Researchers found increased concentrations of air pollutants downwind from oil and gas wells in California, likely affecting millions of Californians who live near them.
A bill under debate in Congress would pave the way to verifying and paying for farms’ carbon savings. Stanford scientists explore this and other opportunities for growing climate change solutions on U.S. farms.
A sweeping analysis of marine fossils from most of the past half-billion years shows the usual rules of body size evolution change during mass extinctions and their recoveries. The discovery is an early step toward predicting how evolution will play out on the other side of the current extinction crisis.
A new Stanford University study shows rising oxygen levels may explain why global extinction rates slowed down over the past 541 million years. Below 40 percent of present atmospheric oxygen, ocean dead zones rapidly expand, and extinctions ramp up.
Analyses lay out a blueprint for speeding development of atmospheric removal and modeling how the approach could improve human health and have an outsized effect on reducing future peak temperatures.
A deep learning approach to classifying buildings with wildfire damage may help responders focus their recovery efforts and offer more immediate information to displaced residents.
The most devastating tornadoes are often preceded by a cloudy plume of ice and water vapor billowing above a severe thunderstorm. New research reveals the mechanism for these plumes could be tied to “hydraulic jumps” – a phenomenon Leonardo Da Vinci observed more than 500 years ago.
Smoke from wildfires may have contributed to thousands of additional premature births in California between 2007 and 2012. The findings underscore the value of reducing the risk of big, extreme wildfires and suggest pregnant people should avoid very smoky air.
Stanford Associate Professor Paula Welander and her student Marisa Mayer discuss how microscopic traces of early life – called microbial lipid biomarkers – could help demystify the origins of life and life beyond Earth.
A new machine learning approach helps scientists understand why extreme precipitation days in the Midwest are becoming more frequent. It could also help scientists better predict how these and other extreme weather events will change in the future.
