How much gas can we get from grass?

Grass biomethane has been shown to be a sustainable gaseous transport biofuel, with a good energy balance, and significant potential for economic viability. Of issue for the designer is the variation in characteristics of the grass depending on location of source, time of cut and species. Further confusion arises from the biomethane potential tests (BMP) which have a tendency to give varying results. This paper has dual ambitions. One of these is to highlight the various results for biomethane potential that may be obtained from the same grass silage. The results indicated that methane potential from the same grass silage varied from 350 to 493 L CH4 kg−1 VS added for three different BMP procedures. The second ambition is to attempt to compare two distinct digestion systems again using the same grass: a two stage continuously stirred tank reactor (CSTR); and a sequentially fed leach bed reactor connected to an upflow anaerobic sludge blanket (SLBR–UASB). The two engineered systems were designed, fabricated, commissioned and operated at small pilot scale until stable optimal operating conditions were reached. The CSTR system achieved 451 L CH4 kg−1 VS added over a 50 day retention period. The SLBR–UASB achieved 341 L CH4 kg−1 VS added at a 30 day retention time.

Soil Moisture Deficit as a predictor of the trend in soil water status of grass fields

Nutrient loss from agricultural land following organic fertilizer spreading can lead to eutrophication and poor water quality. The risk of pollution is partly related to the soil water status during and after spreading. In response to these issues, a decision support system (DSS) for nutrient management has been developed to predict when soil and weather conditions are suitable for slurry spreading. At the core of the DSS, the Hybrid Soil Moisture Deficit (HSMD) model estimates soil water status relative to field capacity (FC) for three soil classes (well, moderately and poorly drained) and has potential to predict the occurrence of a transport vector when the soil is wetter than FC. Three years of field observation of volumetric water content was used to validate HSMD model predictions of water status and to ensure correct use and interpretation of the drainage classes. Point HSMD model predictions were validated with respect to the temporal and spatial variations in volumetric water content and soil strength properties. It was found that the HSMD model predictions were well related to topsoil water content through time, but a new class intermediate between poor and moderate, perhaps ‘imperfectly drained’, was needed. With correct allocations of a field into a drainage class, the HSMD model predictions reflect field scale trends in water status and therefore the model is suitable for use at the core of a DSS.

Trait directed de novo population transcriptome dissects genetic regulation of a balanced polymorphism in phosphorus nutrition/arsenate tolerance in a wild grass Holcus lanatus

The aim of this study was to characterize the transcriptome of a balanced polymorphism, under the regulation of a single gene, for phosphate fertilizer responsiveness/arsenate toler- ance in wild grass Holcus lanatus genotypes screened from the same habitat.

De novo transcriptome sequencing, RNAseq (RNA sequencing) and single nucleotide poly- morphism (SNP) calling were conducted on RNA extracted from H.lanatus. Roche 454 sequencing data were assembled into c. 22 000 isotigs, and paired-end Illumina reads for phosphorus-starved (P) and phosphorus-treated (P+) genovars of tolerant (T) and nontoler- ant (N) phenotypes were mapped to this reference transcriptome.

Heatmaps of the gene expression data showed strong clustering of each P+/P treated genovar, as well as clustering by N/T phenotype. Statistical analysis identified 87 isotigs to be significantly differentially expressed between N and T phenotypes and 258 between P+ and P treated plants. SNPs and transcript expression that systematically differed between N and T phenotypes had regulatory function, namely proteases, kinases and ribonuclear RNA- binding protein and transposable elements.

A single gene for arsenate tolerance led to distinct phenotype transcriptomes and SNP pro- files, with large differences in upstream post-translational and post-transcriptional regulatory genes rather than in genes directly involved in P nutrition transport and metabolism per se.

A 30-Day-Ahead Forecast Model for Grass Pollen in North London, United Kingdom

A 30-day ahead forecast method has been developed for grass pollen at north London. The total period of the grass pollen season is covered by eight multiple regression models, each covering a 10-day period running consecutively from 21st May to 8th August. This means that three models were used for each 30-day forecast. The forecast models were produced using grass pollen and environmental data from 1961-1999 and tested on data from 2000 and 2002. Model accuracy was judged in two ways: the number of times the forecast model was able to successfully predict the severity (relative to the 1961-1999 dataset as a whole) of grass pollen counts in each of the eight forecast periods on a scale of one to four; and the number of times the forecast model was able to predict whether grass pollen counts were higher or lower than the mean. The models achieved 62.5% accuracy in both assessment years when predicting the relative severity of grass pollen counts on a scale of one to four, which equates to six of the eight 10-day periods being forecast correctly. The models attained 87.5% and 100% accuracy in 2000 and 2002 respectively when predicting whether grass pollen counts would be higher or lower than the mean. Attempting to predict pollen counts during distinct 10-day periods throughout the grass pollen season is a novel approach. The models also employed original methodology in the use of winter averages of the North Atlantic Oscillation to forecast 10-day means of allergenic pollen counts.

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.

Examining High Magnitude Grass Pollen Episodes at Worcester, United Kingdom, Using Back-Trajectory Analysis

Trajectory analysis is a valuable tool that has been used before in aerobiological studies, to investigate the movement of airborne pollen. This study has employed back-trajectories to examine the four highest grass pollen episodes at Worcester, during the 2001 grass pollen season. The results have shown that the highest grass pollen counts of the 2001 season were reached when air masses arrived from a westerly direction. Back-trajectory analysis has a limited value to forecasters because the method is retrospective and cannot be employed directly for forecasting. However, when used in conjunction with meteorological data this technique can be used to examine high magnitude events in order to identify conditions that lead to high pollen counts.

Characteristics of Grass Pollen Seasons in Areas of Southern Spain and the United Kingdom

Spatial and temporal variations in daily grass pollen counts and weather variables are described for two regions with different bio-geographical and climatic regimes, southern Spain and the United Kingdom. Daily average grass pollen counts are considered from six pollen-monitoring sites, three in southern Spain (Ciudad Real, Córdoba and Priego) and three in the United Kingdom (Edinburgh, Worcester and Cambridge). Analysis shows that rainfall and maximum temperatures are important factors controlling the magnitude of the grass pollen season in both southern Spain and the United Kingdom, and that the strength and direction of the influence exerted by these variables varies with geographical location and time.

Temporal and Georaphical Variations in Grass Pollen Seasons in Areas of Western Europe: an Analysis of Season dates at Sites of the European Pollen Information System.

Geographical and temporal variations in the start dates of grass pollen seasons are described for selected sites of the European Pollen Information Service. Daily average grass pollen counts are derived from Network sites in Finland, the Netherlands, Denmark, United Kingdom, Austria, Italy and Spain, giving a broad longitudinal transect over Western Europe. The study is part of a larger project that also examines annual and regional variations in the severity, timing of the peak and duration of the grass pollen seasons. For several sites, data are available for over twenty years enabling long term trends to be discerned. The analyses show notable contrasts in the progression of the seasons annually with differing lag times occurring between southern and northern sites in various years depending on the weather conditions. The patterns identified provide some insight into geographical differences and temporal trends in the incidence of pollinosis. The paper discusses the main difficulties involved in this type of analysis and notes possibilities for using data from the European Pollen Information service to construct pan European predictive models for pollen seasons.

Influence of the North Atlantic Oscillation on Grass Pollen Counts in Europe

Relationships between temporal variations in the North Atlantic Oscillation (NAO) and grass pollen counts at 13 sites in Europe, ranging from Córdoba in the South-West and Turku in the North-East, were studied in order to determine spatial differences in the amount of influence exerted by the NAO on the timing and magnitude of grass pollen seasons. There were a number of significant (p<0.05) relationships between the NAO and start dates of the grass pollen season at the 13 pollen-monitoring sites. The strongest associations were generally recorded near to the Atlantic coast. Several significant correlations also existed between winter averages of the NAO and grass pollen season severity. Traditional methods for predicting the start or magnitude of grass pollen seasons have centred on the use of local meteorological observations, but this study has shown the importance of considering large-scale patterns of climate variability like the NAO.

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.

Do Urban Canyons Influence Street Level Grass Pollen Concentrations

In epidemiological studies, outdoor exposure to pollen is typically estimated using rooftop monitoring station data, whilst exposure overwhelmingly occurs at street level. In this study the relationship between street level and roof level grass pollen concentrations was investigated for city centre street canyon environments in Aarhus, Denmark, and London, UK, during the grass pollen seasons of 2010 and 2011 respectively. For the period mid-day to late evening, street level concentrations in both cities tended to be lower than roof-level concentrations, though this difference was found to be statistically significant only in London. The ratio of street/roof level concentrations was compared with temperature, relative humidity, wind speed and direction, and solar radiation. Results indicated that the concentration ratio responds to wind direction with respect to relative canyon orientation and local source distribution. In the London study, an increase in relative humidity was linked to a significant decrease in street/roof level concentration ratio, and a possible causative mechanism involving moisture mediated pollen grain buoyancy is proposed. Relationships with the other weather variables were not found to be significant in either location. These results suggest a tendency for monitoring station data to overestimate exposure in the canyon environment.

Personal Exposure to Grass Pollen: Relating Inhaled Dose to Background Concentration

Background Very few studies on human exposure to allergenic pollen have been conducted using direct methods, with background concentrations measured at city center monitoring stations typically taken as a proxy for exposure despite the inhomogeneous nature of atmospheric pollen concentrations. A 2003 World Health Organization report highlighted the need for an improved understanding of the relation between monitoring station data and actual exposure. Objective To investigate the relation between grass pollen dose and background concentrations measured at a monitoring station, to assess the fidelity of monitoring station data as a qualitative proxy for dose, and to evaluate the ratio of dose rate to background concentration. Methods Grass pollen dose data were collected in Aarhus, Denmark, in an area where grass pollen sources were prevalent, using Nasal Air Samplers. Sample collection lasted for approximately 25 to 30 minutes and was performed at 2-hour intervals from noon to midevening under moderate exercise by 2 individuals. Results A median ratio of dose rate to background concentration of 0.018 was recorded, with higher ratio values frequently occurring at 12 to 2 pm, the time of day when grass species likely to be present in the area are expected to flower. From 4 to 8 pm, dose rate and background concentration data were found to be strongly and significantly correlated (rs = 0.81). Averaged dose rate and background concentration data showed opposing temporal trends. Conclusion Where local emissions are not a factor, background concentration data constitute a good quantitative proxy for inhaled dose. The present ratio of dose rate to background concentration may aid the study of dose–response relations.