## 1 Introduction  
The function of water vapor in atmospheric circulation and climate change is significant. Many meteorological phenomena are linked to precipitable water vapor (PWV). Monitoring water vapor in real time is crucial for weather forecasting and severe weather alerts. In 1992, [PERSON] proposed, for the first time, a system using global navigation satellite system technology to retrieve atmospheric PWV (GNSS PWV) [1]. Since then, the retrieval of PWV through GNSS technology has garnered the interest of several academics [2, 3]. The advantages of GNSS technology include high accuracy, high spatial and temporal resolution, all-weather monitoring, cheap cost, etc., in comparison to the conventional methods of PWV observation (e.g., microwave radiation and radiosonde). Thus, it is frequently used for weather monitoring and forecasting [4, 5, 6, 7]. Precise point positioning (PPP) offers more computing efficiency as well as operational and implementation simplicity compared to relative positioning. Through various error correction and parameter estimates, PPP technology enables high-precision GNSS positioning technology to be obtained for any point location on Earth by using single-station receivers and precise orbit and clock error data based on combined carrying phase and pseudorange observation values [8; 9]. In recent years, with the rapid construction and development of GPS, GLONASS, BDS, Galileo, and other systems, multisystem GNSS (multi-GNSS) data processing and associated applications have become the focus of development trends in GNSS technology. On the other hand, multi-GNSS has the potential to improve GNSS positioning accuracy and dependability by supplying more readily visible satellites, an improved geometric structure of satellite constellations, and more frequencies and signals [10; 11]. [PERSON] et al. reviewed the performance, availability, modernization, and hybridization of multi-GNSS [12]. [PERSON] et al. derived the function model and stochastic model of GPS/GLONASS PPP in detail. The results of static examples show that the addition of GLONASS observations can significantly accelerate the convergence speed of positioning solutions, but the improvement in positioning accuracy is not obvious. The kinematic example shows that the positioning accuracy of the three components (east, north, and up) can be improved by more than 50% by adding GLONASS observations [13]. [PERSON] and [PERSON] investigated the GPS/GLONASS/BDS/Galileo combined PPP model, and analyzed the positioning performance of various systems, to validate the positioning performance of multi-GNSS fusion. The results demonstrated that PPP's positioning performance and reliability may benefit greatly from the incorporation of multisystem observations. The convergence speed of PPP may be greatly improved by including multi-GNSS observations in addition to single GPS PPP [14; 15]. A lack of studies on multi-GNSS PPP at low latitudes represents a major gap in the above research. Most studies focus on mid-high latitudes or address the worldwide distribution. PPP technology can estimate the absolute zenith tropospheric delay (ZTD) of a station. Delivery of real-time tropospheric products is a key consideration for the use of PPP technology in meteorology [8]. The International GNSS Service Real-time Pilot Project's (IGS RTPP) quick development can keep up with the needs of real-time tropospheric products. For ZTD or PWV retrieval in real time, [PERSON] and [PERSON] relied on GPS PPP. Millimeter-level accuracy is possible when referencing meteorological data or the results of postprocessing [16; 17]. [PERSON] and [PERSON] et al. carried out real-time four-constellation integrated PPP ZTD retrieval research. Compared with the pure GPS PPP solution, the ZTD estimation of the four-constellation integrated PPP solution demonstrates a certain improvement in initialization time and accuracy [18; 19]. [PERSON]'s theory deduces the relationship between ZTD and PWV, providing a theoretical framework for the retrieval of PWV by GNSS [20].