Microplastics (MPs) and oil pollution are major concerns in oceans. Although their coexistence in oceans and the associated MP-oil-dispersant agglomerates (MODAs) have been reported, limited attention is given to the behavior of the co-contaminants. This study investigated MODA transport in a simulated ocean system and explored related mechanisms under various oil types, salinities, and mineral concentrations. We found that more than 90% of the heavy oil-formed MODAs stayed at the seawater surface, while the light oil-formed MODAs were widely distributed throughout the seawater column. The increased salinity promoted MODAs formed by 7 and 90 μm MPs to transport from the seawater surface to the column. This was elucidated by the Derjaguin-Landau-Verwey-Overbeek theory as more MODAs formed under higher salinities and dispersants kept them stable in the seawater column. Minerals facilitated the sinking of large MP-formed MODAs (e.g., 40 μm) as minerals were adsorbed on the MODA surface, but their impact on small MP-formed MODAs (e.g., 7 μm) was negligible. A MODA-mineral system was proposed to explain their interaction. Rubey's equation was recommended to predict the sinking velocity of MODAs. This study is the first attempt to reveal MODA transport. Findings will contribute to the model development to facilitate their environmental risk evaluation in oceans.

Synthetic musks (SMs) are odor additives commonly used in the personal care products. Their wide existence in the environment and the recently reported adverse impact on the production and activity of progesterone and estrogen have raised pregnancy red flags and even lead to a pregnancy loss. Apart from the suggestion of limiting SM contact and exposure, effective abortion risk control measures for SMs remain to be blank. Facing the above challenges, this study tried to establish a new theoretical circumvention strategy to reduce the abortion risk of SMs to pregnant women by designing the supplementary diet plan and environmentally friendly SMs derivatives using molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) models. According to the supplementary diet plan, the diet combination of vitamin E, vitamin B₂, niacin, vitamin A, and vitamin B₆ were confirmed to not only provide essential nutrients for human health, but also reduce the abortion risk in pregnant women in daily life. The multi-activity (binding ability of SMs with progesterone-estrogen) 3D-QSAR model was constructed to screen SMs derivatives. The LibDock score, a parameter reflecting the binding ability between SMs' Derivative-24 with progesterone-estrogen, decreased as much as 137.67% compared with its precursor galaxolide (HHCB). The 3D-QSAR models assisted screening indicated that Derivative-24 had lower environmental impacts (i.e., bioconcentration and mobility) and improved functional properties (odor stability, musky scent, and odor intensity). The integration of the optimum candidate, Derivative-24, with optimum three supplementary diet plans exhibited a much lower abortion risk than HHCB, demonstrating the effectiveness of the proposed theoretical circumvention strategy as a comprehensive abortion risk control measure. It also shed light on the design of new pharmaceutical and personal care products using advanced computing tools.

Synthetic musks (SMs) have been widely used as odor additives in personal care products (PCPs). Dermal exposure to SMs is the main pathway of the accumulation of these chemicals in human kerateins and poses potential health risks. In this study, in silico methods were established to reduce the human health risk of SMs from dermal exposure by investigating the risk mechanisms, designing lower bioaccumulation ability SMs and suggesting proper PCP ingredients using molecular docking, molecular dynamics simulation, and quantitative structure-activity relationship (QSAR) models. The binding energy, a parameter reflecting the binding ability of SMs and human keratin protein (4ZRY), was used as the indicator to assess the human health risk of SMs. According to the mechanism analysis, total energy was found as the most influential molecular structural feature influencing the bioaccumulation ability of a SM, and as one of the main factors influencing the function (i.e., odor sensitivity) of an SM. The 3D-QSAR models were constructed to control the human health risk of SMs by designing lower-risk SMs derivatives. The phantolide (PHAN)- 58 was determined to be the optimum SM derivative with lower bioaccumulation ability (reduced 17.25%) and improved odor sensitivity (increased 7.91%). A further reduction of bioaccumulation ability of PHAN-58 was found when adding proper body wash ingredients (i.e., alkyl ethoxylate sulfate (AES), dimethyloldimethyl (DMDM), EDTA-Na4, ethylene glycol distearate (EGDS), hydroxyethyl cellulose (HEC), lemon yellow and octyl glucose), leading to a significant reduction of the bioaccumulation ability (42.27%) compared with that of PHAN. Results demonstrated that the proposed theoretical mechanism and control strategies could effectively reduce the human health risk of SMs from dermal exposure.

Links between environmental conditions (e.g., meteorological factors and air quality) and COVID-19 severity have been reported worldwide. However, the existing frameworks of data analysis are insufficient or inefficient to investigate the potential causality behind the associations involving multidimensional factors and complicated interrelationships. Thus, a causal inference framework equipped with the structural causal model aided by machine learning methods was proposed and applied to examine the potential causal relationships between COVID-19 severity and 10 environmental factors (NO₂, O₃, PM2.5, PM10, SO₂, CO, average air temperature, atmospheric pressure, relative humidity, and wind speed) in 166 Chinese cities. The cities were grouped into three clusters based on the socio-economic features. Time-series data from these cities in each cluster were analyzed in different pandemic phases. The robustness check refuted most potential causal relationships’ estimations (89 out of 90). Only one potential relationship about air temperature passed the final test with a causal effect of 0.041 under a specific cluster-phase condition. The results indicate that the environmental factors are unlikely to cause noticeable aggravation of the COVID-19 pandemic. This study also demonstrated the high value and potential of the proposed method in investigating causal problems with observational data in environmental or other fields.