CMAQ Simulations of Long-range Transport of Air Pollutants in Northeast Asia

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CMAQ Simulations of Long-range Transport of Air Pollutants in Northeast Asia Fan Meng Youjiang He Chinese Research Academy of Environmental Sciences A&WMA International Specialty Conference: Leapfrogging Opportunities for Air Quality Improvement May 11, 2010, Xian, China

Introduction of LTP project The first workshop for long-range transport air pollutants over Northeast Asia held in Seoul, Korea 1995. The project was promoted to tripartite joint research by the agreement of TEMM and detailed research plan was established in 1999. Joint researches have been conducted with 2 research stages (1 st stage: 2000~2004, 2 nd stage: 2005~2007). Korea, China and Japan agreed on the extension of research period to 2012 (3 rd stage:2008~2012) at the 9 th Expert meeting held in Daegu, Korea 2006.

Project contents Working Group Monitoring Sub-working Group Intensive monitoring of air pollutants in China, Japan and Korea ground based and aerial measurements (SO 2, NO 2, O3, PM 10, PM 2.5 and ionic components) Modeling Subworking Group Modeling for long-range transport air pollutants concentrations, deposition and source-receptor relationship Emission inventory development of air pollutants

Modeling in LTP Project

Regional Modeling Systems in LTP Model system China Japan Korea Models-3 / CMAQ coordinate 14 layers, 70 66 grids, 60km resolution (Byun and Ching, 1999) RAQM (Regional Air quality Model) terrain following coordinate 12 layers, 110 80 grids, 60km resolution (An et al., 2002) CADM (Comprehensive Acid Deposition Model) terrain following coordinates 12 layers, 110 80 grids, 60km resolution (Lee et al., 1998) Domain 20 50N, 100 150E 20 50N, 100 150E 20 50N, 100 150E Meteorologic al Model Chemical Mechanism MM5/WRF 34 layers with FDDA using NCEP reanalysis CBM-IV Chemistry (36species 93 reactions) MM5 125 95 (45km), 23 layers, FDDA using NCEP FNL reanalysis CBM-IV mechanism (36species 93 reactions) CSU-RAMS 110 80, 29 vertical layer FDDA using NCEP FNL reanalysis RADM Chemistry (57species 158 reactions) Cloud Model Physical option Diagnostic cloud model in RADM Simple explicit moisture scheme Grell cumulus schemes, MRF Emission SO 2, NOx, VOC, NH 3, CO, PM 10, biogenic VOC provided by LTP for the base year of 1998 (1 1 resolution) Dry deposition Wet deposition Land use type Wesely's parameterization (Wesely, 1989) RADM Module (Chang et al, 1987) EPA/NOAA global ecosystem (11 categories) Cloud model in MM5 Betts-Miller cumulus scheme, MRF RRTM Same as Modified Wesely's parameterization (Walmsley & Wesely, 1996) RADM Module (Chang et al, 1987) DeFries & Townshend (1994) Cloud model in CSU-RAMS Anthes-Kuo cumulus scheme, MRF Same as Dry deposition module in RADM (Lee et al, 1998) RADM Module (Chang et al, 1987) EPA/NOAA global ecosystem (11 categories)

CMAQ Chemical Transport Model & Interface Processors MM5,WRF MCIP Meteorology- Chemistry Interface Processor SMOKE LUROC Land Use Processor CMAQ Chemical Transport Model (CCTM) ICON & BCON Initial and Boundary Conditions Processor JPROC Photolysis Rate Processor Advection Diffusion Gas Phase Chemistry Plume-in- Grid Treatment Aerosol Chemistry and Dynamics Cloud Chemistry and Dynamics Process Analysis Visualization

Vertical Layers: sigma-pressure coordinate top is 100 hpa(15km). MM5/WRF: 32 sigma levels an 31 half sigma levels (layer) 1.0000(15km) 0.9975, 0.9950, 0.9900, 0.9800, 0.9700, 0.9600, 0.9400, 0.9200, 0.9000, 0.8750, 0.8500, 0.8200, 0.7900, 0.7550, 0.7200, 0.6850, 0.6500, 0.6150, 0.5800, 0.5500, 0.5000, 0.4500, 0.4000, 0.3500, 0.3000, 0.2500, 0.2000, 0.1500, 0.1000, 0.0500, 0.0000 (0m) CMAQ:15 Levels (14 Layers) 1.0 (15km) 0.995, 0.99, 0.98, 0.96, 0.94, 0.91, 0.86, 0.80, 0.74, 0.65, 0.55, 0.4, 0.2, 0.0 (0m)

Domain of LTP Project MM5 77x73, 60km CMAQ 70x66, 60km Lambert conformal center is at (120E,36N), two standard parallels are 25N and 47N.

Meteorology MM5 used in simulation of 2002. (LTP-2008). The PSU/NCAR mesoscale model is a limited-area, nonhydrostatic or hydrostatic to simulate or predict mesoscale and regional-scale atmospheric circulation that developed from a mesoscale model used by Anthes at Penn State in the early 70's. Since that time, it has undergone many changes designed to broaden its usage. These include (i) a multiple-nest capability, (ii) nonhydrostatic dynamics, which allows the model to be used at a few-kilometer scale, (iii) multitasking capability on shared- and distributed-memory machines, (iv) a fourdimensional data-assimilation capability, and (v) more physics options. WRF used in simulation of 2002. (LTP-2009) The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. It features multiple dynamical cores, a 3-dimensional variational (3DVAR) data assimilation system, and a software architecture allowing for computational parallelism and system extensibility. WRF is suitable for a broad spectrum of applications across scales ranging from meters to thousands of kilometers.

Air Pollutants Emission Control in China SO 2 Emission (10 6 tonne/a)

LTP 项目中国模式污染源排放分布

Modeling Results for Northeast Asia in March, June, Sep., Nov., of 2002 and March, July, Oct., Dec, of 2006

SO2 concentration of 2002 SO2 concentration of 2006 NOx concentration of 2002 NOx concentration of 2006

SO2 concentration of four seasons in 2006 NOx concentration of four seasons in 2006

Ground Level Sulfate Concentration in 2002 Ground Level Sulfate Concentration in 2006

Nitrate Concentration in 2002 Nitrate Concentration in 2006

Total dry deposition of sulfate and nitrate aerosols in 2002 Total wet deposition of sulfate and nitrate aerosols in 2002

Annual averaged dry deposition of sulfate and nitrate aerosols in 2006 Annual averaged wet deposition of sulfate and nitrate aerosols in 2006

Comparisons with measurements for SO2 Models v.s. surface sites Models v.s. aircraft measurements

Comparisons among three models for annual averaged SO2 concentration of 2002

Comparisons among three models Observation Simulation (China) Simulation (Japan) Simulation (Korea) Sample size 1939 173 1939 1939 Range(ppb) ~ 13.51 0.7 ~ 50.4 0.001-59.85 0.003-49.4 Mean(ppb) 3.75 2.84 1.55 3.78 Standard deviation (ppb) 6.79 2.50 1.90 5.54 Mean of ratio model/obs (S/O) 0.91 1.99 4.25 Standard deviation of ratio model/obs (S/O) 1.37 6.81 19.0 Absolute gross error 8.30 3.02 3.33 Correlation coefficient 0.54 0.22 0.53 Mean difference 8.06 2.20-0.03 Difference standard deviation 8.48 6.62 6.58 Root-mean square error 11.69 6.97 6.58 Mean square error. MSEN 5.05 3.44 24.66 Mean square error. MSES 132.01 45.29 19.71 Index of agreement 0.40 0.29 0.62 Mean fractional error 1.03 0.16-0.28

Source-Receptor Relationship 100% SO2 emis. cut or 20% NOx emis. cut region I region II region III region IV region V Receptor region I region II region III region IV region V

SO2 Emission Scenarios for LTP Project: I I II III IV V

(a) Contribution from region I (b) Contribution from region II (c) Contribution from region III Fig 9. Contribution of SO2 emission to S deposition for 2002 (Source-Receptor Relationship) (%). (d) Contribution from region IV (e) Contribution from region V

350 400 350 350 300 350 300 300 250 th V n o m200 I V / S I I I n o150 t I I k 100 I 50 0 I I I I I I I V V S deposi t i on of Mar ch 300 h t n250 o m / S200 n o t k150 100 50 0 I I I I I I I V V S deposi t i on of Jul y V I V I I I I I I 250 h t n200 o m / S150 n o t k100 50 0-50 I I I I I I I V V S deposi t i on of Oct ober V I V I I I I I I h250 t n o m200 / S n o150 t k 100 50 0 I I I I I I I V V S deposi t i on of December V I V I I I I I I 4500 4000 3500 r a3000 e y / 2500 S n o2000 t k 1500 1000 V I V I I I I I I 500 0 I I I I I I I V V S deposi t i on of 2002 S deposition of 5 LTP regions

Contribution from sources to receptors of sulfur deposition in 2002 0. 49 I 1. 17 I I I I I 1. 21 I V V 7. 38 0. 39 0. 87 3. 91 I I I I I I I V V 0. 25 0. 66 1. 27 17. 65 I I I I I I I V V 39. 63 57. 49 Cont r i but i on t o r egi on I ( %) 87. 45 Cont r i but i on t o r egi on I I ( %) 80. 17 Cont r i but i on t o r egi on I I I ( %) 2. 01 4. 26 13. 7 2. 09 I I I I I I I V V 5. 11 21. 78 I I I I I I I V V 1. 48 64. 6 7. 02 77. 94 Cont r i but i on t o r egi on I V ( %) Cont r i but i on t o r egi on V ( %) Note: SO2 emission from the volcanic island of Miyakejima, Japan not included. The total SO2 emission amounts to 18 Mt from mid of Aug. 2002 to at least December 2003.

S/R Table 5. Contributions of 20% NOx emission of sources regions to the total nitrate deposition of receptor regions in 2006 I II III IV V Total Dep. from 20% NOx Emi. of ith Region ton % ton % ton % ton % ton % ton % 0.78 0.34 I 42210 6.41 14096 5352 4952 1.91 27207 1.78 93817 11.2 9 2 27469 II 91574 13.9 15.4 121505 7.76 24222 9.35 87465 5.72 599461 52.1 5 III 2676 0.406 70626 3.96 167030 10.7 2864 1.11 12610 0.82 255806 17.0 0.12 0.15 IV 2064 0.313 2308 2376 9911 3.82 31068 2.03 47727 6.44 9 2 0.10 0.13 V 567 0.086 1808 2109 6324 2.44 134122 8.77 144930 11.5 1 5 Total Dep. of ith Region 13909 1 21.1 36353 3 20.4 298372 19.1 48273 18.6 292472 19.1 1141741 98.3 Tot al Ni t r at e Deposi t i on f r om 20% NOx Emi. of i t h Regi on 400 350 300 250 200 150 100 50 0 a / N n o t k I I I I I I I V V V I V I I I I I I

Climate and Visibility Aerosol Extinction by CMAQ 1. Mie extinction 2. Reconstructed extinction, an empirical approach by Malm et al., 1994, Sisler,1998 Byun, D.W., Ching, J.K.S., 1999. Science algorithms the EPA Model-3 community multiscale air quality(cmaq) modeling system.

Mie extinction at1 st layer in 2002, Reconstructed extinction at1 st layer in 2002, Mie extinction at 6 st layer in 2002, Reconstructed extinction at6 st layer in 2002,

Summary MM5/WRF-SMOKE-CMAQ modeling system has been installed for LTP simulation in northeast Asia. Preliminary comparison showed that the modeling work is acceptable for long-term simulation. The source-receptor relationship for sulfate and nitrate base on long term simulation have been conducted. There are still uncertainties for detailed or short term simulation. Emission inventory is the biggest source of uncertainties especially for mobile source, bio mass burning and biogenic source. Emission data and the preprocessing such as spatial allocation, temporal profile method need to be improved. Volcanoemissions are not included in 2002 and 2006.

Thank You