Development and testing of a spatio-temporally dynamic pollen emission module, for the BEIS/CMAQ/MENTOR modeling framework through an integrated remote sensing and observation database for the Northeast United States
C. Efstathiou1, N. Lahoti1, A. Unal2, P.G. Georgopoulos1
1Environmental and Occupational Health Sciences Institute, UMDNJ - R.W. Johnson Medical School and Rutgers University, Piscataway, NJ; 2MACTEC, Trenton, NJ
Allergic diseases represent a complex health problem that is receiving increased attention. Europe has succeeded in unifying a network of 400 monitoring stations that share pollen counts through the European Aeroallergen Network (EAN) pollen database. Similarly, the emission and dispersion of particles of biogenic origin, such as aeroallergens, is getting an increasing interest. There is strong evidence supporting the hypothesis that in urban areas, the synergism of pollen and other air pollutants exacerbates respiratory diseases like asthma and allergic rhinitis. A prototype algorithm for simulating the emissions of allergenic particles originating from major tree families of the New York/New Jersey region was developed by extending the approach used for estimating biogenic gas emissions in the Biogenic Emission Inventory System (BEIS). A spatio-temporal vegetation map was derived from a number of different remote sensing sources. Ground level measurements of pollen levels were analyzed and correlated with environmental conditions in order to establish source strengths and the temporal extent of the pollen-shedding period. Photosynthetically Active Radiation (PAR), the absorbed fraction of radiation, which is a major indicator of the state and productivity of vegetation, was closely examined. A preliminary comparison of results derived from simulations utilizing the new pollen emission model is presented: PAR estimates from the 5th generation Mesoscale Model (MM5) and the Community Multiscale Air Quality (CMAQ) model are compared with observations from the Surface Radiation monitoring network (SURFRAD) and with estimates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Finally, a framework for aerobiological modeling applications is introduced and its advantages are discussed vis-à-vis the limitations posed by the lack of temporally resolved dynamic vegetation mapping and of a modern, automated pollen monitoring network for the US.
This work is funded in part by a University Partnership Agreement between USEPA and EOHSI. Viewpoints expressed here are do not necessarily reflect views of the USEPA or its contractors.