Abstract
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.
Similar content being viewed by others
References
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 57:197–205
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 25:27–38
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 203:208–216
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723
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 32:1218–1225
Belmonte J, Canela M (2002) Modelling aerobiological time series. Application to Urticaceae. Aerobiologia 18:287–295
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 27:265–276
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):596–604
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 30:25–33
Brockwell PJ, Davis RA (2002) Introduction to time series and forecasting, 2nd edn. Springer-Verlag, New York, USA
Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD (2007) Shifting plant phenology in response to global change. Trends Ecol Evol 22(7):357–365
Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990) STL: a seasonal-trend decomposition procedure based on loess. J Off Stat 6(1):3–33
Cryer JD, Chan KS (2008) Time series analysis with applications in R, 2nd edn. Springer, New York, USA
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 104:5–22
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 62:976–990
Dagum EB, Luati A (2003) Global and local statistical properties of fixed-length nonparametric smoothers. Stat Method Appl 11:313–333
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 51:49–59
Fernández-Llamazares A, Belmonte J, Boada M, Fraixedas S (2014) Airborne pollen records and their potential applications to the conservation of biodiversity. Aerobiologia 30:111–122
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 31:1–10
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):223–227
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, Spain
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 45:59–63
García-Mozo H (2011) The use of aerobiological data on agronomical studies. Ann Agric Environ Med 18:159–164
García-Mozo H, Mestre A, Galán C (2010) Phenological trends in southern Spain: a response to climate change. Agric For Meteorol 150:575–580
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 548-549:221–228
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 473-474:103–109
Gordo O, Sanz JJ (2009) Long-term temporal changes of plant phenology in the Western Mediterranean. Global Change Biol 15:1930–1948
Graham MH (2003) Confronting multicollinearity in ecological multiple regression. Ecology 84:2809–2815
Gucel S, Guvensen A, Ozturk M, Celik A (2013) Analysis of airborne pollen fall in Nicosia (Cyprus). Environ Monit Assess 185:157–169
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):53–67
Harvey AC, Peters S (1990) Estimation procedures for structural time series model. J Forecast 9:89–108
Hirst JM (1952) An automatic volumetric spore trap. Ann Appl Biol 36:257–344
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 22:13–25
Kasprzyk I (2006) Comparative study of seasonal and intradiurnal variation of airborne herbaceous pollen in urban and rural areas. Aerobiologia 22:185–195
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 12:906–916
Kasprzyk I, Walanus A (2014) Gamma, Gaussian and logistic distribution models for airborne pollen grains and fungal spore season dynamics. Aerobiologia 30:369–383
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 27:37–50
Ljung GM, Box GEP (1978) On a measure of lack of fit in time series models. Biometrika 65:297–303
Mabberley DJ (1987) The plant book. Cambridge University press, Cambridge
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 45:6010–6018
Meier U (2001) Growth stages of mono- and dicotyledonous plants. BBCH monograph. 2nd ed. Federal Biological Research Centre for Agriculture and Forestry
Mevik BH, Wehrens R (2007) The pls package: principal component and partial least squares regressions in R. J Stat Softw 18(2):1–24
Moseholm L, Weeke ER, Petersen BN (1987) Forecast of pollen concentrations of Poaceae (grasses) in the air by time series analysis. Pollen Spores 2-3:305–322
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–S63
Oteros J, García-Mozo H, Hervás-Martínez C, Galán C (2013a) Year clustering analysis for modelling olive flowering phenology. Int J Biometeorol 57(4):545–555
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 179:62–68
Pauling A, Gehrig R, Clot B (2014) Toward optimized temperature sum parameterizations for forecasting the start of the pollen season. Aerobiologia 30:45–57
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 11:821–832
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):531–544
Pérez-Badia R, Bouso V, Rojo J, Vaquero C, Sabariego S (2013) Dynamics and behaviour of airborne Quercus pollen in central Iberian Peninsula. Aerobiologia 29:419–428
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 17:139–151
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 15:1585–1598
Preda C, Saporta G (2005) PLS regression on a stochastic process. Comput Stat Data An 48:149–158
Prieto-Baena JC, Hidalgo PJ, Domínguez-Vilches E, Galán C (2003) Pollen production in the Poaceae family. Grana 42:153–160
R Core Team (2015) A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (accessed 11 Sept 2015)
Ranzi A, Lauriola P, Marletto V, Zinoni F (2003) Forecasting airborne pollen concentrations: development of local models. Aerobiologia 19:39–45
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):931–940
Reyment RA, Jvreskog KG (1996) Applied factor analysis in the natural sciences. Cambridge University Press
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 19:213–225
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 13:25–32
Rojo J, Pérez-Badia R (2015) Models for forecasting the flowering of Cornicabra olive groves. Int J Biometeorol 59:1547–1556
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 538:672–682
Rummel RJ (1988) Applied factor analysis. Northwestern University Press, Evanston
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):253–258
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 49:355–362
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 32:1606–1612
Sawa T (1978) Criteria for discriminating among alternative regression models. Econometrica 46(6):1273–1291
Seasholtz MB, Kowalski B (1993) The parsimony principle applied to multivariate calibration. Anal Chim Acta 277:165–177
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
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 62:323–332
Subiza J (2003) Gramínes: Aerobiología y polinosis en España. Alergol Inmunol Clin 18(3):7–11
Valderrama MJ, Ocaña FA, Aguilera AM, Ocaña-Peinado FM (2010) Forecasting pollen concentration by a two-step functional model. Biometrics 66:578–585
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 48:202–212
Veriankaitė L, Šaulienė I, Bukantis A (2011) Evaluation of meteorological parameters influence upon pollen spread in the atmosphere. J Environ Eng Landsc 19(1):5–11
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 44:5101–5111
Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Manage Sci 6:324–342
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):735–743
Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Itell Lab 58:109–130
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 119:181–191
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Rojo, J., Rivero, R., Romero-Morte, J. et al. Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing. Int J Biometeorol 61, 335–348 (2017). https://doi.org/10.1007/s00484-016-1215-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00484-016-1215-y