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