Diurnal variations of individual organic compound constituents of ultrafine and accumulation mode particulate matter in the Los Angeles Basin

Individual organic compounds can be used as tracers for primary sources of ambient particulate matter (PM) in chemical mass balance receptor models. Previous work has examined PM2.5 only and usually over long sampling periods encompassing entire days or longer. In this study, a high-flow-rate, low-pressure-drop ultrafine particle separator was deployed to collect sufficient mass for organic speciation of ultrafine and accumulation mode aerosol on a diurnal basis. Particles between 0.18 and 2.5 microm in diameter were collected on a quartz-fiber impaction substrate, and ultrafine particles below 0.18 microm were collected downstream on a high-volume filter. Four daily time period samples (morning, midday, evening, and overnight) were sampled over five weekdays to form a weekly average composite for each diurnal period. Sampling was conducted at two sites over two seasons; summer (August) and winter (January) samples were collected at both an urban site near downtown Los Angeles (University of Southern California) and a downwind, inland site in Riverside, CA. Hopanes, used as organic markers for vehicular emissions, were found to exist primarily in the ultrafine mode. Levoglucosan, an indicator of wood combustion, was quantified in both size ranges, but more was present in the accumulation mode particles. An indicator of photochemical secondary organic aerosol formation, 1,2-benzenedicarboxylic acid, was found primarily in the accumulation mode and varied with site, season, and time of day as one would expect for a photochemical product. The atmospheric variations of particulate cholesterol and other organic acids were also considered. By examining the diurnal variation, size-fractionation, and intercorrelations of individual organic compounds, the sources and atmospheric fate of these tracers can be better understood and their utility as molecular markers can be assessed.     

Sonolysis of natural phenolic compounds in aqueous solutions: degradation pathways and biodegradability

The sonochemical degradation in water of p-coumaric acid and p-hydroxybenzaldehyde, compounds that are commonly found in effluents of agricultural origin, was investigated using an 80 kHz horn-type sonicator. Experiments were performed at initial concentrations varying between 10 and 100 mg L(-1), an applied power varying between 75 and 150 W and liquid bulk temperatures varying between ambient and 70 degrees C. At the conditions in question, substrate removal was found to increase with increasing power and decreasing initial concentration and temperature. Addition of 1-butanol as a radical scavenger at a concentration of 1000 mg L(-1) nearly completely suppressed p-coumaric acid degradation. Conversely, addition of radical promoters (Fe2+ ions or H2O2) had a positive effect on removal, thus implying that degradation proceeds predominantly via hydroxyl radical-induced reactions. Reaction intermediates were determined using liquid and gas chromatography and a reaction network for p-coumaric acid degradation is suggested. Shake flask tests with activated sludge were performed to assess the aerobic biodegradability (in terms of chemical oxygen demand removal) before and after sonochemical treatment. At the conditions under consideration, the use of ultrasound enhanced the aerobic degradability of the substrates in question.     

Efficient BDDs for bounded arithmetic constraints

Symbolic model checkers use BDDs to represent arithmetic constraints over bounded integer variables. The size of such BDDs can in the worst case be exponential in the number and size (in bits) of the integer variables. In this paper we show how to construct linear-sized BDDs for linear integer arithmetic constraints. We present basic constructions for atomic equality and inequality constraints and generalize our complexity results for arbitrary linear arithmetic formulas. We also present three alternative ways of handling out-of-bounds transitions and discuss heterogeneous bounds on integer variables. We experimentally compare our approach to other BDD-based symbolic model checkers and demonstrate that the algorithms presented in this paper can be used to improve their performance significantly.     

Flexural Behaviour of Unreinforced and Z-Fibre Reinforced 3D Carbon/Epoxy Composites

Applied Composite Materials - This study compares the flexural properties of unreinforced and Z-fibre reinforced 3D carbon/epoxy composites. Z-fibre reinforcement is introduced by a novel stitching...     

Review on Manufacture of Military Composite Helmet

Applied Composite Materials - Despite of the fact that more and more accessory devices are integrated to functionalize a ballistic helmet system, its core ballistic protective function needs to be...     

A Gaussian sampling heuristic estimation model for developing synthetic trip sets

In this paper, we develop a heuristic model based on Gaussian processes to determine synthetic sets of trips in urban networks, considering only supply-related information. This is an alternative to the benchmark method used in the literature, which consists of repeating several trials of Monte Carlo simulations and therefore requiring a complex calibration task and large computational resources. The developed heuristic model explicitly leverages the probabilistic nature of Gaussian processes and exploits their properties to iteratively select origin–destination (od) pairs of nodes in the city network. The model then determines the shortest trip in distance for the selected od pairs and appends it to the synthetic set. We discuss the implementation and performance of both the benchmark method and the developed heuristic model on two city networks. We show that the presented model is more robust and computationally efficient than the benchmark method. This is evidenced by its ability to determine synthetic sets with much smaller sizes, naturally reducing the computational burden, when compared to the benchmark method. We also discuss how the choice of the kernel function and calibration of the hyperparameters influence the performance of the presented heuristic model.     

Pecunia olet: The role of incidental disgust in the ultimatum game.

We address the role of the incidental emotion of disgust in the Ultimatum Game. Participants had to choose whether or not to accept a ? offer from a ?0 pot made by another participant; 120 were in a room where a disgusting smell was released and 120 were in a room with no particular smell. Acceptance rates were higher in the room with the disgusting smell. The effect was mainly carried by the male participants who also reported more disgust with the disgusting smell and judged the offer as less unfair than females. We propose a spontaneous discounting explanation. Acceptance rates were higher in the room with the disgusting smell because participants misattributed the disgust induced by the offer to the ambient disgusting smell. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Determination of pesticide residues in olive oils with protected geographical indication or designation of origin

Seventy olive oil samples with protected geographical indication (PGI) or designation of origin (PDO) were analysed for fifty‐one target pesticides by means of gas chromatography coupled to mass spectrometry. The highest detection rates were observed for penconazole (n = 20), α‐endosulfan (n = 18), β‐endosulfan (n = 16) and flufenoxuron (n = 12). Four of the seventy investigated samples contained pesticide residues in levels exceeding MRLs. However, the investigated samples showed decreased occurrence and levels of pesticides residues in comparison with previous studies concerning samples from Greek conventional and organic cultivations. According to Spearman matrix, powerful correlations were obtained between α‐endosulfan and β‐endosulfan, detected as pair in thirteen samples and between flufenoxuron and penconazole detected as pair in eleven samples. Among Greek olive oil samples, the Cretan ones showed the lower detection rates and the lowest average number of detected pesticides per sample (1.75) in the case of pesticides positive samples.     

Optimal location of measurements for parameter estimation of distributed parameter systems

Systematic methodologies for the optimal location of spatial measurements, for efficient estimation of parameters of distributed systems, are investigated. A review of relevant methods in the literature is presented, and a comparison between the results obtained with three distinctive existing techniques is given. In addition, a new approach based on the Proper Orthogonal Decomposition (POD), to address this important problem is introduced and discussed with the aid of illustrative benchmark case studies from the literature. Based on the results obtained here, it was observed that the method based on the Gram determinant evolution (Vande Wouwer et al., 2000), does not always produce accurate results. It is strongly dependent on the behaviour of sensitivity coefficients and requires extensive calculations. The method based on max-min optimisation (Alonso, Kevrekidis, Banga, & Frouzakis, 2004) assigns optimal sensor locations to the positions where system outputs reach their extrema values; however, in some cases it produces more than one optimal solution. The D-optimal design method, Uciński (2003, June 18-20), produces as results the optimal number and spatial positions of measurements based on the behaviour (rather than the magnitude) of the sensitivity functions. Here we show that the extrema values of POD modes can be used directly to compute optimal sensor locations (as opposed e.g. to Alonso, Kevrekidis, et al., 2004, where PODs are merely used to reduce the system and further calculations are needed to compute sensor locations). Furthermore, we demonstrate the equivalence between the extrema of POD modes and of sensitivity functions. The added value of directly using PODs for the computation of optimal sensor locations is the computational efficiency of the method, side-stepping the tedious computation of sensitivity coefficient Jacobian matrices and using only system responses and/or experimental results directly. Furthermore, the inherent combination of model reduction and sensor location estimation in this method becomes more important as the complexity of the original distributed parameter system increases.