1. Recent Development of the PKU-Fuel and PKU-Emission Inventory
College of Urban and Environmental Sciences, Peking University, Beijing, China
Speaker: Huizhong Shen

2. Introduction Emissions Transport RF Climate Implications Fuel Exposure
Health

3. PKU-FUEL Fuel scale: 222 countries/regions period: 1960~2030
spatial resolution: country level
temporal resolution: monthly
source category: 64
scale: global
period: 2002, 2007, 2008, 2011
spatial resolution: 0.1º×0.1º
temporal resolution: monthly
source category: 64

4. Global inventory of fuel combustion
Spatially-resolved
0.1º×0.1º: sub-national fuel for 45 countries, especially large countries (2002, 2007, 2008, 2011)
Country-level: 1960~2030
Reduce the spatial bias caused by uneven distribution of per-capita fuel consumption within countries
The uneven distribution of per-capita fuel consumption within large countries confirms the spatial bias if we use simple population-based aggregation at national scale
Provincial, county level, more than 8000 administrative units
(Wang et al., ACP 2013)

5. Global inventory of fuel combustion
Source information
64 source categories, 5 fuel types, 6 main sectors
coal
oil
natural gas
biomass
waste
Coal
Oil
Natural Gas
Solid waste
Biomass
Power stations
Industry
Residential
Agriculture
Transportation
Wildfires
Sector
Fuel
64 source categories

6. Global inventory of fuel combustion
Temporally-resolved: monthly variation
Residential sector
Climate factors (Heating degree days, Cooling degree days)
Socioeconomic indexes (Icap, UR, electricity price, housing unit)

7. Global inventory of fuel combustion
Temporally-resolved: monthly variation
Open fire (open straw burning, deforestation, wildfire)
GFED database
Transportation (in process)
Other sectors (constant)

8. Rural residential energy consumption
Fuel
Rural residential energy consumption
Energy structure in Chinese rural residence
50,000 questionnaires
Urban
Rural-Urban migrants
Population flows in China
Spatial reallocation of emissions
Pollutants cluster at where population gather
1300 questionnaires

9. Small scale coke production in Shanxi
Fuel
Small scale coke production in Shanxi
Satellite: Landsat-5 ( TM 751 )- short-wave length IR band, THERMO-SENSITIVE
Data source: CEODE, USGS, CSDB, GLCF
Year:

10. Small scale coking Fuel
A higher resolution of beehive coking emission inventory (30m×30m)
National scale

11. Development of emissions inventory
bottom-up method
Emission
Activity
Emission factor
Technology divisions
Time trends of technology divisions
Time trends of EFPAHs
Correlated with socioeconomic indexes
Index-dependent regression models
EFPAHs varied with indexes
S-shaped regression model
Regression

12. Development of emissions inventory
Greenhouse gases and air pollutants
CO2, PM (PM2.5, TSP, PM2.5), Black Carbon, CO, Organic Carbon, Hg, PAHs (naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benz(a)anthracene, chrysene, benzo(b)fluoranthene, benzo(k)fluoranthen, benzo(a)pyrene, dibenz(a,h)anthracene, indeno(l,2,3-cd)pyrene, benzo(g,h,i)perylene), SO2, N2O, NH3
PKU-FUEL can be used to estimate emissions of green house gasses and air pollutants
Desulfurization rate
Sulfur Dioxide, Nitrous Oxide, ammonia

13. Development of emissions inventory
Spatially resolved  2007
CO2
PM2.5 BC
PAHs Hg

14. Development of emissions inventory
Temporal trends Hg
(Chen et al., EST 2014) BC
(Wang et al., EST 2012)
PAHs
(Shen et al., EST 2013)

15. Thank You !