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 (150model, which was accurate (correlation between observed and predicted values varied from rs=0.57 to rs=0.68).

A Computer Model of Drafting Effects on Collective Behavior in Elite 10,000 m Runners

Purpose Drafting in cycling influences collective behaviour of pelotons. Whilst evidence for collective behaviour in competitive running events exists, it is not clear if this results from energetic savings conferred by drafting. This study modelled the effects of drafting on behavior in elite 10,000 m runners. Methods Using performance data from a men’s elite 10,000 m track running event, computer simulations were constructed using Netlogo 5.1 to test the effects of three different drafting quantities on collective behaviour: no drafting, drafting to 3m behind with up to ~8% energy savings (a realistic running draft); and drafting up to 3m behind with up to 38% energy savings (a realistic cycling draft). Three measures of collective behaviour were analysed in each condition; mean speed, mean group stretch (distance between first and last placed runner), and Runner Convergence Ratio (RCR) which represents the degree of drafting benefit obtained by the follower in a pair of coupled runners. Results Mean speeds were 6.32±0.28m.s-1, 5.57±0.18 m.s-1, and 5.51±0.13 m.s-1 in the cycling draft, runner draft, and no draft conditions respectively (all P<0.001). RCR was lower in the cycling draft condition, but did not differ between the other two. Mean stretch did not differ between conditions. Conclusions Collective behaviours observed in running events cannot be fully explained through energetic savings conferred by realistic drafting benefits. They may therefore result from other, possibly psychological, processes. The benefits or otherwise of engaging in such behavior are, as yet, unclear.

A Simple Model Describes Development of Early Peaks in Oomycete Zoospore Inoculum Detected in Southern UK Outdoors Horticultural Reservoirs

The numbers of water-borne oomycete propagules in outdoor reservoirs used in horticultural nurseries within the UK are investigated in this study. Water samples were recovered from 11 different horticultural nurseries in the southern UK during Jan-May in two ‘cool’ years (2010.and 2013; winter temperatures 2.0 and 0.4oC below UK Met Office 30 year winter average respectively) and two ‘warm’ years (2008 and 2012; winter temperatures 1.2 and 0.9oC above UK Met Office 30 year winter average respectively). Samples were analysed for total number of oomycete colony forming units (CFU), predominantly members of the families Saprolegniaceae and Pythiaceae, and these were combined to give monthly mean counts. The numbers of CFU were investigated with respect to prevailing climate in the region: mean monthly air temperatures calculated by using daily observations from the nearest climatological station. The investigations show that the number of CFU during spring can be explained by a linear first-order equation and a statistically significant r2 value of 0.66 with the simple relationship: [CFU] = a(T-Tb )-b, where a is the rate of inoculum development with temperature T, and b is the baseload population at temperatures below Tb. Despite the majority of oomycete CFU detected being non-phytopathogenic members of the Saprolegniaceae, total oomycete CFU counts are still of considerable value as indicators of irrigation water treatment efficacy and cleanliness of storage tanks. The presence/absence of Pythium spp. was also determined for all samples tested, and Pythium CFU were found to be present in the majority, the exceptions all being particularly cold months (January and February 2010 and January 2008). A simple scenario study (+2 deg C) suggests that abundance of water-borne oomycetes during spring could be affected by increased temperatures due to climate change.

A Parameter Estimation and Identifiability Analysis Methodology Applied to a Street Canyon Air Pollution Model

Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street Pollution Model (OSPMr). To assess the predictive validity of the model, the data is split into an estimation and a prediction data set using two data splitting approaches and data preparation techniques (clustering and outlier detection) are analysed. The sensitivity analysis, being part of the identifiability analysis, showed that some model parameters were significantly more sensitive than others. The application of the determined optimal parameter values was shown to succesfully equilibrate the model biases among the individual streets and species. It was as well shown that the frequentist approach applied for the uncertainty calculations underestimated the parameter uncertainties. The model parameter uncertainty was qualitatively assessed to be significant, and reduction strategies were identified.