# Indoor Emission Sources Detection by Pollutants Interaction Analysis
## 4 Conclusions  
In this study, the correlation of pollutants is mathematically calculated for the detection of emission sources in indoor environment. Extensive experiments have confirmed the effectiveness and efficiency of our correlation calculation in real-time detecting emission sources under various experimental settings, which covers seven emission events: (1) frying canola oil on electric hob, (2) frying olive oil on an electric hob, (3) frying olive oil on a gas hob, (4) spray of household pesticide, (5) lighting a cigarette and allowing it to smoulder, (6) no activities, and (7) venting session.  
Its worth noting that compared to the case without capturing pollutants interaction data, the detection accuracy of the proposed correlation calculation increases over 20%. It follows that pollutants interaction is indicating a predictive relationship that can be exploited to identify an emission event. The additional information (pollutants correlation) related to the emission sources is necessary and useful for the identification of the emission source in indoor environment.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Feature Extraction Method**} & \multicolumn{8}{c}{**The Sliding Window Size**} \\ \cline{3-10}  & & **30** & **60** & **90** & **120** & **150** & **180** & **210** & **240** \\ \hline \multirow{3}{*}{Correlation coefficient} & average accuracy & 19.32\% & 49.06\% & 61.50 \% & 68.36\% & 72.59\% & 78.19\% & 80.35\% & 80.14\% \\  & growth & - & 29.74\% & 12.44 \% & 6.86 \% & 4.23 \% & 5.60\% & 2.17\% & \(-\)0.21 \% \\  & stdev & 0.0001 & 0.6185 & 0.7064 & 0.6170 & 0.4945 & 0.4358 & 0.4906 & 0.4958 \\ \hline \multirow{3}{*}{LDA} & average accuracy & 46.06\% & 46.32 \% & 47.77\% & 46.81\% & 48.70\% & 49.32\% & 50.01\% & 49.54\% \\  & growth & - & 0.26\% & 1.45\% & \(-\)0.96\% & 1.89\% & 0.63\% & 0.69\% & \(-\)0.47\% \\  & stdev & 4.6216 & 2.9167 & 3.4096 & 4.3471 & 4.1156 & 3.67/75 & 3.9270 & 4.7798 \\ \hline \multirow{3}{*}{PCA} & average accuracy & 21.61\% & 26.44\% & 25.91\% & 29.82\% & 26.68\% & 27.63\% & 28.07\% & 29.49\% \\  & growth & - & 4.83\% & \(-\)0.53\% & 3.92\% & \(-\)3.15\% & 0.96\% & 0.44\% & 1.42\% \\  & stdev & 8.1703 & 5.4214 & 4.1153 & 5.1970 & 6.6487 & 4.3547 & 5.1603 & 5.8455 \\ \hline \multirow{3}{*}{Without data filtering} & average accuracy & 51.74\% & 47.27\% & 47.17\% & 52.45\% & 54.69\% & 56.01\% & 58.47\% & 57.95\% \\  & growth & - & \(-\)4.47\% & \(-\)0.10 \% & 5.28\% & 2.24\% & 1.33\% & 2.45\% & \(-\)0.52\% \\ \cline{1-1}  & stdev & 4.5254 & 5.1515 & 4.8287 & 4.95 & 4.5594 & 4.3840 & 1.4620 & 1.0551 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of four feature extractions under the condition of different sliding window sizes.  
Figure 4: The performance variation of target-feature-extraction methods under the condition of different sliding window sizes.  
**Author Contributions:** Conceptualization, S.P. and G.C.; methodology, S.P. and L.S.; data collection, L.S., I.L. and G.O.; validation, S.P., G.C., I.L. and A.S.; writing--original draft preparation, L.S.; writing--review and editing, S.P. and G.C.; supervision, A.S.; project administration, S.P., G.C. and A.S.; funding acquisition, S.P. and A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.