* Michigan’s Department of Environmental Quality (DEQ) had good news last week regarding the State’s 2018 state-wide sampling of public, school, and tribal water supplies for per- and polyfluoroalkyl substances (PFAS). This was the first such study in the nation, and it was extensive, including 1,114 public water systems, 461 schools that operate their own wells, and 17 tribal systems. Importantly, 90 percent of these supplies showed no detection for any PFAS. Very low levels, below 10 parts per trillion (ppt) were detected in 7 percent of systems. Levels between 10 and 70 ppt were detected in 3 percent. Work will continue: MI will pay for quarterly monitoring of the systems with levels above 10 ppt. In addition, the ad-hoc “Michigan PFAS Action Response Team” (MPART) will continue with a new, more formal status. In 2019, Governor Gretchen Whitmer established MPART as a permanent body within the MDEQ.
* U.S. Department of Energy announced a rather generous version of its own Green New Deal last week: up to $51.5 million for new and innovative research of technologies for trucks, off-road vehicles, and the fuels that power them. This FOA – “funding opportunity announcement” – is focused on gaseous fuels research, including natural gas, biopower, and hydrogen; heavy-duty freight electrification; hydrogen infrastructure and fuel cell technologies for heavy-duty applications; and energy efficient off-road vehicles. The FOA has five topical areas, including novel materials for high-density gas storage and transport, advanced waste to energy technologies, and technology integration that focuses on lowering costs and overcoming technical barriers to the use of medium- and heavy-duty natural gas and hydrogen-fueled vehicles. Another focus is on battery electric heavy-duty freight and technical barriers to advanced batteries, electric drive systems, and charging systems. Concept papers are due to DOE by March 29; full applications by May 15.
* There is a fascinating story out of “9to5Google” about how two Alphabet divisions are working together to “train a neural network on weather forecasts and historical turbine data.” Scientists there then use the DeepMind system to “predict wind power output 36 hours ahead of actual generation.” Variability with wind and solar electric generation is a critical weakness. Civilization doesn’t run on electricity, it runs on electrical systems, with dependability and timeliness two of the most critical factors. If you know when the wind is going to blow you can plan on using it. On the other hand, if you know wind won’t be there, 36 hours is plenty of lead time to make other, non-panicky arrangements. Google’s report states that its algorithm is still being refined, but Google notes how machine learning — compared to no time-based commitments to the grid — has “boosted the value of our wind energy by roughly 20 percent.” The company is applying this optimization to its wind farms in the central United States that generate 700 megawatts of wind power.