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).

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.

Acute Asthma Epidemics, Weather and Pollen in England, 1987-1994

Recent epidemics of acute asthma have caused speculation that, if their causes were known, early warnings might be feasible. In particular, some epidemics seemed to be associated with thunderstorms. We wondered what risk factors predicting epidemics could be identified. Daily asthma admissions counts during 1987-1994, for two age groups (0-14 yrs and > or = 15 yrs), were measured using the Hospital Episodes System (HES). Epidemics were defined as combinations of date, age group and English Regional Health Authority (RHA) with exceptionally high asthma admission counts compared to the predictions of a log-linear autoregression model. They were compared with control days 1 week before and afterwards, regarding seven meteorological variables and 5 day average pollen counts for four species. Fifty six asthma epidemics were identified. The mean density of sferics (lightning flashes), temperature and rainfall on epidemic days were greater than those on control days. High sferics densities were overrepresented in epidemics. Simultaneously high sferics and grass pollen further increased the probability of an epidemic, but only to 15% (95% confidence interval 2-45%). Two thirds of epidemics were not preceded by thunderstorms. Thunderstorms and high grass pollen levels precede asthma epidemics more often than expected by chance. However, most epidemics are not associated with thunderstorms or unusual weather conditions, and most thunderstorms, even following high grass pollen levels, do not precede epidemics. An early warning system based on the indicators examined here would, therefore, detect few epidemics and generate an unacceptably high rate of false alarms.

Long-term and Short-term Forecast Models for Poaceae (Grass) Pollen in Poznan, Poland, Constructed Using Regression Analysis

Airborne concentrations of Poaceae pollen have been monitored in Poznań for more than ten years and the length of the dataset is now considered sufficient for statistical analysis. The objective of this paper is to produce long-range forecasts that predict certain characteristics of the grass pollen season (such as the start, peak and end dates of the grass pollen season) as well as short-term forecasts that predict daily variations in grass pollen counts for the next day or next few days throughout the main grass pollen season. The method of forecasting was regression analysis. Correlation analysis was used to examine the relationship between grass pollen counts and the factors that affect its production, release and dispersal. The models were constructed with data from 1994-2004 and tested on data from 2005 and 2006. The forecast models predicted the start of the grass pollen season to within 2 days and achieved 61% and 70% accuracy on a scale of 1-4 when forecasting variations in daily grass pollen counts in 2005 and 2006 respectively. This study has emphasised how important the weather during the few weeks or months preceding pollination is to grass pollen production, and draws attention to the importance of considering large-scale patterns of climate variability (indices of the North Atlantic Oscillation) when constructing forecast models for allergenic pollen.

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 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.

Potential Impact of Climate Change on Fungal Distributions: Analysis of 2 Years of Contrasting Weather in the UK

The impact of climate change on fungal growth and spore production is less well documented than for allergenic pollen grains, although similar implications for respiratory tract diseases in humans occur. Fungal spores are commonly described as either “dry” or “wet” according to the type of weather associated with their occurrence in the air. This study examined the distribution of selected fungal spores (Alternaria spp., Cladosporium spp., Didymella spp., Epicoccum spp., Leptosphaeria spp. and rusts) occurring in the West Midlands of UK during 2 years of contrasting weather. Spore specimens were collected using a 7-day volumetric air sampler and then analysed with the aid of light microscopy. Distributions of spores were then studied using normality tests and Mann–Whitney U test, while relationships with meteorological parameters were investigated using Spearman’s rank test and angular-linear correlation for wind direction analysis. Our results showed that so-called wet spores were more sensitive to the weather changes showing statistically significant differences between the 2 years of study, in contrast to “dry” spores. We predict that in following years we will observe accelerated levels in allergenic fungal spore production as well as changes in species diversity. This study could be a starting point to revise the grouping system of fungal spores as either “dry” or “wet” types and their response to climate change

Investigating and Predicting Atmospheric Concentrations of Allergenic Fungal Spores (Alternaria, Cladosporium, Didymella and Ganoderma)

Air quality is an increasing concern of the European Union, local authorities, scientists and most of all inhabitants that become more aware of the quality of the surrounding environment. Bioaerosols may be consisted of various elements, and the most important are pollen grains, fungal spores, bacteria, viruses. More than 100 genera of fungal spores have been identified as potential allergens that cause immunological response in susceptible individuals. Alternaria and Cladosporium have been recognised as the most important fungal species responsible for respiratory tract diseases, such as asthma, eczema, rhinitis and chronic sinusitis. While a lot of attention has been given to these fungal species, a limited number of studies can be found on Didymella and Ganoderma, although their allergenic properties were proved clinically. Monitoring of allergenic fungal spore concentration in the air is therefore very important, and in particular at densely populated areas like Worcester, UK. In this thesis a five year spore data set was presented, which was collected using a 7-day volumetric spore trap, analysed with the aid of light microscopy, statistical tests and geographic information system techniques. Although Kruskal-Wallis test detected statistically significant differences between annual concentrations of all examined fungal spore types, specific patterns in their distribution were also found. Alternaria spores were present in the air between mid-May/mid-June until September-October with peak occurring in August. Cladosporium sporulated between mid-May and October, with maximum concentration recorded in July. Didymella spores were seen from June/July up to September, while peaks were found in August. Ganoderma produced spores for 6 months (May-October), and maximum concentration could be found in September. With respect to diurnal fluctuations, Alternaria peaked between 22:00h and 23:00h, Cladosporium 13:00-15:00h, Didymella 04:00-05:00h and 22:00h-23:00h and Ganoderma from 03:00h to 06:00h. Spatial analysis showed that sources of all fungal species were located in England, and there was no evidence for a long distance transport from the continent. The maximum concentration of spores was found several hours delayed in comparison to the approximate time of the spore release from the crops. This was in agreement with diurnal profiles of the spore concentration recorded in Worcester, UK. Spores of Alternaria, Didymella and Ganoderma revealed a regional origin, in contrast to Cladosporium, which sources were situated locally. Hence, the weather conditions registered locally did not exhibit strong statistically significant correlations with fungal spore concentrations. This has had also an impact on the performance of the forecasting models. The best model was obtained for Cladosporium with 66% of the accuracy.