CMAQ Simulations of Long-range Transport of Air Pollutants in Northeast Asia
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1 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
2 Introduction of LTP project The first workshop for long-range transport air pollutants over Northeast Asia held in Seoul, Korea The project was promoted to tripartite joint research by the agreement of TEMM and detailed research plan was established in 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.
3 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
4 Modeling in LTP Project
5 Regional Modeling Systems in LTP Model system China Japan Korea Models-3 / CMAQ coordinate 14 layers, grids, 60km resolution (Byun and Ching, 1999) RAQM (Regional Air quality Model) terrain following coordinate 12 layers, grids, 60km resolution (An et al., 2002) CADM (Comprehensive Acid Deposition Model) terrain following coordinates 12 layers, grids, 60km resolution (Lee et al., 1998) Domain 20 50N, E 20 50N, E 20 50N, E Meteorologic al Model Chemical Mechanism MM5/WRF 34 layers with FDDA using NCEP reanalysis CBM-IV Chemistry (36species 93 reactions) MM (45km), 23 layers, FDDA using NCEP FNL reanalysis CBM-IV mechanism (36species 93 reactions) CSU-RAMS , 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)
6 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
7 Vertical Layers: sigma-pressure coordinate top is 100 hpa(15km). MM5/WRF: 32 sigma levels an 31 half sigma levels (layer) (15km) , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , (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)
8 Domain of LTP Project MM5 77x73, 60km CMAQ 70x66, 60km Lambert conformal center is at (120E,36N), two standard parallels are 25N and 47N.
9 Meteorology MM5 used in simulation of (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 (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.
10 Air Pollutants Emission Control in China SO 2 Emission (10 6 tonne/a)
11 LTP 项目中国模式污染源排放分布
12 Modeling Results for Northeast Asia in March, June, Sep., Nov., of 2002 and March, July, Oct., Dec, of 2006
13 SO2 concentration of 2002 SO2 concentration of 2006 NOx concentration of 2002 NOx concentration of 2006
14 SO2 concentration of four seasons in 2006 NOx concentration of four seasons in 2006
15 Ground Level Sulfate Concentration in 2002 Ground Level Sulfate Concentration in 2006
16 Nitrate Concentration in 2002 Nitrate Concentration in 2006
17 Total dry deposition of sulfate and nitrate aerosols in 2002 Total wet deposition of sulfate and nitrate aerosols in 2002
18 Annual averaged dry deposition of sulfate and nitrate aerosols in 2006 Annual averaged wet deposition of sulfate and nitrate aerosols in 2006
19 Comparisons with measurements for SO2 Models v.s. surface sites Models v.s. aircraft measurements
20 Comparisons among three models for annual averaged SO2 concentration of 2002
21 Comparisons among three models Observation Simulation (China) Simulation (Japan) Simulation (Korea) Sample size Range(ppb) ~ ~ Mean(ppb) Standard deviation (ppb) Mean of ratio model/obs (S/O) Standard deviation of ratio model/obs (S/O) Absolute gross error Correlation coefficient Mean difference Difference standard deviation Root-mean square error Mean square error. MSEN Mean square error. MSES Index of agreement Mean fractional error
22 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
23 SO2 Emission Scenarios for LTP Project: I I II III IV V
24 (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
25 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 k 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 k 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 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 r a3000 e y / 2500 S n o2000 t k V I V I I I I I I I I I I I I I V V S deposi t i on of 2002 S deposition of 5 LTP regions
26 Contribution from sources to receptors of sulfur deposition in I I I I I I I V V I I I I I I I V V I I I I I I I V V Cont r i but i on t o r egi on I ( %) Cont r i but i on t o r egi on I I ( %) Cont r i but i on t o r egi on I I I ( %) I I I I I I I V V I I I I I I I V V 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 to at least December 2003.
27 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 % I II III IV V Total Dep. of ith Region Tot al Ni t r at e Deposi t i on f r om 20% NOx Emi. of i t h Regi on a / N n o t k I I I I I I I V V V I V I I I I I I
28 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., Science algorithms the EPA Model-3 community multiscale air quality(cmaq) modeling system.
29 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,
30
31 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.
32 Thank You
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