环境与职业医学2025，Vol.42Issue(2)：171-178,8.DOI:10.11836/JEOM24236
PM2.5时空序列缺失数据的反距离权重插值方法补缺研究
Inverse distance weight interpolation method for missing data of PM2.5 spatiotemporal series
摘要
Abstract
[Background]Fine particulate matter(PM2.5)monitoring stations may generate missing data for a certain period of time due to various factors.This data loss will adversely affect air quality as-sessment and pollution control decision-making. [Objective]To propose an inverse distance weighted(IDW)spatiotemporal interpolation method based on particle swarm optimization(PSO)to interpolate and fill missing PM2.5 spa-tiotemporal sequence data and increase interpolation accuracy. [Methods]An interpolation experiment was designed into two parts.The first part used hourly PM2.5 observational data from four moments on January 1,2017 in the Yangtze River Delta re-gion.The second part employed daily PM2.5 observational data from the first 10 d of January 2017 in the Beijing-Tianjin-Hebei region.Interpolation accuracy was evaluated using four metrics:root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and mean relative error(MRE). [Results]IDW spatiotemporal interpolation method optimized with PSO significantly improved the accuracy of filling missing PM2.5 spa-tiotemporal sequence data.In the hourly-scale experiment conducted in the Yangtze River Delta region,compared to a distance index of 2,the accuracy metrics RMSE,MAE,MAPE,and MRE generated by the proposed method improved on average by 0.17 μg·m-3,0.27 μg·m-3,0.17%,and 0.01%,respectively.The PM2.5 spatial field maps generated for four moments based on this method clearly illus-trated the spatiotemporal distribution characteristics of hourly PM2.5 concentrations in the Yangtze River Delta region.In the daily-scale experiment conducted in the Beijing-Tianjin-Hebei region,the PSO-optimized distance index outperformed the traditional method,with interpolation accuracy improvements of approximately 0.215 μg·m-3,0.283 μg·m-3,0.174%,and 0.014%,respectively.Furthermore,the seasonal PM2.5 spatial field maps generated by this method revealed the spatiotemporal distribution characteristics of PM2.5 concentrations in the Beijing-Tianjin-Hebei region across different seasons,further validating the effectiveness and applicability of this method. [Conclusion]The IDW spatiotemporal interpolation method optimized with PSO is highly accurate and reliable for interpolating the missing data in the Yangtze River Delta region and the Beijing-Tianjin-Hebei region,providing valuable insights for air pollution control and public health protection.关键词
细颗粒物/时空序列/插值/反距离权重/粒子群算法Key words
fine particulate matter/spatiotemporal series/interpolation/inverse distance weighted/particle swarm optimization分类
预防医学引用本文复制引用
梁玉柔,伍红玲,王伟鹏,程峰,段平..PM2.5时空序列缺失数据的反距离权重插值方法补缺研究[J].环境与职业医学,2025,42(2):171-178,8.基金项目
国家自然科学基金项目(41901336) （41901336）
云南省基础研究计划项目(202101AT070078) （202101AT070078）
云南省高层次人才培养支持计划项目(YNWR-QNBJ-2020-103) （YNWR-QNBJ-2020-103）
