Validation and Spatial–Temporal Variability of Particulate Matter in Urban area Using WRF-Chem with Local and Global Emission Inventories

Abstract

This study examines the validation and spatial–temporal variability of simulated PM₁₀ and PM_2.5 concentrations over Ahmedabad city using the WRF-Chem model with EDGAR global emissions and locally developed PM emissions as inputs. The validation process involves comparing simulated PM₁₀, PM_2.5, and meteorological parameters with in-situ measurements from stations at Pirana and S P Stadium during 16th-17th May and 16th-17th December 2018. The analysis focuses on six-hourly averaged data, highlighting the significant role of Wind Speed, Wind Direction, and Planetary Boundary Layer Height (PBLH) in the dispersion of air pollutants. The results show that the model using locally developed PM emissions significantly reduces bias and provides better spatial distribution patterns compared to EDGAR emissions. For instance, in May, the average simulated PM₁₀ using EDGAR emissions was 85 µg/m³, while the locally developed emissions yielded an average of 183 µg/m³, closer to the observed in-situ average of 178 µg/m³. Similarly, in December, the simulated PM₁₀ using EDGAR emissions was 97 µg/m³ compared to 232 µg/m³ from local emissions, with an in-situ average of 147 µg/m³. The spatial analysis reveals that during May, 52% of the western areas experienced 'Good' air quality in the morning, decreasing to 40% by the evening. In contrast, December showed more severe pollution, with 45% of the North–North Western city experiencing 'Moderately Polluted' air quality by evening. The correlation coefficients (R²) for PM₁₀ validation at Pirana and S P Stadium were 0.73 and 0.93 respectively in May using EDGAR emissions, improving to 0.77 and 0.91 with local emissions. This study underscores the need for improved emission inventories and mitigation strategies to enhance air quality in Ahmedabad. The significant seasonal variations and the impact of meteorological parameters on pollutant dispersion highlight the importance of localized data and targeted interventions for effective air quality management.