Forecasting Methodologies for Ganoderma Spore Concentration Using Combined Statistical Approaches and Model Evaluations

High concentration levels of Ganoderma spp. spores were observed in Worcester, UK, during 2006–2010.These basidiospores are known to cause sensitization due to the allergen content and their small dimensions. This enables them to penetrate the lower part of the respiratory tract in humans. Establishment of a link between occurring symptoms of sensitization to Ganoderma spp. and other basidiospores is challenging due to lack of information regarding spore concentration in the air. Hence, aerobiological monitoring should be conducted, and if possible extended with the construction of forecast models. Daily mean concentration of allergenic Ganoderma spp. spores in the atmosphere of Worcester was measured using 7-day volumetric spore sampler through five consecutive years. The relationships between the presence of spores in the air and the weather parameters were examined. Forecast models were constructed for Ganoderma spp. spores using advanced statistical techniques, i.e. multivariate regression trees and artificial neural networks. Dew point temperature along with maximumtemperature was the most important factor influencing the presence of spores in the air of Worcester. Based on these two major factors and several others of lesser importance, thresholds for certain levels of fungal spore concentration, i.e. low (0–49 s m−3), moderate(50–99 s m−3), high (100–149 s m−3) and very high (150forecasting model, which was accurate (correlation between observed and predicted values varied from rs=0.57 to rs=0.68).

Application of the WRF-Chem Model for a High Ozone Episode in SW Poland – Preliminary Results

The Weather Research and Forecasting model, integrated online with chemistry module, is a multi-scale model suitable for both research and operational forecasts of meteorology and air quality. It is used by many institutions for a variety of applications. In this study, the WRF v3.5 with chemistry (WRF-Chem) is applied to the area of Poland, for a period of 3-20 July 2006, when high concentrations of ground level ozone were observed. The meteorological and chemistry simulations were initiated with ERA-Interim reanalysis and TNO MACC II emissions database, respectively. The model physical parameterization includes RRTM shortwave radiation, Kain-Fritsch cumulus scheme, Purdue Lin microphysics and ACM2 PBL, established previously as the optimal configuration. Chemical mechanism used for the study was RADM2 with MADE/SORGAM aerosols. Simulations were performed for three one-way nested domains covering Europe (36 km x 36 km), Central Europe (12 km x 12 km) and Poland (4 km x 4 km). The results from the innermost domain were analyzed and compared to measurements of ozone concentration at three stations in different environments. The results show underestimation of observed values and daily amplitude of ozone concentrations.

Constructing a 7-day Ahead Forecast Model for Grass Pollen at North London, United Kingdom

A number of media outlets now issue medium-range (~7 day) weather forecasts on a regular basis. It is therefore logical that aerobiologists should attempt to produce medium-range forecasts for allergenic pollen that cover the same time period as the weather forecasts. The objective of this study is to construct a medium-range (< 7 day) forecast model for grass pollen at north London. The forecast models were produced using regression analysis based on grass pollen and meteorological data from 1990-1999 and tested on data from 2000 and 2002. The modelling process was improved by dividing the grass pollen season into three periods; the pre-peak, peak and post peak periods of grass pollen release. The forecast consisted of five regression models. Two simple linear regression models predicting the start and end date of the peak period, and three multiple regression models forecasting daily average grass pollen counts in the pre-peak, peak and post-peak periods. Overall the forecast models achieved 62% accuracy in 2000 and 47% in 2002, reflecting the fact that the 2002 grass pollen season was of a higher magnitude than any of the other seasons included in the analysis. This study has the potential to make a notable contribution to the field of aerobiology. Winter averages of the North Atlantic Oscillation were used to predict certain characteristics of the grass pollen season, which presents an important advance in aerobiological work. The ability to predict allergenic pollen counts for a period between five and seven days will benefit allergy sufferers. Furthermore, medium-range forecasts for allergenic pollen will be of assistance to the medical profession, including allergists planning treatment and physicians scheduling clinical trials.

Application of WRF-Chem to Forecasting PM10 Concentration over Poland

The meteorological and chemical transport model WRF-Chem was implemented to forecast PM10 concentrations over Poland. WRF-Chem version 3.5 was configured with three one way nested domains using the GFS meteorological data and the TNO MACC II emissions. Forecasts, with 48h lead time, were run for a winter and summer period 2014. WRF-Chem in general captures the variability in observed PM10 concentrations, but underestimates some peak concentrations during winter-time. The peaks coincide with either stable atmospheric condition during nighttime in the lower part of the planetary boundary layer or on days with very low surface temperatures. Such episodes lead to increased combustion in residential heating, where hard coal is the main fuel in Poland. This suggests that a key to improvement in the model performance for the peak concentrations is to focus on the simulation of PBL processes and the distribution of emissions with high resolution in WRF-Chem.

Application of WRF-Chem to Forecasting PM10 Concentration Over Poland

The meteorological and chemical transport model WRF-Chem was implemented to forecast PM10 concentrations over Poland. WRF-Chem version 3.5 was configured with three one-way nested domains using the GFS meteorological data and the TNO MACC II emissions. The 48 hour forecasts were run for each day of the winter and summer period of 2014 and there is only a small decrease in model performance for winter with respect to forecast lead time. The model in general captures the variability in observed PM10 concentrations for most of the stations. However, for some locations and specific episodes, the model performance is poor and the results cannot yet be used by official authorities. We argue that a higher resolution sector-based emission data will be helpful for this analysis in connection with a focus on planetary boundary layer processes in WRF-Chem and their impact on the initial distribution of emissions on both time and space.