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Air Quality Publications

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National Air Quality Capability (NAQFC) Publications

Publications related to the National Air Quality Forecast Capability (NAQFC) are separated into sections by the component of the capability that they are most closely associated with: ozone, smoke, dust or fine particulate matter (PM2.5) predictions, and system overview.

NAQFC system overview

  1. Davidson P., K. Schere, R. Draxler, S. Kondrangunta, R. A. Wayland, J. F. Meagher, and R. Mathur (2008), Toward a US National Air Quality Forecast Capability: Current and Planned Capabilities, Air Pollution Modeling and Its Application XIX, C. Borrego and A.I. Miranda (Eds.), 226-234, ISBN 978-1-4020-8452-2, Springer, The Netherlands.
    (https://link.springer.com/chapter/10.1007%2F978-1-4020-8453-9_25)
  2. Stajner I., P. Davidson, D. Byun, J. McQueen, R. Draxler, P. Dickerson, and J. Meagher (2012), US National Air Quality Forecast Capability: Expanding Coverage to Include Particulate Matter, NATO/ITM Air Pollution Modeling and Its Application XXI, Douw G. Steyn & Silvia Trini Castelli (ed.), Springer, Netherlands, pp 379-384, DOI: 10.1007/978-94-007-1359-8_64.
    (https://link.springer.com/chapter/10.1007%2F978-94-007-1359-8_64)
  3. REID, Margaret et al. Cross-Disciplinary Consultancy to Enhance Predictions of Asthma Exacerbation Risk in Boston. Online Journal of Public Health Informatics, [S.l.], v. 8, n. 3, dec. 2016. ISSN 1947-2579.
    (https://ojphi.org/ojs/index.php/ojphi/article/view/6902)

Ozone Prediction

  1. Chai T., H. Kim, P. Lee, D. Tong, L. Pan, Y. Tang, J. Huang, J. McQueen, M. Tsidulko, and I. Stajner, (2013), Evaluation of the United States National Air Quality Forecast Capability experimental real-time predictions in 2010 using Air Quality System ozone and NO2 measurements, Geosci. Model Dev. 6, 1831-1850 doi:10.5194/gmd-6-1831-2013.
    (https://www.geosci-model-dev.net/6/1831/2013/gmd-6-1831-2013.html)
  2. Chang C.-Y., E. Faust, X. Hou, P. Lee, H. Kim, B. C. Hedquist, and K.-J. Liao, (2016), Investigating ambient ozone formation regimes in neighboring cities of shale plays in the Northeast United States using photochemical modeling and satellite retrievals, Atmos. Environ. Doi:10.1016/j.atmosenv.2016.06.058.
    (https://www.sciencedirect.com/science/article/pii/S1352231016304915).
  3. Choi Y., H. Kim, D. Tong, and P. Lee, (2012), Summertime weekly cycles of observed and modeled NOx and O-3 concentrations as a function of satellite-derived ozone production sensitivity and land use types over the Continental United States. Atmospheric Chemistry and Physics 12(14), 6291-6307. doi:10.5194/acp-12-6291-2012.
    (https://www.atmos-chemphys.net/12/6291/2012/acp-12-6291-2012.html)
  4. Monache L. D., J. Wilczak, S. Mckeen, G. Grell, M. Pagowski, S. Peckham, R. Stull, J. Mchenry, and Jeffrey Mcqueen, (2008), A Kalman-filter bias correction method applied to deterministic, ensemble averaged and probabilistic forecasts of surface ozone, Tellus Ser B, 60(2), 238.
    (https://www.tellusb.net/index.php/tellusb/article/view/16918)
  5. Eder B., D. Kang, R. Marthur, J. Pleim, S. Yu, T. Otte, and G. Pouliot,  (2009), A performance evaluation of the National Air Quality Forecast Capability for the summer of 2007, Atmos. Environ., 43, 2312–2320.
    (https://www.sciencedirect.com/science/article/pii/S1352231009000624)
  6. Garner G., A. Thompson, P. Lee, and D. Matins, (2015), Evaluation of NAQFC Model Performance in Forecasting Surface Ozone during the 2011 DISCOVER-AQ Campaign, J. Atmospheric Chemistry, 72, 483-501, doi: 10.1007/s10874-013-9251-z.
    (https://link.springer.com/article/10.1007%2Fs10874-013-9251-z)
  7. Kang D., R. Mathur, S. T. Rao, and S. Yu, (2008), Bias adjustment techniques for improving ozone air quality forecasts, J. Geophys. Res., 113, D23308, doi:10.1029/2008JD010151.
    (https://onlinelibrary.wiley.com/doi/10.1029/2008JD010151/pdf)
  8. Kang D., R. Mathur, and S. T. Rao, (2010), Real-time bias-adjusted O3 and PM2.5 air quality index forecasts and their performance evaluations over the continental United States, Atmosphere Environment, 44(18), 2203.
    (https://www.sciencedirect.com/science/article/pii/S1352231010002128)
  9. Kim H. C., P. Lee, L. Judd, L. Pan, and B. Lefer, (2016), OMI NO 2 column densities over North American urban cities: the effect of satellite footprint resolution, Geosci. Model Dev., 9, 1111-1123, doi:10.5194/gmd-9- 1111-2016.
    (https://www.geosci-model- dev.net/9/1111/2016/gmd-9- 1111-2016.pdf)
  10. Lee P., D. Kang, J. McQueen, M. Tsidulko, M. Hart, G. DiMego, N. Seaman, and P. Davidson (2008), Impact of Domain Size on Modeled Ozone Forecast for the Northeastern United States. J. Meteo. and Climate., 47, 443–461.
    (https://journals.ametsoc.org/doi/abs/10.1175/2007JAMC1408.1)
  11. Lee P., Y.-H. Tang, D. Kang, J. McQueen, M. Tsidulko, H.-C. Huang, S. Lu, M. Hart, H.-M. Lin, S. Yu, G. DiMego, I. Stajner, and P. Davidson, (2009), Impact of Consistent Boundary Layer Mixing Approaches Between NAM and CMAQ, Environmental Fluid Mechanics, 9:23-42. doi:10.1007/s10652-008-9089-0.
    (https://link.springer.com/article/10.1007%2Fs10652-008-9089- 0)
  12. Lee P., F. Ngan, H. Kim, D. Tong, Y. Tang, T. Chai, R. Saylor, A. Stein, D. Byun and M. Tsidulko, J. McQueen, and I. Stajner, (2012), Incremental Development of Air Quality Forecasting System with Off-Line/On-Line Capability: Coupling CMAQ to NCEP National Mesoscale Model, NATO/ITM Air Pollution Modeling and Its Application XXI, Douw G. Steyn & Silvia Trini Castelli (ed.), Springer, Netherlands, pp 187-192, DOI: 10.1007/978-94-007-1359-8_32.
    (https://link.springer.com/chapter/10.1007%2F978-94- 007-1359-8_32)
  13. McKeen S., et al. (2005), Assessment of an ensemble of seven real-time ozone forecasts over eastern North America during the summer of 2004, J. Geophys. Res., 110, D21307, doi:10.1029/2005JD005858.
    (https://onlinelibrary.wiley.com/doi/10.1029/2005JD005858/full)
  14. McKeen S., et al. (2009), An evaluation of real-time air quality forecasts and their urban emissions over eastern Texas during the summer of 2006 Second Texas Air Quality Study field study, J. Geophys. Res., 114, D00F11, doi:10.1029/2008JD011697, [printed 115(D7), 2010].
    (https://onlinelibrary.wiley.com/doi/10.1029/2008JD011697/full)
  15. Otte, T. L., G. Pouliot, J. E. Pleim, J. O. Young, K. L. Schere, D. C. Wong, P.C. Lee, M. Tsidulko, J.T.McQueen, P. Davidson, R. Mathur, H. Y. Chuang, G. DiMego and N. Seaman (2005), Linking the Eta Model with the Community Multiscale Air Quality (CMAQ) modeling system to build a national air quality forecasting system. Wea. Forecasting, 20, 367-384.
    (https://journals.ametsoc.org/doi/pdf/10.1175/WAF855.1)
  16. Pagowski, M., and G. A. Grell (2006), Ensemble-based ozone forecasts: Skill and economic value, J. Geophys. Res., 111, D23S30, doi:10.1029/2006JD007124.
    (https://onlinelibrary.wiley.com/doi/10.1029/2006JD007124/full)
  17. Pan, L., D.Q. Tong, P. Lee, H. Kim and T. Chai,(2014), Assessment of NOx and O3 forecasting performances in the U.S. National Air Quality Forecasting Capability before and after the 2012 major emissions updates, Atmospheric Environment, 95(2014), Pages 610-619. doi:10.1016/j.atmosenv.2014.06.020.
    (https://www.sciencedirect.com/science/article/pii/S135223101400466X)
  18. Saylor, R. D. and A. F. Stein, (2012), Identifying the causes of differences in ozone production from the CB05 and CBMIV chemical mechanisms, Geosci. Model Dev., 5, 257-268, doi:10.5194/gmd-5- 257-2012.
    (https://www.geosci-model-dev.net/5/257/2012/gmd-5-257-2012.html)
  19. Sullivan J., T. McGee, A. Langford, R. J. Alvarez, C. Senff, P. Reddy, A. M. Thompson, L. Twigg, G. Sumnicht, P. Lee, A. Weinheimer, C. Knote, R. Long, and R. Hoff, (2016), Quantifying the contribution of thermally-driven recirculation to a high ozone event along the Colorado Front Range using lidar, J. Geophys. Res. 2016, in press.
    (https://onlinelibrary.wiley.com/doi/10.1002/2016JD025229/abstract)
  20. Tang, Y., P. Lee, M. Tsidulko, H.-C. Huang, J. T. McQueen, G. J. DiMego, L. K. Emmons, R. B. Pierce, H.-M. Lin, D. Kang, D. Tong, S. Yu, R. Mathur, J. E. Pleim, T. L. Otte, G. Pouliot, J. O. Young, K. L. Schere, P. M. Davidson, and I. Stajner, (2009), The Impact of Chemical Lateral Boundary Conditions on CMAQ Predictions of Tropospheric Ozone over the Continental United States, Environmental Fluid Mechanics, 9 (1), 43-58, doi:10.1007/s10652-008-9092-5.
    (https://link.springer.com/article/10.1007%2Fs10652-008-9092-5)
  21. Tong D., L. Pan, W. Chan, L. Lamsal, P. Lee, Y. Tang, H. Kim, S. Kondragunta, and I. Stajner, (2016), Impact of the 2008 Global Recession on air quality over the United States: Implications for surface ozone levels from changes in NOx emissions, Geophys. Res. Letters, doi:10.1002/2016GL069885.
    (https://onlinelibrary.wiley.com/doi/10.1002/2016GL069885/full)
  22. Tong D. Q., R. Mathur, D. Kang, S. Yu, K. L. Schere, and G. Pouliot, (2009), Vegetation exposure to ozone over the continental United States: Assessment of exposure indices by the Eta-CMAQ air quality forecast model, Atmosphere Environment, 43(3),724.
    (https://www.sciencedirect.com/science/article/pii/S1352231008009047)
  23. Tong, D.Q., L. Lamsal, L. Pan, C. Ding, H. Kim, P. Lee, T. Chai, and K.E. Pickering, and I. Stajner, (2015), Long-term NOx trends over large cities in the United States during the 2008 Recession: Intercomparison of satellite retrievals, ground observations, and emission inventories, Atmospheric Environment, doi:10.1016/j.atmosenv.2015.01.035.
    (https://www.sciencedirect.com/science/article/pii/S1352231015000564)
  24. Wilczak, J., et al. (2006), Bias-corrected ensemble and probabilistic forecasts of surface ozone over eastern North America during the summer of 2004, J. Geophys. Res., 111, D23S28, doi:10.1029/2006JD007598.( https://onlinelibrary.wiley.com/doi/10.1029/2006JD007598/full)
  25. Wilczak, J. M., I. Djalalova, S. McKeen, L. Bianco, J.-W. Bao, G. Grell, S. Peckham, R. Mathur, J.McQueen, and P. Lee (2009), Analysis of regional meteorology and surface ozone during the TexAQS II field program and an evaluation of the NMM-CMAQ and WRF-Chem air quality models, J. Geophys. Res., 114, D00F14, doi:10.1029/2008JD011675, [printed 115(D7), 2010].
    (https://onlinelibrary.wiley.com/doi/10.1029/2008JD011675/full)
  26. Yu S., R. Mathur, K. Schere, D. Kang, J. Pleim, and T. L. Otte, (2007), A detailed evaluation of the Eta-CMAQ forecast model performance for O3, its related precursors, and meteorological parameters during the 2004 ICARTT study, J Geophys Res, 112, D12S14.
    (https://onlinelibrary.wiley.com/doi/10.1029/2006JD007715/full)
  27. Yu S., R. Mathur, D. Kang, K. Schere, and D. Tong, (2009), A study of the ozone formation by ensemble back trajectory-process analysis using the Eta–CMAQ forecast model over the northeastern U.S. during the 2004 ICARTT period, Atmosphere Environment,43(2), 355-263.
    (https://www.sciencedirect.com/science/article/pii/S1352231008008303)
  28. Yu S., R. Mathur, G. Sarwar, D. Kang, D. Tong, G. Pouliot, and J. Pleim, (2010), Eta-CMAQ air quality forecasts for O3 and related species using three different photochemical mechanisms (CB4, CB05, SAPRC-99): comparisons with measurements during the 2004 ICARTT study, Atmos. Chem. Phys., 10, 3001-3025, doi:10.5194/acp-10-3001-2010.
    (https://www.atmos-chemphys.net/10/3001/2010/acp-10-3001-2010.html)

Prediction of smoke from wildfires

  1. Christopher, S., P. Gupta, U. Nair, T. A. Jones, S. Kondragunta, Y-L Wu, J. Hand, and X. Zhang (2009), Satellite Remote Sensing and Mesoscale Modeling of the 2007 Georgia/Florida Fires, IEEE J. of Selected Topics in Applied Earth Sciences and Remote Sensing, 2 (3), 163-175, doi:10.1109/JSTARS.2009.2026626.
    (https://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5208307&url=https%3A%2F%2Fiee explore.ieee.org%2Fiel5%2F4609443%2F4609444%2F05208307.pdf%3Farnumber%3D5208 307)
  2. Green, M., S. Kondragunta, P. Ciren, and C. Xu, (2009), Comparison of GOES and MODIS Aerosol Optical Depth (AOD) to AEosol RObotic NETwork (AERONET) AOD and IMPROVE PM2.5 mass at Bondville, Illinois, Journal of the Air & Waste Management Association, 59, 1082-1091.
    (https://www.tandfonline.com/doi/abs/10.3155/1047-3289.59.9.1082)
  3. Kondragunta S., et al. (2008), Air quality forecast verification using satellite data, J. of Applied Meteorology and Climatology, 47, No. 2, 425-442, doi:10.1175/2007JAMC1392.1.
    (https://journals.ametsoc.org/doi/pdf/10.1175/2007JAMC1392.1)
  4. O’Neill S., N. Larkin, J. Hoadley, G. Mills, J. Vaughn, R.Draxler, G. Rolph, M. Ruminski, and S. Ferguson, (2008), Regional real-time smoke prediction systems, in Wildland Fires and Air Pollution, A. Bytnerowicz et al., Eds., Developments in Environmental Science Series, Vol. 8, Elsevier, 499-534.
    (https://www.sciencedirect.com/science/article/pii/S1474817708000223)
  5. Prados A., S. Kondragunta, P. Ciren, and K. Knapp (2007), The GOES Aerosol/Smoke Product (GASP) over North America: Comparisons to AERONET and MODIS Observations, J. of Geophys. Res., 112, D15201, doi:10.1029/2006JD007968.
    (https://onlinelibrary.wiley.com/doi/10.1029/2006JD007968/pdf)
  6. Rolph et al. (2009), Description and Verification of the NOAA Smoke Forecasting System: The 2007 Fire Season. Weather and Forecasting, Volume 24, pp 361-378.
    (https://journals.ametsoc.org/doi/abs/10.1175/2008WAF2222165.1)
  7. Stein et al. (2009), Verification of the NOAA Smoke Forecasting System: Model sensitivity to the injection height. Weather and Forecasting, Volume 24, pp. 379-394.
    (https://journals.ametsoc.org/doi/abs/10.1175/2008WAF2222166.1)

Preditiction of dust from storms

  1. Draxler R. R., and G. D. Hess, (1998), An overview of the HYSPLIT_4 modelling system for trajectories, dispersion, and deposition, Aust. Meteorol. Mag., 47, 295–308.
    (https://www.bom.gov.au/amm/docs/1998/draxler.pdf)
  2. Draxler D., P. Ginoux, and A. F. Stein, (2010), An empirically derived emission algorithm for windblown dust. Journal of Geophysical Research, 115, D16212, doi:10.1029/2009JD013167.
    (https://onlinelibrary.wiley.com/doi/10.1029/2009JD013167/full)
  3. Ginoux P., D. Garbuzov, and N. C. Hsu, (2010), Identification of anthropogenic and natural dust sources using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue level 2 data. Journal of Geophysical Research, 115, D05204, doi:10.1029/2009JD012398.
    (https://onlinelibrary.wiley.com/doi/10.1029/2009JD012398/full)

Prediction of fine particulate matter (PM2.5)

  1. Djalalova I., L. D. Monache, and J. Wilczak, (2015), PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model (vol 108, pg 76, 2015). Atmos. Environ., PERGAMON-ELSEVIER SCIENCE LTD: Vol. 119, 430-430, issn: 1352-2310, ids: CT8PJ, doi: 10.1016/j.atmosenv.2015.05.058.
    (https://www.sciencedirect.com/science/article/pii/S1352231015001405)
  2. Djalalova I., J. Wilczak, S. McKeen, G. Grell, S. Peckham, M. Pagowski, L. DelleMonache, J. McQueen, Y. Tang, P. Lee, J. McHenry, W. Gong, V. Bouchet, and R. Mathur, (2010), Ensemble and bias-correction techniques for air quality model forecasts of surface O-3 and PM2.5 during the TEXAQS-II experiment of 2006. Atmos. Environ., 44 (4) 455-467, ISSN: 1352-2310, ids: 556TD, doi: 10.1016/j.atmosenv.2009.11.007.
    (https://www.sciencedirect.com/science/article/pii/S1352231009009510)
  3. Huang J., J. McQueen, J. Wilczak, I. Djalalova, I. Stajner, P. Shafran, D. Allured, P. Lee, L. Pan, D. Tong, H. Huang, J. Gorline, G. DiMego, S. Upadhayay, and L. D. Monache, (2017), Improving NOAA NAQFC PM2.5 predictions with a bias correction approach, Weather and Forecasting, vol. 32, no. 2, 407-421, https://dx.doi.org/10.1175/WAF-D-16-0118.1. 
    (https://journals.ametsoc.org/doi/10.1175/WAF-D-16-0118.1) 
  4. Lee, P., J. McQueen, I. Stajner, J. Huang, L. Pan, D. Tong, H. Kim, Y. Tang, S. Kondragunta, M. Ruminski, S. Lu, E. Rogers, R. Saylor, P. Shafran, H. Huang, J. Gorline, S. Upadhayay, and R. Artz (2017), NAQFC Developmental Forecast Guidance for Fine Particulate Matter (PM2.5). Weather and Forecasting, 32, 343–360, doi: 10.1175/WAF-D-15-0163.1.
    (https://journals.ametsoc.org/doi/abs/10.1175/WAF-D-15-0163.1)
  5. Lee P., Y. Liu, (2014), Preliminary evaluation of a regional atmospheric chemical data assimilation system for environmental surveillance, Int. J. Environ. Res. Public Health 2014, 11(12), 12795-12816.
    (https://www.mdpi.com/1660-4601/11/12/12795)
  6. Lee P., and F. Ngan, (2011), Coupling of Important Physical Processes in the Planetary Boundary Layer between Meteorological and Chemistry Models for Regional to Continental Scale Air Quality Forecasting: An Overview. Atmosphere, 2, 464-483.
    (https://www.mdpi.com/2073- 4433/2/3/464)
  7. Mathur, R., S. Yu, D. Kang, and K. L. Schere, (2008), Assessment of the wintertime performance of developmental particulate matter forecasts with the Eta-Community Multiscale Air Quality modeling system, J Geophys Res, 113, D02303.
    (https://onlinelibrary.wiley.com/doi/10.1029/2007JD008580/full)
  8. McKeen, S., et al. (2007), Evaluation of several PM2.5 forecast models using data collected during the ICARTT/NEAQS 2004 field study, J. Geophys. Res., 112, D10S20, doi:10.1029/2006JD007608.
    (https://onlinelibrary.wiley.com/doi/10.1029/2006JD007608/pdf)
  9. Pagowski, M., G. A. Grell, S. A. McKeen, S. E. Peckham, and D. Devenyi (2010), Threedimensional variational data assimilation of ozone and fine particulate matter observations: some results using the Weather Research and Forecasting-Chemistry model and Grid-point Statistical Interpolation, Q J R Meteorol Soc, 136(653), 2010.
    (https://onlinelibrary.wiley.com/doi/10.1002/qj.700/abstract)
  10. . Yu, S., R. Mathur, K. Schere, D. Kang, J. Pleim, J. Young, D. Tong, G. Pouliot, S. A. McKeen, and S. T. Rao (2008), Evaluation of real-time PM2.5 forecasts and process analysis for PM2.5 formation over the eastern United States using the Eta-CMAQ forecast model during the 2004 ICARTT study, J. Geophys. Res., 113, D06204, doi:10.1029/2007JD009226.
    (https://onlinelibrary.wiley.com/doi/10.1029/2007JD009226/full)
  11. Zhao H., D. Tong, P. Lee, H. Kim, and H. Lei, (2016), Reconstruction fire records from ground-based routine aerosol monitoring, Atmosphere. 2016, 7, 43.
    (https://www.mdpi.com/2073-4433/7/3/43).

NAQFC Applications

  1. He H., C. P. Loughner, J. W. Stehr, H. L. Arkinson, L. C. Brent, M. B. Follette-Cook, M. A. Tzortziou, K. E. Pickering, A. M. Thompson, D. K. Martins, G. S. Diskin, B. E. Anderson, J. H. Crawford, A. J. Weinheimer, P. Lee, J. C. Hains, and R. R. Dickerson, (2014), An elevated reservoir of air pollutants over the Mid-Atlantic States during the 2011DISCOVER-AQ campaign: airborne measurements and numerical simulations, Atmos. Environ, 85, 18-30.
    (https://www.sciencedirect.com/science/article/pii/S1352231013008741)
  2. Hu Y., M. T. Odman, P. Lee, D. Tong, S. Spak, and A. G. Russell, (2014), A clearer view of tomorrow’s haze: Improvement in air quality forecasting, Environmental Management, Air Waste management Association, February 2014, pp 11-15.
    (https://pubs.awma.org/gsearch/em/2014/2/hu.pdf)
  3. Pickering E. K., and P. Lee, (2014), Discover-AQ Air quality forecasting guides flight plans, Environ. Manager, Sep 2014, 39-43.
    (https://discover-aq.larc.nasa.gov/pdf/EM0914-60pFNL%28L%29-Copyright-1.pdf)