Source Attribution Research Articles

In generative AI we trust: can chatbots effectively verify political information?

This article presents a comparative analysis of the potential of two large language model (LLM)-based chatbots—ChatGPT and Bing Chat (recently rebranded to Microsoft Copilot)—to detect veracity of political information. We use AI auditing methodology to investigate how chatbots evaluate true, false, and borderline statements on five topics: COVID-19, Russian aggression against Ukraine, the Holocaust, climate change, and LGBTQ + -related debates. We compare how the chatbots respond in high- and low-resource languages by using prompts in English, Russian, and Ukrainian. Furthermore, we explore chatbots’ ability to evaluate statements according to political communication concepts of disinformation, misinformation, and conspiracy theory, using definition-oriented prompts. We also systematically test how such evaluations are influenced by source attribution. The results show high potential of ChatGPT for the baseline veracity evaluation task, with 72% of the cases evaluated in accordance with the baseline on average across languages without pre-training. Bing Chat evaluated 67% of the cases in accordance with the baseline. We observe significant disparities in how chatbots evaluate prompts in high- and low-resource languages and how they adapt their evaluations to political communication concepts with ChatGPT providing more nuanced outputs than Bing Chat. These findings highlight the potential of LLM-based chatbots in tackling different forms of false information in online environments, but also point to the substantial variation in terms of how such potential is realized due to specific factors (e.g. language of the prompt or the topic).

Ozone source attribution in polluted European areas during summer 2017 as simulated with MECO(n)

Abstract. Emissions of land transport and anthropogenic non-traffic emissions (e.g. industry, households and power generation) are significant sources of nitrogen oxides, carbon monoxide and volatile organic compounds (VOCs). These emissions are important precursors of tropospheric ozone and affect air quality. The contribution of the emission sectors to ozone cannot be measured directly but can only be calculated using sophisticated atmospheric chemistry models. For this study we apply the MECO(n) model system (MESSy-fied ECHAM and COSMO models nested n times) equipped with a source attribution method to investigate the contribution of various sources to ground-level ozone in Europe. Compared to previous source apportionment studies for Europe, for the first time we apply a combined NOx–VOC tagging implemented in an online nested global–regional chemistry–climate model to achieve a finer resolution over central Europe (12 km) but concurrently incorporating the effect of long-range transport. We distinguish 10 different source sectors and 4 geographical source regions, analysing especially the contribution from the land transport sector. Our analysis focuses on large ozone events during summer in four different regions, two major polluted regions (Po Valley and Benelux) and two more remote regions (Iberian Peninsula and Ireland). The analysis concentrates on results for summer 2017, during which measurement campaign EMeRGe took place. Measurement data from this campaign are used for model evaluation. Our analysis shows that European land transport emissions contribute largely (42 % and 44 %, respectively) to ground-level NOy mixing ratios over Benelux and the Po Valley. Due to the overall lower ozone production efficiency over Benelux compared to the Po Valley, however, the contributions to ground-level ozone are larger in the Po Valley (12 %) compared to Benelux (8 %). In line with previous publications using different source apportionment methods, our results underline the large importance of long-range transport of ozone, especially from North America (Benelux, Ireland), but also from Africa (Iberian Peninsula), and provide additional information about the sectoral contribution not available before. Our analysis shows that the contributions of European emissions from land transport and anthropogenic non-traffic sectors strongly increase with increasing values of MDA8 (daily maximum 8 h average) ozone over the Po Valley and in the Benelux region. Accordingly, these two sectors drive large MDA8 values in these regions. Inter-comparisons of results for 2018 and with a coarser model resolution (50 instead of 12 km) show that these results are robust with respect to inter-annual variability and model resolution. Comparing our results with results from other source attribution methods we find that the contributions to ozone from individual sectors, which have large NOx but rather low VOC emissions, are estimated to be lower, if their emissions of NOx and VOCs are regarded concurrently.

Cross-sectoral genomic surveillance reveals a lack of insight in sources of human infections with Shiga toxin-producing Escherichia coli, the Netherlands, 2017 to 2023.

Shiga toxin-producing Escherichia coli (STEC) is a zoonotic pathogen associated with illness ranging from mild diarrhoea to haemolytic uremic syndrome (HUS) or even death. Cross-sectoral data sharing provides an opportunity to gain insight in reservoirs and sources of human infections and starting points for pro-active measures. Nevertheless, phylogenetic clustering of STEC strains from animals, food and human cases is low in the Dutch surveillance system. This is partly due to the substantial contribution of international travel and person-to-person spread in the STEC epidemiology. Furthermore, some STEC strains causing disease in humans may have a human reservoir. Although the main reservoirs and sources are included in the Dutch monitoring programmes, some animals and food products may be under-recognised as potential sources of human infections. More effort in investigating the role of other reservoirs beyond the well-known can provide a better understanding on STEC ecology in general, improving surveillance and source attribution, and ultimately provide better guidance for monitoring and source finding. This also implies having good diagnostics in place and isolates available for typing. Therefore, on the human side of the surveillance, the decision has been made to start isolating STEC at national level.

Unveiling the organic chemical composition and sources of organic carbon in PM2.5 at an urban site in Greater Cairo (Egypt): A comprehensive analysis of primary and secondary compounds

This work presents an exhaustive chemical characterization of the organic fraction of fine particulate matter (PM2.5) collected at an urban site in the Greater Cairo Area, Egypt, one of the most polluted megacities in the world. An intensive 2-month sampling campaign was conducted at an urban site in Giza (Dokki), from November 26, 2019, to January 28, 2020. Daily (24-h integrated) PM2.5 filter samples were then analyzed for their carbonaceous (OC, EC) and organic fractions including primary (n-alkanes, phthalates, fatty acids, polycyclic aromatic hydrocarbons, hopanes, sugars, and sugar alcohols) and secondary (isoprene and β-caryophyllene oxidation products, and dicarboxylic acids) compounds. Average organic (OC) and elemental carbon (EC) concentrations were 17.8 ± 6.6 μg/m3 and 4.4 ± 1.5 μg/m3, respectively. Biomass burning was confirmed by high daily concentration levels of levoglucosan, mannosan, and galactosan (sum equals to 288 ng/m3). Road traffic was also highlighted by the relative abundance of tetracosane and a carbon preference index close to unity as well as by the concentration ratios of PAHs and hopanes. Moreover, phthalates were identified for the first time in Cairo with high concentrations (654 ng/m3) that might be attributable to open waste burning activities. Fatty acids and sugars were also investigated and assigned to cooking activities and primary biogenic sources, respectively. The average concentration of isoprene and β-caryophyllene oxidation products were 0.89 ± 0.83 ng/m3, and 0.01 ± 0.02 ng/m3, respectively. These low values are expected since no pine trees or even forests exist in Egypt. The macrotracer approach was employed alongside Monte Carlo simulation to identify sources of primary OC and evaluate the uncertainties associated with source attribution and OC reconstruction. The findings revealed a strong contribution from cooking (31% of observed OC) and biomass burning (18%), with median reconstructed OC levels showing significant uncertainty (64%) as expected.

Effect on the spectral purity of photon-pairs due to on-chip temporal manipulation of the pump

Photonic Integrated Circuits (PICs) are a promising contender for quantum information technologies. The spectral purity of photons is one of the key attributes of photon-pair sources based on nonlinear optics. We can obtain >99% purity in PIC ring-resonator photon-pair sources by manipulating the pump pulse in the time domain. Here, we have developed a PIC to shape a pulse into dual, triple, and quadruple pulses and investigated the effect of these pump pulse configurations on the purity. Our results show that more complex configurations, compared to a two-pulse configuration, do not result in comparatively higher purity but allow more accurate control over choosing arbitrary values of the purity.

Spatiotemporal Analysis and Source Attribution of Severe PM2.5 Pollution Episodes in Beijing during October-November 2023

Spatiotemporal Analysis and Source Attribution of Severe PM2.5 Pollution Episodes in Beijing during October-November 2023

EXPRESS: Observation inflation as source confusion: Symmetrical conflation of memories based on action performance and observation.

People often cannot remember the source of their memories despite recalling other elements of a remembered event correctly. Observation inflation is one such error of source monitoring. It refers to remembering actions of another agent as self-performed. While the existence of this memory error is well documented, it is not clear how it relates to other errors of source attribution: It is not evident whether the phenomenon reflects (1) a specific tendency to appropriate the actions of other agents, (2) a general confusion of sources with overlapping features or (3) whether it is a confound induced by the complex structure of the conventionally used experimental paradigm. We conducted two online experiments to assess these potential contributions to observation inflation. Crucially, administering a full source monitoring test revealed a symmetrical pattern: Recognizing other's actions as one's own occurred at the same rate as misattributing one's own actions to another agent. The findings resonate with source-monitoring frameworks by suggesting that source attribution errors arise due to the similarity of the sources, whereas the evidence speaks against a special status for appropriating observed actions.

Anomalous individuals searching framework for space debris detection in single optical astronomical image

With the growing concern over space debris, effective space surveillance is significant to prevent potential collisions. Traditional approaches often design specific algorithms for distinct surveillance tasks, thereby neglecting the shared aspects across these tasks. Such methods frequently exhibit limited robustness, particularly when confronted with external interference or shifts in scene dynamics. This study introduces a space debris detection framework within single astronomical image, conceptualizing the task as an anomalous individuals searching challenge. The framework is structured around three core modules: individuals extraction, feature extraction and anomaly detection. Utilizing this framework, a versatile methodology is designed, which has been rigorously tested across two primary observational contexts. According to the similarity between ideal and actual imaging, our method begins by extracting sources within a normalized correlation space. It then compiles a comprehensive feature matrix for each source, encompassing motion, intensity, and morphological attributes. By exploiting the inherent low-rank characteristics and sparsity of the feature matrix, we identify foundational feature vectors for stars. Anomalous sources are subsequently identified via the Mahalanobis distance, facilitating the identification of targets. The method is validated through both simulated and actual observed datasets, with 97.7% average detection accuracy, outperforms than eight classical methods across various scenarios. Given the modular nature of the framework, each component can be refined to accommodate more complex situations. Moreover, the uniform anomaly scores generated offer valuable confidence for subsequent tracking algorithms, which underscore the potential of the framework in advancing practical space surveillance endeavors.

Costanzo Festa’s (?) motet ‘O altitudo divitiarum’ re-examined: new suggestions regarding its source context, attribution and function

In the manuscript VatS 38, copied around 1550–63, there are two anonymous motets: Gaude felix ecclesia (ff. 114v-122r) and O altitudo divitiarum (ff. 122v-126r). Joseph Llorens attributed them to Costanzo Festa (c. 1490–1545) in his catalogue of the musical manuscripts of the Sistine Chapel and both are included in Festa’s Opera omnia. What Llorens apparently missed is the fact that the former, with the text Gaude felix Florentia and attributed to Andreas de Silva – who probably wrote it in honor of Pope Leo X on the occasion of his election in 1513 – appears in the manuscript RomeV 35-40 (the Vallicelliana Manuscript). The analysis of O altitudo divitiarum, on the other hand, its stylistic features, transmission, and liturgical and historical context has made me suggest that the motet, if indeed by Festa, may have been intended for the peace treaty in Nice in 1538 with participation of Pope Paul III, Emperor Charles V, and King Francis I of France. If so, it could be considered as a companion to Cristóbal de Morales’s six-voice motet in two movements Jubilate Deo omnis terra which was specifically written for this occasion.

Effects of AI versus human source attribution on trust and forgiveness in the identical corporate apology statement for a data breach scandal

This study investigates the effects of AI versus human source attribution on trust and forgiveness in the identical AI-generated corporate apology statement for a simulated data breach scandal. While AI-generated messages hold promise in crisis communication, their impact on public perception remains understudied. The research was inspired by incidents where ChatGPT was used to generate official apology statements, raising questions about the authenticity of AI-generated apologies. Using a fictitious retail company’s apology statement, crafted with the assistance of ChatGPT, participants were randomly assigned to conditions indicating the statement was AI-aided, human-written, or unspecified (control). The results indicate that participants attributed higher levels of forgiveness intention and trust to the statement credited to humans compared to AI-generated statements. Additionally, the human-attributed statement was perceived as more empathetic and sincere than the AI-attributed statement. Mediation analysis results revealed that empathy mediated forgiveness intention and trust in human-authored statements, while perceived sincerity mediated these factors in AI-aided statements. These findings suggest that source attribution significantly influences public perception of organizational apologies during crises. This study contributes to understanding the evolving role of AI in crisis management and underscores the importance of ethical and transparent communication practices.

Oxford Nanopore's 2024 sequencing technology for Listeria monocytogenes outbreak detection and source attribution: progress and clone-specific challenges.

Whole genome sequencing is an essential cornerstone of pathogen surveillance and outbreak detection. Established sequencing technologies are currently being challenged by Oxford Nanopore Technologies (ONT), which offers an accessible and cost-effective alternative enabling gap-free assemblies of chromosomes and plasmids. Limited accuracy has hindered its use for investigating pathogen transmission, but recent technology updates have brought significant improvements. To evaluate its readiness for outbreak detection, we selected 78 Listeria monocytogenes isolates from diverse lineages or known epidemiological clusters for sequencing with ONT's V14 Rapid Barcoding Kit and R10.4.1 flow cells. The most accurate of several tested workflows generated assemblies with a median of one error (SNP or indel) per assembly. For 66 isolates, the cgMLST profiles from ONT-only assemblies were identical to those generated from Illumina data. Eight assemblies were of lower quality, with more than 20 erroneous sites each, primarily caused by methylations at the GAAGAC motif (5'-GAAG6mAC-3'/5'-GT4mCTTC-3'). This led to inaccurate clustering, failing to group isolates from a persistence-associated clone that carried the responsible restriction-modification system. Out of 50 methylation motifs detected among the 78 isolates, only the GAAGAC motif was linked to substantially increased error rates. Our study shows that most L. monocytogenes genomes assembled from ONT-only data are suitable for high-resolution genotyping, but further improvements of chemistries or basecallers are required for reliable routine use in outbreak and food safety investigations.

Isotopic Constraints on Nitrous Oxide Emissions From the US Corn Belt

AbstractAgriculture is the dominant source of anthropogenic nitrous oxide (N2O) –a greenhouse gas and a stratospheric ozone depleting substance. The US Corn Belt is a large global N2O source, but there remain large uncertainties regarding its source attribution and biogeochemical pathways. Here, we interpret high frequency stable N2O isotope observations from a very tall tower to improve our understanding of regional source attribution. We detected significant seasonal variability in δ15Nbulk (6.47–7.33‰) and the isotope site preference (δ15NSP = δ15Nα–δ15Nβ, 18.22–25.19‰) indicating a predominance of denitrification during the growing period but of nitrification during the snowmelt period. Isotope mixing models and atmospheric inversions both indicate that indirect emissions contribute substantially (>35%) to total N2O emissions. Despite the relatively large uncertainties, the upper bound of bottom‐up indirect emission estimates are at the lower bound of the isotopic constraint, implying significant discrepancies that require further investigation.

Protein Consumption and Risk of CVD Among U.S. Adults: The Multi-Ethnic Study of Atherosclerosis (MESA).

Although some randomized trials have reported beneficial effects of protein intake on cardiometabolic risk factors, evidence from prospective studies have not supported a strong link between protein intake and cardiovascular disease (CVD) risk. It is also unclear whether diversity in protein intake plays a role in CVD risk. We investigated prospective associations of (1) protein intake, overall and by food source and (2) diversity of protein sources with risk of CVD, coronary heart disease (CHD), and stroke. In a multi-ethnic cohort of 5879 U.S. adults (45-84 years), who were free of CVD at baseline, protein intake was assessed at baseline (2000-2002) using a validated 120-item food frequency questionnaire. Two different aspects of protein diversity were assessed including count (number of protein food consumed at least once/week) and dissimilarity (diversity of the attributes of the protein sources consumed). Relationships with incident CVD outcomes through 2019 were assessed using Cox proportional hazards models adjusting for sociodemographic, lifestyle, and comorbidity factors. During 83,430 person-years, 1045 CVD cases were identified, including 668 CHD and 332 stroke cases. In multivariable models, we found no significant associations between protein intake, overall and by food source, with incident CVD, CHD, or stroke. Protein count, but not protein dissimilarity, was weakly associated with CVD risk. We found no significant associations between diversity of consumption of animal or plant food source and CVD outcomes. Our findings suggest protein consumption may not significantly impact CVD risk in middle-aged adults.

Branding locally produced chicken in Ghana: An application of discrete choice experiment

Branding is gradually becoming an essential component of successful agribusinesses and a significant motivation to boost modern agriculture. The study aimed to investigate broiler farmers' choices and preferences for branding locally produced chicken in Ghana. Primary data was collected from 562 broiler producers using a multistage sampling technique. The Discrete Choice Experiment model, which comprises random parameter logit and conditional logit models, was used to analyse farmers' preferences for branding attributes and willingness to brand locally produced chicken. Branding attributes of brand source, production claim, form of branded chicken, percentage of farm use, and price were analysed. Averagely, farmers were willing to pay US$0.59 to brand 1.9 kg of processed chicken. Further, household size, brand source, production claim, form of branded chicken, percentage of farm use, and membership of a farmer-based organisation positively influenced farmers' willingness to brand locally produced chicken. In contrast, price negatively influenced farmers’ preference for branding. Farmers should produce brands with these unique attributes to boost sales, improve income and enhance the competitiveness in the poultry industry.

Methods for Quantifying Source-Specific Air Pollution Exposure to Serve Epidemiology, Risk Assessment, and Environmental Justice.

Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source-specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source-specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data-Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on-road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult-since source-specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models.

The contribution of transport emissions to ozone mixing ratios and methane lifetime in 2015 and 2050 in the Shared Socioeconomic Pathways (SSPs)

Abstract. We quantify the contributions of emissions from the transport sector to tropospheric ozone and the hydroxyl radical (OH) by means of model simulations with a global chemistry-climate model equipped with a source attribution method. For the first time we applied a method which also allows for quantifying contributions to OH which is invariant upon disaggregation or recombination and additive. Based on these quantified contributions, we analyse the ozone radiative forcing (RF) and methane lifetime reductions attributable to emissions from the transport sectors. The contributions were analysed for each transport sector separately and for 2015 as well as for 2050 under the Shared Socioeconomic Pathways (SSPs) SSP1-1.9, SSP2-4.5, and SSP3-7.0. In line with previous publications using the source attribution approach, we quantify ozone RF attributable to emissions from land transport, shipping, and aviation for the year 2015 of 121, 60, and 31 mW m−2, respectively. At the same time, we diagnose a relative reduction in methane lifetime due to transport emissions of 14.3 % (land transport), 8.5 % (shipping), and 3.8 % (aviation). These reductions are significantly larger than reported by previous studies due to the application of the source attribution method. Compared to 2015, only SSP1-1.9 shows a strong decrease in ozone RF and methane lifetime reduction attributable to the entire transport sector in 2050. For the projections of SSP2-4.5, we find similar effects of the total transport sector as for 2015, while the effects in SSP3-7.0 increase compared to 2015. This small change in the effects for the two projections compared to 2015 is caused by two main factors. Firstly, aviation emissions are projected to increase in SSP2-4.5 (increase of 107 %) and SSP3-7.0 (+86 %) compared to 2015, resulting in projected ozone RF of 55 mW m−2 (+78 %) and 50 mW m−2 (+61 %) for the year 2050 from aviation emissions. Secondly, the non-linear effects of atmospheric chemistry in polluted regions such as Europe and North America lead to rather small reductions in ozone and OH in response to emission reductions, especially from land transport emissions. In addition, the increase in emissions from land transport in other parts of the world, particularly in South Asia, leads to an increased contribution of ozone and OH. In particular, ozone formed by land transport emissions from South Asia causes strong RF that partially offsets the reductions in Europe and North America. Moreover, our results show that besides the non-linear response, lack of international cooperation, as in the SSP3-7.0 projection, hinders mitigation of ground-level ozone.

Source attribution of Listeria monocytogenes in the Netherlands

The aim of this study was to determine the relative contributions of various potential food sources of human listeriosis and to identify source-specific risk factors, at exposure level, for human Listeria monocytogenes (Lm) infection. To achieve this, available Lm isolates from human cases (n = 756) and food/animal sources (n = 950) from national surveillance systems in the Netherlands (2010−2020) were whole genome sequenced. Additionally, questionnaire-based exposure data for human cases was collected. Source attribution analysis was performed using a Random Forest model based on core-genome multilocus sequence typing (cgMLST). Risk factors for human Lm infection of cattle, chicken and seafood origin were determined using beta regression analysis on the cgMLST-based attribution estimates. Results indicated that the 756 human Lm isolates were mainly attributed to cattle (62.3 %), chicken (19.4 %), and seafood (16.9 %). Specifically, fresh meat (86.2 %), including fresh bovine meat (43.7 %) and fresh chicken meat (39.3 %), accounted for most cases. These attributions stemmed from Lm contamination of either the food products or their production environments. Consumption of steak tartare and smoked salmon was associated with an increased risk of human Lm infections attributed to cattle and seafood, respectively, while no specific risk factors for chicken-borne listeriosis were identified. This study indicated that Lm isolates of cattle origin, particularly those from fresh bovine meat and associated production environments, are estimated to be the primary cause of human listeriosis in the Netherlands. This aligns with several other European source attribution studies on Lm. Moreover, the identified risk factors for human Lm infection from cattle (i.e. steak tartare) and seafood (i.e. smoked salmon) clearly indicated their attributable sources. This joint analysis of core genome and epidemiological data provided novel insights into the origins and transmission pathways of human listeriosis.

Differences in perspectives on sustainability attributes of dietary protein sources between reduced animal-based dieters and nondieters

The United Nations Sustainable Development Goals envision a sustainable future that includes a shift in food consumption, aiming to reduce anthropogenic environmental impacts. Protein sources are crucial in sustainable diets because of their high environmental influence. Although consumers play a pivotal role in driving the shift towards more sustainable food consumption patterns, research on the perceptions of specific dietary groups regarding protein sources is limited. In this study, the perceived sustainability attributes of eight common protein sources between reduced animal-based dieters (RABs) and nondieters (NDs) were evaluated. Using a cross-sectional survey, we measured demographic data, food consumption frequency, perceived healthiness, and environmental friendliness. We performed conjoint analysis on the importance of the protein sources’ sustainability attributes (nutritional score, ecological score, price, product category). In total, 294 RABs and 247 NDs participated. The majority of the sample consisted of highly educated, childless women aged 18–39 years with high incomes, residing in cities, and responsible for food in their households. The presence of a nutritional score had a positive effect on both consumer groups’ perceptions of product sustainability, whereas the presence of an ecological score negatively affected consumer preferences for more sustainable products. The perceptions of the sustainability of protein sources varied significantly between RABs and NDs. Overall, RABs underestimated the healthiness of animal-based products, whereas NDs underestimated the environmental impact of protein sources. Among the animal-based protein sources, eggs and fish were seen as the most sustainable, with significantly higher means for NDs (p < 0.001). Among the sustainability attributes, plant-based foods and nutritional scores had a significant positive effect on both groups’ choices. For RABs, all animal-based protein sources had a significant negative effect on food choice. These results support the assumption that nutritional scores can improve the nutritional quality of food choices, whereas ecological scores can reduce the environmental impact of food choices. These findings underscore the need for tailored communication strategies and interventions to promote sustainable food choices among diverse dietary groups.

Heavy metal contamination assessment and source attribution in the Vicinity of an iron slag pile in Hechi, China: Integrating multi-medium analysis

Heavy metals, such as mercury, cadmium, and nickel, may contaminate human inhabited environments, with critical consequences for human health. This study examines the health impacts of heavy metal pollution from an iron slag pile in Hechi, China, by analyzing heavy metal contamination in water, sediment, soil, and crops. Here, the Nemerow pollution index (NI) indicated severe pollution at most sampling sites, the mean NI of groundwater, and surface water had reached 594.13 and 26.79, respectively. Bioaccumulation of mercury (Hg), cadmium (Cd), and nickel (Ni) was noted in crops, cucumbers showed comparatively lower risk levels. Logarithmic surface water-sediment partition coefficient calculations indicated that heavy metals such as chromium (Cr), ferrum (Fe), zinc (Zn), copper (Cu), Ni, arsenic (As), and lead (Pb) tend to accumulate in sediments. There was a high risk in groundwater (67.48–6590.54) and surface water (13.73–2500.85). Variably influenced by rainfall, these metals can be diluted and mobilized from surface water and sediments, thereby changing the contamination levels and ecological risks. Probabilistic health risk assessments indicated that health risks were higher in children than in adults, the mean total carcinogenic risk values of soil, groundwater, and surface water, were 6.79E-04, 4.20E-06, and 1.15E-6 for children, respectively. Moderate soil pollution is the main health hazard. A Positive Matrix Factorization model attributed over 60% of the pollution to slag stacking. Biotechnologies, solidification/stabilization techniques, field management, and institutional controls, driven by principles of green, low-carbon, and economic efficiency may mitigate. These findings contribute to the management of heavy metal pollution in iron slag pile areas.

Aerosol particle number concentration, ultrafine particle number fraction, and new particle formation measurements near the international airports in Berlin, Germany – First results from the BEAR study

Studies revealed airports as a prominent source of ultrafine particles (UFP), which can disperse downwind to residential areas, raising health concerns. To expand our understanding of how air traffic-related emissions influence total particle number concentration (PNC) in the airport’s surrounding areas, we conduct long-term assessment of airborne particulate exposure before and after relocation of air traffic from “Otto Lilienthal” Airport (TXL) to Berlin Brandenburg Airport “Willy Brandt” (BER) in Berlin, Germany. Here, we provide insights into the spatial–temporal variability of PNC measured in 16 schools recruited for Berlin-Brandenburg Air Study (BEAR).The results show that the average PNC in Berlin was 7900 ± 7000 cm−3, consistent with other European cities. The highest median PNC was recorded in spring (6700 cm−3) and the lowest in winter (5100 cm−3). PNC showed a bi-modal increase during morning and evening hours at most measurement sites due to road-traffic emissions. A comparison between measurements at the schools and fixed monitoring sites revealed good agreement at distances up to 5 km. A noticeable decline in this agreement occurred as the distance between measurement sites increased. After TXL was closed, PNC in surrounding areas decreased by 30 %. The opposite trend was not seen after BER was re-opened after the COVID-lock-down, as the air traffic has not reached the full capacity yet. The analysis of particle number size distribution data showed that UFP number fraction exhibit seasonal variations, with higher values in spring and autumn. This can be explained by nucleation events, which notably affected PNC.The presented findings will play a pivotal role in forthcoming source attribution and epidemiological investigations, offering a holistic understanding of airports’ impact on airborne pollutant levels and their health implications. The study calls for further investigations of air-traffic-related physical–chemical pollutant properties in areas found further away (> 10 km) from airports.