The LA Fires Spewed Out Toxic Nanoparticles. Nicholas Spada is one of the only scientists in the world using a nuclear x-ray process to study deadly nanoparticles in wildfire smoke. What he's uncovered in California is a nightmare. Nicholas Spada was used to fielding urgent requests when wildfire smoke blanketed cities. Winter was supposed to be the quiet period when wildfires die down and researchers like Spada perform instrument maintenance, write grant proposals and go home for dinner. Instead, 2025's so-called offseason ignited January 7, when the Santa Ana winds came howling through Los Angeles, bringing gusts upwards of 100 miles per hour, after more than eight months without meaningful rainfall. By nightfall, thousands of homes in Los Angeles' swanky Pacific Palisades neighborhood and the Altadena community north of the city were gone. The next morning, Spada was fielding call after call at the University of California, Davis, from fellow air researchers at universities across the country who were packing instruments and other gear and heading for Los Angeles, many on their own dime. They would be studying urban fires--not normal wildfires or even urban-wildland interface fires--but urban fires in which most of the fuel was manmade: lawn chemicals, asbestos insulation, lead paint, lithium batteries. They asked Spada which instruments to bring, what measurements to take, where to set up downwind and when he would be there. The calls quickly morphed into a WhatsApp group that's still going strong, as results continue to roll in sporadically all these months later. Spada, a trim, energetic man with a close-trimmed beard and reddish hair, is a project scientist at UC Davis' Air Quality Research Center. He is one of only a handful of scientists in the world proficient at using a nuclear method for detecting toxic substances in air particles to understand their impact on human health and the environment.

Mechanizing the manual harvesting of fresh market fruits constitutes one of the biggest challenges to the sustainability of the fruit industry. During manual harvesting of some fresh-market crops like strawberries and table grapes, pickers spend significant amounts of time walking to carry full trays to a collection station at the edge of the field. A step toward increasing harvest automation for such crops is to deploy harvest-aid collaborative robots (co-bots) that transport the empty and full trays, thus increasing harvest efficiency by reducing pickers' non-productive walking times. This work presents the development of a co-robotic harvest-aid system and its evaluation during commercial strawberry harvesting. At the heart of the system lies a predictive stochastic scheduling algorithm that minimizes the expected non-picking time, thus maximizing the harvest efficiency. During the evaluation experiments, the co-robots improved the mean harvesting efficiency by around 10% and reduced the mean non-productive time by 60%, when the robot-to-picker ratio was 1:3. The concepts developed in this work can be applied to robotic harvest-aids for other manually harvested crops that involve walking for crop transportation.