Advertisement

Air Quality, Atmosphere & Health

, Volume 11, Issue 1, pp 69–82 | Cite as

Mapping air pollutants at municipality level in Italy and Spain in support to health impact evaluations

  • Stefania GhigoEmail author
  • Stefano Bande
  • Luisella Ciancarella
  • Mihaela Mircea
  • Antonio Piersanti
  • Gaia Righini
  • José María Baldasano
  • Xavier Basagaña
  • Ennio Cadum
  • on behalf of the MED HISS Study group
Article

Abstract

A growing health concern, due to poor air quality, recently led to an increased number of studies regarding air pollution effects on public health. Consequently, close attention is paid to estimation methods of exposure to atmospheric pollutants. This paper aims to meet a specific requirement of epidemiological researchers, that is providing annual air pollution maps at municipality scale for health impact assessment purposes on national basis. Firstly, data fusion through kriging with external drift is implemented, combining pollution data from two different sources, models and measurements, in order to improve the spatial distribution of surface concentrations at grid level. Then, the assimilated data of air pollution are upscaled, so as to obtain concentrations at municipality level. This methodology was applied to Italy and Spain (in Spain, only the second step was carried out since the modeled concentration already included an assimilation procedure). In both countries, for each municipality, an estimate of the concentration value for atmospheric pollutants of major concern for human health (PM10 and NO2) was provided, offering more relevant information from a surveillance point of view.

Keywords

Data fusion Atmospheric pollution CTM models Health impact assessment Upscaling 

Notes

Acknowledgements

The research described in this article was conducted under the grant agreement European Commission, Environment LIFE12 ENV/IT/000834. The authors declare they have no actual or potential competing financial interests.

The authors wish to thank Nino Küenzli from Swiss Tropical and Public Health Institute and Xavier Querol from Institute for Environmental. Assessment and Water Research IDAEA-CSIC (Spanish Research Council) for their assistance during the project and their valuable suggestions.

Supplementary material

11869_2017_520_MOESM1_ESM.pdf (920 kb)
(PDF 919 KB)

References

  1. Aguilera I, Basagaña X, Pay M, Agis D, Bouso L, Foraster M, Rivera M, Baldasano JM, Künzli N (2013) Evaluation of the CALIOPE air quality forecasting system for epidemiological research: the example of NO2 in the province of Girona (Spain). Atmos Environ 72:134–141CrossRefGoogle Scholar
  2. ARIA/ARIANET (2008) EMMA (EMGR/make) User manual. Arianet, Milano, Italy, R2008.99Google Scholar
  3. Arunachalam S, Valencia A, Akita Y, Serre M, Omary M, Garcia V, Isakov V (2014) A method for estimating urban background concentrations in support of hybrid air pollution modeling for environmental health studies. Int J Environ Res Publ Health 11:10, 518–10,536CrossRefGoogle Scholar
  4. Baccini M, Grisotto L, Catelan D, Consonni D, Bertazzi PA, Biggeri A (2015) Commuting-adjusted short-term health impact assessment of airborne fine particles with uncertainty quantification via Monte Carlo simulation. Environ Health Perspect 123(1):27–33.  https://doi.org/10.1289/ehp.1408218 Google Scholar
  5. Baldasano JM, Jiménez-Guerrero P, Jorba O, Pérez C, López E, Güereca P, Martin F, García-Vivanco M, Palomino I, Querol X, Pandolfi M, Sanz M, Diéguez J (2008) CALIOPE: An operational air quality forecasting system for the Iberian Peninsula, Balearic Islands and Canary Islands—first annual evaluation and ongoing developments. Adv Sci Res 2:89–98. http://www.adv-sci-res.net/2/89/2008/ CrossRefGoogle Scholar
  6. Baldasano JM, Pay MT, Jorba O, Gassó S, Jiménez-Guerrero P (2011) An annual assessment of air quality with the CALIOPE modeling system over Spain. Sci Total Environ 409(11):2163–2178CrossRefGoogle Scholar
  7. Bessagnet B, Pirovano G, Mircea M, Cuvelier C, Aulinger A, Calori G, Ciarelli G, Manders A, Stern R, Tsyro S, García Vivanco M, Thunis P, Pay MT, Colette A, Couvidat F, Meleux F, Rouïl L, Ung A, Aksoyoglu S, Baldasano JM, Bieser J, Briganti G, Cappelletti A, D’Isidoro M, Finardi S, Kranenburg R, Silibello C, Carnevale C, Aas W, Dupont JC, Fagerli H, Gonzalez L, Menut L, Prévôt ASH, Roberts P, White L (2016) Presentation of the EURODELTA III intercomparison exercise—evaluation of the chemistry transport models’ performance on criteria pollutants and joint analysis with meteorology. Atmos Chem Phys 16(19):12,667–12,701CrossRefGoogle Scholar
  8. Binkowski FS (1999) The aerosol portion of models-3 CMAQ. In: Byun DW, Ching JKS (eds) Science algorithms of the EPA models-3 community multiscale air quality (CMAQ) modeling system, pp 1–23. EPA-600/R-99/030Google Scholar
  9. Blangiardo M, Cameletti M, Baio G, Rue H (2013) Spatial and spatio-temporal models with R-INLA. Spat Spatio-Temporal Epidemiol 4:33–49CrossRefGoogle Scholar
  10. Box GEP, Cox DR (1964) An analysis of transformations. J Royal Stat Soc Ser B 26:211–246Google Scholar
  11. 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. Appl Mech Rev 59(2):51–77CrossRefGoogle Scholar
  12. Cadum E, Rowinski M, Berti G, Basagaña X, Ciancarella L, Spadea T, Annesi-Maesano I, Otorepec P, Zanini G, Costa G (2016) LIFE MED HISS ENV/it/000834: an ‘health surveillance’ pilot project on long term effects exposure to air pollution to implement a european system. In: Abstracts of the 2016 meeting of the International Society for Environmental Epidemiology (ISEE), pp P2–310.  https://doi.org/10.1289/ehp.isee2016
  13. Calori G, Finardi S, Nanni A, Radice P, Riccardo S, Bertello A, Pavone F (2008) Long-term air quality assessment: modeling sources contribution and scenarios in Ivrea and Torino areas. Environ Model Assess 13(3):329–335CrossRefGoogle Scholar
  14. Cameletti M (2013) The change of support problem through the INLA approach. Stat Appl Special Issue:29–43Google Scholar
  15. Carnevale C, Finzi G, Pisoni E, Singh V, Volta M (2011) An integrated air quality forecast system for a metropolitan area. J Environ Monit 13:3437–3447CrossRefGoogle Scholar
  16. Carnevale C, Finzi G, Pederzoli A, Pisoni E, Thunis P, Turrini E, Volta M (2015) A methodology for the evaluation of re-analyzed PM10 concentration fields: a case study over the PO valley. Air Quality. Atmos Health 8(6):533–544CrossRefGoogle Scholar
  17. Carter WPL (1999) Documentation of the SAPRC-99 mechanism for VOC reactivity assessment. Technical report, http://www.cert.ucr.edu/carter/reactdat.htm
  18. Chang JC, Hanna S (2004) Air quality model performance evaluation. Meteorol Atmos Phys 87:167–196CrossRefGoogle Scholar
  19. Chen G, Li J, Ying Q, Sherman S, Perkins N, Rajeshwari S, Mendola P (2014) Evaluation of observation-fused regional air quality model results for population air pollution exposure estimation. Sci Total Environ 485–486:563–574CrossRefGoogle Scholar
  20. Ciancarella L, Adani M, Briganti G, Cappelletti A, Ciucci A, Cremona G, D’Elia I, D’Isidoro M, Mircea M, Piersanti A, Righini G, Russo F, Vitali L, Zanini G (2016) La simulazione nazionale di AMS-MINNI relativa all’anno 2010. Technical Report RT-2016-12-ENEA ENEA, BolognaGoogle Scholar
  21. Cotton WR, Pielke Sr RA, Walko RL, Liston GE, Tremback CJ, Jiang H, McAnelly RL, Harrington JY, Nicholls ME, Carrio GG, McFadden JP (2003) RAMS 2001: current status and future directions. Meteorol Atmos Phys 82(1):5–29CrossRefGoogle Scholar
  22. Cressie N A (1993) Statistics for spatial data. Wiley, New YorkGoogle Scholar
  23. de Keijzer C, Agis D, Ambrós A, Arévalo G, Baldasano JM, Bande S, Barrera-Gómez J, Benach J, Cirach M, Dadvand P, Ghigo S, Martinez-Solanas E, Nieuwenhuijsen M, Cadum E, Basagaña X (2017) The association of air pollution and greenness with mortality and life expectancy in spain: a small-area study. Environ Int 99:170–176CrossRefGoogle Scholar
  24. Denby B, Georgieva E, Lükewille A (2011) The application of models under the European Union’s Air Quality Directive: a technical reference guide. Technical Report 10/2011, European Environmental Agency, CopenhagenGoogle Scholar
  25. Development Core Team (2010) A language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  26. Fuentes M, Raftery A (2005) Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models. Biometrics 61(1):36–45CrossRefGoogle Scholar
  27. Gariazzo C, Silibello C, Finardi S, Radice P, Piersanti A, Calori G, Cecinato A, Perrino C, Nusio F, Cagnoli M, Pelliccioni A, Gobbi G P, Di Filippo P (2007) A gas/aerosol air pollutants study over the urban area of rome using a comprehensive chemical transport model. Atmos Environ 41:7286–7303CrossRefGoogle Scholar
  28. Gariazzo C, Hänninen O, Amicarelli A, Pelliccioni A, Silibello C, Sozzi R, Jantunen M (2011) Integrated model for the estimation of annual, seasonal, and episode PM10 exposures of children in Rome, Italy. Air Qual. Atmos. Health 4:169–178CrossRefGoogle Scholar
  29. Gelfand A, Sahu S, O’Hagan A, West M (2010) Combining monitoring data and computer model output in assessing environmental exposure. In: The Oxford Handbook of Applied Bayesian Analysis. Oxford University Press, OxfordGoogle Scholar
  30. Gelfand A, Zhu L, Carlin BP (2001) On the change of support problem for spatio-temporal data. Biostatistics 2(1):31–45CrossRefGoogle Scholar
  31. Guevara M, Martínez F, Arévalo S, Gassó G, Baldasano J (2013) Improved system for modelling spanish emissions: HERMESv2.0. Atmos Environ 81:209–221.  https://doi.org/10.1016/j.atmosenv.2013.08.053 CrossRefGoogle Scholar
  32. Hystad P, Demers PA, Johnson KC, Brook J, van Donkelaar A, Lamsa L, Martin R, Brauer M (2012) Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study. Environ Health 11:22CrossRefGoogle Scholar
  33. Ignaccolo R, Ghigo S, Bande S (2013) Functional zoning for air quality. Environ Ecol Stat 20(3):321–339Google Scholar
  34. Kiesewetter G, Borken-Kleefeld J, Schöpp W, Heyes C, Thunis P, Bessagnet B, Terrenoire E, Gsella A, Amann M (2014) Modelling NO2 concentrations at the street level in the GAINS integrated assessment model: projections under current legislation. Atmos Chem Phys 14:813–829CrossRefGoogle Scholar
  35. Kim SY, Yi SJ, Eum YS, Choi HJ, Shin H, Ryou HG, Kim H (2014) Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities. Environmental Health and Toxicology 29:8.  https://doi.org/10.5620/eht.e2014012
  36. Michalakes J, Dudhia J, Gill D, Henderson T, Klemp J, Skamarock W, Wang W, Zwieflhofer W, Mozdzynski G (2005) The weather research and forecast model: software architecture and performance. In: Proceedings of the Eleventh ECMWF Workshop on the Use of High Performance Computing in Meteorology, pp 156–168Google Scholar
  37. Mircea M, Zanini G, Briganti G, Cappelletti A, Pederzoli A, Vitali L, Pace G, Marri P, Silibello C, Finardi S, Calori G (2010) Modelling air quality over Italy with MINNI atmospheric modelling system: from regional to local scale. In: Steyn D G (ed) STC Air Pollution Modelling and its Application,  https://doi.org/10.1007/978-94-007-1359-9_82
  38. Mircea M, Ciancarella L, Briganti G, Calori G, Cappelletti A, Cionni I, Costa M, Cremona G, D’Isidoro M, Finardi S, Pace G, Piersanti A, Righini G, Silibello C, Vitali L, Zanini G (2014) Assessment of the AMS-MINNI system capabilities to predict air quality over Italy for the calendar year 2005. Atmos Environ 84:178–188CrossRefGoogle Scholar
  39. Mircea M, Grigoras G, D’Isidoro M, Righini G, Adani M, Briganti G, Ciancarella L, Cappelletti A, Calori G, Cionni CGI, Finardi S, Larsen BR, Pace G, Perrino C, Piersanti A, Silibello VLC, Zanini G (2016) Impact of grid resolution on aerosol predictions: a case study over italy. Aerosol Air Qual Res 16:1253–1267.  https://doi.org/10.4209/aaqr.2015.02.0058 CrossRefGoogle Scholar
  40. Monforti F, Pederzoli A (2005) THOSCANE: a tool to detail CORINAIR emission inventories, vol 20Google Scholar
  41. Montero J, Fernández-Avilés G (2015) Functional kriging prediction of pollution series: the geostatistical alternative for spatially-fixed data. Estud Econ Apl 1:145–174Google Scholar
  42. Park N (2016) Time-series mapping of PM10 concentration using multigaussian space-time kriging: a case study in the Seoul Metropolitan Area, Korea. Advances in Meteorology 2016,  https://doi.org/10.1155/2016/9452080
  43. Pay MT, Jiménez-Guerrero P, Jorba O, Basart S, Querol X, Pandolfi M, Baldasano JM (2012) Spatio-temporal variability of concentrations and speciation of particulate matter across Spain in the CALIOPE modeling system. Atmos Environ 46:376–396CrossRefGoogle Scholar
  44. Pérez C, Nickovic S, Baldasano JM, Sicard M, Rocadenbosch F, Cachorro VE (2006a) A long Saharan dust event over the Western Mediterranean: Lidar, Sun photometer observations, and regional dust modeling. J Geophys Res, 111, D15.  https://doi.org/10.1029/2005JD006579
  45. Pérez C, Nickovic S, Pejanovic G, Baldasano JM, Özsoy E (2006b) Interactive dust-radiation modeling: a step to improve weather forecasts. J Geophys Res, 111, D16.  https://doi.org/10.1029/2005JD006717
  46. Pernigotti D, Thunis P, Cuvelier C, Georgieva E, Gsella A, De Meij A, Pirovano G, Balzarini A, Riva GM, Carnevale C, Pisoni E, Volta M, Bessagnet B, Kerschbaumer A, Viaene P, De Ridder K, Nyiri A, Wind P (2013) POMI: a model inter-comparison exercise over the Po Valley. Air Qual Atmos Health 6(4):701–715CrossRefGoogle Scholar
  47. Ribeiro JR, Diggle PJ (2001) geoR: a package for geostatistical analysis. R-NEWS 1(2):15–18Google Scholar
  48. Shao X, Stein M, Ching J (2007) Statistical comparisons of methods for interpolating the output of a numerical air quality model. J Stat Plann Infer 137(7):2277–2293CrossRefGoogle Scholar
  49. Sicardi V, Ortiz A, Rincón J, Jorba O, Pay MT, Gassó S, Baldasano JM (2012) Assessment of Kalman filter bias-adjustment technique to improve the simulation of ground-level ozone over Spain. Sci Total Environ 416:329–342CrossRefGoogle Scholar
  50. Silibello C, Calori G, Brusasca G, Giudici A, Angelino E, Fossati G, Peroni E, Buganza E (2008) Modelling of PM10 concentrations over Milano urban area using two aerosol modules. Environ Modell Softw 23:333–343CrossRefGoogle Scholar
  51. Silibello C, Bolignano A, Sozzi R, Gariazzo C (2014) Application on chemical transport model and optimized data assimilation methods to provide air quality assessment. Air Quality Atmosphere & Health  https://doi.org/10.1007/s11869-014-0235-1
  52. Skamarock WC, Klemp JB (2008) A time-split nonhydrostatic atmospheric model for weather and forecasting applications. J Comput Phys 227:3465–3485CrossRefGoogle Scholar
  53. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2005) A description of the advanced research WRF version 2. NCAR Technical noteGoogle Scholar
  54. Van de Kassteele J, Stein A, Dekkers ALM, Velders GJM (2009) External drift kriging of NOx concentrations with dispersion model output in a reduced air quality monitoring network. Environ Ecol Stat 16 (3):321–339CrossRefGoogle Scholar
  55. Wackernagel H (2003) Multivariate geostatistics: an introduction with applications. Springer, BerlinCrossRefGoogle Scholar
  56. World Health Organization (2015a) Economic cost of the health impact of air pollution in Europe: clean air, health and wealth. Technical report, WHO Regional Office for Europe, CopenhagenGoogle Scholar
  57. World Health Organization (2015b) Health and the environment: addressing the health impact of air pollution. Draft resolution proposed by the delegations of Albania, Chile, Colombia, France, Germany, Monaco, Norway, Panama, Sweden, Switzerland, Ukraine, United States of America, Uruguay and Zambia. In: WHA68, 68th World Health Assembly, Geneva, Switzerland, http://www.who.int/iris/handle/10665/253206
  58. Zanini G, Pignatelli T, Monforti F, Vialetto G, Vitali L, Brusasca G, Calori G, Finardi S, Radice P, Silibello C (2005) The MINNI Project: an integrated assessment modelling system for policy making. Proceedings of MODSIM 2005 International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia and New Zealand, 2005–2011, https://www.mssanz.org.au/modsim05/papers/zanini.pdf

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Stefania Ghigo
    • 1
    Email author
  • Stefano Bande
    • 1
  • Luisella Ciancarella
    • 2
  • Mihaela Mircea
    • 2
  • Antonio Piersanti
    • 2
  • Gaia Righini
    • 2
  • José María Baldasano
    • 3
    • 4
  • Xavier Basagaña
    • 5
  • Ennio Cadum
    • 6
  • on behalf of the MED HISS Study group
  1. 1.Dipartimento Tematico Sistemi PrevisionaliQualitá dell’aria, ARPA PiemonteTurinItaly
  2. 2.ENEA, Agenzia Nazionale per le Nuove Tecnologie, L’energia e lo Sviluppo Economico SostenibileBolognaItaly
  3. 3.Earth Sciences DepartmentBarcelona Supercomputing Center (BSC)BarcelonaSpain
  4. 4.Environmental Modelling LaboratoryTechnical University of Catalonia (UPC)BarcelonaSpain
  5. 5.ISGlobal, Centre for Research in Environmental Epidemiology (CREAL)BarcelonaSpain
  6. 6.Dipartimento Tematico Epidemiologia e Salute AmbientaleARPA PiemonteTurinItaly

Personalised recommendations