Abstract: Space-time trends of PM<sub>2.5 </sub>constituents in the United States estimated by machine learning models, 2005-2015 (AGU Fall Meeting 2018)

Space-time trends of PM_2.5constituents in the United States estimated by machine learning models, 2005-2015

Yang Liu, Emory University, Gangarosa Department of Environmental Health, Atlanta, GA, United States, Xia Meng, Emory University, Department of Environmental Health, Rollins School of Public Health, Atlanta, GA, United States, Jenny L Hand, Colorado State Univ, Fort Collins, CO, United States and Bret A Schichtel, National Park Service, Air Resources Division, Denver, CO, United States

Back to: Airborne Particulate Matter: Linking Sources and Composition to Specific Health Effects II Posters

Space-time trends of PM2.5 constituents in the United States estimated by machine learning models, 2005-2015

Space-time trends of PM_2.5constituents in the United States estimated by machine learning models, 2005-2015