Due to the rapid urbanization of many cities around the world, industrial manufacturing plants have been expanded quickly, leading to the discharge of large amounts of pollutants into the environment. Consequently, a significant deterioration in local air quality is recorded, representing a high health risk for the city's residents. In this context, the main objective of this work is to understand the dispersion of gas pollution in high-density urban environments, specifically the Hail region of Saudi Arabia. The simulations carried out with Ansys Fluent 19.0 were based on actual climatic conditions, with particular attention paid to accurately reproducing the exact topography of the study area. The main results concern the characterization of flow behavior and the dispersion of gas pollutants emitted by power plants. Several factors, including building geometry and wind speed, are examined. The study reveals that for a reference wind speed of more than 7 m/s, gaseous pollution exhibits a significant tendency to accumulate within buildings, resulting in significant concentrations.

Background: Climate change poses increasing environmental and public health risk, particularly in arid regions, understanding how population perceives this risk and translating awareness into adoptive behavior is essential for effective climate resilience strategies. This study examined climate change risk perception and adoptive behavior among Hail region population in Saudi Araba using Health Belief Model (HBM). Methods: Cross-Sectional study was conducted among 460 participants using structured questionnaire based on HBM constructs. Data was analyzed by applying descriptive statistics, superman's correlation, logistic regression, and structural equation modeling to assess the predictors and pathways influencing adoptive behavior. Results: About half of participants stated high climate change risk perception (50.7%) and adoptive behavior (53.9%). Education, gender, and employment sector were significant predictors of risk perception, however adoptive behavior displayed constrained sociodemographic associations. Correlation analysis revealed strong relationships adoptive behavior and enabling constructs, particulary perceived benefits and self-efficacy than with susceptibility and severity. Path analysis demonstrated that risk perception significantly influenced adoptive behavior (β=1.00, p < 0.001) however, perceived barrier exerted strongest effect (β=0.70), followed by benefits, cues to action, and self-efficacy. The model displayed satisfactory fit and illuminated a substantial proportion of variance in adoptive behavior.Conclusion: The results confirm a perception- behaviors gap, indicate that climate change awearness alone does not ensure adoptive action. Enabling and related factors play a decisive role in behavioral adoption. Interventions should therefore be more beyond risk communication toward barrier reduction, capacity building, and supportive policy measures to enhance climate adaptation and public health resilience in arid sitting areas.