Rong Wang Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, China Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ, France Rong Wang, Shu Tao, Philippe Ciais, Huizhon Shen, Ye Huang, Han Chen, Xuhui Wang, Shilong Piao, Guofeng Shen, Bin Wan, Yanyan Zhang

An Introduction for the PKU-??? Emission Products

Motivations  A growing need for CO 2 emission inventory with a high resolution because of the emerging of high resolution observation platforms and models.  Detailed categorization of fuel consumption by sector is important for making the sector-based mitigation polices as well as identifying the different emission rate.  Traditional national population/nightlight proxy may lead to a high bias in space.  A high-resolution fuel combustion inventory can provide other atmospheric researches with a reliable emission inventory, such as BC, PAHs, Hg, CO, et al.

by Gurney et al., Environ. Sci. Technol. 2009, 43, 5535–5541 Influence of sub-national fuel data on modeling CO 2 in the atmosphere

by Wang and Tao, in preparation Influence of sub-national fuel data on modeling black carbon in the atmosphere

64 types of fuel combustion process CoalOil Natural Gas Solid waste Biomass Energy Production Industry ResidentialAgriculture Transporta tion Wildfires 64 combustion sources A total of 223 countries/territories in 11 regions : Eastern and Southern Africa Northern Africa Western and Central Africa East Asia Oceania South and South-east Asia Western and Central Asia Europe Caribbean and Central America North America South America FuelSector Methodology

Detailed Classification of 64 Combustion Processes

Detailed Classification of 64 Combustion Processes (continued)

Key for PKU-FUEL and PKU-CO2: Subnational disaggregation method Sub-national fuel consumption data of the major fuel types were collected for 45 countries (  ×0.5°grids for 36 European countries (EC-36), 7942 counties for China, Mexico, and U.S., 161 states/provinces for India, Brazil, Canada, Australia, Turkey, and South Africa). Sub-national data for sub-national units of the 45 countries and national data for other countries were disaggregated to 0.1  ×0.1°grids using various proxies. Wang, R.; Tao, S.; et al. Atmos. Chem. Phys. Discuss., 2012a, 12,

VULCAN INVENTORY

Monte Carlo Simulation Variations in inputs were quantified by coefficients of variations (CVs) of given distributions: CVs for shipping fuels (20%) and wildfire (18%) were from the literature (normal distribution) CV of 5% was used for all other fuels from energy statistics (uniform distribution) CVs for uncombusted rates and emission factors were 20 and 5%, respectively (normal distribution) CVs for the uncertainties in population aggregation were assigned to individual countries depend on their areas. 1000% was used for Asian Russia (225,829 grids), and CVs for other countries were calculated as1000%  log(N)/log(225,829), where N is the number of grids in individual countries (normal distribution) Uncertainties are presented as CI 95 (95% confidence interval) for normal distribution or R 90 (95th minus 5th percentile range, in absolute term) and R 90 /M (R 90 over median, in relative term) for abnormal distribution Uncertainty Analysis

A summary of PKU-FUEL and PKU-CO2 1)A original sub-national spatial aggregation system was developed and applied for 45 countries, especially large countries 2)Fossil, biomass, and solid waste fuels were further divided into 64 sub-types in 6 economic sectors 3)Uncertainty of PKU-FUEL and PKU-CO2 was quantified, including that caused in spatial allocation 4)PKU-FUEL can be used to estimate emissions of many air pollutants

Results Map of fuel consumption (0.1°×0.1°) in 2007Map of Per capital fuel consumption (0.1°×0.1°) in 2007 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

Map of CO 2 emission from all combustions (0.1°×0.1°) in 2007

Uncertainty of CO 2 emission map Mean R 90 /M of the emissions in all grids were lower than 20% in the 45 sub- nationally aggregated countries and 187% in other nationally aggregated countries, indicating a significant reduction in the uncertainty by sub-national aggregation method. Uncertainty of total CO 2 emission Absolute Uncertainty Relative Uncertainty

Comparison to national aggregation approaches National aggregationSub-national aggregation The calculated RD represents the relative deviations induced by national aggregation, which was just the uncertainty reduced by sub-national aggregation approaches. Vs Map of relative difference (RD) between the two inventories

Comparison to national aggregation approaches The average RD a were 121, 93.8, and 43.7% for all counties in China, Mexico, and U.S. and 36.8, 55.0, and 35.2% for all provinces/states in India, Brazil, and Australia, respectively. Implying : 1. Spatial bias of the national aggregation method was larger for developing countries with relatively high unbalanced development 2. The smaller the regions used in the sub-national aggregation, the greater the reduction in the bias would be

Comparison to Vulcan CO 2 inventory Vulcan CO 2 inventory: 2002PKU-CO 2 inventory: 2007 Plot of the two inventories 1)Detailed traffic data were not available in our study 2)Area sources were uniformly allocated to 0.1°×0.1° grids within counties in the Vulcan inventory 3)Shipping emissions in the Great Lakes were not included in the Vulcan inventory Major differences:

Comparison to ODIAC: a nightlight-based inventory ODIAC CO 2 inventoryPKU-CO 2 inventory While intensive nightlights do reflect high CO 2 emissions in metropolitans, it cannot reflect the emissions in rural regions, small cities, or areas where industry / agricultural activities were concentrated.

Implications of PKU-CO2

Implications 1: Inverse modeling The average relative difference for China, Mexico, and U.S. (county-level) were 71%, 61%, 49%, while that for India, Brazil, and Australia (state-level) were 54%, 13%, and 28%, respectively. National aggregation inventory Sub-national aggregation inventory Global carbon balance of terrestrial ecosystem was calculated based on both nationally and sub-nationally aggregated inventories using CarbonTracker. The difference between them can be used to evaluate the effect of the sub-national aggregation on the calculated geographical pattern of carbon balance.

Difference of carbon balance between using national and subnational disaggregation data

Implications 2: Urban/rural CO 2 emission The difference in CO 2 emissions between developed and developing countries occurred mainly in rural areas, and it was close in urban areas. Due to the fast urbanization rate in developing countries, rapid increase in carbon emission seems to be inevitable without effective measures taken.

How to make use of PKU-CO2 to study the contribution of forcing in the atmosphere by CO2 emitted in China?

Temporal Change

谢谢！