Skip to main content
SpringerLink
Log in
Book cover

Geospatial Technologies for Urban Health pp 53–72Cite as

Evaluating the Effect of Domain Size of the Community Multiscale Air Quality (CMAQ) Model on Regional PM2.5 Simulations

  • Chapter
  • First Online:

  • 720 Accesses

Part of the book series: Global Perspectives on Health Geography ((GPHG))

Abstract

A growing number of urban health impact studies use Community Multiscale Air Quality (CMAQ) models for air pollution exposure estimation, although the performance of CMAQ models is likely to be affected by multiple parameters, including the configuration setting of the study domain. We presented an approach for CMAQ model uncertainty assessment with respect to domain size and reported spatial and temporal variations of CMAQ model performance over two study domains, a relatively small domain (DS) and a large domain (DL). Specifically, we simulated daily PM2.5 concentrations over two domains during 2011 and quantified the difference between the model predictions. The model performance was assessed by comparing modeled PM2.5 against measured PM2.5 values at monitoring sites located in the region of overlap for each domain. The results suggest that the CMAQ simulations over two domains were in good agreement across the study area except in southwestern areas. We also found that the overall model performance was better for CMAQ simulations with a large domain relative to the smaller domain. Based on our findings, we recommend applying a large domain for PM2.5 simulations, particularly for urban health risk assessments conducted over summer months, which generally contain more emissions.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Abbreviations

AQS:

Air Quality System

BCs:

Boundary conditions

CMAQ:

Community Multiscale Air Quality

EPA:

Environmental Protection Agency

FB:

Fractional bias

FE:

Fractional error

NEI:

National Emission Inventory

NYC:

New York City

PM2.5:

Fine particulate matter with aerodynamic diameter less than or equal to 2.5 μm

SMOKE:

Sparse Matrix Operator Kernel Emission

WRF:

Weather Research and Forecasting

References

  • Adams, K., Greenbaum, D. S., Shaikh, R., van Erp, A. M., & Russell, A. G. (2015). Particulate matter components, sources, and health: Systematic approaches to testing effects. Journal of the Air & Waste Management Association, 65(5), 544–558.

    Article  Google Scholar 

  • Appel, K. W., Foley, K., Bash, J., Pinder, R., Dennis, R., Allen, D., & Pickering, K. (2011). A multi-resolution assessment of the Community Multiscale Air Quality (CMAQ) model v4. 7 wet deposition estimates for 2002–2006. Geoscientific Model Development, 4(2), 357.

    Article  Google Scholar 

  • Appel, K. W., Napelenok, S. L., Foley, K. M., Pye, H. O., Hogrefe, C., Luecken, D. J., ... & Hutzell, W. T. (2017). Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1. Geoscientific Model Development, 10(4), 1703–1732.

    Google Scholar 

  • Barna, M. G., & Knipping, E. M. (2006). Insights from the BRAVO study on nesting global models to specify boundary conditions in regional air quality modeling simulations. Atmospheric Environment, 40, 574–582.

    Article  Google Scholar 

  • Baxter, L. K., Dionisio, K. L., Burke, J., Sarnat, S. E., Sarnat, J. A., Hodas, N., ... & Kumar, N. (2013). Exposure prediction approaches used in air pollution epidemiology studies: Key findings and future recommendations. Journal of Exposure Science and Environmental Epidemiology, 23(6), 654.

    Google Scholar 

  • Beddows, A. V., Kitwiroon, N., Williams, M. L., & Beevers, S. D. (2017). Emulation and sensitivity analysis of the Community Multiscale Air Quality Model for a UK ozone pollution episode. Environmental Science & Technology, 51(11), 6229–6236.

    Article  Google Scholar 

  • Bell, M. L., Ebisu, K., Peng, R. D., Walker, J., Samet, J. M., Zeger, S. L., & Dominici, F. (2008). Seasonal and regional short-term effects of fine particles on hospital admissions in 202 US counties, 1999–2005. American Journal of Epidemiology, 168(11), 1301–1310.

    Article  Google Scholar 

  • Borge, R., López, J., Lumbreras, J., Narros, A., & Rodríguez, E. (2010). Influence of boundary conditions on CMAQ simulations over the Iberian Peninsula. Atmospheric Environment, 44(23), 2681–2695.

    Article  Google Scholar 

  • Boylan, J. W., & Russell, A. G. (2006). PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models. Atmospheric Environment, 40(26), 4946–4959.

    Article  Google Scholar 

  • Bravo, M. A., Fuentes, M., Zhang, Y., Burr, M. J., & Bell, M. L. (2012). Comparison of exposure estimation methods for air pollutants: Ambient monitoring data and regional air quality simulation. Environmental Research, 116, 1–10.

    Article  Google Scholar 

  • Bravo, M. A., Ebisu, K., Dominici, F., Wang, Y., Peng, R. D., & Bell, M. L. (2016). Airborne fine particles and risk of hospital admissions for understudied populations: Effects by urbanicity and short-term cumulative exposures in 708 U.S. counties. Environmental Health Perspectives, 125(4), 594–601.

    Article  Google Scholar 

  • Burr, M. J., & Zhang, Y. (2011). Source apportionment of fine particulate matter over the Eastern US Part I: Source sensitivity simulations using CMAQ with the Brute Force method. Atmospheric Pollution Research, 2(3), 300–317.

    Article  Google Scholar 

  • Byun, D., & Schere, K. L. (2006). Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system. Applied Mechanics Reviews, 59(2), 51–77.

    Article  Google Scholar 

  • Cefalu, M., & Dominici, F. (2014). Does exposure prediction bias health effect estimation? The relationship between confounding adjustment and exposure prediction. Epidemiology (Cambridge, Mass.), 25(4), 583.

    Article  Google Scholar 

  • CMAQ version 5.0 (February 2010 release) OGD. (2015, December 4). CMASWIKI, Retrieved 14:35, May 5, 2019 from https://www.airqualitymodeling.org/index.php?title=CMAQ_version_5.0_(February_2010_release)_OGD&oldid=682.

    Google Scholar 

  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.

    Article  Google Scholar 

  • Dockery, D. W. (2009). Health effects of particulate air pollution. Annuals of Epidemiology, 19(4), 257–263.

    Article  Google Scholar 

  • Du, Y., Xu, X., Chu, M., Guo, Y., & Wang, J. (2016). Air particulate matter and cardiovascular disease: The epidemiological, biomedical and clinical evidence. Journal of Thoracic Disease, 8(1), E8.

    Google Scholar 

  • Ebisu, K., & Bell, M. L. (2012). Airborne PM2.5 chemical components and low birth weight in the northeastern and mid-Atlantic regions of the United States. Environmental Health Perspectives, 120(12), 1746.

    Article  Google Scholar 

  • EPA. (2014). Modeling guidance for demonstrating attainment of air quality goals for ozone, PM2.5, and regional haze-December 2014 DRAFT. US Environmental Protection Agency, Office of Air Quality Planning and Standards. https://www3.epa.gov/scram001/guidance/guide/Draft_O3-PM-RH_Modeling_Guidance-2014.pdf.

  • Eyth, A., & Vukovich, J. (2016). Technical Support Document (TSD) preparation of emissions inventories for the version 6.3, 2011 emissions modeling platform. US Environmental Protection Agency, Office of Air Quality Planning and Standards.

    Google Scholar 

  • Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201.

    Article  Google Scholar 

  • Fountoukis, C., Koraj, D., van der Gon, H. D., Charalampidis, P., Pilinis, C., & Pandis, S. (2013). Impact of grid resolution on the predicted fine PM by a regional 3-D chemical transport model. Atmospheric Environment, 68, 24–32.

    Article  Google Scholar 

  • Gentner, D. R., Jathar, S. H., Gordon, T. D., Bahreini, R., Day, D. A., El Haddad, I., ... & Goldstein, A. H. (2017). Review of urban secondary organic aerosol formation from gasoline and diesel motor vehicle emissions. Environmental Science & Technology, 51(3), 1074–1093.

    Google Scholar 

  • Griffith, D. A. (1980). Towards a theory of spatial statistics. Geographical Analysis, 12(4), 325–339.

    Article  Google Scholar 

  • Griffith, D. A., & Amrhein, C. G. (1983). An evaluation of correction techniques for boundary effects in spatial statistical analysis: Traditional methods. Geographical Analysis, 15(4), 352–360.

    Article  Google Scholar 

  • Hoek, G., Krishnan, R. M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B., & Kaufman, J. D. (2013). Long-term air pollution exposure and cardio-respiratory mortality: A review. Environmental Health, 12(1), 43.

    Article  Google Scholar 

  • Hogrefe, C., Liu, P., Pouliot, G., Mathur, R., Roselle, S., Flemming, J., Lin, M., & Park, R. J. (2018). Impacts of different characterizations of large-scale background on simulated regional-scale ozone over the continental United States. Atmospheric Chemistry and Physics, 18(5), 3839.

    Google Scholar 

  • Hu, J., Li, X., Huang, L., Qi, Y., Zhang, Q., Zhao, B., Wang, S., & Zhang, H. (2017). Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China. Atmospheric Chemistry and Physics, 17(21), 13103.

    Google Scholar 

  • Jiang, X., & Yoo, E.-h. (2018). The importance of spatial resolutions of Community Multiscale Air Quality (CMAQ) models on health impact assessment. Science of the Total Environment, 627, 1528–1543.

    Article  Google Scholar 

  • Jiménez, P., Parra, R., & Baldasano, J. M. (2007). Influence of initial and boundary conditions for ozone modeling in very complex terrains: A case study in the northeastern Iberian Peninsula. Environmental Modelling & Software, 22(9), 1294–1306.

    Article  Google Scholar 

  • Karambelas, A., Holloway, T., Kinney, P. L., Fiore, A. M., DeFries, R., Kiesewetter, G., & Heyes, C. (2018). Urban versus rural health impacts attributable to PM2.5 and O3 in northern India. Environmental Research Letters, 13(6), 064010.

    Article  Google Scholar 

  • Kloog, I., Ridgway, B., Koutrakis, P., Coull, B. A., & Schwartz, J. D. (2013). Long- and short-term exposure to PM2.5 and mortality: Using novel exposure models. Epidemiology (Cambridge, Mass.), 24(4), 555.

    Article  Google Scholar 

  • Krall, J. R., Chang, H. H., Sarnat, S. E., Peng, R. D., & Waller, L. A. (2015). Current methods and challenges for epidemiological studies of the associations between chemical constituents of particulate matter and health. Current Environmental Health Reports, 2(4), 388–398.

    Article  Google Scholar 

  • Lee, P., Kang, D., McQueen, J., Tsidulko, M., Hart, M., DiMego, G., Seaman, N., & Davidson, P. (2008). Impact of domain size on modeled ozone forecast for the northeastern United States. Journal of Applied Meteorology and Climatology, 47(2), 443–461.

    Article  Google Scholar 

  • Lee, H., Liu, Y., Coull, B., Schwartz, J., & Koutrakis, P. (2011). A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations. Atmospheric Chemistry and Physics, 11(15), 7991–8002.

    Article  Google Scholar 

  • Lee, D., Wang, J., Jiang, X., Lee, Y., & Jang, K. (2012). Comparison between atmospheric chemistry model and observations utilizing the RAQMS–CMAQ linkage. Atmospheric Environment, 61, 85–93.

    Article  Google Scholar 

  • Makar, P. A., Gong, W., Mooney, C., Zhang, J., Davignon, D., Samaali, M., ... & Chen, J. (2010). Dynamic adjustment of climatological ozone boundary conditions for air-quality forecasts. Atmospheric Chemistry and Physics, 10(18), 8997–9015.

    Google Scholar 

  • Mancilla, Y., Herckes, P., Fraser, M. P., & Mendoza, A. (2015). Secondary organic aerosol contributions to PM2.5 in Monterrey, Mexico: Temporal and seasonal variation. Atmospheric Research, 153, 348–359.

    Article  Google Scholar 

  • McGuinn, L. A., Ward-Caviness, C., Neas, L. M., Schneider, A., Di, Q., Chudnovsky, A., ... & Kraus, W. E. (2017). Fine particulate matter and cardiovascular disease: Comparison of assessment methods for long-term exposure. Environmental Research, 159, 16–23.

    Google Scholar 

  • Morris, R. E., McNally, D. E., Tesche, T. W., Tonnesen, G., Boylan, J. W., & Brewer, P. (2005). Preliminary evaluation of the Community Multiscale Air Quality model for 2002 over the Southeastern United States. Journal of the Air & Waste Management Association, 55(11), 1694–1708.

    Article  Google Scholar 

  • Murray, N., Chang, H. H., Holmes, H., & Liu, Y. (2018). Combining satellite imagery and numerical model simulation to estimate ambient air pollution: An ensemble averaging approach. arXiv preprint arXiv: 1802.03077.

    Google Scholar 

  • Nolte, C., Appel, K., Kelly, J., Bhave, P., Fahey, K., Collett, J., Jr., Zhang, L., & Young, J. (2015). Evaluation of the Community Multiscale Air Quality (CMAQ) model v5. 0 against size-resolved measurements of inorganic particle composition across sites in North America. Geoscientific Model Development, 8(9), 2877–2892.

    Article  Google Scholar 

  • Özkaynak, H., Baxter, L. K., Dionisio, K. L., & Burke, J. (2013). Air pollution exposure prediction approaches used in air pollution epidemiology studies. Journal of Exposure Science and Environmental Epidemiology, 23(6), 566–572.

    Article  Google Scholar 

  • Pour-Biazar, A., Khan, M., Wang, L., Park, Y.-H., Newchurch, M., McNider, R. T., Liu, X., Byun, D. W., & Cameron, R. (2011). Utilization of satellite observation of ozone and aerosols in providing initial and boundary condition for regional air quality studies. Journal of Geophysical Research: Atmospheres, 116(D18).

    Google Scholar 

  • Queen, A., & Zhang, Y. (2008). Examining the sensitivity of MM5–CMAQ predictions to explicit microphysics schemes and horizontal grid resolutions, Part III–The impact of horizontal grid resolution. Atmospheric Environment, 42(16), 3869–3881.

    Article  Google Scholar 

  • Reyes, J. M., Xu, Y., Vizuete, W., & Serre, M. L. (2017). Regionalized PM2.5 Community Multiscale Air Quality model performance evaluation across a continuous spatiotemporal domain. Atmospheric Environment, 148, 258–265.

    Google Scholar 

  • Ripley, B. D. (1981). Spatial statistics. New York: Wiley.

    Book  Google Scholar 

  • Roy, B., Mathur, R., Gilliland, A. B., & Howard, S. C. (2007). A comparison of CMAQ-based aerosol properties with IMPROVE, MODIS, and AERONET data. Journal of Geophysical Research: Atmospheres, (D14), 112.

    Google Scholar 

  • Samaali, M., Moran, M. D., Bouchet, V. S., Pavlovic, R., Cousineau, S., & Sassi, M. (2009). On the influence of chemical initial and boundary conditions on annual regional air quality model simulations for North America. Atmospheric Environment, 43(32), 4873–4885.

    Article  Google Scholar 

  • Seinfeld, J. H., & Pandis, S. N. (2016). Atmospheric chemistry and physics: From air pollution to climate change. Wiley.

    Google Scholar 

  • Tang, Y., Carmichael, G. R., Thongboonchoo, N., Chai, T., Horowitz, L. W., Pierce, R. B., ... & Sachse, G. W. (2007). Influence of lateral and top boundary conditions on regional air quality prediction: A multiscale study coupling regional and global chemical transport models. Journal of Geophysical Research: Atmospheres, 112(D10).

    Google Scholar 

  • Tang, Y., Lee, P., Tsidulko, M., Huang, H. C., McQueen, J. T., DiMego, G. J., ... & Kang, D. (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.

    Google Scholar 

  • Wang, C., Tu, Y., Yu, Z., & Lu, R. (2015). PM2.5 and cardiovascular disease in the elderly: An overview. International Journal of Environmental Research and Public Health, 12(7), 8187–8197.

    Article  Google Scholar 

  • Wang, W., Barker, D., Bray, J., Bruyere, C., Duda, M., Dudhia, J., Gill, D., & Michalakes, J. (2016). User’s guide for the Advanced Research WRF (ARW) modeling system version 3.7. http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3.7/ARWUsersGuideV3.7.pdf.

  • Warner, T. T., Peterson, R. A., & Treadon, R. E. (1997). A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bulletin of the American Meteorological Society, 78(11), 2599–2617.

    Article  Google Scholar 

  • Weber, S. A., Insaf, T. Z., Hall, E. S., Talbot, T. O., & Huff, A. K. (2016). Assessing the impact of fine particulate matter (PM2.5) on respiratory-cardiovascular chronic diseases in the New York City Metropolitan area using Hierarchical Bayesian Model estimates. Environmental Research, 151, 399–409.

    Article  Google Scholar 

  • Xiao, Q., Liu, Y., Mulholland, J. A., Russell, A. G., Darrow, L. A., Tolbert, P. E., & Strickland, M. J. (2016). Pediatric emergency department visits and ambient air pollution in the US State of Georgia: A case-crossover study. Environmental Health, 15(1), 115.

    Article  Google Scholar 

  • Xing, Y.-F., Xu, Y.-H., Shi, M.-H., & Lian, Y.-X. (2016). The impact of PM2.5. On the human respiratory system. Journal of Thoracic Disease, 8(1), E69.

    Google Scholar 

  • Yoo, E.-H., & Kyriakidis, P. (2008). Area-to-point prediction under boundary conditions. Geographical Analysis, 40(4), 355–379.

    Article  Google Scholar 

  • Zhang, H., Chen, G., Hu, J., Chen, S.-H., Wiedinmyer, C., Kleeman, M., & Ying, Q. (2014). Evaluation of a seven-year air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States. Science of the Total Environment, 473, 275–285.

    Google Scholar 

  • Zhu, X. (2016). GIS for environmental applications: A practical approach. Routledge.

    Google Scholar 

Download references

Acknowledgments

The authors thank for the support provided by the Center for Computational Research (CCR) as well as the seed grant from University at Buffalo’s Research and Education in Energy, Environment & Water (RENEW) Institute.

Author information

Authors and Affiliations

  1. Department of Geography, State University of New York at Buffalo, Buffalo, NY, USA

    Xiangyu Jiang & Eun-Hye Yoo

Authors
  1. Xiangyu Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eun-Hye Yoo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eun-Hye Yoo .

Editor information

Editors and Affiliations

  1. Department of Geography, Texas State University, San Marcos, TX, USA

    Yongmei Lu

  2. Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA

    Eric Delmelle

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jiang, X., Yoo, EH. (2020). Evaluating the Effect of Domain Size of the Community Multiscale Air Quality (CMAQ) Model on Regional PM2.5 Simulations. In: Lu, Y., Delmelle, E. (eds) Geospatial Technologies for Urban Health. Global Perspectives on Health Geography. Springer, Cham. https://doi.org/10.1007/978-3-030-19573-1_4

Download citation

Publish with us

Policies and ethics

Search

Navigation