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Abstract
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 (PWVERA5) is used as the input data of machine learning. The PWV derived by radiosonde data (PWVRAD) 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.
Highlights
Introduction distributed under the terms andIn 2020, huge wildfires that raged through California killed 91 people, and fiercer wildfires occur in the western United States more frequently [1], impinging on the ecosystem.wildfires cannot only cause great economic damage, and produce incalculable impacts on the environment
Most of the information of Global Navigation Satellite System (GNSS)-derived like precipitable water vapor (LPWV) is caused by water vapor, and a small part of the information is caused by particulate matter
This paper aims to use the GNSS technique to detect particulate matter changes caused by the 2020 California wildfires
Summary
Wildfires cannot only cause great economic damage, and produce incalculable impacts on the environment. Wildfires produce a large number of smoke particles [2], of which. Long-term exposure to particles seriously damages human bodies. Anjali et al studied the Australian wildfires in 2019, concluding through experimenters that long-term exposure to wildfires smoke has a serious impact on the respiratory tract [3]. The occurrence of wildfires is often triggered by natural causes such as heatwaves and droughts in summer and lightning, as well as smoking, picnics, and other human activities [5,6]. The earlier detection of wildfires can significantly alleviate the impact on people and the loss [7,8].
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