Identification Of Sources Research Articles

Reassessing the origins of Near Eastern obsidian vessels: Not as simple as “Central Anatolia”

In the 1960s, Renfrew and colleagues tested an obsidian vessel from Tepe Gawra in northern Iraq. The vessel was attributed to their “Group 1e-f” chemical type, which matched obsidian from the Acıgöl volcanic complex in central Turkey as well as unclear locations in eastern Turkey and Armenia. Renfrew and colleagues favored an attribution of the vessel to Acıgöl, and consequently, an association between obsidian vessels and central Turkey became widely held in the literature. Given the destructive nature of chemical analysis until the 21st century, obsidian vessels and fragments were almost never tested, so there were few chances to overturn or support this association. Furthermore, such vessels have attracted much attention as likely prestige objects. To consider value and meanings derived from a material’s source, however, it is crucial to have the correct identification of its origin. First I consider the available sourcing data for previously studied vessel fragments. Second I report my new source identifications for a vessel with an uncertain provenience in the Metropolitan Museum of Art, fragments from Ur, and the same vessel from Tepe Gawra tested by Renfrew and colleagues. Only the site located in south-central Turkey (Domuztepe) had polished artifact fragments from the obsidian sources of Cappadocia. For Mesopotamian (Tepe Gawra, Ur, Kenan Tepe) and Zagros (Tal-e Malyan) sites, the vessels instead derived from four sources in eastern Turkey: Sarıkamış 2, Nemrut Dağ 2, Bingöl B, and Meydan Dağ. Thus, the association between obsidian vessels and central Turkey must be abandoned, as must narratives based on this association (e.g., exchange between early states or elites in Cappadocia and Mesopotamia).

RDA Training Methods and Application Examples

The first standard rules for cataloging, Panizzi, were introduced. These rules, which have changed and developed in various periods, have been tried to be applied with Machine Readable Cataloging (MARC), which is a machine-readable data chart, with the change in information technologies in 1966. In the ongoing process, the second edition of AACR was published in 1978 due to difficulties in bibliographic identification of the diversified information sources. Between 2004-2005, the JSC (Joint Steering Committee for Revision of Anglo-American Cataloging Rules) group wanted to publish AACR 3 as a result of new needs in cataloging, but RDA (Resource Description and Access) was appropriate as the name for the new rules. seen. Since 2010, the new cataloging standard, RDA, has become widespread worldwide. The transition to RDA brought many changes with it. Providing the necessary training for the implementation of RDA is one of the starting points of this change. In the studies conducted on RDA in different countries, it has been understood that the subject of “RDA Education” is one of the important issues in the transition process. The methods to be followed while giving this training are very important. Similar and different methods for education have been developed between countries. The aim of this article is to help develop a method in RDA education in our country by examining the RDA education in these different countries.

Characterization and toxicological effects of microplastics in PM2.5 in background sites of the southern coast of China

Microplastics (MPs) are widely present in atmosphere without understanding about their influences on human health. Spatial variations of MPs in fine particulate matter (PM2.5), along with source contribution and health effects analyses, were investigated at two distinct background air quality locations in Hong Kong: TMS, a coastal site, and HT, a high-altitude site, both remote from anthropogenic activities. The higher concentration of total MPs concentration (∑MPs) was observed at HT (31.67 ± 10.25 pg/m3) compared to TMS (11.00 ± 4.69 pg/m3). Polyethylene (PE) was the dominant MP in both locations, and other MPs showed inconsistent concentration patterns in the analysis. Source identification analysis indicated that the MPs at HT was predominantly affected by marine sources, whereas the MPs at TMS was potentially due to continental/local sources. The HT was not an ideal location for monitoring atmospheric background concentration of MP due to high contributions from marine source. Cellular exposure to MPs experiments (A549 cells) demonstrated stronger correlations (p < 0.05) between exposure to MPs and oxidative stress (determined by lactate dehydrogenase (LDH) and malondialdehyde (MDA)) than inflammation factors. The polyvinyl chloride, nylon 6,6 and polystyrene were the chemical compounds that can induce oxidative stress, and interactions among MPs. The results of this study will aid in the development of a new MPs control policy.

The cascade failure model under ecological network is effective for quantifying the resilience of urban regions

Human activities and natural disasters degrade urban ecosystems and cause ecological fragmentation at regional scale. It is therefore necessary to create or rehabilitate useful linkages between ecological spaces. Although ecosystem resilience is receiving increasing attention, most studies focus on the spatiotemporal changes of alternative indicators of ecosystem resilience, without considering the changes in the structure and function of internal ecological nodes. Therefore, this study presents a novel research framework that uses cascade failure model based on the ecological network (EN) to evaluate the resilience of typical urban areas. The framework combines “importance-vulnerability” scored ecological sources identification with minimum cumulative resistance simulation to quantitatively characterize the urban region as an EN and assesses the resilience of the urban EN using a capacity-load cascade failure model. The framework was tested in the Xi'an metropolitan region in China resulted in the regional EN with 191 nodes and 571 edges. It was found that the EN of the Xi'an metropolitan region was relatively stable. The network will completely break down only when the node failure rate exceeds 85 %. The framework we propose offers a novel and powerful approach for policymakers to plan for the conservation and development of ecological infrastructure and land uses in urban region context.

Radio U-Net: a convolutional neural network to detect diffuse radio sources in galaxy clusters and beyond

ABSTRACT The forthcoming generation of radio telescope arrays promises significant advancements in sensitivity and resolution, enabling the identification and characterization of many new faint and diffuse radio sources. Conventional manual cataloguing methodologies are anticipated to be insufficient to exploit the capabilities of new radio surveys. Radio interferometric images of diffuse sources present a challenge for image segmentation tasks due to noise, artifacts, and embedded radio sources. In response to these challenges, we introduce Radio U-Net, a fully convolutional neural network based on the U-Net architecture. Radio U-Net is designed to detect faint and extended sources in radio surveys, such as radio haloes, relics, and cosmic web filaments. Radio U-Net was trained on synthetic radio observations built upon cosmological simulations and then tested on a sample of galaxy clusters, where the detection of cluster diffuse radio sources relied on customized data reduction and visual inspection of Low-Frequency Array Two metre Sky Survey (LoTSS) data. The 83 per cent of clusters exhibiting diffuse radio emission were accurately identified, and the segmentation successfully recovered the morphology of the sources even in low-quality images. In a test sample comprising 246 galaxy clusters, we achieved a 73 per cent accuracy rate in distinguishing between clusters with and without diffuse radio emission. Our results establish the applicability of Radio U-Net to extensive radio survey data sets, probing its efficiency on cutting-edge high-performance computing systems. This approach represents an advancement in optimizing the exploitation of forthcoming large radio surveys for scientific exploration.

Distribution, ecological risk assessment, and source identification of potential toxic elements (PTEs) in Muttukadu backwater sediments, Southern India

Muttukadu Backwater is a robust estuary on the east coast of Tamil Nadu is contaminated by industrial, residential, and tourism-related activity (especially boat houses). In this present research, we investigated the heavy metal pollution, spatial distribution and statistical analysis of muttukadu coastal sediments, east coast of the Bay of Bengal in south India. In this study, we present the distribution of heavy metals in backwater sediments reveals enhanced levels of Fe, Mn, Cr, Zn, Cu, Ni, Pb, and Co and the highest concentrations of these metals are found at stations 2, 4, and 8. Based on contamination indices such as CD, Igeo, and PLI, moderate contamination is clear evident. However, the present data of Cr, Cu, and Pb metals have indicated low to moderate anthropogenic influence. We have identified the metals of Cr, Cu, and Pb indicated the High habitat risks are highlighted by ecological risk assessments, and it is due to the anthropogenic factors. Cu, Mn, Zn, and Cr are sourced from anthropogenic sources, while Pb, As, Co, and Ni originate from natural sources, according to spatial distribution analysis. A significant disturbance to the aquatic environment is caused by human activity. In this present study, underscores the necessity of pre-flooded dataset for future comparative research and ecological conservation in Muttukadu Backwater and their environments.

Outlier Detection in Streaming Data for Telecommunications and Industrial Applications: A Survey

Streaming data are present all around us. From traditional radio systems streaming audio to today’s connected end-user devices constantly sending information or accessing services, data are flowing constantly between nodes across various networks. The demand for appropriate outlier detection (OD) methods in the fields of fault detection, special events detection, and malicious activities detection and prevention is not only persistent over time but increasing, especially with the recent developments in Telecommunication systems such as Fifth Generation (5G) networks facilitating the expansion of the Internet of Things (IoT). The process of selecting a computationally efficient OD method, adapted for a specific field and accounting for the existence of empirical data, or lack thereof, is non-trivial. This paper presents a thorough survey of OD methods, categorized by the applications they are implemented in, the basic assumptions that they use according to the characteristics of the streaming data, and a summary of the emerging challenges, such as the evolving structure and nature of the data and their dimensionality and temporality. A categorization of commonly used datasets in the context of streaming data is produced to aid data source identification for researchers in this field. Based on this, guidelines for OD method selection are defined, which consider flexibility and sample size requirements and facilitate the design of such algorithms in Telecommunications and other industries.

Sound source identification of a cylindrical shell by merging near-field acoustic holography with operational transfer path analysis

The purpose of this paper is to propose a new sound source identification method to identify and separate the sound sources generated by the cross-coupled vibration sources inside the cylindrical shell structure. Near-field acoustic holography (NAH) has fundamentally changed sound source identification in that it has enabled the identification of sound sources and the visualization of the 3-D sound field. Nevertheless, the NAH technique is still unable to identify the vibration sources inside a structure and also finds it difficult to identify the contribution of a single sound source to sound fields due to cross-coupling among the vibration sources. To overcome these limitations, a modified operational transfer path analysis (OPA) technique has also been proposed, which can address the cross-coupling between vibration sources. In practice, however, a single identification method often appears to be inadequate. Thus, in this paper, a novel method of merging the NAH technique and the modified OPA technique has been adopted and used to identify the structure-borne sound source of a cylindrical shell. Finally, the adaptability of the proposed method has been demonstrated by numerical simulations and experimentally and it has been shown that the novel method can not only compute the sound field distribution of a cylindrical surface, but also reconstruct other 3-D field distributions, and moreover, can locate a sound source and predict the sound field.

Crucial time of emergency monitoring for reliable numerical pollution source identification

The Pollution source identification (PSI) is an important issue on river water quality management especially for urban receiving water. Numerical inversion method is theoretically an effective PSI technique, which employs monitored downstream pollutant breakthrough curves to identify the pollution source. In practice, it is important to know how much monitoring data should be accumulated to provide PSI results with acceptable accuracy and uncertainty. However, no literature reports on this key point and it seriously handers the numerical PSI technology to mature practical applications. To seek a monitoring guideline for PSI, we conducted extensively numerical experiments for single-point source instantaneous release taking Bayesian-MCMC method as the baseline inversion technique. The crucial time (Tc) phenomenon was found during the data accumulation process for Bayesian source inversion. After Tc, estimated source parameters subsequent sustained low error levels and uncertainty convergence. Results shown the presence of Tc impacted by the number and location of monitoring sections, while monitoring frequency and data error do not. Under different river hydrodynamic conditions, relative crucial time (Λ) is determined by the river's Peclet number, and minimum effective Λ was controlled by dispersion coefficient (Dx). Analytic spatial structure of Λ(U, Dx) was uncovered and this relationship successfully explained by the information entropy theory. Based on these findings, a novel design method of PSI emergency monitoring network for preparedness plan and a practical framework of PSI for emergency response were established. These findings fill the important knowledge gap in PSI applications and the guidelines provide valuable references for river water quality management.

Seasonal variations and the prevalence of phenolic profiles in ambient fine particulate matter and their impact on oxidative potential

Exposure to fine particulate matter (PM2.5) poses numerous health risks, with oxidative potential (OP) serving as a critical marker of its toxicity. Synthetic phenolic antioxidants (SPAs) and bisphenols (BPs) influence reactive oxygen species (ROS) levels in PM2.5, and exposure to these compounds induces oxidative stress in organisms, thereby potentially affecting the OP of PM2.5. We detected 26 phenols (including 12 SPAs, 5 transformation products (TPs), and 9 BPs) in PM2.5 sample collected from October 2018 to September 2021 in Wuhan, China. Among them, 19 substances were detected at a detection frequency greater than 50 % in PM2.5 sample. AO 2246 and BHT were the main components of SPAs, and BHT-Q and BPA had the highest concentrations in TPs and BPs, respectively. PM2.5 mass concentrations and phenolic levels were higher in winter and autumn. Substances within groups were strongly correlated, suggesting the same or similar source of exposure. This finding aid in more precise pollution source identification and is crucial for comprehensively evaluating their combined health effects. Furthermore, we determined the OP of PM2.5 and found that BPs were related to increased OP and ROS. This suggests that the toxicity of PM2.5 is influenced not only by its concentration but also by its chemical composition, with BPs potentially enhancing its toxic effects. These factors should be fully considered when assessing the health impacts of PM2.5.

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.

International airport emissions and their impact on local air quality: chemical speciation of ambient aerosols at Madrid–Barajas Airport during the AVIATOR campaign

Abstract. Madrid–Barajas Airport (MAD) is the fourth-busiest airport in Europe. The aerosol chemical composition and the concentrations of other key pollutants were measured at the airport perimeter during October 2021 to assess the impact of airport emissions on local air quality. A high-fidelity ambient instrumentation system was deployed at Madrid–Barajas Airport to measure the following: concentrations of organic aerosols (with their composition), black carbon (eBC), carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NOx), sulfur dioxide (SO2), particulate matter (PM2.5, PM10), total hydrocarbon (THC), and total particle number. The average concentrations of eBC, NOx, SO2, PM2.5, PM10, CO, and THC at the airport for the entire campaign were 1.07 µg m−3, 22.7 µg m−3, 4.10 µg m−3, 9.35 µg m−3, 16.43 µg m−3, 0.23 mg m−3, and 2.30 mg m−3, respectively. The source apportionment analysis of the non-refractory organic aerosol (OA) using positive matrix factorisation (PMF) allowed us to discriminate between different sources of pollution, namely less oxidised oxygenated organic aerosol (LO-OOA), alkane organic aerosol (AlkOA), and more oxidised oxygenated organic aerosol (MO-OOA). The results showed that LO-OOA and MO-OOA account for more than 80 % of the total organic particle mass measured near the runway. Trace gases correlate better with the AlkOA factor than LO-OOA and MO-OOA, indicating that AlkOA is mainly related to primary combustion emissions. Bivariate polar plots were used for pollutant source identification. Significantly higher concentrations of the obtained factors were observed at low wind speeds (&lt;3 m s−1) from the southwest, where two of the runways and all terminals are located. Higher SO2/NOx and CO/eBC ratios were observed when the winds originated from the northeast, where the two northern runways are located. These elevated ratios are attributed to the aircraft activity being the major pollutant source in the northeast area.

Identification of new sources of resistance against late leaf spot disease of groundnut using molecular and field screenings

ABSTRACT Late leaf spot (LLS), caused by the fungus Nothopassalora personata, is an important foliar disease in groundnut (Arachis hypogaea L.) production. The present study used previously reported three single nucleotide polymorphism (SNP) markers to screen a set of groundnut germplasm and compared them with phenotypic values of three hotspot locations. The results indicated that marker-identified genotypes had LLS-resistant or moderately resistant phenotypes. About 56% of the phenotypically confirmed genotypes were confirmed with the SNP genotype. Some of the phenotypically resistant genotypes were not confirmed with marker genotypes, indicating some other quantitative trait locus (QTL) responsible for the LLS resistance might have been involved. The findings in this study are relevant to future LLS resistance breeding programs.

Ecological source identification and ecological security pattern construction from the perspective of ecosystem service supply and demand: A case study of Baiyangdian Basin in China

Ecological source identification and ecological security pattern construction from the perspective of ecosystem service supply and demand: A case study of Baiyangdian Basin in China

An analysis of abnormal vibration and noise caused by shaft coupling misalignment of high-speed train

This study conducts on-site testing and simulation analysis of the abnormal noise phenomenon generated during the operation of high-speed trains. The sound pressure characteristics in the cabin and the vibration of different vehicle components are recorded and subjected to comprehensive analysis. Test results indicate that significant abnormal noise occurs inside the cabin at the speed of 250 km/h, with frequencies of 87 Hz, 173 Hz, 259 Hz, and 346 Hz. Through sound source identification, vibration transmission analysis and theoretical derivation, it has been determined that the abnormal noise primarily originates from the transmission system, and it is further speculated that these abnormal vibrations and noises may be correlated with the shaft coupling misalignment within the transmission system. To verify the conjecture, a high-speed vehicle electromechanical coupling simulation model was established, incorporating the shaft coupling misalignment in the transmission system. The vibration characteristics of vehicle components were analyzed, confirming that shaft coupling misalignment leads to uneven meshing forces in gears, resulting in significant vibration and noise that affect the riding comfort of the vehicle. We propose replacing the shaft coupling to solve this problem, the test results showed that the abnormal vibration amplitudes of the transmission system at 87 Hz, 173 Hz, 259 Hz, and 346 Hz were significantly reduced after replacing the shaft coupling, and the amplitude of the corresponding frequencies on the cabin floor decreased by 91 %, 78 %, 98 %, and 66 %, respectively. The issue of abnormal noise has been effectively resolved.

Which pollutants and sources should be prioritized for control in multi-pollutants complex contaminated areas?

Risk assessment and source identification of multi-pollutants are essential for accurate control of soil contamination. However, complexity in pollutant properties and diversity in source types raise challenges to the target. Therefore, this study constructed a hierarchical ecological risk quantification method combined with risk ranking, risk of single pollutant using potential affected fraction (PAF), and joint risk of multi-pollutants employing msPAF. Taking regional contamination in South China as a case, the risk ranking was determined, while single and joint effects showed msPAF reaching 79.4 %, with risk as heavy metals (HMs) > per- and polyfluoroalkyl substances (PFASs) > polycyclic aromatic hydrocarbons (PAHs). Meanwhile, an integrated source apportionment method was established from three layers by principal component analysis to classify source types, multiple linear regression of distance to identify key sources, and positive matrix factorization to track omitted sources. Consequently, key sources were captured, with 80.8 %−93.2 % contribution of farmland and electroplating to three main HMs, 52.2 %−69.4 % contribution of roads to three main PAHs, and 71.1 %−73.2 % contribution of electroplating to two main PFASs. Further, omitted sources were tracked with contribution of 31.2 %−84.1 % to eight pollutants. The established methods can identify control targets, including high-risk pollutants and their key sources.

In-Silico Screening of Natural Compound Library against COVID-19 Main Protease

Aims: To perform in-silico screening of the natural phytoconstituents against COVID-19 main protease Background: COVID-19 is an atypical virus that is universal around the globe. WHO (World Health Organization) has also affirmed the emergency public health concerns regarding COVID-19. As COVID-19 spreads globally within a shorter duration, there is a great demand for effective therapy with minimal side effects. Objective: To perform SP and XP docking of the naturally obtained ligand Quercetin (flavonoid) based on HTVS. To identify the effectiveness of natural phytoconstituents for COVID-19 main protease virus. Methods: In the present study, natural ligands were used for performing the virtual screening, which was obtained from Prestwick Phytochemical Library by applying hierarchal screening. Several docking protocols, such as SP and XP, are used to screen the best natural hit compounds. The three best-hit compounds and hydroxychloroquine were further studied for SAR analysis. Moreover, the ADME study by QikProp and the identification of biological sources were carried out by Duke's Phytochemical and Ethnobotanical Databases. result: The results suggest that Quercetin showed good binding affinity towards 6LU7 (-56.689 kcal/mol) which could be the effective natural compound for COVID-19. Results: The results suggest that Quercetin showed good binding affinity towards 6LU7 (-56.689 kcal/mol), which could be an effective natural phytoconstituent for COVID-19. Conclusion: Based on HTVS, SP, and XP docking, the naturally obtained ligand Quercetin (a flavonoid) can be used in place of Hydroxychloroquine against COVID-19's main protease virus. This scientific exploration will help to identify the effective natural compound for the COVID-19 main protease virus.

Source Identification of Potentially Toxic Metals in Plants of Alpine Ecosystems of Mt. Madra by Positive Matrix Factorization.

In this study, the concentrations of cadmium (Cd), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn) in plants sampled from Mt. Madra were investigated. Furthermore, the distribution characteristics and source identification of potentially toxic metals were investigated with the application of Positive Matrix Factorization (PMF) modelling. Samples of 26 different plant species were taken from Mt. Madra at elevations ranging from 177 to 1347m using the multi-point sampling approach. The metal quantities measured by ICP-OES are the following sequences (mean ± SD) (mg/kg): Fe (974.96 ± 29.6) > Mn (111.81 ± 2.6) > Zn (27.28 ± 0.2) > Ni (2.17 ± 0.03) > Pb (0.77 ± 0.01) > Cd (0.12 ± 0.01). According to the plant samples in which the highest values were determined, the metals are as follows: Cd (Lathyrus laxiflorus, 0.401mg/kg), Fe (Ajuga orientalis, 7621.207mg/kg), Mn (Castanea sativa, 724.927mg/kg), Ni (Prunella laciniata, 6.947mg/kg), Pb (Crataegus stevenii, 3.955mg/kg) and Zn (Prunella laciniata, 50.802mg/kg). The results of the PMF model showed that Cd had an atmospheric transport factor originated and transported from industrial activites, Ni had a substrate factor, Fe, Mn, Pb and Zn were influenced by different anthropogenic factors.

Systematic assessment of source identification and ecological and probabilistic health risks of potentially toxic elements (PTEs) in soils of a typical coal mining area in Guanzhong region

Mining activities may cause the accumulation of potentially toxic elements (PTEs) in surrounding soils, posing ecological threats and health dangers to the local population. Therefore, a comprehensive assessment using multiple indicators was used to quantify the level of risk in the region. The results showed that the mean values of the nine potentially toxic elements in the study area were lower than the background values only for Cr, and the lowest coefficient of variation was 17.1 % for As, and the spatial distribution characteristics of the elements indicate that they are enriched by different factors. The elements Hg and Cd, which have substantial cumulative features, are the key contributors to ecological risk in the study region, which is overall at moderate risk. APCS-MLR model parses out 4 possible sources: mixed industrial, mining and transportation sources (53.98 %), natural sources (24.56 %), atmospheric deposition sources (12.60 %), and agricultural production sources (8.76 %). The probabilistic health risks show that children are more susceptible to health risks than adults; among children, the safety criteria (HI < 1 and CR < 10−4) were surpassed by 29.29 % of THI and 8.58 % of TCR. According to source-orientated health hazards, the element Ni significantly increases the risk of cancer. Mixed sources from industry, mining, and transportation are important sources of health risks. The results of this research provide some scientific references for the management and decrease of regional ecological and health risks.

A multi-analytical approach for the identification of pollutant sources on black crust samples: Stable isotope ratio of carbon, sulphur, and oxygen

This study is focused on the identification of pollutant sources on black crust (BC) samples from the Monumental Cemetery of Milan (Italy), through a multi-analytical approach based on the determination of stable isotope ratios of carbon, sulphur, and oxygen. Six black crust samples, mainly developed on marble sculptures over a time span of 100–150 years, were analysed.For the first time, δ13C was measured for BC samples: δ13C values of the pulverized samples (from −1.2 to +1.3 ‰) are very close to the values obtained from the carbonate matrix, whereas after the removal of the matrix through acidification, δ13C values of BC samples from Milan range from −27.2 to −22.1 ‰, with no significant variation between samples with different ratios of organic carbon to elemental carbon. In sum, the δ13C values obtained for all BC samples fall within the range of anthropogenic emissions such as vehicle traffic, coal combustion and industrial emissions.δ34S and δ18O values of sulphate from BC samples range from −6.3 to +7.0 ‰ and from +7.6 to +10.5 ‰, respectively. Coupling the analysis of the oxygen isotope ratio with that of sulphur enables a more precise identification of the origin of sulphates: the observed isotopic composition falls in the range typical for anthropogenic emission of sulphur dioxide.Overall, in this study, C, S and O isotopes were combined for the first time to assess pollutant sources on black crust samples: this multi-stable isotope approach allowed to show that the BC formation on monuments from the Monumental Cemetery of Milan mostly results from anthropogenic emissions from fossil fuels combustion by road vehicles and factories, as well as domestic heating.