Skip to main content

Advertisement

Log in

Spatiotemporal calibration of atmospheric nitrogen dioxide concentration estimates from an air quality model for Connecticut

  • Published:
Environmental and Ecological Statistics Aims and scope Submit manuscript

Abstract

A spatiotemporal calibration and resolution refinement model was fitted to calibrate nitrogen dioxide (\(\hbox {NO}_2\)) concentration estimates from the Community Multiscale Air Quality (CMAQ) model, using two sources of observed data on \(\hbox {NO}_2\) that differed in their spatial and temporal resolutions. To refine the spatial resolution of the CMAQ model estimates, we leveraged information using additional local covariates including total traffic volume within 2 km, population density, elevation, and land use characteristics. Predictions from this model greatly improved the bias in the CMAQ estimates, as observed by the much lower mean squared error (MSE) at the \(\hbox {NO}_2\) monitor sites. The final model was used to predict the daily concentration of ambient \(\hbox {NO}_2\) over the entire state of Connecticut on a grid with pixels of size 300 \(\times \) 300 m. A comparison of the prediction map with a similar map for the CMAQ estimates showed marked improvement in the spatial resolution. The effect of local covariates was evident in the finer spatial resolution map, where the contribution of traffic on major highways to ambient \(\hbox {NO}_2\) concentration stands out. An animation was also provided to show the change in the concentration of ambient \(\hbox {NO}_2\) over space and time for 1994 and 1995.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Alkuwari FA, Guillas S, Wang Y (2013) Statistical downscaling of an air quality model using fitted empirical orthogonal functions. Atmos Environ 81:1–10

    Article  CAS  Google Scholar 

  • Berrocal VJ, Gelfand AE, Holland DM (2010) A spatio-temporal downscaler for output from numerical models. J Agric Biol Environ Stat 15(2):176–197

    Article  Google Scholar 

  • Beyer HL (2012) Geospatial Modeling Environment (Version 0.7.2.1). (software). http://www.spatialecology.com/gme. Accessed 10 Apr 2012

  • Brown P, Diggle P, Lord M, Young P (2001) Space-time calibration of radar rainfall data. J R Stat Soc Ser C 50(2):221–241

    Article  Google Scholar 

  • Byun D, Schere K (2006) Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system. Appl Mech Rev 59:51–77

    Article  Google Scholar 

  • Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM (2006) Measurement error in nonlinear models: a modern perspective. CRC Press, Boca Raton

    Book  Google Scholar 

  • Chang HH, Hu X, Liu Y (2014) Calibrating MODIS aerosol optical depth for predicting daily PM\(_{2.5}\) concentrations via statistical downscaling. J Expo Sci Environ Epidemiol 24(4):398–404

    Article  CAS  Google Scholar 

  • Connecticut Department of Transportation (2001) 2000 Traffic Volumes. State Maintained Highway Network, Bureau of Policy and Planning

  • Cressie N (1993) Statistics for spatial data: Wiley series in probability and statistics. Wiley, New York

    Book  Google Scholar 

  • ESRI (2003) StreetMapUSA. Redlands, CA, Environmental Systems Research Institute

  • Fuentes M, Raftery AE (2005) Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models. Biometrics 61(1):36–45

    Article  Google Scholar 

  • Gesch D (2007) The national elevation dataset. In: Maune D (ed) Digital elevation model technologies and applications: the DEM users manual. Asprs Publications, Bethesda

    Google Scholar 

  • Gesch D, Oimoen M, Greenlee S, Nelson C, Steuck M, Tyler D (2002) The national elevation dataset. Photogramm Eng Remote Sens 68(1):5–11

    Google Scholar 

  • Gilani O, McKay LA, Gregoire TG, Guan Y, Leaderer BP, Holford TR (2016) Spatiotemporal calibration and resolution refinement of output from deterministic models. Stat Med. https://doi.org/10.1002/sim.6867

    Article  PubMed  Google Scholar 

  • Hogrefe C, Lynn B, Goldberg R, Rosenzweig C, Zalewsky E, Hao W, Doraiswamy P, Civerolo K, Ku JY, Sistla G, Kinney PL (2009) A combined model–observation approach to estimate historic gridded fields of PM\(_{2.5}\) mass and species concentrations. Atmos Environ 43(16):2561–2570

    Article  CAS  Google Scholar 

  • Holford T, Ebisu K, McKay L, Gent J, Triche E, Bracken M, Leaderer B (2010) Integrated exposure modeling: a model using GIS and GLM. Stat Med 29(1):116–129

    PubMed  PubMed Central  Google Scholar 

  • Jerrett M, Arain A, Kanaroglou P, Beckerman B, Potoglou D, Sahsuvaroglu T, Morrison J, Giovis C (2004) A review and evaluation of intraurban air pollution exposure models. J Expo Sci Environ Epidemiol 15(2):185–204

    Article  CAS  Google Scholar 

  • Kalman R (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82(1):35–45

    Article  Google Scholar 

  • Li B, Eriksson M, Srinivasan R, Sherman M (2008) A geostatistical method for Texas NexRad data calibration. Environmetrics 19(1):1–19

    Article  CAS  Google Scholar 

  • Massachusetts Department of Transportation - Highway Division (2012) Traffic Volume Counts. http://www.mhd.state.ma.us/default.asp?pgid=content/traffic01&sid=about. Accessed 29 Mar 2013

  • Meiring W, Sampson P, Guttorp P (1998) Space-time estimation of grid-cell hourly ozone levels for assessment of a deterministic model. Environ Ecol Stat 5(3):197–222

    Article  Google Scholar 

  • New York State Department of Transportation (2012) Department of Transportation - Historic Traffic Data 1977-2011. https://www.dot.ny.gov/divisions/engineering/technical-services/hds-respository/. Accessed 29 Mar 2013

  • Palmes E, Gunnison A, DiMattio J, Tomczyk C (1976) Personal sampler for nitrogen dioxide. Am Ind Hyg Assoc J 37(10):570–577

    Article  CAS  Google Scholar 

  • Petris G (2010) An R package for dynamic linear models. J Stat Softw 36(12):1–16

    Article  Google Scholar 

  • R Core Team (2013) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/

  • Skene KJ, Gent JF, McKay LA, Belanger K, Leaderer BP, Holford TR (2010) Modeling effects of traffic and landscape characteristics on ambient nitrogen dioxide levels in Connecticut. Atmos Environ 44(39):5156–5164. https://doi.org/10.1016/j.atmosenv.2010.08.058. http://www.sciencedirect.com/science/article/B6VH3-511G1YK-9/2/4258d4a4b1934748a7a99324663259f8

    Article  CAS  Google Scholar 

  • Triche E, Belanger K, Beckett W, Bracken M, Holford T, Gent J, Jankun T, McSharry J, Leaderer B (2002) Infant respiratory symptoms associated with indoor heating sources. Am J Respir Crit Care Med 166(8):1105–1111

    Article  Google Scholar 

  • US Census Bureau (1990) US Census Data 1990. ftp.census.gov/census\(\_\)1990/. Accessed 01 Apr 2013

  • US Census Bureau (2012) Cartographic Boundary Files - U.S. Census Bureau. http://www.census.gov/geo/www/cob/tr1990.html. Accessed 01 Apr 2013

  • US Environmental Protection Agency (2008) Integrated Science Assessment for oxides of nitrogen—Health criteria. National Center for Environmental Assessment, Office of Research and Development, Research Triangle Park Durham, NC

  • US Environmental Protection Agency (2011) Technology Transfer Network (TTN) Air Quality System (AQS)—Nitrogen Dioxide. http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm. Accessed 14 Apr 2011

  • US Environmental Protection Agency (2014) Community Multi-scale Air Quality Model (CMAQ). http://www.epa.gov/amad/Research/RIA/cmaq.html. Accessed 03 Feb 2014

  • US Geological Survey (2012) USGS National Land Cover Dataset 1992. http://landcover.usgs.gov/natllandcover.php. Accessed 29 Mar 2013

  • WHO (2003) World Health Organization, Bonn, Germany. Health aspects of air pollution with particulate matter, ozone and nitrogen dioxide. http://www.euro.who.int/document/e79097.pdf . Accessed 21 Apr 2011

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Owais Gilani.

Additional information

Handling Editor Pierre Dutilleul.

The authors thank Dr. Lance Waller for useful feedback on the manuscript, as well as the anonymous reviewers whose comments helped improve the manuscript. This research was partially funded by Grant R01ES017416 from the National Institutes of Health.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gilani, O., McKay, L.A., Gregoire, T.G. et al. Spatiotemporal calibration of atmospheric nitrogen dioxide concentration estimates from an air quality model for Connecticut. Environ Ecol Stat 26, 325–349 (2019). https://doi.org/10.1007/s10651-019-00430-7

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10651-019-00430-7

Keywords

Navigation