a b s t r a c tOne major concern with the use of antifouling paints is the release of its biocides (mainly copper and zinc) into natural waters, where they may exhibit toxicity to non-target organisms. While many studies have quantified the release of biocides from antifouling paints, very little is known about the physicochemical state of released copper. For proper risk assessment of antifouling paints, characterization of copper released into water is necessary because the physicochemical state determines the metal's environmental fate and effects. In this study, we monitored release of different fractions of copper (dissolved, nano, and bulk) from a commercial copper-based antifouling paint. Release from painted wood and aluminum mini-bars that were submerged in natural waters was monitored for 180 days. Leachates contained both dissolved and particulate copper species. X-ray diffraction and X-ray photoelectron spectroscopy were used to determine the chemical phase of particles in the leachate. The amount of copper released was strongly dependent on water salinity, painted surface, and paint drying time. The presence of nanosized Cu 2 O particles was confirmed in paint and its leachate using singleparticle inductively coupled plasma-mass spectrometry and electron microscopy. Toxicity of paint leachate to a marine phytoplankton was also evaluated.
h i g h l i g h t s ChemFate is a multi-media dynamic chemical fate and transport tool. ChemFate includes four models: organoFate, ionOFate, metalFate, nanoFate. ChemFate predicts daily chemical environmental concentrations. ChemFate considers daily meteorology, chemical release, and region characteristics.
Species Sensitivity Distribution (SSD) is a key metric for understanding the potential ecotoxicological impacts of chemicals. However, SSDs have been developed to estimate for only handful of chemicals due to the scarcity of experimental toxicity data. Here we present a novel approach to expand the chemical coverage of SSDs using Artificial Neural Network (ANN). We collected over 2000 experimental toxicity data in Lethal Concentration 50 (LC50) for 8 aquatic species and trained an ANN model for each of the 8 aquatic species based on molecular structure. The R2 values of resulting ANN models range from 0.54 to 0.75 (median R2 = 0.69). We applied the predicted LC50 values to fit SSD curves using bootstrapping method, generating SSDs for 8424 chemicals in the ToX21 database. The dataset is expected to serve as a screening-level reference SSD database for understanding potential ecotoxicological impacts of chemicals.
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