural domestic wastewater, decentralized sewage systems, and small-scale industries should be prioritized in monitoring and regulatory frameworks. The elevated integrated risk scores of many emerging OPEs further underscore the need to expand current pollutant lists and incorporate environmentally persistent flame-retardant substitutes into future guideline development. In addition, the multidimensional toxicity patterns captured by the C. elegans-ToxPi approach demonstrate its value as a screening tool for risk prioritization in contexts where toxicological thresholds are lacking. We also acknowledge several limitations, including the limited number of sampling campaigns, the lack of full hydrological replication, and reliance on a single upstream reservoir as a relative reference site. These constraints may influence temporal representativeness and spatial resolution. Future work should include higher-frequency sampling across diverse hydrological conditions and establish multiple reference locations to more accurately quantify pollutant transport processes.