High-resolution dynamical downscaling of present and future air quality over the South Asia-Cordex domain with a focus on the megacity Delhi, India

<p>Through a NERC/MOES funded project, PROMOTE, analysis based on WRF and CMAQ models has been conducted to understand the impact of road transport emissions on air quality over Delhi. NCEP/FNL ¹ data was used to drive WRF with four nested domains over India with resolutions of 45km, 15 km, 5 km, and 1.6 km for 2018. EDGAR v5.0 emission inventory (for 2015) ² and Cam-Chem initial and boundary condition data ³ were used to drive the CMAQ model. In the baseline runs, all domains were considered without any change in emissions, while in Scenario 1, road transport sector was removed in the third domain (5km) covering Delhi region. Model performance for NO_x, NO₂, PM₁₀, PM_2.5 and O₃ was evaluated with available observations, recognising that air quality and meteorological datasets were limited for the period analysed. In the later part of the study, OSCAR model ⁴ was used to predict the high-resolution air quality over Delhi and estimate the contributions from road transport emissions. Relative contributions to Delhi's air quality from local and regional long range transport sources are discussed.&#160;</p> <p>As part of a NERC funded COP26 project on Climate Adaptation for India, an overarching goal of this study is to quantify how air quality changes in South Asia in a changing climate under SSP245 (middle-of-the-road scenario) ⁵. A dynamical downscaling process was implemented and bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) data ⁶ was used to drive Weather Research and Forecasting (WRF) model simulations. Simulations have been conducted for the South Asia-Cordex domain for 2015 (representative of 2011-2020 years) and 2050 (representative of 2046-2055 years) with a 27 km grid resolution. The Community Multiscale Air Quality (CMAQ) model was driven with an average of ten years meteorology with future land use land cover, initial and boundary conditions, and future emissions ⁷for India. To assess the impact of averaged meteorology on the CMAQ performance, the CMAQ model was run with both ten years' averaged meteorology around 2015 (2011-2020) and only 2015 meteorology.</p> <p>Although averaging ten years around the desired year suppresses the diurnal variations, it provides an indication of monthly changes in climate and air quality variables. Under the selected SSP245 scenario, the CMAQ model predicted monthly means of PM_2.5 anomalies (2050-2015) range over India between 8 to 41 &#956;g/m³. Significant change is PM_2.5, PM₁₀, NO_x, and O₃ anomalies, especially in the urban regions of India, such as Delhi, before and after the Monsoon months (June to October) have been observed.</p> <p><strong>Financial Support:</strong> We acknowledge funding from NERC/MOES (Reference: NE/P016391/1) for the PROMOTE project and NERC funding (Reference: 2021COPA&R48Sokhi) for the COP26 Improving adaptation strategies for climate extremes and air pollution affecting India project.</p> <p><strong>References </strong></p> <p>¹ NCEP/NOAA/U.S. 2015,&#160; https://doi.org/10.5065/D65Q4T4Z.</p> <p>² Crippa M. et al. (2019): http://data.europa.eu/89h/377801af-b094-4943-8fdc-f79a7c0c2d19</p> <p>³ Buchholz, R. R. et al, (2019). https://doi.org/10.5065/NMP7-EP60</p> <p>^4&#160;Singh V, et al. (2020) Environmental Pollution, 257, 113623</p> <p>⁵ van Vuuren, D.P. et al. (2011). Climatic Change 109, 5. https://doi.org/10.1007/s10584-011-0148-z</p> <p>⁶ Xu, Z. <em>et al.</em>&#160; (2021). <em>Sci Data</em>&#160;8,&#160;293. https://doi.org/10.1038/s41597-021-01079-3</p> <p>⁷ SMoG-India v1 2015 and 2050 emissions dataset, NERC project.</p>