Assessing the Impacts of Integrating High-Resolution Land Use Data in WRF-Chem on Air Quality Forecasting in New Delhi, India

Air quality is of huge concern and poses a significant challenge in urban areas worldwide over several cities including New Delhi, India. Accurate air quality forecasting is crucial for timely alerting the public, effective pollution management, and public health protection. This study aims to assess the impacts of integrating high-resolution land use data with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) on air quality forecasts in New Delhi. The high resolution (100 m) land use data is obtained from CGLC-MODIS-LCZ dataset. This study addresses this limitation by enabling this coupling and performing a suite of sensitivity experiments to understand the impact to understand the implications of this coupling for critical pollutants like fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃).

Specifically, we are comparing air quality forecasts between control runs of WRF Chem and experimental WRF Chem runs coupled with enhanced land use data. Our goal is to use this newly developed and evaluated model configuration to understand sensitivity of air quality in New Delhi to anthropogenic emissions from various sources, such as vehicular traffic and mitigation strategies. The model's performance will be assessed by comparing its predictions with observations and analysis. If successful, this approach could yield an user-friendly and scalable framework of air quality management not only in New Delhi but also in other global megacities facing similar challenges.