Transportation planning is facing heightened complexity because the dynamic parameters influenced by globalization and unpredictable technological disruptions. Traditional models are not capable to handle interval‐based uncertainties related to supply, demand, and costs, especially as the scale of suppliers and customers expands. To bridge this gap, the current study develops a hybrid framework using interval‐valued Fermatean neutrosophic fuzzy numbers (IVFNFNs) in interval data–based transportation problems. Unlike classical fuzzy or neutrosophic techniques, IVFNFNs combine Fermatean fuzzy sets’ ability to capture higher‐order uncertainties with interval‐valued neutrosophic logic’s granular representation of ambiguity, offering unmatched flexibility in modeling incomplete, inconsistent, and indeterminate transportation data. The major contributions of this study are threefold. First, IVFNNs utilize cubic constraints that contribute tighter bounds on feasible solutions compared with Pythagorean or intuitionistic fuzzy sets, reducing solution space overestimation. Second, we derive a novel scoring function that preserves interval uncertainty through endpoint‐weighted aggregating while maintaining consistency with Fermatean constraints, enabling direct integration with classical transportation algorithms. The practical superiority of our methodology is demonstrated through extensive numerical analysis. When applied to large‐scale transportation networks, IVFNNs achieve 15.3% lower average costs than interval‐valued intuitionistic models under identical uncertainty conditions while completely eliminating constraint violations in unbalanced scenarios. By maintaining cost efficiency with environmental and social sustainability, our method provides actionable comprehension for supply chain managers and policymakers.

Due to the rapid urbanization of many cities around the world, industrial manufacturing plants have grown rapidly, thus leading to the release of large amounts of pollutants into the environment. This is the main reason for the degradation of the local air quality, resulting in an increased risk of unfavorable sanitary conditions for city dwellers. Understanding the dispersion of pollutants in local population environments, meteorological conditions and other physical characteristics is fundamental for predicting and evaluating air quality. This paper provides comprehensive details on the study of flow patterns and pollutant dispersion processes in urban areas. Several factors which include building geometry, local atmospheric effects, structural obstructions, and velocity of exhaust pollutants are examined considering field data, wind tunnel tests, operational simulation techniques, and computational fluid dynamics. Good agreements are noticeable. Simultaneous evolutions of the velocity, thermal and scalar mass fraction fields of the pollutant emitting from a three-dimensional elevated source around a rectangular obstacle placed on a turbulent boundary layer wall, and also downstream the obstacle, have been successfully carried out. The most serious pollutant levels in urban areas under various high wind velocities are identified.