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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
  • 摘要:
    背景 细颗粒物（PM2.5）监测站点因受各种因素的影响，导致在某一段时间的数据缺失。这种数据缺失会影响空气质量评估和污染治理决策的制定。
    目的 提出一种基于粒子群算法（PSO）优化距离指数的反距离权重（IDW）时空插值方法，用于PM2.5时空序列缺失数据的插值补缺，提升插值补缺精度。
    方法 研究分为两部分：第一部分以长三角地区为研究区，基于2017年1月1日四个时刻的小时尺度PM2.5观测数据进行插值补缺实验；第二部分以京津冀地区为研究区，基于2017年1月1日至10日共10 d的天尺度PM2.5观测数据进行插值补缺实验。实验分别选取均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和平均相对误差（MRE）4种指标对补缺精度进行评估。
    结果 基于PSO优化距离指数的IDW时空插值方法提升了PM2.5时空序列缺失数据补缺的精度。在长三角地区的小时尺度实验中，相较于距离指数取2时，上述方法的RMSE、MAE、MAPE、MRE平均提升了0.17 μg·m−3、0.27 μg·m−3、0.17%、0.01%。基于该方法生成的4个时间段PM2.5空间场图，清晰展示了长三角地区小时尺度PM2.5浓度的时空分布特征。在京津冀地区的天尺度实验中，PSO优化的距离指数相比传统方法表现更优，插值精度提升约为0.215 μg·m−3、0.283 μg·m−3、0.174%、0.014%。此外，该方法生成的四个季节的PM2.5空间场图展现了京津冀地区在不同季节的PM2.5浓度时空分布特征，进一步验证了该方法的有效性和适用性。
    结论 基于PSO优化的IDW时空插值补缺方法在长三角和京津冀地区的PM2.5时空序列缺失数据应用中展现出较优的插值精度和可靠性，为大气污染防治和公众健康保护提供了重要参考。

     

    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 assessment 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 spatiotemporal 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 region. 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 spatiotemporal 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 illustrated 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.

     

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