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- W4316924748 abstract "Background and Aim: Estimating ground-level ozone concentrations with high spatial resolution is crucial to assess the adverse health effects associated with exposure to ozone. Despite its importance, there is no study estimating high spatial resolution of ozone concentration in Korea. This study aims to estimate monthly average of daily maximum 8 hours (8 h) ozone at a resolution of 1 km × 1 km across Korea from 2001 to 2020. Methods: This study used an ensemble model that integrated multiple machine learning algorithms (random forest, light gradient boosting, and neural network) to estimate monthly average of daily maximum 8 h ozone at a resolution of 1 km × 1 km across the contiguous Korea. We used a generalized additive model that accounted for geographic difference to combine ozone estimates from random forest, light gradient boosting, and neural network. The three machine learning models include multiple predictors with satellite data, meteorological variables, spatially weighted ground-level air pollutants, land-use variables, reanalysis datasets for meteorological variables, and others. Results: Total number of monitoring stations for ozone was 480 during the period 2001-2020. In the total area, our ensemble model showed a 10-fold cross-validated R2 of 0.840 during the entire study period. Urban areas showed the better prediction performance (R2 of 0.842), compared to the non-urban areas (R2 of 0.764). Conclusions: This study can provide the high-resolution ozone prediction estimates with excellent performance, and our estimates can be used to estimate the more precise health impacts attributable to ozone. Keywords: Ground-level Ozone, High spatial resolution, Machine learning model, Republic of Korea" @default.
- W4316924748 created "2023-01-18" @default.
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- W4316924748 date "2022-09-18" @default.
- W4316924748 modified "2023-09-27" @default.
- W4316924748 title "Estimate High-Spatial Resolution of Ground-Level Ozone in Korea during 2001-2020 using Ensemble Model" @default.
- W4316924748 doi "https://doi.org/10.1289/isee.2022.p-0177" @default.
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