Study Finds High Levels of Toxic Pollutants in Stranded Dolphins and Whales

Researchers examined toxins in tissue concentrations and pathology data from 83 stranded dolphins and whales from 2012 to 2018. They looked at 11 different animal species to test for 17 different substances. The study is the first to report on concentrations in blubber tissues of stranded cetaceans of atrazine, DEP, NPE and triclosan. It also is the first to report concentrations of toxicants in a white-beaked dolphin and in Gervais’ beaked whales.

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Foundation donates $1 million to UCI’s Henry Samueli School of Engineering

Irvine, Calif., June 24, 2020 – The Lincoln Dynamic Foundation, created by University of California, Irvine alumnus John D. Lincoln, has made a $1 million gift to the university’s Henry Samueli School of Engineering to establish the World Institute for Sustainable Development of Materials. The new institute will advance interdisciplinary research, education and knowledge translation in an effort to innovate, evaluate and adopt technologies that utilize safer, nontoxic chemicals and materials, with the goal of mitigating environmental impacts.

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Study: Air pollution from fracking linked to deaths in Pennsylvania

Approximately 20 people in Pennsylvania lost their lives during a seven-year period because of particulate matter pollution emitted by shale gas wells, according to a recent study including faculty at Binghamton University, State University of New York.

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Penn’s Center of Excellence in Environmental Toxicology (CEET) Receives $8 Million Grant from the National Institute of Environmental Health Sciences

The Center of Excellence in Environmental Toxicology (CEET) at the University of Pennsylvania received an $8 million grant, to be distributed over the next five years, from the National Institute of Environmental Health Sciences, a renewal of its P30 Environmental Health Sciences Core Center (EHSCC) grant.

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Using Big Data to Design Gas Separation Membranes

Researchers at Columbia Engineering and the University of South Carolina have developed a method that combines big data and machine learning to selectively design gas-filtering polymer membranes to reduce greenhouse gas emissions. Their study, published today in Science Advances, is the first to apply an experimentally validated machine learning method to rapidly design and develop advanced gas separation membranes.

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