1,847
Views
29
CrossRef citations to date
0
Altmetric
Technical Papers

Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

&
Pages 628-640 | Received 17 Sep 2014, Accepted 07 Jan 2015, Published online: 14 Apr 2015

References

  • Abdul-Wahab, S.A., C.S. Bakheit, and S.M. Al-Alawi. 2005. Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environ. Model. Softw. 20:1263–1271. doi:10.1016/j.envsoft.2004.09.001
  • Arian, M.A., R. Blair, N. Finkelstein, J.R. Brook, T. Sahsuvaroglu, B. Beckerman, L. Zhang, and M. Jerrett. 2007. The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies. Atmos. Environ. 41:3453–3464. doi:10.1016/j.atmosenv.2006.11.063
  • Briggs, D.J., C. de Hoogh, J. Gulliver, J. Wills, P. Elliott, S. Kingham, and K. Smallbone. 2000. A regression-based method for mapping traffic-related air pollution: Application and testing in four contrasting urban environments. Sci. Total Environ. 253:151–167. doi:10.1016/S0048-9697(00)00429-0
  • Chaloulakou, A., G. Grivas, and N. Spyrellis. 2003. Neural network and multiple regression models for PM10 prediction in Athens: A comparative assessment. J. Air Waste Manage. Assoc. 53:1138–1190. doi:10.1080/10473289.2003.10466276
  • Center for International Earth Science Information Network (CIESIN). 2013. Gridded Population of the World (GPW), version 3. http://sedac.ciesin.columbia.edu/gpw (accessed April 2, 2013).
  • Chen, C., C. Wu, H. Yu, C. Chan, and T. Cheng. 2012a. Spatiotemporal modelling with temporal-invariant variogram subgroups to estimate fine particle matter PM2.5 concentraions. Atmos. Environ. 54:1–8. doi:10.1016/j.atmosenv.2012.02.015
  • Chen, L., Z. Bai, S. Kong, B. Han, Y. You, X. Ding, S. Du, and A. Liu. 2010. A land use regression for predicting NO2 and PM10 concentrations in different seasons in Tianjin region, China. J. Environ. Sci. 22:1364–1373. doi:10.1016/S1001-0742(09)60263-1
  • Chen, L., Y. Wang, P. Li, Y. Ji, S. Kong, Z. Li, and Z. Bai. 2012b. A land use regression model incorporating data on industrial point source pollution. J. Environ. Sci. 24:1251–1258. doi:10.1016/S1001-0742(11)60902-9
  • Cobourn, W.G., L. Dolcine, M. French, and M.C. Hubbard. 2000. A comparison of non-linear regression and neural network models for ground-level ozone forecasting. J. Air Waste Manage. Assoc. 50:1999–2009. doi:10.1080/10473289.2000.10464228
  • Comrie, A.C. 1997. Comparing neural networks and regression models for ozone forecasting. J. Air Waste Manage. Assoc. 47:653–663. doi:10.1080/10473289.1997.10463925
  • Central Statistics Office. 2013. Census 2011 Small Area Population Statistics (SAPS). http://www.cso.ie/en/census/census2011smallareapopulationstatisticssaps/ (accessed January 14, 2015).
  • Diem, J.E., and A.C. Comrie. 2002.Predictive mapping of air pollution involving sparse spatial observations. Environ. Pollut. 119:99–117. doi:10.1016/S0269-7491(01)00308-6
  • Donnelly, A., B.D.R. Misstear, and B. Broderick. 2011a. Relationship of background NO2 concentrations in air to back trajectories through parametric and nonparametric regression methods: Application at two background sites in Ireland. J. Environ. Model. Assess. 17:363–373.
  • Donnelly, A., B.D.R. Misstear, and B. Broderick. 2011b. Application of nonparametric regression methods to study the relationship between NO2 concentrations and local wind direction and speed at background sites. Sci. Total Environ. 409:1134–1144. doi:10.1016/j.scitotenv.2010.12.001
  • Dons, E., M. Van Poppel, L. Int Panis, S. De Prins, P. Berghmans, G. Koppen, and C. Matheeussen. 2014. Land use regression models as a tool for short, medium and long term exposure to traffic related air pollution. Sci. Total Environ. 476–477:378–386. doi:10.1016/j.scitotenv.2014.01.025
  • Dons, E., M. Van Poppel, B. Kochan, G. Wets, and L. Int Panis. 2013. Modeling temporal and spatial variability of traffic-related air pollution: Hourly land use regression models for black carbon. Atmos. Environ. 74:237–246. doi:10.1016/j.atmosenv.2013.03.050
  • ESRI (Environmental Systems Resource Institute). 2012. Arc-GIS: ArcMap 10.1. ESRI, Redlands, California. http://www.esri.com/news/arcnews/spring12articles/introducing-arcgis-101.html, (accessed January 15, 2013).
  • European Environment Agency. 2013a. Corine land cover 2006 seamless vector data and population density disaggregated with Corine land cover 2000. http://www.eea.europa.eu/data-and-maps/data/clc-2006-vector-data-version-2 (accessed on January 2, 2013).
  • European Environment Agency. 2013b. Population density. http://www.eea.europa.eu/data-and-maps/data/population-density#tab-european-data (accessed on January 2, 2013); http://www.eea.europa.eu/data-and-maps/data/population-density#tab-methodology (accessed January 3, 2013).
  • Freund, R.J., W.J. Wilson, and P. Sa. 2006. Regression Analysis: Statistical modelling of a Response Variable, 2nd ed. London: Academic Press.
  • Fox, J. and S. Weisberg. 2011. An {R} Companion to Applied Regression, Second Edition. Thousand Oaks CA: Sage. http://socserv.socsci.mcmaster.ca/jfox/Books/Companion (accessed January 20, 2013).
  • Gonzales, M., O. Myers, L. Smith, H.A. Olvera, S. Mukerjee, W.W. Li, N. Pingitore, M. Amaya, S. Burchiel, M. Berwick, and ARCH Study Team. 2012. Evaluation of land use regression models for NO2 in El Paso, Texas, USA. Sci. Total Environ. 432:135–142. doi:10.1016/j.scitotenv.2012.05.062
  • Gulliver, J., K. de Hoogh, D. Fecht, D. Vienneau, and D. Briggs. 2011. Comparative assessment of GIS-based methods and metrics for estimating long-term exposures to air pollution. Atmos. Environ. 45:7072–7080. doi:10.1016/j.atmosenv.2011.09.042
  • Hoek, G., R. Beelen, K. de Hoogh, D. Vienneau, J. Gulliver, P. Fischer, and D. Briggs. 2008. A review of lands regression models to assess the spatial variation of outdoor air pollution. Atmos. Environ. 42:7561–7578. doi:10.1016/j.atmosenv.2008.05.057
  • Ibarra-Berastegui, G., A. Elias, A. Barona, J. Saenz, A. Ezcurra, and J. Diaz de Argandõna. 2008. From diagnosis to prognosis for forecasting air pollution using neural network: Air pollution monitoring in Bilbao. Environ. Modell. Softw. 23:622–637.
  • Ireland Environmental Protection Agency. 2015. Air Quality. http://www.epa.ie/air/quality/ (accessed January 14, 2015).
  • Ireland Environmental Protection Agency. 2014. Air quality. http://www.epa.ie/pubs/reports/air/quality/aqreview2010.html#.VFN2oPmsVig 2/3 (accessed November 1, 2014).
  • Jason, G.S., M. Brauer, B. Ainslie, D. Steyn, T. Larson, and M. Buzzelli. 2008. An innovative land use regression model incorporating meteorology for exposure analysis. Sci. Total Environ. 390:520–529.
  • Kurz, C., R. Orthofer, P. Sturm, A. Kaiser, U. Uhrner, R. Reifeltshammer, and M. Rexeis. 2014. Projection of the air quality in Vienna between 2005 and 2020 for NO2 and PM10. Urban Climate 10(4):703–719. doi:10.1016/j.uclim.2014.03.008
  • Lee, S., C.-H. Ho, Y.G. Lee, H.J. Choi, and C.K. Song. 2013. Influence of transboundary air pollutants from China on the high-PM10 episode in Seoul, Korea for the period October 16−20, 2008. Atmos. Environ. 77:430–439. doi:10.1016/j.atmosenv.2013.05.006
  • Lenschow, P., H.-J. Abraham, K. Kutzner, M. Lutz, J.-D. Preuß, and W. Reichenbächer. 2001. Some ideas about the sources of PM10. Atmos. Environ. 35(Suppl. 1):S23–S33. doi:10.1016/S1352-2310(01)00122-4
  • MacIntyre, E.A., C.J. Karr, M. Koehoorn, P.A. Demers, L. Tamburic, C. Lencar, and M. Brauer. 2011. Residential air pollution and otitis media during the first two years of life. Epidemiology 22:81–89. doi:10.1097/EDE.0b013e3181fdb60f
  • McNabola, A., B.M. Broderick, and L.W. Gill. 2009. A principal components analysis of the factors effecting personal exposure to air pollution in urban commuters in Dublin, Ireland. J. Environ. Sci. Health A 44:1–9. doi:10.1080/10934520903139928
  • Mölter, A., S. Lindley, F. de Vocht, A. Simpson, and R. Agius. 2010. Modelling air pollution for epidemiologic research—Part II: Predicting temporal variation through land use regression. Sci. Total Environ. 409:211–217. doi:10.1016/j.scitotenv.2010.10.005
  • National Oceanic and Atmospheric Administration Air Resources Laboratory. 2013. HYSPLIT—Hybrid Single Particle Lagrangian Integrated Trajectory Model. http://ready.arl.noaa.gov/HYSPLIT.php (accessed February 12, 2013).
  • Organisation for Economic Co-operation and Development. 1994. Creating Rural Indicators For Shaping Territorial Policy. Paris: Organisation for Economic Co-operation and Development.
  • OpenStreetMap. 2013. OpenStreetMap: Data extracts. http://download.geofabrik.de/europe.html (accessed January 2, 2013).
  • Pardoe, I. 2012. Applied Regression Modelling, 2nd ed. New York: Wiley & Sons.
  • Pitard, A., A. Zeghnoun, A. Courseaux, J. Lamberty, V. Delmas, J. Luc Fossard, and H. Villet. 2004. Short term associations between air pollution and respiratory drug sales. Environ. Res. 95:43–52. doi:10.1016/j.envres.2003.08.006
  • R Core Team. 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (accessed January 1, 2013).
  • Sahsuvaroglu, T., A. Arain, P. Kanaroglou, N. Finkelstein, N. Newbold, M. Jerrett, B. Beckerman, J. Brook, M. Finkelstein, and N.L. Gilbert. 2012. A land use regression model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Ontario, Canada. J. Air Waste Manage. Assoc. 56:1059–1069. doi:10.1080/10473289.2006.10464542
  • Sider, T., A. Alam, M. Zukari, H. Dugum, N. Goldstein, N. Eluru, and M. Hatzopoulou. 2013. Land-use and socio-economics as determinants of traffic emissions and individual exposure to air pollution, J. Transport Geogr. 33:230–239. doi:10.1016/j.jtrangeo.2013.08.006
  • Smith, L.A., S. Mukerjee, K.C. Chung, and J. Afgani. 2011. Spatial analysis and land use regression of VOCs and NO2 in Dallas, Texas during two seasons. J. Environ. Monit. 13:999–1007. doi:10.1039/c0em00724b
  • Vienna City Administration. 2006. Vienna Environmental Report 2004/2005. http://www.wien.gv.at/english/environment/protection/reports/pdf/complete-report-04.pdf (accessed November 1, 2014).
  • Wang, M., R. Beelen, M. Eeftens, K. Meliefste, G. Hoek, and B. Brunekreef. 2012. Systematic evaluation of land use regression models for NO2. Environ. Sci. Technol. 46:4481–4489. doi:10.1021/es204183v
  • World Health Organization. 2006. Health risks of particulate matter from long-range transboundary air pollution: Joint WHO/Conventation Task Force on the Health Aspects of Air Pollution. http://www.euro.who.int/__data/assets/pdf_file/0006/78657/E88189.pdf (accessed January 12, 2013).
  • Yann, S., J. Galineau, A. Hulin, F. Caini, N. Marquis, V. Navel, S. Bottagisi, L. Giorgis-Allemand, C. Jacquier, R. Slama, and J. Lepeule. 2014. Health effects of ambient air pollution: Do different methods for estimating exposure lead to different results? Environ. Int. 66:165–173.
  • ZAMG (Zentralanstalt für Meteorologie und Geodynamik). 2013. Central Institute for Meteorology and Geodynamics, Vienna, e-mail correspondence, April 11, 2013.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.