Naturally Acquired Bovine Besnoitiosis

The pathogenesis of bovine besnoitiosis, a disease of increasing concern within Europe, is still incompletely understood. In this study, disease progression after natural infection with the causative apicomplexan Besnoitia besnoiti was monitored in histological skin sections of 5 individual female cattle over time. High-frequency skin sampling of 2 cattle with mild and 2 with severe acute, subacute, and chronic besnoitiosis, as well as from 1 animal during subclinical disease, enabled documentation from the beginning of the disease. Skin sections were stained with hematoxylin and eosin and Giemsa, periodic acid–Schiff reaction, and anti-Besnoitia immunohistochemistry. In all 4 clinically affected animals, tachyzoite-like endozoites could be detected for the first time by immunohistochemistry, and tissue cyst evolution was monitored. Besnoitiosis-associated lesions were not detected in the animal showing the subclinical course. Because of the inconsistency of the nomenclature of Besnoitia tissue cyst layers in the literature, a new nomenclature for B. besnoiti cyst wall layers is proposed: tissue cysts consist of a hypertrophied host cell with enlarged nuclei, an intracytoplasmic parasitophorous vacuole with bradyzoites, a sometimes vacuolated inner cyst wall, and an outer cyst wall in more developed cysts. Inner and outer cyst walls can be readily distinguished by using special stains. In 1 animal, extracystic B. besnoiti zoites were immunohistochemically detected during the chronic stage. At necropsy, the 2 severely affected cows displayed large numbers of B. besnoiti cysts in a variety of tissues, including the corium of the claws, contributing mainly to the development of chronic laminitis in these 2 cases

Organic nitrate and secondary organic aerosol yield from NO3 oxidation of ß-pinene evaluated using a gas-phase kinetics/aerosol partitioning model

The yields of organic nitrates and of secondary organic aerosol (SOA) particle formation were measured for the reaction NO3+beta-pinene under dry and humid conditions in the atmosphere simulation chamber SAPHIR at Research Center Julich. These experiments were conducted at low concentrations of NO3 (NO3+N2O5 < 10 ppb) and beta-pinene (peak similar to 15 ppb), with no seed aerosol. SOA formation was observed to be prompt and substantial (similar to 50% mass yield under both dry conditions and at 60% RH), and highly correlated with organic nitrate formation. The observed gas/aerosol partitioning of organic nitrates can be simulated using an absorptive partitioning model to derive an estimated vapor pressure of the condensing nitrate species of p(vap) similar to 5x10(-6) Torr (6.67x10(-4) Pa), which constrains speculation about the oxidation mechanism and chemical identity of the organic nitrate. Once formed the SOA in this system continues to evolve, resulting in measurable aerosol volume decrease with time. The observations of high aerosol yield from NOx-dependent oxidation of monoterpenes provide an example of a significant anthropogenic source of SOA from biogenic hydrocarbon precursors. Estimates of the NO3+beta-pinene SOA source strength for California and the globe indicate that NO3 reactions with monoterpenes are likely an important source (0.5-8% of the global total) of organic aerosol on regional and global scales

The formation, properties and impact of secondary organic aerosol: current and emerging issues

Secondary organic aerosol (SOA) accounts for a significant fraction of ambient tropospheric aerosol and a detailed knowledge of the formation, properties and transformation of SOA is therefore required to evaluate its impact on atmospheric processes, climate and human health. The chemical and physical processes associated with SOA formation are complex and varied, and, despite considerable progress in recent years, a quantitative and predictive understanding of SOA formation does not exist and therefore represents a major research challenge in atmospheric science. This review begins with an update on the current state of knowledge on the global SOA budget and is followed by an overview of the atmospheric degradation mechanisms for SOA precursors, gas-particle partitioning theory and the analytical techniques used to determine the chemical composition of SOA. A survey of recent laboratory, field and modeling studies is also presented. The following topical and emerging issues are highlighted and discussed in detail: molecular characterization of biogenic SOA constituents, condensed phase reactions and oligomerization, the interaction of atmospheric organic components with sulfuric acid, the chemical and photochemical processing of organics in the atmospheric aqueous phase, aerosol formation from real plant emissions, interaction of atmospheric organic components with water, thermodynamics and mixtures in atmospheric models. Finally, the major challenges ahead in laboratory, field and modeling studies of SOA are discussed and recommendations for future research directions are proposed

Enhancement of the aerosol direct radiative effect by semi-volatile aerosol components: airborne measurements in North-Western Europe

A case study of atmospheric aerosol measurements exploring the impact of the vertical distribution of aerosol chemical composition upon the radiative budget in North-Western Europe is presented. Sub-micron aerosol chemical composition was measured by an Aerodyne Aerosol Mass Spectrometer (AMS) on both an airborne platform and a ground-based site at Cabauw in the Netherlands. The examined period in May 2008 was characterised by enhanced pollution loadings in North-Western Europe and was dominated by ammonium nitrate and Organic Matter (OM). Both ammonium nitrate and OM were observed to increase with altitude in the atmospheric boundary layer. This is primarily attributed to partitioning of semi-volatile gas phase species to the particle phase at reduced temperature and enhanced relative humidity. Increased ammonium nitrate concentrations in particular were found to strongly increase the ambient scattering potential of the aerosol burden, which was a consequence of the large amount of associated water as well as the enhanced mass. During particularly polluted conditions, increases in aerosol optical depth of 50–100% were estimated to occur due to the observed increase in secondary aerosol mass and associated water uptake. Furthermore, the single scattering albedo was also shown to increase with height in the boundary layer. These enhancements combined to increase the negative direct aerosol radiative forcing by close to a factor of two at the median percentile level. Such increases have major ramifications for regional climate predictions as semi-volatile components are often not included in aerosol models. The results presented here provide an ideal opportunity to test regional and global representations of both the aerosol vertical distribution and subsequent impacts in North-Western Europe. North-Western Europe can be viewed as an analogue for the possible future air quality over other polluted regions of the Northern Hemisphere, where substantial reductions in sulphur dioxide emissions have yet to occur. Anticipated reductions in sulphur dioxide in polluted regions will result in an increase in the availability of ammonia to form ammonium nitrate as opposed to ammonium sulphate. This will be most important where intensive agricultural practises occur. Our observations over North-Western Europe, a region where sulphur dioxide emissions have already been reduced, indicate that failure to include the semi-volatile behaviour of ammonium nitrate will result in significant errors in predicted aerosol direct radiative forcing. Such errors will be particularly significant on regional scales

Deep Learning Based Prediction of Sun-Induced Fluorescence from Hyplant Imagery

The retrieval of sun-induced fluorescence (SIF) from hyperspectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method combining semi-supervised deep learning with an existing spectral fitting method. A validation study with in-situ SIF measurements shows high sensitivity of the deep learning method to SIF changes even though systematic shifts deteriorate its absolute prediction accuracy. A detailed analysis of diurnal SIF dynamics and SIF prediction in topographically variable terrain highlights the benefits of this deep learning approach