A sensitive biomarker for the detection of aquatic contamination based on behavioral assays using zebrafish larvae.

An effective biological early warning system for the detection of water contamination should employ undemanding species that rapidly react to the presence of contaminants in their environment. The demonstrated reaction should be comprehensible and unambiguously evidential of the contamination event. This study utilized 96h post fertilization zebrafish larvae and tested their behavioral response to acute exposure to low concentrations of cadmium chloride (CdCl2) (5.0, 2.5, 1.25, 0.625mg/L) and permethrin (0.05, 0.029, 0.017, 0.01µg/L). We hypothesize that the number of larvae that show advanced trajectories in a group corresponds with water contamination, as the latter triggers avoidance behavior in the organisms. The proportion of advanced trajectories in the control and treated groups during the first minute of darkness was designated as a segregation parameter. It was parametrized and a threshold value was set using one CdCl2 trial and then applied to the remaining CdCl2 and permethrin replicates. For all cases, the method allowed distinguishing between the control and treated groups within two cycles of light: dark. The calculated parameter was statistically significantly different between the treated and control groups, except for the lowest CdCl2 concentration (0.625mg/L) in one replicate. This proof-of-concept study shows the potential of the proposed methodology for utilization as part of a multispecies biomonitoring system.

Global L-band equivalent AI-based vegetation optical depth dataset.

The L-band vegetation optical depth data garners significant interest for its ability to effectively monitor vegetation, thanks to minimal saturation within this frequency range. However, the existing datasets have limited temporal coverage, constrained by the start of the respective satellite missions. Global L-band equivalent AI-Based Vegetation Optical Depth or GLAB-VOD is a global long-term consistent microwave vegetation optical depth dataset created using machine learning to expand the SMAP-IB VOD dataset temporal coverage from 2015-2020 to 2002-2020. The GLAB-VOD dataset has an 18-day temporal resolution and 25 km spatial resolution on the EASE2 grid and covers 2002-2020. An auxiliary consistent daily brightness temperature product, called GLAB-TB, is developed in parallel and ensures the consistency of the VOD product across time periods with different microwave satellites. As a result of its temporal consistency, this dataset can be used to study long-term global and regional trends in vegetation biomass and utilized in any other applications where long-term consistency is necessary. The GLAB-VOD dataset shows excellent spatial correlation globally when compared with biomass (up to R = 0.92) and canopy height (R = 0.93), outperforming its target dataset, SMAP-IB VOD.

A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset.

The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data and is aimed at extrapolating SMAP-like quality SM back in time using previous satellite microwave platforms. CASM represents SM in the top soil layer, and it is defined on a global 25 km EASE-2 grid and for 2002-2020 with a 3-day temporal resolution. The seasonal cycle is removed for the neural network training to ensure its skill is targeted at predicting SM extremes. CASM comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. CASM dataset can be used to study a wide range of hydrological, carbon cycle, and energy processes since only a consistent long-term dataset allows assessing changes in water availability and water stress.