Source Identification Techniques Research Articles

Bioaccumulation of metals in muscle tissues of economically important fish species from black sea Lagoon lakes in Türkiye: Consumer health risk and nutritional value assessment

This study is the first comprehensive investigation into the levels of potentially toxic elements (PTEs) in the muscle tissues of various fish species (Carassius gibelio, Squalis cephalus, Esox lucius, and Tinca tinca) in Lagoon Lakes Akgöl and Semenit (LLAS), Samsun, Türkiye. A multidisciplinary approach was employed, integrating elemental analysis, health risk assessment, and source identification techniques to evaluate the distribution, sources, and health risks of PTE contamination in fish. The mean concentrations of toxic and trace elements (µg/kg) were determined in ascending order: Cd (5) < Co (8) < Cr (18) = Pb (18) < Ni (66) < As (163) < Cu (290) < Mn (798) < Fe (6150) < Zn (6250). In contrast, the concentrations of macro elements are provided in units of mg/kg, as follows: Mg (391) < Na (677) < Ca (1698) < P (2314) < K (3837), with notable variations across species. Health risk assessment methodologies indicated that PTE levels in fish muscle tissues generally fell below regulatory limits, indicating their safety for consumption. Noncarcinogenic risk assessment further documented the safety of consuming these fish species, with elevated maximum safe consumption quantity (MSCQ) values observed for all metals. In addition, the estimated daily intake of PTEs was found to be below reference doses, confirming the safety of consumption. Esox lucius demonstrated a highest abundance of essential nutrients (e.g., Ca, K, Mg, and P). Source identification techniques indicated that residential waste, lithogenic materials, and agricultural practices are significant contributors to PTE contamination. Overall, this research stresses the importance of monitoring PTE levels in fish, ensuring food safety and protect human health.

Using sulfur isotopes to constrain the sources of sulfate in PM2.5 during the winter in Jiaozuo City

Sulfate (SO42−) is one of the main driving forces behind the explosive growth of atmospheric fine particulate matter (PM2.5), and it scatters sunlight, contributing to atmospheric cooling. Investigating the sources and transformation of SO42− is crucial for understanding PM2.5 pollution and its implications for climate change. Despite a continuous decline in PM2.5 concentrations in China since 2015, it remains unclear whether there have been changes in the sources and transformation processes of SO42−. Furthermore, previous studies that utilized isotope techniques for source identification have often overlooked the impact of isotope fractionation. This study focuses on atmospheric PM2.5 in Jiaozuo based on samples collected from December 2017 to February 2018. The analysis encompassed both the water-soluble ions and sulfur isotope compositions of SO42− (δ34S–SO42−). By employing a Bayesian isotope mixing model, quantitative analysis of SO42− sources in winter atmospheric PM2.5 in the study area was conducted. The findings revealed that the δ34S–SO42− values of winter PM2.5 ranged from −0.1‰ to 9.8‰, with an arithmetic mean of 4.9 ± 1.5‰ (n = 74). After considering sulfur isotope fractionation effects and the impact of primary SO42−, the calculated results suggested that on average, the contributions of primary SO42−, coal combustion, vehicle exhaust, biomass burning, and oil combustion to the SO42− in winter PM2.5 were 24.9 ± 15.7%, 41.2 ± 2.6%, 27.9 ± 5.3%, 4.8 ± 3.7%, and 1.2 ± 0.7%, respectively. Compared with the clean period, the contribution of the primary SO42− decreased significantly during haze episodes, while the contribution of the secondary SO42− increased. The contribution of coal combustion to the secondary SO42− increased notably by 21.9%, while the contributions from other secondary sulfate remained relatively stable. The lower δ34S values of the atmospheric PM2.5 in Jiaozuo affirm the significant impact of the anthropogenic SO2 input on the sulfur isotope composition of the atmospheric sulfate. Analysis confirmed that the decrease in the contribution of coal combustion to atmospheric SO42− in the study area, indicating the positive effects of regional air pollution control measures. Nevertheless, continued attention should be paid to the influences of coal combustion and vehicle exhaust on regional air pollution.

OMI-based emission source classification in East China and its spatial redistribution in view of pollution control measures

This study aims to generate a satellite-based qualitative emission source characterization for the heavily polluted eastern part of China in the 2010–2016 time period. The applied source identification technique relies on satellite-based NOx and SO2 emission estimates by OMI, their SO2:NOx ratio, and the MIX anthropogenic emission inventory to distinguish emissions from different emission categories (urban, industrial, natural) and characterize the dominant source per 0.25° × 0.25° grid cell in East China. Overall, we find good agreement between the satellite- and emission inventory–based spatiotemporal distribution and characterization of the dominant emission sources in East China in 2010–2016. In 2010, the satellite measurements suggest an emission distribution less dominated by industrial areas, a somewhat larger role for urban/transportation areas and agricultural activities, and more natural emissions in the southern part compared to the bottom-up emission categorization. In 2016, more than half of the classified emission categories over East China have remained the same. At the same time, there is a notable increase of agricultural lands and decrease of areas dominated by industry/transportation in 2016, suggestive of an overall decrease in heavy air pollution in East China over the course of 7 years. This is likely attributed to the sustained efforts of the Chinese government to drastically improve the air quality, especially since 2013 when the National Air Pollution Prevention and Control Action Plan was enacted. However, signs of urban expansion (urbanization) and rural–urban migration (“Go West” motion) stemmed from China’s rapid economic growth and labour demand are evident; escalating industrialization (even with cleaner means) and the urban population growth in East China resulted in stronger emissions from sources representing consumption and transportation which are strongly related to NO2 and PM10 pollution (rather than SO2) and are directly influenced by the population size. This resulted to a shift of the emissions from the east mainly to the north and northwest of East China. Overall, although the effectiveness of the Chinese environmental control policies has been successful, the air pollution problem remains an important concern.

Source term estimation for continuous plume dispersion in Fusion Field Trial-07: Bayesian inference probability adjoint inverse method

In scenarios involving sudden releases of unidentified gases or concealed pollution emergencies, source control emerges as a critical procedure to safeguard residential air quality. Appropriate inverse source tracking methodology depending on diverse measurement data could be utilized to promptly identify pollutant source parameters. In this study, source term estimation (STE) method, i.e., jointly combining probability adjoint method with the Bayesian inference method, has been proposed. General form of the pollutant inverse transport equation was firstly established. Subsequently, the pollution source information, assumed from single continuous point releases during Fusion Field Trials 2007 under an unsteady wind field, was identified using the Bayesian inference probability adjoint inverse method. Metropolis-Hastings Markov Chain Monte Carlo (MH-MCMC) and Differential Evolution Markov Chain Monte Carlo (DE-MCMC) were then compared as sampling methods for Bayesian inference. Results indicated that the DE-MCMC algorithm has superior convergence and could present higher accuracy of pollutant source information than that of MH-MCMC algorithm, particularly for highly nonlinear and multi-modal distribution systems. Furthermore, the integration of Union standard Adjoint Location Probability (UALP) as prior information into the Bayesian inference probability adjoint inverse method effectively narrowed the sampling range, enhancing both the accuracy and robustness of the proposed approach. Finally, the impact of the covariance matrix on the inverse identification accuracy was explored. Overall, this research has provided insights into the future applicability of this Bayesian inference inversion technique for point source identification.

Flow Confinement Effects on sUAS Rotor Noise

The goal of this paper is to investigate the effects of flow confinement on the noise generated by a small unmanned aerial system rotor measured and simulated in a partially closed test room. More specifically, the influence of the flow recirculation in the test room and the consequent blade turbulence interaction on both the tonal unsteady loading noise and the broadband laminar separation noise components are scrutinized. The study also aims at improving our prediction capabilities of drone rotor noise, and deepening our fundamental knowledge of rotor aeroacoustics in transitional boundary layer regimes. Numerical simulations are conducted by means of a scale-resolving wall-modelled lattice Boltzmann solver to calculate the unsteady flow and the noise generated by the same reference configuration used in our previous works, which consists in a rotor operated at a chord-based Reynolds number of about 70000 at 75% of the tip radius, both in hover and axial flow conditions. Results for a rotor in pristine flow conditions are compared to results obtained by simulating the rotor in a confined environment that reproduces the A-Tunnel test chamber of Delft University of Technology. One one hand, our study reveals that previously observed discrepancies between measured and predicted noise spectra in hover conditions are not due to the influence of the flow confinement on the laminar separation mechanisms, as hypothesized in our previous study, rather to a lack of numerical resolution. On the other hand, the new results reveal that, in hover conditions, the dominant effect of the flow recirculation is to increase the unsteady loading tonal noise components, whereas, in axial flow conditions, no significant effect due to the test environment is observed. Some of our conclusive observations are supported by results obtained using a source identification technique herein presented for the first time.

Source Camera Identification with a Robust Device Fingerprint: Evolution from Image-Based to Video-Based Approaches.

With the increasing prevalence of digital multimedia content, the need for reliable and accurate source camera identification has become crucial in applications such as digital forensics. While effective techniques exist for identifying the source camera of images, video-based source identification presents unique challenges due to disruptive effects introduced during video processing, such as compression artifacts and pixel misalignment caused by techniques like video coding and stabilization. These effects render existing approaches, which rely on high-frequency camera fingerprints like Photo Response Non-Uniformity (PRNU), inadequate for video-based identification. To address this challenge, we propose a novel approach that builds upon the image-based source identification technique. Leveraging a global stochastic fingerprint residing in the low- and mid-frequency bands, we exploit its resilience to disruptive effects in the high-frequency bands, envisioning its potential for video-based source identification. Through comprehensive evaluation on recent smartphones dataset, we establish new benchmarks for source camera model and individual device identification, surpassing state-of-the-art techniques. While conventional image-based methods struggle in video contexts, our approach unifies image and video source identification through a single framework powered by the novel non-PRNU device-specific fingerprint. This contribution expands the existing body of knowledge in the field of multimedia forensics.

Vibration response and acoustic radiation of a thin floating plate

Remote sensing may be used to detect and locate acoustic sources on a thin floating plate as well as predict the geometry and material of the structure based on recordings of vibratory and acoustic waves. Remote detection and localization of impacts on a thin floating structure is desirable. For a transient excited thin floating structure, the coupling of the vibration waves in the structure and the acoustic waves radiating from it into the air above and water below can introduce complexity not encountered with traditional source identification techniques. To study this problem, repeatable axisymmetric transient impact experiments are conducted in a 1.2-m diameter cylindrical water tank by dropping a stainless-steel ball bearing onto a 6.4-mm-thick aluminum plate suspended at the air-water interface. Accelerometers and laser Doppler vibrometry are used to record the vibration of the plate. Acoustic radiation is recorded in the air above the plate and in the water below it using linear arrays of 6 microphones and hydrophones, respectively. Wave speeds in the plate ranging between 430 m/s and 1550 m/s are considered. Results are compared to a combined acoustic-structure finite element model. [Sponsored by NAVSEA through the NEEC.]

Microbiome-based source identification of microbial contamination in nebulizers used by inpatients

Nebulizers are essential for the delivery of aerosolized medication for respiratory patients in hospital. Microbial contamination of nebulizers increases the risk of healthcare-associated infections, presenting the critical need to identify sources of contamination in order to develop effective infection prevention and control practices in hospitals. Using an innovative microbiome-based cultivation-independent microbial source identification technique, the hospital indoor environment was identified as a significant source contributing to microbial contaminants in nebulizers, providing important information to develop strategies for targeted decontamination and enhance the effectiveness of infection prevention and control practices.

Accurate real-time monitoring of fine dust using a densely connected convolutional networks with measured plasma emissions

Accurate identification and monitoring of fine dust are emerging as a primary global issue for addressing the harmful effects of fine dust on public health. Identifying the source of fine dust is indispensable for ensuring the human lifespan as well as preventing environmental disasters. Here a simple yet effective spark-induced plasma spectroscopy (SIPS) unit combined with deep learning for real-time classification is verified as a fast and precise PM (particulate matter) source identification technique. SIPS promises portable use, label-free detection, source identification, and chemical susceptibility in a single step with acceptable speed and accuracy. In particular, the densely connected convolutional networks (DenseNet) are used with measured spark-induced plasma emission datasets to identify PM sources at above 98%. The identification performance was compared with other common classification methods, and DenseNet with dropouts (30%), optimized batch size (16), and cyclic learning rate training emerged as the most promising source identification method.

Open-Set Source Camera Device Identification of Digital Images Using Deep Learning

Source camera identification plays an important role in forensics investigations on images. It is a forensic problem of linking an image in question to the camera used to capture it. Several source identification techniques have been developed in the literature since this may be a facilitating tool that help to trace back the images to the camera device held by the accused in various forensic applications. However, one of the key disadvantages is that the existing techniques fail if the image in question was taken by a new camera that is not used in the training process. Under a real-world forensic scenario, it is not possible to presume that each image being analyzed comes from one of the cameras used to train the source identification system. To address this issue, we propose a data-driven system based on convolutional neural network to identify the source camera device in an open-set scenario. The experimental results on various sets of cameras show that it is possible to leverage the data-driven model as the feature extractor paired with an open-set classifier to trace back the images to the open-set cameras. The results show that the proposed system outperforms the state-of-the-art techniques in identifying the exact device that are never seen before with considerably high accuracy and is resilient to unknown post-processing applied by the social network platforms. Moreover, the experimental results demonstrate the good generalization capability of the proposed system in extracting the source information, making it more suitable for open-set scenarios.

Remediation of soils and sediments polluted with polycyclic aromatic hydrocarbons: To immobilize, mobilize, or degrade?

Polycyclic aromatic hydrocarbons (PAHs) are generated due to incomplete burning of organic substances. Use of fossil fuels is the primary anthropogenic cause of PAHs emission in natural settings. Although several PAH compounds exist in the natural environmental setting, only 16 of these compounds are considered priority pollutants. PAHs imposes several health impacts on humans and other living organisms due to their carcinogenic, mutagenic, or teratogenic properties. The specific characteristics of PAHs, such as their high hydrophobicity and low water solubility, influence their active adsorption onto soils and sediments, affecting their bioavailability and subsequent degradation. Therefore, this review first discusses various sources of PAHs, including source identification techniques, bioavailability, and interactions of PAHs with soils and sediments. Then this review addresses the remediation technologies adopted so far of PAHs in soils and sediments using immobilization techniques (capping, stabilization, dredging, and excavation), mobilization techniques (thermal desorption, washing, electrokinetics, and surfactant assisted), and biological degradation techniques. The pros and cons of each technology are discussed. A detailed systematic compilation of eco-friendly approaches used to degrade PAHs, such as phytoremediation, microbial remediation, and emerging hybrid or integrated technologies are reviewed along with case studies and provided prospects for future research.

Deep learning Blazar classification based on multifrequency spectral energy distribution data

ABSTRACT Blazars are among the most studied sources in high-energy astrophysics as they form the largest fraction of extragalactic gamma-ray sources and are considered prime candidates for being the counterparts of high-energy astrophysical neutrinos. Their reliable identification amid the many faint radio sources is a crucial step for multimessenger counterpart associations. As the astronomical community prepares for the coming of a number of new facilities able to survey the non-thermal sky at unprecedented depths, from radio to gamma-rays, machine-learning techniques for fast and reliable source identification are ever more relevant. The purpose of this work was to develop a deep learning architecture to identify Blazar within a population of active galactic nucleus (AGN) based solely on non-contemporaneous spectral energy distribution information, collected from publicly available multifrequency catalogues. This study uses an unprecedented amount of data, with spectral energy distributions (SEDs) for ≈14 000 sources collected with the Open Universe VOU-Blazars tool. It uses a convolutional long short-term memory neural network purposefully built for the problem of SED classification, which we describe in detail and validate. The network was able to distinguish Blazars from other types of active galactic nuclei (AGNs) to a satisfying degree (achieving a receiver operating characteristic area under curve of 0.98), even when trained on a reduced subset of the whole sample. This initial study does not attempt to classify Blazars among their different sub-classes, or quantify the likelihood of any multifrequency or multimessenger association, but is presented as a step towards these more practically oriented applications.

WITHDRAWN: Prediction of rumour source identification through spam detection on social Networks- A survey

Online social networks (OSNs) are becoming part of everyone’s virtual world which helps people to connect and disseminate all kind of opinions, news, and other information in real-time. In this era of social networking, an increased amount of novel information being spread with different viewpoints in an unprecedented scale. However, along with useful information propagation, OSNs also serve as fertile land for false information or rumour propagation. This causes irreversible damage to society during emergency events as a negative effect and hinder them from the actual information. Normally, internet media like OSNs, gossip circulate quicker than average. Therefore, studying the rumour dynamics and controlling the rumours in OSNs is of critical research interest in recent times. To achieve this purpose, a robust and reliable centrality model need to be introduced to discover influential spreader by considering the interest of individual’s on neighbor. The opinion of an individual with neighbors is the primary source for this method along with their location in the network. Identifying the rumours during inception stages is difficult due to the lesser availability of data related to the rumour. In this paper a brief survey is done on different rumour source identification techniques.

Application of AERMOD for the identification of a point-source release in the FFT-07 experiment

This study explores the feasibility of using the AERMOD dispersion model as a source-receptor relationship for a point-source identification. For this purpose, an inversion technique based on the least squares approach is adopted. An evaluation of this methodology for the estimation of source location and its intensity is carried out utilizing concentration measurements obtained from the Fusion Field Trial 07 diffusion experiment for continuous trials. The source location is estimated with an average error of 25.5 m from the true values while the corresponding maximum and minimum errors are found to be 57.6 m and 10.8 m, respectively. The source intensity is retrieved within a factor of 1.6 in all the trials. Further, a comparative analysis with the studies reported in the literature suggests that the point source is estimated with reasonable accuracy if the AERMOD dispersion model is utilized along with the least squares inversion technique for source identification. AERMOD along with a source identification algorithm enhances the potential applicability of a widely used regulatory dispersion model.

Contamination and Sources Identification of Polycyclic Aromatic Hydrocarbons in Water and Sediments of Annaba Bay Basin, Algeria

Annaba gulf is a coastal area (southwestern Mediterranean Sea) that receives large diffuse inputs from Fertial plant, Seybouse wadi and Boujemaâ wadi, which are influenced by anthropogenic activities. Wastewaters and surface waters and sediments from these sites were analyzed for Polycyclic Aromatic Hydrocarbons (16 PAHs) by using gas chromatography system operating with the flame ionizing detector (FID). Total PAHs ranged from 0.033 to 0.503 µg L–1 in water, and from 32.44 to 509.58 µg kg–1dw in sediments. In order to identify pollution emission sources of PAHs, three source identification techniques were used, including diagnostic ratios, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). The results show that the pollution originated mainly from pyrogenic inputs in water and sediments. Annaba Bay basin is contaminated mainly by heavy PAHs discards originated from domestic and industrial wastewaters and traffic emissions.

A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes

Variation propagation modelling in multistage machining processes through use of analytical approaches has been widely investigated for the purposes of dimension prediction and variation source identification. Yet the variation prediction of complex features is non-trivial task to model mathematically. Moreover, the application of the variation propagation approaches and associated variation source identification techniques using Skin Model Shapes is unclear. This paper proposes a multilayer shallow neural network regression approach to predict geometrical deviations of parts given manufacturing errors. The neural network is trained on a simulated data, generated from machining simulation of a point cloud of a part. Further, given a point cloud data of a machined feature, the source of variation can be identified by optimally matching the deviation patterns of the actual surface with that of shallow neural network generated surface. To demonstrate the method, a two-stage machining process and a virtual part that has planar, cylindrical and torus features was considered. The geometric characteristics of machined features and the sources variation could be predicted at an error of 1% and 4.25%, respectively. This work extends the application of Skin Model Shapes in variation propagation analysis in multistage manufacturing.

The acoustic inverse problem in the framework of alternating direction method of multipliers

Acoustic imaging is an important technique for sound source identification which is widely used in noise control and fault detection. It can be described as a method for reconstructing the amplitude and phase distribution of an acoustic field in a given plane. When the size of radiating object is huge and measurement environment is noisy, accuracy and efficiency of acoustic imaging algorithm deteriorate easily. In this paper, a novel computational framework is investigated to simplify the algorithm and balance computation speed with accuracy. It converts acoustic imaging into an inverse problem and then divides this problem into two parts including forward model and denoising model. This computation framework is based on the alternating direction method of multipliers, which makes parallel implementation and distributed optimization possible, especially for large scale measurements. It is assumed that the acoustic field is composed of a cluster of monopole sources and the equivalent source method is taken to solve the acoustic inverse problem. Both L2-norm penalization based on the minimal energy assumption and Lp-norm (0<p⩽1) penalization based on the sparsity hypothesis are taken in this framework. Thus, the regularization parameter in the algorithm, which balances the standard least square error and the norm of the source solution, can be coupled with some pre-existing methods incluing L-curve, GCV and Bayesian strategies. Different regularization parameters are proposed and the comparison is made in this paper. The proposed method provides a general and convenient method for different type of sound source priors and it is validated with an experiment.

A Revisit to the Infection Source Identification Problem under Classical Graph Centrality Measures

The high connectivity of the modern day world has lead to the easy diffusion of harmful information content like rumors, computer viruses, and contagions like diseases. This is detrimental to human societies in terms of compromised security, monetary loss and life threats. Therefore, to mitigate such damages, identifying and timely quarantining the sources of such diffusion processes is highly critical. Despite the difficulty and challenges of the problem, researchers have extensively studied source identification in complex networks over the past decade. Several approaches have been proposed, but limited attention has been given to the classical graph centrality measures. Moreover, researchers have generally advised against employing these measures for identifying infection sources. Motivated by the same, in this paper, we revisit these measures in context to source identification and raise the question: “Are classical graph centrality measures really not good enough?”, and perform an extensive experimental journey. We pick five such measures, viz., betweenness, closeness, degree, eigenvector and eccentricity, and conduct an in-depth analysis of their effectiveness in source detection. Our extensive results show that, contrary to the popular belief, a combination of eccentricity and closeness (EC+CC) generally performs better than several state-of-the-art source identification techniques, with higher accuracy and lower average hop error. We also analyze the impact of infection size on source identification and observe that EC+CC is generally highly scalable and stable as well. We also understand that as the infection size increases, the detection accuracy decreases, irrespective of the technique used. However, when we examine the effect of graph density, we observe that as the graph density increases, the detection accuracy increases as well, with EC+CC again outperforming state-of-the-art.

An alternating iterative algorithm for sound source identification based on equivalent source method

Near-field acoustical holography (NAH) based on equivalent source method (ESM) is an efficient technique for sound source identification. Conventional ESM with Tikhonov regularization (TRESM), ESM based on CVX MATLAB toolbox (CVX) and wideband acoustic holography (WBH) are commonly used methods for calculating equivalent source strengths. However, all of them have their respective limitations. To address some of these, an alternating iterative algorithm for sound source identification based on equivalent source method (AIESM) is proposed in this article, which is a combination of alternating direction method and a non-monotone line search technique. The method makes use of sparse regularization under the principle of compressive sensing (CS) to calculate equivalent source strengths. Moreover, inspired by the idea of functional beamforming (FB), AIESM with order n can yield an improved dynamic range when detecting the source location. Numerical simulations are carried out at different frequencies, and the results suggest that the computational efficiency of the proposed method is close to that of TRESM. In addition, AIESM has a better reconstruction accuracy than TRESM and WBH in a relatively wide frequency range. Compared with ESM based on CVX, AIESM is slightly better in reconstruction accuracy and has a higher computational efficiency. Meanwhile, AIESM with order n can provide more accurate source position and better resolution. The validity and practicality of the proposed method are further supported by experimental results.

Interactions of soil metals with glomalin-related soil protein as soil pollution bioindicators in mangrove wetland ecosystems

Through binding of mineral particles and elements, glomalin-related soil protein (GRSP) plays a critical role in sustaining terrestrial soil quality and contributes to the fate of elements from terrestrial to aquatic ecosystems. There is little knowledge, however, of the metal sequestration patterns of GRSP in both terrestrial and aquatic soils, and this limits progress in understanding how environmental conditions influence GRSP characteristics. Here, we employed microcosm experiments to determine the molecular composition of original GRSP derived from three arbuscular mycorrhizal fungi, Glomus intraradices, Glomus versiforme and Acaulospora laevis. To gain insight into the metal sequestration patterns of environmental GRSP, we investigated major subtropical and tropical mangrove wetlands in southern China. GRSP-bound metals were significantly and positively correlated with total metals, and the metal binding contributed to the metal sequestration of mangrove soils. Fourier-transform infrared spectroscopy results showed that original- and environmental GRSP fractions contained hydroxyl, carboxyl, amide and carbonyl functional groups, which enhanced metal binding. Environmental process had no effect on the type of functional groups of the GRSP, while it significantly changed the relative content of the functional groups. The infrared fingerprint analyses of original- and environmental GRSP revealed field-specific, however, no taxon-specific characteristics of GRSP. Biostatistical analysis of the GRSP molecular composition further revealed that the soil pollution sources regulated the ratios of functional group contents associated with hydrocarbons, proteins, polysaccharides and nucleic acids. By GRSP infrared fingerprints coupled with multivariate analyses, we developed a technique for source identification of heavy metal pollution, giving more reliable evidence about contributing sources.