Nearby Point Research Articles

Effects of point source emission heights in WRF–STILT: a step towards exploiting nocturnal observations in models

Abstract. An appropriate representation of point source emissions in atmospheric transport models is very challenging. In the Stochastic Time-Inverted Lagrangian Transport model (STILT), all point source emissions are typically released from the surface, meaning that the actual emission stack height plus subsequent plume rise is not considered. This can lead to erroneous predictions of trace gas concentrations, especially during nighttime when vertical atmospheric mixing is minimal. In this study we use two Weather Research and Forecasting (WRF)–STILT model approaches to simulate fossil fuel CO2 (ffCO2) concentrations: (1) the standard “surface source influence (SSI)” approach and (2) an alternative “volume source influence (VSI)” approach where nearby point sources release CO2 according to their effective emission height profiles. The comparison with 14C-based measured ffCO2 data from 2-week integrated afternoon and nighttime samples collected at Heidelberg, 30 m above ground level shows that the root-mean-square deviation (RMSD) between modelled and measured ffCO2 is indeed almost twice as high during the night (RMSD =6.3 ppm) compared to the afternoon (RMSD =3.7 ppm) when using the standard SSI approach. In contrast, the VSI approach leads to a much better performance at nighttime (RMSD =3.4 ppm), which is similar to its performance during afternoon (RMSD =3.7 ppm). Representing nearby point source emissions with the VSI approach could thus be a first step towards exploiting nocturnal observations in STILT. The ability to use nighttime observations in atmospheric inversions would dramatically increase the observational data and allow for the investigation of different source mixtures or diurnal cycles. To further investigate the differences between these two approaches, we conducted a model experiment in which we simulated the ffCO2 contributions from 12 artificial power plants with typical annual emissions of 1 million tonnes of CO2 and with distances between 5 and 200 km from the Heidelberg observation site. We find that such a power plant must be more than 50 km away from the observation site in order for the mean modelled ffCO2 concentration difference between the SSI and VSI approach to fall below 0.1 ppm during situations with low mixing heights smaller than 500 m.

A Metastable Amorphous Intermediate Is Responsible for Laser-Induced Nucleation of Glycine.

Laser-induced crystal nucleation through optical tweezing, and in particular polymorph selection through laser polarization, promises unprecedented control over crystallization. However, in the absence of a nearby liquid-liquid critical point or miscibility gap, the origin of the required mesoscale clusters remains unclear. A number of recent studies of so-called nonclassical nucleation have suggested the presence of large amorphous clusters. Here, we show that supersaturated aqueous glycine solutions form metastable intermediate particles that are off the direct path to crystal nucleation. Laser-induced crystal nucleation only occurs when the laser "activates" one of these particles. In situ low-frequency Raman spectroscopy is used to demonstrate their amorphous or partially ordered character and transformation to various crystal polymorphs. The requirement for solution aging in many previously reported laser-induced crystal nucleation experiments strongly suggests that the presence of amorphous intermediates is a general requirement.

Free-energy barriers in the Sherrington-Kirkpatrick model.

The free-energy landscape of the Sherrington-Kirkpatrick (SK) Ising spin glass is simple in the framework of the Thouless-Anderson-Palmer (TAP) equationsas each solution (which are minima of the free energy) has associated with it a nearby index-one saddle point. The free-energy barrier to escape the minimum is just the difference between the saddle point free energy and that at its associated minimum. This difference is calculated for the states with free energies f>f_{c}. It is very small for these states, decreasing as 1/N^{2}, where N is the number of spins in the system. These states are not marginally stable. We argue that such small barriers are why numerical studies never find these states when N is large. Instead, the states that are found are those that have marginal stability. For them the barriers are at least of O(1). f_{c} is the free energy per spin below which the states develop broken replica-symmetry-like overlaps with each other. In the regime f<f_{c} we can only offer some possibilities based around scaling arguments. One of these suggest that the barriers might become as large as N^{1/3}. That might be consistent with recent numerical studies on the Viana-Bray model, which were at variance with the expectations of Cugliandolo and Kurchan for the SK model.

Optimized proportional-integral controller for a photovoltaic-virtual synchronous generator system

Due to the lack of a physical rotating rotor, the photovoltaic generators (PVG) have no inertia. Therefore, replacing traditional synchronous generators (SG), (source of inertia), with PVG will reduce the inertia of overall power systems. A reduced inertia and damping feature will frequently cause instability issues due to high rate of change of frequency (RoCoF), and less stringent voltage at the nearby point of common coupling (PCC). In this paper, the concept of virtual synchronous generators (VSG) is adopted in to couple the source with the grid frequency in order to provide virtual inertia. This is created by using energy storage for short time, direct current (DC) to alternating current (AC) converter, and a suitable control mechanism. In implementing VSG, the important aspects to focus on are reducing the fluctuation of DC-link voltage, stabilize the frequency and voltage, and power flow. So, the particle swarm optimization (PSO) algorithm was used to adjust the parameters of proportional integral (PI) controller by reducing the error of the current controller and voltage regulator in the VSG controller. The simulation results illustrate the advantages of the PI tuning using PSO, where the overshoot is decreased by 68.9% and the settling time is decreased by 34% due to load fluctuations.

Point Cloud Segmentation from iPhone-Based LiDAR Sensors Using the Tensor Feature

With widely used LiDAR sensors included in consumer electronic devices, it is increasingly convenient to acquire point cloud data, but it is also difficult to segment the point cloud data obtained from these unprofessional LiDAR devices, due to their low accuracy and high noise. To address the issue, a point cloud segmentation method using the tensor feature is proposed. The normal vectors of the point cloud are computed based on initial tensor encoding, which are further encoded into the tensor of each point. Using the tensor from a nearby point, the tensor of the center point is aggregated in all dimensions from its neighborhood. Then, the tensor feature in the point is decomposed and different dimensional shape features are detected, and the point cloud dataset is segmented based on the clustering of the tensor feature. Using the point cloud dataset acquired from the iPhone-based LiDAR sensor, experiments were conducted, and results show that both normal vectors and tensors are computed, then the dataset is successfully segmented.

Hybrid inflation and waterfall field in string theory from D7-branes

We present an explicit string realisation of a cosmological inflationary scenario we proposed recently within the framework of type IIB flux compactifications in the presence of three magnetised D7-brane stacks. Inflation takes place around a metastable de Sitter vacuum. The inflaton is identified with the volume modulus and has a potential with a very shallow minimum near the maximum. Inflation ends due to the presence of “waterfall” fields that drive the evolution of the Universe from a nearby saddle point towards a global minimum with tuneable vacuum energy describing the present state of our Universe.

Human Impact on Planetary Temperature and Glacial Volume: Extending a Toy Climate Model to a New Millennium

Starting with a toy climate model from the literature, we employ a system of two nonlinear differential equations to model the reciprocal effects of the average temperature and the percentage of glacial volume on Earth. In the literature, this model is used to demonstrate the potential for a stable periodic orbit over a long time span in the form of an attracting limit cycle. In the roughly twenty five years since this model appeared in the literature, the effects of global warming and human-impacted climate change have become much more well known and apparent. We demonstrate modification of initial conditions to understand how human activity could affect the model results. Although we too see the attracting limit cycle that yields a periodic orbit, we demonstrate that small perturbations in initial conditions can lead to extreme outcomes due to the presence of a nearby saddle point. We simulate the results over time to to highlight the critical nature of perturbations that in effect change the initial conditions and to determine how soon drastic climate events might take place.

Nutrient Improvements in Chesapeake Bay: Direct Effect of Load Reductions and Implications for Coastal Management

In Chesapeake Bay in the United States, decades of management efforts have resulted in modest reductions of nutrient loads from the watershed, but the corresponding improvements in estuarine water quality have not consistently followed. Generalized additive models were used to directly link river flows and nutrient loads from the watershed to nutrient trends in the estuary on a station-by-station basis, which allowed for identification of exactly when and where responses are happening. Results show that Chesapeake Bay's total nitrogen and total phosphorus conditions are mostly improving after accounting for variation in freshwater flow. Almost all of these improving nutrient concentrations in the estuary can be explained by reductions in watershed loads entering through 16 rivers and 145 nearby point sources, with the nearby point source reductions being slightly more effective at explaining estuarine nutrient trends. Overall, these two major types of loads from multiple locations across the watershed are together necessary and responsible for the improving estuarine nutrient conditions, a finding that is highly relevant to managing valuable estuarine resources worldwide.

Quantification of Spatial and Temporal Trends in Atmospheric Mercury Deposition across Canada over the Past 30 Years.

Mercury (Hg) is a pollutant of concern across Canada and transboundary anthropogenic Hg sources presently account for over 95% of national anthropogenic Hg deposition. This study applies novel statistical analyses of 82 high-resolution dated lake sediment cores collected from 19 regions across Canada, including nearby point sources and in remote regions and spanning a full west-east geographical range of ∼4900 km (south of 60°N and between 132 and 64°W) to quantify the recent (1990-2018) spatial and temporal trends in anthropogenic atmospheric Hg deposition. Temporal trend analysis shows significant synchronous decreasing trends in post-1990 anthropogenic Hg fluxes in western Canada in contrast to increasing trends in the east, with spatial patterns largely driven by longitude and proximity to known point source(s). Recent sediment-derived Hg fluxes agreed well with the available wet deposition monitoring. Sediment-derived atmospheric Hg deposition rates also compared well to the modeled values derived from the Hg model, when lake sites located nearby (<100 km) point sources were omitted due to difficulties in comparison between the sediment-derived and modeled values at deposition "hot spots". This highlights the applicability of multi-core approaches to quantify spatio-temporal changes in Hg deposition over broad geographic ranges and assess the effectiveness of regional and global Hg emission reductions to address global Hg pollution concerns.

From Point Vortices to Vortex Patches in Self-Similar Expanding Configurations

The main result is that given a self-similarly expanding configuration of 3 point vortices that start sufficiently far out, we can instead take compactly supported vorticity functions, and the resulting solution to 2D incompressible Euler will evolve like a nearby point vortex configuration for all time, with the size of the patches growing at most as \(t^{1/4+\epsilon }\) and the distance between them growing as \(\sqrt{t}\).

A systematic capacity assessment and classification of geologic CO2 storage systems in India

With rising carbon dioxide emissions and ambitious global targets for climate change mitigation, carbon sequestration has become one of the most promising carbon dioxide removal technologies. It can prove especially beneficial to an emerging country such as India, which must balance its rapid economic growth with a reduction in emissions. A detailed assessment of the cumulative storage capacity is necessary to facilitate the development of CO2 storage pathways in India. Previous assessments for India have primarily estimated the theoretical storage capacity based on limited data. In this study, we have reviewed different methodologies for estimating storage capacities globally, and based on the most current data available, in a first, developed a systematic assessment for theoretical and effective CO2 storage capacities for different geological formations in India. Four storage pathways with adequate potential have been identified: storage through CO2 enhanced oil recovery (EOR), enhanced coalbed methane recovery (ECBMR), storage in deep saline aquifers, and basalt formations. The results indicate considerable potential for CO2 storage in India, especially in saline aquifers (291 Gt) and basalt (97–316 Gt). Even though the storage capacity estimated through EOR (3.4 Gt) and ECBMR (3.7 Gt) is comparatively less, it is adequate to store emissions from nearby large point sources. These methods are also highly feasible due to the ready availability of infrastructure and extensive geological information about the basins involved. In addition, we have developed novel classification systems for different basins in India to represent their prospectivity for CO2 storage.

CO_{2} Convection in Hydrocarbon Under Flowing Conditions

Carbon-neutral oil production is one way to improve the sustainability of petroleum resources. The emissions from produced hydrocarbons can be offset by injecting capture CO_{2} from a nearby point source into a saline aquifer for storage or a producing oil reservoir. The latter is referred to as enhanced oil recovery (EOR) and would enhance the economic viability of CO_{2} sequestration. The injected CO_{2} will interact with the oil and cause it to flow more freely within the reservoir. Consequently, the overall recovery of oil from the reservoir will increase. This enhanced oil recovery (EOR) technique is perceived as the most cost-effective method for disposing captured CO_{2} emissions and has been performed for many decades with the focus on oil recovery. The interaction between existing oil and injected CO_{2} needs to be fully understood to effectively manage CO_{2} migration and storage efficiency. When CO_{2} and oil mix in a fully miscible setting, the density can change non-linearly and cause density instabilities. These instabilities involve complex convective-diffusive processes, which are hard to model and simulate. The interactions occur at the sub-centimeter scale, and it is important to understand its implications for the field scale migration of CO_{2} and oil. In this work, we simulate gravity effects, namely gravity override and convective mixing, during miscible displacement of CO_{2} and oil. The flow behavior due to the competition between viscous and gravity effects is complex, and can only be accurately simulated with a very fine grid. We demonstrate that convection occurs rapidly, and has a strong effect on breakthrough of CO_{2} at the outlet. This work for the first time quantifies these effects for a simple system under realistic conditions.

Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing

Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server’s capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consumption and ensure uninterrupted connectivity with hybrid access point. However, the smart division of application into k subtasks as well as intelligent partitioning of time slot for harvesting energy and offloading data is a complex problem. In this paper, we propose a novel deep-learning-based offloading and time allocation policy (DOTP) for training a deep neural network that divides the computation application into optimal number of subtasks, decides for the subtasks to be offloaded or executed locally (offloading policy), and divides the time slot for data offloading and energy harvesting (time allocation policy). DOTP takes into account the current battery level, energy consumption, and time delay of mobile terminal. A comprehensive cost function is formulated, which uses all the aforementioned metrics to calculate the cost for all k number of subtasks. We propose an algorithm that selects the optimal number of subtasks, partial offloading policy, and time allocation policy to generate a huge dataset for training a deep neural network and hence avoid huge computational overhead in partial offloading. Simulation results are compared with the benchmark schemes of total offloading, local execution, and partial offloading. It is evident from the results that the proposed algorithm outperforms the other schemes in terms of battery life, time delay, and energy consumption, with 75% accuracy of the trained deep neural network. The achieved decrease in total energy consumption of mobile terminal through DOTP is 45.74%, 36.69%, and 30.59% as compared to total offloading, partial offloading, and local offloading schemes, respectively.

Water Quality Prediction Method Based on Transfer Learning and Echo State Network

Due to the adjacency effect of space, the water quality information of adjacent monitoring points in the same water area is relevant. However, traditional prediction methods use the historical water quality information of only one monitoring point rather than other nearby monitoring points. It is important to note that the sample quantity has a great influence on the prediction accuracy of the prediction methods which require lots of training samples. In addition, the water quality information is time series. Whether the time sequence of water quality information can be effectively used has direct impact on the prediction accuracy. Therefore, this paper proposes a water quality prediction method based on transfer learning and echo state network (ESN). First, we transfer the features of water quality samples of the nearby monitoring point to the prediction monitoring point based on transfer component analysis, and further select the high-quality samples based on similarity and time sequence. In this way the sample quantity of the prediction monitoring point is increased on the premise of ensuring the sample quality. Second, we construct a water quality prediction model based on ESN which takes the advantage of the time sequence of water quality information, improving the prediction accuracy on the premise of ensuring the training time. Finally, the proposed method is applied in the actual water quality dataset of Hong Kong. The experiment results demonstrate that the proposed method can expand the quantity of training samples and further improve the accuracy of water quality prediction by making full use of the space-time characteristics of water quality information.

Characterization of ground-based atmospheric pollution and meteorology sampling stations during the Lake Michigan Ozone Study 2017

ABSTRACT The Lake Michigan Ozone Study 2017 (LMOS 2017) in May and June 2017 enabled study of transport, emissions, and chemical evolution related to ozone air pollution in the Lake Michigan airshed. Two highly instrumented ground sampling sites were part of a wider sampling strategy of aircraft, shipborne, and ground-based mobile sampling. The Zion, Illinois site (on the coast of Lake Michigan, 67 km north of Chicago) was selected to sample higher NOx air parcels having undergone less photochemical processing. The Sheboygan, Wisconsin site (on the coast of Lake Michigan, 211 km north of Chicago) was selected due to its favorable location for the observation of photochemically aged plumes during ozone episodes involving southerly winds with lake breeze. The study encountered elevated ozone during three multiday periods. Daytime ozone episode concentrations at Zion were 60 ppb for ozone, 3.8 ppb for NOx, 1.2 ppb for nitric acid, and 8.2 μg m-3 for fine particulate matter. At Sheboygan daytime, ozone episode concentrations were 60 ppb for ozone, 2.6 ppb for NOx, and 3.0 ppb for NOy. To facilitate informed use of the LMOS 2017 data repository, we here present comprehensive site description, including airmass influences during high ozone periods of the campaign, overview of meteorological and pollutant measurements, analysis of continuous emission monitor data from nearby large point sources, and characterization of local source impacts from vehicle traffic, large point sources, and rail. Consistent with previous field campaigns and the conceptual model of ozone episodes in the area, trajectories from the southwest, south, and lake breeze trajectories (south or southeast) were overrepresented during pollution episodes. Local source impacts from vehicle traffic, large point sources, and rail were assessed and found to represent less than about 15% of typical concentrations measured. Implications for model-observation comparison and design of future field campaigns are discussed. Implications: The Lake Michigan Ozone Study 2017 (LMOS 2017) was conducted along the western shore of Lake Michigan, and involved two well-instrumented coastal ground sites (Zion, IL, and Sheboygan, WI). LMOS 2017 data are publicly available, and this paper provides detailed site characterization and measurement summary to enable informed use of repository data. Minor local source impacts were detected but were largely confined to nighttime conditions of less interest for ozone episode analysis and modeling. The role of these sites in the wider field campaign and their detailed description facilitates future campaign planning, informed data repository use, and model-observation comparison.

Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather

A worldwide increase in the number of vehicles on the road has led to an increase in the frequency of serious traffic accidents, causing loss of life and property. Autonomous vehicles could be part of the solution, but their safe operation is dependent on the onboard LiDAR (light detection and ranging) systems used for the detection of the environment outside the vehicle. Unfortunately, problems with the application of LiDAR in autonomous vehicles remain, for example, the weakening of the echo detection capability in adverse weather conditions. The signal is also affected, even drowned out, by sensory noise outside the vehicles, and the problem can become so severe that the autonomous vehicle cannot move. Clearly, the accuracy of the stereo images sensed by the LiDAR must be improved. In this study, we developed a method to improve the acquisition of LiDAR data in adverse weather by using a combination of a Kalman filter and nearby point cloud denoising. The overall LiDAR framework was tested in experiments in a space 2 m in length and width and 0.6 m high. Normal weather and three kinds of adverse weather conditions (rain, thick smoke, and rain and thick smoke) were simulated. The results show that this system can be used to recover normal weather data from data measured by LiDAR even in adverse weather conditions. The results showed an effective improvement of 10% to 30% in the LiDAR stereo images. This method can be developed and widely applied in the future.

A Surface Geometry Model for LiDAR Depth Completion

LiDAR depth completion is a task that predicts depth values for every pixel on the corresponding camera frame, although only sparse LiDAR points are available. Most of the existing state-of-the-art solutions are based on deep neural networks, which need a large amount of data and heavy computations for training the models. In this letter, a novel non-learning depth completion method is proposed by exploiting the local surface geometry that is enhanced by an outlier removal algorithm. The proposed surface geometry model is inspired by the observation that most pixels with unknown depth have a nearby LiDAR point. Therefore, it is assumed those pixels share the same surface with the nearest LiDAR point, and their respective depth can be estimated as the nearest LiDAR depth value plus a residual error. The residual error is calculated by using a derived equation with several physical parameters as input, including the known camera intrinsic parameters, estimated normal vector, and offset distance on the image plane. The proposed method is further enhanced by an outlier removal algorithm that is designed to remove incorrectly mapped LiDAR points from occluded regions. On KITTI dataset, the proposed solution achieves the best error performance among all existing non-learning methods and is comparable to the best self-supervised learning method and some supervised learning methods. Moreover, since outlier points from occluded regions is a commonly existing problem, the proposed outlier removal algorithm is a general preprocessing step that is applicable to many robotic systems with both camera and LiDAR sensors.

X-Ray-to-radio Offset Inference from Low-count X-Ray Jets

Abstract Observations of positional offsets between the location of X-ray and radio features in many resolved, extragalactic jets indicates that the emitting regions are not cospatial, an important piece of evidence in the debate over the origin of the X-ray emission on kiloparsec scales. The existing literature is nearly exclusively focused on jets with sufficiently deep Chandra observations to yield accurate positions for X-ray features, but most of the known X-ray jets are detected with tens of counts or fewer, making detailed morphological comparisons difficult. Here we report the detection of X-ray-to-radio positional offsets in 15 extragalactic jets from an analysis of 22 sources with low-count Chandra observations, where we utilized the Low-count Image Reconstruction Algorithm. This algorithm has allowed us to account for effects such as Poisson background fluctuations and nearby point sources which have previously made the detection of offsets difficult in shallow observations. Using this method, we find that in 55% of knots with detectable offsets, the X-rays peak upstream of the radio, questioning the applicability of one-zone models, including the IC/CMB model for explaining the X-ray emission. We also report the nondetection of two previously claimed X-ray jets. Many, but not all, of our sources follow a loose trend of increasing offset between the X-ray and radio emission, as well as a decreasing X-ray-to-radio flux ratio along the jet.

Chaos in conservative discrete-time systems subjected to parameter drift.

Based on the example of a paradigmatic area preserving low-dimensional mapping subjected to different scenarios of parameter drifts, we illustrate that the dynamics can best be understood by following ensembles of initial conditions corresponding to the tori of the initial system. When such ensembles are followed, snapshot tori are obtained, which change their location and shape. Within a time-dependent snapshot chaotic sea, we demonstrate the existence of snapshot stable and unstable foliations. Two easily visualizable conditions for torus breakup are found: one in relation to a discontinuity of the map and the other to a specific snapshot stable manifold, indicating that points of the torus are going to become subjected to strong stretching. In a more general setup, the latter can be formulated in terms of the so-called stable pseudo-foliation, which is shown to be able to extend beyond the instantaneous chaotic sea. The average distance of nearby point pairs initiated on an original torus crosses over into an exponential growth when the snapshot torus breaks up according to the second condition. As a consequence of the strongly non-monotonous change of phase portraits in maps, the exponential regime is found to split up into shorter periods characterized by different finite-time Lyapunov exponents. In scenarios with plateau ending, the divided phase space of the plateau might lead to the Lyapunov exponent averaged over the ensemble of a torus being much smaller than that of the stationary map of the plateau.

Is accommodation a confounder in pupillometry research?

Much psychological research uses pupil diameter measurements to investigate the cognitive and emotional effects of visual stimuli. A potential problem is that accommodating at a nearby point causes the pupil to constrict. This study examined to what extent accommodation is a confounder in pupillometry research. Participants solved multiplication problems at different distances (Experiment 1) and looked at line drawings with different monocular depth cues (Experiment 2) while their pupil diameter, refraction, and vergence angle were recorded using a photorefractor. Experiment 1 showed that the pupils dilated while performing the multiplications, for all presentation distances. Pupillary constriction due to accommodation was not strong enough to override pupil dilation due to cognitive load. Experiment 2 showed that monocular depth cues caused a small shift in refraction in the expected direction. We conclude that, for the young student sample we used, pupil diameter measurements are not substantially affected by accommodation.