Satellite-based estimation of full-coverage ozone (O₃) concentration and health effect assessment across Hainan Island

Ambient ozone (O₃) poses great damage to human health, biodiversity, and climate change. Although the health risk due to O₃ pollution has been widely evaluated, the spatiotemporal variation of O₃ level and its potential health impact in a remote tropical island was less concerned. In the present study, we firstly employed an efficient machine learning model named random forest (RF) to fill the missing O₃ column amount over Hainan Island of China based on the meteorological factors and other geographical covariates. The result suggested that the RF model achieved the best performance (R² = 0.87, RMSE = 6.36 DU) among all of the filling methods. We further used an extreme gradient boosting (XGBoost) algorithm to estimate the surface O₃ level around Hainan Island on the basis of the full-coverage O₃ column amount and other predictors. The XGBoost method showed the better predictive performance (R² = 0.59, RMSE = 24.14 μg/m³) compared with other estimation models. The predicted O₃ concentration exhibited remarkably spatial difference with the highest value observed in Dongfang (77.61 ± 5.25 μg/m³), followed by that in Changjiang (72.51 ± 5.22 μg/m³), and the lowest one in Lingshui (64.16 ± 2.71 μg/m³). Based on the estimated O₃ level, we found that the annually mean nonattainment days in Dongfang reached 29.26 ± 35.20 days and about one third of days in the winter fell into the severe O₃ pollution over Hainan Island. Besides, the mean all-cause mortalities owing to severe O₃ pollution over Hainan Island were calculated to be 391 (95%CI: (212–570)) cases each year, accounting for 0.004% of all the population in Hainan Island. The statistical simulation of O₃ level at a remote island updated the traditional knowledge and aroused the public concern on island air pollution.