Abstract: In response to the dust storm event that occurred in northern China on March 15, 2021, a method based on the precipitable water vapor (PWV) difference (ΔGNSS PWV) is proposed to investigate the correlation between PWV retrieved from Global Navigation Satellite System (GNSS) stations and the concentration of atmospheric particulate matter (PM). Three GNSS stations located in Zhongwei, Ningxia (NXZW), Fangshan, Beijing (BJFS), and Changchun, Jilin (CHAN), along with nearby atmospheric particulate matter concentration data, were selected for analysis. The results indicate that under non-dust storm conditions, the PWV accuracy derived from GNSS calculations is satisfactory, with mean and standard deviation differences from ERA5_PWV both around 2 mm, demonstrating the reliability of the retrieval results. Prior to the occurrence of the dust storm, the correlation between PWV at each station and atmospheric particulate matter concentration is less than 20%, indicating a weak correlation. During the dust storm event, this correlation significantly increases, particularly at BJFS and CHAN stations, where the correlation between PWV and atmospheric particulate matter concentration exceeds 60%. After eliminating phase lag, the correlation at the NXZW station even reaches 70.25%. Further analysis reveals that during the dust storm occurrence, the correlation between ΔGNSS PWV and SUM_PM (PM₁₀+PM_2.5) also significantly increases, with correlations exceeding 70% at BJFS and CHAN stations. Comprehensive analysis suggests that during dust storms, the correlation between ΔGNSS PWV and SUM_PM further intensifies, indicating that atmospheric particulate matter contributes significantly more to ΔGNSS PWV than to PWV, highlighting the potential application value of the PWV difference method in monitoring atmospheric particulate matter concentration. Therefore, this study provides a novel research approach and method, laying the foundation for further exploration of the complex interplay between atmospheric particulate matter concentration and meteorological conditions.