reactions fueled by higher temperatures and solar radiation [11; 12]. These seasonal patterns highlight the intricate interactions between pollutant sources, meteorological conditions, and atmospheric processes [13; 14].  
The relationship between air pollutants and meteorological parameters is inherently nonlinear, influenced by local climate conditions, topography, and anthropogenic activities [12; 15]. For instance, higher WS can facilitate pollutant dispersion, while stagnant conditions with low WS and high humidity can exacerbate air quality issues [7; 16; 17]. Furthermore, elevated temperatures can enhance the photochemical formation of secondary pollutants such as ozone [6; 18].  
Several predictive models have been developed to estimate air pollutant concentrations based on meteorological inputs. Techniques such as multiple linear regression (MLR) [19], generalized additive models [15], and machine learning algorithms [20; 21] have shown considerable success in explaining variations in air quality. For example, studies in China and India have demonstrated that meteorological models coupled with land use regression can capture up to 80% of the variability in pollutant concentrations [22; 23]. However, the dynamic and spatially heterogeneous nature of meteorological influences requires region-specific analyses to improve model performance [4; 14].  
In one study, [PERSON] [24] forecasted PM\({}_{10}\), PM\({}_{2.5}\), and NO\({}_{2}\) in a city using ANN and multivariate regression, incorporating local and upwind (Beijing) air quality data to reflect long-range pollutant transport. Among the models tested, the ANN-tanh model showed the highest accuracy, outperforming others before, during, and after Yellow Dust events. In addition, using 15 input variables--including recent local measurements and 48-h prior data from a Chinese city--the ANN-tanh model, built with a feed-forward MLP structure and backpropagation training, demonstrated the highest accuracy [25].  
This study focuses on Sakarya, Turkjye, a rapidly developing urban-industrial region characterized by significant emissions from vehicular traffic and industrial activities. Its unique climatic and topographical conditions make it an ideal location to explore the interactions between meteorological factors and air pollution. The study aimed (1) to analyze seasonal trends in key air pollutants, including PM\({}_{10}\), NO, NO\({}_{2}\), and CO, within the region; (2) to assess the influence of meteorological parameters such as WS, temperature, RH, and rainfall (RainF) on pollutant concentrations; (3) to develop and evaluate predictive models for air pollutant concentrations using statistical and machine learning methods; and (4) to propose evidence-based recommendations to support effective air quality management strategies in urban-industrial regions like Sakarya.  
The findings of this research aim to enhance the understanding of air quality dynamics in urban-industrial environments and provide actionable insights for policymakers. By addressing the specific challenges of air pollution in Sakarya, this study seeks to inform targeted mitigation strategies, protect public health, and contribute to sustainable urban development. Moreover, the methodologies and insights developed here can serve as a framework for similar studies in regions facing comparable air quality challenges.