Terrestrial oil spill mapping using satellite earth observation and machine learning: A case study in South Sudan

The cost of (Un)regulation: Shrinking Earth's orbits and the need for sustainable space governance

Outer space is infinite, useable planetary orbits are not. This makes the Earth's orbit a unique case of an Area Beyond National Jurisdiction (ABNJ) complex to address, difficult to use in a sustainable and equitable way and almost intractable to regulate at an international level. As of 2023, we remain far from attaining a sustainable orbital environment, and future uses of the Earth's orbits for new satellites constellations appear now increasingly at risk. Adopting a probability-based empirical model to project the growth trajectory of objects in space, this article argues that the sector will cross a 'critical density' threshold within the upcoming years unless strong remedial actions to clear up the orbits are implemented and estimates the potential costs of active debris removal measures. Our findings suggest that orbital sustainability is unlikely to come from technology alone, no matter how advanced or ground-breaking. A long-term solution will necessarily require a radical rewriting of the outdated, often conflicting international regulatory framework, which contributed to creating this debris crisis in the first place, shrinking the Earth's orbit to (almost) the point of no return.

Tracking the behavior of an accidental oil spill and its impacts on the marine environment in the Eastern Mediterranean

The eastern Mediterranean region is a vital hub for oil transportation and production because of its strategic location between Europe, Asia, and Africa. But its unique attributes, including narrow shipping routes, heavy marine traffic, and proximity to vulnerable ecosystems, render it particularly susceptible to accidental oil spills. In this research, an oil spill detection model, along with bathymetric and oceanographic parameters, was used to track oil spills that occurred at the Syrian Baniyas Station in the Eastern Mediterranean on August 23, 2021. Furthermore, the study employed a pairwise comparison matrix (PWCM) to assess the relative importance of wind speed and direction, water depth, and sea surface temperature (SST) in the dispersion of oil spills. Analysis of Sentinel-1 data obtained prior to, during, and after the incident revealed the accumulation of oil slicks along the Syrian coast from Baniyas to Latakia for up to twenty days. The spilled oil reached the coast of Cyprus six days after the incident. The study determined that wind speed and direction played a critical role in the dispersion of spilled oil, while water depth and SST were comparatively less significant factors in this process. The overall accuracy (OA) and Kappa coefficient (KC) for land, water, and oil slick classes derived from the random forest (RF) algorithm ranged from 90 % to 98 % and from 0.86 to 0.98, respectively. The spread of oil slicks at the incident location was revealed by the decorrelation stretch and band ratios of Sentinel-2 MultiSpectral Instrument (MSI) data. The accidental oil spill could have negative effects on the organic carbon cycle, chlorophyll production, and ecosystem productivity. It is essential to consider the vulnerability of specific regions in the Eastern Mediterranean to oil spills when developing adaptation strategies.

Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives

Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in the early stages, with knowledge gaps, technical bottlenecks in data quality, high-dimensional/heterogeneous/small-sample data analysis and model interpretability, and a lack of an in-depth understanding of environmental toxicology. Given the above problems, we review the recent progress in the literature and highlight state-of-the-art toxicological studies using ML (such as learning and predicting toxicity in complicated biosystems and multiple-factor environmental scenarios of long-term and large-scale pollution). Beyond predicting simple biological endpoints by integrating untargeted omics and adverse outcome pathways, ML development should focus on revealing toxicological mechanisms. The integration of data-driven ML with other methods (e.g., omics analysis and adverse outcome pathway frameworks) endows ML with widely promising application in revealing toxicological mechanisms. High-quality databases and interpretable algorithms are urgently needed for toxicology and environmental science. Addressing the core issues and future challenges for ML in this review may narrow the knowledge gap between environmental toxicity and computational science and facilitate the control of environmental risk in the future.