Evaluation and improvement of Air Pollutant emission inventory for Asian region by using Satellite column densities data. Gakuji KURATA*, Pichnaree Lalitaporn, Yuzuru Matsuoka, Kyoto University, Japan *E-mail: [email protected] 2012 ACCENT-IGAC-GEIA Conference, Emission to Address Science and Policy Needs, 11-13 June, 2012, Toulouse, France Acknowledgments This research was partially supported by the Ministry of Education, Science, Sports and Culture, Japan, Grant-in-Aid for Science Research (B) , 21360254 , 2012. and the Global Environment Research Fund (S-6) by the Ministry of the Environment of Japan. Satellite observations of tropospheric NO 2 vertical column densities (VCDs) over Southeast Asia including China and Japan are analyzed based on measurements from four satellite sensors; GOME, SCIAMACHY, OMI, and GOME-2 during the time period from 1996 to 2011. The inter-annual variations and the consistency between the different satellite datasets are investigated and compared with several emission inventory for Asian region. The tropospheric NO 2 VCDs over the study area have been simulated with Community Multi- scale Air Quality (CMAQ) model and then comparably analyzed with those retrieved from satellite observations in order to validate the accuracy of the emission inventories. The fifteen years tropospheric NO 2 VCDs data (1996-2011) from GOME, SCIAMACHY, OMI, and GOME-2 shows high increasing trends in China, especially in Beijing and Shanghai. Most of the results from the model simulations of horizontal tropospheric NO 2 VCDs distribution generally agree well with satellite measurements. Overall, the discrepancies among the CMAQ model and satellite retrievals are mainly due to inaccurate emission inventories fed into the model and the uncertainties in the satellite retrievals. However, as a result of the consistency between satellite-retrieved and model simulated tropospheric NO 2 VCDs, it suggests that integration of satellite data with air quality model can be used to evaluate and improve the accuracy of emission inventories. Abstract 1. Satellite retrievals: Satellite-based tropospheric NO 2 columns are retrieved from level-2 products of GOME, SCIAMACHY, OMI and GOME-2 published in the TEMIS website (http://www.temis.nl). 2. Emission inventory: REAS emission: Regional Emission inventory in Asia. MACCity emission: Global emission inventory. Kyoto Univ. emission(AIM): Regional Emission in Asia. 3. Model description: WRF 3.3 80km mesh (Jan –Dec , 2005) NCEP-CFSR (0.5degree) Noah land-surface model WSM 6-class graupel scheme CMAQ 4.7 Chemistry: CB-05- AERO5 Boundary condition : MOZART4 Methodology and Data Outline of the study GCM Output Landuse Terrain WRF Emission Mesh data Meteo. Field Calculated Concentration Health Impact Boundary Condition Chemical Transport Model CMAQ Co-benefit Analysis Death Disease Impact Assessment Exposure Outdoor Micro Environment Indoor ●Indoor Emission (Cooking, Heating, Hot water, Lighting) (Oil, Coal, Wood, Charcoal, etc) ●Time use data (Each Cohort) ● Room / House / Building parameter ● Ventilation condition Meteorological Model Target Area Local Administrative level N = 6,695 Large Point Source N = 16,956 Sectors Power Plant Iron and Steel Cement Petrochemical Paper and Pulp other Industry Passenger transportation Freight transportation Commercial Residential Target Year : 2005 (2010) (2020) (2030) Emission Inventory of Asian Countries Application to Asian Countries Emission Mesh Estimation of Emission Collection and Organization of Information of Large Point Source and Area activity ArcGIS Monthly average of CMAQ NO 2 VCD at Satellite over-pass time(10:30 LST): There are clear annual variation in northern part of China. It seems that there is no influence of a long-range transport. FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC (molecs/cm 2 ) Model output for NO 2 VCDs JAN Comparison between CMAQ output and Satellite NO 2 VCD The comparison between NO 2 VCDs from CMAQ & SCIAMACHY at Satellite over-pass (10:30 LST) The Ratio of NO 2 VCDs of Model vs. SCIAMACHY at satellite over-pass (10:30 LST) for every 3 month average. Summary • Regarding the qualitative relationship between the satellite NO 2 VCDs data and emission inventory around the megacities, it became clear that it is well in agreement especially in Beijing and Shanghai. • It was clearly shown that systematic errors exists in our original emission inventory used in the CMAQ simulation by the comparison between model simulation and satellite observation for Year 2005. • In particular, the systematic underestimate exists in the area along the shore of China and the Indochinese Peninsula. • On the other hand, overestimation was seen around several area and cities, such as northern India and Singapore. • The tendency of an underestimate may be strong in the winter of the Northern Hemisphere at high latitude. Our assumption of a seasonal variation may not be right. • It can be expected that this kind of analyses can provide compensation of emission source data with useful information. Seasonal Variability of NO 2 0 50 100 Jan-96 Jul-96 Jan-97 Jul-97 Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 Jan-11 Jul-11 NO 2 (10 15 molec. cm -2 ) Tropospheric NO 2 columns over Beijing GOME GOME-2 SCIAMACHY OMI 0 10 20 Jan-96 Jul-96 Jan-97 Jul-97 Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 Jan-11 Jul-11 NO 2 (10 15 molec. cm -2 ) Tropospheric NO 2 columns over Bangkok GOME GOME-2 SCIAMACHY OMI 0 10 20 Jan-96 Jul-96 Jan-97 Jul-97 Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 Jan-11 Jul-11 NO 2 (10 15 molec. cm -2 ) Tropospheric NO 2 columns over Jakarta GOME GOME-2 SCIAMACHY OMI Time series of monthly tropospheric NO 2 columns from GOME, SCIAMACHY, OMI & GOME-2 satellites for the Megacities in SEA including China & Japan from 1996-2011 were compared. Shanghai has the highest increasing trend of 21.5% per year followed by Beijing with 14.1% per year (Ref. year 1996). Mid/Low-latitude zone: maximum of tropospheric NO 2 columns can be seen during wintertime (November-February) & minimum during summertime (June-August). Equator-latitude zone: maximum of tropospheric NO 2 columns can be seen during dry season (June-August) & minimum during rainy season (December-February). Comparison of Satellite data and Emission inventories (REAS) The comparison of REAS NO x emissions & annual average of tropospheric NO 2 columns from GOME, SCIAMACHY & GOME-2 satellites during 1996-2009. The cities that located in mainland (Shanghai, Beijing, Bangkok, Hanoi and Phnom Penh): present relatively good relationships between REAS NO x emissions and tropospheric NO 2 columns (R > 0.7). The cities that located near coastal area (Naypyidaw, Dili, Singapore): the relationships between REAS NO x emissions and tropospheric NO 2 columns didn’t show a good agreement. We need to identify the reason. (emission? or meteorology? ) NO 2 columns: 27.80 % yr -1 REAS NO x : 5.00 % yr -1 R = 0.90 0 50000 100000 150000 0 10 20 30 40 REAS NO x (10 15 molec. cm -2 yr -1 ) Satellite NO 2 (10 15 molec. cm -2 ) NO x emissions & NO 2 columns: Shanghai GOME GOME-2 SCIAMACHY avg all NOx NO 2 columns: 14.56 % yr -1 REAS NO x : -0.75 % yr -1 R = -0.51 60000 65000 70000 75000 80000 0 5 10 15 REAS NO x (10 15 molec. cm -2 yr -1 ) Satellite NO 2 (10 15 molec. cm -2 ) NO x emissions & NO 2 columns: Singapore GOME GOME-2 SCIAMACHY avg all NOx linear(avg all) linear(NOx) linear(NOx) linear(avg all) Comparison of Satellite data & Emission inventory(MACCity) The long-term trend including seasonal variation were compared between MACCity NO x emissions & tropospheric NO 2 columns from satellites during 1996-2010. The cities that located in mainland (Shanghai, Beijing and Hanoi): the seasonal cycle of NO x emissions and tropospheric NO 2 columns are in good agreement (R > 0.65). The cities that located near coastal area: the correlations of MACCity NO x emissions and tropospheric NO 2 columns are low and the seasonal variation of tropospheric NO 2 columns from satellites were not clear. NO 2 columns: 16.39% yr -1 MACCity NO x : 7.02% yr -1 R = 0.68 0 10000 20000 30000 40000 50000 0 20 40 60 80 100 Jan-96 Jul-96 Jan-97 Jul-97 Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 MACCity NO x (10 15 molec. cm -2 yr -1 ) Satellite NO 2 (10 15 molec. cm -2 ) NO x emissions & NO 2 columns: Beijing GOME GOME-2 SCIA OMI avg all NOx NO 2 columns: 13.81% yr -1 MACCity NO x : -0.80% yr -1 R = -0.36 0 10000 20000 30000 40000 50000 0 5 10 15 20 Jan-96 Jul-96 Jan-97 Jul-97 Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 MACCity NO x (10 15 molec. cm -2 yr -1 ) Satellite NO 2 (10 15 molec. cm -2 ) NO x emissions & NO 2 columns: Singapore GOME GOME-2 SCIA OMI avg all NOx linear(avg all) linear(NOx) linear(NOx) linear(avg all)