Skip to main content

Advertisement

Log in

Modelling local uncertainty in relations between birth weight and air quality within an urban area: combining geographically weighted regression with geostatistical simulation

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

In this study, we combine known methods to present a new approach to assess local distributions of estimated parameters measuring associations between air quality and birth weight in the urban area of Sines (Portugal). To model exposure and capture short-distance variations in air quality, we use a Regression Kriging estimator combining air quality point data with land use auxiliary data. To assess uncertainty of exposure, the Kriging estimator is incorporated in a sequential Gaussian simulation algorithm (sGs) providing a set of simulated exposure maps with similar spatial structural dependence and statistical properties of observed data. Following the completion of the simulation runs, we fit a geographically weighted generalized linear model (GWGLM) for each mother’s place of residence, using observed health data and simulated exposure data, and repeat this procedure for each simulated map. Once the fit of GWGLM with all exposure maps is finished, we take the distribution of local estimated parameters measuring associations between exposure and birth weight, thus providing a measure of uncertainty in the local estimates. Results reveal that the distribution of local parameters did not vary substantially. Combining both methods (GWGLM and sGs), however, we are able to incorporate local uncertainty on the estimated associations providing an additional tool for analysis of the impacts of place in health.

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.

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

Similar content being viewed by others

References

  • Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki F (eds) Second International Symposium on Information Theory, pp 267–281

  • Asta J, Erhardt W, Ferretti M, Fornasier F (2002) European guideline for mapping lichen diversity as an indicator of environmental stress. Br Lichen 1–20

  • Brunsdon C, Singleton AD (2015) Geocomputation: a pratical primer, 1st edn. SAGE Publications Ltd, London

    Google Scholar 

  • Brunsdon C, Fotheringham S, Charlton M (1998) Geographically weighted regression—modelling spatial non-stationarity. J R Stat Soc Ser D 47:431–443

    Article  Google Scholar 

  • Canha N, Almeida SM, Freitas MC, Wolterbeek HT (2014) Indoor and outdoor biomonitoring using lichens at urban and rural primary schools. J Toxicol Environ Health A 77:900–915. https://doi.org/10.1080/15287394.2014.911130

    Article  CAS  Google Scholar 

  • Chen VY-J, Yang T-C (2012) SAS macro programs for geographically weighted generalized linear modeling with spatial point data: applications to health research. Comput Methods Prog Biomed 107:262–273. https://doi.org/10.1016/j.cmpb.2011.10.006

    Article  Google Scholar 

  • Conti ME, Cecchetti G (2001) Biological monitoring: lichens as bioindicators of air pollution assessment—a review. Environ Pollut 114:471–492

    Article  CAS  Google Scholar 

  • CTT Correios (2010) Geoindex standard. http://geoindex.ctt.pt/. Accessed 2 May 2011

  • da Silva AR, Rodrigues TCV (2013) Geographically weighted negative binomial regression—incorporating overdispersion. Stat Comput 24:769–783. https://doi.org/10.1007/s11222-013-9401-9

    Article  Google Scholar 

  • de Hoogh K, Korek M, Vienneau D, Keuken M, Kukkonen J, Nieuwenhuijsen MJ, Badaloni C, Beelen R, Bolignano A, Cesaroni G, Pradas MC, Cyrys J, Douros J, Eeftens M, Forastiere F, Forsberg B, Fuks K, Gehring U, Gryparis A, Gulliver J, Hansell AL, Hoffmann B, Johansson C, Jonkers S, Kangas L, Katsouyanni K, Künzli N, Lanki T, Memmesheimer M, Moussiopoulos N, Modig L, Pershagen G, Probst-Hensch N, Schindler C, Schikowski T, Sugiri D, Teixidó O, Tsai MY, Yli-Tuomi T, Brunekreef B, Hoek G, Bellander T (2014) Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies. Environ Int 73:382–392. https://doi.org/10.1016/j.envint.2014.08.011

    Article  CAS  Google Scholar 

  • Environmental Systems Research Institute (2006) ArcGIS. ESRI Inc 2011

  • Faria MV, Duarte GO, Baptista PC, Farias TL (2017) Scenario-based analysis of traffic-related PM2.5 concentration: Lisbon case study. Environ Sci Pollut Res 24:12026–12037. https://doi.org/10.1007/s11356-015-5556-6

    Article  CAS  Google Scholar 

  • Fei J-C, Min X-B, Wang Z-X, Pang ZH, Liang YJ, Ke Y (2017) Health and ecological risk assessment of heavy metals pollution in an antimony mining region: a case study from South China. Environ Sci Pollut Res 24:27573–27586. https://doi.org/10.1007/s11356-017-0310-x

    Article  CAS  Google Scholar 

  • Fortin M-J, James PMA, MacKenzie A et al (2012) Spatial statistics, spatial regression, and graph theory in ecology. Spatial Statistics 1:100–109. https://doi.org/10.1016/j.spasta.2012.02.004

    Article  Google Scholar 

  • Fotheringham AS, Charlton ME, Brunsdon C (1998) Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environ Plan A 30:1905–1927. https://doi.org/10.1068/a301905

    Article  Google Scholar 

  • Garty J (1993) Lichens as biomonitors of heavy metal pollution. In: Weinheim MB (ed) Plants as biomonitors: indicators for heavy metals in the terrestrial environment. pp 193–257

  • Glinianaia SV, Rankin J, Bell R, Pless-Mulloli T, Howel D (2004) Particulate air pollution and fetal health: a systematic review of the epidemiologic evidence. Epidemiology 15:36–45. https://doi.org/10.1097/01.ede.0000101023.41844.ac

    Article  Google Scholar 

  • Goldman GT, Mulholland JA, Russell AG, Gass K, Strickland MJ, Tolbert PE (2012) Characterization of ambient air pollution measurement error in a time-series health study using a geostatistical simulation approach. Atmos Environ 57:101–108. https://doi.org/10.1016/j.atmosenv.2012.04.045

    Article  CAS  Google Scholar 

  • Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press

  • Goovaerts P (2009) Medical geography: a promising field of application for geostatistics. Math Geosci 41:243–264

    Article  CAS  Google Scholar 

  • Goovaerts P, Jacquez GM, Greiling D (2005) Exploring scale-dependent correlations between cancer mortality rates using factorial kriging and population-weighted semivariograms. Geogr Anal 37:152–182. https://doi.org/10.1111/j.1538-4632.2005.00634.x

    Article  Google Scholar 

  • Gryparis A, Paciorek CJ, Zeka A, Schwartz J, Coull BA (2009) Measurement error caused by spatial misalignment in environmental epidemiology. Biostatistics 10:258–274. https://doi.org/10.1093/biostatistics/kxn033

    Article  Google Scholar 

  • Hampton KH, Serre ML, Gesink DC, Pilcher CD, Miller WC (2011) Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping. Int J Health Geogr 10:54. https://doi.org/10.1186/1476-072X-10-54

    Article  Google Scholar 

  • Harris P, Fotheringham AS, Crespo R, Charlton M (2010) The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets. Math Geosci 42:657–680. https://doi.org/10.1007/s11004-010-9284-7

    Article  CAS  Google Scholar 

  • Hengl T (2009) A practical guide to geostatistical mapping, 2nd edn. Office for Official Publications of the European Communities, Luxembourg

    Google Scholar 

  • Hengl T, Heuvelink GBM, Rossiter DG (2007) About regression-kriging: from equations to case studies. Comput Geosci 33:1301–1315. https://doi.org/10.1016/j.cageo.2007.05.001

    Article  Google Scholar 

  • Hijmans RJ, van Etten J (2012) Raster: geographic analysis and modeling with raster data. R package version 2.4.20

  • Hoffmann B, Moebus S, Dragano N, Stang A, Möhlenkamp S, Schmermund A, Memmesheimer M, Bröcker-Preuss M, Mann K, Erbel R, Jöckel KH (2009) Chronic residential exposure to particulate matter air pollution and systemic inflammatory markers. Environ Health Perspect 117:1302–1308. https://doi.org/10.1289/ehp.0800362

    Article  CAS  Google Scholar 

  • Jerrett M, Arain A, Kanaroglou P, Beckerman B, Potoglou D, Sahsuvaroglu T, Morrison J, Giovis C (2005) A review and evaluation of intraurban air pollution exposure models. J Expo Anal Environ Epidemiol 15:185–204. https://doi.org/10.1038/sj.jea.7500388

    Article  CAS  Google Scholar 

  • Jin Y, Ge Y, Wang J, Chen Y, Heuvelink GBM, Atkinson PM (2018) Downscaling AMSR-2 soil moisture data with geographically weighted area-to-area regression kriging. IEEE Trans Geosci Remote Sens 56:2362–2376. https://doi.org/10.1109/TGRS.2017.2778420

    Article  Google Scholar 

  • Kalkbrenner AE, Windham GC, Serre ML, Akita Y, Wang X, Hoffman K, Thayer BP, Daniels JL (2015) Particulate matter exposure, prenatal and postnatal windows of susceptibility, and autism spectrum disorders. Epidemiology 26:30–42. https://doi.org/10.1097/EDE.0000000000000173

    Article  Google Scholar 

  • Kanaroglou P, Jerrett M, Morrison J et al (2005) Establishing an air pollution monitoring network for intra-urban population exposure assessment: a location-allocation approach. Atmos Environ 39:2399–2409. https://doi.org/10.1016/j.atmosenv.2004.06.049

    Article  CAS  Google Scholar 

  • Kirby RS, Delmelle E, Eberth JM (2017) Advances in spatial epidemiology and geographic information systems. Ann Epidemiol 27:1–9. https://doi.org/10.1016/j.annepidem.2016.12.001

    Article  Google Scholar 

  • Kitanidis PK (1993) Generalized covariance functions in estimation. Math Geol 25:525–540. https://doi.org/10.1007/BF00890244

    Article  Google Scholar 

  • Kramer MS (2003) The epidemiology of adverse pregnancy outcomes: an overview. J Nutr 133:1592S–1596S

    Article  CAS  Google Scholar 

  • Kyriakidis P (2004) A geostatistical framework for area to point spatial interpolation. Geogr Anal 36:259–289

    Article  Google Scholar 

  • Lawson A, Banerjee S, Haining R, Ugarte L (2016) Handbook of spatial epidemiology. CRC Press-Taylor & Francis Group

  • Lee S, Serre ML, Van DA et al (2012) Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States. Environ Health Perspect 120:1727–1733

    Article  CAS  Google Scholar 

  • Li Z, Wang W, Liu P, Bigham JM, Ragland DR (2013) Using geographically weighted Poisson regression for county-level crash modeling in California. Saf Sci 58:89–97. https://doi.org/10.1016/j.ssci2013.04.005

    Article  CAS  Google Scholar 

  • Llop E, Pinho P, Matos P, Pereira MJ, Branquinho C (2012) The use of lichen functional groups as indicators of air quality in a Mediterranean urban environment. Ecol Indic 13:215–221. https://doi.org/10.1016/j.ecolind.2011.06.005

    Article  CAS  Google Scholar 

  • Llop E, Pinho P, Ribeiro MC, Pereira MJ, Branquinho C (2017) Traffic represents the main source of pollution in small Mediterranean urban areas as seen by lichen functional groups. Environ Sci Pollut Res 24:1–10. https://doi.org/10.1007/s11356-017-8598-0

    Article  Google Scholar 

  • Loppi S, Ivanov D, Boccardi R (2002) Biodiversity of epiphytic lichens and air pollution in the town of Siena (Central Italy). Environ Pollut 116:123–128

    Article  CAS  Google Scholar 

  • Minasny B, McBratney AB (2007) Spatial prediction of soil properties using EBLUP with the Matérn covariance function. Geoderma 140:324–336. https://doi.org/10.1016/j.geoderma.2007.04.028

    Article  Google Scholar 

  • Munzi S, Correia O, Silva P, Lopes N, Freitas C, Branquinho C, Pinho P (2014) Lichens as ecological indicators in urban areas: beyond the effects of pollutants. J Appl Ecol 51:1750–1757. https://doi.org/10.1111/1365-2664.12304

    Article  Google Scholar 

  • Nakaya T, Fotheringham S, Brunsdon C, Charlton M (2005) Geographically weighted Poisson regression for disease association mapping. Stat Med 24:2695–2717. https://doi.org/10.1002/sim.2129

    Article  CAS  Google Scholar 

  • Nelder JA, Wedderburn RW (1972) Generalized linear models. J R Stat Soc 135:370–384

    Google Scholar 

  • Neuman SP, Jacobson EA (1984) Analysis of nonintrinsic spatial variability by residual kriging with application to regional groundwater levels. J Int Assoc Math Geol 16:499–521. https://doi.org/10.1007/BF01886329

    Article  Google Scholar 

  • Odeh IOA, Mcbratney AB, Chittleborough DJ (1994) Spatial prediction of soil properties from landform attributes derived from a digital elevation model. Geoderma 63:197–214

    Article  Google Scholar 

  • Paoli L, Munzi S, Guttová A, et al (2015) Lichens as suitable indicators of the biological effects of atmospheric pollutants around a municipal solid waste incinerator (S Italy). Ecol Indic 52:362–370. https://doi.org/10.1016/j.ecolind.2014.12.018

  • Pasquier A, André M (2017) Considering criteria related to spatial variabilities for the assessment of air pollution from traffic. Transportation Research Procedia 25:3354–3369. https://doi.org/10.1016/j.trpro.2017.05.210

    Article  Google Scholar 

  • Pebesma EJ (2004) Multivariable geostatistics in S: the gstat package. Comput Geosci 30:683–691

    Article  Google Scholar 

  • Pinho P, Augusto S, Branquinho C, Bio A, Pereira MJ, Soares A, Catarino F (2004) Mapping lichen diversity as a first step for air quality assessment. J Atmos Chem 49:377–389. https://doi.org/10.1007/s10874-004-1253-4

    Article  CAS  Google Scholar 

  • Pinho P, Augusto S, Máguas C, Pereira MJ, Soares A, Branquinho C (2008a) Impact of neighbourhood land-cover in epiphytic lichen diversity: analysis of multiple factors working at different spatial scales. Environ Pollut 151:414–422. https://doi.org/10.1016/j.envpol.2007.06.015

    Article  CAS  Google Scholar 

  • Pinho P, Augusto S, Martins-Loução M et al (2008b) Causes of change in nitrophytic and oligotrophic lichen species in a Mediterranean climate: impact of land cover and atmospheric pollutants. Environ Pollut 154:380–389. https://doi.org/10.1016/j.envpol.2007.11.028

    Article  CAS  Google Scholar 

  • Pinho P, Llop E, Ribeiro MC, Cruz C, Soares A, Pereira MJ, Branquinho C (2014) Tools for determining critical levels of atmospheric ammonia under the influence of multiple disturbances. Environ Pollut 188:88–93. https://doi.org/10.1016/j.envpol.2014.01.024

    Article  CAS  Google Scholar 

  • R Core Team (2014) R: a language and environment for statistical computing.

  • Ribeiro MC, Llop E, Branquinho C et al (2012) A retrospective cohort study to assess the association between outdoor air quality and low birth weight. Arch Dis Child 97:A283. https://doi.org/10.1136/archdischild-2012-302724.0990

    Article  Google Scholar 

  • Ribeiro MC, Pinho P, Llop E et al (2016) Geostatistical uncertainty of assessing air quality using high-spatial-resolution lichen data: a health study in the urban area of Sines, Portugal. Sci Total Environ 562:740–750

    Article  CAS  Google Scholar 

  • Rose CI, Hawksworth DL (1981) Lichen recolonization in London’s cleaner air. Nature 289:289–292

    Article  Google Scholar 

  • Waller LA, Gotway CA (2004) Applied spatial statistics for public health data. Wiley, Hoboken

    Book  Google Scholar 

  • Waller LA, Zhu L, Gotway CA, Gorman DM, Gruenewald PJ (2007) Quantifying geographic variations in associations between alcohol distribution and violence: a comparison of geographically weighted regression and spatially varying coefficient models. Stoch Environ Res Risk A 21:573–588. https://doi.org/10.1007/s00477-007-0139-9

    Article  Google Scholar 

  • Wolterbeek HT, Garty J, Reis MA, Freitas MC (2003) Chapter 11 Biomonitors in use: lichens and metal air pollution. In: Markert BA, Breure AM, HGZBT-TM and other C in the E (eds) Bioindicators & biomonitors principles, concepts and applications. Elsevier, pp 377–419

  • Young LJ, Gotway CA, Yang J, Kearney G, DuClos C (2008) Assessing the association between environmental impacts and health outcomes: a case study from Florida. Stat Med 27:3998–4015. https://doi.org/10.1002/sim

    Article  Google Scholar 

  • Zandbergen PA (2007) Influence of geocoding quality on environmental exposure assessment of children living near high traffic roads. BMC Public Health 7:1–13. https://doi.org/10.1186/1471-2458-7-37

    Article  Google Scholar 

Download references

Funding

This study was funded by the CERENA (strategic project FCT-UID/ECI/04028/2013).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Manuel Castro Ribeiro.

Additional information

Responsible editor: Philippe Garrigues

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ribeiro, M.C., Pereira, M.J. Modelling local uncertainty in relations between birth weight and air quality within an urban area: combining geographically weighted regression with geostatistical simulation. Environ Sci Pollut Res 25, 25942–25954 (2018). https://doi.org/10.1007/s11356-018-2614-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-018-2614-x

Keywords

Navigation