Seasonal trends in concentrations and fluxes of volatile organic compounds above central London

Concentrations and fluxes of seven volatile organic compounds (VOCs) were measured between August and December 2012 at a roof-top site in central London as part of the ClearfLo project (Clean Air for London). VOC concentrations were quantified using a proton transfer reaction-mass spectrometer and fluxes were calculated using a virtual disjunct eddy covariance technique. The median VOC fluxes, including aromatics, oxygenated compounds and isoprene, ranged from 0.07 to 0.33 mg m−2 h−1 and mixing ratios were 7.27 ppb for methanol (m / z 33) and <1 ppb for the remaining compounds. Strong relationships were observed between most VOC fluxes and concentrations with traffic density, but also with photosynthetically active radiation (PAR) and temperature for the oxygenated compounds and isoprene. An estimated 50–90 % of aromatic fluxes were attributable to traffic activity, which showed little seasonal variation, suggesting boundary layer effects or possibly advected pollution may be the primary causes of increased concentrations of aromatics in winter. PAR and temperature-dependent processes accounted for the majority of isoprene, methanol and acetaldehyde fluxes and concentrations in August and September, when fluxes and concentrations were largest. Modelled biogenic isoprene fluxes using the G95 algorithm agreed well with measured fluxes in August and September, due to urban vegetation. Comparisons of estimated annual benzene emissions from the London and National Atmospheric Emissions Inventory agreed well with measured benzene fluxes. Flux footprint analysis indicated emission sources were localized and that boundary layer dynamics and source strengths were responsible for temporal and spatial VOC flux and concentration variability during the measurement period

Canopy-scale flux measurements and bottom-up emission estimates of volatile organic compounds from a mixed oak and hornbeam forest in northern Italy

This paper reports the fluxes and mixing ratios of biogenically emitted volatile organic compounds (BVOCs) 4aEuro-m above a mixed oak and hornbeam forest in northern Italy. Fluxes of methanol, acetaldehyde, isoprene, methyl vinyl ketoneaEuro-+aEuro-methacrolein, methyl ethyl ketone and monoterpenes were obtained using both a proton-transfer-reaction mass spectrometer (PTR-MS) and a proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) together with the methods of virtual disjunct eddy covariance (using PTR-MS) and eddy covariance (using PTR-ToF-MS). Isoprene was the dominant emitted compound with a mean daytime flux of 1.9aEuro-mgaEuro-m(-2)aEuro-h(-1). Mixing ratios, recorded 4aEuro-m above the canopy, were dominated by methanol with a mean value of 6.2aEuro-ppbv over the 28-day measurement period. Comparison of isoprene fluxes calculated using the PTR-MS and PTR-ToF-MS showed very good agreement while comparison of the monoterpene fluxes suggested a slight over estimation of the flux by the PTR-MS. A basal isoprene emission rate for the forest of 1.7aEuro-mgaEuro-m(-2)aEuro-h(-1) was calculated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) isoprene emission algorithms (Guenther et al., 2006). A detailed tree-species distribution map for the site enabled the leaf-level emission of isoprene and monoterpenes recorded using gas-chromatography mass spectrometry (GC-MS) to be scaled up to produce a bottom-up canopy-scale flux. This was compared with the top-down canopy-scale flux obtained by measurements. For monoterpenes, the two estimates were closely correlated and this correlation improved when the plant-species composition in the individual flux footprint was taken into account. However, the bottom-up approach significantly underestimated the isoprene flux, compared with the top-down measurements, suggesting that the leaf-level measurements were not representative of actual emission rates.Peer reviewe

Comparison of embedded and added motor imagery training in patients after stroke: Study protocol of a randomised controlled pilot trial using a mixed methods approach

Copyright @ 2009 Schuster et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Two different approaches have been adopted when applying motor imagery (MI) to stroke patients. MI can be conducted either added to conventional physiotherapy or integrated within therapy sessions. The proposed study aims to compare the efficacy of embedded MI to an added MI intervention. Evidence from pilot studies reported in the literature suggests that both approaches can improve performance of a complex motor skill involving whole body movements, however, it remains to be demonstrated, which is the more effective one.Methods/Design: A single blinded, randomised controlled trial (RCT) with a pre-post intervention design will be carried out. The study design includes two experimental groups and a control group (CG). Both experimental groups (EG1, EG2) will receive physical practice of a clinical relevant motor task ('Going down, laying on the floor, and getting up again') over a two week intervention period: EG1 with embedded MI training, EG2 with MI training added after physiotherapy. The CG will receive standard physiotherapy intervention and an additional control intervention not related to MI.The primary study outcome is the time difference to perform the task from pre to post-intervention. Secondary outcomes include level of help needed, stages of motor task completion, degree of motor impairment, balance ability, fear of falling measure, motivation score, and motor imagery ability score. Four data collection points are proposed: twice during baseline phase, once following the intervention period, and once after a two week follow up. A nested qualitative part should add an important insight into patients' experience and attitudes towards MI. Semi-structured interviews of six to ten patients, who participate in the RCT, will be conducted to investigate patients' previous experience with MI and their expectations towards the MI intervention in the study. Patients will be interviewed prior and after the intervention period.Discussion: Results will determine whether embedded MI is superior to added MI. Findings of the semi-structured interviews will help to integrate patient's expectations of MI interventions in the design of research studies to improve practical applicability using MI as an adjunct therapy technique

Seasonality of isoprene emissions and oxidation products above the remote Amazon

The Amazon rainforest is the largest source of isoprene emissions to the atmosphere globally. Under low nitric oxide (NO) conditions (i.e. at NO mixing ratios less than about 40 pptv), isoprene reacts rapidly with hydroxyl (OH) to form isoprene-derived peroxy radicals (ISOPOO), which subsequently react with the hydroperoxyl radical (HO2) to form isoprene epoxydiols (IEPOX). IEPOX compounds are efficient precursors to the formation of secondary organic aerosols (SOA). Natural isoprene emissions, therefore, have the potential to influence cloudiness, rainfall, radiation balance and climate. Here, we present the first seasonal analysis of isoprene emissions and concentrations above the Amazon based on eddy covariance flux measurements made at a remote forest location. We reveal the forest to maintain a constant emission potential of isoprene throughout the year (6.9 mg m-2 h-1). The emission potential of isoprene is calculated by normalising the measured fluxes to a set of standard conditions (303 K and 1500 mmol m-2 s-1). During the wet season a factor of two reduction in absolute emissions was observed but this is explained entirely on the basis of meteorology and leaf area index, not by a change in isoprene emissions potential. Using an innovative analysis of the isoprene fluxes, in combination with measurements of its oxidation products and detailed chemical box-modelling, we explore whether concentrations of IEPOX follow the same seasonal cycle as the isoprene precursor. Our analysis implies that during the dry season (Sep–Jan) air pollution from regional biomass burning provides a modest increase in NO concentrations (indirectly inferred from a combination of other anthropogenic tracer measurements and box-modelling) which creates a competing oxidation pathway for ISOPOO; rather than forming IEPOX, alternative products are formed with less propensity to produce aerosol. This competition decreases IEPOX formation rates by a factor of two in the dry season compared with a scenario with no anthropogenic NO pollution, and by 30% throughout the year. The abundance of biogenic SOA precursors in the Amazon appears not to be dictated by the seasonality of natural isoprene emissions as previously thought, but is instead driven by regional anthropogenic pollution which modifies the atmospheric chemistry of isoprene

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

The Physical Processes of CME/ICME Evolution

As observed in Thomson-scattered white light, coronal mass ejections (CMEs) are manifest as large-scale expulsions of plasma magnetically driven from the corona in the most energetic eruptions from the Sun. It remains a tantalizing mystery as to how these erupting magnetic fields evolve to form the complex structures we observe in the solar wind at Earth. Here, we strive to provide a fresh perspective on the post-eruption and interplanetary evolution of CMEs, focusing on the physical processes that define the many complex interactions of the ejected plasma with its surroundings as it departs the corona and propagates through the heliosphere. We summarize the ways CMEs and their interplanetary CMEs (ICMEs) are rotated, reconfigured, deformed, deflected, decelerated and disguised during their journey through the solar wind. This study then leads to consideration of how structures originating in coronal eruptions can be connected to their far removed interplanetary counterparts. Given that ICMEs are the drivers of most geomagnetic storms (and the sole driver of extreme storms), this work provides a guide to the processes that must be considered in making space weather forecasts from remote observations of the corona.Peer reviewe

Genetic tool development in marine protists: emerging model organisms for experimental cell biology

Abstract: Diverse microbial ecosystems underpin life in the sea. Among these microbes are many unicellular eukaryotes that span the diversity of the eukaryotic tree of life. However, genetic tractability has been limited to a few species, which do not represent eukaryotic diversity or environmentally relevant taxa. Here, we report on the development of genetic tools in a range of protists primarily from marine environments. We present evidence for foreign DNA delivery and expression in 13 species never before transformed and for advancement of tools for eight other species, as well as potential reasons for why transformation of yet another 17 species tested was not achieved. Our resource in genetic manipulation will provide insights into the ancestral eukaryotic lifeforms, general eukaryote cell biology, protein diversification and the evolution of cellular pathways