Simulation of average monthly ozone exposure concentrations in China: A temporal and spatial estimation method

Ozone has adverse effects on human health, it is necessary to obtain the refined ozone exposure concentration. At present, most of existing research based ground air quality monitoring station (MS) which gather urban area information only. It diffcult estimate in areas where MSs are scarce. By combining accurate but uneven data outdoor concentrations and unbiased coverage RS China, we can improve accuracy simulation average monthly monitor-free area. Since usually low at night, assessed during day (e.g., 10:00–18:00). We proposed a space–time geostatistical kriging interpolation composite space/time mean trend model (CSTM) predict mainland having obtained concentration map from an MS. verified results remote sensing (RS) data, while simultaneously determining distance threshold (according accuracy) hybrid map. used smoothing filter reduce influence spatial seasonal trends found cutoff separation 175 km two showed equal estimation accuracy, result was fused with through threshold. Finally, The multi-source best were In mainly gathers northern eastern regions as well part central mainland. be characterize when 24th-layer MS for combined temporal China. China explored further provide suggestions health regional economic development. • combine eliminate bias data. fuse between via them. fusion more detailed.