Worldwide heavy oil and bitumen deposits amount to 9 trillion barrels of oil distributed in over 280 basins around the world(1), with Canada home to oil sands deposits of 1.7 trillion barrels(2). The global development of this resource and the increase in oil production from oil sands has caused environmental concerns over the presence of toxic compounds in nearby ecosystems(3,4) and acid deposition(5,6). The contribution of oil sands exploration to secondary organic aerosol formation, an important component of atmospheric particulate matter that affects air quality and climate(7), remains poorly understood. Here we use data from airborne measurements over the Canadian oil sands, laboratory experiments and a box-model study to provide a quantitative assessment of the magnitude of secondary organic aerosol production from oil sands emissions. We find that the evaporation and atmospheric oxidation of low-volatility organic vapours from the mined oil sands material is directly responsible for the majority of the observed secondary organic aerosol mass. The resultant production rates of 45-84 tonnes per day make the oil sands one of the largest sources of anthropogenic secondary organic aerosols in North America. Heavy oil and bitumen account for over ten per cent of global oil production today(8), and this figure continues to grow(9). Our findings suggest that the production of the more viscous crude oils could be a large source of secondary organic aerosols in many production and refining regions worldwide, and that such production should be considered when assessing the environmental impacts of current and planned bitumen and heavy oil extraction projects globally.
This paper examines optimal monetary policy in a two-country New Keynesian model with international trade in intermediate inputs. We derive the loss function of a cooperative monetary policymaker and find that the optimal monetary policy must target intermediate-goods price inflation rates, final-goods price inflation rates, final-goods output gaps, and relative-price gaps. We use the welfare loss under the optimal monetary policy as a benchmark to evaluate the welfare implications of three Taylor-type monetary policy rules. A main finding is that the degree of price stickiness at the stage of intermediate-goods production is a key factor to determine which policy rule should be followed. Specifically, when the degree of price stickiness at the stage of intermediate-goods production is high, the policymaker should follow intermediate-goods PPI-based Taylor rule, whereas CPI-based Taylor rule should be followed when the degree of price stickiness at the stage of intermediate-goods production is intermediate or low.
We apply the ideal octahedron model and the relaxation algorithm in generating octahedron packings. The cubatic order parameter [P4]1">[P4]1, bond-orientational order metric Q6">Q6, and local cubatic order parameter P4local">P4local of the packings are calculated and their correlations with the packing density are investigated in the order maps. The border curve of packing density separates the geometrically feasible and infeasible regions in the order maps. Observing the transition phenomenon on the border curve, we propose the concept of the maximally dense random packing (MDRP) as the densest packing in the random state in which the particle positions and orientations are randomly distributed and there is no nontrivial spatial correlations among particles. The MDRP characterizes the onset of nontrivial spatial correlations among particles. A special packing with a density about 0.7 is found in the order maps and considered to be the MDRP of octahedra. The P4local">P4local is proposed as a new order parameter for octahedron packings, which measures the average order degree in the neighborhoods of particles. The [P4]1">[P4]1, Q6">Q6 and P4local">P4local evaluate the order degree of orientation, bond orientation and local structures, respectively and are applied simultaneously to measure the order degree of the octahedron packings. Their thresholds in the random state are determined by Monte Carlo simulations.
Organic peroxides, important species in the atmosphere, promote secondary organic aerosol (SOA) aging, affect HOx radicals cycling, and cause adverse health effects. However, the formation, gas-particle partitioning, and evolution of organic peroxides are complicated and still unclear. In this study, we investigated in the laboratory the production and gas-particle partitioning of peroxides from the ozonolysis of a-pinene, which is one of the major biogenic volatile organic compounds in the atmosphere and an important precursor for SOA at a global scale. We have determined the molar yields of hydrogen peroxide (H2O2), hydromethyl hydroperoxide (HMHP), peroxyformic acid (PFA), peroxyacetic acid (PAA), and total peroxides (TPOs, including unknown peroxides) and the fraction of peroxides in a-pinene/O3 SOA. Comparing the gas-phase peroxides with the particle-phase peroxides, we find that gas-particle partitioning coefficients of PFA and PAA are 104 times higher than the values from the theoretical prediction, indicating that organic peroxides play a more important role in SOA formation than previously expected. Here, the partitioning coefficients of TPO were determined to be as high as (2–3)*104 m3 mg-1. Even so, more than 80% of the peroxides formed in the reaction remain in the gas phase. Water changes the distribution of gaseous peroxides, while it does not affect the total amount of peroxides in either the gas or the particle phase. Approx. 18% of gaseous peroxides undergo rapid heterogeneous decomposition on SOA particles in the presence of water vapor, resulting in the additional production of H2O2. This process can partially explain the unexpectedly high H2O2 yields under wet conditions. Transformation of organic peroxides to H2O2 also preserves OH in the atmosphere, helping to improve the understanding of OH cycling.
Objectives Oxidative DNA damage may be increased among nightshift workers because of suppression of melatonin, a cellular antioxidant, and/or inflammation related to sleep disruption. However, oxidative DNA damage has received limited attention in previous studies of nightshift work.Methods From two previous cross-sectional studies, urine samples collected during a night sleep period for 217 dayshift workers and during day and night sleep (on their first day off) periods for 223 nightshift workers were assayed for 8-hydroxydeoxyguanosine (8-OH-dG), a marker of oxidative DNA damage, using high-performance liquid chromatography with electrochemical detection. Urinary measures of 6-sulfatoxymelatonin (aMT6s), a marker of circulating melatonin levels, and actigraphy-based sleep quality data were also available.Results Nightshift workers during their day sleep periods excreted 83% (p=0.2) and 77% (p=0.03) of the 8-OH-dG that dayshift workers and they themselves, respectively, excreted during their night sleep periods. Among nightshift workers, higher aMT6s levels were associated with higher urinary 8-OH-dG levels, and an inverse U-shaped trend was observed between 8-OH-dG levels and sleep efficiency and sleep duration.Conclusions Reduced excretion of 8-OH-dG among nightshift workers during day sleep may reflect reduced functioning of DNA repair machinery, which could potentially lead to increased cellular levels of oxidative DNA damage. Melatonin disruption among nightshift workers may be responsible for the observed effect, as melatonin is known to enhance repair of oxidative DNA damage. Quality of sleep may similarly impact DNA repair. Cellular levels of DNA damage will need to be evaluated in future studies to help interpret these findings.
The cycling of iron at the Earth’s near surface is profoundly influenced by dissimilatory metal reducing microorganisms, and many studies have focused on unraveling electron transfer mechanisms between these bacteria and Fe(III)-(oxyhydr)oxides. However, these efforts have been complicated by the fact that these minerals often occur in the micro- to nanosize regime, and in relevant natural environments as well as in the laboratory are subject to aggregation. The nature of the physical interface between the cellular envelope, the outer-membrane cytochromesresponsible for facilitating the interfacial electron transfer step, and these complex mineral particulates is thus difficult to probe. Previous studies using whole cells have reported reduction rates that do not correlate with particle size. In the present study we isolate the interaction between the decaheme outer-membrane cytochrome OmcA of Shewanella oneidensisand nanoparticulate hematite, examining the reduction rate as a function of particle size and reaction products through detailed characterization of the electron balance and the structure and valence of iron at particle surfaces. By comparison with abiotic reduction via the smaller molecule ascorbic acid, we show that the reduction rate is systematically controlled by the sterically accessible interfacial contact area between OmcA and hematite in particle aggregates; rates increase once pore throat sizes in aggregates become as large as OmcA. Simultaneous measure of OmcA oxidation against Fe(II) release shows a ratio of 1:10, consistent with a cascade OmcA oxidation mechanism heme by heme. X-rayabsorption spectroscopies reveal incipient magnetite on the reacted surfaces of the hematite nanoparticles after reaction. The collective findings establish the importance of accessibility of physical contact between the terminal reductases and iron oxide surfaces, and through apparent consistency of observations help reconcile behavior reported at the larger more complex scale of whole cell studies.
Visual perceptual learning models, as constrained by orientation and location specificities, propose that learning either reflects changes in V1 neuronal tuning or reweighting specific V1 inputs in either the visual cortex or higher areas. Here we demonstrate that, with a training-plus-exposure procedure, in which observers are trained at one orientation and either simultaneously or subsequently passively exposed to a second transfer orientation, perceptual learning can completely transfer to the second orientation in tasks known to be orientation-specific. However, transfer fails if exposure precedes the training. These results challenge the existing specific perceptual learning models by suggesting a more general perceptual learning process. We propose a rule-based learning model to explain perceptual learning and its specificity and transfer. In this model, a decision unit in high-level brain areas learns the rules of reweighting the V1 inputs through training. However, these rules cannot be applied to a new orientation/location because the decision unit cannot functionally connect to the new V1 inputs that are unattended or even suppressed after training at a different orientation/location, which leads to specificity. Repeated orientation exposure or location training reactivates these inputs to establish the functional connections and enable the transfer of learning.
Visual perceptual learning models, as constrained by orientation and location specificities, propose that learning either reflects changes in V1 neuronal tuning or reweighting specific V1 inputs in either the visual cortex or higher areas. Here we demonstrate that, with a training-plus-exposure procedure, in which observers are trained at one orientation and either simultaneously or subsequently passively exposed to a second transfer orientation, perceptual learning can completely transfer to the second orientation in tasks known to be orientation-specific. However, transfer fails if exposure precedes the training. These results challenge the existing specific perceptual learning models by suggesting a more general perceptual learning process. We propose a rule-based learning model to explain perceptual learning and its specificity and transfer. In this model, a decision unit in high-level brain areas learns the rules of reweighting the V1 inputs through training. However, these rules cannot be applied to a new orientation/location because the decision unit cannot functionally connect to the new V1 inputs that are unattended or even suppressed after training at a different orientation/location, which leads to specificity. Repeated orientation exposure or location training reactivates these inputs to establish the functional connections and enable the transfer of learning.
Microbial communities of activated sludge (AS) play a key role in the performance of wastewater treatment processes. However, seasonal variability of microbial population in varying AS-based processes has been poorly correlated with operation of full-scale wastewater treatment systems (WWTSs). In this paper, significant seasonal variability of AS microbial communities in eight WWTSs located in the city of Guangzhou were revealed in terms of 16S rRNA-based Miseq sequencing. Furthermore, variation redundancy analysis (RDA) demonstrated that the microbial community compositions closely correlated with WWTS operation parameters such as temperature, BOD, NH4+-N and TN. Consequently, support vector regression models which reasonably predicted effluent BOD, SS and TN in WWTSs were established based on microbial community compositions. This work provided an alternative tool for rapid assessment on performance of full-scale wastewater treatment plants.