How simulations could help get PFAS out of soil

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There are many ways PFAS can enter the environment, all of which increase the odds of finding these chemicals in our food or water. Credit: Michigan Department of Environment, Great Lakes and Energy

Michigan State University chemists are discovering new information to help remediate “forever chemicals” by showing for the first time how they interact with soil at the molecular level.

The researchers, Narasimhan Loganathan and Angela K. Wilson in the College of Natural Science, published their findings online in the journal Environmental Science & Technology.

“Forever chemicals”—more formally known as PFAS or perfluoroalkyl and polyfluoroalkyl substances—earned the label because they don’t break down naturally. When PFAS pollute soil and water, they can enter the food system through plants, livestock and drinking water.

A Centers for Disease Control and Prevention report from 2015 estimated that PFAS is in the blood of 97% of Americans. Other, more recent studies have put that number closer to 99%.

What makes PFAS so ubiquitous is a combination of persistence and utility. More than 9,000 chemicals qualify as PFAS and they’re found in a wide range of applications, including food packaging, nonstick cookware, firefighting foams and many more. While time and nature can degrade certain components of these products—and of the waste generated in producing them—the PFAS lingers, accumulating in the environment.

Removing PFAS from soil and water, then, is important for reducing exposure to these chemicals and the harm they can cause, including thyroid disease and increased risk of some cancers.

“When you start looking at , you see a lot about removing PFAS from water, but there’s very little about PFAS in soil,” said Loganathan, a senior research associate in MSU’s Department of Chemistry.

“And some of the studies are ‘molecule blind,'” said Wilson, John A. Hannah Distinguished Professor of chemistry and a scientist with the MSU Center for PFAS Research. “That is, they’re not paying attention to the chemistry.”

Wilson and Loganathan decided to help change that by performing the first molecular-level simulations of interactions between PFAS with a soil component, kaolinite.

For the study, the duo focused on some of the most prevalent and problematic PFAS chemicals. They chose kaolinite on the soil side because it is a common soil mineral, especially in Michigan.

PFAS are a concern everywhere, but they present a unique challenge in Michigan. Michigan has an abundance of PFAS, with more than 200 known PFAS-contaminated sites. On top of that, agriculture and the Great Lakes are foundational to the state’s identity. Protecting Michigan’s land and water is a shared goal of many of the state’s communities, legislators and companies.

“Even before this work, we were going to huge meetings and talking about PFAS with people from different municipalities, farms, and more,” Wilson said. “A lot of people are looking for solutions.”

The study was inspired by a Michigan engineering firm that asked Wilson about how PFAS might spread in soil and how best to remediate the chemicals. She didn’t have the answers, but she knew Loganathan could help her start finding some.

She recruited him to join this project, supported by the National Science Foundation. The duo also had access to provided by the National Energy Research Scientific Computing Center and MSU’s Institute for Cyber-Enabled Research, or iCER.

The results of the simulations did provide some reasons for optimism with regard to remediation. For example, some of the PFAS the researchers studied that had longer carbon chains serving as their backbones congregated on the kaolinite.

“Ideally, this is what you’d want. You’d like all PFAS just to sit in a clump so you can grab it and filter it out,” Wilson said. The flipside is that the shorter-chained PFAS were less likely to clump, remaining more mobile in soil.

“The take-home message is that not all PFAS behave similarly,” Wilson said. “And not all soils behave the same with regard to PFAS.”

“The components in the soil play a big role,” Loganathan said. “The soil composition around any contaminated site is going to be critical for how far PFAS make it into the subsurface, where they can then reach groundwater.”

Although the idea of examining the myriad combinations of PFAS and soil components is imposing, the researchers have shown their computational approach is well-suited to tackling the diversity of problems inherent to PFAS pollution.

“The beauty of computational chemistry is that you can study so many different systems,” said Wilson, whose research team is also examining interactions of PFAS with proteins in the body. Her team is also studying PFAS in different fish species with support from Great Lakes Fisheries Trust and the Strategic Environmental Research and Development Program, which are state and federal organizations, respectively, that fund environmental projects. The goal, in the and biology projects, is to reveal interactions that could help protect more people from PFAS exposure.

“Such insights are going to be incredibly important for any remediation strategy,” Loganathan said.



More information:
Narasimhan Loganathan et al, Adsorption, Structure, and Dynamics of Short- and Long-Chain PFAS Molecules in Kaolinite: Molecular-Level Insights, Environmental Science & Technology (2022). DOI: 10.1021/acs.est.2c01054

Citation:
How simulations could help get PFAS out of soi

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Credit: Pixabay/CC0 Public Domain

Social media has supercharged the spread of information—and misinformation, which presents significant challenges when trying to distinguish between fact and fiction on social media platforms like Twitter.

One of the most prolific, widely shared, and highly scrutinized Twitter accounts of the past several years belonged to former U.S. President Donald Trump. In the final year of his presidency, Trump tweeted, on average, more than 33 times each day. These tweets ranged from easily verifiable statements of fact to comments that were demonstrably false.

The sheer volume of Trump’s record and its thorough analysis by fact checkers allowed a team of researchers to conduct a unique comparison of his word choices when he shared either true or .

The results of this study, published in the journal Psychological Science, show that Trump’s word choices differed in clear and predictable ways when he shared information that he knew to be factually incorrect. Van der Zee and her colleagues then used this information to create a to predict whether a single was factually correct or incorrect. Similar personalized linguistic models may eventually help detect lies in other real-world settings.

“We created a personalized language model that could predict which statements from the former president were correct and which potentially deceitful,” said Sophie van der Zee, a researcher at Erasmus University in Rotterdam and first author on the paper. “His language was so consistent that in about three quarters of the cases, our model could correctly predict if Trump’s tweets were factual or not based solely on his word use.”







A personal model of Trumpery: Linguistic deception detection in a real-world high-stakes setting. Credit: APS

For their analysis, the researchers collected two separate data sets, each containing 3 months’ worth of presidential tweets sent by the @realDonaldTrump Twitter account. The researchers then cross-referenced these data sets with a fact-checked data set of tweets from the Washington Post to determine if a tweet was correct or incorrect.

To avoid data pollution, the researchers removed all tweets that did not reflect Trump’s own language use (e.g., retweets, long quotes).

The first data set revealed large differences in language use between Trump’s factually correct and incorrect tweets. Van der Zee and her colleagues then used this information to create a model to predict whether an individual tweet was factual.

“Using this model, we could predict how truthful Trump was in three out of four tweets,” said van der Zee. “We also compared our new personalized model with other similar detection models and found it outperformed them by at least 5 percentage points.”

Given these results, the researchers speculate that their personalized model could help distinguish fact from fiction in Trump’s future communications. Similar models could also be made for other politicians who are systematically fact-checked.

“Our paper also constitutes a warning for all people sharing information online,” said van der Zee. “It was already known that information people post online can be used against them. We now show, using only publicly available data, that the words people use when sharing information online can reveal sensitive about the sender, including an indication of their trustworthiness.”



More information:
Sophie Van Der Zee et al, A Personal Model of Trumpery: Linguistic Deception Detection in a Real-World High-Stakes Setting, Psychological Science (2021). DOI: 10.1177/09567976211015941

Citation:
Trump’s Tweets: telling truth from fiction from the words he used (2022, January 27)
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