Detection of Particulate Matter Changes Caused by 2020 California Wildfires Based on GNSS and Radiosonde Station
From August to October 2020, a serious wildfire occurred in California, USA, which produced a large number of particulate matter and harmful gases, resulting in huge economic losses and environmental pollution. Particulate matter delays the GNSS signal, which affects the like precipitable water vapo...
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| Format: | Article |
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MDPI AG
2021-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/22/4557 |
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| author | Jinyun Guo Rui Hou Maosheng Zhou Xin Jin Guowei Li |
| author_facet | Jinyun Guo Rui Hou Maosheng Zhou Xin Jin Guowei Li |
| author_sort | Jinyun Guo |
| collection | DOAJ |
| description | From August to October 2020, a serious wildfire occurred in California, USA, which produced a large number of particulate matter and harmful gases, resulting in huge economic losses and environmental pollution. Particulate matter delays the GNSS signal, which affects the like precipitable water vapor (LPWV) derived by the GNSS non-hydrostatic delay. Most of the information of GNSS-derived LPWV is caused by water vapor, and a small part of the information is caused by particulate matter. A new method based on the difference (ΔPWV) between the PWV of virtual radiosonde stations network and GNSS-derived LPWV is proposed to detect the changes of particulate matter in the atmosphere during the 2020 California wildfires. There are few radiosonde stations in the experimental area and they are far away from the GNSS station. In order to solve this problem, we propose to use the multilayer perceptron (MLP) neural network method to establish the virtual radiosonde network in the experimental area. The PWV derived by the fifth-generation European center for medium-range weather forecasts reanalysis model (PWV<sub>ERA5</sub>) is used as the input data of machine learning. The PWV derived by radiosonde data (PWV<sub>RAD</sub>) is used as the training target data of machine learning. The ΔPWV is obtained based on PWV derived by the virtual radiosonde station network and GNSS in the experimental area. In order to further reduce the influence of noise and other factors on ΔPWV, this paper attempts to decompose ΔPWV time series by using the singular spectrum analysis method, and obtain its principal components, subsequently, analyzing the relationship between the principal components of ΔPWV with particulate matter. The results indicate that the accuracy of PWV predicted by the virtual radiosonde network is significantly better than the fifth-generation European center for the medium-range weather forecast reanalysis model, and the change trend of ΔPWV is basically consistent with the change law of particulate matter in which the value of ΔPWV in the case of fire is significantly higher than that before and after the fire. The mean of correlation coefficients between ΔPWV and PM10 at each GNSS station before, during and after wildfires are 0.068, 0.397 and 0.065, respectively, which show the evident enhancement of the correlation between ΔPWV and particulate matter during wildfires. It is concluded that because of the high sensitiveness of ΔPWV to the change of particulate matter, the GNSS technique can be used as an effective new approach to detect the change of particulate matter and, then, to detect wildfires effectively. |
| first_indexed | 2024-03-10T05:06:07Z |
| format | Article |
| id | doaj.art-7d1441833f5242c1b42ee9a2a55f0c01 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T05:06:07Z |
| publishDate | 2021-11-01 |
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| spelling | doaj.art-7d1441833f5242c1b42ee9a2a55f0c012023-11-23T01:19:22ZengMDPI AGRemote Sensing2072-42922021-11-011322455710.3390/rs13224557Detection of Particulate Matter Changes Caused by 2020 California Wildfires Based on GNSS and Radiosonde StationJinyun Guo0Rui Hou1Maosheng Zhou2Xin Jin3Guowei Li4College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaDepartment of Space and Technology, Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250100, ChinaFrom August to October 2020, a serious wildfire occurred in California, USA, which produced a large number of particulate matter and harmful gases, resulting in huge economic losses and environmental pollution. Particulate matter delays the GNSS signal, which affects the like precipitable water vapor (LPWV) derived by the GNSS non-hydrostatic delay. Most of the information of GNSS-derived LPWV is caused by water vapor, and a small part of the information is caused by particulate matter. A new method based on the difference (ΔPWV) between the PWV of virtual radiosonde stations network and GNSS-derived LPWV is proposed to detect the changes of particulate matter in the atmosphere during the 2020 California wildfires. There are few radiosonde stations in the experimental area and they are far away from the GNSS station. In order to solve this problem, we propose to use the multilayer perceptron (MLP) neural network method to establish the virtual radiosonde network in the experimental area. The PWV derived by the fifth-generation European center for medium-range weather forecasts reanalysis model (PWV<sub>ERA5</sub>) is used as the input data of machine learning. The PWV derived by radiosonde data (PWV<sub>RAD</sub>) is used as the training target data of machine learning. The ΔPWV is obtained based on PWV derived by the virtual radiosonde station network and GNSS in the experimental area. In order to further reduce the influence of noise and other factors on ΔPWV, this paper attempts to decompose ΔPWV time series by using the singular spectrum analysis method, and obtain its principal components, subsequently, analyzing the relationship between the principal components of ΔPWV with particulate matter. The results indicate that the accuracy of PWV predicted by the virtual radiosonde network is significantly better than the fifth-generation European center for the medium-range weather forecast reanalysis model, and the change trend of ΔPWV is basically consistent with the change law of particulate matter in which the value of ΔPWV in the case of fire is significantly higher than that before and after the fire. The mean of correlation coefficients between ΔPWV and PM10 at each GNSS station before, during and after wildfires are 0.068, 0.397 and 0.065, respectively, which show the evident enhancement of the correlation between ΔPWV and particulate matter during wildfires. It is concluded that because of the high sensitiveness of ΔPWV to the change of particulate matter, the GNSS technique can be used as an effective new approach to detect the change of particulate matter and, then, to detect wildfires effectively.https://www.mdpi.com/2072-4292/13/22/4557global navigation satellite system2020 California wildfiresvirtual radiosonde stations networkmultilayer perceptron neural networkPM10/PM2.5 |
| spellingShingle | Jinyun Guo Rui Hou Maosheng Zhou Xin Jin Guowei Li Detection of Particulate Matter Changes Caused by 2020 California Wildfires Based on GNSS and Radiosonde Station Remote Sensing global navigation satellite system 2020 California wildfires virtual radiosonde stations network multilayer perceptron neural network PM10/PM2.5 |
| title | Detection of Particulate Matter Changes Caused by 2020 California Wildfires Based on GNSS and Radiosonde Station |
| title_full | Detection of Particulate Matter Changes Caused by 2020 California Wildfires Based on GNSS and Radiosonde Station |
| title_fullStr | Detection of Particulate Matter Changes Caused by 2020 California Wildfires Based on GNSS and Radiosonde Station |
| title_full_unstemmed | Detection of Particulate Matter Changes Caused by 2020 California Wildfires Based on GNSS and Radiosonde Station |
| title_short | Detection of Particulate Matter Changes Caused by 2020 California Wildfires Based on GNSS and Radiosonde Station |
| title_sort | detection of particulate matter changes caused by 2020 california wildfires based on gnss and radiosonde station |
| topic | global navigation satellite system 2020 California wildfires virtual radiosonde stations network multilayer perceptron neural network PM10/PM2.5 |
| url | https://www.mdpi.com/2072-4292/13/22/4557 |
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