1. Introduction
In 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. Additionally, wildfires cannot only cause great economic damage, but also produce incalculable impacts on the environment. Therefore, the research on wildfires detection methods grows in popularity. Wildfires produce a large number of smoke particles [
2], of which PM10 (particles from 0 microns to 10 microns) and PM2.5 (particles from 2.5 microns or less) are considered as two important indicators, affecting human health. 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]. Angeliki et al. collected statistics on particulate matter from 1980 to 2020. Moreover, it was concluded that PM10 and PM2.5 concentrations are associated with mortality, and that the risk of cardiovascular death and the incidence rate of the respiratory system will mount with the increase in PM2.5 concentration in smoke [
4]. 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]. Wildfires are unpredictable and harmful. The earlier detection of wildfires can significantly alleviate the impact on people and the loss [
7,
8]. Antonio et al. developed a layered wireless sensor network, which is combined with originated wildfires in dangerous areas and integrated with a fire command center, geographic information system, and fire detector so as to detect wildfires [
9]. Hu et al. applied Sentinel-2 remote sensing data to classify wildfire areas and non-fire areas, and established a fully automatic algorithm based on adaptive thresholds which can be applied to onboard processing [
10]. Taking the 2020 California wildfires as the research object, Alan et al. modeled 54 million wildfires and 25 million building locations in the study area, conducting a risk assessment and experiments for wildfire prediction [
11]. Currently, the research methods of wildfire detection mainly use unmanned aerial vehicles, remote sensing, various smoke sensors, etc., but these methods have the disadvantages of a huge cost and poor real-time performance.
The Global Navigation Satellite System (GNSS) has been widely used in the field of meteorology by virtue of its advantages of wide distribution of stations, all-weather observation, low cost, and high temporal and spatial resolution [
12,
13,
14,
15]. The content of precipitable water vapor (PWV) in the atmosphere changes sharply with time and space [
16,
17,
18], so the PWV is widely used in meteorology research [
19,
20,
21]. The GNSS-derived PWV (PWV
GNSS) has been verified, and its accuracy is the same as that of traditional technologies such as a radiosonde station, airborne radiometer, water vapor radiometer, and lidar [
22,
23]. Moreover, Abbasy et al. conducted experiments on GNSS stations in the province of Zanjan, Iran, and compared PWV
GNSS with the radiosonde-derived PWV (PWV
RAD) value and PWV value in the global reanalysis datasets [
24]. It has been concluded that GNSS inversion PWV has a better inversion accuracy. Yahaya et al. studied the correlation between GNSS inversion PWV and particulate matter in Nigeria, and determined statistics on the correlation between GNSS inversion PWV and particulate matter PM10 [
25]. It has been concluded that GNSS inversion PWV and PM10 are highly correlated. Guo et al. studied the correlation between the inversion of the zenith total delay (ZTD) and PM2.5 by GNSS, and predicted the particulate matter PM2.5 in short-term, proving the influence of the GNSS-based inversion on ZTD, including particulate matter [
26]. Wen et al. verified the relationship between the zenith wet delay and PM2.5 based on GNSS and meteorological factors, and predicted the PM2.5 value in short-term [
27]. The feasibility of particle detection based on GNSS has been proved by relevant research.
The GNSS signal is delayed due to the influence of the troposphere. The hydrostatic delay can be obtained by the tropospheric model with millimeter accuracy, mainly considering the influence of the standard atmosphere without particles such as PM10/PM2.5. Therefore, the influence of particulate matter is included in the non-hydrostatic delay [
26] and GNSS-derived like PWV(LPWV). 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. The PWV observed by the radiosonde station is mainly caused by water vapor, without being affected by particles. Therefore, the difference (
PWV) between GNSS-derived LPWV and PWV
RAD is mainly caused by particles in theory [
28]. Because of a few available ground sounding stations without colocation, this paper proposes a method to detect the change of atmospheric particles during the 2020 California wildfires by obtaining the
PWV based on the PWV of virtual sounding stations and GNSS stations.
This paper aims to use the GNSS technique to detect particulate matter changes caused by the 2020 California wildfires. A new method based on the
PWV between the PWV of the virtual radiosonde stations network and GNSS-derived LPWV is proposed. The relationship between particulate matter and
PWV is studied.
Section 2 introduces study areas, data collection and research methods. Processing results and analyses are presented in
Section 3. Finally, the conclusion is given in
Section 4.
4. Conclusions
The study took the 2020 California wildfires as an example, and the data of doy 001 to 366 in 2020 from 10 GNSS stations were calculated to obtain LPWV. A new method base on PWV to detect the changes of particulate matter in the atmosphere during the 2020 California wildfires was proposed. The results showed that the variation trend of PWV was highly consistent with that of particulate matter data, with a high correlation between them.
The specific conclusions are as follows:
- (1)
The virtual radiosonde station network in the Rocky Mountain region was constructed based on the MLP neural network. The accuracy of PWVVR was significantly higher than that of PWVERA5, and the system deviation between PWVERA5 and PWVRAD could be greatly reduced.
- (2)
The PWV at fire occurrence was significantly higher than that at early and late stages of fire occurrence, showing the same change pattern with particulate matter.
- (3)
The PWVPC was obtained by decomposing and reconstructing PWV with the SSA method. The correlation coefficient between PWVPC and particulate matter data was significantly improved, showing that the decomposition and reconstruction of PWV by SSA can significantly increase the contribution rate of particulate matter to PWV. At the same time, the correlation coefficient between PWVPC and particulate matter data was significantly higher during the fire occurrence period than before and after the fire occurrence.
In conclusion, the PWV method based on the virtual radiosonde station network could effectively detect the change of particulate matter and, thus, provides a new technology and method for wildfire detection.