## 5 Conclusions  
RFsp-based quantile regression predictions, visualized by spatial-temporal maps of the ozone levels, across Australia and the nearby ocean, confirmed our hypothesis that  
Figure 23: Intervention Analysis Output for Region 3 with Model R3. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variable** & **Estimate** & **Std. Error** & **t Value** & **Pr(\textgreater{}) [t]** \\ \hline (Intercept) & 5462.56 & 633.00 & 8.63 & \textless{}0.0001 *** \\ L(Y.t, k = 1) & 0.50 & 0.05 & 8.45 & \textless{}0.0001 *** \\ L(Y.t, k = 5) & \(-\)0.20 & 0.05 & \(-\)3.71 & 0.000282 *** \\ S.t & \(-\)234.58 & 59.74 & \(-\)3.92 & 0.000126 *** \\ Pt.t & \(-\)431.34 & 76.54 & \(-\)5.63 & \textless{}0.0001 *** \\ Q.t.1 & \(-\)232.57 & 57.86 & \(-\)4.02 & \textless{}0.0001 *** \\ L(Aerosol\_Region3, 1) & 2.79 & 1.30 & 2.13 & 0.033857 * \\ L(Aerosol\_Region3, 3) & \(-\)2.41 & 1.26 & \(-\)1.90 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model Results. bushfires result in a localized effect on the ozone layer in the vicinity of the bushfire or downwind of it. Noteworthy also, is that our findings, along with those from [9; 10] which discuss how aerosols affect ground ozone levels, in addition to the findings of [15; 19; 20] on stratospheric ozone depletion, indicate that there is significant evidence that ozone levels are affected by aerosols from bushfires. Random forest spatial-temporal (RFsp) analysis of the satellite data from [34] of ozone and aerosols (NASA/NOAA) has shown very accurate ozone predictions from aerosol measurements (achieving an OOB R\({}^{2}\) of 0.996 and MSE of 4.32, with a five-fold cross-validation R\({}^{2}\) of 0.995 and MSE of 5.33), indicating a significant association between bushfire incidence in Australia and ozone depletion in the vicinity of the bushfires. The use of the total column/sum ozone (TSO) and total sum aerosol (TSA) data for three regions of eastern Australia (Pacific Ocean, Victoria, and NSW) demonstrated significant change-points (or times of change in states) of ozone with respect to aerosol exceedances, as established by binary segmentation and also by Hidden Markov models (HMMs). The HMM approach established that the ozone TSO time series exhibited four or five hidden states. We also observed a significant and quantifiable temporal and lagged effect between times of significant change in aerosol of 10-25 days and change-points in ozone levels in all three regions. Intervention methods gave similar times of aerosol exceedance to those found by HMM. Intervention analysis increases in aerosol in all three regions had a significant and ongoing impact 1-5 days later in reducing ozone levels (\(p<0.0001\)). For all three regions, there was a lag of 10-25 days between times of aerosol exceedances (or change-points) and subsequent ozone depletion. Specifically, in Region 1 (Pacific Ocean) the exceedance (CP) in Aerosol occurred around 7 November 2019. The associated subsequent decrease in ozone then occurred around 12 days later on 19 November 2019. About 40 days later on 18 January 2020 ozone levels were starting to increase. In Region 2 (Vic) the exceedance (CP) in Aerosol occurred around 13-17 November 2019. Then, 18 days later around 7-12 December 2019, there was a significant decrease in ozone; 25 days later ozone levels began to increase. In Region 3 (Pacific Ocean) the exceedance (CP) in Aerosol occurred around 5 November 2019; followed 13-16 days later by a decrease in Ozone around the 30 November 2019. About 40-45 days later, ozone levels begin increasing somewhat around 15 January 2020. Further work based on HMMs and SSM intervention analyses will incorporate meteorological and climate variables, such as wind and temperature, seasonal effects, and possibly random effects, along with forecasting future ozone levels. More mathematically rigorous Binary Segmentation methods [58], are topics of future work; as are HMM-Gamma [69] models, recently proposed for ozone zone identification models.