International Journal of Biometeorology

, Volume 61, Issue 2, pp 335–348 | Cite as

Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing

  • Jesús RojoEmail author
  • Rosario Rivero
  • Jorge Romero-Morte
  • Federico Fernández-González
  • Rosa Pérez-Badia
Original Paper


Analysis of airborne pollen concentrations provides valuable information on plant phenology and is thus a useful tool in agriculture—for predicting harvests in crops such as the olive and for deciding when to apply phytosanitary treatments—as well as in medicine and the environmental sciences. Variations in airborne pollen concentrations, moreover, are indicators of changing plant life cycles. By modeling pollen time series, we can not only identify the variables influencing pollen levels but also predict future pollen concentrations. In this study, airborne pollen time series were modeled using a seasonal-trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) smoothing (STL). The data series—daily Poaceae pollen concentrations over the period 2006–2014—was broken up into seasonal and residual (stochastic) components. The seasonal component was compared with data on Poaceae flowering phenology obtained by field sampling. Residuals were fitted to a model generated from daily temperature and rainfall values, and daily pollen concentrations, using partial least squares regression (PLSR). This method was then applied to predict daily pollen concentrations for 2014 (independent validation data) using results for the seasonal component of the time series and estimates of the residual component for the period 2006–2013. Correlation between predicted and observed values was r = 0.79 (correlation coefficient) for the pre-peak period (i.e., the period prior to the peak pollen concentration) and r = 0.63 for the post-peak period. Separate analysis of each of the components of the pollen data series enables the sources of variability to be identified more accurately than by analysis of the original non-decomposed data series, and for this reason, this procedure has proved to be a suitable technique for analyzing the main environmental factors influencing airborne pollen concentrations.


Seasonality Daily variations Grass pollen Flowering phenology Meteorological variables 


  1. Aboulaich N, Achmakh L, Bouziane H, Trigo MM, Recio M, Kadiri M, Cabezudo B, Riadi H, Kazzaz M (2013) Effect of meteorological parameters on Poaceae pollen in the atmosphere of Tetouan (NW Morocco). Int J Biometeorol 57:197–205CrossRefGoogle Scholar
  2. Aboulaich N, Bouziane H, Kadiri M, Trigo MM, Riadi H, Kazzaz M, Merzouki A (2009) Pollen production in anemophilous species of the Poaceae family in Tetouan (NW Morocco). Aerobiologia 25:27–38CrossRefGoogle Scholar
  3. Aguilera F, Orlandi F, Ruiz-Valenzuela L, Msallem M, Fornaciari M (2015) Analysis and interpretation of long temporal trends in cumulative temperatures and olive reproductive features using a seasonal trend decomposition procedure. Agric For Meteorol 203:208–216CrossRefGoogle Scholar
  4. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723CrossRefGoogle Scholar
  5. Aznarte JL, Benítez JM, Nieto D, de Linares C, Díaz de la Guardia C, Alba F (2007) Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models. Expert Syst Appl 32:1218–1225CrossRefGoogle Scholar
  6. Belmonte J, Canela M (2002) Modelling aerobiological time series. Application to Urticaceae. Aerobiologia 18:287–295CrossRefGoogle Scholar
  7. Bourjea J, Dalleau M, Derville S, Beudard F, Marmoex C, M’Soili A, Roos D, Ciccione S, Frazier J (2015) Seasonality, abundance, and fifteen-year trend in green turtle nesting activity at Itsamia, Moheli, Comoros. Endang Species Res 27:265–276CrossRefGoogle Scholar
  8. Bousquet J, Anto J, Auffray C, Akdis M, Cambon-Thomsen A, Keil T, Haahtela T, Lambrecht BN, Postma DS, Sunyer J, Valenta R, Akdis CA, Annesi-Maesano I, Arno A, Bachert C, Ballester F, Basagana X, Baumgartner U, Bindslev-Jensen C, Brunekreef B, Carlsen KH, Chatzi L, Crameri R, Eveno E, Forastiere F, Garcia-Aymerich J, Guerra S, Hammad H, Heinrich J, Hirsch D, Jacquemin B, Kauffmann F, Kerkhof M, Kogevinas M, Koppelman GH, Kowalski ML, Lau S, Lodrup-Carlsen KC, Lopez-Botet M, Lotvall J, Lupinek C, Maier D, Makela MJ, Martinez FD, Mestres J, Momas I, Nawijn MC, Neubauer A, Oddie S, Palkonen S, Pin I, Pison C, Rancé F, Reitamo S, Rial-Sebbag E, Salapatas M, Siroux V, Smagghe D, Torrent M, Toskala E, van Cauwenberge P, van Oosterhout AJ, Varraso R, von Hertzen L, Wickman M, Wijmenga C, Worm M, Wright J, Zuberbier T (2011) MeDALL (Mechanisms of the Development of ALLergy): an integrated approach from phenotypes to systems medicine. Allergy 66(5):596–604CrossRefGoogle Scholar
  9. Brighetti MA, Costa C, Menesatti P, Antonucci F, Tripodi S, Travaglini A (2014) Multivariate statistical forecasting modeling to predict Poaceae pollen critical concentrations by meteoclimatic data. Aerobiologia 30:25–33CrossRefGoogle Scholar
  10. Brockwell PJ, Davis RA (2002) Introduction to time series and forecasting, 2nd edn. Springer-Verlag, New York, USACrossRefGoogle Scholar
  11. Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22(7):357–365CrossRefGoogle Scholar
  12. Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990) STL: a seasonal-trend decomposition procedure based on loess. J Off Stat 6(1):3–33Google Scholar
  13. Cryer JD, Chan KS (2008) Time series analysis with applications in R, 2nd edn. Springer, New York, USAGoogle Scholar
  14. Currie KI, Brailsford G, Nichol S, Gomez A, Sparks R, Lassey KR, Riedel K (2011) Tropospheric 14CO2 at Wellington, New Zealand: the world’s longest record. Biogeochemistry 104:5–22CrossRefGoogle Scholar
  15. D’Amato G, Cecchi L, Bonini S, Nunes C, Annesi-Maesano I, Behrendt H, Liccardi G, Popov T, van Cauwenberge P (2007) Allergenic pollen and pollen allergy in Europe. Allergy 62:976–990CrossRefGoogle Scholar
  16. Dagum EB, Luati A (2003) Global and local statistical properties of fixed-length nonparametric smoothers. Stat Method Appl 11:313–333CrossRefGoogle Scholar
  17. Estrella N, Menzel A, Krämer U, Behrendt H (2006) Integration of flowering dates in phenology and pollen counts in aerobiology: analysis of their spatial and temporal coherence in Germany (1992–1999). Int J Biometeorol 51:49–59CrossRefGoogle Scholar
  18. Fernández-Llamazares A, Belmonte J, Boada M, Fraixedas S (2014) Airborne pollen records and their potential applications to the conservation of biodiversity. Aerobiologia 30:111–122CrossRefGoogle Scholar
  19. Fernández-Rodríguez S, Adams-Groom B, Silva-Palacios I, Caeiro E, Brandao R, Ferro R, Gonzalo-Garijo A, Smith M, Tormo R (2015) Comparison of Poaceae pollen counts recorded at sites in Portugal, Spain and the UK. Aerobiologia 31:1–10CrossRefGoogle Scholar
  20. Frenguelli G, Passalacqua G, Bonini S, Fiocchi A, Incorvaia C, Marcucci F, Tedeschini E, Canonica GW, Frati F (2010) Bridging allergologic and botanical knowledge in seasonal allergy: a role for phenology. Ann Allerg Asthma Im 105(3):223–227CrossRefGoogle Scholar
  21. Galán C, Cariñanos P, Alcázar P, Domínguez-Vilches E (2007) Spanish aerobiology network (REA): management and quality manual. Servicio de Publicaciones, Universidad de Córdoba, Córdoba, SpainGoogle Scholar
  22. Galán C, Cariñanos P, García-Mozo H, Alcázar P, Domínguez-Vilches E (2001) Model for forecasting Olea europaea L. airborne pollen in South-West Andalusia, Spain. Int J Biometeorol 45:59–63CrossRefGoogle Scholar
  23. García-Mozo H (2011) The use of aerobiological data on agronomical studies. Ann Agric Environ Med 18:159–164Google Scholar
  24. García-Mozo H, Mestre A, Galán C (2010) Phenological trends in southern Spain: a response to climate change. Agric For Meteorol 150:575–580CrossRefGoogle Scholar
  25. García-Mozo H, Oteros JA, Galán C (2016) Impact of land cover changes and climate on the main airborne pollen types in Southern Spain. Sci Total Environ 548-549:221–228CrossRefGoogle Scholar
  26. García-Mozo H, Yaezel L, Oteros J, Galán C (2014) Statistical approach to the analysis of olive long-term pollen season trends in southern Spain. Sci Total Environ 473-474:103–109CrossRefGoogle Scholar
  27. Gordo O, Sanz JJ (2009) Long-term temporal changes of plant phenology in the Western Mediterranean. Global Change Biol 15:1930–1948CrossRefGoogle Scholar
  28. Graham MH (2003) Confronting multicollinearity in ecological multiple regression. Ecology 84:2809–2815CrossRefGoogle Scholar
  29. Gucel S, Guvensen A, Ozturk M, Celik A (2013) Analysis of airborne pollen fall in Nicosia (Cyprus). Environ Monit Assess 185:157–169CrossRefGoogle Scholar
  30. Hamid N, Ali SM, Talib F, Sadiq I, Ghufran MA (2015) Spatial and temporal variations of pollen concentrations in Islamabad (Pakistan): effect of meteorological parameters and impact on human health. Grana 54(1):53–67CrossRefGoogle Scholar
  31. Harvey AC, Peters S (1990) Estimation procedures for structural time series model. J Forecast 9:89–108Google Scholar
  32. Hirst JM (1952) An automatic volumetric spore trap. Ann Appl Biol 36:257–344CrossRefGoogle Scholar
  33. Jato V, Rodríguez-Rajo FJ, Alcázar P, De Nuntiis P, Galán C, Mandrioli P (2006) May the definition of pollen season influence aerobiological results? Aerobiologia 22:13–25CrossRefGoogle Scholar
  34. Kasprzyk I (2006) Comparative study of seasonal and intradiurnal variation of airborne herbaceous pollen in urban and rural areas. Aerobiologia 22:185–195CrossRefGoogle Scholar
  35. Kasprzyk I, Walanus A (2010) Description of the main Poaceae pollen season using bi-Gaussian curves, and forecasting methods for the start and peak dates for this type of season in Rzeszów and Ostrowiec Św. (SE Poland). J Environ Monit 12:906–916CrossRefGoogle Scholar
  36. Kasprzyk I, Walanus A (2014) Gamma, Gaussian and logistic distribution models for airborne pollen grains and fungal spore season dynamics. Aerobiologia 30:369–383CrossRefGoogle Scholar
  37. León-Ruiz E, Alcázar P, Domínguez-Vilches E, Galán C (2011) Study of Poaceae phenology in a Mediterranean climate. Which species contribute most to airborne pollen counts? Aerobiologia 27:37–50CrossRefGoogle Scholar
  38. Ljung GM, Box GEP (1978) On a measure of lack of fit in time series models. Biometrika 65:297–303CrossRefGoogle Scholar
  39. Mabberley DJ (1987) The plant book. Cambridge University press, CambridgeGoogle Scholar
  40. Makra L, Matyasovszky I, Deàk AJ (2011) Trends in the characteristics of allergenic pollen circulation in central Europe based on the example of Szeged, Hungary. Atmos Environ 45:6010–6018CrossRefGoogle Scholar
  41. Meier U (2001) Growth stages of mono- and dicotyledonous plants. BBCH monograph. 2nd ed. Federal Biological Research Centre for Agriculture and ForestryGoogle Scholar
  42. Mevik BH, Wehrens R (2007) The pls package: principal component and partial least squares regressions in R. J Stat Softw 18(2):1–24CrossRefGoogle Scholar
  43. Moseholm L, Weeke ER, Petersen BN (1987) Forecast of pollen concentrations of Poaceae (grasses) in the air by time series analysis. Pollen Spores 2-3:305–322Google Scholar
  44. Ocaña-Peinado F, Valderrama MJ, Aguilera AM (2008) A dynamic regression model for air pollen concentration. Stoch Environ Res Risk Assess 22(Suppl 1):S59–S63CrossRefGoogle Scholar
  45. Oteros J, García-Mozo H, Hervás-Martínez C, Galán C (2013a) Year clustering analysis for modelling olive flowering phenology. Int J Biometeorol 57(4):545–555CrossRefGoogle Scholar
  46. Oteros J, García-Mozo H, Vázquez L, Mestre A, Domínguez-Vilches E, Galán C (2013b) Modelling olive phenological response to weather and topography. Agric Ecosyst Environ 179:62–68CrossRefGoogle Scholar
  47. Pauling A, Gehrig R, Clot B (2014) Toward optimized temperature sum parameterizations for forecasting the start of the pollen season. Aerobiologia 30:45–57CrossRefGoogle Scholar
  48. Peel RG, Ørby PV, Skjøth CA, Kennedy R, Schlünssen V, Smith M, Sommer J, Hertel O (2014) Seasonal variation in diurnal atmospheric grass pollen concentration profiles. Biogeosciences 11:821–832CrossRefGoogle Scholar
  49. Peñuelas J, Filella I, Comas P (2002) Changed plant and animal life cycles from 1952 to 2000 in the Mediterranean region. Global Change Biol 8(6):531–544CrossRefGoogle Scholar
  50. Pérez-Badia R, Bouso V, Rojo J, Vaquero C, Sabariego S (2013) Dynamics and behaviour of airborne Quercus pollen in central Iberian Peninsula. Aerobiologia 29:419–428CrossRefGoogle Scholar
  51. Pérez-Badia R, Rapp A, Morales C, Sardinero S, Galán C, García-Mozo H (2010) Pollen spectrum and risk of pollen allergy in central Spain. Ann Agric Environ Med 17:139–151Google Scholar
  52. Petropavlovskikh I, Evans R, McConville G, Manney GL, Rieder HE (2015) The influence of the North Atlantic Oscillation and El Niño-Southern Oscillation on mean and extreme values of column ozone over the United States. Atmos Chem Phys 15:1585–1598CrossRefGoogle Scholar
  53. Preda C, Saporta G (2005) PLS regression on a stochastic process. Comput Stat Data An 48:149–158CrossRefGoogle Scholar
  54. Prieto-Baena JC, Hidalgo PJ, Domínguez-Vilches E, Galán C (2003) Pollen production in the Poaceae family. Grana 42:153–160CrossRefGoogle Scholar
  55. R Core Team (2015) A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (accessed 11 Sept 2015)
  56. Ranzi A, Lauriola P, Marletto V, Zinoni F (2003) Forecasting airborne pollen concentrations: development of local models. Aerobiologia 19:39–45CrossRefGoogle Scholar
  57. Recio M, Docampo S, García-Sánchez J, Trigo MM, Melgar M, Cabezudo B (2010) Influence of temperature, rainfall and wind trends on grass pollination in Malaga (western Mediterranean coast). Agric For Meteorol 150(7-8):931–940CrossRefGoogle Scholar
  58. Reyment RA, Jvreskog KG (1996) Applied factor analysis in the natural sciences. Cambridge University PressGoogle Scholar
  59. Rodríguez-Rajo FJ, Jato V, Aira MJ (2003) Pollen content in the atmosphere of Lugo (NW Spain) with reference to meteorological factors (1999-2001). Aerobiologia 19:213–225CrossRefGoogle Scholar
  60. Rodríguez-Rajo F, Valencia-Barrera RM, Vega-Maray AM, Suárez FJ, Fernández-González D, Jato V (2006) Prediction of airborne Alnus pollen concentration by using ARIMA models. Ann Agric Environ Med 13:25–32Google Scholar
  61. Rojo J, Pérez-Badia R (2015) Models for forecasting the flowering of Cornicabra olive groves. Int J Biometeorol 59:1547–1556CrossRefGoogle Scholar
  62. Rojo J, Rapp A, Lara B, Fernández-González F, Pérez-Badia R (2015) Effect of land uses and wind direction on the contribution of local sources to airborne pollen. Sci Total Environ 538:672–682CrossRefGoogle Scholar
  63. Rummel RJ (1988) Applied factor analysis. Northwestern University Press, EvanstonGoogle Scholar
  64. Sabariego S, Cuesta P, Fernández-González F, Pérez-Badia R (2012) Models for forecasting airborne Cupressaceae pollen levels in central Spain. Int J Biometeorol 56(2):253–258CrossRefGoogle Scholar
  65. Sánchez-Mesa JA, Galán C, Martínez-Heras JA, Hervás-Martínez C (2005) The use of discriminant analysis and neural networks to forecast the severity of the Poaceae pollen season in a region with typical Mediterranean climate. Int J Biometeorol 49:355–362CrossRefGoogle Scholar
  66. Sánchez-Mesa JA, Galán C, Martínez-Heras JA, Hervás-Martínez C (2002) The use of a neural network to forecast daily grass pollen concentration in a Mediterranean region: the southern part of the Iberian Peninsula. Clin Exp Allergy 32:1606–1612CrossRefGoogle Scholar
  67. Sawa T (1978) Criteria for discriminating among alternative regression models. Econometrica 46(6):1273–1291CrossRefGoogle Scholar
  68. Seasholtz MB, Kowalski B (1993) The parsimony principle applied to multivariate calibration. Anal Chim Acta 277:165–177CrossRefGoogle Scholar
  69. Silva-Palacios I, Fernández-Rodríguez S, Durán-Barroso P, Tormo-Molina R, Maya-Manzano JM, Gonzalo-Garijo A (2015) Temporal modelling and forecasting of the airborne pollen of Cupressaceae on the southwestern Iberian Peninsula. Int J Biometeorol. doi: 10.1007/s00484-015-1026-6 Google Scholar
  70. Stach A, Smith M, Prieto-Baena JC, Emberlin J (2008) Long-term and short-term forecast models for Poaceae (grass) pollen in Poznań, Poland, constructed using regression analysis. Environ Exp Bot 62:323–332CrossRefGoogle Scholar
  71. Subiza J (2003) Gramínes: Aerobiología y polinosis en España. Alergol Inmunol Clin 18(3):7–11Google Scholar
  72. Valderrama MJ, Ocaña FA, Aguilera AM, Ocaña-Peinado FM (2010) Forecasting pollen concentration by a two-step functional model. Biometrics 66:578–585CrossRefGoogle Scholar
  73. Vallejos RO, Fabré NN, Batista VS, Acosta J (2013) The application of a general time series model to floodplain fisheries in the Amazon. Environ Modell Softw 48:202–212CrossRefGoogle Scholar
  74. Veriankaitė L, Šaulienė I, Bukantis A (2011) Evaluation of meteorological parameters influence upon pollen spread in the atmosphere. J Environ Eng Landsc 19(1):5–11CrossRefGoogle Scholar
  75. Voukantsis D, Niska H, Karatzas K, Riga M, Damialis A, Vokou D (2010) Forecasting daily pollen concentrations using data-driven modeling methods in Thessaloniki, Greece. Atmos Environ 44:5101–5111CrossRefGoogle Scholar
  76. Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Manage Sci 6:324–342CrossRefGoogle Scholar
  77. Wold S, Ruhe A, Wold H, Dunn WJ (1984) The collinearity problem in linear regression. The Partial Least Squares (PLS) approach to generalized inverses. SIAM J Sci Comput 5(3):735–743CrossRefGoogle Scholar
  78. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Itell Lab 58:109–130CrossRefGoogle Scholar
  79. Zhou J, Liang Z, Liu Y, Guo H, He D, Zhao L (2015) Six-decade temporal change and seasonal decomposition of climate variables in Lake Dianchi watershed (China): stable trend or abrupt shift? Theor Appl Climatol 119:181–191CrossRefGoogle Scholar

Copyright information

© ISB 2016

Authors and Affiliations

  1. 1.Institute of Environmental SciencesUniversity of Castilla-La ManchaToledoSpain

Personalised recommendations