A large eddy simulation of the dispersion of traffic emissions by moving vehicles at an intersection

Traffic induced flow within urban areas can have a significant effect on pollution dispersion, particularly for traffic emissions. Traffic movement results in increased turbulence within the street and the dispersion of pollutants by vehicles as they move through the street. In order to accurately model urban air quality and perform meaningful exposure analysis at the microscale, these effects cannot be ignored. In this paper we introduce a method to simulate traffic induced dispersion at high resolution. The computational fluid dynamics software, Fluidity, is used to model the moving vehicles through a domain consisting of an idealised intersection. A multi-fluid method is used where vehicles are represented as a second fluid which displaces the air as it moves through the domain. The vehicle model is coupled with an instantaneous emissions model which calculates the emission rate of each vehicle at each time step. A comparison is made with a second Fluidity model which simulates the traffic emissions as a line source and does not include moving vehicles. The method is used to demonstrate how moving vehicles can have a significant effect on street level concentration fields and how large vehicles such as buses can also cause acute high concentration events at the roadside which can contribute significantly to overall exposure

The dehydration, rehydration and tectonic setting of greenstone belts in a portion of the northern Kaapvaal Craton, South Africa

High-grade gneiss terranes and low-grade granite-greenstone terranes are well known in several Archaean domains. The geological relationship between these different crustal regions, however, is still controversial. One school of thought favors fundamental genetic differences between high-grade and low-grade terranes while others argue for a depth-controlled crustal evolution. The detailed examination of well-exposed Archaean terranes at different metamorphic grades, therefore, is not only an important source of information about the crustal levels exposed, but also is critical to the understanding of the possible tectonic and metamorphic evolution of greenstone belts with time. Three South African greenstone belts are compared

Do we need high temporal resolution modelling of exposure in urban areas? A test case

Roadside concentrations of harmful pollutants such as NOx are highly variable in both space and time. This is rarely considered when assessing pedestrian and cyclist exposures. We aim to fully describe the spatio-temporal variability of exposures of pedestrians and cyclists travelling along a road at high resolution. We evaluate the value added of high spatio-temporal resolution compared to high spatial resolution only. We also compare high resolution vehicle emissions modelling to using a constant volume source. We highlight conditions of peak exposures, and discuss implications for health impact assessments. Using the large eddy simulation code Fluidity we simulate NOx concentrations at a resolution of 2 m and 1 s along a 350 m road segment in a complex real-world street geometry including an intersection and bus stops. We then simulate pedestrian and cyclist journeys for different routes and departure times. For the high spatio-temporal method, the standard deviation in 1 s concentration experienced by pedestrians (50.9 μg.m-3) is nearly three times greater than that predicted by the high-spatial only (17.5 μg.m-3) or constant volume source (17.6 μg.m-3) methods. This exposure is characterised by low concentrations punctuated by short duration, peak exposures which elevate the mean exposure and are not captured by the other two methods. We also find that the mean exposure of cyclists on the road (31.8 μg.m-3) is significantly greater than that of cyclists on a roadside path (25.6 μg.m-3) and that of pedestrians on a sidewalk (17.6 μg.m-3). We conclude that ignoring high resolution temporal air pollution variability experienced at the breathing time scale can lead to a mischaracterization of pedestrian and cyclist exposures, and therefore also potentially the harm caused. High resolution methods reveal that peaks, and hence mean exposures, can be meaningfully reduced by avoiding hyper-local hotspots such as bus stops and junctions

Gravity Evidence for a Larger Limpopo Belt in Southern Africa and Geodynamic Implications

The Limpopo Belt of southern Africa is a Neoarchean orogenic belt located between two older Archean provinces, the Zimbabwe craton to the north and the Kaapvaal craton to the south. Previous studies considered the Limpopo Belt to be a linearly trending east-northeast belt with a width of ∼250 km and ∼600 km long. We provide evidence from gravity data constrained by seismic and geochronologic data suggesting that the Limpopo Belt is much larger than previously assumed and includes the Shashe Belt in Botswana, thus defining a southward convex orogenic arc sandwiched between the two cratons. The 2 Ga Magondi orogenic belt truncates the Limpopo-Shahse Belt to the west. The northern marginal, central and southern marginal tectonic zones define a single gravity anomaly on upward continued maps, indicating that they had the same exhumation history. This interpretation requires a tectonic model involving convergence between the Kaapvaal and Zimbabwe cratons during a Neoarchean orogeny that preserved the thick cratonic keel that has been imaged in tomographic models

Influence of functionalized fullerene structure on polymer photovoltaic degradation

The time dependence of device performance has been measured for photocells using blends containing the conjugated polymer, poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene] (MEH-PPV) with two different functionalized C60 electron acceptor molecules: commercially available [6,6]-phenyl C61 butyric acid methyl ester (PCBM) or [6,6]-phenyl C61 butyric acid octadecyl ester (PCBOD) produced in this laboratory. Performance was characterized by the short-circuit current output of the devices, with the time dependence of PCBM samples typically degrading exponentially. Variations in the characteristic lifetime of the devices were observed to depend on the molar fraction of the electron acceptor molecules (calculated with respect to the MEH-PPV monomer fraction). In comparison to the PCBM samples, the stability of the PCBOD blends was significantly enhanced, with a one or two order of magnitude improvement. Corresponding spectroscopic data with similar time evolution as the transport measurements suggest an independent means for determining and understanding degradation mechanisms

The trade-off between taxi time and fuel consumption in airport ground movement

Environmental impact is a very important agenda item in many sectors nowadays, which the air transportation sector is also trying to reduce as much as possible. One area which has remained relatively unexplored in this context is the ground movement problem for aircraft on the airport’s surface. Aircraft have to be routed from a gate to a runway and vice versa and it is still unknown whether fuel burn and environmental impact reductions will best result from purely minimising the taxi times or whether it is also important to avoid multiple acceleration phases. This paper presents a newly developed multi-objective approach for analysing the trade-off between taxi time and fuel consumption during taxiing. The approach consists of a combination of a graph-based routing algorithm and a population adaptive immune algorithm to discover different speed profiles of aircraft. Analysis with data from a European hub airport has highlighted the impressive performance of the new approach. Furthermore, it is shown that the trade-off between taxi time and fuel consumption is very sensitive to the fuel-related objective function which is used

Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles.

OBJECTIVE: Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. METHODS: Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. RESULTS: InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. CONCLUSIONS: The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

OBJECTIVES: Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. METHODS: We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. RESULTS: The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. CONCLUSIONS: The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA