Risk assessment of Arctic aquatic species using ecotoxicological biomarkers and Bayesian network

Human health risk model for microplastic exposure in the Arctic region

Microplastics enriched with carcinogens like heavy metals, polycyclic aromatic hydrocarbons (PAHs), and their derivatives are ubiquitous in Arctic waters. They contaminate the local land and sea-based food sources, which is a significant health hazard. It is thus imperative to assess the risk posed by them to the nearby communities, which primarily rely on locally available food sources to meet their energy requirements. This paper proposes a novel ecotoxicity model to assess the human health risk posed by microplastics. The region's geophysical and environmental conditions affecting human microplastic intake, along with the human physiological parameters influencing biotransformation, are incorporated into the developed causation model. It investigates the carcinogenic risk associated with microplastic intake in humans via ingestion in terms of incremental excess lifetime cancer risk (IELCR). The model first evaluates microplastic intake and then uses reactive metabolites produced due to the interaction of microplastics with xenobiotic metabolizing enzymes to assess cellular mutations that result in cancer. All these conditions are mapped in an Object-Oriented Bayesian Network (OOBN) framework to evaluate IELCR. The study will provide a vital tool for formulating better risk management strategies and policies in the Arctic region, especially concerning Arctic Indigenous peoples.

Assessing metal(loid)s concentrations and biomarkers in tilapia (Oreochromis niloticus) and largemouth bass (Micropterus salmoides) of three ecosystems of the Yaqui River Basin, Mexico

Aquatic ecosystems have been suffering deleterious effects due to the development of different economic activities. Metal(loid)s are one of the most persistent chemicals in environmental reservoirs, and may produce adverse effects on different organisms. Since fishes have been largely used in studies of metal(loid)s exposure, tilapia and largemouth bass were collected in three ecosystems from the Yaqui River Basin to measure the concentrations of metal(loid)s (chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), zinc (Zn) arsenic (As), mercury (Hg), and selenium (Se)) and some biomarkers (somatic indices, metallothionein expression and histopathological analysis) in tissues of both species. Metal(loid) concentrations varied seasonally among ecosystems in tissues of both species. The elements varied seasonally and spatially in tissues of both species, with a general distribution of liver > gills > gonads. Also, biomarkers showed variations indicative that the fish species were exposed to different environmental stressor conditions. The highest values of some biomarkers were in largemouth bass, possibly due to differences in their biological characteristics, mainly feeding habits. The multivariate analysis showed positive associations between metal(loid)s and biomarkers, which are usually associated to the use of these elements in metabolic and/or regulatory physiological processes. Both fish species presented histological damage at different levels, from SI types (changes that are reversible for organ structure) to SII types (changes that are more severe but may be repairable). Taken together, the results from this study suggest that the Yaqui River Basin is moderately impacted by metals and metalloids.

An ecotoxicological risk model for the microplastics in arctic waters

The risk posed to Arctic marine life by microplastics, a Contaminants of Emerging Arctic Concern (CEAC), is poorly known. The reason is the limited understanding of the dose-response relationship due to the region's peculiar environmental and geophysical properties and the unique physiological properties of the species living there. The properties of microplastics in the region and their distribution across the oceanic profile further complicate the problem. This paper addresses the knowledge gap by proposing a novel comprehensive ecotoxicity model. The model uses oxidative stress caused by the Reactive Oxygen Species (ROS) to assess cell mortality. Cell mortality has been used as an indicator of ecological risk. The model is implemented in the Bayesian Network (BN) framework to evaluate the cytotoxicity, measured as the probability of causing mortality. The work enhances the understanding and assessment of the cytotoxicity of microplastics in polar cod and associated risks.

Anaerobic-petroleum degrading bacteria: Diversity and biotechnological applications for improving coastal soil

Due to the industrial emissions and accidental spills, the critical material for modern industrial society petroleum pollution causes severe ecological damage. The prosperous oil exploitation and transportation causes the recalcitrant, hazardous, and carcinogenic sludge widespread in the coastal wetlands. The costly physicochemical-based remediation remains the secondary and inadequate treatment for the derivatives along with the tailings. Anaerobic microbial petroleum degrading biotechnology has received extensive attention for its cost acceptable, eco-friendly, and fewer health hazards. As a result of the advances in biotechnology and microbiology, the anaerobic oil-degrading bacteria have been well developing to achieve the same remediation effects with lower operating costs. This review summarizes the advantages and potential scenarios of the anaerobic degrading bacteria, such as sulfate-reducing bacteria, denitrifying bacteria, and metal-reducing bacteria in the coastal area decomposing the alkanes, alkenes, aromatic hydrocarbons, polycyclic aromatic, and related derivatives. In the future, a complete theoretical basis of microbiological biotechnology, molecular biology, and electrochemistry is necessary to make efficient and environmental-friendly use of anaerobic degradation bacteria to mineralize oil sludge organic wastes.

A food chain-based ecological risk assessment model for oil spills in the Arctic environment

This paper investigates the linkage between the acute impacts on apex marine mammals with polar cod responses to an oil spill. It proposes a Bayesian network-based model to link these direct and indirect effects on the apex marine mammals. The model predicts a recruitment collapse (for the scenarios considered), causing a higher risk of mortality of polar bears, beluga whales, and Narwhals in the Arctic region. Whales (adult and calves) were predicted to be at higher risk when the spill was under thick ice, while adult polar bears were at higher risk when the spill occurred on thin ice. A spill over the thick ice caused the least risk to whale and adult polar bears. The spill's timing and location have a significant impact on the animals in the Arctic region due to its unique sea ice dynamics, simple food web, and short periods of food abundance.