Environ Sci Pollut Res Int. 2025 Sep 12. doi: 10.1007/s11356-025-36928-w. Online ahead of print.
ABSTRACT
The overarching goals of this work is to explore best practices for micro-scale modeling of a real case, identify relevant phenomena by using high-resolution modeling, and to explore their implications for public health, and climate resilience strategies in Hampton Roads, VA, USA. This project employs the Weather Research and Forecasting (WRF) model to conduct a comprehensive study of Hampton Roads, utilizing a coupled mesoscale to microscale modeling capable of resolving boundary layer turbulence. This study has three primary objectives: (1) to establish the optimal mesoscale to Large-Eddy Simulation (LES) configurations for complex geographical regions such as the Hampton Roads (HR) domain and address challenges inherent to multi-scale modeling; (2) as a demonstration, to identify extreme heat episodes and urban heat islands within the study area; and (3) to explore the correlation between these heat islands and the socio-economic characteristics of HR neighborhoods. Model performance was evaluated using observational data, applying standard statistical metrics such as correlation coefficient, mean bias, and root mean square error to select the most realistic model configuration. Similar statistical methods were used to assess the relationship between heat exposure and socio-economic factors. We also introduce a new metric, cooling energy demand, to quantify the potential economic burden of extreme heat. The Results show that lower-income communities are disproportionately exposed to higher heat levels and face greater cooling energy demands compared to rural areas. In addition, through extensive testing, we identified the cell-perturbation method as an effective approach for producing physically realistic LES simulations validated against observations. Future work will extend this approach to neighborhood-scale air quality modeling to develop a more comprehensive understanding of environmental stressors and support targeted climate resilience strategies for vulnerable communities.
PMID:40938554 | DOI:10.1007/s11356-025-36928-w
