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

Abstract

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 36km x 36km 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 (NO 2) 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 km 2. In contrast, modeled surface NO 2 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 km 2 (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

Urban versus rural health impacts attributable to PM 2.5 and O 3 in northern India

“…In addition estimating rural emissions by extrapolation from urban data can be problematic. Karambelas et al (2018) found large low biases across modeled concentrations evaluated with satellite and surface observations, demonstrating the importance of having representative inventories and high-resolution chemical transport models. Emissions inventories are invaluable to air quality modeling and for estimating human health impacts due to pollution exposure, playing a vital role in advancing air quality and health understanding in India.…”

“…Air quality modeling was performed using the regional CMAQ model version 5.1. Previously, CMAQ has been used in India to assess O 3 regimes (Sharma et al 2016, Sharma andKhare 2017) and evaluate urban and rural emissions uncertainties (Karambelas et al 2018). We completed simulations for four seasonally representative months using 2010 meteorology and emissions: January, April, July, and October for winter, pre-monsoon spring, monsoon, and postmonsoon fall respectively.…”

“…Model processes include surface-and upper-level emissions, photolysis, gasand particle-phase chemistry, deposition, and dispersion Byun and Schere (2006). Simulations include the Carbon Bond 05 (CB05) chemical mechanism (Yarwood et al 2005) and AERO 6 aerosol mechanism, the inclusion of windblown dust (Dong et al 2015) with enhancements following Karambelas et al (2018), and in-line lightning NO x production (Allen et al 2012). Boundary and initial conditions are from a larger, 36 km by 36 km domain (Karambelas et al 2018).…”

“…Simulations include the Carbon Bond 05 (CB05) chemical mechanism (Yarwood et al 2005) and AERO 6 aerosol mechanism, the inclusion of windblown dust (Dong et al 2015) with enhancements following Karambelas et al (2018), and in-line lightning NO x production (Allen et al 2012). Boundary and initial conditions are from a larger, 36 km by 36 km domain (Karambelas et al 2018). Meteorology is simulated using the Weather Research and Forecasting (WRF) model v3.2 and Preprocessing System with European Center for Medium-Range Weather Forecasting ERA-Interim globally gridded reanalysis data (Dee et al 2011).…”

“…We use biogenic, biomass burning, and anthropogenic emissions for year 2010 gridded to 12 by 12 km and vertically distributed (supplemental Karambelas et al (2018). Annual total anthropogenic emissions for 2010 are from the GAINS Model gridded globally following the ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) project version 5a (Stohl et al 2015, Klimont et al 2017.…”