U.K. HiGEM: Simulations of desert dust and biomass burning aerosols with a high-resolution atmospheric GCM

The atmospheric component of the United Kingdom’s new High-resolution Global Environmental Model (HiGEM) has been run with interactive aerosol schemes that include biomass burning and mineral dust. Dust emission, transport, and deposition are parameterized within the model using six particle size divisions, which are treated independently. The biomass is modeled in three nonindependent modes, and emissions are prescribed from an external dataset. The model is shown to produce realistic horizontal and vertical distributions of these aerosols for each season when compared with available satellite- and ground-based observations and with other models. Combined aerosol optical depths off the coast of North Africa exceed 0.5 both in boreal winter, when biomass is the main contributor, and also in summer, when the dust dominates. The model is capable of resolving smaller-scale features, such as dust storms emanating from the Bode´ le´ and Saharan regions of North Africa and the wintertime Bode´ le´ low-level jet. This is illustrated by February and July case studies, in which the diurnal cycles of model variables in relation to dust emission and transport are examined. The top-of-atmosphere annual mean radiative forcing of the dust is calculated and found to be globally quite small but locally very large, exceeding 20 W m22 over the Sahara, where inclusion of dust aerosol is shown to improve the model radiative balance. This work extends previous aerosol studies by combining complexity with increased global resolution and represents a step toward the next generation of models to investigate aerosol–climate interactions. 1. Introduction Accurate modeling of mineral dust is known to be important because of its radiative impact in both numerical weather prediction models (Milton et al. 2008; Haywood et

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression

We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm. Compared with the state-of-the-art regularisation assisted orthogonal least square algorithm based on the LOO MR for selecting fixednode RBF classifiers, the proposed PSO aided OFR algorithm for constructing tunable-node RBF classifiers offers significant advantages in terms of better generalisation performance and smaller model size as well as imposes lower computational complexity in classifier construction process. Moreover, the proposed algorithm does not have any hyperparameter that requires costly tuning based on cross validation.

Assessing the impact of nano- and micro-scale zerovalent iron particles on soil microbial activities: Particle reactivity interferes with assay conditions and interpretation of genuine microbial effects

The effects of nano-scale and micro-scale zerovalent iron (nZVI and mZVI) particles on general (dehydrogenase and hydrolase) and specific (ammonia oxidation potential, AOP) activities mediated by the microbial community in an uncontaminated soil were examined. nZVI (diameter 12.5 nm; 10 mg gÿ1 soil)apparently inhibited AOP and nZVI and mZVI apparently stimulated dehydrogenase activity but had minimal influence on hydrolase activity. Sterile experiments revealed that the apparent inhibition of AOP could not be interpreted as such due to the confounding action of the particles, whereas, the nZVIenhanced dehydrogenase activity could represent the genuine response of a stimulated microbial population or an artifact of ZVI reactivity. Overall, there was no evidence for negative effects of nZVI or mZVI on the processes studied. When examining the impact of redox active particles such as ZVI on microbial oxidation–reduction reactions, potential confounding effects of the test particles on assay conditions should be considered.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Integrating features for man-made target tracking from FLIR image sequence using particle filter

A new man-made target tracking algorithm integrating features from (Forward Looking InfraRed) image sequence is presented based on particle filter. Firstly, a multiscale fractal feature is used to enhance targets in FLIR images. Secondly, the gray space feature is defined by Bhattacharyya distance between intensity histograms of the reference target and a sample target from MFF (Multi-scale Fractal Feature) image. Thirdly, the motion feature is obtained by differencing between two MFF images. Fourthly, a fusion coefficient can be automatically obtained by online feature selection method for features integrating based on fuzzy logic. Finally, a particle filtering framework is developed to fulfill the target tracking. Experimental results have shown that the proposed algorithm can accurately track weak or small man-made target in FLIR images with complicated background. The algorithm is effective, robust and satisfied to real time tracking.

Chemical and aerosol characterisation of the troposphere over West Africa during the monsoon period as part of AMMA

During June, July and August 2006 five aircraft took part in a campaign over West Africa to observe the aerosol content and chemical composition of the troposphere and lower stratosphere as part of the African Monsoon Multidisciplinary Analysis (AMMA) project. These are the first such measurements in this region during the monsoon period. In addition to providing an overview of the tropospheric composition, this paper provides a description of the measurement strategy (flights performed, instrumental payloads, wing-tip to wing-tip comparisons) and points to some of the important findings discussed in more detail in other papers in this special issue. The ozone data exhibits an "S" shaped vertical profile which appears to result from significant losses in the lower troposphere due to rapid deposition to forested areas and photochemical destruction in the moist monsoon air, and convective uplift of ozone-poor air to the upper troposphere. This profile is disturbed, particularly in the south of the region, by the intrusions in the lower and middle troposphere of air from the southern hemisphere impacted by biomass burning. Comparisons with longer term data sets suggest the impact of these intrusions on West Africa in 2006 was greater than in other recent wet seasons. There is evidence for net photochemical production of ozone in these biomass burning plumes as well as in urban plumes, in particular that from Lagos, convective outflow in the upper troposphere and in boundary layer air affected by nitrogen oxide emissions from recently wetted soils. This latter effect, along with enhanced deposition to the forested areas, contributes to a latitudinal gradient of ozone in the lower troposphere. Biogenic volatile organic compounds are also important in defining the composition both for the boundary layer and upper tropospheric convective outflow. Mineral dust was found to be the most abundant and ubiquitous aerosol type in the atmosphere over Western Africa. Data collected within AMMA indicate that injection of dust to altitudes favourable for long-range transport (i.e. in the upper Sahelian planetary boundary layer) can occur behind the leading edge of mesoscale convective system (MCS) cold-pools. Research within AMMA also provides the first estimates of secondary organic aerosols across the West African Sahel and have shown that organic mass loadings vary between 0 and 2 μg m−3 with a median concentration of 1.07 μg m−3. The vertical distribution of nucleation mode particle concentrations reveals that significant and fairly strong particle formation events did occur for a considerable fraction of measurement time above 8 km (and only there). Very low concentrations were observed in general in the fresh outflow of active MCSs, likely as the result of efficient wet removal of aerosol particles due to heavy precipitation inside the convective cells of the MCSs. This wet removal initially affects all particle size ranges as clearly shown by all measurements in the vicinity of MCSs.

Transport in the low-latitude tropopause zone diagnosed using particle trajectories

Recent literature has described a “transition zone” between the average top of deep convection in the Tropics and the stratosphere. Here transport across this zone is investigated using an offline trajectory model. Particles were advected by the resolved winds from the European Centre for Medium-Range Weather Forecasts reanalyses. For each boreal winter clusters of particles were released in the upper troposphere over the four main regions of tropical deep convection (Indonesia, central Pacific, South America, and Africa). Most particles remain in the troposphere, descending on average for every cluster. The horizontal components of 5-day trajectories are strongly influenced by the El Niño–Southern Oscillation (ENSO), but the Lagrangian average descent does not have a clear ENSO signature. Tropopause crossing locations are first identified by recording events when trajectories from the same release regions cross the World Meteorological Organization lapse rate tropopause. Most crossing events occur 5–15 days after release, and 30-day trajectories are sufficiently long to estimate crossing number densities. In a further two experiments slight excursions across the lapse rate tropopause are differentiated from the drift deeper into the stratosphere by defining the “tropopause zone” as a layer bounded by the average potential temperature of the lapse rate tropopause and the profile temperature minimum. Transport upward across this zone is studied using forward trajectories released from the lower bound and back trajectories arriving at the upper bound. Histograms of particle potential temperature (θ) show marked differences between the transition zone, where there is a slow spread in θ values about a peak that shifts slowly upward, and the troposphere below 350 K. There forward trajectories experience slow radiative cooling interspersed with bursts of convective heating resulting in a well-mixed distribution. In contrast θ histograms for back trajectories arriving in the stratosphere have two distinct peaks just above 300 and 350 K, indicating the sharp change from rapid convective heating in the well-mixed troposphere to slow ascent in the transition zone. Although trajectories slowly cross the tropopause zone throughout the Tropics, all three experiments show that most trajectories reaching the stratosphere from the lower troposphere within 30 days do so over the west Pacific warm pool. This preferred location moves about 30°–50° farther east in an El Niño year (1982/83) and about 30° farther west in a La Niña year (1988/89). These results could have important implications for upper-troposphere–lower-stratosphere pollution and chemistry studies.

The effect of feeding modified soyabean oil enriched with C18 : 4 n-3 to broilers on the deposition of n-3 fatty acids in chicken meat

Supplementing broiler diets with conventional vegetable oils has little effect on the long-chain n-3 PUFA (LC n-3 PUFA) content of the meat. The present study investigated the effect on fatty acid composition and sensory characteristics of chicken meat when broilers were fed oil extracted from soyabeans (SDASOY) that had been genetically engineered to produce C18 : 4n-3 (stearidonic acid (SDA), 240 mg/g oil). Three diets were fed to 120 birds (eight replicate pens of five birds) from 15 d to slaughter (41–50 d). Diets were identical apart from the oil added to them (45 and 50 g/kg as fed in the grower and finisher phases, respectively), which was either SDASOY, near-isogenic soya (CON) or fish oil (FISH). The LC n-3 PUFA content of the meat increased in the order CON, SDASOY and FISH. In breast meat with skin, the SDA concentration was 522, 13 and 37 (sem 14·4) mg/100 g meat for SDASOY, CON and FISH, respectively. Equivalent values for C20 : 5n-3 (EPA) were 53, 13 and 140 (sem 8·4); for C22 : 5n-3 (docosapentaenoic acid (DPA)) 65, 15 and 101 (sem 3·5); for C22 : 6n-3 (DHA) 19, 9 and 181 (sem 4·4). Leg meat (with skin) values for SDA were 861, 23 and 68 (sem 30·1); for EPA 87, 9 and 258 (sem 7·5); for DPA 95, 20 and 165 (sem 5·0); for DHA 29, 10 and 278 (sem 8·4). Aroma, taste and aftertaste of freshly cooked breast meat were not affected. Fishy aromas, tastes and aftertastes were associated with LC n-3 PUFA content of the meat, being most noticeable in the FISH leg meat (both freshly cooked and reheated) and in the reheated SDASOY leg meat.

A cluster computing implementation of a Monte Carlo simulation of a particle growth mechanism

Clusters of computers can be used together to provide a powerful computing resource. Large Monte Carlo simulations, such as those used to model particle growth, are computationally intensive and take considerable time to execute on conventional workstations. By spreading the work of the simulation across a cluster of computers, the elapsed execution time can be greatly reduced. Thus a user has apparently the performance of a supercomputer by using the spare cycles on other workstations.

Still water, hidden depths: the deposition of Bronze Age metalwork in the English Fenland

Finds of metalwork always raise the question of why they were deposited: a smith's collection, a concealed hoard or a votive offering? Findspots in water suggest offerings, since they would be awkward to retrieve. But understanding the context of deposition means knowing the prehistoric environment. The Fenland area of England has many Bronze Age sites, and deposits of metalwork and a well-mapped ancient environment too. Putting all three together the authors begin to assemble a grammar of deposition: swords and rapiers in rivers, some mixed collections placed in still water and others on once-dry land with burnt mounds.

Advancements in the estimation of ice particle fall speeds using laboratory and field measurements

Accurate estimates for the fall speed of natural hydrometeors are vital if their evolution in clouds is to be understood quantitatively. In this study, laboratory measurements of the terminal velocity vt for a variety of ice particle models settling in viscous fluids, along with wind-tunnel and field measurements of ice particles settling in air, have been analyzed and compared to common methods of computing vt from the literature. It is observed that while these methods work well for a number of particle types, they fail for particles with open geometries, specifically those particles for which the area ratio Ar is small (Ar is defined as the area of the particle projected normal to the flow divided by the area of a circumscribing disc). In particular, the fall speeds of stellar and dendritic crystals, needles, open bullet rosettes, and low-density aggregates are all overestimated. These particle types are important in many cloud types: aggregates in particular often dominate snow precipitation at the ground and vertically pointing Doppler radar measurements. Based on the laboratory data, a simple modification to previous computational methods is proposed, based on the area ratio. This new method collapses the available drag data onto an approximately universal curve, and the resulting errors in the computed fall speeds relative to the tank data are less than 25% in all cases. Comparison with the (much more scattered) measurements of ice particles falling in air show strong support for this new method, with the area ratio bias apparently eliminated.

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.