Yeongkwon Son is an associate professor in the Department of Biostatistics, Epidemiology and Environmental Health at the University of Nevada, Reno School of Public Health. His research focuses on environmental health and exposure science, with the goal of understanding how environmental contaminants and complex chemical mixtures contribute to human exposure and health outcomes.
His laboratory develops and applies analytical methods to measure contaminants in environmental and biological samples. His current research includes biomonitoring and chemical analysis of emerging contaminants, including per- and polyfluoroalkyl substances, commonly known as PFAS, flame retardants and microplastics. He also studies airborne pollutants, including wildfire smoke and household air pollutants, using mass spectrometry to improve exposure assessments.
Son’s research also integrates environmental monitoring, low-cost sensor networks, satellite remote sensing and machine learning and artificial intelligence to characterize population exposure to environmental hazards. Current applications include wildfire smoke, extreme heat and air pollution, with an emphasis on improving exposure estimates and supporting environmental health research.
Yeongkwon Son is an associate professor in the Department of Biostatistics, Epidemiology and Environmental Health at the University of Nevada, Reno School of Public Health. His research focuses on environmental health and exposure science, with the goal of understanding how environmental contaminants and complex chemical mixtures contribute to human exposure and health outcomes.
His laboratory develops and applies analytical methods to measure contaminants in environmental and biological samples. His current research includes biomonitoring and chemical analysis of emerging contaminants, including per- and polyfluoroalkyl substances, commonly known as PFAS, flame retardants and microplastics. He also studies airborne pollutants, including wildfire smoke and household air pollutants, using mass spectrometry to improve exposure assessments.
Son’s research also integrates environmental monitoring, low-cost sensor networks, satellite remote sensing and machine learning and artificial intelligence to characterize population exposure to environmental hazards. Current applications include wildfire smoke, extreme heat and air pollution, with an emphasis on improving exposure estimates and supporting environmental health research.