Potential Source Locations Research Articles

Life cycle assessment of Calanus finmarchicus products

Abstract Purpose The ambitions for a more sustainable aquaculture sector in Norway demand the need for new ingredients that could be incorporated into feeds. In this context, Calanus finmarchicus products (powder, oil, and hydrolysate) emerge as potential local and scalable sources of oil and protein. Despite this, there are no studies in the literature analyzing the possible environmental impact of these products. The goal of this study is to measure the environmental impacts of Calanus finmarchicus products. Methods We applied an attributional life cycle assessment method to measure the environmental impacts of Calanus finmarchicus products. We consider the supply chain from the processing company CALANUS AS in Norway and data from fishers of Calanus finmarchicus, as inputs for the analysis. The product system boundaries were cradle-to-gate, and sixteen environmental impact categories were considered for the analysis according to the environmental footprint (EF) assessment method. A sensitivity analysis was applied to evaluate the impact of the allocation method considered and the electricity mix used in the production stage. Results The results indicate that considering economic allocation, Calanus oil accounts for the highest impacts on the different categories, especially due to the combustion of fossil fuels needed for harvesting, freezing on board, and transport to the port. Moreover, the scenario analysis showed that the allocation method and electricity mix were considered, impacting drastically on the results of the analysis. In addition, it was found that Calanus finmarchicus products showed better results than other sources of protein and oil in some impact categories. Conclusions The results of the environmental assessment suggest that Calanus products show promising potential to increase the sustainability of aquaculture in Norway. Future research highlights the need for a more holistic sustainability analysis incorporating economic and social aspects. In addition, future studies are needed to account for possible impacts on biodiversity and ecosystems, especially during Calanus harvesting. Graphical Abstract

Local contributions and climate change effects on organochlorine pesticide levels in soil and sediments in Svalbard

The Arctic region, including Svalbard, faces unique environmental challenges from the presence and persistence of organochlorine pesticides (OCPs), pollutants known for their long-range atmospheric transport and potential local sources. In Svalbard, the melting of sea ice and glaciers due to climate change may release OCPs trapped over decades, while human activities in the area could contribute additional local contamination. This study aimed to identify and quantify different sources of OCPs in soil and marine sediments at Svalbard. Samples were collected from Kongsfjorden, Rijpfjorden, and in Ny-Ålesund. The concentrations of 23 OCPs in sediments sampled were in the range of 0.36–0.90 ng/g, which were lower than those in the soils from Ny-Ålesund (0.28 ng/g to 3.6 ng/g). The highest OCP levels were detected at locations near the research station in Ny-Ålesund, where local contamination from research activities, mining, and dumpsites could occur. Hexachlorobenzene (HCB) were the most prominent compound, followed by various DDTs and HCHs. Dignostic ratios and the Positive Matrix Factorization (PMF) model were employed to determine the primary sources of OCPs. The results from modeling showed that historically used pollutants were the primary contributor, accounting for 90% of OCPs present, while recently input OCPs were a minor contributor. However, newly input pollutants significantly contributed to HCHs (43%). It is suggested that the contribution of legacy OCPs mainly comes from the melting of sea ice and glaciers. This was especially true for Rijpfjorden (95%), while it was also significant for Kongsfjorden (55%). The local contamination and fresh inputs played a substantial role in the area near the research station in Ny-Ålesund. The study emphasizes the importance of secondly source, especially the role of melting sea ice and glaciers as well as local contaminations as sources of OCPs in Svalbard's marine sediment, which highlight the urgent need to address the impact of climate change on the Arctic environment.

A semi-probabilistic Bayesian method to identify the number and location of potential sources in 3D unconfined aquifer using limited observed concentration

Source identification of a contaminant has always been challenging for accurately modeling groundwater transport. Source identification problems are classified into several parts, such as identifying the location of contamination, the strength of contamination, the time the contaminant is introduced into the groundwater, and the duration of its activity. Identifying the sources considering all the parts as variables increases the computational complexity. Reducing the number of variables in source identification problems is necessary for a swift solution through optimization approaches. The most challenging variable in source identification modeling is the location of contamination, as it is a discrete variable for almost all the numerical solutions of groundwater models. In this research study, we have created a methodology to narrow the location of contamination from a random distribution throughout the aquifer to a reasonable number of probable locations. Although methods to identify the location of contamination were devised earlier, we have attempted an approach of combining a particle tracking approach with Bayesian method of updating the probabilities as a novel approach, where the observation data is limited. We have considered the aquifer parameters and observation well data and devised a method with a Lagrangian approach to particle movement to identify the potential source locations. We have refined the source locations to a narrower probability distribution using the Bayesian method of updating the probability through new information of refined grid space. We have tested the models to identify the potential sources with different hypothetical problems and identified the sources in advective dominant transport with an average probability of 0.53, diffusion dominant transport with an average probability of 0.62, heterogenous soils with an average probability of 0.99, anisotropic aquifer with an average probability of 0.91, and aquifer with irregular boundary with an average probability of 0.96 to identify the location nearest to the actual contaminant source. The results are satisfactory in identifying the number of potential sources with an accuracy of 88 % (15 identified out of 17 sources with a probability greater than 0.4) and their locations in the aquifer with a probability of 0.223 for exact location identification. The probability of finding a source nearest to the actual location is 0.745 at an average distance of 11.6 m from the actual source location.

Localizing North Atlantic Right Whales using a deformable sonobuoy grid

In 2018, a large-scale data collection effort was conducted in the Gulf of St. Lawrence, a known feeding ground for North Atlantic Right Whales (NARW), over two days. On each day, visual surveys were conducted, 32 sonobuoys were deployed to gather directional acoustic time series, and a Slocum glider operated in the area to collect oceanographic data. Following the collection phase, the acoustic data were manually annotated with a focus on NARW vocalizations. This project uses the multi-modal dataset to test and compare the results of three localization algorithms for NARW calls. The first method of localization used the directionality of the calls, where probability density maps were created by overlapping the bearing statistics across each of the relevant sonobuoys, commonly referred to as cross-fixing. To improve the bearing distributions, the signals were isolated with thresholding after using a conditional whitener and power-law statistic on its short-time Fourier transform. In the next approach, localization was performed using a spherical interpolation method to initialize a maximum likelihood time-difference-of-arrival algorithm. Finally, matched-field processing was used to model replica fields at potential source locations and correlate the replicas with the received pressure on the hydrophones.

An Approach to Calculate Source Locations for Thicker Plates when Acoustic Emission Signals are Dominated by Trailing Waves

In many acoustic emission (AE) applications, signal arrival times from an array of sensors are coupled with an appropriate velocity to calculate the source location of AE events. To date, the AE signals present in these applications are Lamb and/or Rayleigh waves. In a previous publication [1], finite element modeled (FEM) AE signals in thicker plates were dominated by trailing wave (TW) packets, when no Rayleigh wave was present. The typical character of the TW dominated signals was described as a spaced-in-time train of short-in-time wave packets. Further, it was pointed out, that when TW dominate the signals, it is not clear how arrival times for source location calculations might be obtained along with an appropriate propagation velocity. The goal of this work is to develop a source location approach for cases of dominant TWs in the AE signals in thicker plates. The FEM-signal database from the previous publication was used for this study. The database consisted of in-plane dipole sources at five different depths and four different steel plate thicknesses (50, 75, 100 and 125 mm). Bottom-surface out-of-plane displacement signals versus time for propagation distances from 250 to 1500 mm were examined. The FEM signals were frequency filtered to examine a broadband case of 80 to 500 kHz and a narrowband case of 100 to 300 kHz. To study the potential source location accuracy, a total of seven four-sensor arrays were employed. The study results describe a process to select appropriate arrival times and obtain a propagation “velocity.” This information was used to calculate source locations for the sensor arrays. The location errors were shown to be less than the plate thickness.

High-Resolution Identification of Sound Sources Based on Sparse Bayesian Learning with Grid Adaptive Split Refinement

Sound source identification technology based on a microphone array has many application scenarios. The compressive beamforming method has attracted much attention due to its high accuracy and high-resolution performance. However, for the far-field measurement problem of large microphone arrays, existing methods based on fixed grids have the defect of basis mismatch. Due to the large number of grid points representing potential sound source locations, the identification accuracy of traditional grid adjustment methods also needs to be improved. To solve this problem, this paper proposes a sound source identification method based on adaptive grid splitting and refinement. First, the initial source locations are obtained through a sparse Bayesian learning framework. Then, higher-weight candidate grids are retained, and local regions near them are split and updated. During the iteration process, Green’s function and the source strength obtained in the previous iteration are multiplied to get the sound pressure matrix. The robust principal component analysis model of the Gaussian mixture separates and replaces the sound pressure matrix with a low-rank matrix. The actual sound source locations are gradually approximated through the dynamically adjusted sound pressure low-rank matrix and optimized grid transfer matrix. The performance of the method is verified through numerical simulations. In addition, experiments on a standard aircraft model are conducted in a wind tunnel and speakers are installed on the model, proving that the proposed method can achieve fast, high-precision imaging of low-frequency sound sources in an extensive dynamic range at long distances.

Toxic elements, sources and health risk assessment of PM2.5 in an industrial area of Surabaya, Indonesia

ABSTRACT A comprehensive study on air quality in the industrial area of Indonesia, which significantly impacts respiratory health and poses breathing hazards, is still lacking. This study aims to uncover the contents of toxic elements, identify potential sources of fine particles, and evaluate their contributions to health risks in Surabaya’s industrial area. The study focuses on identifying chemical composition, sources, and assessing health risks for both children and adults. The PM2.5 concentration from 2021 to 2022 ranged from 3.29 to 27.05 µg m−3, with an average value of 13.63 ± 4.54 µg m−3, exceeding the WHO’s annual standard of 5 µg m−3. Black carbon (BC) accounted for 27% of PM2.5 composition. A seasonal variation was found; the highest PM2.5 and BC were observed during the dry season, with the highest peak in June. The highest concentration of elements in PM2.5 was observed for S, followed by Si, K, Zn, Fe, and Pb, with maximum values of 3.53, 1.16, 1.01, 1.86, 0.29, and 0.37 µg m−3, respectively. Source apportionment using positive matrix factorisation (PMF) revealed eight sources: galvanising industry, ammonium chloride, secondary sulphate, biomass burning emission, soil, steel industry, vehicle emissions, and lead smelting, with metal industries accounting for 37.3%. Investigation into their potential source locations was conducted using the conditional bivariate probability function (CBPF) method. Human health assessment for adults and children indicates carcinogenic risks with a total risk of 9.50 × 10−5 and 2.38 × 10−5, respectively. The results emphasise the significance of managing sources linked to industry and transportation-related sources, crucial in decreasing high PM2.5 levels and several elevated elements and mitigating the health impact in Surabaya’s industrial area, Indonesia.

Measuring short-chain per- and polyfluoroalkyl substances in Central New Jersey air using chemical ionization mass spectrometry

ABSTRACT Real-time measurements of short-chain (C < 8) per- and polyfluoroalkyl substances (PFAS) were performed in Central New Jersey air using chemical ionization mass spectrometry (CIMS). The CIMS was calibrated for C2–C6 perfluorinated carboxylic acids, and 4:2 and 6:2 fluorotelomer alcohols. Of these, only trifluoroacetic acid (TFA) was detected in ambient air above instrumental detection limits. However, instrumental sensitivities (and thus ambient mixing ratios) were estimated for other detected PFAS including C3H2F6O and C6HF11O3. TFA mixing ratios reached up to 0.7 parts-per-trillion by volume (pptv). Estimated C3H2F6O and C6HF11O3 mixing ratios reached the single pptv level. These latter two formulas are consistent with hexafluoroisopropanol (HFIP) and hexafluoropropylene oxide dimer acid (HFPO-DA), respectively, though they may potentially represent multiple isomers. Diel profiles of detected PFAS along with local meteorological data can provide information on potential local sources of these compounds. However, only limited discussion of potential sources was provided here given the sparse detection of these compounds above instrument detection limits. These results demonstrate the potential of online CIMS instrumentation for measuring certain PFAS in ambient outdoor air in real time at or below the pptv level. This technique also has potential for fenceline monitoring and other near-source applications. Implications: Online chemical ionization mass spectrometry (CIMS) has potential for fast, real-time measurements of certain airborne per- and polyfluoroalkyl substances (PFAS). Three short-chain (C < 8) PFAS were detected by CIMS in Central New Jersey ambient air near or above the parts-per-trillion by volume (pptv) level. This technique also has potential for fenceline monitoring and other near-source applications for airborne PFAS.

Synthesis of biodiesel from an unique potential oil reserve Delonix regia using a novel biocatalyst bamboo stem – A comparative study by RSM and ANN

Establishing sustainable energy sources is now necessary due to the depletion of fossil fuels and the rising global need for energy. On the other side, burning fossil fuels releases dangerous pollutants into the atmosphere, such as particulates and oxides of carbon, sulfur, and nitrogen. People all over the world are accepting a sustainable, renewable, and cost-effective source for the development of biofuels. Identification of a native reserve is a crucial part to hunt a suitable resource for biodiesel production due to its commercial transmission. Biodiesel is a prominent clean energy substitute due to its positive environmental concerns. In this work, a local potential source Delonix regia seeds are used for oil extraction and converted it into biodiesel directly due to its low free fatty acid content. A newly developed bio-nanocatalyst bamboo stem powder was facilitate as a catalyst and characterized by various spectroscopic techniques for efficient biodiesel conversion. Response surface methodology and Artificial Neural Network were employed to predict the optimized process parameters and maximum of around 97% biodiesel yield was achieved utilizing methanol/oil ratio - 0.25 v/v, catalyst dosage - 4 wt%, mixing speed - 950 rpm, reaction temperature - 65ºC and processing time - 95 min. Then, the biodiesel was characterized by gas chromatography to find the fatty acid combination and the properties were analysed for further processing in the automobiles. Moreover, the catalyst reusability was evaluated for economical biodiesel production was established effectively.

Aerosol absorption using in situ filter-based photometers and ground-based sun photometry in the Po Valley urban atmosphere

Abstract. Light-absorbing aerosols (LAAs) are short-lived climate forcers with a significant impact on Earth's radiative balance. LAAs include dust aerosols, black carbon (BC) and organic light-absorbing carbonaceous aerosol (collectively termed brown carbon, BrC), which have also been proven to be highly toxic. In this study, aerosol absorption at five wavelengths (ranging from ultraviolet to infrared) was monitored continuously using filter-based photometers during two winter seasons in 2020 and 2021 in the city of Modena (southern central Po Valley, northern Italy), at two regulatory air quality monitoring sites, along with other pollutants (coarse particulate matter, PM10; fine particulate matter, PM2.5; O3; NO; NO2; and C6H6) and the vehicular traffic rate. The aerosol optical depth (AOD) and other column aerosol optical properties were concurrently monitored at four wavelengths by an AErosol RObotic NETwork (AERONET) sun photometer under urban background conditions within Modena. In situ absorption levels were apportioned to both sources (fossil fuel and biomass burning) and species (BC and BrC), while columnar absorption was apportioned to BC, BrC and mineral dust. The combined analysis of the atmospheric aerosol and gas measurements and of the meteorological conditions (in situ and from the ERA5 reanalysis) identified the location of potential urban sources of BC and BrC, most likely related to traffic and biomass burning. In situ data show different diurnal/weekly patterns for BrC from biomass burning and BC from traffic, with minor differences between the background and the urban traffic conditions. AERONET version 3 absorption aerosol optical depth (AAOD) retrievals at four wavelengths allowed the estimation of the absorptive direct radiative effect due to LAAs over the same period under the reasonable assumption that the AOD signal is concentrated within the mixing layer. AERONET retrievals showed a modest correlation of columnar absorption with planetary boundary layer (PBL)-scaled in situ observations, although the correlation improved significantly during a desert dust transport event that affected both in situ aerosol and columnar absorption, particularly in the blue spectrum range. A low correlation occurred between the contribution of BrC to aerosol absorption for the in situ and the columnar observations, with the BrC contribution being generally larger for in situ observations. Finally, evidence of a highly layered atmosphere during the study period, featuring significant spatial mixing and modest vertical mixing, was shown by ERA5-based atmospheric temperature profiles and by the large correlation of concurrent AERONET AOD retrievals in Modena and in Ispra (on the northwestern side of the Po Valley, ca. 225 km from Modena).

Environmental distribution of per- and polyfluoroalkyl substances (PFAS) on Svalbard: Local sources and long-range transport to the Arctic

The environmental distribution of per- and polyfluoroalkyl substances (PFAS) in water, snow, sediment and soil samples taken along the west coast of Spitsbergen in the Svalbard archipelago, Norwegian Arctic, was determined. The contribution of potential local primary sources (wastewater, firefighting training site at Svalbard airport, landfill) to PFAS concentrations and long-range transport (atmosphere, ocean currents) were then compared, based on measured PFAS levels and composition profiles. In remote coastal and inland areas of Spitsbergen, meltwater had the highest mean ΣPFAS concentration (6.5 ± 1.3 ng L−1), followed by surface snow (2.5 ± 1.7 ng L−1), freshwater (2.3 ± 1.1 ng L−1), seawater (1.05 ± 0.64 ng L−1), lake sediments (0.084 ± 0.038 ng g−1 dry weight (dw)) and marine sediments (<method detection limit (MDL)–0.46 ng g−1 dw, median 0.015 ng g−1 dw). Perfluoroalkyl sulfonates (PFSA) and 6:2 fluorotelomer sulfonate (FTSA) were predominant in water and soil samples influenced by local sources, while perfluoroalkyl carboxylates (PFCA) were predominant in water and sediment from remote coastal and inland areas of Svalbard. The PFAS composition profiles observed in remote areas indicated that atmospheric transport and oxidation of volatile precursors is an important source of PFCA on Svalbard. Shorter-chain PFAS such as perfluorobutanoate (PFBA) were the predominant PFAS in freshwater, reflecting replacement of C8-chained PFAS with shorter-chained compounds. The comparatively high PFAS (especially PFBA) concentration in meltwater indicated that melting of snow and ice during the Arctic spring is an important diffuse local PFAS source. This source may become even more important with climate warming-induced melting of Arctic glaciers and ice sheets. Further studies of mobilisation and transport of PFAS in the Arctic region are needed to confirm this trend.

Analysis of O3 Pollution Characteristics, Formation Sensitivity, and Transport Impact in Southern Nanjing

As one of the most important city clusters in China, surface ozone (O3) pollution in the Yangtze River Delta (YRD) Region has become a prominent air quality problem in recent years. Online observations of ambient O3, nitrogen oxides (NOx), and volatile organic compounds (VOCs) were conducted in southern Nanjing from July-September 2020 and April-May 2021. On this basis, the pollution characteristics of O3 were analyzed. The O3-VOCs-NOx sensitivity and the transport influence of its precursors were further discussed using models. The frequency statistics of the daily maximum 8-hour moving average (DMA-8h O3) and hourly concentration (O3-1h) showed that O3 pollution in southern Nanjing was more serious than that in urban areas. Three typical O3 pollution episodes were selected during the whole observation period, which included August 16th-27th, 2020; September 3rd-11th, 2020; and May 17th-25th, 2021. The O3-VOCs-NOx sensitivities in these three pollution episodes were analyzed using the observation-based model (OBM). The results of the relative incremental reactivity (RIR) and empirical kinetics modeling approach (EKMA) showed that the sensitivities of O3 formation in the three pollution episodes were in the NOx-limited regime, the transition regime, and the VOCs-limited regime, respectively. This difference in O3-VOCs-NOx sensitivity reflects that the site may have been influenced by transport. Therefore, the potential source contribution function (PSCF) and the concentration weighted trajectory (CWT) method were further used to analyze the potential source areas of NOx, VOCs, and O3 in these three pollution episodes. The results showed that there were obvious regional transport effects of NOx, VOCs, and O3 in these three pollution episodes. The location of potential sources differed in these three pollution episodes, which were affected by the eastern cities of the Lishui site; the urban area of Nanjing and eastern area of Anhui Province; and the urban area of Nanjing and the middle of the YRD Region, respectively. The impact of transport on O3 and its precursors also indicated the necessity of regional joint prevention and control of O3 pollution in the YRD Region.

Sumaki Höyük (Batman, Türkiye) Neolitik Yerleşiminde Bulunan Bazalt Parçalarının Çok Perspektifli Jeokimyasal ve Mineralojik Analizi

This study aims to determine the provenance of grinding stone tools unearthed from the Neolithic phases of Sumaki Höyük settlement using a portable Energy Dispersive X-ray Fluorescence Spectrometer (P-EDXRF) and X-ray Diffraction spectrometer (XRD). Sumaki Höyük is located in the Lower Garzan Basin of Batman province, Turkey. The settlement is dated to 9084 - 8123 cal BP. Grinding stone tools in this settlement are usually made of basalt. Albeit at low amounts, limestone was also used in the production of grinding stones. The Lower Garzan Basin, located to the east of Diyarbakır Basin, is surrounded by Mount Kıradağı and Mount Raman to the west and the Garzan and Kentalan Anticlinal to the east with Kıradağı extending in a northwest-southeast direction. The basalt flow occurred in the Quaternary period. Samples collected from different parts of the Neolithic phase of Sumaki Höyük and the Kıradağı basalt flows were analyzed using P-EDXRF to determine their chemical composition. The same samples were also analyzed using XRD to determine their mineral composition. P-EDXRF and XRD analyses reveal that the samples from Sumaki Höyük and Kıradağı are in good accordance with each other. It is therefore understood that the basalt stone tools used in the settlement were taken from the Kıradağı basalts. Data gathered on the characteristics of potential source locations provide an insight into the settlement strategy and mobility of Neolithic communities.

Locating volcanic tremor using azimuth coherence of cross-correlation

Accurate localization of volcanic tremors can provide important information about volcanic activities. However, the seismic waveform of volcanic tremor is complicated, resulting in that its location cannot be accurately determined from traditional traveltime inversion methods due to the unidentifiable first arrivals. The cross-correlation-based back-projection method, which stacked the back-projected spatial images utilizing the cross-correlation data from different station pairs, is considered as one of the most effective waveform stacking location methods for the volcanic tremor. In this method, the obtained coherence from different station pairs can improve the signal-to-noise ratio of projected energy distribution, and then constrain the potential source location. Nevertheless, not all back-projected images of station pairs are helpful to enhance the positioning effect in practice because the resolution of the energy peak in the back-projection method is affected by the selection of used station pairs. In this study, we optimized the back-projection method by establishing some criteria for the selection of the station pairs, including the distance between two stations in each pair, and the calculated azimuth angle, the cover area, and the central location between each two of all station pairs. In addition, the phase information in cross-correlation data has been also used in our method to limit the energy divergence in the resulting map. Taking the Katla volcano as a real example, we demonstrate that the proposed second-order azimuth coherent location method can increase the accuracy of locating volcanic tremors compared to the traditional methods, resulting in an improvement of approximately 70 % in location resolution.

Airborne microbiomes at a subtropical island in southern China: Importance of the northwest and southeast monsoons

Airborne microbes can survive in the atmosphere, travel intercontinental distances by air mass movement, and colonize in remote area through dry and wet deposition, thus forming a connection between oceanic, atmospheric, and terrestrial systems. However, studies linking the monsoon and bioaerosol transport are extremely rare. Here we applied high-throughput sequencing to investigate airborne microbial community succession on an island in Pearl River Estuary under summer and winter monsoon influence. The microbial communities from local potential sources (i.e., soil, leaf and surface seawater), water-soluble ions, organic carbon and elemental carbon were used to evaluate marine and terrestrial contribution. Our results revealed that airborne microbial communities underwent rapid changes during different monsoons influence (ANOSIM, P < 0.005), especially fungal aerosol. Fungal aerosols were dominant by Cladosporium, Debaryomyces, Rhodotorula, Fusarium, Aureobasidium and Candida during winter monsoon, while Coriolopsis, Ceriporia, Fomes, Phanerochaete, Trametes, Phlebia, and Antrodia were predominant fungal genera during summer monsoon. Redundant analysis showed that changes of air temperature and relative humidity caused by monsoon were the most important driven factors of airborne microbial communities in summer monsoon for airborne microbial communities. In addition, terrestrial sources were the most important contribution, especially that of soil for airborne bacteria. Some taxa were affected by atmospheric transport influenced by monsoon synoptic air mass movement.

A physics‐based spectral matching (PBSM) method for generating fully site‐related ground motions

AbstractThe response spectrum is generally adopted in the seismic design to represent the seismic fortification level, but the structural dynamic analysis requires the ground motions as an input. However, ground motions generated by the traditional spectral matching methods do not have site‐related physical backgrounds for the target site. In this study, a physics‐based spectral matching (PBSM) method is developed to generate fully site‐related broadband ground motions (i.e., the generated ground motions have real physical backgrounds for the target site, including the source process, propagation path, and local site conditions) that are compatible to the target spectrum. In this method, the three‐dimensional (3D) numerical model around the target site is constructed to calculate the strain Green's tensors of all potential source locations using adjoint simulations. The variable space dimension of the seismic source is significantly reduced by applying the self‐similar feature of the multidimension source model, so that the optimization algorithm can be used to search for the rupture process that generates the physics‐based and spectrum‐matched ground motions (abbreviated to PBSM ground motions). The proposed method is applied to the Xiluodu dam in China. Compared with the traditional techniques, the generated ground motions have fully site‐related physical backgrounds and are compatible to the target spectrum. Additionally, as this method generates broadband ground motions based on the deterministic rupture process, any features of ground motions, such as large velocity pulses, can be taken into account throughout the optimization process. This study introduces the deterministic physical backgrounds of earthquakes to performance‐based seismic design and analysis. The proposed method may have a significant application potential in earthquake engineering.

The Origin and Invasion Pathway of Brown Rats Rattus norvegicus on Dok-Do Island Revealed by Genome-Wide Markers from 3-RADseq Approach

Biological invasions are known to cause local extinctions on islands. Dok-do, a small, remote volcanic island in the East Sea of Korea in the western Pacific, has recently been invaded by rats, posing ecological problems. To infer their origin and invasion pathway, we collected rats from Dok-do and from the potential introduction source locations, Ulleung-do in the Pacific Ocean, and four east coastal ports. First, we identified that the brown rat (Rattus norvegicus) was the only rat species occurring at collecting sites based on the key morphological characteristics. To determine the population-level genetic diversity pattern, we applied the 3-RADseq approach. After a series of filtrations (minor allele frequency < 0.05, Hardy-Weinberg equilibrium p < 1 × 10-7), 4042 SNPs were retained for the final dataset from the 25,439 SNPs initially isolated. The spatial structure and genetic diversity pattern of brown rats suggested that the rat population on Dok-do was likely introduced from Ulleung-do. Our work provides practical information that will assist in the management of invasive brown rats in vulnerable island ecosystems.

Identification of Clandestine Groundwater Pollution Sources in Aquifer Polluted with BTEX

One of the biggest challenges in characterizing clandestine groundwater pollution sources is correctly estimating the number of such sources and their physical locations. Indiscreet underground disposal of toxic waste leading to groundwater pollution is widespread, and it is extremely difficult to detect the number of such sources and their locations. In this study, a real-life scenario of groundwater pollution in suburban New South Wales, Australia, is solved using a simulated annealing (SA)–based linked simulation optimization (LSO) technique with Benzene, Toluene, Ethylbenzene and Xylene (BTEX). The developed LSO considers the number of pollutant sources and their locations as explicit unknowns. This overcomes a crucial limitation in the earlier methods that limited the search to a few known potential source locations. The results show the applicability of the developed methodology to real-life groundwater pollution problems without making limiting assumptions about the number of sources or potential source locations being known, which has not been addressed to date.

Passive source localization based on multipath arrival angles with a vertical line array using sparse Bayesian learning.

In deep water, multipath time delays or frequency-domain interference periods of the acoustic intensity combined with multipath arrival angles are typically used for source localization. However, depth estimate is hard to achieve for a narrowband source at a remote part of the direct arrival zone as the required bandwidth increases with the source range. In this paper, a passive source localization method with a vertical line array, suitable for both broadband and narrowband sources, is proposed. Based on the variation trends of multipath angles with source range and depth, source localization is achieved by only matching the measured angles of the direct path and surface-reflected path with model-based values of a predefined grid of potential source locations. Considering the angle resolution limited by the array aperture and the presence of coherent multipath, sparse Bayesian learning is used and compared with the conventional beamforming and the minimum-variance distortionless-response beamforming to resolve and estimate the multipath angles. Simulations and experimental data of explosive sources collected by a vertical line array in the South China Sea are carried out to illustrate the method and demonstrate the performance.

Maximum Likelihood-Based Gridless DoA Estimation Using Structured Covariance Matrix Recovery and SBL With Grid Refinement

We consider the parametric data model employed in applications such as line spectral estimation and direction-of-arrival estimation. We focus on the stochastic maximum likelihood estimation (MLE) framework and offer approaches to estimate the parameter of interest in a gridless manner, overcoming the model complexities of the past. This progress is enabled by the modern trend of reparameterization of the objective and exploiting the sparse Bayesian learning (SBL) approach. The latter is shown to be a correlation-aware method, and for the underlying problem it is identified as a grid-based technique for recovering a structured covariance matrix of the measurements. For the case when the structured matrix is expressible as a sampled Toeplitz matrix, such as when measurements are sampled in time or space at regular intervals, additional constraints and reparameterization of the SBL objective leads to the proposed structured matrix recovery technique based on MLE. The proposed optimization problem is non-convex, and we propose a majorization-minimization based iterative procedure to estimate the structured matrix; each iteration solves a semidefinite program. We recover the parameter of interest in a gridless manner by appealing to the Caratheodory-Fejer result on decomposition of PSD Toeplitz matrices. For the general case of irregularly spaced time or spatial samples, we propose an iterative SBL procedure that refines grid points to increase resolution near potential source locations, while maintaining a low per iteration complexity. We provide numerical results to evaluate and compare the performance of the proposed techniques with other gridless techniques, and the CRB. The proposed correlation-aware approach is more robust to environmental/system effects such as low number of snapshots, correlated sources, small separation between source locations and improves sources identifiability.