0000000000755905
AUTHOR
Zhou Wang
From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model
The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO(2), O(3), and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO(2) and particulate matter with aerodynamic diameters <2.5 μm by –30.1% and –17.5%, respectively, bu…
Effective field theory search for high-energy nuclear recoils using the XENON100 dark matter detector
International audience; We report on weakly interacting massive particles (WIMPs) search results in the XENON100 detector using a nonrelativistic effective field theory approach. The data from science run II (34 kg×224.6 live days) were reanalyzed, with an increased recoil energy interval compared to previous analyses, ranging from (6.6–240) keVnr. The data are found to be compatible with the background-only hypothesis. We present 90% confidence level exclusion limits on the coupling constants of WIMP-nucleon effective operators using a binned profile likelihood method. We also consider the case of inelastic WIMP scattering, where incident WIMPs may up-scatter to a higher mass state, and …