5/24/2018 Sikoparija B

1/8

FORECASTING RAGWEEDPOLLEN DISPERSAL IN

EUROPEBranko ikoparija, Michel Thibaudon,

Carsten A. Skjth, Matt Smith

Research Group Aerobiology and PollenInformation

Department of Oto-Rhino-Laryngology

5/24/2018 Sikoparija B

2/8

What to forecast?

1. The beginning of the exposure

(Start of pollen season)e.g. Laaidi, 2003

2. The intensity of exposure

(daily or bi-hourly pollen concentration)

BASED ON HEALTH IMPACT OF RAGWEED POLLEN:

5/24/2018 Sikoparija B

3/8

Observation-orientated models

Predict daily concentrations based onregularity of flowering phenophaze andenvironmental factors influencing itsdevelopment

Using mathematical models helps in taking intoconsideration all important factors

Traditionaly by relating pollen concentrations to

measurable/predictable variable using variousstatistical procedure:

Regression analysis

Time-series analysis

Computational intelligence techniques(e.g. neural networks)

e.g.Makra et al., 2011 using Time-series analysisPredictors: pollen concentration of the previous

day, precipitation, temperature, wind

Stark et al., 1997 using Regression analysisPredictors: rainfall, wind speed, temperature,time measured from the start of the season

Pollen concentration =

local source flowering + pollen transport

5/24/2018 Sikoparija B

4/8

Source-orientated models

take into consideration both knowledge of source conditions anddiffusion and dispersion of airborne particles.

Mathematical formulae of diffusion are used to calculateconcentrations at various distances from the source or release site.

5/24/2018 Sikoparija B

5/8

HU vs. DAll sources

Source inventory

COSMO-ART (Zink et al., 2011)

red = proportion of correct scores

Location and amount of sources in a grid the biggest uncertainty

??????

5/24/2018 Sikoparija B

6/8

Airborne pollen modelling studies

??? ???

EMISSION PATTERN

5/24/2018 Sikoparija B

7/8

Conclusion

COSMO-ART (Zink et al., 2011)

CHIMERE (Chaxel, 2011))

ragweed pollenconcentrations simulated byCOSMO-ART and CHIMEREmodels and presented using

geostatistics

5/24/2018 Sikoparija B

8/8

Refferences (small print):

Chaxel, E., C. Rieux, C., Rios, I., Thibaudon, M., Oliver, G. 2012., Modeling the 2011 ragweed season in the Frenchregion Rhne-Alpes with the dispersion model CHIMERE. 2ndIRC Lyon, France

Chaxel, E 2011., Modelling Ambrosia pollen in the air : Forecast and prospective. ATOPICA meeting (LSCE, Saclay)

Efstathiou, C., Isukapalli, S., Georgopoulos, P. 2011. A mechanistic modeling system for estimating large-scaleemissions and transport of pollen and co-allergens. Atmospheric Environment 45, 2260-2276.

.Helbig, N., Vogel, B., Vogel, H., Fiedler, F., 2004. Numerical modelling of pollen dispersion on the regional scale.Aerobiologia 20, 3-19.

Kawashima, S., Takahashi, Y., 1999. An improved simulation of mesoscale dispersion of airborne cedar pollen using a

flowering-time map. Grana 38, 316-324.

Laaidi M, Thibaudon M, Besancenot JP 2003 Two statistical approaches to forecasting the start and duration of thepollen season of Ambrosia in the area of Lyon (France). Int J Biometeorol 48:6573.

Pasken, R., Pietrowicz, J., 2005. Using dispersion and mesoscale meteorological models to forecast pollenconcentrations. Atmospheric Environment 39, 7689-7701.

Prank, M., Sofiev, M., Chapman, D. 2012., Emission and dispersion of the ragweed pollen in Europe: first results andevaluation of the SILAM model. 2ndIRC Lyon, France

Schueler, S., Schlnzen, K., 2006. Modeling of oak pollen dispersal on the landscape level with a mesoscaleatmospheric model. Environmental Modeling and Assessment 11, 179-194.

Sofiev, M., Siljamo, P., Ranta, H., Rantio-Lehtimki, A., 2006. Towards numerical forecasting of long-range air

transport of birch pollen: theoretical considerations and a feasibility study. International Journal of Biometeorology 50,392-402.

Stark, P., Ryan, L., McDonald, J., Burhe, H. 1997. Using meteorologic data to predict daily ragweed pollen levels.

Aerobiologia 13, 177-184

Vogel, H., Pauling, A., Vogel, B., 2008. Numerical simulation of birch pollen dispersion with an operational weatherforecast system. International Journal of Biometeorology 52, 805-814.

Zink, K., Vogel, H., Vogel, B., Magyar, D., Kottmeier, C. 2011 Modeling the dispersion of Ambrosia artemisiifoliaL.pollen with the model system COSMO-ART. Int J Biometeorol, DOI 10.1007/s00484-011-0468-8

Thank You!!

Research Group Aerobiology and Pollen Information

Department of Oto-Rhino-Laryngology