雾霾与GPS对流层天顶延迟相关性探究

利用国际GNSS服务（International GNSS Service，IGS）提供的对流层天顶延迟（zenith path delay，ZPD）产品，研究其与雾霾的相关性，并探究了造成雾霾的“元凶”——悬浮颗粒物与气压、温度和湿度的变化关系。首先，研究了中国境内4个IGS站30 d的日平均ZPD与量化评定雾霾的空气质量指数（air quality index，AQI）的变化趋势，发现二者基本同步增大（减小）。内陆3个站点的相关系数绝对值均大于0.5，说明ZPD与表征雾霾的AQI有着较强的相关关系，雾霾对对流层延迟产生影响。其次，对1 h采样率的北京房山空气质量分指数（individual air quality index，IAQI）与ZPD进行分析，二者的变化趋势基本一致，其中PM2.5、PM10、AQI与ZPD的相关系数分别为0.504 2、0.539 1和0.555 4。同时，当AQI达到300以上重度污染时，会对ZPD产生5 cm以上差值的显著影响。最后，利用IGS的M文件探究了北京房山各IAQI与气压、温度、湿度24 h变化，一天中IAQI、气压、湿度均呈“U”变化趋势，而温度则呈现倒“U”变化，说明雾霾的形成与气压、温度、湿度相关，并利用逐步线性回归给出了概略模型。

Correlation of the Haze and GPS Troposphere Zenith Path Delay

The main part of the tropospheric delay in satellite navigation and positioning is caused by the refraction inherent in the composition of the atmosphere and changes in atmospheric composition, due to haze, an air polluting condition that places enormous impact on production and life. There must be some degree of correlation between these two factors. By using the zenith path delay (ZPD) production supplied by the IGS, we study the correlation between haze and probe suspended particulate matter, the "culprit" of haze, and variation in air pressure, temperature, and humidity. A study of 30-day daily-average ZPDand air quality index (AQI) and quantitative assessment of haze trends showed at four IGS stations in China that both synchronously increased and decreased. The correlation coefficient of three inland stations was greater than the absolute value of 0.5, indicating ZPD and AQI, the characterization of haze, were strongly correlated and that haze has an impact on tropospheric delay. Trends individual air quality index (IAQI) and ZPD at Beijing Fangshan (BJFS) with a one-hour sampling rate were analyzed, both changed basically the same way.The correlation coefficients for PM2.5, PM10, AQI and ZPD were 0.504 2, 0.5391 and 0.558 3. Meanwhile, the AQI, when it reached 300 or more, had a significant impact on ZPD with more than 5cm bias. M-file of IGS is exploited to probe the changing tendencies of each IAQI with the air pressure, temperature and humidity over a 24-hour period. It shows that IAQI, air pressure, and humidity have a "U" trend, and the temperature has inverted "U" change in one day. This suggests that the air pressure, temperature, and humidity make great contribution to the formation of haze, a schematic model is given using stepwise linear regression.