MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP

Abstract

The MIX inventory is developed for the years 2008 and 2010 to support the Model Inter-Comparison Study for Asia (MICS-Asia) and the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) by a mosaic of up-to-date regional emission inventories. Emissions are estimated for all major anthropogenic sources in 29 countries and regions in Asia. We conducted detailed comparisons of different regional emission inventories and incorporated the best available ones for each region into the mosaic inventory at a uniform spatial and temporal resolution. Emissions are aggregated to five anthropogenic sectors: power, industry, residential, transportation, and agriculture. We estimate the total Asian emissions of 10 species in 2010 as follows: 51.3 Tg SO2, 52.1 Tg NOx, 336.6 Tg CO, 67.0 Tg NMVOC (non-methane volatile organic compounds), 28.8 Tg NH3, 31.7 Tg PM10, 22.7 Tg PM2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO2. Emissions from China and India dominate the emissions of Asia for most of the species. We also estimated Asian emissions in 2006 using the same methodology of MIX. The relative change rates of Asian emissions for the period of 2006–2010 are estimated as follows: −8.1 % for SO2, +19.2 % for NOx, +3.9 % for CO, +15.5 % for NMVOC, +1.7 % for NH3, −3.4 % for PM10, −1.6 % for PM2.5, +5.5 % for BC, +1.8 % for OC, and +19.9 % for CO2. Model-ready speciated NMVOC emissions for SAPRC-99 and CB05 mechanisms were developed following a profile-assignment approach. Monthly gridded emissions at a spatial resolution of 0.25° × 0.25° are developed and can be accessed from http://www.meicmodel.org/dataset-mix

A 15-year record (2001–2015) of the ratio of nitrate to non-sea-salt sulfate in precipitation over East Asia

“…1). It has been recognized that anthropogenic emissions centered over this region (Kurokawa et al, 2013;Li et al, 2017), the related atmospheric concentration, and depositions are severe in China. To overcome this limitation and advance our knowledge of precipitation chemistry over the whole of China, we evaluated additional sources of data for the chemical concentration of precipitation over China.…”

Dome effect of black carbon and its key influencing factors: A one-dimensional modelling study

“…That is, BC would exert a more intensive dome effect over rural surface. It should be noted that residential combustion of raw coal and biofuel in a small domestic stove in rural area is responsible for the majority of BC emission in China (Zhi et al, 2008;Li et al, 2017a). More 10 stable PBL and more significant dome effect over rural surface would further favor the formation and accumulation of regional air pollution.…”

Source influence on emission pathways and ambient PM<sub>2.5</sub> pollution over India (2015–2050)

“…As is quite evident ( Emission datasets for India in global emission inventories have been developed either through combination of regional inventories for specific base years (Janssens-Maenhout et al, 2015) or using integrated assessment models, e.g., the GAINS 25 model (Amann et al, 2011), to generate scenarios of air pollutants (Klimont et al, 2009(Klimont et al, , 2018Purohit et al, 2010;Stohl et al, 2015). Indian emissions for 2008 and 2010 under the HTAP framework (Janssens-Maenhout et al, 2015), originate from the MIX inventory (Li et al, 2017), based on earlier Asia inventories like INTEX-B (Lu et al, 2011;Lu and Streets, 2012) and REAS (Kurokawa et al, 2013). Inconsistencies are reported from merging datasets, calculating different pollutants using differing assumptions (Li et al, 2017).…”

“…Indian emissions for 2008 and 2010 under the HTAP framework (Janssens-Maenhout et al, 2015), originate from the MIX inventory (Li et al, 2017), based on earlier Asia inventories like INTEX-B (Lu et al, 2011;Lu and Streets, 2012) and REAS (Kurokawa et al, 2013). Inconsistencies are reported from merging datasets, calculating different pollutants using differing assumptions (Li et al, 2017). The datasets do not include some important regional emission sources like the open 30 burning of agricultural residues (Janssens-Maenhout et al, 2015).…”

“…The GEOS-Chem model has been previously applied to study PM2.5 over India (e.g., Boys et al 2014;Kharol et al 2013;Philip et al 2014a;Li et al 2017) including relating satellite observations of aerosol optical depth to ground-level PM2.5 for the GBD assessment (Brauer et al 2012van Donkelaar et al 2010van Donkelaar et al , 2015van Donkelaar et al , 2016. The simulations undertaken in this work represent one of the finest resolution efforts to date to both represent India, and global scale processes.…”