To study the feasibility of monitoring forest fire using the ΔPWV technique, the paper will follow the following steps: First, the PWVGNSS is calculated using GAMIT/GLOBK 10.71. PWVGNSS is obtained using GPS phase observations. PWVGNSS data are taken every 2 h. We take an average of 12 PWV data every day to get the PWV of a single day. Second, ΔPWV is calculated by Equation (8). Third, the relationship between ΔPWV and PM2.5/PM10 is analyzed to verify the feasibility of monitoring forest fires using the ΔPWV technique.
3.2. Analysis of Forest Fire Monitoring in Southeastern Australia Based on GNSS
In order to verify the feasibility of monitoring forest fires in southeastern Australia based on the GNSS, the STD, MEAN, and MAX of ΔPWV and PM10 during Australian forest fires are analyzed. The analyzed results are shown in
Table 4 and
Figure 2,
Figure 3,
Figure 4,
Figure 5 and
Figure 6 (the fire period is in the red box). The PM10 data of the GNSS station are measured by the air quality monitoring station nearest to the GNSS station. The matching of the GNSS station and air quality monitoring station is listed in
Table 2.
It can be seen from
Table 4 and
Figure 2 that ΔPWV and PM10 at the PTSV station show the same change pattern. The STD, MEAN, and MAX of ΔPWV and PM10 are all largest during the forest fire, and the STD, MEAN, and MAX of ΔPWV and PM10 before the forest fire are smaller than those after the forest fire.
Figure 2 shows that before the forest fire, the STD, MEAN, and MAX of ΔPWV and PM10 are small with relatively smooth fluctuations. During the forest fires, ΔPWV and PM10 both have a sharp upward trend, and the trend of change is large. After the fire, PM10 decreases and gradually returns to stability. ΔPWV is still large and the fluctuation is obvious. Before and during the fire, the trend of ΔPWV is similar to that of PM10.
The PTSV station has a Mediterranean climate, alternatingly controlled by westerly winds and subtropical high pressure. Before the fire, ΔPWV and PM10 show approximately the same change pattern, with smaller values and less fluctuation. During the fire, ΔPWV and PM10 show an increasing trend. Fires occur in the summer, in Mediterranean climates where rainfall is less. Therefore, ΔPWV is less affected by precipitation. Forest fires lead to an increase in PM10 in air pollutants, leading to an increase in ΔPWV affected by particulate matter. ΔPWV is similar to PM10 before and during the fire. This is due to the influence of PM10 on ΔPWV in case of forest fire. After the fire, PM10 decreases because the air quality monitoring station can only monitor the particulate pollutants on the surface, but it is difficult to monitor the particulate pollutants in the upper air. ΔPWV remains large and varies dramatically after the fire, caused by particle stagnation in the environment. Therefore, it is feasible to use ΔPWV to study particulate matter pollution from fires.
It can be seen from
Table 4 and
Figure 3 that ΔPWV and PM10 at the STNY station show the same change pattern before and during the forest fire. The STD and MEAN of ΔPWV are small before the fire and largest after the fire. The MAX of ΔPWV at STNY is largest during the fire and smallest before the fire. The STD, MEAN, and MAX of PM10 are all largest during the forest fire, and smaller before the forest fire than after.
Figure 3 shows that ΔPWV and PM10 show the same change pattern before the fire. The STD, MEAN, and MAX of ΔPWV and PM10 are all small with small fluctuations. During the fire, ΔPWV and PM10 both have a sharp upward trend with relatively large values. After the fire, PM10 decreases and gradually returns to stability. ΔPWV is still large with obvious fluctuation. Before and during the fire, the trend of ΔPWV is similar to that of PM10.
The STNY station has a temperate marine climate, which is influenced by westerly winds at midlatitudes and is humid all year round. Before the fire, ΔPWV and PM10 show approximately the same change pattern, with smaller values and less fluctuation. During the fire, ΔPWV and PM10 show an increasing trend. Fires break out in the summer, in temperate marine climates where it is humid all year round. Therefore, ΔPWV is less affected by precipitation. Forest fires lead to an increase in PM10 in air pollutants, leading to an increase in ΔPWV affected by particulate matter. ΔPWV is similar to PM10 before and during the fire. This is due to the fact that ΔPWV is affected by PM10 when fires occur. After the fire, PM10 decreases because the air quality monitoring station can only monitor the particulate pollutants on the surface, and it is difficult to monitor particulate pollutants in the upper air. ΔPWV remains large and varies dramatically after the fire caused by particle stagnation in the environment. Therefore, it is feasible to use ΔPWV to study particulate matter pollution from fires.
It can be seen from
Table 4 and
Figure 4 that ΔPWV and PM10 at the SPBY station show the same change pattern. The STD, MEAN, and MAX of ΔPWV and PM10 are all largest during the forest fire.
Figure 4 shows that before the fire, PM10 and ΔPWV show the same change pattern. The STD, MEAN, and MAX of PM10 and ΔPWV are small with relatively smooth fluctuations. During the fire, both ΔPWV and PM10 show a sharp upward trend.
The SPBY station also has a temperate marine climate. Before the fire, ΔPWV and PM10 show approximately the same change pattern, with smaller values and less fluctuation. During the fire, ΔPWV and PM10 also have a sharp rise and a large change trend. Fires occur in the summer, in temperate marine climates where it is humid all year round. Therefore, ΔPWV is less affected by precipitation. Forest fires lead to an increase in PM10 in air pollutants, leading to an increase in ΔPWV affected by particulate matter. ΔPWV is similar to PM10 before and during the fire. This is due to the fact that ΔPWV is affected by PM10 when fires occur.
It can be seen from
Table 4 and
Figure 5 that ΔPWV and PM10 at CLEV and ROBI show different change patterns. During the fire, the STD, MEAN, and MAX of ΔPWV are lower than those before the fire at CLEV and ROBI. The STD, MEAN, and MAX of ΔPWV are largest after the fire. The STD, MEAN, and MAX of PM10 at CLEV and ROBI are largest during the forest fire. Before the fire, the STD, MEAN, and MAX of PM10 are smaller than those after the fire.
Figure 5 shows that ΔPWV and PM10 at CLEV and ROBI show different change patterns. The STD, MEAN, and MAX of PM10 are small with less fluctuations before and after the fire, and are largest when the fire occurs. In
Figure 5a STD statistics, ΔPWVs at CLEV and ROBI are small before the fire and gradually increase during the fire. After the fire, ΔPWV is larger and more stable. In
Figure 5b MEAN statistics, ΔPWVs at CLEV and ROBI show an upward trend during the forest fire. After the fire, ΔPWV is larger and the fluctuation is large. In
Figure 5c MAX statistics, when the fire occurs at CLEV and ROBI, ΔPWV fluctuates largely and shows an upward trend. ΔPWVs at CLEV and ROBI are not similar to PM10.
CLEV and ROBI have a humid subtropical climate. ΔPWVs at CLEV and ROBI show different change trends with PM10 before and during the fire. The fact that CLEV and ROBI are located at the low latitude with more precipitation and humidity all year round can have a greater impact on PWV [
25]. During the fire in the spring and summer, the southeast monsoon prevails, and the fire area is located in the northwest direction. However, the two stations are located on the coastal area on the northern edge of the Great Dividing Range, which is significantly affected by the difference in thermal properties between land and sea. This may be the reason that ΔPWV during the fire does not increase with PM10 affected by the Great Dividing Range. Therefore, natural factors such as precipitation, topography, and climate cast a large influence on ΔPWV. These factors cause ΔPWV to be different from PM10 during the fire. Since the fire occurs in the northwest and burns in a large area, particulate matter would have long-term stagnation in the air. The northwest monsoon blows in the winter, which causes ΔPWV to fluctuate violently after the fire.
It can be seen from
Table 4 and
Figure 6 that before the fire, ΔPWV and PM10 at PTKL and SYDN show the same change pattern. During the fire, the STD, MEAN, and MAX of ΔPWV at PTKL are largest. After the fire, they are larger than before the fire. The STD and MAX of ΔPWV at SYDN are smallest before the fire and largest after the fire. The MEAN of ΔPWV at SYDN is largest during the fire. The STD, MEAN, and MAX of PM10 at PTKL and SYDN are largest during the fire, and after the fire, PM10 is slightly larger than before the fire.
Figure 6 shows that before the forest fire, the STD, MEAN, and MAX of PM10 and ΔPWV are small and the fluctuations are relatively smooth. When the fire breaks out, ΔPWV does not increase immediately with PM10 until December. After the fire, PM10 decreases and gradually returns to stability, but ΔPWV is still large and fluctuates greatly.
PTKL and SYDN have a humid subtropical climate. Before the fire, ΔPWV is similar to PM10, both stable and small. When the fire breaks out in October, PM10 rises sharply, but ΔPWV does not rise until December. SYDN and PTKL are located on the eastern windward slope of the Great Dividing Range. During the fire, it is close to summer, mainly affected by the southeast monsoon, and from October to November, forest fires occur in the northwest of the stations. Therefore, particulate matter is blocked by the Great Dividing Range, causing the delay of ΔPWV. After the fire, the study area are affected by the northwest monsoon in autumn. Affected by the climate, the particulate matters are in the upper air. ΔPWV does not drop immediately and remains large.