Within　the　context　of　PM2.5　concentrations　decreasing　annually,　ozone　concentrations　have　increased　instead　of　decreased,　and　ozone　has　become　one　of　the　main　pollutants　in　the　warm　season　in　China.　Based　on　the　idea　of　big　data　association　analysis,　the　extreme　gradient　boosting　(XGBoost)　ozone　concentration　estimation　model　was　constructed　and　developed　to　estimate　the　maximum　daily　8　h　average　ozone　concentration　(O3_8h)　in　China　in　2019　for　human　exposure　assessment.　The　model　input　ground　monitoring　station　data,　high-resolution　remote-sensing　satellite　data,　meteorological　data,　emission　inventory　data,　digital　elevation　model　(DEM)　data,　and　population　data　were　used　to　capture　the　temporal　and　spatial　variation　of　O3_8h.　In　this　study,　ten-fold　cross-validation　was　used　to　evaluate　the　estimation　performance　of　the　model　(R2=0.871,　RMSE=11.7　μg•m-3).　Compared　to　those　with　the　random　forest　(RF)　model　and　kernel　ridge　regression　(KRR)　model,　due　to　the　improvement　in　the　algorithm　itself　and　the　advancement　of　parallel　processing,　the　estimation　results　of　the　XGBoost　model　showed　higher　accuracy　(RF:　R2=0.864,　RMSE=12.387　μg•m-3).　The　KRR　model　was　as　follows:　R2=0.582,　RMSE=23.1　μg•m-3,　and　the　computational　efficiency　of　the　model　was　significantly　improved.　At　the　same　time,　the　level　of　ozone　exposure　and　the　relative　risk　of　death　due　to　chronic　obstructive　pulmonary　disease　(COPD)　in　China＇s　provinces　and　cities　were　evaluated.　The　results　showed　that　the　top　five　number　of　days　exceeding　the　standard　occurred　in　Shandong　Province,　Henan　Province,　Hebei　Province,　Anhui　Province,　and　the　Ningxia　Hui　Autonomous　Region.　In　terms　of　exposure　intensity,　Hebei　Province,　Shandong　Province,　Shanxi　Province,　Tianjin　City,　and　Jiangsu　Province　ranked　the　top　five　in　terms　of　population　weighted　ozone　concentration.　In　terms　of　health　effects,　the　relative　risk　of　COPD　death　showed　seasonal　changes,　with　the　highest　in　summer　and　the　lowest　in　winter.　©　2022,　Science　Press.　All　right　reserved.