Hyung Joo Lee

Abstract. Epidemiological studies investigating the human health effects of PM2.5 are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM2.5 monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing our ability to estimate location- or subject-specific exposures to PM2.5, but some have reported poor predictive power. A new methodology was developed to calibrate a

We estimated daily ground-level PM2.5 concentrations combining Collection 6 deep blue (DB) Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) data (10 km resolution) with land use regression in California, United States, for the period 2006-2012. The Collection 6 DB method for AOD provided more reliable data retrievals over California's bright surface areas than previous data sets. Our DB AOD and PM2.5 data suggested that the PM2.5 predictability could be enhanced

Although ground measurements have contributed to revealing the association between ambient air pollution and health effects in epidemiological studies, exposure measurement errors are likely to be caused because of the sparse spatial distribution of ground monitors. In this study, we estimate daily ground NO2 concentrations in the New England region, U.S., for the period 2005-2010 using satellite remote sensing data in combination with land use regression. To estimate ground-level NO2 concentrat

This study estimated annual average ambient fine particulate matter (PM_2.5) concentrations at 1 km resolution using satellite Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD), land use parameters, and meteorology in California for the year 2016 (cross-validation <i>R</i>² = 0.73 (site-based) and 0.81 (observation-based)). Using these high-resolution PM_2.5 estimates, regionally varying urban enhancements of PM_2.5

We investigated spatial variability in aerosol optical properties, including aerosol optical depth (AOD), fine-mode fraction (FMF), and single scattering albedo (SSA), observed at 21 Aerosol Robotic Network (AERONET) sites and satellite remote sensing data in South Korea during the spring of 2012. These dense AERONET networks established in a National Aeronautics and Space Administration (NASA) field campaign enabled us to examine the spatially detailed aerosol size distribution and composition

Abstract Epidemiological studies have reported the associations of adverse health outcomes with ambient particulate matter with aerodynamic diameter ≤2.5μm (PM 2.5 ). While these studies have accumulated increasingly refined evidence on PM 2.5 -health associations, the needs for more advanced PM 2.5 exposure models have also grown. For the last two decades, PM 2.5 estimation approaches using satellite remote sensing have been developed and advanced, taking advantage of quantitative aerosol data

High Resolution Image Download MS PowerPoint Slide Research has typically estimated NO 2 concentrations over several kilometers; thus, NO 2 data at finer spatial resolution remain limited. This study used tropospheric NO 2 data from the TROPOspheric Monitoring Instrument (TROPOMI) and traffic-related land use parameters to estimate long-term average NO 2 concentrations at a spatial resolution of 500 m in South Korea from 2018 to 2022. Our satellite-land use hybrid regression model showed reasona