## 5 Conclusions  
This study explored the seasonal dynamics of key air pollutants (PM\({}_{10}\), NO, NO\({}_{2}\), and CO) and their relationships with meteorological parameters (WS, temperature, RH, and RainF) in Sakarya city from 2021 to 2023. The findings emphasize the critical roles of WS and RainF in reducing PM\({}_{10}\) and NO levels, particularly through wet deposition and atmospheric dispersion. While RF outperformed MLR in capturing non-linear pollutant-meteorological interactions, the models struggled to predict CO concentrations due to its localized sources and weak meteorological dependencies. Temperature and RH exhibited complex, pollutant-specific effects, with PM\({}_{10}\) showing increased levels during summer but reduced levels under humid spring conditions. These results underscore the intricate interplay between meteorological factors and air pollutant dynamics, providing actionable insights for data-driven air quality management.  
This study is limited by its focus on Sakarya, Turkiye, which may restrict the generalizability of its findings to other regions with different geographic, climatic, and emission characteristics. Additionally, the accuracy of the results depends on the availability and quality of air pollution and meteorological data, as any gaps or inconsistencies in the dataset could affect model performance. The study's temporal scope may also limit its ability to capture long-term trends or the impacts of extreme environmental events and policy changes on air quality.  
The study highlights the necessity of incorporating meteorological data into air quality assessments and developing seasonally adaptive, pollutant-specific strategies. To address persistent PM\({}_{10}\) emissions and the challenges associated with CO prediction, stricter industrial and vehicular emission regulations, along with advanced hybrid modeling approaches, are essential. Furthermore, integrating renewable energy adoption, real-time monitoring systems, and green urban planning can significantly enhance air quality in urban-industrial regions like Sakarya. Future research should expand the scope to include long-term analyses, detailed emission inventories, and cross-regional comparisons to validate these findings and identify scalable solutions. These efforts will contribute to a more comprehensive understanding of air pollution dynamics and the development of effective, sustainable mitigation strategies.  
Conceptualization, B.E.; methodology, B.E.; software, B.E. and S.S.; validation, B.E. and S.S.; formal analysis, B.E. and S.S.; investigation, B.E.; resources, B.E. and S.S.; data curation, Y.D.A.; writing--original draft preparation, S.S.; writing--review and editing, B.E. and S.O.; visualization, S.S.; supervision, B.E.; project administration, B.E. and S.S.; funding acquisition, B.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Sakarya University Scientific Research Projects Coordination Unit (SAU BAP), grant number 2024-25-59-139.  
**Data Availability Statement:** Restrictions apply to the availability of these data. Data were obtained from Provincial Directorate of the Ministry of Environment, Urbanization, and Climate Change and and Sakarya Meteorological Office and are available from the authors with the permission of Provincial Directorate of the Ministry of Environment, Urbanization, and Climate Change and Sakarya Meteorological Office.  
**Conflicts of Interest:** The authors declare that the research was funded by the Sakarya University Scientific Research Projects Coordination Unit (SAU BAP), which may cause potential conflicts of interest.