Conventional and nano-copper pesticides are equally toxic to the estuarine amphipod Leptocheirus plumulosus.

Modern nano-engineered pesticides have great promise for agriculture due to their extended, low dose release profiles that are intended to increase effectiveness but reduce environmental harm. Whether nanopesticides, including copper (Cu) formulations, cause reduced levels of toxicity to non-target aquatic organisms is unclear but important to assess. Predicting how aquatic species respond to incidental exposure to Cu-based nanopesticides is challenging because of the expected very low concentrations in the environment, and the two forms of exposure that may occur, namely to Cu ions and Cu nanoparticles. We conducted Cu speciation, tissue uptake, and 7-day toxicity laboratory experiments to test how a model estuarine organism, the amphipod Leptocheirus plumulosus, responded to two popular Cu-based nanopesticides, CuPRO and Kocide, and conventional CuCl. Exposure concentrations ranged from 0 to 2.5 ppm, which were similar to those found in estuarine water located downstream of agricultural fields. Cu dissolution rates were much slower for the nanopesticides than the ionic formula, and Cu body burden in amphipods increased approximately linearly with the nominal exposure concentration. Amphipod survival declined in a normal dose-response manner with no difference among Cu formulations. Growth and movement rates after 7 days revealed no difference among exposure levels when analyzed with conventional statistical methods. By contrast, analysis of respiration rates, inferred from biomass measurements, with a bioenergetic toxicodynamic model indicated potential for population-level effects of exposure to very low-levels of the two nanopesticides, as well as the control contaminant CuCl. Our results indicate that toxicity assessment of environmental trace pollutant concentrations may go undetected with traditional ecotoxicological tests. We present a process integrating toxicity test results and toxicodynamic modeling that can improve our capacity to detect and predict environmental impacts of very low levels of nanomaterials released into the environment.