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

Wen, Hao; Dang, Yamin; Li, Liwei

doi:10.1109/access.2020.3003580

Cited by 26 publications

(14 citation statements)

References 31 publications

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“…This kind of model effectively analyzes the movement of PM2.5 occurrence and development, Mechanism prediction modeling effectively analyzes the movement mechanism of PM2.5 occurrence and development, so that people can better understand the generation and development process of PM2.5. However, the modeling of the mechanism prediction model needs more analysis processes, the model is more complex, the calculation time is more time-consuming, and the simulation results are also vulnerable to the impact of pollutant emissions and selected variables [4].…”

Section: B Related Work About Prediction Modelsmentioning

confidence: 99%

A Novel Particulate Matter 2.5 Concentration Prediction Model Based on Double-Layer Decomposition and Feedback of Model Learning Effect

Wang

Chen

2022

IEEE Access

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Accurate and effective particulate matter 2.5(PM2.5) concentration prediction can provide early warning information for decision-making departments, so as to take governance and preventive measures. A combined model based on double-layer decomposition(DLD) and feedback of model learning effect for PM 2.5 concentration prediction is proposed in this paper. Firstly, ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) were used for double-level decomposition for the PM2.5 concentration series, to reduce the nonstationarity and nonlinearity of the concentration series and improve the predictability; Secondly, a Wavelet neural network (WNN) prediction model based on the feedback of model learning effect is established for the subsequence obtained by double-layer decomposition. Finally, the prediction results of each subsequence are superimposed to obtain the final prediction results. The case study shows that the prediction model proposed in this paper is scientific.

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Section: B Related Work About Prediction Modelsmentioning

confidence: 99%

A Novel Particulate Matter 2.5 Concentration Prediction Model Based on Double-Layer Decomposition and Feedback of Model Learning Effect

Wang

Chen

2022

IEEE Access

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“…ε j i,f contains other unmodelled errors for phase observation. The zenith total delay (ZTD) as unknown parameters is estimated [20], and the zenith hydrostatic delay (ZHD) is calculated using the Saastamoinen (SAAS) model [43,44]. The equation is…”

Section: Datamentioning

confidence: 99%

“…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. Therefore, it is possible to monitor forest fires based on GNSS technique.…”

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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“…With the development of GNSS technology, the global navigation satellite system's (GNSS) zenith tropospheric delay (ZTD), zenith wet delay (ZWD) and precipitable water vapor (PWV) data are able to reflect certain atmospheric water vapor information and can also reflect changes in meteorological conditions [13,14]. Therefore, some scholars have begun to introduce GNSS data into the PM 2.5 research field; for example, Wen et al [15] explored the correlation between PM 2.5 and ZWD in Baoding, Hebei, China, and found that the correlation coefficient between the daily average PM 2.5 and ZWD was mainly greater than 0.4 in autumn and winter in this region, while the correlation coefficient between the hourly average PM 2.5 and ZWD was mainly greater than 0.3. Guo et al [16] took the GNSS data from Beijing Fangshan Station (BJFS) as an example to analyze the correlations among GNSS-derived PWV and ZTD and PM 2.5 hourly sequences.…”

Section: Introductionmentioning

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

Cited by 26 publications

References 31 publications

A Novel Particulate Matter 2.5 Concentration Prediction Model Based on Double-Layer Decomposition and Feedback of Model Learning Effect

A Novel Particulate Matter 2.5 Concentration Prediction Model Based on Double-Layer Decomposition and Feedback of Model Learning Effect

Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

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

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