In silico investigation of gas/particle partitioning equilibrium of polybrominated diphenyl ethers (PBDEs)

Gas/particle partitioning of PCDD/Fs: Distributions and implications for available models

Polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs) are semi-volatile organic compounds (SVOCs) existing in the atmosphere in the gas and particulate phase, remain persistent for a long time and pose a high risk to the environment and human health. In this study, PCDD/F measurements were made in an urban area between June 2022 and April 2023. In order to understand the fate of PCDD/Fs, the gas/particle (G/P) partitioning was studied. Although various models have been developed to determine the G/P partitioning of SVOCs, only logKp-logPL0, Junge-Pankow and Harner-Bidleman models are generally used for PCDD/Fs. In this study, nine different models (Junge-Pankow, Harner-Bidleman, Dachs-Eisenreich, Li-Ma-Yang, pp-LFER, mp-pp-LFER, QSPR, logKp-logPL0, logKp-logKOA) were employed to determine the G/P partitioning. To the best of our knowledge, pp-LFER, mp-pp-LFER and QSPR models were evaluated for PCDD/Fs for the first time in this study. In addition, the performance of the models within the equilibrium (EQ), non-equilibrium (NE) and maximum partitioning (MP) domain was investigated for PCDD/Fs for the first time in this study. Accordingly, models based on absorption in the EQ domain, adsorption in the NE domain and adsorption and absorption mechanisms in the MP domain were found to be effective in explaining the G/P transitions. It was determined that there is no equilibrium situation in the G/P partitioning. The Junge-Pankow, pp-LFER, Li-Ma-Yang and QSPR models under-predicted the particle fraction values while the other models showed a high prediction profile. The Li-Ma-Yang model showed the closest results to the measured particle fraction values, and it determined that deposition mechanisms are of non-negligible importance in the G/P partitioning of PCDD/Fs. One of the new models, the pp-LFER model, has shown remarkable success at high logKOA values. The mp-pp-LFER model, which overestimated the contribution of the adsorption mechanism, showed a very high prediction profile compared to the measured values.

Molecular Interactions Governing the Rat Aryl Hydrocarbon Receptor Activities of Polycyclic Aromatic Compounds and Predictive Model Development

Polycyclic aromatic compounds (PACs) exhibit rat aryl hydrocarbon receptor (rAhR) activities, leading to diverse biological or toxic effects. In this study, the key amino residues and molecular interactions that govern the rAhR activity of PACs were investigated using in silico strategies. The homology model of rAhR was first docked with 90 PACs to yield complexes, and the results of the molecular dynamics simulations of 16 typical complexes showed that the binding energies of the complexes range from -7.37 to -26.39 kcal/mol. The major contribution to the molecular interaction comes from van der Waals forces, and Pro295 and Arg316 become the key residues involved in most complexes. Two QSAR models were further developed to predict the rAhR activity of PACs (in terms of log IEQ for PACs without halogen substitutions and log%-TCDD-max for halogenated PACs). Both models have good predictive ability, robustness, and extrapolation ability. Molecular polarizability, electronegativity, size, and nucleophilicity are identified as the important factors affecting the rAhR activity of PACs. The developed models could be employed to predict the rAhR activity of other reactive PACs. This work provides insight into the mechanisms and interactions of the rAhR activity of PACs and assists in the assessment of their fate and risk in organisms.

Statistical-physical method for simulating the transport of microplastic-antibiotic compound pollutants in typical bay area

Microplastics and antibiotics are emerging pollutants in the environment and have received widespread attention globally. In coastal areas, microplastic and antibiotic pollution is ubiquitous and often overlapping. Microplastic-antibiotic compound pollutants that are formed through adsorption have thus become a major concern. However, modeling knowledge of microplastic transport in coastal areas is still limited, and research on the impact of compound pollutants caused by Polythene (PE)-antibiotics in such settings is in early stages. In this study, using a lattice Boltzmann method (LBM) and temporal Markov method (TMM) under a statistical-physical framework, we simulated pollutant transport and PE-antibiotic compound pollutants in coastal areas. First, a series of models are proposed, including an LBM wave-current coupling model, an LBM antibiotic transport model, an LBM particle-tracking model, a TMM microplastic transport model and the final LBM-TMM hybrid compound pollutant model. Then, the suitability and applicability of the models was validated using experimental data and numerical simulations. Finally, the models were applied to a study area, Laizhou Bay (China). The simulation results demonstrate that adsorption will reduce the concentration of antibiotics in the water environment. Within 44 days, the adsorbed antibiotic carried by PE particles migrate further, and the width of the pollution zone escalates from 234.2 m to 689.0 m.

Release Behavior of Liquid Crystal Monomers from Waste Smartphone Screens: Occurrence, Distribution, and Mechanistic Modeling

Liquid crystal display (LCD) screens can release many organic pollutants into the indoor environment, including liquid crystal monomers (LCMs), which have been proposed as a novel class of emerging pollutants. Knowing the release pathways and mechanisms of LCMs from various components of LCD screens is important to accurately assess the LCM release and reveal their environmental transport behavior and fate in the ambient environment. A total of 47, 43, and 33 out of 64 target LCMs were detected in three disassembled parts of waste smartphone screens, including the LCM layer (LL), light guide plate (LGP), and screen protector (SP), respectively. Correlation analysis confirmed LL was the source of LCMs detected in LGP and SP. The emission factors of LCMs from waste screen, SP, and LGP parts were estimated as 2.38 × 10^-3, 1.36 × 10^-3, and 1.02 × 10^-3, respectively. A mechanism model was developed to describe the release behaviors of LCMs from waste screens, where three characteristics parameters of released LCMs, including average mass proportion (AP), predicted subcooled vapor pressures (P_L), and octanol-air partitioning coefficients (K_oa), involving coexistence of absorption and adsorption mechanisms, could control the diffusion-partitioning. The released LCMs in LGP could reach diffusion-partition equilibrium more quickly than those in SP, indicating that LCM release could be mainly governed through SP diffusions.

Prediction study on the distribution of polycyclic aromatic hydrocarbons and their halogenated derivatives in the atmospheric particulate phase

Polycyclic aromatic hydrocarbons (PAHs) and their halogenated derivatives (X-PAHs), which generally produced from photochemical and thermal reactions of parent PAHs, widely exist in the environment. They are semi-volatile organic chemicals (SVOCs) and the partitioning between gas/particulate phases affects their environmental migration, transformation and fate, which further impacts their toxicity and health risk to human. However, there is a large data missing of the experimental distribution ratio in the atmospheric particulate phase (f), especially for X-PAHs. In this study, we first checked the correlation between experimental f values of 53 PAH derivatives and their octanol-air partitioning coefficients (log K_OA), which is frequently used to characterize the distribution of chemicals in organic phase, and yielded R² = 0.803. Then, quantum chemical descriptors derived from molecular structural optimization by M06-2X/6-311 +G (d,p) method were further employed to develop Quantitative Structure-Property Relationship (QSPR) model. The model contains two descriptors, the average molecular polarizability (α) and the equilibrium parameter of molecular electrostatic potential (τ), and yields better performance with R² = 0.846 and RMSE = 0.122. The mechanism analysis and validation results by different strategies prove that the model can reveal the molecular properties that dominate the distribution between gas and particulate phases and it can be used to predict f values of other PAHs/X-PAHs, providing basic data for their environmental ecological risk assessment.

QSPR models for predicting the adsorption capacity for microplastics of polyethylene, polypropylene and polystyrene

Microplastics have become an emerging concerned global environmental pollution problem. Their strong adsorption towards the coexisting organic pollutants can cause additional environmental risks. Therefore, the adsorption capacity and mechanisms are necessary information for the comprehensive environmental assessments of both microplastics and organic pollutants. To overcome the lack of adsorption information, five quantitative structure–property relationship (QSPR) models were developed for predicting the microplastic/water partition coefficients (log K_d) of organics between polyethylene/seawater, polyethylene/freshwater, polyethylene/pure water, polypropylene/seawater, and polystyrene/seawater. All the QSPR models show good fitting ability (R² = 0.811–0.939), predictive ability (Q²_ext = 0.835–0.910, RMSE_ext = 0.369–0.752), and robustness (Q_cv² = 0.882–0.957). They can be used to predict the K_d values of organic pollutants (such as polychlorinated biphenyls, chlorobenzene, polycyclic aromatic hydrocarbons, antibiotics perfluorinated compounds, etc.) under different pH conditions. The hydrophobic interaction has been indicated as an important mechanism for the adsorption of organic pollutants to microplastics. In sea waters, the role of hydrogen bond interaction in adsorption is considerable. For polystyrene, π–π interaction contributes to the partitioning. The developed models can be used to quickly estimate the adsorption capacity of organic pollutants on microplastics in different types of water, providing necessary information for ecological risk studies of microplastics.

Modeling gas/particle partitioning of polybrominated diphenyl ethers (PBDEs) in the atmosphere: A review

Gas/particle (G/P) partitioning of semi-volatile organic compounds (SVOCs) such as polybrominated diphenyl ethers (PBDEs), is an important atmospheric process due to its significance in governing atmospheric fate, wet/dry deposition, and long-range atmospheric transport. In this article, eight models published to predict the G/P partitioning of PBDEs are reviewed. These eight models are used to calculate the G/P partitioning quotient and particulate phase fraction of selected PBDE congeners. A comparison of the predicted results from the eight models with monitoring data published by several research groups worldwide leads to the following conclusions: 1) when the values of the logarithm of the octanol-air partition coefficient (logK_OA) fall below 11.4 (the first threshold value, logK_OA1), all 8 models perform well in predicting the G/P partitioning of PBDEs in the atmosphere, and 2) when logK_OA is >11.4, and especially above 12.5 (the second threshold value, logK_OA2), the Li-Ma-Yang model, a steady-state model developed based on wet and dry deposition of the particles (Li et al., Atmos. Chem. Phys. 2015; 15:1669-1681), shows the best performance with highest conformity to the measurements for selected PBDEs (94.4 ± 1.6% data points within ±1 log unit). Overall, the Li-Ma-Yang model appears to capture the most important factors that affect the partitioning of PBDEs between gaseous and particular phases in the atmosphere.

Understanding and predicting the diffusivity of organic chemicals for diffusive gradients in thin-films using a QSPR model

The diffusion coefficient (D) is a key physicochemical parameter for the diffusive gradients in thin films technique (DGT) for environmental sampling, which has been extended to organic chemicals (so called o-DGT). D can be measured in the laboratory, although for organic chemicals this parameter might be predicted based on chemical structure. Here we developed for the first time a Quantitative Structure-Property Relationship (QSPR) model to predict the D values. Twenty quantum chemical descriptors that quantify the electronic and energy properties of 120 organic compounds were selected together with molecular mass, solubility and hydrophobicity. The best QSPR model was established by using genetic algorithm and multiple linear regression (GA-MLR). The results indicated that the model derived from the average molecular polarizability (α), the chemical potential (ξ) and the global electrophilicity index (ω) could explain the diffusion of organics in o-DGT and had good statistical performance (R² = 0.767, RMSE = 0.101). Different validation strategies confirmed that the developed model was robust and predictive. 93% of tested compounds were within the applicability domain (AD) and predicted accurately. We concluded that the proposed QSPR model can serve as an efficient predictive tool for new chemicals in the AD, would be useful to cross validate measured D values and provide a better the understanding of the diffusive behaviour of organics in o-DGT and measurements in the environment. It might also be useful in the non-target analysis with o-DGT for chemicals without measured D values.

Development of predictive models for silicone rubber-water partition coefficients of hydrophobic organic contaminants

The silicone rubber passive sampling technique is extensively applied to monitor the aqueous freely dissolved concentration of hydrophobic organic compounds (HOCs). The silicone rubber-water partition coefficient (Ksrw) is an important parameter to accurately measure the concentrations of chemicals using passive sampling devices. In this study, two theoretical linear solvation energy relationship (TLSER) models and a quantitative structure-property relationship (QSPR) model were developed for predicting the Ksrw of HOCs. The 119 model compounds studied here included 31 personal care products, such as musks, UV-filters, and organophosphate flame retardants, as well as "conventional" pollutants, such as polycyclic aromatic hydrocarbons and polychlorinated biphenyls. The statistical parameters indicated that the final QSPR model with seven descriptors for all 119 chemicals had a satisfactory goodness-of-fit (Radj2 = 0.898), robustness (QLOO2 = 0.881) and predictive ability (Qext-F1,2,32 = 0.897-0.926). In comparison, the results of one TLSER model with four descriptors for 113 chemicals (Radj2 = 0.826, QLOO2 = 0.790, Qext-F1,2,32 = 0.805-0.837) and another TLSER model with one descriptor for 5 chemicals (Radj2 = 0.747, QLOO2 = 0.647) were also acceptable. The applicability domains of the obtained models covered chemicals containing hydroxyl, imino groups, carbonyl groups, ether bonds, halogen atoms, sulfur atoms, phosphorus atoms, nitro groups, and cyano groups. In addition, the structural features governing the partition behavior of chemicals between silicone rubber and water were explored through interpretation of appropriate mechanisms.

In silico study for inhibiting thyroid hormone sulfotransferase activity by halogenated phenolic chemicals

Thyroid hormones (THs) are essential to proper growth and development of human bodies. Inhibiting the sulfation metabolism of THs has been demonstrated to be an important way for some environmental pollutants, such as halogenated phenolic compounds, to interfere THs homeostasis, thereby causing health problems. However, the important property characteristics that govern the sulfation inhibition of these chemicals are not well understood, and the experimental data on inhibition potential is limited. In this work, an in silico approach was developed to investigate the structure-activity relationship for their sulfotransferases (SULTs) inhibition. A series of quantum chemical descriptors that quantify the electronic and energy properties of 22 halogenated phenolic compounds have been calculated to establish a predictive model and analyzed their corresponding contributions to SULTs inhibition. Density functional theory (DFT) B3LYP/6-31G** has been employed to optimize molecular geometries to obtain a total of 15 descriptors for every compound. The implementation of linear regression shows three descriptors that represent molecular mass, positive charges on hydrogen atoms, and energy of frontier orbitals strongly correlate with SULTs inhibition potential. This indicates molecular size, hydrogen-bond strength, and nucleophilic-electrophilic reactivity may play important roles in SULTs inhibition. The derived regression model has good statistical performance (r² = 0.84, rms = 0.35), and different validation strategies indicate it can serve as an efficient predictive tool for other chemicals in application domain but with no experimental data, consequently assisting in their THs sulfation inhibition and health risk assessment.

The occurrence of polybrominated diphenyl ether (PBDE) contamination in soil, water/sediment, and air

As a kind of brominated flame retardants (BFRs), polybrominated diphenyl ethers (PBDEs) are extensively used in different types of electronic equipment, furniture, plastics, and textiles. PBDEs are ubiquitous environmental contaminants that may impact human health and ecosystems. Here we highlight recent findings on the occurrence, contamination status, and transport of PBDEs in soil, water/sediment, and air. Four aspects are discussed in detail: (1) sources of PBDEs to the environment; (2) occurrence and transport of PBDEs in soil; (3) PBDEs in aquatic ecosystems (water/sediment) and their water-sediment partitioning; and (4) the occurrence of PBDEs in the atmosphere and their gas-particle partitioning. Future prospects for the investigation on PBDEs occurrence are also discussed based on current scientific and practical needs.

Developing Predictive Models for Carrying Ability of Micro-Plastics towards Organic Pollutants

Microplastics, which have been frequently detected worldwide, are strong adsorbents for organic pollutants and may alter their environmental behavior and toxicity in the environment. To completely state the risk of microplastics and their coexisting organics, the adsorption behavior of microplastics is a critical issue that needs to be clarified. Thus, the microplastic/water partition coefficient (log K_d) of organics was investigated by in silico method here. Five log K_d predictive models were developed for the partition of organics in polyethylene/seawater, polyethylene/freshwater, polyethylene/pure water, polypropylene/seawater, and polystyrene/seawater. The statistical results indicate that the established models have good robustness and predictive ability. Analyzing the descriptors selected by different models finds that hydrophobic interaction is the main adsorption mechanism, and π−π interaction also plays a crucial role for the microplastics containing benzene rings. Hydrogen bond basicity and cavity formation energy of compounds can determine their partition tendency. The distinct crystallinity and aromaticity make different microplastics exhibit disparate adsorption carrying ability. Environmental medium with high salinity can enhance the adsorption of organics and microplastics by increasing their induced dipole effect. The models developed in this study can not only be used to estimate the log K_d values, but also provide some necessary mechanism information for the further risk studies of microplastics.

Reductive Debromination of Polybrominated Diphenyl Ethers: Dependence on Br Number of the Br-Rich Phenyl Ring

Reductive debromination has been widely studied for the degradation of polybrominated diphenyl ethers (PBDEs), although the reaction mechanisms are not so clear. In the present study, the photocatalytic degradation and debromination of ten PBDEs were carried out with CuO/TiO₂ nanocomposites as the photocatalyst under an anaerobic condition. The pseudo-first-order rate constants were obtained for the photocatalytic debromination of PBDEs, and their relative rate constants ( k_R) were evaluated against k_R= 1 for BDE209. Unlike the generally accepted summary that k_R is dependent on the total Br number ( N) of PBDEs, k_R is found to depend on the Br number on a phenyl ring with more Br atoms than the other one. In other words, a phenyl ring substituted by more Br is more reactive for the reductive debromination. The calculated LUMO energies ( E_LUMO) of all PBDEs are well correlated to the more reactive phenyl ring with more Br, compared with the N of two phenyl rings. The result was explained by LUMO localization on the Br-rich phenyl ring, suggesting that the reductive debromination occurs on the phenyl ring.