An Integrated Analysis of Asian Outflow Events Using Comprehensive Emissions in Support of ACE- Asia and TRACE-P J.-H. Woo 1, G.R. Carmichael 1, D.G. Streets 2, Y. Tang 1, C. Jang 3, J. S. Fu 4, L. Pan 1, N. Thongboonchoo 1 1. Center for Global and Regional Environ. Research, Univ. Iowa, USA; 2. Argonne National Laboratory, USA; 3. U.S. Environmental Protection Agency, Research Triangle Park, USA; 4. Department of Civil and Environmental Engineering, University of Tennessee, USA Reconstruction of Emissions and Suggestions to Improve Understanding Emissions Development Schematic methodology for the development of Asian emission estimates Definition of the inventory domain, its constituent countries, international shipping lanes, and sub-regions An inventory of air pollutant emissions in Asia in the year 2000 is developed to support atmospheric modeling and analysis of observations taken during the TRACE-P and the ACE-Asia experiment. We estimate total Asian emissions as follows: 34.3 Tg SO 2, 26.8 Tg NO x, 9870 Tg CO 2, 279 Tg CO, 107 Tg CH 4, 52.2 Tg NMVOC, 2.54 Tg black carbon (BC), 10.4 Tg organic carbon (OC), and 27.5 Tg NH 3. In addition, NMVOC are speciated into 19 sub-categories according to functional groups and reactivity. Thus, we are able to identify the major source regions and types for many of the significant gaseous and particle emissions that influence pollutant concentrations in the vicinity of the TRACE-P and ACE-Asia field measurements Emissions Processing To support field experiments and complex atmospheric models, highly resolved levels of spatial, temporal, and species-component detail are needed in the emission inventories. It is also important that these emission fields correctly reflect the spatial and temporal emission profiles of sources that were operating during the time period of the field campaigns, otherwise good agreement between models and experiment cannot be expected. High-Resol (2 min) Gridded Emissions (~ 300 maps) Low-Resol (1 deg) Gridded Emissions (39 Maps) 3 Months 2 Types 1(3) Sectors 3304~830 Species SectorsSpecies Biogenic by grid(1deg) Biomass Burning by Region(52) Anthropogenic by Region(77) Spatial Interpolation Spatial/ Temporal Allocation Land/Sea Masking Spatial Allocation Spatial Aggregation Re-arrange by sector/layer Map projection Unit Change DB Export Multi-Resol (6 & 18min) Gridded Emission (~ 600 maps) Multi-Resol (12 & 36km) Gridded Emission (~ 600 maps) Multi-Resol (12 & 36km) Gridded Emission (22 DB files) Clip Domain Feed into Chemical Transport Models Start with Tabular database Emissions Distribution Knowledge of source strengths and locations is also a valuable aid for interpreting observations and model results and ultimately choosing appropriate mitigation strategies. Fossil Biofuel Source strength by emissions categories March 1~3, 2001 March 3~6, 2001 March 7~9, 2001 March 10~12, 2001 Biomass Burning March 13~15, 2001 March 16~18, 2001 March 19~21, 2001 NMVOC Composition by Energy Sectors In summary, for all of Asia we estimate that 27% of NMVOC emissions are alkanes, 22% alkenes, 5% ethyne, 17% aromatics, 9% aldehydes and ketones, and 20% halocarbons and other organic compounds. This kind of distribution is typical of all of Asia except the developed or industrializing parts of East Asia (e.g. Japan; S. Korea; and Taiwan). In this region, the share of alkanes is higher (~ 35%) and the share of alkenes is much lower (~ 9%). This is indicative of greater use of petroleum products, particularly for transportation, and far less vegetation burning. Particle Composition by Different Source Categories Biofuel use dominates the emission profiles of ethane (45%), ethene (50%), and ethyne (45%). Emissions of butanes are primarily from transportation(58%). Formaldehyde emissions (56%) are mainly from biomass burning, and toluene emissions (45%) are mainly from industry. Integrate Analysis using Aerosol Compositions Satellite Observation vs. Process-based Emissions Not done yet! A sample analysis that combines satellite image, measured concentration (BC from C130), measured wind vector, emissions distribution, forward CTM (STEM 2K1) and backward receptor model (EPA, CMB 8.0) showed consistent features for ACE-Asia C130 Flight 7. The elemental compositions of five source categories were used as source signatures. These information is new and useful to interpret source contribution from the CMB model for each selected outflow event. TOMS Aerosol Index Dust Dust & BB Emissions April 7, 2001 April 9, 2001 April 14, 2001 From STEM 2K1 Model (ACE-Asia, Apr 12, 2001) Red : Fossil Fuel Combustion Pink : Biofuel Combustion and Biomass Burning Yellow : Wind-Blown Dust (Crustal) From Chemical Mass Balance Model (Bottom Fig.) Background : Satellite Image (VIS channel) Red Dots : BC Emissions (Anthropogenic) Colored Arrows : Measured Wind Vector with Black Carbon Concentration (C130-7R, 2001/4/12) Apr 12, 03:00 Apr 12, 08:00 Process-based emissions estimates reveal the source of Asian dust and Biomass burning outflow observed from NASA TOMS satellite. Biomass Burning Ship, Flight, and Ground Measurements Model vs. Model Modeling domain with countries/regions sets for source-receptor calculations, measurement site locations ( ) receptor locations (×): 1) Kangwha; 2) Komae; 3) Taichung; 4) Nanjing; 5) Jinan; 6) Beijing; 7) Nangoku; 8) Otobe; 9) Amami; 10) Kashima; 11) Yangyang; 12) Tokoro; 13) Hachijo; 14) Oki; 15) Tsushima; 16) Fukue; 17) Miyako; 18) Taitong. Source : MICS-Asia ( Model InterComparison of Long-Range Transport and Sulfur Deposition in East Asia ) Phase 1 Kangwha (Korea) Tsushima (Japan) Dots: Measurements Whiskers: Model results The CTMs that are participated in MICS-Asia (Model InterComparison of Long-Range Transport and Sulfur Deposition in East Asia) show consistency as well as inconsistency among models and between observation vs. prediction. The CTM gives good scientific understandings with some degree of uncertainties, though. Intercomparison with monitoring data and models for the January period 2-D and 3-D analysis features for DC8 flight 9 (March 9th). Left: 3-D back trajectories (5-day) colored with measured trace gas mixing ratio (light purple line: 3.4GMT, purple line: 3.3GMT, red line: 2.5GMT); fossil fuel (light blue dots), biofuel (yellow dots), biomass burning (red dots) emissions; background map - topography colored by landcover. Right: Source sector contribution by CMB model and 5day 2-D back trajectories. 2-D and 3-D analysis features for DC8 flight9 (March 10th), Left: same as uppermost figure, but (light blue: 3.3GMT, red: 3.5GMT, light red: 5.2GMT, orange: 7.6GMT, yellow: 7.9GMT), Right: same as uppermost figure. Selected DC8 flight and measurement points used in the analysis Geographical distribution of temporally averaged (monthly) biomass burning CO emissions Evaluation measures of the CMB model estimates Five DC8 flights (e.g. flight 6, 8, 9, 10, and 12) with16 flight segments were selected as outflow events. Four chemical species (ethane, propane, butanes, and acetylene) out of 27 were selected as Chemical Mass Balance (CMB) model input We analyzed spatial and temporally resolved emission data, backward trajectory analysis, 3D chemical source model, and wind field information to interpret source contribution from the CMB model for each selected outflow event. Flights in the post frontal regions at high latitudes and low altitudes were found to have a high contribution of fossil fuel emissions. Flights in the warm sector of cold fronts were dominated by biomass burning contributions (about 70%). Biofuel contributions were high (about 70%) when air masses come from central China. Integrate Analysis using Gaseous Chemical Compositions > 3.7 Estimated Emissions Observation Based Reconstruction Reconstruction of emissions field using measurement & trajectory Reconstruction of emissions field can one way to re-create useful information to improve our understanding.

> 3.7 Can Useful Emissions Information Be Reconstructed Using Observed Ratios (or Concentrations)? Estimated Emissions Observation Based Reconstruction Reconstruction of emissions field using measurement & trajectory Emission & Modeling Analysis (DC8-08, Mar. 9 th, Biomass Emission) DC8 flight track Back trajectory Observer’s track Flyby1 Red : Fire Count(Mar. 6 th ) Blue : CO Emis(Anthro.) Flyby2 PM2.5 in Beijing (Model) PM2.5 in Beijing (Monitoring) M3/CMAQ (12-km) Jan. & July avg., 2001 PM 2.5: (ug/m 3 ) Annual Average for all sites (2000): PM 2.5: 95.5 (ug/m 3 ) (Courtesy of Prof. Zhang, Beijing Univ., 2002) RF16 highly processed plume RF7 highly processed plume Field Experiments Employ Mobile “Super- Sites” & Provide Opportunities to Characterize Emissions Modeling domain with countries/regions sets for source-receptor calculations, measurement site locations ( ) receptor locations (×): 1) Kangwha; 2) Komae; 3) Taichung; 4) Nanjing; 5) Jinan; 6) Beijing; 7) Nangoku; 8) Otobe; 9) Amami; 10) Kashima; 11) Yangyang; 12) Tokoro; 13) Hachijo; 14) Oki; 15) Tsushima; 16) Fukue; 17) Miyako; 18) Taitong. Source : MICS-Asia ( Model InterComparison of Long-Range Transport and Sulfur Deposition in East Asia ) Phase 1 MICS-Asia (Comparison with monitoring data for the January period) Kangwha (Korea) Tsushima (Japan) Dots: Measurements Whiskers: Model results

1 deg. Lake Tai’s effect can be seen at 30' × 30' resolution, but Yangtze River’s effect cannot be clearly seen even in 5' × 5' resolution. 5 min. 30 sec. An Integrated Analysis of Asian Outflow Events Using Comprehensive Emissions in Support of ACE-Asia and TRACE-P J.-H. Woo, G.R. Carmichael, D.G. Streets, Y. Tang, L Pan, N. Thongboonchoo Center for Global and Regional Environ. Research, Univ. Iowa, USA Argonne National Laboratory, USA Population Landcover Region boundary LPSs Volcano Emission Road Network Shiplanes Regional Emission Urban Pop. by Regions LPSs by Regions Road by Regions Shiplane by Types Rural Pop. by Regions Area Emission by Fine Grid Mobile Emissionb y Fine Grid Ship Emission by Fine Grid LPSs Emission Emission Sectors Sectoral Aggregation Spatial Aggregation 30 min. Original Fire Count (FC_Org) (AVHRR, WFW) Moving Averaged FC (FC_MA) AI adjusted FC (FC_AIadj) Regression FC&AI Normalized FC (FC_Norm) 3-day Fire Count (For only 0 FC) Satellite Cov./Cloudiness (AVHRR), Latitude adjust factor Normalize Moving Average AI(TOMS) Satellite Cov./Cloudiness (AVHRR), Precipitation(NCEP), Landcover(ORNL) Regional Biomass Burned (Dr. David Streets) AI adjust Consistency test - Biomass burned.vs. FC - Annual, typical, regional - Change criteria for each FC adjust step - Implement new information for adjust Daily, Gridded Annual, Regional Finalize FC Summarize FC Temporal - Monthly, Seasonal, Annual Spatial - Regional Allocation Factor Temporal/Spatial/Type(LC) Daily FC, Landcover class, Biomass density Region Area Fraction (For anthro. gridding 30 min, Shifted) LPSs & Volcano Population Road network Ship lanes Landcover Development Methodology Schematic methodology for the development of Asian emission estimates Definition of the inventory domain, its constituent countries, international shipping lanes, and sub-regions An inventory of air pollutant emissions in Asia in the year 2000 is developed to support atmospheric modeling and analysis of observations taken during the TRACE-P and the ACE-Asia experiment. We estimate total Asian emissions as follows: 34.3 Tg SO 2, 26.8 Tg NO x, 9870 Tg CO 2, 279 Tg CO, 107 Tg CH 4, 52.2 Tg NMVOC, 2.54 Tg black carbon (BC), 10.4 Tg organic carbon (OC), and 27.5 Tg NH 3. In addition, NMVOC are speciated into 19 sub-categories according to functional groups and reactivity. Thus, we are able to identify the major source regions and types for many of the significant gaseous and particle emissions that influence pollutant concentrations in the vicinity of the TRACE-P and ACE-Asia field measurements Regional emissions The anthropogenic emissions are initially calculated for 64 regions of Asia. Regional emissions are then gridded at a variety of spatial resolutions for input to the atmospheric simulation models, ranging from 1º × 1º for most regional model applications down to 30 sec × 30 sec resolution for urban-scale studies. Each type of emission source is represented by a different spatially resolved surrogate parameter, using such data sets as urban and rural population, road networks, land cover, and ship lanes. Large Asian power plants are separately identified in the RAINS-Asia model and located according to their latitude/longitude coordinates. Emissions are initially calculated for an annual time period. However, it is recognized that there is considerable seasonal variation for some species, associated with such activities as the burning of fossil fuels for home heating in winter and the temperature dependence of releases of NH 3 from fertilizer application and CH 4 from manure. Biomass burning clearly has a very high degree of seasonality, determined by local agricultural practices and meteorological conditions. Such seasonality is important in preparing emissions for comparison with time-specific field experiments. For this reason we have apportioned annual emissions to daily and monthly emissions using a variety of techniques. The techniques used to develop biomass burning estimates are elaborate and based on national surveys of fires, AVHRR satellite fire counts, and TOMS AI data. Efforts concentrated on realistically apportioning annual emissions to the months of March and April, which correspond to the timing of the TRACE-P and ACE- Asia field campaigns. To account for missing data due to cloud cover, and satellite coverage, we applied a normalized factor to the fire count data to adjust for missing data. However this process does not account for no-data grid cells or data error conditions. The zero fire detection due to the lack of satellite information case, we used 3-day moving averages (only applied for zero-fire counts cells). If there was trouble in the satellite on-board system or at the receiving station, or if clouds persisted more than several days, the moving average scheme can not improve the AVHRR fire count data. In this case we used TOMS-AI data as an additional information source. However, the TOMS-AI data should be used with caution because it detects all (absorbing) aerosols, including dust and man-made smoke. So we applied several masks to help filter the information that is not caused by biomass burning. These masks include: 1) the classification of cloud conditions with and without rain using NCEP daily precipitation fields; 2) landcover maps to omit dust interference; and 3) maps of anthropogenic smoke sources including coal mine fires, oil wells, and gas drilling sites. Fire detection from Xinjiang and Pakistan could be flares from Oil/Gas drilling site Landcover Fraction (Crop/Grass /Forest) Landcover Fraction estimation to allocate type of burning Emission Inventory FeaturesEmission-Based Analysis We used the annual-regional emission data from Yarber et al. [manuscript in preparation, 2002] to estimate daily emissions for the period from Feb 26 to May 10, These estimates of total biomass burning emissions by regions were further analyzed to provide daily emissions. The AVHRR fire count, cloud, and satellite coverage data was used to analyze daily fire events. The basic allocation method was to distribute the emissions within a region in time and space according to the daily fire count statistics normalized by the total number of detected fires. Comparison of country surveys with various AVHRR fire-count adjustments reveals problem areas for further investigation Xinjiang Mongolia Indonesia Vietnam fire count > country surveys fire count < country surveys Regional emissions with sectoral contribution (Upper : SO 2, Lower : CO) Uncertainty (%) in pollutant emission estimates (±95% confidence intervals) We estimate the error for each emitting sub-sector by combining the coefficients of variation (CV, or the standard deviation divided by the mean) of the contributing factors. We then combine these uncertainties to estimate the uncertainty of emission estimates for each country. We tried wherever possible to use harmonized data across different source types and regions, even though the reporting practices of countries are not always consistent and available. The monthly biomass burning CO emissions by region The daily biomass burning CO emissions (March, Year 2001) A number of features stand out: the source strengths of NH 3 in the agricultural areas of central China and northern India, the relatively strong signature of NO x from transportation sources in Japan, significant emissions of NMVOC from biomass burning in Southeast Asia, and large SO 2 emissions in the Sichuan Basin and industrialized eastern China. The CO from fossil fuel and biofuel combustion shows the contrast between source categories across developed and rural areas of China, where coal and biofuel combustion are prevalent. The distributions of four NMVOC species shows the dominant source associations of these species: alkanes and ethene with biofuel combustion, toluene with industry, and formaldehyde with biomass burning. These maps are specifically for the month of January, and therefore they emphasize emissions from domestic heating in China and biomass burning in Southeast Asia Area source emission distributions at 30 min grid resolution: SO 2 (leftmost), NO x (middle left), NMVOC (middle right), and NH 3 (rightmost). The unit is Mg yr -1 per grid cell. The same as above, but : CO from fossil fuel (left) and biofuel (right) Area source emission at 30 min grid resolution: total alkanes (leftmost), ethene (middle left), toluene (middle right), and formaldehyde (rightmost). Unit : Moles/sec/grid The initial allocation procedures are conducted at the highest possible resolution (30 sec × 30 sec—about 1 km × 1 km) then aggregated to lower resolution to meet the needs of modeling and analysis. In theory, any resolution is possible that is lower than or equal to 30 sec × 30 sec. Left figure shows CO emission distributions at the 1 deg. (upper right), 30 min. (upper left), 5 min. (lower left), and 30 sec. (lower right) grid cell near Shanghai area. The effect of Lake Tai could be shown in 30 min. grid resolution, whereas Yangtze river is showed only with the highest resolution. In this figure, the effects of regional and sectoral differences can be clearly seen for the four different regions. Right figure shows SO 2 (Upper) and CO (Lower) emission distributions at the highest resolution in the Pearl River Delta, as an example. In this figure, the effects of regional and sectoral differences can be clearly seen for the two different chemical species. This makes focused studies of large metropolitan air- sheds feasible with this inventory. CO emissions by region and source-category overlayed with regional CO/CO 2 emission ratios (left), BC/OC emission ratio by regions overlayed with regional BC/OC ratios by source category (right). Source profiles from regional groups (left : major species, right : NMVOC species) Chemical groups identified from hierarchical cluster analysis Chemical distributions identified in rotated factor space Regional groups identified from hierarchical cluster analysis We analyzed regional and chemical characteristics using the region/chemical species information in our inventory. From cluster analysis SO 2 shows up as a separate group. This reflects the fact that it is dominated by fossil fuel usage. NO x, CO 2 and halocarbons are grouped as one, and they relate to the stage of development. CO, BC, OC, ethane, propane, ethene, propene, terminal alkenes, internal alkenes, and acetylene have similar regional distributions and are identified as a group. CH 4 and NH 3 are identified as a group and represent species not highly related to combustion. The hydrocarbon species are further clustered into four groups that reflect source category contribution. As an alternative analysis, we performed factor analysis using the rotated axis scheme. The factor loadings results are clustered into groups that are similar to those identified through the cluster analysis, and provide same confidence in the groups identified. The results of regional cluster analysis reduced the 52 regions into 11 regional groups as shown in rightmost dendrogram. Lower left figures show the emission profiles of the regional groupings. 2-D and 3-D analysis features for DC8 flight 6 (March 4th). Left: 3-D back trajectories (5-day) colored with measured trace gas mixing ratio (light purple line: 4.6GMT, purple line: 5.2GMT, red line: 6.9GMT, green line: 7.0GMT); fossil fuel (light blue dots), biofuel (yellow dots), biomass burning (red dots) emissions; background map - topography colored by landcover. Right: Source sector contribution by CMB model and 5day 2-D back trajectories. 2-D and 3-D analysis features for DC8 flight8 (March 9th), Left: same as uppermost figure, but (light blue: 3.4GMT, purple: 3.3GMT, red: 2.5GMT); Right: same as uppermost figure. 2-D and 3-D analysis features for DC8 flight9 (March 10th), Left: same as uppermost figure, but (light blue: 3.3GMT, red: 3.5GMT, light red: 5.2GMT, orange: 7.6GMT, yellow: 7.9GMT), Right: same as uppermost figure. 2-D and 3-D analysis features for DC8 flight10 (March 13th) and DC8 flight 12 (March 18th), Left: same as uppermost figure, but (yellow: flight10-5.5GMT, orange: flight12-3.5GMT, red: flight12-5.2GMT, purple: flight12-5.7GMT), Right: same as uppermost figure. Selected DC8 flight and measurement points used in the analysis Geographical distribution of temporally averaged (monthly) biomass burning CO emissions Evaluation measures of the CMB model estimates In general, Asian outflow is usually a complex mixture of biofuel, biomass and fossil sources. Flights in the post frontal regions at high latitudes and low altitudes were found to have a high contribution of fossil fuel emissions. Flights in the warm sector of cold fronts were dominated by biomass burning contributions (about 70%). Biofuel contributions were high (about 70%) when air masses come from central China. The receptor model results were shown to be consistent with 3D chemical model sensitivity studies for two common flight cases. Our receptor based approached showed consistency with biomass burning emission sensitivity tests using 3D chemical “source” models [Tang et al., 2002; Zhang et al., 2002]. In addition the results are consistent with source indicators. Ma et al., [2002] identified the contributions of bio-emission (e.g. biomass and biofuel) using the P-3B flight aerosol measurement data. They used dK+/dSO 4 2- slopes from biomass(biofuel + biomass burning) and fossil plumes to analyze contribution of source categories. Both P-3B flight 10 and DC8 flight 8 flew a similar path along the 20°N latitude on the same day (March 9th). Their results using dK+/dSO 4 2- slopes showed that the bio-emission contributed 80~100% of total mass. This result is consistent with ours, i.e. 87~96%. The ratios of traditional source tracers including acetonitrile (CH 3 CN : biomass combustion sources), tetrachloroethene (C 2 Cl 4 : fossil fuel sources) and SO y (fossil fuel sources), and CO (general sources) were also analyzed. CH 3 CN/SO y ratios for the selected data points for DC8 flight 6 and flight 12 (high fossil fuel source contribution) showed lower values, whereas the selected data points for flight 8 (high biomass combustion source contribution) showed higher numbers. The SO y /CO and C 2 Cl 4 /CO ratios were anti- correlated with CH 3 CN/SO y ratios for the same data points. The CMB receptor model, 3D chemical model and source tracer ratios showed consistent results for the selected flight cases. Five DC8 flights (e.g. flight 6, 8, 9, 10, and 12) with16 flight segments were selected as outflow events. Four chemical species (ethane, propane, butanes, and acetylene) out of 27 were selected as Chemical Mass Balance (CMB) model input We analyzed spatial and temporally resolved emission data, backward trajectory analysis, 3D chemical source model, and wind field information to interpret source contribution from the CMB model for each selected outflow event. References Ma, Y., R. J. Weber, Y.-N. Lee, D. C. Thornton, A. R. Bandy, A. D. Clarke, D. R. Blake, G. W. Sachse, H. E. Fuelberg, J.-H. Woo, D. Streets, G. R. Carmichael, F. L. Eisele, The Characteristics and Influence of Biomass Burning Aerosols on Fine Particle Ionic Composition Measured in Asian Outflow During TRACE-P, J. Geophys. Res., submitted in Jung-Hun Woo, David Streets, Gregory R. Carmichael, Youhua Tang, Bongin Yoo, Won-Chan Lee, Narisara Thongboonchoo, Simon Pinnock, Gakuji Kurata, Itsushi Uno, Qingyan Fu, Stephanie Vay, Glen W. Sachse, Donald R. Blake, Alan Fried, D. C. Thornton, The Contribution of Biomass and Biofuel Emissions to Trace Gas Distributions in Asia during the TRACE-P Experiment, J. Geophys. Res., submitted in Streets, D. G., T. C. Bond, G. R. Carmichael, S. D. Fernandes, Q. Fu, D. He, Z. Klimont, S. M. Nelson, N. Y. Tsai, M.. Q. Wang, J.-H. Woo, and K. F. Yarber, A year-2000 inventory of gaseous and primary aerosol emissions in Asia to support TRACE-P modeling and analysis, J. Geophys. Res., submitted in Tang, Y., G. R. Carmichael, J.-H. Woo, N. Thongboonchoo, G. Kurata, I. Uno, and D. G. Streets, The Influences of Biomass Burning during TRACE-P Experiment Identified by the Regional Chemical Transport Model, J. Geophys. Res., submitted in Yarber, K.F., Streets, D.G., Woo, J.-H., and Carmichael, G.R., Biomass burning in Asia: annual and seasonal estimates and atmospheric emissions, in preparation, Zhang, M., I. Uno, Z. Wang, H. Akimoto, G.R. Carmichael, Y. Tang, J.-H. Woo, D.G. structure of trace gas and aerosol distributions over the Western Pacific Ocean during TRACE-P, J. Geophys. Res., submitted in Acknowledgement : This work was supported by the NASA ACMAP and GTE Programs and the NSF Atmospheric Chemistry Program. Fire Counts Precipitation Aerosol Index