## 7 Conclusions and Future Work  
By modeling individual field-measured values of target variables S, Sr, and g02 instead of their long-term upper bounds, we were able to significantly increase the accuracy of the trained regression models. This progress was made possible by the additional input attributes that were capable of capturing seasonality and other significant local events (e.g., building or surface mining activities), in combination with suitable model types and appropriate methods of attribute selection. The new input attributes consisted of the date of measurement along with a large number of attributes derived from multispectral satellite imagery by various double combinations of six basic statistical functions. Our experiments confirmed that these attributes indirectly capture significant events and processes affecting local environmental pollution levels. We tested the statistical significance of the increases in model accuracy after the addition of extra attributes using a nonparametric permutation test, which confirmed that the increase was significant. The magnitude of this increase was shown to depend on the method of attribute selection and on the type of regression model as well. We found out that the effect of adding the measurement date alone was similar in size to the joint effect of all of the new satellite-derived attributes. This means that local pollution levels exhibit some strong and relatively stable seasonal cycles. Nevertheless, the experiments also confirmed that there was independent information in the satellite-derived attributes, because the most accurate models were those combining the satellite attributes with the measurement date. We believe that this combination would also prove to be the most stable of all combinations investigated so far. In the future, we would like to derive more input attributes from satellite images in order to extract more information from them. We also plan to exploit daily or even hourly values of the SHMU input attributes, which, by virtue of representing the variation of local pollution levels more accurately, could further improve the accuracy of the models. Another potential increase in model accuracy could come from the use of the NEIS database, which contains information on main environmental polluters--industrial producers and companies. Yet another interesting research direction is the modeling of the true statistical distributions of local pollution levels, as embodied in target variables S, Sr, and g02, which might lead to a more detailed knowledge of their behavior and, consequently, to higher estimation accuracy. It is also possible that we might discover new domain knowledge potentially useful, not only for the electric power industry, but also for ecology and related disciplines. Data curation, P.K., J.M., L.P. and L.S. ; Formal analysis, P.K. and M.K. ([PERSON]); Methodology, P.K. and M.K. ([PERSON]); Project administration, M.K. ([PERSON]) and L.H. ; Resources, J.M. ; Software, P.K. ; Supervision, L.H. ; Validation, P.K. and M.K. ([PERSON]); Writing--original draft, M.K. ([PERSON]) and M.O. ; Writing--review & editing, P.K., M.K. ([PERSON]) and M.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the project VEGA 2/0125/20 and by the Slovak Research and Development Agency under the contract no. APVV APVV-20-0548 (ARIEN). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Certain portion of our data cannot be made public due to commercial agreement limitations under which we obtained it from SHMU and VUJE companies. The publicly available part of our data can be downloaded from [[https://zenodo.org/record/6373788](https://zenodo.org/record/6373788)]([https://zenodo.org/record/6373788](https://zenodo.org/record/6373788)) (accessed on 26 February 2022).