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27 November 2009

Introduction of REAS and adjoint inverse modeling of NO x emissions over eastern China using satellite observations. 27 November 2009. Jun-ichi Kurokawa National Institute for Environmental Studies, (NIES), Tsukuba, Japan. Brief review and future plans for REAS

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27 November 2009

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  1. Introduction of REAS and adjoint inverse modeling of NOx emissions over eastern China using satellite observations 27 November 2009 Jun-ichi Kurokawa National Institute for Environmental Studies, (NIES), Tsukuba, Japan

  2. Brief review and future plans for REAS (Regional emission inventory in Asia) J. Kurokawa1,*, T. Ohara1, H. Akimoto2, N. Horii3, K. Yamaji4, X. Yan5, and T. Hayasaka6. 1. National Institute for Environmental Studies, Tsukuba, Japan 2. Acid Deposition and Oxidant Research Center, Niigata, Japan 3. Faculty of Economics, Kyushu University, Fukuoka, Japan 4. Frontier Research Center for Global Change, Yokohama, Japan 5. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Science, Nanjing, China 6. Research Institute for Humanity and Nature, Kyoto, Japan.

  3. Summary of REAS version 1.1 Ohara et al.,ACP,7,4419-4444,2007; http://w3.jamstec.go.jp/frcgc/research/p3/emission.htm

  4. Basic methodology for REAS 1.1 Stationary Sources (Combustion) E = Σ(A/NCV)×S×(１－SR)×(１－R) SO2 E = ΣA×EF× (１－R) NOx, CO, BC, OC, … E Emissions A Energy Consumption NCV Net Calorific Value S Sulfur Contents of Fuel SR Sulfur Retention in Ash R Removal Efficiency EF Emission Factor Σ Fuel and Sector Type Stationary Sources (Non-Combustion) E = A×EF× (１－R) A Production/Population/… Road Transport E = ΣA × EF × FE / (SG×NCV) A Energy Consumption EF Emission Factor FE Fuel Economy SG Specific Gravity NCV Net Calorific Value Σ Vehicle Type Grid Allocation: ・Large Power Plants → Large Point Sources → Location of LPS ・Other Sources → Area Sources → Population (rural/urban/total), Road Network ・ Averaged emission factors for each fuel and sector type were used. ・ Temporal variations of EFs by emission regulation measuresetc. were not considered in detail. ・ Rcently, many new studies of Asian emissions have been reported. But REAS has not utilized them, yet.

  5. Historical emissions between 1980 and 2003 SO2 [Mt/yr] NOx [Mt/yr] CO [Mt/yr] NMVOC [Mt/yr] ・ Total energy consumptions in Asia more than doubledbetween 1980 and 2003. ・ Asian emissions show rapid growth rates: SO2:119%, NOx:176%, CO:64%, NMVOC:108% ・ NOx emissions in China show a marked increase of 280%over 1980 levels and in particular, the growth after 2000 is high.

  6. Time sereis of NOx emissions in China High NO2 VCDs Middle NOx (All data are normalized to 1996) Low Coal-fired Power Plants Increasing rates of REAS (00 → 05) 11.8% (SO2) 11.2% (NOx) 7.7% (CO) Gasoline Vehicles Zhang et al., ACP, 9, 5131-5153,2009

  7. Future plans for REAS ・Main focus is on China and, if possible,other East Asia, India and SE Asia. ・New information from recent literatures and databases will be collected. ・Emission factors will be updated considering the detailed technologies and their implementation reates for each year. ・Information about LPS including newly constructed or closed will be updated, and if we can get good data, large industrial plants will be treated as LPS. ・ GIS database, Monthly statistics , Meteorologica data, Satellite observations. ・ Results of inverse modeling will be utilized to update the data sets. ・ Future scenarios will be generated by AIM (Asia-Pacific Integrated Model) developed by NIES, Kyoto University, and other research institute.

  8. Adjoint inverse modeling of NOx emissions over eastern China using satellite observations of NO2 vertical column densities Jun-ichi Kurokawa1,*, Toshimasa Ohara1 Keiya Yumimoto2, Itsushi Uno2 1. National Institute for Environmental Studies, Tsukuba, Japan 2. Research Institute for Applied Mechanics, Kyushu University, Japan

  9. Introduction It is essential to prepare accurate emission inventories for atmospheric chemistry modeling. Bottom-up Approach Inverse Modeling Combinations of activity statistics and source- or region-specific EFs. There are uncertainties for statistics, parameters, and temporal and grid allocation profiles. The publication of basic statistics is generally a couple of years behind. Emissions are optimized to reduce the differences between simulated and observed data. It is a powerful method that helps solving the problems of bottom-up approach. We developed a 4DVAR data assimilation system for the optimization of NOx emissions based on a regional CTM and satellite observation data

  10. Model description – 4DVAR X(t) ４-Dimensional VARiational (４DVAR) data assimilation Optimal Solution Observation Simulation First Guess X０＊ The cost function is defined as a function of some input parameters in a numerical model. X０ｂ ０ T In 4DVAR data assimilation system, the parameters are adjusted to minimize the value of the cost function. In our inverse modeling system, surface NOx emissions are assigned as the parameters to be adjusted and then, the results will be the optimized emissions.

  11. Model description – RC4-NOx i = 1 A Priori Emissions CMAQ Base Model: Yumimoto and Uno, 2006 RAMS/CTM-4DVAR-CO A parameterized NOx Chemistry scheme was introduced into RAMS. Key parameters such as chemical production and loss terms of NOx were calculated in advance using full chemistry model CMAQ RAMS/CTM-4DVAR-NOx P(NOx), L(NOx) NO2/NOx Vd(NO), Vd(NO2) Forward Model Initial Condition P(NOx), L(NOx) NO2/NOx Vd(NO), Vd(NO2) Adjoint Model Observation GOME NO2 Column ① Forward Model → Cost Function ② Adjoint Model → Gradient of Cost Function ③ Optimization → Update of Emissions ④ Iteration（① ～ ③） ※ For each iteration step, parameters for NOx chemistry are also updated by running CMAQ. ⑤ Emissions passing the convergence criterion are the a posteriori emissions. Optimization Met. Fields RAMS (Pielke et al.,1992) ( in offline manner) A Posteriori Emissions Yes The Convergence Criterion No The Convergence Criterion Norm of the gradient of the cost function is reduced by 1% with respect to the initial norm. Updated Emissions i = i + 1 CMAQ

  12. Inversion experiments’ set-up 【Target Region for Emission Optimization】 The emission optimization and comparison between observed and model-simulated results were performed only over EC region in order to save the computational costs. 【A Priori Emissions】 ・ REAS 1.1NOx emissions for fuel combustion and soil sources. ・ TRACE-Pfor biomass burning emissions. ・ Seasonalityisonlyconsideredforsoilemissions. ・ The uncertainty of 500% is assigned for the background errors. 【Observation Data】 ・ Monthly averaged GOME NO2 vertical column densities (VCDs) ・ The observation errors are assumed to be the MAX(the absolute error 1.0×1015 molec./cm2, 20% of the observed NO2 VCDs) RAMS, CMAQ modeling domain with NOx emissions in July 2002 Horizontal Resolution : 80km Vertical Layer : RAMS/CMAQ 23/14with stretching grid layers (150m at the surface) EC : eastern China, BR : the Beijing region NCP : the North China Plain, YD : the Yangtze Delta 【Simulation period】 July 1996-2002

  13. Comparison of observed and model-simulated NO2 VCDs GOME-observed NO2VCDs NO2 VCDs increased clearly from both 1996 to 1999 and 1999 to 2002, especially around BR, NCP, and YD. Overall, NO2 VCDs increased nearly monotonically from 1996 to 2002. １９９６ NO2 VCDs without assimilation NO2 VCDs over EC increased monotonously from 1996 to 2002. However, the rate of increase was obviously lower than that of the GOME observation. The temporal variations over BR, NCP, and YD were much different from each other and from those of GOME observation. １９９９ ２００２ Assimilated NO2VCDs The spatial distribution and time evolution of GOME-observed data were generally reproduced. GOME Assimilated Without Assimilation

  14. A posteriori NOx emissions Differences between a priori and a posteriori NOx emissions (a posteriori minus a priori) -A priori emissions over BR were reduced by optimization during the whole simulation period. →The reductions at extremely high emission grid cells might be excessive because GOME has a coarse horizontal resolution and the NO2 VCDs for each observation grid were smoothed values. → In northern China, more energy is required for heating in winter, thus emissions in summer might be lower than annual average. - In NCP, emissions increased both in 1999 and 2002, but the area of increase was different. In 2002, emissions largerly increased in YD, in contrast to the changes in 1996 and 1999. - A priori emissions in 1996 were reduced in a wide area where soil NOx emissions were relatively high. → Soil NOx emissions in 1996 were prepared by linear interpolation of data for 1990 and 2001. Interannual differences are also important for emissions from soil sources.

  15. Trends of NOx emissions before and after optimization Emission differences between 1999 and 1996 (1999 minus 1996) Emission differences between 2002 and 1999 (2002 minus 1999) ・In general, changes in the spatial distributions of a posteriori emissions are larger than those of a priori emissions, especially in the eastern partof EC. ・The area of increasing emissions from 1996 to 1999 and from 1999 to 2002 are around BR, YD, and the center of NCP both in a priori and a posteriori emissions. ・The amounts of increase of a posteriori emissions are larger than those of a priori emissions. In particular, the differences are extremely large around YD. A priori emissions A posteriori emissions

  16. Trends of NOx emissions before and after optimization Eastern China The Beijing Region The rates of increase of a posteriori emission(▲) were larger than those of a priori emissions(■) and their trends became similar to those of GOME (●). The increase in emissions rates from 1996 to 2002 became much larger over each region. EC : 19%→49% BR : 14%→63% YD : 23%→101% NCP : 20%→54% Running RC4-NOx through a whole year is needed to evaluate annual emission amounts and their trends. The Yangtze Delta The North China Plain It is suggested that REAS 1.1 underestimates the rate of increase of NOx emissions from 1996 to 2002. All trends are normalized to 1999

  17. Conclusion REAS reveals that anthropogenic emissions in Asia show marked increases in these two decades. It is important not only to extend the target years but also consider the introducion of new technologies for Asian emissoin inventories. RAMS/CTM-4DVAR-NOx which optimizes NOx emissions using GOME observed NO2 VCDs was developed and applied to NOx emissions over eastern China in July from 1996 to 2002. - The increase in emissions rates from 1996 to 2002became larger overeastern China from a priori to a posteriori emissions. - It is suggested that REAS underestimates the rate of increase of NOx emissions for China from 1996 to 2002.

  18. Thank You for your Attention !

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