Naslov
RAGWEED POLLEN FORECAST IN THE PANNONIAN BIOGEOGRAPHICAL REGION: LESSONS LEARNED

Autori
Magyar, Donat ; Novák, R ; Páldy, Anna ; Udvardy, O ; Kajtor- Apatini, D ; Környei- Bócsi, E ; Pál ; V ; Szigeti, T ; Stjepanović, Barbara ; Hrga, Ivana ; Večenaj, Ana ; Vucić, Anita ; Peroš Pucar, Danijela ; Šikoparija, BNranko ; Radišić, Predrag ; Škorić, Tatjan a ; Ščevková, J ; Simon-Csete, E ; Nagy, M ; Topa, Z ; Kofol- Seliger, Andreja ; Leru, P.M. ; Eftimie, A- M ; Bastl, M ; Berger, Uwe ; Leelőssy, A

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Ecocycles, Vol. 8, No. 3. SPECIAL ISSUE, pp. 6-68. FINAL PROGRAM AND BOOK OF ABSTRACTS OF THE WORLD CONFERENCE OF THE INTERNATIONAL RAGWEED SOCIETY / Thibaudon, Michel ; Magyar, Donát ; Szigeti, Tamás ; Kazinczi, Gabriella ; Komives, Tamas ; Botta-Dukát, Zoltan ; Orlóci, László ; Makra, László - Budimpešta : International Ragweed Society (IRS), 2022, 28-29

Skup
WORLD CONFERENCE OF THE INTERNATIONAL RAGWEED SOCIETY. RAGWEED: A SUCCESSFUL STORY. Tackling Ragweed: a multidisciplinary and international approach

Mjesto i datum
Budapest, Hungary, 08.09.2022. - 09.09.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
ragweed, Pannonian Biogeographical Region, Ragweed Pollen Alarm System (R-PAS)

Sažetak
Common ragweed (Ambrosia artemisiifolia L.) is a major concern of public health and agriculture in the Pannonian Biogeographical Region (PBR). Pollen monitoring started more than 30 years ago in Hungary. Since then, the Seasonal Pollen Index of this plant shows an increasing tendency. Ragweed pollen affects approximately 15-20 % of the population ; 93 % of them regularly use pollen information services. Allergic patients and doctors need information about the onset of the season to start the intake of preventive medication two weeks ahead of the appearance of symptoms (10 pollen/m3 /day). During the main season, the short-term (i.e. 3 days) ragweed pollen forecast is crucial –this information can be achieved by different methods. Season start: in the early period of the season, a calendar method is useful (it is a conservative, bell-shaped curve of the 7-day moving average of 10 years’ data). The positioning of the ragweed season to the start date is important for the calculation of a reliable forecast for the first weeks. Therefore, several attempts were made to forecast the start of the season, including already existing and new 29 methods. It was concluded that phenological observations provide the most appropriate input to forecast the start of the ragweed pollen season. Concerning the PRB, the season started 9 times out of the last 10 years in Hungary, most frequently (4 times) in Debrecen. Thus, phenological data were collected from this region weekly to enable a precise prediction of pollen seasons’ start in the last 3 years. In most of the years, the onset of pollen season fell within the same five-day period of 27-31 July, however, anomalies were also detected. Extremely early peaks of pollen concentrations were observed at several monitoring stations in Hungary in June 2017 and 2018, one month before the usual onset. During the nationwide, biweekly field surveys, early blooming A. artemisiifolia plants were found, mostly in North-East Hungary. These field observations matched the source areas identified by trajectory analyses. Main season: For the prediction of the short-term changes in the pollen concentration in the main season the Ragweed Pollen Alarm System (R-PAS) was elaborated with the international collaboration of 28 monitoring stations in 2017 to generate a forecast in the PBR. The most critical point in the routine operation of such a large pollen alarm system is the coordination of data updates. We experienced that appropriate forecast models can handle some delay in data transmission, i.e., 3-day lagged pollen data provide acceptable forecast results in R-PAS. On the other hand, 1- or 2-day lagged pollen data would require unnecessarily high operational costs vs. the added value. As a first version a neural network model was used, nowadays a source-based dispersal model is applied to provide hourly forecasts too. Season end: Further research is needed to forecast the end of the ragweed season, using a different approach due to the re- aerosolization of pollen grains.

Izvorni jezik
Engleski

Znanstvena područja
Interdisciplinarne prirodne znanosti, Javno zdravstvo i zdravstvena zaštita

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