7,707
Views
23
CrossRef citations to date
0
Altmetric
Research Article

Spatio-temporal regression kriging for modelling urban NO2 concentrations

ORCID Icon, , &
Pages 851-865 | Received 10 Apr 2019, Accepted 10 Sep 2019, Published online: 27 Sep 2019

References

  • Beelen, R., et al., 2009. Mapping of background air pollution at a fine spatial scale across the European Union. Science of the Total Environment, 407 (6), 1852–1867. doi:10.1016/j.scitotenv.2008.11.048
  • Brook, R.D., et al., 2010. Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American heart association. Circulation, 121 (21), 2331–2378. doi:10.1161/CIR.0b013e3181dbece1
  • Byrd, R.H., et al., 1995. A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16 (5), 1190–1208. doi:10.1137/0916069
  • CBS, 2018. Wijk- en Buurtkaart 2016 versie 3. Nationaal Georegister.
  • Close, J.P., ed., 2016. AiREAS: sustainocracy for a healthy city. The invisible made visible phase 1. Basel: Springer.
  • Cohen, A.J., et al., 2017. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the global burden of diseases study 2015. The Lancet, 389 (10082), 1907–1918. doi:10.1016/S0140-6736(17)30505-6
  • Cressie, N. and Wikle, C.K., 2011. Statistics for spatio-temporal data. Hoboken, NJ: John Wiley & Sons.
  • Diggle, P.J. and Ribeiro, P.J., 2007. Model-based geostatistics. New York: Springer.
  • Fenger, J., 2009. Urban air pollution. In: C.N. Hewitt and A.V. Jackson, eds. Atmospheric science for environmental scientists. Chichester: Wiley & Sons Ltd., 243–267.
  • Gräler, B., Pebesma, E., and Heuvelink, G., 2016. Spatio-temporal interpolation using gstat. The R Journal, 8 (1), 204–218. doi:10.32614/RJ-2016-014
  • Harrell, F.E., 2018. Function aregImpute, package Hmisc 4.1-1. Nashville, TN: Vanderbilt University School of Medicine.
  • Hoek, G., et al., 2008. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric Environment, 42 (33), 7561–7578. doi:10.1016/j.atmosenv.2008.05.057
  • Hu, Y., et al., 2015. Spatio-temporal transmission and environmental determinants of Schistosomiasis Japonica in Anhui Province, China. PLoS Neglected Tropical Diseases, 9 (2), e0003470. doi:10.1371/journal.pntd.0003470
  • Kadaster, 2018. TOP10NL [online]. Apeldoorn. Available from: http://nationaalgeoregister.nl/geonetwork/srv/dut/catalog.search#/metadata/29d5310f-dd0d-45ba-abad-b4ffc6b8785f [Accessed 4 June 2018].
  • Kashima, S., et al., 2018. Comparison of land use regression models for NO2 based on routine and campaign monitoring data from an urban area of Japan. Science of the Total Environment, 631–632, 1029–1037. doi:10.1016/j.scitotenv.2018.02.334
  • Kilibarda, M., et al., 2014. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. Journal of Geophysical Research: Atmospheres, 119 (5), 2294–2313.
  • Klompmaker, J.O., et al., 2015. Spatial variation of ultrafine particles and black carbon in two cities: results from a short-term measurement campaign. Science of the Total Environment, 508, 266–275. doi:10.1016/j.scitotenv.2014.11.088
  • KNMI, 2016. Uurgegevens van het weer in Nederland - download [online]. Available from: http://projects.knmi.nl/klimatologie/uurgegevens/selectie.cgi [Accessed 16 January 2017].
  • Lee, M., et al., 2017. Land use regression modelling of air pollution in high density high rise cities: a case study in Hong Kong. Science of the Total Environment, 592, 306–315. doi:10.1016/j.scitotenv.2017.03.094
  • Sharker, M.H. and Karimi, H.A., 2014. Computing least air pollution exposure routes. International Journal of Geographical Information Science, 28 (2), 343–362. doi:10.1080/13658816.2013.841317
  • Sherman, M., 2011. Spatial statistics and spatio-temporal data: covariance functions and directional properties. Chichester: John Wiley & Sons, Ltd.
  • Snyder, E.G., et al., 2013. The changing paradigm of air pollution monitoring. Environmental Science & Technology, 47 (20), 11369–11377. doi:10.1021/es4022602
  • Van de Kassteele, J., et al., 2009. External drift kriging of NOx concentrations with dispersion model output in a reduced air quality monitoring network. Environmental and Ecological Statistics, 16 (3), 321–339. doi:10.1007/s10651-007-0052-x
  • Van Zoest, V., et al., 2019. Calibration of low-cost NO2 sensors in an urban air quality network. Atmospheric Environment, 210, 66–75. doi:10.1016/j.atmosenv.2019.04.048
  • Van Zoest, V.M., Stein, A., and Hoek, G., 2018. Outlier detection in urban air quality sensor networks. Water, Air, & Soil Pollution, 229 (4), 111. doi:10.1007/s11270-018-3756-7
  • Webster, R. and Oliver, M.A., 2001. Geostatistics for environmental scientists. 2nd ed. Chichester: John Wiley & Sons Ltd.
  • Weissert, L.F., et al., 2018. Development of a microscale land use regression model for predicting NO2 concentrations at a heavy trafficked suburban area in Auckland, NZ. Science of the Total Environment, 619, 112–119. doi:10.1016/j.scitotenv.2017.11.028
  • Zimmerman, D.L., et al., 2010. Classical geostatistical methods. In: A.E. Gelfand, ed. Handbook of spatial statistics. Boca Raton, FL: CRC Press, 29–44.