## 5 Conclusions  
In this work, the methodology of nonlinear regression as a correction factor in measurements from low-cost air pollutant sensors was presented. The advantage of this methodology is that it can be implemented in IoT devices, as it does not require high computational power. The proposed methodology proves excellent results, as the results indicate that a second-degree polynomial is adequate to show an improvement in the degree of correlation (R\({}^{2}\)) of up to 9% for ozone and up to 4% for nitrogen dioxide, while experiments with a higher degree polynomial were not adopted due to increased system complexity. The results of both scale methods demonstrate satisfactory performance, with corrected measurements closely aligning with reference measurements.  
For the purpose of evaluating the results, the RMSE, MAD, and MAE methods were employed for both primary and corrected data. The RMSE method indicated a deviation between the primary and corrected results, suggesting the correction impact on the data. However, both the MAD and MAE methods showed an improvement in the corrected results, since for both types of air pollutant sensors both the mean absolute deviation and the mean absolute error show a smaller value in the corrected values compared to the reference values. In summary, Table 10 shows the improvements of average degree per method was used.  
This means that the correction by the nonlinear regression method, although not directly improving the correlation degree, accelerates the results through the linear coefficients, with the final effect of reducing the occurrence of the reduction in both the MAD and MAE methods. In line with the previous values, the corrected values indicate closer alignment with the reference data and, therefore, increased reliability.  
Nonlinear regression serves as an effective correction factor for data from low-cost sensors, feasible on both weekly and monthly bases. Notably, weekly correction demonstrates slightly superior performance in the corrected results, as evidenced by the RMSE, MAD, and MAE tables.  
Given the global concern regarding air quality, expanding spatial coverage, especially in large urban areas, through air quality monitoring networks is crucial.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RMSE** & **MAD** & **MAE** \\ \hline O\({}_{3}\) & 32\% & 16\% & 10\% \\ NO\({}_{2}\) & 13\% & 26\% & 5\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Average degree of improvements per method: RMSE, MAD, and MAE.  
**Author Contributions:** Conceptualization, I.S. and I.C.; methodology, I.S., O.T., E.S., D.T. and I.C.; software, I.C.; validation, I.S., O.T., E.S., D.T. and I.C.; formal analysis, E.S., D.T. and I.C.; investigation, I.C.; resources, E.S., D.T. and I.C.; data curation, O.T. and I.C.; writing--original draft preparation, I.C., E.S. and D.T.; writing--review and editing, O.T., E.S., D.T. and I.S.; visualization, I.C.; supervision, D.T. and I.S.; project administration, I.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.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.