Global navigation satellite system precipitable water vapour combined with other atmospheric factors to predict the short-term change of PM2.5 mass concentration
With the rapid development of the global navigation satellite system (GNSS), GNSS-derived precipitable water vapour (PWV) is of great significance in weather forecasting. The present study reports the effect of monitoring haze (mainly PM2.5) using GNSS PWV data from the Beijing Fangshan station (BJF...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Borntraeger
2021-10-01
|
| Series: | Meteorologische Zeitschrift |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1127/metz/2021/1061 |
| _version_ | 1818897671200440320 |
|---|---|
| author | Min Guo Pengfei Xia Pengjie Li Hanwei Zhang |
| author_facet | Min Guo Pengfei Xia Pengjie Li Hanwei Zhang |
| author_sort | Min Guo |
| collection | DOAJ |
| description | With the rapid development of the global navigation satellite system (GNSS), GNSS-derived precipitable water vapour (PWV) is of great significance in weather forecasting. The present study reports the effect of monitoring haze (mainly PM2.5) using GNSS PWV data from the Beijing Fangshan station (BJFS, International GNSS Service). However, the correlation between the PWV time series based on data from the BJFS and corresponding PM2.5 mass concentration series data from the Temple of Heaven station in Beijing is low over long periods (for DOYs 1–60, 2016 and DOYs 275–366 in 2016). In addition, the correlation between the two was low in summer when considering the DOYs 183–244, 2016. We suspect the low correlation is due to the influence of meteorological factors, such as the relative humidity, wind speed, pressure, and heavy rainfall. To accurately analyze the relationship between the two, we select a short period with relatively stable weather conditions (e.g., no heavy rainfall and relatively continuous data) during haze periods., which improves the correlation coefficient between the GNSS PWV and PM2.5 time series to a value of 0.66. To further improve the correlation between PWV and the PM2.5 time series, two datasets were reconstructed by use of the fourth-layer low-frequency coefficient (a4) with the standard orthogonal wavelet Daubechies method (db5). By removing the influence of high-frequency noise with this method, the correlation is improved, with the value of the maximum correlation coefficient reaching 0.73. Although the real change in PM2.5 mass concentration is affected by other factors (e.g., NO2, SO2, relative humidity), the reconstructed NO2, relative humidity, average wind speed, PWV, O3, CO and SO2 time series serve as the independent variables, with reconstructed PM2.5 as the dependent variable, to establish a multiple-regression model for the prediction of the change in the PM2.5 mass concentration. This approach is successful in predicting a short-term change in PM2.5 mass concentration based on four statistical values of the regression model. Therefore, the GNSS PWV data combined with other parameters can be used to forecast haze weather with a linear-multiple regression method after wavelet decomposition. |
| first_indexed | 2024-12-19T19:19:53Z |
| format | Article |
| id | doaj.art-714cbeadd7a844c2a57fc76d5b2c922b |
| institution | Directory Open Access Journal |
| issn | 0941-2948 |
| language | English |
| last_indexed | 2024-12-19T19:19:53Z |
| publishDate | 2021-10-01 |
| publisher | Borntraeger |
| record_format | Article |
| series | Meteorologische Zeitschrift |
| spelling | doaj.art-714cbeadd7a844c2a57fc76d5b2c922b2022-12-21T20:09:02ZengBorntraegerMeteorologische Zeitschrift0941-29482021-10-0130542944410.1127/metz/2021/106199662Global navigation satellite system precipitable water vapour combined with other atmospheric factors to predict the short-term change of PM2.5 mass concentrationMin GuoPengfei XiaPengjie LiHanwei ZhangWith the rapid development of the global navigation satellite system (GNSS), GNSS-derived precipitable water vapour (PWV) is of great significance in weather forecasting. The present study reports the effect of monitoring haze (mainly PM2.5) using GNSS PWV data from the Beijing Fangshan station (BJFS, International GNSS Service). However, the correlation between the PWV time series based on data from the BJFS and corresponding PM2.5 mass concentration series data from the Temple of Heaven station in Beijing is low over long periods (for DOYs 1–60, 2016 and DOYs 275–366 in 2016). In addition, the correlation between the two was low in summer when considering the DOYs 183–244, 2016. We suspect the low correlation is due to the influence of meteorological factors, such as the relative humidity, wind speed, pressure, and heavy rainfall. To accurately analyze the relationship between the two, we select a short period with relatively stable weather conditions (e.g., no heavy rainfall and relatively continuous data) during haze periods., which improves the correlation coefficient between the GNSS PWV and PM2.5 time series to a value of 0.66. To further improve the correlation between PWV and the PM2.5 time series, two datasets were reconstructed by use of the fourth-layer low-frequency coefficient (a4) with the standard orthogonal wavelet Daubechies method (db5). By removing the influence of high-frequency noise with this method, the correlation is improved, with the value of the maximum correlation coefficient reaching 0.73. Although the real change in PM2.5 mass concentration is affected by other factors (e.g., NO2, SO2, relative humidity), the reconstructed NO2, relative humidity, average wind speed, PWV, O3, CO and SO2 time series serve as the independent variables, with reconstructed PM2.5 as the dependent variable, to establish a multiple-regression model for the prediction of the change in the PM2.5 mass concentration. This approach is successful in predicting a short-term change in PM2.5 mass concentration based on four statistical values of the regression model. Therefore, the GNSS PWV data combined with other parameters can be used to forecast haze weather with a linear-multiple regression method after wavelet decomposition.http://dx.doi.org/10.1127/metz/2021/1061gnss pwvfine particulate matter (pm2.5)standard orthogonal wavelet daubechies (db5)multiple regression analysis |
| spellingShingle | Min Guo Pengfei Xia Pengjie Li Hanwei Zhang Global navigation satellite system precipitable water vapour combined with other atmospheric factors to predict the short-term change of PM2.5 mass concentration Meteorologische Zeitschrift gnss pwv fine particulate matter (pm2.5) standard orthogonal wavelet daubechies (db5) multiple regression analysis |
| title | Global navigation satellite system precipitable water vapour combined with other atmospheric factors to predict the short-term change of PM2.5 mass concentration |
| title_full | Global navigation satellite system precipitable water vapour combined with other atmospheric factors to predict the short-term change of PM2.5 mass concentration |
| title_fullStr | Global navigation satellite system precipitable water vapour combined with other atmospheric factors to predict the short-term change of PM2.5 mass concentration |
| title_full_unstemmed | Global navigation satellite system precipitable water vapour combined with other atmospheric factors to predict the short-term change of PM2.5 mass concentration |
| title_short | Global navigation satellite system precipitable water vapour combined with other atmospheric factors to predict the short-term change of PM2.5 mass concentration |
| title_sort | global navigation satellite system precipitable water vapour combined with other atmospheric factors to predict the short term change of pm2 5 mass concentration |
| topic | gnss pwv fine particulate matter (pm2.5) standard orthogonal wavelet daubechies (db5) multiple regression analysis |
| url | http://dx.doi.org/10.1127/metz/2021/1061 |
| work_keys_str_mv | AT minguo globalnavigationsatellitesystemprecipitablewatervapourcombinedwithotheratmosphericfactorstopredicttheshorttermchangeofpm25massconcentration AT pengfeixia globalnavigationsatellitesystemprecipitablewatervapourcombinedwithotheratmosphericfactorstopredicttheshorttermchangeofpm25massconcentration AT pengjieli globalnavigationsatellitesystemprecipitablewatervapourcombinedwithotheratmosphericfactorstopredicttheshorttermchangeofpm25massconcentration AT hanweizhang globalnavigationsatellitesystemprecipitablewatervapourcombinedwithotheratmosphericfactorstopredicttheshorttermchangeofpm25massconcentration |