<p>With the rapid advancement of urbanization, the management of traffic pollution has become a critical issue demanding immediate resolution. Among various pollutants, vehicular noise and exhaust emissions represent primary sources of traffic pollution. To analyze the complex mechanisms underlying noise and exhaust pollution on roadways, this study proposes a mesoscopic-scale collaborative analysis method based on the Cellular Transmission Model (CTM). By discretizing the road into homogeneous cells, statistical vehicle motion characteristics are captured. This approach integrates established models of noise energy and exhaust emissions, enabling dynamic spatiotemporal correlation between noise and pollutant data. The results indicate that fluctuations in acceleration are significantly positively correlated with noise levels and NOx emissions, further revealing a “growth misalignment” phenomenon between noise and exhaust due to their distinct underlying mechanisms. The main findings are as follows: (1) Vehicle acceleration and deceleration behaviors are identified as the core factors driving high NOx emissions, while CO and HC emissions are primarily controlled by total traffic demand; (2) Moving beyond the limitations of traditional single-pollutant control strategies, a unified quantitative evaluation of multiple pollutants is achieved using dynamic weights and a Synergistic Benefit Index (SBI); (3) The dynamic variation of the SBI effectively quantifies the dominant pollutant at different stages—noise dominates when V/C ≤ 0.4 (SBI &gt; 0.5), while exhaust emissions become dominant when V/C &gt; 0.5 (SBI &lt; 0.5). Compared to existing macroscopic and microscopic models, the proposed method demonstrates enhanced capability in capturing local heterogeneity with lower computational complexity.</p>