Analysis of the correlation between GNSS PWV and atmospheric particulate matter during dust storms based on PWV difference method
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摘要: 针对2021年3月15日中国北方发生的沙尘暴事件，提出了一种基于大气可降水量差值的方法，旨在探究GNSS站点反演的大气可降水量与大气颗粒物浓度之间的相关性. 选取了位于宁夏中卫(NXZW)、北京房山(BJFS)和吉林长春(CHAN)的3个GNSS站点及附近的大气颗粒物浓度数据进行分析. 结果显示，在非沙尘暴条件下，GNSS解算的大气可降水量(precipitable water vapor，PWV)精度表现良好，其与ERA5模型的PWV的差值均值和标准差均约在2 mm，证明了解算结果的可靠性. 沙尘暴发生前，各站点PWV与大气颗粒物浓度的相关性均低于20%，表现出较弱的相关性. 在沙尘暴期间，该相关性显著提高，尤其在BJFS和CHAN站点，PWV与大气颗粒物浓度的相关性超过60%. 相位滞后消除后，NXZW站点的相关性更是达到70.25%. 进一步分析还发现，沙尘暴发生时，PWV差值与大气颗粒物浓度的相关性也显著提高，其中BJFS和CHAN站点的相关性超过70%. 综合分析表明，沙尘暴发生时，PWV差值与大气颗粒物浓度的相关性进一步增高，这表明大气颗粒物对PWV差值的贡献比对PWV本身的贡献显著增加，从而说明了PWV差值方法在大气颗粒物浓度监测方面的潜在应用价值. 因此，本研究提供了一种新的研究思路和方法，为大气颗粒物浓度和气象条件之间复杂交互关系的进一步研究奠定了基础.
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关键词:
- 大气可降水量(PWV)差值 /
- 全球卫星导航系统(GNSS) /
- 大气颗粒物浓度 /
- 沙尘暴 /
- 相关性
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+PM2.5) 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. -
表 1 GNSS数据解算精度
mm 指标 N E U GNSS PWV 与ERA5 PWV
差值最大值 16.1 12.2 20.6 7.2 8.70 最小值 −15.2 −13.8 −18.1 −6.9 −5.20 平均值 1.1 1.5 5.1 1.5 2.19 标准差 3.3 3.8 7.5 1.8 2.10 
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表 2 沙尘暴不同时期GNSS PWV与SUM_PM之间的相关性
时期 测站 相关性/% P值 沙尘暴前 BJFS 8.24 9.24×10−4 CHAN 12.36 1.32×10−3 NXZW 15.98 5.36×10−3 沙尘暴时 BJFS 68.25 3.32×10−4 CHAN 64.58 5.74×10−3 NXZW 36.29 7.25×10−3 沙尘暴后 BJFS 24.35 6.35×10−4 CHAN 5.01 9.31×10−5 NXZW 20.58 7.12×10−3
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表 3 沙尘暴不同时期ΔGNSS PWV与SUM_PM之间的相关性
时期 测站 相关性/% P值 沙尘暴前 BJFS 7.24 9.45×10−4 CHAN 19.06 1.67×10−5 NXZW 16.01 5.24×10−3 沙尘暴时 BJFS 72.57 3.84×10−4 CHAN 70.68 5.27×10−4 NXZW 38.09 7.75×10−3 沙尘暴后 BJFS 20.15 6.72×10−3 CHAN 6.14 9.27×10−4 NXZW 19.31 7.13×10−5
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