CAMS outreach to health professionals European pollen forecasts

Transcription

1 CAMS outreach to health professionals European pollen forecasts Mikhail Sofiev, Pilvi Siljamo, Olga Ritenberga, Uwe Berger, Joaquim Arteta, Jordina Belmonte, Karl-Christian Bergmann, Françoise Chéroux, Hendrik Elbern, Elmar Friese, Carmen Galan, Regula Gehrig, Dmitry Khvorostyanov, Richard Kranenburg, Virginie Marécal, Frederik Meleux, Laurent Menut, Anna-Mari Pessi, Lennart Robertson,Viktoria Rodinkova, Annika Saarto, Arjo Segers, Elena Severova, Ingrida Sauliene, Birthe Marie Steensen, Erik Teinemaa, Michel Thibaudon, Vincent-Henri Peuch

2 He inhaled a breath of humid morning breeze and let in nitrogen, oxygen, argon, xenon & radon, steam, carbon monoxide, nitrogen dioxide, tetra-ethyl lead, benzene, some mould spores, a bacteria fleet, anonymous body hair, a pigeon ectoparasite, anemophilous pollen, a drop of sulphur dioxide flown from a distant factory, and a particle of dust carried by the night sirocco. In other words he breathed air of the city (Stefano Benni "Achille piè veloce", Mondadori, Italy, 2003) Courtesy of G.D Amato

3 Outlook Introduction Pollen: an air pollutant causing human allergy CAMS pollen forecasts How to use? Most-important parameters Interaction with chemical air pollution Mitigation and / or adaptation Next steps of pollen forecasting

4 Prevalence of allergenic rhinitis in Europe Mean prevalence 24,5% 33.6 Prevalence of respiratory allergy disorders (% of population) Austria Denmark Finland Germany Italy Netherlands Norw ay Spain Sw eden UK Adapted from Dahl, de Monchy, Chivato, Valovirta et al. Respir Med 2004; 98: Flags courtesy of used with permission

5 European Parliament Allergy Declaration During discussion and voting, EAACI offered sensitization testing for passers-by in the EP s building 350 tests made for most-common allergies 47% tested positive 178 MPs voted for the Declaration second strongest EP vote on environmental topics, ever

6 Timely proper medication reduces symptoms Mean medication score

7 What to forecast? Local season is quite well predicted by local-scale statistical models based on measurements or by localized deterministic phenology model regional deterministic model usually has lower scores Regional and long-range pollen transport episodes are not predictable from local measurements Birch pollen grains in m -3 air Turku flushing 20th of May Turku Oulu Oulu flushing 24th of May SILAM model first pollen forecasting season, EAN observations courtesy Season of S.Jaeger 1999

8 CAMS approach: models + measurements Pollen modelling since mid-2000s developed into reliable forecasting and analysis tool in combination with measurements, shows good results in operational use 7-models-strong CAMS European ensemble (+2 and +2 coming) European Aeroallergen Network: started in 1970s well-organized, coordinated and standardized observations throughout Europe manual microscopic analysis: slow, expensive, long delays limited number of sites daily standard resolution (bi-hourly in some countries) Link to patients: load thresholds COST EUPOL unified approach: medical data, observations and modelling for clinically-relevant threshold of pollen concentrations

9 Season 2013: CAMS, 7 models CAMS regional ensemble for birch pollen standard CAMS domain, forecast parameters similar to chemical AQF birch, grass, olive pollen source terms of FMI daily forecasts available during the season 2017: extension to operational grass, olive Evaluation against European Aeroallergen Network (EAN) world-wide unique pollen network, >160 stations over Europe: voluntary unfunded contribution state of the art (manual microscopic analysis of pollen slides) sofar did not allow for NRT comparison and data assimilation no public funding: complicated arrangements to ensure sustainability Sofiev et al, ACP, 2015

10 Seasonal pollen index Sofiev et al, ACP, 2015

11 How to use pollen information / forecast? Short time scale (forecasts) Behavioral adaptation Medication Limited but existing possibilities for mitigation Long time scale (seasonal index, typical season timing) Substantial mitigation options Long-term adaptation measures

12 CAMS / Europe Norkko-pollen forecasts for Finland: University of Turku and FMI Helsinki / FMI Turku / Aerobiolgy Unit High-resolution SILAM pollen forecasts FMI production machine DATA Pollen concentrations Rain T2m V10m DATA* SmartMet editor Smart Met Pollen cnc* Checked/Corrected SILAM data

13 Pollen forecast at norkko.fi: Birch flowering started in the Southern Finland May 3, 2016 Former pollen forecasts New Norkko-pollen forecasts

14 Pollen forecast at norkko.fi: Birch flowering started in the Southern Finland May 3, 2016 Former pollen forecasts New Norkko-pollen forecasts

15 Pollen mobile application app.norkko.fi Helsinki, Tuesday, May 3, 2016 University of Turku (UTU) and FMI co-operation Application by UTU Web-based application Based on SILAM-pollen forecasts (FMI) Aerobiological observations (UTU) Weather forecasts (FMI) Human-made corrections (UTU) SmartMet-workstation (FMI)

16 preprocessor postprocessor New frontier: Personal Allergy SYmptom FOrecasting CAMS ensemble AQ, pollen fcst AQ, pollen fcst Europe SFM AQ, pollen fcst Europe SILAM AQ, pollen fcst regional Interface files DB symptom models Interface files DB PHFD symptoms personal symptom forecasts mobile app PASYFO Web site mean symptom forecasts

17 Quality assurance: past self-reports Evaluation of the 2018 season is on-going Pre-operational assessment: ~14,000 daily entries, 4 yrs Patient Klaipeda nasal symptomspatient mean-nasal symptoms Patient Šauliai overall symptoms Patient mean overall symptoms

18 Operational prototype: Latvia & Lithuania

19 PASYFO mobile app

20 Next-next frontier: online pollen monitoring Major problems: pollen data are not near-real-time and usually not open historical reasons of bottom-up movement currently gold-standard Hirst-type pollen monitor has been invented in 1951, did not change till today. Manual analysis The world-leading pollen network EAN: a club of volunteers with largely private ownership and manual Emerging technology: real-time monitors very complicated, expensive and yet-to-be-proven operationally viable potential breakthrough in pollen monitoring A series of related projects, AutoPollen programme of EUMETNET

21 Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss AutoPollen Automatic Pollen Detection Network EUMETNET Optional Programme

22 AutoPollen Automatic Pollen Detection Network Prepared by the Task Team on Automatic Pollen Detection Network Clot B. (Chair) MeteoSwiss, Switzerland Gilge S. DWD, Germany Hajkova L. CHMI, Czech Republic Magyar D. NPHC, Hungary Scheifinger H. ZAMG, Austria Sofiev M. FMI, Finland With the support of EUMETNET secretariat and MeteoSwiss International Team Approved by EUMETNET A19, November 2017

23 AutoPollen Technical Proposal 10 objectives / 33 tasks and deliverables Requirements for instruments Site design = site selection and site description Network design Data requirements References sites QA / QC; intercomparison exercise Products Expected impact Prototype of network Interaction with stakeholders, collaboration and communication

24 Example mitigation/adaptation measures Long-term mitigation city planning: ornamental plants, species used in parks, recreation zones and their location, etc agriculture planning: location of fields with allergenic species with regard to cities public activity planning: avoid peak-pollen days/hours UK study: allergic students score worse in spring exams than in winter eradication measures against invasive species (ragweed) Short-term mitigation mowing grass not only in parks. Goal is not only to make it looking nice, but to prevent it from flowering. Timing is crucial clean streets, roofs, ventilation systems during the season Short-term adaptation Timely medication: start shortly before the pollen season Skip the local season by proper holiday travelling

25 on city planning Birch pollen Exposure NOx, BC, OC, Grass pollen Exposure

26 Thank you for your attention!