Atmospheric Sciences [A]

A12C  MW:3014   Monday
Evaluation of Air Quality Models and Assessment of Emissions Inventories Using Bottom- Up and Top-Down Approaches I
Presiding: J Fast, Pacific Northwest National Laboratory; S McKeen, NOAA ESRL/CIRES

A12C-01 INVITED 

Modeling Overview of the MILAGRO Field Campaign

* Madronich, S (sasha@ucar.edu), National Center for Atmospheric Research, P.O.Box 3000, Boulder, CO 80307, United States Hodzic, A (alma@ucar.edu), National Center for Atmospheric Research, P.O.Box 3000, Boulder, CO 80307, United States Tie, X (xxtie@ucar.edu), National Center for Atmospheric Research, P.O.Box 3000, Boulder, CO 80307, United States Wiedinmyer, C (christin@ucar.edu), National Center for Atmospheric Research, P.O.Box 3000, Boulder, CO 80307, United States Zaveri, R (rahul.zaveri@pnl.gov), Pacific Northwest National Laboratory, P.O.Box 999, Richland, WA 99352, United States Fast, J (jerome.fast@pnl.gov), Pacific Northwest National Laboratory, P.O.Box 999, Richland, WA 99352, United States

The MILAGRO field campaign (Mexico City, March 2006) provided an interesting and challenging data base for evaluating tropospheric chemistry models on the urban and regional scale. This urban to regional transition spans a large range of VOC/NOx chemical regimes, and is complicated by the presence of aerosol of various origin including biomass burning, fossil fuel use, and dust. Complex terrain, urban effects, and frequently weak synoptic forcing challenge the representation of dispersion in the models. While together these factors are unique to Mexico City and its surroundings, gains in understanding processes and model improvements may also apply to many other urbanized regions. The MILAGRO data are being used to evaluate a hierarchy of models, from process-level chemistry, microphysics, and radiation, to more parameterized 3D chemistry- transport models that are applied to regional and global scales. Areas of special interest include the evolution of aerosol (especially secondary organic), budgets and partitioning of reactive nitrogen and other photochemically active gases, heterogeneous interactions, and radiative closure. Some specific examples (e.g. with the WRF- Chem model) will be presented and additional opportunities will be discussed.

A12C-02 

Evaluation of Air Quality Predictions from MOZART-4

* Emmons, L (emmons@ucar.edu), National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, United States Pfister, G (pfister@ucar.edu), National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, United States Hess, P (pgh25@cornell.edu), National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, United States Hess, P (pgh25@cornell.edu), Cornell University, Dept. of Biological and Environmental Engineering, Ithaca, NY 14853, United States Lamarque, J (lamar@ucar.edu), National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, United States Edwards, D (edwards@ucar.edu), National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, United States

Chemical simulations from the Model for Ozone and Related Chemical Tracers, version 4 (MOZART-4) have been used in numerous studies to analyze the impact of various sources, such as wildfires and megacities, on global air quality. MOZART-4 has also been used to provide chemical forecasts in support of aircraft experiments, most recently for the NSF-organized Megacities Impact on Regional and Global Environment (MIRAGE) and NASA's Intercontinental Chemical Transport Experiment (INTEX-B). Direct comparisons of MOZART, interpolated to the time and location of measurements, have shown generally good agreement with the aircraft measurements of MIRAGE and INTEX-B, as well as with satellite observations, such as MOPITT CO. Where there are discrepancies, it is challenging to distinguish between errors in emission inventories and errors in the representation of chemistry or physical processes in the model. Comparisons of tracer-tracer correlations (such as for volatile organic compounds with different photochemical lifetimes or different sources) between measurements and simulations will be used to help identify the source of model errors. Artificial tracers, such as temporally and spatially "tagged" CO, in MOZART simulations will be compared with the determinations of photochemical age derived from the observations. Several regional and global inventories (e.g., Mexico NEI, D. Streets' Asia Inventory, POET, RETRO) will be used in MOZART and the results assessed. The sensitivity of model simulations to horizontal resolution will also be examined. The numerous investigators who have provided the unprecedented high quality observations as part of the MIRAGE, INTEX-B and other experiments are gratefully acknowledged for making their data available for model evaluations such as this.

A12C-03 

Evaluation of the Volatility Basis-Set Approach for Modeling Primary and Secondary Organic Aerosol in the Mexico City Metropolitan Area

* Tsimpidi, A P (tsimpidi@chemeng.upatras.gr), Dept. of Chemical Engineering, University of Patras, 1 Karatheodori,Rio, Patra, 26504, Greece Karydis, V A (vlkarydis@chemeng.upatras.gr), Dept. of Chemical Engineering, University of Patras, 1 Karatheodori,Rio, Patra, 26504, Greece Pandis, S N (spyros@chemeng.upatras.gr), Dept. of Chemical Engineering, University of Patras, 1 Karatheodori,Rio, Patra, 26504, Greece Zavala, M (miguelz@MIT.EDU), Dept. of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology (MIT), 77 Massachusetts Avenue, Cambridge, MA 02139, United States Zavala, M (miguelz@MIT.EDU), Molina Center for Energy and the Environment (MCE2), 3262 Holiday Ct., Suite 2001, La Jolla, CA 92037, United States Lei, W (wflei@MIT.EDU), Dept. of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology (MIT), 77 Massachusetts Avenue, Cambridge, MA 02139, United States Lei, W (wflei@MIT.EDU), Molina Center for Energy and the Environment (MCE2), 3262 Holiday Ct., Suite 2001, La Jolla, CA 92037, United States Molina, L T (ltmolina@MIT.EDU), Dept. of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology (MIT), 77 Massachusetts Avenue, Cambridge, MA 02139, United States Molina, L T (ltmolina@MIT.EDU), Molina Center for Energy and the Environment (MCE2), 3262 Holiday Ct., Suite 2001, La Jolla, CA 92037, United States

Anthropogenic air pollution is an increasingly serious problem for public health, agriculture, and global climate. Organic material (OM) contributes ~ 20-50% to the total fine aerosol mass at continental mid-latitudes. Although OM accounts for a large fraction of PM2.5 concentration worldwide, the contributions of primary and secondary organic aerosol have been difficult to quantify. In this study, new primary and secondary organic aerosol modules were added to PMCAMx, a three dimensional chemical transport model (Gaydos et al., 2007), for use with the SAPRC99 chemistry mechanism (Carter, 2000; ENVIRON, 2006) based on recent smog chamber studies (Robinson et al., 2007). The new modeling framework is based on the volatility basis-set approach (Lane et al., 2007): both primary and secondary organic components are assumed to be semivolatile and photochemically reactive and are distributed in logarithmically spaced volatility bins. The emission inventory, which uses as starting point the MCMA 2004 official inventory (CAM, 2006), is modified and the primary organic aerosol (POA) emissions are distributed by volatility based on dilution experiments (Robinson et al., 2007). Sensitivity tests where POA is considered as nonvolatile and POA and SOA as chemically reactive are also described. In all cases PMCAMx is applied in the Mexico City Metropolitan Area during March 2006. The modeling domain covers a 180x180x6 km region in the MCMA with 3x3 km grid resolution. The model predictions are compared with Aerodyne's Aerosol Mass Spectrometry (AMS) observations from the MILAGRO Campaign. References Robinson, A. L.; Donahue, N. M.; Shrivastava, M. K.; Weitkamp, E. A.; Sage, A. M.; Grieshop, A. P.; Lane, T. E.; Pandis, S. N.; Pierce, J. R., 2007. Rethinking organic aerosols: semivolatile emissions and photochemical aging. Science 315, 1259-1262. Gaydos, T. M.; Pinder, R. W.; Koo, B.; Fahey, K. M.; Pandis, S. N., 2007. Development and application of a three- dimensional aerosol chemical transport model, PMCAMx. Atmospheric Environment 41, 2594-2611. Carter, W.P.L., 2000. Programs and Files Implementing the SAPRC-99 Mechanism and its Associates Emissions Processing Procedures for Models-3 and Other Regional Models. January 31, 2000. http://pah.cert.ucr.edu/~carter/SAPRC99.htm. Environ, 2006. User's guide to the comprehensive air quality model with extensions (CAMx). Version 4.30. Report prepared by ENVIRON International Corporation, Novato, CA. Lane, T.E.; Donahue, N. M.; Pandis, S. N. 2007. Simulating Secondary Organic Aerosol Formation using the Votality Basis-Set Approach in a Chemical Transport Model, in preperation. CAM (Comision Ambiental Metropolitana) 2006: Inventario de Emisiones 2004 de la Zona Metropolitana del Valle de Mexico, Mexico. Robinson, A. L.; Donahue, N. M.; Shrivastava, M. K.; Weitkamp, E. A.; Sage, A. M.; Grieshop, A. P.; Lane, T. E.; Pandis, S. N.; Pierce, J. R., 2007. Rethinking organic aerosols: semivolatile emissions and photochemical aging. Science 315, 1259-1262.

A12C-04 

Evaluation of New Secondary Organic Aerosol Formation Models Based on Mexico City Field Measurements

Dzepina, K (katja.dzepina@colorado.edu), University of Colorado-Boulder, UCB 216, Boulder, CO 80309-0216, United States * Jimenez, J L (jose.jimenez@colorado.edu), University of Colorado-Boulder, UCB 216, Boulder, CO 80309-0216, United States Volkamer, R (rainer.volkamer@colorado.edu), University of Colorado-Boulder, UCB 216, Boulder, CO 80309-0216, United States Aiken, A (allison.aiken@colorado.edu), University of Colorado-Boulder, UCB 216, Boulder, CO 80309-0216, United States Huffman, J A (alex.huffman@colorado.edu), University of Colorado-Boulder, UCB 216, Boulder, CO 80309-0216, United States

Recent field studies have found large discrepancies in the measured vs. modeled SOA mass loadings in both urban and regional polluted atmospheres [Volkamer et al., 2006 and references therein]. The reasons for these large differences are unclear. Here we revisit the SOA formation measurements from Mexico City described by Volkamer et al. and compared them to very recently published SOA formation models, including the updated aromatic SOA yields of Ng et al. (2007), the formation of SOA from primary semivolatile and intermediate volatility species (SVOCs and IVOCs) proposed by Robinson et al. (2007), the lack of partitioning of SOA in POA surrogates (Zaveri et al., 2007), and the formation of SOA from glyoxal (Volkamer et al., 2007). Traditional SOA precursors (mainly aromatics) still fail to produce enough SOA to match the observations by a large factor. The low-NOx aromatic pathways of Ng. et al., which have higher SOA yields, make a very small contribution in this urban environment as the RO2 + NO reaction dominates the fate of the RO2 radicals. Glyoxal makes a significant contribution to SOA formation, with similar timing and oxygen-to-carbon ratio (O/C) as the measurements. SVOCs and IVOCs introduce a large amount of carbon that was not in models before, and which has a high SOA yield. With the parameters presented by Robinson et al., this mechanism can close the gap in SOA mass between measurements and models in our case studies. However the O/C ratio of the SOA produced by this mechanism is too low when compared with observations, and the timing of formation is also slightly delayed with respect to the observations, due to the need for several generations of oxidation to bring a significant fraction of the SVOCs and IVOCs into the particle phase. Much experimental work is needed for a realistic assessment of the importance, and for constraining of the parameters, of the Robinson mechanism, especially of the real concentrations and volatility distribution of SVOCs and IVOCs in urban air, but also of the reaction rates, oxygen gain upon oxidation, and activity coefficients. The sensitivities of the model to the various uncertain parameters are evaluated. The volatility of the model SOA is compared to field measurements using a thermal denuder. Finally the evaporation upon dilution and the evolution of the SOA after 3 more days of oxidation are evaluated.

A12C-05 INVITED 

An assessment of the performance of NAM-CMAQ air quality forecast with measurements from surface networks and specialized field campaigns

* Mathur, R (mathur.rohit@epa.gov), ASMD/ARL/OAR/NOAA, U.S. EPA, Mail Drop E243-03, RTP, NC 27711, United States Yu, S (yu.shaocai@epa.gov), STC, U.S. EPA, Mail Drop E243-03, RTP, NC 27711, United States Kang, D (kang.daiwen@epa.gov), STC, U.S. EPA, Mail Drop E243-03, RTP, NC 27711, United States Pleim, J (pleim.jon@epa.gov), ASMD/ARL/OAR/NOAA, U.S. EPA, Mail Drop E243-03, RTP, NC 27711, United States Lin, H (lin.hsin-mu@epa.gov), STC, U.S. EPA, Mail Drop E243-03, RTP, NC 27711, United States Schere, K (schere.kenneth@epa.gov), ASMD/ARL/OAR/NOAA, U.S. EPA, Mail Drop E243-03, RTP, NC 27711, United States Pouliot, G (pouliot.george@epa.gov), ASMD/ARL/OAR/NOAA, U.S. EPA, Mail Drop E243-03, RTP, NC 27711, United States Young, J (young.jeff@epa.gov), ASMD/ARL/OAR/NOAA, U.S. EPA, Mail Drop E243-03, RTP, NC 27711, United States Otte, T (otte.tanya@epa.gov), ASMD/ARL/OAR/NOAA, U.S. EPA, Mail Drop E243-03, RTP, NC 27711, United States Tong, D (tong.daniel@epa.gov), STC, U.S. EPA, Mail Drop E243-03, RTP, NC 27711, United States McQueen, J (jeff.mcqueen@noaa.gov), NCEP/NWS/NOAA, 5200 Auth Road, Camp Springs, MD 20746, United States Lee, P (pius.lee@noaa.gov), SAIC, 5200 Auth Road, Camp Springs, MD 20746, United States Tang, Y (youhua.tang@noaa.gov), SAIC, 5200 Auth Road, Camp Springs, MD 20746, United States Tsidulko, M (marina.tsidulko@noaa.gov), SAIC, 5200 Auth Road, Camp Springs, MD 20746, United States Davidson, P (paula.davidson@noaa.gov), OST/NWS/NOAA, 1325 East West Highway, Silver Spring, MD 20910, United States

It is desirable for local air quality agencies to accurately forecast tropospheric ozone and fine particulate matter (PM2.5) concentrations to alert the sensitive population of the onset, severity and duration of unhealthy air, and to encourage the public and industry to reduce emissions-producing activities. The availability of increased computational power coupled with advances in the computational structure of the models has now enabled the use of comprehensive atmospheric chemistry/transport models in real-time air quality forecasting. An air-quality forecasting (AQF) system based on the National Weather Service (NWS) Weather Research and Forecast Nonhydrostatic Mesoscale Model (WRF-NMM) North American Mesoscale (NAM) model and the U.S. EPA's Community Multiscale Air Quality (CMAQ) Modeling System was used to simulate in real-time the distributions of tropospheric ozone and PM2.5 over the Continental United States during 2006. The refinements and adaptation of the CMAQ model for O3 and PM2.5 forecast applications will be discussed. The ability of the model to forecast the co-evolution of surface level ozone and PM2.5 pollution will be assessed through comparisons with regional ozone and continuous PM2.5 measurements from the AIRNOW system. The strengths and weaknesses of the modeling system in representing the distributions of O3, related photochemical species, and fine PM chemical constituents is analyzed through comparisons with speciated measurements from the surface networks as well as specialized measurements collected on various platforms deployed during the 2006 Texas Air Quality and 2004 ICARTT field studies.

A12C-06 

Preliminary Evaluation of Air Quality Model Performance Utilizing Measurements at the University of Houston Moody Tower and others during the TexAQS-II

Byun, D W (dbyun@mail.uh.edu), University of Houston, Institute for Multidimensional Air Quality Studies, Department of Geoscience, Houston, TX 77204, United States Rappenglueck, B (brappenglueck@uh.edu), University of Houston, Institute for Multidimensional Air Quality Studies, Department of Geoscience, Houston, TX 77204, United States * Lefer, B (blefer@uh.edu), University of Houston, Institute for Multidimensional Air Quality Studies, Department of Geoscience, Houston, TX 77204, United States

Accurate meteorological and photochemical modeling efforts are necessary to understand the measurements made during the Texas Air Quality Study (TexAQS-II). The main objective of the study is to understand the meteorological and chemical processes of high ozone and regional haze events in the Eastern Texas, including the Houston-Galveston metropolitan area. Real-time and retrospective meteorological and photochemical model simulations were performed to study key physical and chemical processes in the Houston Galveston Area. In particular, the Vertical Mixing Experiment (VME) at the University of Houston campus was performed on selected days during the TexAQS-II. Results of the MM5 meteorological model and CMAQ air quality model simulations were compared with the VME and other TexAQS-II measurements to understand the interaction of the boundary layer dynamics and photochemical evolution affecting Houston air quality.

A12C-07 

An Evaluation of Ozone and PM2.5 Air Quality Forecast Model Skill and Several Bias Correction Methods During the TEXAQS 2006 Field Program

* DJALALOVA, I (Irina.V.Djalalova@noaa.gov), NOAA/ESRL/Physical Sciences Division, 325 Broadway, R/PSD, Boulder, CO 80305, United States Wilczak, J (James.M.Wilczak@noaa.gov), NOAA/ESRL/Physical Sciences Division, 325 Broadway, R/PSD, Boulder, CO 80305, United States McKeen, S (Stuart.A.McKeen@noaa.gov), NOAA/ESRL/Chemical Sciences Division, 325 Broadway, R/CSD, Boulder, CO 80305, United States Grell, G (Georg.A.Grell@noaa.gov), NOAA/ESRL/Global Sciences Division, 325 Broadway, R/GSD, Boulder, CO 80305, United States Peckham, S (Steven.Peckham@noaa.gov), NOAA/ESRL/Global Sciences Division, 325 Broadway, R/GSD, Boulder, CO 80305, United States McQueen, J (Jeff.Mcqueen@noaa.gov), NOAA/NWS/Environmental Modeling Center, 5200 Auth Road, W/NP2, Camp Springs, MD 20746, United States Lee, P (Pius.Lee@noaa.gov), NOAA/NWS/Environmental Modeling Center, 5200 Auth Road, W/NP2, Camp Springs, MD 20746, United States McHenry, J (John.McHenry@baronams.com), Baron AMS, North Carolina Supercomputing Center, 3021 Cornwallis Road, Research Triangle Pa, NC 27709, United States Gong, W (Wanmin.Gong@ec.gc.ca), Environment Canada, AQRD/ASTD/STB, 4905 Dufferin Street, Downsview, ON M3H 5T4, Canada Bouchet, V (Veronique.Bouchet@ec.gc.ca), Centre Météorologique Canadien, 2121 Route Transcanadienne, Dorval, Qué H9P 1J3, Canada Tang, Y (Youhua.Tang@noaa.gov), NOAA/NWS/Environmental Modeling Center, 5200 Auth Road, W/NP2, Camp Springs, MD 20746, United States

Surface ozone and PM 2.5 data from 119 AIRNOW sites collected during the TEXAQS August 01-October 15, 2006 field program are used to evaluate 7 different air quality models: AURAMS, BAMS-15km, CHRONOS, CMAQ, WRF-12km, WRF-36km and STEM. Skill from the individual models is compared to that of the ensemble mean of the models, using both simple bulk statistics as well as categorical forecast statistics. In addition, three different bias correction techniques are compared: a simple 7-day mean bias correction, a Kalman Filter approach, and a dynamical weighting method. The advantages of each technique are contrasted for ozone and PM2.5.

A12C-08 

Evaluation of Emissions and Photochemical Processing Within Air Quality Model Forecasts During the 2006 TexAQS/GoMACCS Field Study

* McKeen, S A (Stuart.A.McKeen@noaa.gov), NOAA Earth System Research Laboratory, Chemical Sciences Division, R/CSD4 325 Broadway, Boulder, CO 80305-3328, United States * McKeen, S A (Stuart.A.McKeen@noaa.gov), Cooperative Institute for Research in Environmental Sciences, University of Colorado, CB216, Boulder, CO 80305-3328, United States Grell, G (Georg.A.Grell@noaa.gov), Cooperative Institute for Research in Environmental Sciences, University of Colorado, CB216, Boulder, CO 80305-3328, United States Grell, G (Georg.A.Grell@noaa.gov), NOAA Earth System Research Laboratory, Global Sciences Division, R/GSD1 325 Broadway, Boulder, CO 80305-3328, United States Peckham, S (Steven.Peckham@noaa.gov), Cooperative Institute for Research in Environmental Sciences, University of Colorado, CB216, Boulder, CO 80305-3328, United States Peckham, S (Steven.Peckham@noaa.gov), NOAA Earth System Research Laboratory, Global Sciences Division, R/GSD1 325 Broadway, Boulder, CO 80305-3328, United States McQueen, J (Jeff.Mcqueen@noaa.gov), NOAA National Weather Service, Environmental Modeling Center, W/NP2 5200 Auth Road, Camp Springs, MD 20746, United States Lee, P (Pius.Lee@noaa.gov), NOAA National Weather Service, Environmental Modeling Center, W/NP2 5200 Auth Road, Camp Springs, MD 20746, United States McHenry, J (John.McHenry@baronams.com), Baron AMS, North Carolina Supercomputing Center 3021 Cornwallis Road, Research Triangle Pk, NC 27709, United States Gong, W (Wanmin.Gong@ec.gc.ca), Environment Canada, AQRD/ASTD/STB 4905 Dufferin Street, Downsview, ON M3H 5T4, Canada Bouchet, V (Veronique.Bouchet@ec.gc.ca), Environment Canada, Centre Meteorologique Canada 2121 Route Transcanadienne, Dorval, QU H9P 1J3, Canada Tang, Y (Youhua.Tang@noaa.gov), NOAA National Weather Service, Environmental Modeling Center, W/NP2 5200 Auth Road, Camp Springs, MD 20746, United States Tang, Y (Youhua.Tang@noaa.gov), University of Iowa Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA 52242, United States Carmichael, G (gcarmich@engineering.uiowa.edu), University of Iowa Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA 52242, United States Wilczak, J (James.M.Wilczak@noaa.gov), NOAA Earth System Research Laboratory, Physical Sciences Division, R/PSD 325 Broadway, Boulder, CO 80305-3328, United States Djalalova, I (Irina.V.Djalalova@noaa.gov), Cooperative Institute for Research in Environmental Sciences, University of Colorado, CB216, Boulder, CO 80305-3328, United States Djalalova, I (Irina.V.Djalalova@noaa.gov), NOAA Earth System Research Laboratory, Physical Sciences Division, R/PSD 325 Broadway, Boulder, CO 80305-3328, United States

Several air-quality models provided real-time forecasts of ozone and PM2.5 aerosols during the TexAQS/GoMACCS field campaign. These forecast models include two versions of the NOAA/ESRL/GSD WRF/Chem model, a developmental version of the NWS/NCEP CMAQ/WRF model, the Canadian Meteorological Services CHRONOS and AURAMS models, the MM5 based MAQSIP model from Baron Advanced Meteorological Services Inc., and the University of Iowa STEM model. Statistical evaluations of each model with the U.S. EPA AIRNow ozone and PM2.5 network over Eastern Texas during the summer of 2006 point to persistent model biases in surface predictions of these two criteria pollutants. Uncertainties in emission inventories and photochemical mechanisms are likely sources of forecast error within each model. Detailed observations of dozens of gas-phase ozone precursors and aerosol components collected on board the NOAA-WP3 aircraft during TexAQS/GoMACCS are used to compare model and observed concentrations. Aircraft flight tracks were designed to characterize up-wind conditions and the evolving composition of urban plumes down-wind of Houston and Dallas, TX within the planetary boundary layer. The aircraft data for 10 flights during September of 2006 are used in three diagnostic evaluations of the various models: characterizing the background composition up-wind of the two urban areas, evaluating the photochemical processing leading to ozone and PM2.5 formation various distances down-wind of the urban sources, and using ratios of above-background concentrations to infer and compare emission ratios of key ozone and PM2.5 precursors.