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

Guo, Min; Zhang, Hanwei; Xia, Pengfei

doi:10.1002/met.1866

Cited by 17 publications

(8 citation statements)

References 54 publications

(55 reference statements)

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“…So for long-term prediction, adding ZWD cannot perform well. From section I, the accuracy of various hourly PM2.5 concentration monitoring and prediction models is basically between 20μg/m 3 -60μg/m 3 , due to the difference between mathematical models and external factors (such as region, season, climate, time period, PM2.5 source, PM2.5 concentration change range) [21]- [26], [31], [32]. And based on SVMR method with metabolic method, using ZWD and meteorological factors to online predict PM2.5 concentration can be substantially better than 60μg/m 3 within 3 hours and better than 80μg/m 3 within 6 hours in this paper.…”

Section: Discussionmentioning

confidence: 99%

“…Meanwhile, through the method of Daubechies (DB) wavelet decomposition, it can be found that GPS water vapor reconstructed by low frequency coefficient and PM2.5 concentration sequence has a good correlation in the low frequency domain [30]. With GNSS tropospheric zenith total delay (ZTD), relative humidity, average wind speed, NO2 concentration, establishing multiple linear regression model of PM2.5, through the method of DB wavelet decomposition and reconstruction, can get mathematically meaningful results [31]. With ZTD, air quality pollutants (SO2, NO2) and meteorological factors (wind direction, relative humidity, temperature and wind speed) as input factors, and PM2.5 concentration values as the output factor , Zhou et al [32] establish a neural network model to predict hourly PM2.5 concentration in Guilin.…”

Section: Introductionmentioning

confidence: 99%

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

Wen

Dang

Li³

2020

IEEE Access

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With the development of industrialization, fine particulate matter (PM2.5) severely chills the health of people. Studies have shown that the variation of PM2.5 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 PM2.5 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 PM2.5 and ZWD is investigated. Finally, the experimental data including PM2.5, ZWD, temperature, air pressure, and relative humidity data sampled in one hour are used to establish PM2.5 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 PM2.5 and ZWD is mainly larger than 0.4, and the correlation coefficient between hourly mean PM2.5 and ZWD is mainly larger than 0.3. Meanwhile, in daily cycle analysis, air temperature, air pressure and relative humidity are related to PM2.5 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 PM2.5 concentration.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wen

Dang

Li³

2020

IEEE Access

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“…Satellite-derived tropospheric data have a strong correlation with PM 2.5 [37]. To explore the variation patterns presented by PM 2.5 with PWV and ZTD at different time scales at each station in the five provinces of south-central China, data corresponding to 340 PM 2.5 ground stations were mapped and analyzed.…”

Section: Analysis Of the Relationships Between The Original-sequence Ztd And Pwv With Pm 25mentioning

confidence: 99%

Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM2.5 Concentration in Central and Southern China

Wei

Xie

Huang

et al. 2021

IJERPH

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With the increasing application of global navigation satellite system (GNSS) technology in the field of meteorology, satellite-derived zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data have been used to explore the spatial coverage pattern of PM2.5 concentrations. In this study, the PM2.5 concentration data obtained from 340 PM2.5 ground stations in south-central China were used to analyze the variation patterns of PM2.5 in south-central China at different time periods, and six PM2.5 interpolation models were developed in the region. The spatial and temporal PM2.5 variation patterns in central and southern China were analyzed from the perspectives of time series variations and spatial distribution characteristics, and six types of interpolation models were established in central and southern China. (1) Through correlation analysis, and exploratory regression and geographical detector methods, the correlation analysis of PM2.5-related variables showed that the GNSS-derived PWV and ZTD were negatively correlated with PM2.5, and that their significances and contributions to the spatial analysis were good. (2) Three types of suitable variable combinations were selected for modeling through a collinearity diagnosis, and six types of models (geographically weighted regression (GWR), geographically weighted regression kriging (GWRK), geographically weighted regression—empirical bayesian kriging (GWR-EBK), multiscale geographically weighted regression (MGWR), multiscale geographically weighted regression kriging (MGWRK), and multiscale geographically weighted regression—empirical bayesian kriging (MGWR-EBK)) were constructed. The overall R2 of the GWR-EBK model construction was the best (annual: 0.962, winter: 0.966, spring: 0.926, summer: 0.873, and autumn: 0.908), and the interpolation accuracy of the GWR-EBK model constructed by inputting ZTD was the best overall, with an average RMSE of 3.22 μg/m3 recorded, while the GWR-EBK model constructed by inputting PWV had the highest interpolation accuracy in winter, with an RMSE of 4.5 μg/m3 recorded; these values were 2.17% and 4.26% higher than the RMSE values of the other two types of models (ZTD and temperature) in winter, respectively. (3) The introduction of the empirical Bayesian kriging method to interpolate the residuals of the models (GWR and MGWR) and to then correct the original interpolation results of the models was the most effective, and the accuracy improvement percentage was better than that of the ordinary kriging method. The average improvement ratios of the GWRK and GWR-EBK models compared with that of the GWR model were 5.04% and 14.74%, respectively, and the average improvement ratios of the MGWRK and MGWR-EBK models compared with that of the MGWR model were 2.79% and 12.66%, respectively. (4) Elevation intervals and provinces were classified, and the influence of the elevation and the spatial distribution of the plane on the accuracy of the PM2.5 regional model was discussed. The experiments showed that the accuracy of the constructed regional model decreased as the elevation increased. The accuracies of the models in representing Henan, Hubei and Hunan provinces were lower than those of the models in representing Guangdong and Guangxi provinces.

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“…Guo et al [18] conducted a study on forest fires in northern China to assess the impact of changes in PM2.5 on environmental pollution. Guo et al [19] used the global navigation satellite system (GNSS) for short-term prediction of PM2.5, demonstrating the feasibility of monitoring of particulate matter with GNSS technique. Based on the GNSS and meteorological factors, Wen et al [20] verified the relationship between zenith wet delay (ZWD) and PM2.5, and forecast PM10 values in the short term.…”

Section: Introductionmentioning

confidence: 99%

Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

et al. 2021

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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.

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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

Cited by 17 publications

References 54 publications

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

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

Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM2.5 Concentration in Central and Southern China

Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

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A method for predicting short‐time changes in fine particulate matter (PM2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay

Cited by 17 publications

References 54 publications

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

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

Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM2.5 Concentration in Central and Southern China

Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

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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