【摘要】 目的 本研究旨在利用R代码，通过案例分析实现将气象因素站点数据插值为栅格数据，为气象因素个体暴露水平及其健康风险评估提供数据整理的方法。方法 收集整理2010年1月1日至2021年12月31日，中国2 419个国家级地面气象站的逐日气象数据序列。借助R代码应用反距离加权法(inverse distance weighting, IDW)进行空间插值。结果 当插值算法参数p取2、N取10～15时，可获得最佳的插值精度。同时，利用furrr包进行并行运算，开启8个CPU核心可实现最优的插值效率。结论 该R代码可有效实现将气象因素站点数据插值为栅格数据。更多还原

【Abstract】 Objective To interpolate meteorological factor data into gridded data through case analysis using R script, and to provide a data collation method for individual exposure levels to meteorological factors and health risk assessment.Methods Daily meteorological data sequences were collected from 2 419 national ground-based meteorological stations in China from January 1, 2010 to December 31, 2021. The spatial interpolation was performed by inverse distance weighting with R script.Results The best interpolation precision was achieved when the interpolation algorithm parameters p was set to 2 and N was set between 10 and 15. Meanwhile, the optimal interpolation efficiency was realized by utilizing the “furrr” package to perform parallel computation with 8 CPU cores.Conclusion Meteorological factor data could be effectively interpolated into gridded data with R script.更多还原