Inverse distance weight interpolation method for missing data of PM2.5 spatiotemporal series
- VernacularTitle:PM2.5时空序列缺失数据的反距离权重插值方法补缺研究
- Author:
Yurou LIANG
1
;
Hongling WU
2
;
Weipeng WANG
3
;
Feng CHENG
1
;
Ping DUAN
1
Author Information
- Publication Type:Selectedarticle
- Keywords: fine particulate matter; spatiotemporal series; interpolation; inverse distance weighted; particle swarm optimization
- From: Journal of Environmental and Occupational Medicine 2025;42(2):171-178
- CountryChina
- Language:Chinese
- 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.
