Global marine plastic pollution, which is derived mainly from the input of vast amounts of land-based plastic waste, has drawn increasing public attention. Riverine plastic outflows estimated using models based on the concept of mismanaged plastic waste (MPW) are substantially greater than reported field measurements. Herein, we formulate a robust model using the Human Development Index (HDI) as the main predictor, and the modeled riverine plastic outflows are calibrated and validated by available field data. A strong correlation is achieved between model estimates and field measurements, with a regression coefficient of r 2 = 0.9. The model estimates that the global plastic outflows from 1518 main rivers were in the range of 57,000–265,000 (median: 134,000) MT year–1 in 2018, which were approximately one-tenth of the estimates by MPW-based models. With increased plastic production and human development, the global riverine plastic outflow is projected to peak in 2028 in a modeled trajectory of 2010–2050. The HDI is a better indicator than MPW to estimate global riverine plastic outflows, and plastic pollution can be effectively assessed and contained during human development processes. The much lower global riverine plastic outflows should substantially ease the public’s concern about marine plastic pollution and financial pressure for remediation.

Over a third of the world’s annual chemical production and sales occur in China. Thus, knowledge of the properties of the substances produced and emitted there is important from a global perspective. The chemical Inventory of Existing Chemical Substances of China (IECSC) lists over 45 000 chemicals. When compared to the North American and European chemical inventories, 6916 substances were found to be unique to the IECSC. We retrieved structural information for 14 938 organic chemicals in the IECSC and determined their overall environmental persistence , bioaccumulation factor (BAF), and long-range transport potential (transfer efficiency) using in silico approaches with the goal of identifying new chemicals with properties that might lead to global contamination issues. Overall, 10% of the 14 938 chemicals were unique to the IECSC and their environmental persistence and BAF were statistically higher than the values for the rest of the IECSC chemicals. We prioritized 27 neutral organic compounds predicted to have prolonged environmental persistence, and high potential for bioaccumulation and long-range transport when compared with polychlorinated biphenyls as a benchmark. We also identified 69 organofluorine compounds with three or more perfluorinated moieties, unique to the IECSC. Screening approaches and results from this study help to identify and prioritize those to be considered in further environmental modeling and monitoring assessments.

Transplacental transfer of environmental chemicals results in direct risks to fetal development. Although numerous studies have investigated transplacental transfer efficiencies (TTEs) of environmental chemicals, the underlying mechanisms and influencing factors remain poorly understood. The present study aims to synthesize a current state of knowledge on the TTEs of major environmental chemicals and explore the roles of chemicals' molecular descriptors and placental transporters in the transplacental transfer. The results indicate great variations in TTEs (median: 0.29−2.86) across 51 chemicals. Chemical-dependent TTEs may partially be attributed to the influences of chemicals' molecular descriptors. Predictive models based on experimental TTEs and 1790 computed molecular descriptors indicate that a very limited number of molecular descriptors, such as the topological polar surface area, may substantially influence and efficiently predict chemicals' TTEs. In addition, molecular docking analyses were conducted to determine the binding affinities between 51 chemicals and six selected transporters, including BCRP, MDR1, hENT1, FRα, SERT, and MRP1. The results reveal transporter-and chemical-dependent binding affinities. Therefore, our study demonstrates that molecular descriptors and placental transporters, among a variety of other factors, can play important roles in the transplacental transfer of environmental chemicals. However, the underlying mechanisms and several important knowledge gaps identified herein require further investigations.

Gestational exposure to environmental chemicals and subsequent permeation through the placental barrier represents potential health risks to both pregnant women and their fetuses. In the present study, we explored prenatal exposure to a suite of 46 emerging plasticizers and synthetic antioxidants (including five transformation products of 2,6-di-tert-butyl-4-hydroxytoluene, BHT) and their potency to cross human placenta based on a total of 109 maternal and cord serum pairs. Most of these chemicals have rarely or never been investigated for prenatal exposure and associated health risks. Eleven of them exhibited detection frequency greater than 50% in maternal blood, including dibutyl fumarate (DBF), 2,6-di-tert-butylphenol (2,4-DtBP), 1,3-diphenylguanidine (DPG), methyl-2-(benzoyl)­benzoate (MBB), triethyl citrate (TEC), BHT, and its five metabolites, with a median concentration from 0.05 to 3.1 ng/mL. The transplacental transfer efficiency (TTE) was determined for selected chemicals with valid measurements in more than 10 maternal/cord blood pairs, and the mean TTEs exhibited a large variation (i.e., 0.29–2.14) between chemicals. The determined TTEs for some of the target chemicals were comparable to the predicted values by our previously proposed models developed from molecular descriptors, indicating that their transplacental transfer potency could be largely affected by physicochemical properties and molecular structures. However, additional biological and physiological factors may influence the potency of environmental chemicals to cross human placenta. Overall, our study findings raise concern on human exposure to an increasing list of plastic additives during critical life stages (e.g., pregnancy) and potential health risks.

Identifying potential persistent organic pollutants (POPs) and persistent, bioaccumulative, and toxic (PBT) substances from industrial chemical inventories are essential for chemical risk assessment, management, and pollution control. Inspired by the connections between chemical structures and their properties, a deep convolutional neural network (DCNN) model was developed to screen potential PBT/POP-like chemicals. For each chemical, a two-dimensional molecular descriptor representation matrix based on 2424 molecular descriptors was used as the model input. The DCNN model was trained via a supervised learning algorithm with 1306 PBT/POP-like chemicals and 9990 chemicals currently known as non-POPs/PBTs. The model can achieve an average prediction accuracy of 95.3 ± 0.6% and an F-measurement of 79.3 ± 2.5% for PBT/POP-like chemicals (positive samples only) on external data sets. The DCNN model was further evaluated with 52 experimentally determined PBT chemicals in the REACH PBT assessment list and correctly recognized 47 chemicals as PBT/non-PBT chemicals. The DCNN model yielded a total of 4011 suspected PBT/POP like chemicals from 58 079 chemicals merged from five published industrial chemical lists. The proportions of PBT/POP-like substances in the chemical inventories were 6.9–7.8%, higher than a previous estimate of 3–5%. Although additional PBT/POP chemicals were identified, no new family of PBT/POP-like chemicals was observed.