Kinetic multi-layer model of gas-particle interactions in aerosols and clouds (KM-GAP): linking condensation, evaporation and chemical reactions of organics, oxidants and water

We present a novel kinetic multi-layer model for gas-particle interactions in aerosols and clouds (KM-GAP) that treats explicitly all steps of mass transport and chemical reaction of semi-volatile species partitioning between gas phase, particle surface and particle bulk. KM-GAP is based on the PRA model framework (Pöschl-Rudich-Ammann, 2007), and it includes gas phase diffusion, reversible adsorption, surface reactions, bulk diffusion and reaction, as well as condensation, evaporation and heat transfer. The size change of atmospheric particles and the temporal evolution and spatial profile of the concentration of individual chemical species can be modelled along with gas uptake and accommodation coefficients. Depending on the complexity of the investigated system, unlimited numbers of semi-volatile species, chemical reactions, and physical processes can be treated, and the model shall help to bridge gaps in the understanding and quantification of multiphase chemistry and microphysics in atmo- spheric aerosols and clouds. In this study we demonstrate how KM-GAP can be used to analyze, interpret and design experimental investigations of changes in particle size and chemical composition in response to condensation, evaporation, and chemical reaction. For the condensational growth of water droplets, our kinetic model results provide a direct link between laboratory observations and molecular dynamic simulations, confirming that the accommodation coefficient of water at 270 K is close to unity. Literature data on the evaporation of dioctyl phthalate as a function of particle size and time can be reproduced, and the model results suggest that changes in the experimental conditions like aerosol particle concentration and chamber geometry may influence the evaporation kinetics and can be optimized for eðcient probing of specific physical effects and parameters. With regard to oxidative aging of organic aerosol particles, we illustrate how the formation and evaporation of volatile reaction products like nonanal can cause a decrease in the size of oleic acid particles exposed to ozone.

Hydrogen-bonding-driven self-assembly of PEGylated organosilica nanoparticles with poly(acrylic acid) in aqueous solutions and in layer-by-layer deposition at solid surfaces

PEGylated organosilica nanoparticles have been synthesized through self-condensation of (3-mercaptopropyl)trimethoxysilane in dimethyl sulfoxide into thiolated nanoparticles with their subsequent reaction with methoxypoly(ethylene glycol) maleimide. The PEGylated nanoparticles showed excellent colloidal stability over a wide range of pH in contrast to the parent thiolated nanoparticles, which have a tendency to aggregate irreversibly under acidic conditions (pH < 3.0). Due to the presence of a poly(ethylene glycol)-based corona, the PEGylated nanoparticles are capable of forming hydrogen-bonded interpolymer complexes with poly(acrylic acid) in aqueous solutions under acidic conditions, resulting in larger aggregates. The use of hydrogen-bonding interactions allows more efficient attachment of the nanoparticles to surfaces. The alternating deposition of PEGylated nanoparticles and poly(acrylic acid) on silicon wafer surfaces in a layer-by-layer fashion leads to multilayered coatings. The self-assembly of PEGylated nanoparticles with poly(acrylic acid) in aqueous solutions and at solid surfaces was compared to the behavior of linear poly(ethylene glycol). The nanoparticle system creates thicker layers than the poly(ethylene glycol), and a thicker layer is obtained on a poly(acrylic acid) surface than on a silica surface, because of the effects of hydrogen bonding. Some implications of these hydrogen-bonding-driven interactions between PEGylated nanoparticles and poly(acrylic acid) for pharmaceutical formulations are discussed.

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.

Kinetic multi-layer model of gas-particle interactions in aerosols and clouds (KM-GAP): linking condensation, evaporation and chemical reactions of organics, oxidants and water

We present a novel kinetic multi-layer model for gas-particle interactions in aerosols and clouds (KMGAP) that treats explicitly all steps of mass transport and chemical reaction of semi-volatile species partitioning between gas phase, particle surface and particle bulk. KMGAP is based on the PRA model framework (P¨oschl-Rudich- Ammann, 2007), and it includes gas phase diffusion, reversible adsorption, surface reactions, bulk diffusion and reaction, as well as condensation, evaporation and heat transfer. The size change of atmospheric particles and the temporal evolution and spatial profile of the concentration of individual chemical species can be modeled along with gas uptake and accommodation coefficients. Depending on the complexity of the investigated system and the computational constraints, unlimited numbers of semi-volatile species, chemical reactions, and physical processes can be treated, and the model shall help to bridge gaps in the understanding and quantification of multiphase chemistry and microphysics in atmospheric aerosols and clouds. In this study we demonstrate how KM-GAP can be used to analyze, interpret and design experimental investigations of changes in particle size and chemical composition in response to condensation, evaporation, and chemical reaction. For the condensational growth of water droplets, our kinetic model results provide a direct link between laboratory observations and molecular dynamic simulations, confirming that the accommodation coefficient of water at 270K is close to unity (Winkler et al., 2006). Literature data on the evaporation of dioctyl phthalate as a function of particle size and time can be reproduced, and the model results suggest that changes in the experimental conditions like aerosol particle concentration and chamber geometry may influence the evaporation kinetics and can be optimized for efficient probing of specific physical effects and parameters. With regard to oxidative aging of organic aerosol particles, we illustrate how the formation and evaporation of volatile reaction products like nonanal can cause a decrease in the size of oleic acid particles exposed to ozone.

Optimizing layer-by-layer deposition of interpolymer complexes on solid substrates using Biacore

The layer-by-layer deposition of polymers onto surfaces allows the fabrication of multilayered materials for a wide range of applications, from drug delivery to biosensors. This work describes the analysis of complex formation between poly(acrylic acid) and methylcellulose in aqueous solutions using Biacore, a surface plasmon resonance analytical technique, traditionally used to examine biological interactions. This technique characterized the layer-by-layer deposition of these polymers on the surface of a Biacore sensor chip. The results were subsequently used to optimize the experimental conditions for sequential layer deposition on glass slides. The role of the solution pH and poly(acrylic acid) molecular weight on the formation of interpolymer multilayered coatings was researched, and showed that the optimal deposition of the polymer complexes was achieved at pHs ≤2.5 with a poly(acrylic acid) molecular weight of 450 kDa.

The effects of a changing pollution climate on throughfall deposition and cycling in a forested area in southern England

This study compares two sets of measurements of the composition of bulk precipitation and throughfall at a site in southern England with a 20-year gap between them. During this time, SO2 emissions from the UK fell by 82%, NOx emissions by 35% and NH3 emissions by 7%. These reductions were partly reflected in bulk precipitation, with deposition reductions of 56% in SO4,38% in NO3, 32% in NH4, and 73% in H+. In throughfall under Scots pine, the effects were more dramatic, with an 89% reduction in SO4 deposition and a 98% reduction in H+ deposition. The mean pH under these trees increased from 2.85 to 4.30. Nitrate and ammonium deposition in throughfall increased slightly, however. In the earlier period, the Scots pines were unable to neutralise the high flux of acidity associated with sulphur deposition, even though this was not a highly polluted part of the UK, and deciduous trees (oak and birch) were only able to neutralise it in summer when the leaves were present. In the later period, the sulphur flux had reduced to the point where the acidity could be neutralised by all species — the neutralisation mechanism is thus likely to be largely leaching of base cations and buffering substances from the foliage. The high fluxes are partly due to the fact that these are 60–80 year old trees growing in an open forest structure. The increase in NO3 and NH4 in throughfall in spite of decreased deposition seems likely due to a decrease in foliar uptake, perhaps due to the increasing nitrogen saturation of the catchment soils. These changes may increase the rate of soil microbial activity as nitrogen increases and acidity declines, with consequent effects on water quality of the catchment drainage stream.

Numerical simulation of tephra transport and deposition of the 1982 El Chichón eruption and implications for hazard assessment

El Chichón volcano, Chiapas, Mexico, erupted explosively on March 29th, 1982, after a repose period of about 550 years. Amongst ten eruptive episodes documented between March 29th and April 4th, only the three that occurred on March 29th and April 4th produced significant pyroclastic tephra deposits. Here we use analytical (HAZMAP) and numerical (FALL3D) tephra transport models to reconstruct the deposits and the atmospheric plume dispersal associated with the three main fallout units of the 1982 eruption. On the basis of such a reconstruction, we produce hazard maps of tephra fallout associated to a Plinian eruption and discuss the implications of such a severe eruption scenario.

Copper deposition on TiO2 from copper(II)hexafluoroacetylacetonate

The authors have studied the adsorption of CuII(hfac)2 on the surface of a model oxide system, TiO2(110), and probed the molecular stability with respect to thermal cycling, using atomic scale imaging by scanning tunneling microscopy supported by x-ray photoemission spectroscopy. They find that at 473 K, the adsorbed metal-organic molecules begin to dissociate and release Cu atoms which aggregate and form Cu nanoparticles. These Cu nanoparticles ripen over time and the size (height) distribution develops into a bimodal distribution. Unlike other organometallic systems, which show a bimodal distribution due to enhanced nucleation or growth at surface step edges, the nanoparticles do not preferentially form at steps. The reduced mobility of the Cu islands may be related to the co-adsorbed ligands that remain in very small clusters on the surface.

Using the significant dust deposition event on the glaciers of Mt. Elbrus, Caucasus Mountains, Russia on 5 May 2009 to develop a method for dating and "provenancing" of desert dust events recorded in snow pack

A significant desert dust deposition event occurred on Mt. Elbrus, Caucasus Mountains, Russia on 5 May 2009, where the deposited dust later appeared as a brown layer in the snow pack. An examination of dust transportation history and analysis of chemical and physical properties of the deposited dust were used to develop a new approach for high-resolution “provenancing” of dust deposition events recorded in snow pack using multiple independent techniques. A combination of SEVIRI red-green-blue composite imagery, MODIS atmospheric optical depth fields derived using the Deep Blue algorithm, air mass trajectories derived with HYSPLIT model and analysis of meteorological data enabled identification of dust source regions with high temporal (hours) and spatial (ca. 100 km) resolution. Dust, deposited on 5 May 2009, originated in the foothills of the Djebel Akhdar in eastern Libya where dust sources were activated by the intrusion of cold air from the Mediterranean Sea and Saharan low pressure system and transported to the Caucasus along the eastern Mediterranean coast, Syria and Turkey. Particles with an average diameter below 8 μm accounted for 90% of the measured particles in the sample with a mean of 3.58 μm, median 2.48 μm. The chemical signature of this long-travelled dust was significantly different from the locally-produced dust and close to that of soils collected in a palaeolake in the source region, in concentrations of hematite. Potential addition of dust from a secondary source in northern Mesopotamia introduced uncertainty in the “provenancing” of dust from this event. Nevertheless, the approach adopted here enables other dust horizons in the snowpack to be linked to specific dust transport events recorded in remote sensing and meteorological data archives.

Using the significant dust deposition event on the glaciers of Mt. Elbrus, Caucasus Mountains, Russia on 5 May 2009 to develop a method for dating and provenancing of desert dust events recorded in snow pack.

A significant desert dust deposition event occurred on Mt. Elbrus, Caucasus Mountains, Russia on 5 May 2009, where the deposited dust later appeared as a brown layer in the snow pack. An examination of dust transportation history and analysis of chemical and physical properties of the deposited dust were used to develop a new approach for high-resolution provenancing of dust deposition events recorded in snow pack using multiple independent techniques. A combination of SEVIRI red-green-blue composite imagery, MODIS atmospheric optical depth fields derived using the Deep Blue algorithm, air mass trajectories derived with HYSPLIT model and analysis of meteorological data enabled identification of dust source regions with high temporal (hours) and spatial (ca. 100 km) resolution. Dust, deposited on 5 May 2009, originated in the foothills of the Djebel Akhdar in eastern Libya where dust sources were activated by the intrusion of cold air from the Mediterranean Sea and Saharan low pressure system and transported to the Caucasus along the eastern Mediterranean coast, Syria and Turkey. Particles with an average diameter below 8 μm accounted for 90% of the measured particles in the sample with a mean of 3.58 μm, median 2.48 μm and the dominant mode of 0.60 μm. The chemical signature of this long-travelled dust was significantly different from the locally-produced dust and close to that of soils collected in a palaeolake in the source region, in concentrations of hematite and oxides of aluminium, manganese, and magnesium. Potential addition of dust from a secondary source in northern Mesopotamia introduced uncertainty in the provenancing of dust from this event. Nevertheless, the approach adopted here enables other dust horizons in the snowpack to be linked to specific dust transport events recorded in remote sensing and meteorological data archives.

Croig Cave: a late Bronze Age ornament deposition and 3,500 years of coastal foraging in NW Mull, Scotland

Activity within caves provides an important element of the later prehistoric and historic settlement pattern of western Scotland. This contribution reports on a small-scale excavation within Croig Cave, on the coast of north-west Mull, that exposed a 1.95m sequence of midden deposits and cave floors that dated bewteen c 1700 BC and AD 1400. Midden analysis indicated the processing of a .... 950 BC, a penannular copper bracelet a discrete ritual episode within the cycle of otherwise potentially mundane activities. Lead isotope analysis indicates an Irish origin for the copper ore. A piece of iron slag within later midden deposits, dated to c 400 BC, along with high frequencies of wood charcoal, suggest that smithing or smelting may have occurred within the cave. High zinc levels in the historic levels of the midden c AD 1200 might indicate intensive processing of seaweed.

Odd deposits and average practice. A critical history of the concept of structured deposition.

This paper presents a critical history of the concept of ‘structured deposition’. It examines the long-term development of this idea in archaeology, from its origins in the early 1980s through to the present day, looking at how it has been moulded and transformed. On the basis of this historical account, a number of problems are identified with the way that ‘structured deposition’ has generally been conceptualized and applied. It is suggested that the range of deposits described under a single banner as being ‘structured’ is unhelpfully broad, and that archaeologists have been too willing to view material culture patterning as intentionally produced – the result of symbolic or ritual action. It is also argued that the material signatures of ‘everyday’ practice have been undertheorized and all too often ignored. Ultimately, it is suggested that if we are ever to understand fully the archaeological signatures of past practice, it is vital to consider the ‘everyday’ as well as the ‘ritual’ processes which lie behind the patterns we uncover in the ground.

Particle swarm optimisation assisted classification using elastic net prefiltering

A novel two-stage construction algorithm for linear-in-the-parameters classifier is proposed, aiming at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage to construct a sparse linear-in-the-parameters classifier. For the first stage learning of generating the prefiltered signal, a two-level algorithm is introduced to maximise the model's generalisation capability, in which an elastic net model identification algorithm using singular value decomposition is employed at the lower level while the two regularisation parameters are selected by maximising the Bayesian evidence using a particle swarm optimization algorithm. Analysis is provided to demonstrate how “Occam's razor” is embodied in this approach. The second stage of sparse classifier construction is based on an orthogonal forward regression with the D-optimality algorithm. Extensive experimental results demonstrate that the proposed approach is effective and yields competitive results for noisy data sets.