With the development of industrialization, fine particulate matter (PM25) severely chills the health of people Studies have shown that the variation of PM25 concentration is related to the Global Navigation Satellite System (GNSS) tropospheric delay Therefore, it is possible to use the widely distributed continuous operation reference station (CORS) to monitor and predict PM25 concentrations with high time resolution In this paper, the zenith wet delay (ZWD) of five CORS located in Baoding, Hebei Province, China, from Sep 2014 to Feb 2015, is calculated firstly Then, the correlation between PM25 and ZWD is investigated Finally, the experimental data including PM25, ZWD, temperature, air pressure, and relative humidity data sampled in one hour are used to establish PM25 concentrations online prediction model based on support vector machine regression (SVMR) model with metabolic method The experimental results show that in autumn and winter, the correlation coefficient between daily-mean PM25 and ZWD is mainly larger than 04, and the correlation coefficient between hourly mean PM25 and ZWD is mainly larger than 03 Meanwhile, in daily cycle analysis, air temperature, air pressure and relative humidity are related to PM25 concentration Finally, Using SVMR model with metabolic method, by combining GNSS and meteorological factors, ideal short-term prediction accuracy is achieved, which shows that the use of GNSS and meteorological factors is potential in predicting PM25 concentration

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Short-Term PM2.5 Concentration Prediction by Combining GNSS and Meteorological Factors

We compare the water-vapor weighted temperature Tmb through Tm-Ts relationship of Bevis with the true water-vapor weighted mean temperature Tm based on the radiosonde data observed from 2003 to 2006 in China,and validate the applicability of Tm-Ts relationship of Bevis in China.The conclusion shows that the accuracy of Tmb has highly linear relations with the station heights.Then,we did some corrections to Tmb and the improved Tm-Ts relationship of Bevis can be used to compute the water-vapor-weighted temperature in China with high pricision.

Validation and Analysis of the Water-Vapor-Weighted Mean Temperature from T_m-T_s Relationship

Exploration and analysis of the factors influencing GNSS PWV for nowcasting applications

From late 2019 to early 2020, forest fires in southeastern Australia caused huge economic losses and huge environmental pollution. Monitoring forest fires has become increasingly important. A new method of fire detection using the difference between global navigation satellite system (GNSS)-derived precipitable water vapor and radiosonde-derived precipitable water vapor (ΔPWV) is proposed. To study the feasibility of the new method, the relationship is studied between particulate matter 10 (PM10) (2.5 to 10 microns particulate matter) and ΔPWV based on Global Positioning System (GPS) data, radiosonde data, and PM10 data from 1 June 2019 to 1 June 2020 in southeastern Australia. The results show that before the forest fire, ΔPWV and PM10 were smaller and less fluctuating. When the forest fire happened, ΔPWV and PM10 were increasing. Then after the forest fire, PM10 became small with relatively smooth fluctuations, but ΔPWV was larger and more fluctuating. Correlation between the 15-day moving standard deviation (STD) time series of ΔPWV and PM10 after the fire was significantly higher than that before the fire. This study shows that ΔPWV is effective in monitoring forest fires based on GNSS technique before and during forest fires in climates with more uniform precipitation, and using ΔPWV to detect forest fires based on GNSS needs to be further investigated in climates with more precipitation and severe climate change.

Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

Study on the regional prediction model of PM2.5 concentrations based on multi-source observations

https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/met.1866

A method for predicting short-time changes in fine particulate matter (PM 2.5 ) mass concentration based on the global navigation satellite system zenith tropospheric delay

Min Guo

Papers

A method for predicting short-time changes in fine particulate matter (PM 2.5 ) mass concentration based on the global navigation satellite system zenith tropospheric delay

Exploration and analysis of the factors influencing GNSS PWV for nowcasting applications