Quantitative Spatial Distribution Research Articles

Geostatistical modelling of soil properties towards long-term ecological sustainability of agroecosystems

A profound grasp of the quantitative spatial heterogeneity and distribution of the soil physicochemical attributes is crucial in understanding agricultural landscapes for ensuring the provisioning of soil ecosystem services. However, the analysis of data from remote sensing, like NDVI, can be of help in analysing the capacity of the landscape to provide supporting ecosystem services such as primary productivity. The research investigated and addressed the dispersion of important soil physico-chemical attributes in agricultural lands of the temperate Himalayan region of India using a geostatistical method and combining normalized difference vegetation index (NDVI) time-series data and the regression Kriging method. A 206 soil samples were gathered and assessed for soil parameters like pH, EC, OC, and available N, P, K, Ca, and Mg from Kishtwar district of Jammu. The coefficient of variation (CV) for pH and electrical conductivity (EC) ranged notably from 8.75 % to 118.98 %, highlighting diverse soil characteristics critical for local management practices. Mean elevation averaged 2743.32 m (m), with a moderate NDVI of 0.15, indicating dynamics in vegetation cover. Soil pH ranged from intensely acidic to marginally alkaline, with varying EC levels. Seemingly high organic carbon (OC), nitrogen (N), and potassium (K) levels, accompanied by medium phosphorus (P), calcium (Ca), and magnesium (Mg) levels were found in the region. The study employed ordinary kriging (OK) to map the spatial distribution of soil parameters, utilizing mean square error (MSE), root mean square error (RMSE), and the Moran’s I index. Exponential models were the best fit models for OC, while spherical models were fit for pH, EC, N, P, and Ca. Mathematical models were best fit for K and Mg. Spatial analysis using spherical and exponential models revealed distinct distribution patterns for pH, N, P, Ca, and Mg. The results of the degree of spatial dependence from the semi-variogram analyses indicated a strong (0.06 %) to moderate (0.51 %) to weak (2.81 %) dependence. The interpolated maps showed a distinct gradient in elevation (1053–4413 m), OC (0.13–2.80 %), NDVI (−0.16–0.54), pH (4.80–8.00), EC (0.03–9.80 dS m−1), N (201.15–993.19 kg ha−1), P (3.00–96.00 kg ha−1), K (124.88–1110.71 kg ha−1), Ca (7.00–46.00 meq 100 g soil−1), and Mg (2.30–21.50 meq 100 g soil−1) at the regional scale, indicating a wide range of spatial soil heterogeneity. The heterogeneity maps of soil parameters generated by this research can be effectively used by land planners and farm managers at a regional scale for crop nutrient management to reduce soil contamination risk. These maps serve as baseline materials and effective tools for suitable land management strategies such as conservation-effective tillage, integrated nutrient management, and organic farming based on the spatial distribution of soil properties and they can significantly enhance the long-term ecological sustainability of agro-ecosystems’ management.

MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022)

Abstract. Accurate long-term daily cloud-gap-filled fractional snow cover products are essential for climate change and snow hydrological studies in the Asian Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are not sufficient. In this study, the multiple-endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the multistep spatiotemporal interpolation algorithm (MSTI) are used to produce the MODIS daily cloud-gap-filled fractional snow cover product over the AWT region (AWT MODIS FSC). The AWT MODIS FSC products have a spatial resolution of 0.005° and span from 2000 to 2022. The 2745 scenes of Landsat-8 images are used for the areal-scale accuracy assessment. The fractional snow cover accuracy metrics, including the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE), are 0.80, 0.16 and 0.10, respectively. The binarized identification accuracy metrics, including overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), are 95.17 %, 97.34 % and 97.59 %, respectively. Snow depth data observed at 175 meteorological stations are used to evaluate accuracy at the point scale, yielding the following accuracy metrics: an OA of 93.26 %, a PA of 84.41 %, a UA of 82.14 % and a Cohen kappa (CK) value of 0.79. Snow depth observations from meteorological stations are also used to assess the fractional snow cover resulting from different weather conditions, with an OA of 95.36 % (88.96 %), a PA of 87.75 % (82.26 %), a UA of 86.86 % (78.86 %) and a CK of 0.84 (0.72) under the MODIS clear-sky observations (spatiotemporal reconstruction based on the MSTI algorithm). The AWT MODIS FSC product can provide quantitative spatial distribution information on snowpacks for mountain hydrological models, land surface models and numerical weather prediction in the Asian Water Tower region. This dataset is freely available from the National Tibetan Plateau Data Center at https://doi.org/10.11888/Cryos.tpdc.272503 (Jiang et al., 2022) or from the Zenodo platform at https://doi.org/10.5281/zenodo.10005826 (Jiang et al., 2023a).

Human head sized magnetorelaxometry imaging of magnetic nanoparticles with optically pumped magnetometers — A feasibility study

Magnetic nanoparticles (MNP) offer exciting biomedical applications, e.g. magnetic hyperthermia for tumor treatment. The knowledge of the quantitative spatial distribution of MNP in the human body is a key for treatment safety and efficiency. Here we experimentally demonstrate the reconstruction of the quantitative MNP distribution of a human head phantom by magnetorelaxometry imaging (MRXI). The MRXI setup is composed of a 50 channel optically pumped magnetometer (OPM) system and 72 excitation coils, which are operated inside a magnetically shielded room. With our setup, we were able to reconstruct a glioblastoma phantom with a clinically relevant iron amount. A simulation study reveals, that currently the main limitations in terms of reconstruction quality arise from geometrical uncertainties of the setup. Summing up, we demonstrate the feasibility of OPM-MRXI for large regions of interest, potentially advancing the monitoring of MNP in related treatments.

Diffusion zone evolution around growing crystals in combeite glass

AbstractThe precipitation of nonstoichiometric crystals (of different composition from the parent glass) motivated us to study the stoichiometric Na2O·2CaO·3SiO2 ( N2CS3) glass‐forming system comprehensively. The concentration profiles of Na and Ca in the diffusion zone around Na4+2xCa4−x[Si6O18], , crystals in an N2C3S glass were characterized by energy‐dispersive spectroscopy and were then theoretically described. We show that the diffusion zone is formed because of the rejection of part of calcium by the growing crystal. The radial variation of the Ca concentrations and the crystal nucleation rate through the diffusion zone were analyzed simultaneously. We found that the nucleation rate in the diffusion zone near the crystal–glass interface drastically decreases due to the increase in the work of critical cluster formation resulting from the composition change. In contrast, the diffusion activation barrier remains practically constant. Moreover, the Ca diffusion coefficients, DCa, in Na2O·2CaO·3SiO2 and CaO·SiO2 glasses show the same Arrhenius dependence, demonstrating that (surprisingly) DCa does not depend on the glass composition in the CaO·SiO2–Na2O·SiO2 metasilicate joint. This work provides the quantitative spatial distribution of chemical components in a partially crystallized glass, shedding light on solid solution crystallization in a glass‐forming liquid and its consequences.

Assessing urban pluvial waterlogging resilience based on sewer congestion risk and climate change impacts

Urban waterlogging is a critical issue in urban governance, as without proper preventive measures, stagnant water can lead to damage or even casualties. Identifying waterlogging risk points is often desired in urban waterlogging management. Evaluation strategies should be tailored due to the dynamic features of climate warming and high uncertainty in rainfall patterns. An urban pluvial waterlogging resilience (UPWR) analysis framework is proposed as the basis and the tool for dynamically evaluating the spatial distribution of urban waterlogging resilience. UPWR combines hydrological modelling with drainage capacity calculation, extending the original urban waterlogging analysis into a spatial–temporal resilience analysis paradigm for urban waterlogging management in a future climate. An area in Wuhan city, which has suffered from severe waterlogging disaster events for years, was selected for a case study to test the effectiveness and applicability of the proposed approach. This study found that (1) as the drainage network congestion situation intensifies, the UPWR grade decreases in 14% of the study areas; (2) from 2030 to 2100, the UPWR in Wuhan shows a worsening trend under each Representative Concentration Pathway (RCP), and the UPWR under the RCP45 scenario is the worst; and (3) the analysis results can be used to predict future UPWR changes and determine in advance which areas require special maintenance. The novelty of this research lies in the following: (a) the two levels of precipitation and drainage are combined to construct an UPWR assessment framework, providing a quantitative spatial distribution that lays the foundation for exploring urban waterlogging management solutions; and (b) consideration is given to the dynamic change in climate and urban resistance to waterlogging when predicting the future UPWR via a continuous time series, which can provide a basis for urban waterlogging control planning.

Regional-scale burned area mapping in Mediterranean regions based on the multitemporal composite integration of Sentinel-1 and Sentinel-2 data

ABSTRACT Wildland fires are among the main factors affecting the surrounding territory in terms of ecological and socioeconomic changes at different temporal and spatial scales. In the Mediterranean environment, although fire can positively influence some bio-physical dynamics of habitats, it acts as a pressing disturbance on ecosystems when the severity, spatial scale, and/or frequency are high, thereby determining their degradation. Therefore, knowing and mapping the accurate quantitative spatial distribution of all areas affected by fire during an entire high-frequency fire season and on a relatively large scale (regional/national scale) is an essential step to initialize the numerous subsequent effect monitoring analyses that can be carried out. This work proposes a reliable and open-access workflow to map burned areas on regional and national scales during the entire fire season. To achieve this, we integrated optical (Sentinel-2, S2) and Synthetic Aperture Radar (SAR; Sentinel-1, S1) free high spatial and temporal resolution data into a multitemporal composite criterion. Open-source software and Python-based libraries were used to develop the workflow. In particular, the second-lowest near infra-red (NIR) image composite (secMinNIR) criterion, based on the retrieval of the second minimum values that the NIR values reached in each pixel during the entire time frame considered, was applied to cloud-free S2 imagery to optimize the separability between burned and unburned areas. Subsequently, a second temporal composite criterion was developed and applied to the S1 time series, relying on the SAR capacity to detect vegetation fire-induced structural and humidity changes. It was based on retrieving the S1 pixel value of the first next (or the same) date to the corresponding date of the pixel value previously found by secMinNIR. The burned area map was created using an object-based geographic analysis (GEOBIA) process, using two optical and SAR composite images as input layers. The large-scale mean-shift (LSMS) algorithm was employed to segment the image, while the random forest (RF) classifier was the machine-learning model used to perform supervised classification. GEOBIA-based burned area classification was also performed using only the optical composite. The resulting accuracy values were compared using the precision (p), recall (r), and F-score accuracy metrics. The classification achieved high accuracy levels (F-score value greater than 0.9) in both cases (S1+ S2, 0.956; S2, 0.914), highlighting the increased effectiveness of this approach in detecting burned areas, heterogeneous in terms of amplitude, and affected site-specific characteristics that occurred during the fire season. Although the use of only optical data is sufficient to map the fire-affected areas early, some commission errors, represented by small regions scattered over the entire study area, remain, proving that the integration of SAR data improves the quality of the obtained results.

Spatial characteristics and multifactorial driving analysis of fly-tipping bulky waste in Beijing based on the random forest model

The phenomenon of fly-tipping of bulky waste is becoming increasingly serious with the development of economy and the improvement of living standards. The fly-tipping of bulky waste causes a waste stream with a high proportion of good quality recyclable materials and a wide range of social and environmental problems including damaging the environment and aggravating traffic in a megacity like Beijing. In this study, we analyzed the quantitative spatial distribution of fly-tipping bulky waste in Beijing for the first time and identified its driving forces to explore the fly-tipping rule of bulky waste. We used Anselin's Local Moran I method to reveal the spatial characteristics , and Random Forest machine learning method to examine the driving factors based on multiple data sources such as geographical, population and survey data. The results showed that the spatial agglomeration of fly-tipping presented a typical core edge diffusion spatial distribution pattern. High-High clusters of cases were found in most regions of Dongcheng District and the east part of Xicheng District. In the multifactorial drivers, the three most important driving factors were found to be accommodation, floating population and income level. Surprisingly, any kind of road and educational level had small effect on the fly-tipping case. These results provide a theoretical basis for the government to advance comprehensive prevention and control strategies of bulky waste fly-tipping. And it is helpful to formulate management policies of fly-tipping bulky waste and provide targeted guidance and suggestions for senior decision makers and managers. • Random forest machine learning method was used to analyze bulky waste fly-tipping. • Fly-tipping presented a typical core edge diffusion spatial distribution pattern. • 15 driving factors were extracted from multiple data sources. • Accommodation and floating population had the highest impact on fly-tipping.

Data processing of three-dimensional vibrational spectroscopic chemical images for pharmaceutical applications

Vibrational spectroscopic chemical imaging is a powerful tool in the pharmaceutical industry to assess the spatial distribution of components within pharmaceutical samples. Recently, the combination of vibrational spectroscopic chemical mapping with serial sectioning has provided a means to visualise the three-dimensional (3D) structure of a tablet matrix. There are recognised knowledge gaps in current tablet manufacturing processes, particularly regarding the size, shape and distribution of components within the final drug product. The performance of pharmaceutical tablets is known to be primarily influenced by the physical and chemical properties of the formulation. Here, we describe the data processing methods required to extract quantitative domain size and spatial distribution statistics from 3D vibrational spectroscopic chemical images. This provides a means to quantitatively describe the microstructure of a tablet matrix and is a powerful tool to overcome knowledge gaps in current tablet manufacturing processes, optimising formulation development.

2D Quantitative Imaging of Magnetic Nanoparticles by an AC Biosusceptometry Based Scanning Approach and Inverse Problem.

The use of magnetic nanoparticles (MNPs) in biomedical applications requires the quantitative knowledge of their quantitative distribution within the body. AC Biosusceptometry (ACB) is a biomagnetic technique recently employed to detect MNPs in vivo by measuring the MNPs response when exposed to an alternate magnetic field. The ACB technique presents some interesting characteristics: non-invasiveness, low operational cost, high portability, and no need for magnetic shielding. ACB conventional methods until now provided only qualitative information about the MNPs’ mapping in small animals. We present a theoretical model and experimentally demonstrate the feasibility of ACB reconstructing 2D quantitative images of MNPs’ distributions. We employed an ACB single-channel scanning approach, measuring at 361 sensor positions, to reconstruct MNPs’ spatial distributions. For this, we established a discrete forward problem and solved the ACB system’s inverse problem. Thus, we were able to determine the positions and quantities of MNPs in a field of view of with good precision and accuracy. The results show the ACB system’s capabilities to reconstruct the quantitative spatial distribution of MNPs with a spatial resolution better than , and a sensitivity of of MNPs fixed in gypsum. These results show the system’s potential for biomedical application of MNPs in several studies, for example, electrochemical-functionalized MNPs for cancer cell targeting, quantitative sensing, and possibly in vivo imaging.

Spatial characteristics and driving forces of anthropogenic phosphorus emissions in the Yangtze River Economic Belt, China

Excessive phosphorus emissions by anthropogenic activities are closely related to eutrophication in the Yangtze River. Quantitative spatial distribution of total phosphorus (TP) emissions and identifying their driving forces are important for the management of phosphorus pollution in the Yangtze River Economic Belt (YREB). In this study, anthropogenic TP emissions were estimated using emission factors in the YREB for 2017. Global and local spatial autocorrelation and multiple regression analysis were adopted to reveal the spatial agglomeration effects and driving forces of the TP emissions in 130 cities of the YREB. The TP emissions were estimated to be 155,940 t, and the agricultural TP emissions were 106,181 t, followed by domestic (46,032 t) and industrial emissions (3727 t) in 2017 in the YREB. Livestock breeding was the most significant anthropogenic source of TP emissions. The TP emissions in the middle and lower reaches were larger than those in the upper reaches. A significant spatial agglomeration effect of TP emissions was found in the YREB, with High-high aggregation in the middle reaches and Low-low aggregation in the upper reaches. The paddy land area was the most significant factor affecting TP emissions, followed by the population size and vegetable orchard area. The results provide fundamental data regarding pollution sources for subsequent research investigations. The results enhance the understanding of the TP source contributions; thus, accurate control measures could focus on the important sources and regions of TP pollution in the YREB.

Three-Dimensional Quantitative Intracellular Visualization of Graphene Oxide Nanoparticles by Tomographic Flow Cytometry.

Interaction of nanoparticles (NPs) with cells is of fundamental importance in biology and biomedical sciences. NPs can be taken up by cells, thus interacting with their intracellular elements, modifying the life cycle pathways, and possibly inducing death. Therefore, there is a great interest in understanding and visualizing the process of cellular uptake itself or even secondary effects, for example, toxicity. Nowadays, no method is reported yet in which 3D imaging of NPs distribution can be achieved for suspended cells in flow-cytometry. Here we show that, by means of label-free tomographic flow-cytometry, it is possible to obtain full 3D quantitative spatial distribution of nanographene oxide (nGO) inside each single flowing cell. This can allow the setting of a class of biomarkers that characterize the 3D spatial intracellular deployment of nGO or other NPs clusters, thus opening the route for quantitative descriptions to discover new insights in the realm of NP–cell interactions.

Measurement and modeling of planar airblast spray flux distributions

A liquid sheet breaks up into drops by fast air streams at airblast type atomizers, which are widely utilized for jet engines. Fuel spray flux in space directly determines the local mass ratio of fuel to air inside a combustion chamber. However, it has been difficult to measure the quantitative spatial distribution of spray flux using laser techniques and mechanical patternators. Therefore, in the present study, we have developed a new shape of 3D printed patternator applicable to the measurement of spray flux distribution by a planar airblast atomizer, and quantitative data are presented at the conditions of gas velocity up to 100 m/s with changing atomizer configuration and liquid species as water and kerosene. Laser technique of Phase Doppler Anemometer and high-speed visualization are complementary conducted with theoretical investigation. The experimental results of spray flux distributions are consistently normalized by the proposed dimensionless injection condition based on the square root of momentum ratio of spreading drops to gas stream. The applicable limit of the present modeling framework is given by Weber number based on the ratio of inertia of gas stream to liquid surface tension.

Quantitative spatial distribution model of site-specific loess landslides on the Heifangtai terrace, China

Landslide disasters are associated with severe losses on the Loess Plateau of China. Although early warning systems and susceptibility mapping have mitigated this issue to some extent, most methods are qualitative or semi-quantitative in the site-specific range. In this paper, a quantitative spatial distribution model is presented for site-specific loess landslide hazard assessment. Coupled with multi-temporal remote sensing images and high-precision UAV cloud point data, a total of 98 loess landslides that have occurred since 2004 on the Heifangtai terrace were collected to establish a landslide volume-date and retreating distance database. Eleven loess landslides are selected to construct a numerical model for parameter back analysis, and the accuracy of the simulation results is quantitatively evaluated by the centroid distance and overlapping area. Different volumes and receding distance rates of landslides are fitted to determine the relationship between cracks and potential volume, and different volumes and parameters are combined to simulate the spatial distribution of potential loess landslides. The results of this study reveal that landslide volumes mainly range between 1 × 103 and 5 × 105 m3, and the historical occurrence probability reaches 0.551. The optimal parameters are estimated by the maximum likelihood method to obtain a uniform distribution parameter value probability model, and the results show that the error of the estimated length within a range of 0.05 from the optimal parameter does not exceed 15%. In the selected slope slide case, farmland near the toe of the slope primarily includes exposed hazards with probabilities greater than 0.7. This work provides a useful reference for local disaster reduction and a theoretical methodology for hazard assessments.

Quantitative spatial distribution analysis of mafic monogenic volcanism in the southern Puna, Argentina: Implications for magma production rates and structural control during its ascent

Establishing the factors that control the pathways of magma ascent is an important issue in the study of monogenic mafic volcanism since it provides information about the relationship between the source of the ascending magma and the regional and local tectonic frameworks. We have quantitatively analyzed the spatial distribution of the volcanic centers in the monogenic volcanic fields (MVFs) of the Southern Puna using Nearest-Neighbor Analysis to assess the degree of randomness between individual centers. We also performed alignment analysis which highlights the structural weaknesses that magmas use to ascend. We integrate these novel data with published structural, petrologic, and geophysical data to propose a source-to-surface model which explains the spatial distribution of monogenic mafic centers of the Southern Puna. We find that MVFs display two distinct spatial patterns related to different magmatic production rates. Specifically, in those areas where the magmatic production is long-lasting and relatively high, the magma exerts strain rates suffciently high to reactivate pre-exisiting discontinuities with random orientations with respect to the current local stress conditions. As a result the volcanic centers are arranged with a clustered spatial distribution. We define these as MVFs controlled by magmatism or high flux fields (MVFs-M). On the other hand, low magmatic production rates tend to produce areas where mafic centers display a Poisson distribution, because strain rates are only sufficient to reactivate pre-existing discontinuities that are nearly parallel to the maximum compressive stress. This latter group is defined as MVFs controlled by tectonism or low flux fields (MVFs-T). Available petrological and geophysical data indicate that both groups are fed by a complex lower crustal MASH zone where magma production is associated with lithospheric foundering and subduction-related mantle melting. Spatial distribution of the MVFs in the Southern Puna are also affected by the development of upper crustal magma storage zones.

Quantitative 2D Magnetorelaxometry Imaging of Magnetic Nanoparticles using Optically Pumped Magnetometers.

For biomagnetical applications exploiting physical properties of magnetic nanoparticles (MNP), e.g., magnetic hyperthermia, knowledge about the quantitative spatial MNP distribution is crucial, which can be extracted by magnetorelaxometry (MRX) imaging. In this paper, we present quantification, quantitative 1D reconstruction, and quantitative 2D imaging of MNP by exploiting optically pumped magnetometers for MRX. While highlighting the potential of commercially available optically pumped magnetometers (OPM) for MRXI, we discuss current limitations of the used OPM. We show, that with our OPM setup, MNP can be precisely quantified with iron amounts down to ≈6 μg, which can be improved easily. With a 1D-reconstruction setup, point-like and complex MNP phantoms can be reconstructed quantitatively with high precision and accuracy. We show that with our developed 2D MRX imaging setup, which measures 12 cm by 8 cm, point-like MNP distributions with clinically relevant iron concentrations can be reconstructed precisely and accurately. Our 2D setup has the potential to be easily extended to a tomography styled (and thus slice-selective) 3D scanner, by adding a mechanical axis to the phantom.

Estimation of quantitative spatial and temporal distribution for groundwater storage in agricultural basin of Korea: implications for rational water use

Assessment of spatio-temporal variability in groundwater storage (GWS) is critical for effective water resource planning and management with rational groundwater use. Haean Basin comprising 64 km2 area, mainly depends on groundwater for agricultural, domestic and other water uses. For quantification of GWS variability, sufficient field-based hydrological data of the ongoing decade (2011–2017) were interpolated in ArcGIS, to give reliable results. Despite satellite remote sensing having advantages in the field of hydrology, the limitation of accessibility at larger spatial resolution (~ 200,000 km2) is making it non-applicable for fine-scale observations. From the surface area, saturated thickness and specific yield of aquifer, a decline of 33 million cubic meters in GWS volume was indicated from 230 to 197 during the observed 6 years. An observable drop (4.6 m) in the mean water level due to increased water use (0.49 MCM) during this decade is responsible for the change in the mean groundwater level. A changing water level can change the surface area and saturated thickness of aquifer that have a positive correlation with GWS, and act as main controlling factors for GWS change. An annual recharge of 0.14 m to the total amount of GWS is near negligible, considering the long-term sustainability of groundwater resource. A shift towards more use of surface water, and limited drilling of groundwater wells is recommended for rational water use. The findings of this study will provide basic knowledge for relating studies, specifically which are based on small (local) scale distribution of groundwater resource.

What Is So “Hot” in Heatmap? Qualitative Code Cluster Analysis with Foursquare Venue

Foursquare is a popular Web service and a representative location-based social network (LBSN) service using position data. Heatmap is a widely used means of geovisualization for analyzing social data with locational values. Until now, heatmap analysis of LBSN has focused on identifying quantitative distribution and patterns, with little consideration of the qualitative analysis of data content. Based on a case study of Foursquare venues and user-created content in Seattle, WA, this study conducts analyses assessing both the quantitative spatial distribution and the qualitative characteristics of coffee shops in the Seattle metropolitan area. It specifically proposes a new analytical method referred to as “code cluster,” which is designed to employ quantitative and qualitative approaches simultaneously. The significance of this method is its capacity to explain geographical differences in terms of qualitative traits in cluster regions, in addition to analyzing their spatial characteristics and distributions. In introducing this new hybrid approach, our aims are to reflect the original intent and essence of the data throughout the research process and to make further efforts to analyze and interpret the contextualized meanings. This will be possible through integration of advanced spatial analysis, geovisualization, and qualitative research that build on current geographic and geovisual research with big data.

Reconstruction of Ge spatial distribution in ICF target using PIXE-T

Inertial confinement fusion (ICF) target is a microsphere with diameter at millimeter scale and shell thickness at micrometer scale. Ge-doped glow discharge polymer (GDP) is an ideal material for the target. The homogeneity of dopant is vital for uniform compression and minimization of Rayleigh-Taylor (RT) instabilities in ignition. However, the method of quantitative measurement of dopants’ spatial distribution is still not mature. Quantitation is not accurate and image resolution is low. Particle induced X-ray emission tomography (PIXE-T) is a suitable technique for measuring trace elements’ spatial distribution in small sample at micrometer scale. This paper demonstrates the capabilities of PIXE-T to obtain quantitative spatial distribution of dopant (Germanium) in ICF target for the first time. We also developed a program run in MATLAB to reconstruct Ge spatial distribution 3D image using Filtered Back Projection (FBP) algorithm. When ion beam is transmitted through the target, its energy is attenuated due to collisions. This energy attenuation causes decrease of X-ray emission cross section. For X-ray emitted from target inside, it will be absorbed by target itself before being collected by detector. Both effects cause loss of X-ray counts, especially at the rear of target toward beam. The program can compensate the loss of X-ray counts caused by these two effects, non-linear X-ray production (NLXP) along the path of the beam and X-ray attenuation (XA). This correction is based on a uniform mass density (1.08g/cm3) and a uniform major composition (stoichiometric ratio C:H=5:7 for GDP material) within the sample. Corrections are applied before tomographic reconstruction, producing a more accurate Ge spatial distribution image. After converting X-ray counts in every voxel to Ge concentration using standard reference material (SRM), the image shows quantitative spatial distribution.

Optimization-Based Approach for Joint X-Ray Fluorescence and Transmission Tomographic Inversion

Fluorescence tomographic reconstruction, based on the detection of photons coming from fluorescent emission, can be used for revealing the internal elemental composition of a sample. On the other hand, conventional X-ray transmission tomography can be used for reconstructing the spatial distribution of the absorption coefficient inside a sample. In this work, we integrate both X-ray fluorescence and X-ray transmission data modalities and formulate a nonlinear optimization-based approach for reconstruction of the elemental composition of a given object. This model provides a simultaneous reconstruction of both the quantitative spatial distribution of all elements and the absorption effect in the sample. Mathematically speaking, we show that compared with the single-modality inversion (i.e., the X-ray transmission or fluorescence alone), the joint inversion provides a better-posed problem, which implies a better recovery. Therefore, the challenges in X-ray fluorescence tomography arising mainly from the eff...

A preliminary numerical model of the Geminid meteoroid stream

Last year the work on the qualitative model of the Geminid meteoroid stream was finished [1, 2]. The mentioned publications summarized the results of 20-years efforts. This model explained most of the Geminid’s structural features, including bimodality, but to find quantitative spatial density distribution of particles we need a more precise model. The first runs of the new numerical model were made, and some preliminary results have been obtained.