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Benchmarking Spatial Interpolation Methods for Long-Term Meteorological Exposure Assessment in China: Comparing

Rui Zhang1, Yonghong Li2, Mulei Chen1

  • 1National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine), Beijing, China.

Environmental Health Insights
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

Inverse Distance Weighting (IDW) is superior to Ordinary Kriging (OK) for national-scale daily meteorological exposure assessment. IDW offers better accuracy and efficiency for environmental epidemiology studies.

Keywords:
individual exposureinverse distance weightingmeteorological variablesordinary Krigingspatial interpolation

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Area of Science:

  • Environmental Epidemiology
  • Geospatial Analysis
  • Climate Science

Background:

  • Accurate high-resolution meteorological exposure assessment is crucial for individual-level environmental epidemiology.
  • Methodological guidance on optimal spatial interpolation for daily meteorological variables at a national scale is limited.

Purpose of the Study:

  • To systematically benchmark Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) for national-scale daily meteorological interpolation.
  • To provide practical methodological guidance for large-scale meteorological exposure modeling in climate-health research.

Main Methods:

  • Utilized daily meteorological observations from 2417 stations in mainland China (2010-2021).
  • Benchmarked IDW and OK using 12 representative days and 10-fold cross-validation.
  • Evaluated performance using RMSE, sMAPE, NSE, bias, and computation time.

Main Results:

  • IDW consistently outperformed OK in national-scale cross-validation across 12 representative days.
  • IDW demonstrated lower prediction errors and higher Nash-Sutcliffe efficiency (NSE) for daily mean temperature and relative humidity compared to OK.
  • IDW exhibited modest computational advantages over OK, with average processing times of approximately 96s/day versus 99s/day for OK.

Conclusions:

  • IDW offers a favorable balance of accuracy, computational efficiency, and spatial variability preservation for epidemiological exposure assessment.
  • The findings support IDW as a suitable method for large-scale meteorological exposure reconstruction in environmental epidemiology.
  • This study provides practical methodological guidance for national-scale climate-health research.