Moving beyond Risk Quotients: Advancing Ecological Risk Assessment to Reflect Better, More Robust and Relevant Methods

Organophosphate esters in milk across thirteen countries from 2020 to 2023: Concentrations, sources, temporal trends and ToxPi priority to humans

Recent increases in organophosphate ester (OPE) application have led to their widespread presence, yet little is known about their temporal trends in food. This study collected milk samples from 13 countries across three continents during 2020-2023, finding detectable OPEs in all samples (range: 2.25-19.7; median: 7.06 ng/g ww). Although no statistical temporal differences were found for the total OPEs during the 4-year sampling campaign, it was interesting to observe significant variations in the decreasing trend for Cl-OPEs and concentration variations for aryl-OPEs and alkyl-OPEs (p < 0.05), indicating changing OPE use patterns. Packaged milk exhibited significant higher OPE levels than those found in directly collected raw unpackaged milk, and milk with longer shelf-life showed higher OPE levels, revealing packaging material as a contamination source. No significant geographical differences were observed in milk across countries (p > 0.05), but Shandong Province, a major OPE production site in China, showed relatively higher OPE concentrations. The Monte Carlo simulation of estimated daily intakes indicated no exposure risk from OPEs through milk consumption. The molecular docking method was used to assess human hormone binding affinity with OPEs, amongst which aryl-OPEs had the highest binding energies. The Toxicological-Priority-Index method which integrated chemical property, detection frequency, risk quotients, hazardous quotients and endocrine-disrupting effects was employed to prioritize OPEs. Aryl-OPEs showed the highest scores, deserving attention in the future.

Predicting the potential risks posed by antidepressants as emerging contaminants in fish based on network pharmacological analysis

This study conducted a network pharmacology-based analysis to simultaneously discern a broad spectrum of potential environmental risks and health effects of antidepressants, a common class of pharmaceutical emerging contaminants (PECs) possessing a complex pharmacological profile, and in silico predict the adverse phenotypes potentially occurring in fish associated with exposure to antidepressants and their mixtures under realistic exposure scenarios. Results showed that 24 of the included 39 antidepressants had been detected worldwide in water environment across 50 countries. Using the environmentally realistic exposure scenario for China as an example, the predicted blood concentrations of antidepressant residues that were generated based on the Fish Plasma Model ranged from 37.89 (Alprazolam) to 16,772.05 (Sertraline) ng/L in exposed fish. Hazard-based bioactivity network without regard to concentration data was composed of 148 potential targets and 701 antidepressant-target interactions. After filtering each antidepressant-target interaction node using the predicted drug concentrations in the blood of fish under realistic exposure scenarios in China, an environmental risk-based network was refined and showed that 11 targets, including muscarinic acetylcholine receptor M1, alpha-2B adrenergic receptor, serotonin 2 A receptor, etc. might be modulated by antidepressants at concentrations equal to or below the environmental exposure levels and their mixtures in fish. Environmentally relevant concentrations of antidepressants in water samples from China might perturb the behavior, stress response, phototaxis, and development in exposed fish.

Development of a probabilistic risk model for pharmaceuticals in the environment under population and wastewater treatment scenarios

Preparing for future environmental pressures requires projections of how relevant risks will change over time. Current regulatory models of environmental risk assessment (ERA) of pollutants such as pharmaceuticals could be improved by considering the influence of global change factors (e.g., population growth) and by presenting uncertainty more transparently. In this article, we present the development of a prototype object-oriented Bayesian network (BN) for the prediction of environmental risk for six high-priority pharmaceuticals across 36 scenarios: current and three future population scenarios, combined with infrastructure scenarios, in three Norwegian counties. We compare the risk, characterized by probability distributions of risk quotients (RQs), across scenarios and pharmaceuticals. Our results suggest that RQs would be greatest in rural counties, due to the lower development of current wastewater treatment facilities, but that these areas consequently have the most potential for risk mitigation. This pattern intensifies under higher population growth scenarios. With this prototype, we developed a hierarchical probabilistic model and demonstrated its potential in forecasting the environmental risk of chemical stressors under plausible demographic and management scenarios, contributing to the further development of BNs for ERA. Integr Environ Assess Manag 2024;00:1-21. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Advancing exposure assessment approaches to improve wildlife risk assessment

The exposure assessment component of a Wildlife Ecological Risk Assessment aims to estimate the magnitude, frequency, and duration of exposure to a chemical or environmental contaminant, along with characteristics of the exposed population. This can be challenging in wildlife as there is often high uncertainty and error caused by broad-based, interspecific extrapolation and assumptions often because of a lack of data. Both the US Environmental Protection Agency (USEPA) and European Food Safety Authority (EFSA) have broadly directed exposure assessments to include estimates of the quantity (dose or concentration), frequency, and duration of exposure to a contaminant of interest while considering "all relevant factors." This ambiguity in the inclusion or exclusion of specific factors (e.g., individual and species-specific biology, diet, or proportion time in treated or contaminated area) can significantly influence the overall risk characterization. In this review, we identify four discrete categories of complexity that should be considered in an exposure assessment-chemical, environmental, organismal, and ecological. These may require more data, but a degree of inclusion at all stages of the risk assessment is critical to moving beyond screening-level methods that have a high degree of uncertainty and suffer from conservatism and a lack of realism. We demonstrate that there are many existing and emerging scientific tools and cross-cutting solutions for tackling exposure complexity. To foster greater application of these methods in wildlife exposure assessments, we present a new framework for risk assessors to construct an "exposure matrix." Using three case studies, we illustrate how the matrix can better inform, integrate, and more transparently communicate the important elements of complexity and realism in exposure assessments for wildlife. Modernizing wildlife exposure assessments is long overdue and will require improved collaboration, data sharing, application of standardized exposure scenarios, better communication of assumptions and uncertainty, and postregulatory tracking. Integr Environ Assess Manag 2024;20:674-698. © 2023 SETAC.

A review on in silico prediction of the environmental risks posed by pharmaceutical emerging contaminants

Computer-aided (in silico) prediction has shown good potential to support the environmental risk assessment (ERA) of pharmaceutical emerging contaminants (PECs), allowing low-cost, animal-free, high-throughput screening of multiple potential risks posed by a wide variety of pharmaceuticals in the environment based on insufficient toxicity data. This review provided recent insights regarding the application of in silico approaches in prediction for environmental risks of PECs. Based on the review of 20 included articles from 8 countries published since 2018, we found that the researchers' interest and concern in this research topic were sharply aroused since 2021. Recently, in silico approaches have been widely used for the prediction of bioaccumulation and biodegradability, lethal endpoints, developmental toxicity, mutagenicity, other eco-toxicological effects such as ototoxicity and hematological toxicity, and human health hazards of exposure to PECs. Particular attention has been given to the simultaneous discernment of multiple environmental risks and health effects of PECs based on mechanistic data of pharmaceuticals using advanced bioinformatic methods such as transcriptomic analysis and network pharmacology prediction. In silico software platforms and databases used in the included studies were diversified, and there is currently no standardized and accepted in silico model for ERA of PECs. Date suggested that in silico prediction of the environmental risks posed by PECs is still in its infancy. Considerable critical challenges need to be addressed, including consideration of environmental exposure concentration for PECs, interactions among mixtures of PECs and other contaminants coexisting in environments, and development of in silico models specific to ERA of PECs.

Beach showers as sources of contamination for sunscreen pollution in marine protected areas and areas of intensive beach tourism in Hawaii, USA

In 2019, sands in nearby runoff streams from public beach showers were sampled on three islands in the State of Hawaii and tested for over 18 different petrochemical UV filters. Beach sands that are directly in the plume discharge of beach showers on three of the islands of Hawaii (Maui, Oahu, Hawai'i) were found to be contaminated with a wide array of petrochemical-based UV-filters that are found in sunscreens. Sands from beach showers across all three islands had a mean concentration of 5619 ng/g of oxybenzone with the highest concentration of 34,518 ng/g of oxybenzone at a beach shower in the Waikiki area of Honolulu. Octocrylene was detected at a majority of the beach shower locations, with a mean concentration of 296.3 ng/g across 13 sampling sites with the highest concentration of 1075 ng/g at the beach shower in Waikiki. Avobenzone, octinoxate, 4-methylbenzylidene camphor and benzophenone-2 were detected, as well as breakdown products of oxybenzone, including benzophenone-1, 2,2'-dihydroxy-4-methoxybenzophenone, and 4-hydroxybenzophenone. Dioxybenzone (DHMB) presented the highest concentration in water (75.4 ng/mL), whereas octocrylene was detected in all water samples. Some of these same target analytes were detected in water samples on coral reefs that are adjacent to the beach showers. Risk assessments for both sand and water samples at a majority of the sampling sites had a Risk Quotient > 1, indicating that these chemicals could pose a serious threat to beach zones and coral reef habitats. There are almost a dozen mitigation options that could be employed to quickly reduce contaminant loads associated with discharges from these beach showers, like those currently being employed (post-study sampling and analysis) in the State of Hawaii, including banning the use of sunscreens using petrochemical-based UV filters or educating tourists before they arrive on the beach.