Constraining a Historical Black Carbon Emission Inventory of the United States for 1960–2000

Abstract

We present an observationally constrained United States black carbon emission inventory with explicit representation of activity and technology between 1960 and 2000. We compare measured coefficient of haze data in California and New Jersey between 1965 and 2000 with predicted concentration trends and attribute discrepancies between observations and predicted concentrations among several sources based on seasonal and weekly patterns in observations. Emission factors for sources with distinct fuel trends are then estimated by comparing fuel and concentration trends and further substantiated by in‐depth examination of emission measurements. We recommend (1) increasing emission factors for preregulation vehicles by 80–250%; (2) increasing emission factors for residential heating stoves and boilers by 70% to 200% for 1980s and before; (3) explicitly representing naturally aspired off‐road engines for 1980s and before; and (4) explicitly representing certified wood stoves after 1985. We also evaluate other possible sources for discrepancy between model and measurement, including bias in modeled meteorology, subgrid spatial heterogeneity of concentrations, and inconsistencies in reported fuel consumption. The updated U.S. emissions are higher than the a priori estimate by 80% between 1960 and 1980, totaling 690 Gg/year in 1960 and 620 Gg/year in 1970 (excluding open burning). The revised inventory shows a strongly decreasing trend that was present in the observations but missing in the a priori inventory.

A Mineralogy‐Based Anthropogenic Combustion‐Iron Emission Inventory

“…Technology and emission control distributions are estimated for the year 2010 using region-and time-dependent penetration curves in SPEW (Bond et al, 2004(Bond et al, , 2007Hoesly et al, 2018;Lamarque et al, 2010;Sun et al, 2019) for coal, liquid fuels, and biofuels and added for steel production from World Steel Association (WSA, 2015) database for the year 2010. Coal, wood, and oil are combusted in residential (cook stoves and its types), industrial and power (pulverized, fluidized bed, and stoker), transportation (road, rail, and shipping), and miscellaneous (flares, brick kilns, cement manufacturing, and off-road machineries) sectors and the portion of a fuel combusted in each technology varies by country/region-with high-income countries generally using comparatively higher amount of low-emission technologies (Bond et al, 2004(Bond et al, , 2007 than many middle-and low-income countries in the year 2010; detailed information on such selection of technology distribution is available in Bond et al (2004) but discussed here briefly for the smelting sector.…”

“…Iron emissions from nonferrous metal production dominate in some regions such as Chile where copper production exceeds iron and steel production (Brown et al, 2013). Industrial and power-related iron emissions dominate in coal combustion activities (Table 10.1029/2019JD032114 S6), especially from regions with relatively lower PM control measures (Bond et al, 2007;Hoesly et al, 2018;Lamarque et al, 2010;Sun et al, 2019). Among liquid fuel emissions, heavy fuel oil and diesel combustion dominate with about 80% (at 14 Gg/yr) and 90% (at 16 Gg/yr) of emissions in fine and coarse fractions, respectively (Table S6).…”

The determination of highly time-resolved and source-separated black carbon emission rates using radon as a tracer of atmospheric dynamics

“…Bottom-up emission inventories rely on information on the amount of used fuel combined with fuel-specific emission factors (e.g., Bond et al, 2007;Bond et al, 2013;Klimont et al, 2017). Although current emission inventories agree quite well on the main emission sources and regions, there exist significant uncertainties in the emission factors and activity data, used for emission calculation, with recent observationally constrained estimations much higher than the ones traditionally used (Sun et al, 2019). In contrast to bottom-up emission inventories, top-down constrained methods (such as inverse modelling) focus on minimizing the difference between simulated pollutant concentration, based on estimated emission flux, and measured pollutant concentration (Brioude et al, 2013;Wang et al, 2016b;Guerrette and Henze, 2017).…”

“…According to the European Union emission inventory report (LRTAP, 2018), 0.2 Tg of BC was emitted in 2016 in the EU-28 region, with the dominant energy-related emissions from on-road and non-road diesel engines accounting for about 70 % of all anthropogenic BC emissions (Bond et al, 2013). A recently updated United States black carbon emission inventory (Sun et al, 2019) pointed out a decreasing trend of BC emissions from 1960 to 2000, dominated by the vehicle, industrial and residential sectors. Traffic-related BC emission primarily dominates particulate matter (PM) emission, especially in major cities (e.g., Pakkanen et al, 2000;Klimont et al, 2017).…”

“…Therefore, assessment of BC emission rate requires decoupling of meteorologically driven variation from the dynamics of the sources. On diurnal timescales, atmospheric stability and dynamics play a key role in the variability of primary inert pollutants (e.g., Quan et al, 2013;McGrath-Spangler et al, 2015;Tang et al, 2016), such as BC, and are affected by them (e.g., Ferrero et al, 2014). The evolution of the planetary boundary layer (PBL), the lowest part of the troposphere, is driven by convective heat transfer from the ground surface and by mechanical mixing (due to wind shear and surface roughness), which are responsible for the formation of the turbulent mixing layer (ML).…”