Constraining the uncertainty in emissions over India with a regional air quality model evaluation

Publication date: February 2018
Source:Atmospheric Environment, Volume 174
Author(s): Alexandra Karambelas, Tracey Holloway, Gregor Kiesewetter, Chris Heyes
To evaluate uncertainty in the spatial distribution of air emissions over India, we compare satellite and surface observations with simulations from the U.S. Environmental Protection Agency (EPA) Community Multi-Scale Air Quality (CMAQ) model. Seasonally representative simulations were completed for January, April, July, and October 2010 at 36  km × 36  km using anthropogenic emissions from the Greenhouse Gas-Air Pollution Interaction and Synergies (GAINS) model following version 5a of the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants project (ECLIPSE v5a). We use both tropospheric columns from the Ozone Monitoring Instrument (OMI) and surface observations from the Central Pollution Control Board (CPCB) to closely examine modeled nitrogen dioxide (NO2) biases in urban and rural regions across India. Spatial average evaluation with satellite retrievals indicate a low bias in the modeled tropospheric column (−63.3%), which reflects broad low-biases in majority non-urban regions (−70.1% in rural areas) across the sub-continent to slightly lesser low biases reflected in semi-urban areas (−44.7%), with the threshold between semi-urban and rural defined as 400 people per km². In contrast, modeled surface NO2 concentrations exhibit a slight high bias of +15.6% when compared to surface CPCB observations predominantly located in urban areas. Conversely, in examining extremely population dense urban regions with more than 5000 people per km² (dense-urban), we find model overestimates in both the column (+57.8) and at the surface (+131.2%) compared to observations. Based on these results, we find that existing emission fields for India may overestimate urban emissions in densely populated regions and underestimate rural emissions. However, if we rely on model evaluation with predominantly urban surface observations from the CPCB, comparisons reflect model high biases, contradictory to the knowledge gained using satellite observations. Satellites thus serve as an important emissions and model evaluation metric where surface observations are lacking, such as rural India, and support improved emissions inventory development.



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