THE APPLICATION OF MULTIPLE IMPUTATION METHOD BASED ON HYBRID MULTI-STRATEGY IN HANDLING MISSING AIR QUALITY MONITORING DATA

Air quality monitoring data is a crucial basis for assessing air pollution levels and formulating control measures. However, missing data is a prevalent issue due to instrument malfunctions, human factors, and other reasons, significantly compromising data integrity and usability. To address this problem, this study collected nearly 1 million air quality monitoring records from 12 monitoring stations between 2015 and 2023, summarizing and analyzing the mechanisms and characteristics of missing data in such datasets. Data imputation experiments were conducted using R. Through missing mechanism control and imputation experimental design strategies, the imputation performance of algorithms was evaluated under the criteria of MAE, RMSE, and WMAPE based on completely random missingness. Specifically, data imputation experiments under different missing scenarios were repeated N times, and the mean values were used to evaluate four multiple imputation algorithms, with 95% confidence intervals provided. The experimental results show that: (1) the hybrid multi-strategy imputation method MNPRF demonstrates significant advantages across all datasets, with the smallest confidence limits and interval widths; (2) this method not only inherits the strengths of parent algorithms, substantially improving data quality, but also mitigates the weaknesses of the original algorithms to some extent.