Effect of different emission inventories on modeled ozone and carbon monoxide in Southeast Asia

Abstract

Abstract. In order to improve our understanding of air quality in Southeast Asia, the anthropogenic emissions inventory must be well represented. In this work, we apply different anthropogenic emission inventories in the Weather Research and Forecasting Model with Chemistry (WRF-Chem) version 3.3 using Model for Ozone and Related Chemical Tracers (MOZART) gas-phase chemistry and Global Ozone Chemistry Aerosol Radiation and Transport (GO-CART) aerosols to examine the differences in predicted carbon monoxide (CO) and ozone (O 3 ) surface mixing ratios for Southeast Asia in March and December 2008. The anthropogenic emission inventories include the Reanalysis of the TROpospheric chemical composition (RETRO), the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B), the MACCity emissions (adapted from the Monitoring Atmospheric Composition and Climate and megacity Zoom for the Environment projects), the Southeast Asia Composition, Cloud, Climate Coupling Regional Study (SEAC4RS) emissions, and a combination of MACCity and SEAC4RS emissions. Biomass-burning emissions are from the Fire Inventory from the National Center for Atmospheric Research (NCAR) (FINNv1) model. WRF-Chem reasonably predicts the 2 m temperature, 10 m wind, and precipitation. In general, surface CO is underpredicted by WRF-Chem while surface O 3 is overpredicted. The NO 2 tropospheric column predicted by WRF-Chem has the same magnitude as observations, but tends to underpredict the NO 2 column over the equatorial ocean and near Indonesia. Simulations using different anthropogenic emissions produce only a slight variability of O 3 and CO mixing ratios, while biomass-burning emissions add more variability. The different anthropogenic emissions differ by up to 30 % in CO emissions, but O 3 and CO mixing ratios averaged over the land areas of the model domain differ by ∼ 4.5 % and ∼ 8 %, respectively, among the simulations. Biomass-burning emissions create a substantial increase for both O 3 and CO by ∼ 29 % and ∼ 16 %, respectively, when comparing the March biomass-burning period to the December period with low biomass-burning emissions. The simulations show that none of the anthropogenic emission inventories are better than the others for predicting O 3 surface mixing ratios. However, the simulations with different anthropogenic emission inventories do differ in their predictions of CO surface mixing ratios producing variations of ∼ 30 % for March and 10-20 % for December at Thai surface monitoring sites.

Influence of springtime biomass burning in South Asia on regional ozone (O 3 ): A model based case study

“…We do not attempt to provide a detailed quantitative estimate of the uncertainty in the magnitude of O 3 productions. In reality, the model based estimates of biomass burning impacts are subject to several sources of uncertainty including uncertainties in the magnitude of emission estimates (both FINNv1 and the anthropogenic emission inventories (Amnuaylojaroen et al, 2014), chemistry and transport, meteorology (Kumar et al, 2012a), model resolution etc. The accuracy of FINN depends on the land cover maps used and the emission factors.…”

Comparison of global inventories of CO2 emissions from biomass burning during 2002–2011 derived from multiple satellite products

“…GFAS, as a priori emission, is used to optimize CO emissions using observations from the Infrared Atmospheric Sounding Interferometer (Krol et al, 2013). FINN is employed to predict surface ozone and CO production (Amnuaylojaroen et al, 2014).…”

Study of SO2 Pollution in the Middle East Using MERRA‐2, CAMS Data Assimilation Products, and High‐Resolution WRF‐Chem Simulations

“…Li et al () also pointed out that substantial uncertainties exist in the estimated emissions, especially for regions experiencing rapid changes in the economy such as China and India. For instance, a comparison of different anthropogenic emission inventories using WRF‐Chem over Southeast and South Asia has been performed in Amnuaylojaroen et al () and Sharma et al (). Amnuaylojaroen et al () showed that the uncertainties of carbon monoxide surface mixing ratios in simulations with different anthropogenic emission inventories reach 30%.…”

“…For instance, a comparison of different anthropogenic emission inventories using WRF-Chem over Southeast and South Asia has been performed in Amnuaylojaroen et al (2014) and Sharma et al (2017). Amnuaylojaroen et al (2014) showed that the uncertainties of carbon monoxide surface mixing ratios in simulations with different anthropogenic emission inventories reach 30%. Sharma et al (2017) showed that the modeled ozone mixing ratios during noontime are sensitive to the choice of an emission inventory.…”