Source Attribution Research Articles

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

Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: Our findings suggest protein consumption may not significantly impact CVD risk in middle-aged adults.

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.

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.

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.

Source apportionment and influencing factors of surface water pollution through a combination of multiple receptor models and geodetector

In line with sustainable development goals (SDGs), precise quantification of water pollution and analysis of environmental interactions are crucial for effectively safeguarding water resources. In this study, Nemerow's pollution index was used to evaluate water quality, three receptor models were used to identify pollution sources, and Geodetector analysis was applied to explore environmental interactions in the North Shangyu Plain, Southeast China. Using 5207 surface water samples from September 2023 with 11 physicochemical parameters, the results showed that surface rivers in the North Shangyu Plain exhibited varying degrees of pollution: slight pollution upstream, moderate pollution in midstream and downstream, and concentrated high pollution in certain areas, with TN, CODCr, and TP as the primary pollutants. Multimethod source apportionment significantly improved the accuracy of pollution source attribution and identified five main sources: domestic sewage (1.42%–3.54%) characterized by NO3-N, phytoplankton source (38.43%–50.05%) indicated by chl and PC, agricultural cultivation (16.1%–17.63%) marked by TP and CODMn, industrial wastewater (17.64%–25.1%) primarily associated with TN, and natural source (10.32%–13.26%) characterized by DO, NH3-N, and CODCr. Influencing factor analysis validated the source identification. Natural factors had minor impacts on water parameters, while pollution control from agricultural activities was suggested to diversify fertilizer types rather than merely reduce quantities. The combined effects of industrial and aquaculture activities intensified pollution from TN, chl, and PC, underscoring the need for targeted management practices. This study showed the objectivity and reliability of using a combined approach of multiple receptor models and Geodetector to evaluate the river water quality status, which helps assist decision-makers in formulating more effective water resource protection strategies.

Source Attribution Analysis of an Ozone Concentration Increase Event in the Main Urban Area of Xi’an Using the WRF-CMAQ Model

Source Attribution Analysis of an Ozone Concentration Increase Event in the Main Urban Area of Xi’an Using the WRF-CMAQ Model

Suicidal thoughts are associated with reduced source attribution of emotion.

Approximately 9% of people think about suicide during their lifetime. Suicidal thoughts are consistently associated with perceived failures in emotion regulation. However, factors contributing to these perceptions remain insufficiently clear. New evidence suggests that when people know little about the cause of their emotions (i.e., low source attribution of emotion), they perceive themselves as less successful in regulating them. Therefore, emotion regulation deficits in people with suicidal thoughts might be related to lower knowledge about sources of emotions. We examined this question in two ecological momentary assessment studies (N₁ = 396, N₂ = 195). We found that participants with current suicidal thoughts knew less about the sources of their emotions compared to participants with no suicidal thoughts history (Studies 1 and 2), and even when compared to controls with similar levels of psychiatric symptoms but no history of suicidal thoughts (Study 2). Using language processing, we found that written descriptions of the source of participants' emotions were less concrete among those with suicidal thoughts compared to participants with no suicidal thoughts history. Among suicidal participants, suicidal thoughts were more likely to be present in moments when participants knew less than usual about the source of their negative emotions (Study 2), and low knowledge of the source was associated with more frequent and prolonged suicidal thoughts (Studies 1 and 2). Finally, lower perceived success in emotion regulation mediated the association between source attribution of emotion and the occurrence of suicidal thoughts. Findings suggest that reduced knowledge about the source of negative emotions might increase the risk for suicidal thinking. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

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.

Supplemental Material for Suicidal Thoughts Are Associated With Reduced Source Attribution of Emotion

Supplemental Material for Suicidal Thoughts Are Associated With Reduced Source Attribution of Emotion

Quantification of heavy metal contamination and source in urban water sediments using a statistically determined geochemical baseline

Geochemical baselines (GBs) play a crucial role in discerning natural variability from anthropogenic impacts on elemental composition within the environment. However, their applicability in quantifying the contribution of pollution sources to heavy metal contamination in sediments remains understudied. This research aimed to assess the degree of contamination and local pollution source attribution by leveraging geochemical baselines derived from statistical techniques, specifically the relative cumulative frequency (RCF) and 2σ-iterative (2σ-I) methods. In the urban water systems of Ma’anshan City, the major iron ore centre in eastern China, we observed concentration ranges of Cr, Cu, Ni, Pb and Zn in 36 sediment samples ranging from 66.89 to 352.08 mg/kg, 22.01 to 133.37 mg/kg, 22.66 to 50.80 mg/kg, 14.66to 264.37 mg/kg and 73.30 to 2707.46 mg/kg, respectively. RCF and 2σ-I techniques yielded similar GBs with no significant differences (p > 0.05). The geo-accumulation index and contamination factor analysis showed a sediment heavy metal accumulation rank of Zn > Pb > Cr > Cu > Ni. The contribution percentage of pollution sources varied with land functional type of watershed. For industry-influenced sediments, the contribution of local sources to Cr, Cu, Pb and Zn was significant, with shares of 43%–88%. Overall, this study highlights the valuable insights provided by GBs for effective management of urban aquatic environments.

Source-risk and uncertainty assessment of trace metals in surface sediments of a human-dominated seaward catchment in eastern China

Current total concentration-based methods for source attribution and risk assessment often overestimate metal risks, thereby impeding the formulation of effective risk management strategies. This study aims to develop a framework for source-specific risk assessment based on metal bioavailability in surface river sediments from a human-dominated seaward catchment in eastern China. Metal bioavailability was quantified using chemical fractionation results, and source apportionment was conducted using the positive matrix factorization (PMF) model. Risk assessment integrated these findings using two indices: the Potential Ecological Risk Index (PERI) and the Mean Probable Effect Concentration Quotient (mPEC-Q), with uncertainty addressed via Monte Carlo simulations. Results indicated that average total concentrations of Cu, Pb, Zn, Cr, Hg, Cd, and As exceeded their respective background levels by 1.63 to 15.00 times. The residual fraction constituted the majority, accounting for 53.84 % to 77.79 % of total concentrations, suggesting significant natural origins. However, source apportionment revealed a predominant contribution from anthropogenic activities, including industrial smelting, agricultural practices, and atmospheric deposition. The contributions were found to vary between 5.35 % and 40.03 % when the total concentration was adjusted to bioavailable content. Total concentration-based PERI/mPEC-Q assessments indicated high/moderate risk levels, decreasing to considerable/low risk levels with bioavailability adjustment. Hg and Cd were identified as priority metals. Further incorporating source appointment parameters into the risk assessment, industrial smelting was identified as the primary contributor, accounting for 66.06 % of total risk by total concentration and 65.63 % by bioavailability. This underscores the role of bioavailability in mitigating risk overestimation. Monte Carlo simulations validated industrial smelting as a major risk contributor. This study emphasizes the importance of considering bioavailability in the source-risk assessment of sediment-metals, crucial for targeted risk management in urbanized catchment areas.

Significance of spatial variability in heat generation on nonlinear mixed convective Jeffrey nanofluid flows nearby an impermeable plate

The dynamics of Jeffrey nanofluids over an elongated surface heated by convection are addressed adequately under the nonlinear Boussinesq’s sense. Novel attributes of exponential heat source and thermal radiation are included. Buongiorno’s revised nanoliquid model is implemented to study the uneven dispersion and thermophoresis of nanoparticles subjected to passive control of nanoparticles on the surface. Wall suction or injection effects are also considered. Mathematical modeling is developed and treated numerically using the similarity approach. The results are presented considering numerous values of the physical parameters. Stimulus variables that regulate the resulting dimensionless physical quantities are examined graphically. Nonlinear convection has occurred to reinforce the nanofluid motion and reduce the thermal boundary layer thickness. The results of this study are useful in polymer industries.

Predicting image quality of forensic footwear impressions

The ability of a footwear examiner to confidently discern features of importance in a forensic examination is directly related to impression quality. As a result, quality directly impacts the strength an examiner can ascribe to any opinion of source attribution. Despite the importance of image quality during both the analysis and comparison phases of an examination, there is limited research on the estimation, variation, and prediction of footwear impression quality. In response, this study aims to develop a methodology for evaluating footwear impression quality by regressing image features against subjective judgments of quality. Using a dataset of more than 450 impressions evaluated by more than 40 participants, estimates of intra- and inter-rater consistency were computed. After identifying reliable raters, matrix completion was performed, thereby permitting data imputation. This was based on an approximate 90:10 split between training and testing data, ultimately resulting in 6,000 quality predictions, which exhibited a mean agreement with ground truth > 95%. Each image in the dataset was then reduced to a 10-dimensional feature vector describing image attributes such as complexity, contrast, sharpness, and noise. These features were used to train multiple ordinal and multinomial regression models aimed at predicting image quality. Model comparison resulted in an overall optimal accuracy of approximately 75% when comparing predicted/modeled quality against the ground truth of subjective rater opinions. The resulting semi-automated, reference-free and numerical prediction tool exhibits reasonable success for impression quality prediction when presented with the types of challenging and domain-specific images often encountered in forensic footwear comparisons. The outcomes and challenges associated with this investigation provide a foundation upon which future studies of quality can be modified and built, with the downstream goal of an increased understanding of how quality impacts weight of evidence.

Measurement report: Source attribution and estimation of black carbon levels in an urban hotspot of the central Po Valley – an integrated approach combining high-resolution dispersion modelling and micro-aethalometers

Abstract. Understanding black carbon (BC) levels and its sources in urban environments is of paramount importance due to the far-reaching health, climate, and air quality implications. While several recent studies have assessed BC concentrations at specific fixed urban locations, there is a notable lack of knowledge in the existing literature on spatially resolved data alongside source estimation methods. This study aims to fill this gap by conducting a comprehensive investigation of BC levels and sources in Modena (Po Valley, Italy), which serves as a representative example of a medium-sized urban area in Europe. Using a combination of multi-wavelength micro-aethalometer measurements and a hybrid Eulerian–Lagrangian modelling system, we studied two consecutive winter seasons (February–March 2020 and December 2020–January 2021). Leveraging the multi-wavelength absorption analyser (MWAA) model, we differentiate sources (fossil fuel combustion, FF, and biomass burning, BB) and components (BC vs. brown carbon, BrC) from micro-aethalometer measurements. The analysis reveals consistent, minimal diurnal variability in BrC absorption, in contrast to FF-related sources that exhibit distinctive diurnal peaks during rush hours, while BB sources show less diurnal variation. The city itself contributes significantly to BC concentrations (52 ± 16 %), with BB and FF playing a prominent role (35 ± 15 % and 9 ± 4 %, respectively). Long-distance transport also influences BC concentrations, especially in the case of BB and FF emissions, with 28 ± 1 % and 15 ± 2 %, respectively. When analysing the traffic-related concentrations, Euro 4 diesel passenger cars considerably contribute to the exhaust emissions. These results provide valuable insights for policy makers and urban planners to manage BC levels in medium-sized urban areas, taking into account local and long-distance sources.

Occurrence rate and species and subtypes of Cryptosporidium spp. in pet dogs in Yunnan Province, China

BackgroundCryptosporidium spp. is a ubiquitous, globally distributed intestinal protozoan infecting humans and at least 260 animal hosts. Due to close human contact with pet dogs and identification of zoonotic Cryptosporidium species and subtypes in these animals, dog health is not only a veterinarian issue but also a public health issue. This study aimed to understand occurrence and genetic characterization at both genotype and subtype levels in pet dogs in Yunnan Province, China.ResultsA total of 589 fresh fecal specimens were collected from adult pet dogs in the rural areas of eight cities/autonomous prefectures of Yunnan Province, China. 16 fecal specimens were positive for Cryptosporidium spp. by polymerase chain reaction (PCR) amplification and sequence analysis of the small subunit ribosomal RNA (SSU rRNA) gene, with an average occurrence rate of 2.7% (16/589) being observed. Three zoonotic Cryptosporidium species were identified: C. parvum (n = 7), C. suis (n = 5) and C. canis (n = 4). At the 60-kDa glycoprotein (gp60) locus, only three C. parvum and two C. canis specimens were successfully amplified and sequenced, with subtype IIaA17G2R1 (n = 3) and subtypes XXa4 (n = 1) and XXa5 (n = 1) being identified, respectively.ConclusionsThe present finding of three zoonotic Cryptosporidium species in dogs implied that dogs infected with Cryptosporidium spp. may pose a threat to human health. C. suis was identified in dogs in this study for the first time, expanding the host range of this species. Identification of C. parvum subtype IIaA17G2R1 and C. canis subtypes XXa4 and XXa5 will be helpful to explore the source attribution of infection/contamination and assess the transmission dynamics of C. parvum and C. canis in the investigated areas in the future.

Driving factor, source identification, and health risk of PFAS contamination in groundwater based on the self-organizing map

The complex interactions between groundwater chemical environments and PFAS present challenges for data analysis and factor assessment of the spatial distribution and source attribution of PFAS in groundwater. This study employed spatial response analysis combining self-organizing maps (SOM), K-means clustering, Spearman correlation, positive matrix factorization (PMF) and risk quotient (RQ), to uncover the spatial characteristics, driving factors, sources, and human health risks of groundwater PFAS in the Pearl River Basin. The results indicated that the characteristics of PFAS in groundwater were classified into 16 neurons, which were further divided into 6 clusters (I—VI). This division was due to the contribution of industrial pollution (33.2 %) and domestic pollution (31.5 %) to the composition of PFAS in groundwater. In addition, the hydrochemical indicators such as pH, dissolved organic carbon (DOC), chloride (Cl−), and calcium ions (Ca2+) might also affect the distribution pattern of PFAS. The potential human health risk in the area was minimal, with cluster Ⅱ presenting the highest risk (RQ value 0.25) which is closely related to PFOA emissions from fluoropolymer industry. This study provides a theoretical basis and data support for applying of SOM to the visualization and control of PFAS contamination in groundwater.

Antibiotic residues in the cirata reservoir, Indonesia and their effect on ecology and the selection for antibiotic-resistant bacteria

Antibiotic residues, their mixture toxicity, and the potential selection for antibiotic-resistant bacteria could pose a problem for water use and the ecosystem of reservoirs. This study aims to provide a comprehensive understanding of the occurrence, concentration, distribution, and ecological risks associated with various antibiotics in the Cirata reservoir, Indonesia. In our water and sediment samples, we detected 24 out of the 65 antibiotic residues analyzed, revealing a diverse range of antibiotic classes present. Notably, sulphonamides, diaminopyrimidine, and lincosamides were frequently found in the water, while the sediment predominantly contained tetracyclines and fluoroquinolones. Most antibiotic classes reached their highest concentrations in the water during the dry season. However, fluoroquinolones and tetracyclines showed their highest concentrations in the water during the wet season. Ecotoxicological risk assessments indicated that the impact of most antibiotic residues on aquatic organisms was negligible, except for fluoroquinolones. Looking at the impact on cyanobacteria, however, varying risks were indicated, ranging from medium to critical, with antibiotics like sulfamethoxazole, ciprofloxacin, norfloxacin, and lincomycin posing substantial threats. Among these, ciprofloxacin emerged as the antibiotic with the strongest risk. Furthermore, fluoroquinolones may have the potential to contribute to the selection of antibiotic-resistant bacteria. The presence of mixtures of antibiotic residues during the wet season significantly impacted species loss, with Potentially Affected Fraction of Species (msPAF) values exceeding 0.75 in almost 90% of locations. However, the impact of mixtures of antibiotic residues in sediment remained consistently low across all locations and seasons. Based on their occurrences and associated risks, 12 priority antibiotic residues were identified for monitoring in the reservoir and its tributaries. Moreover, the study suggests that river inflow serves as the most significant source of antibiotic residues in the reservoir. Further investigations into the relative share attribution of antibiotic sources in the reservoir is recommended to help identify effective interventions.

Machine learning to attribute the source of Campylobacter infections in the United States: A retrospective analysis of national surveillance data

Integrating pathogen genomic surveillance with bioinformatics can enhance public health responses by identifying risk and guiding interventions. This study focusses on the two predominant Campylobacter species, which are commonly found in the gut of birds and mammals and often infect humans via contaminated food. Rising incidence and antimicrobial resistance (AMR) are a global concern, and there is an urgent need to quantify the main routes to human infection. During routine US national surveillance (2009-2019), 8856 Campylobacter genomes from human infections and 16,703 from possible sources were sequenced. Using machine learning and probabilistic models, we target genetic variation associated with host adaptation to attribute the source of human infections and estimate the importance of different disease reservoirs. Poultry was identified as the primary source of human infections, responsible for an estimated 68% of cases, followed by cattle (28%), and only a small contribution from wild birds (3%) and pork sources (1%). There was also evidence of an increase in multidrug resistance, particularly among isolates attributed to chickens. National surveillance and source attribution can guide policy, and our study suggests that interventions targeting poultry will yield the greatest reductions in campylobacteriosis and spread of AMR in the US. All sequence reads were uploaded and shared on NCBI's Sequence Read Archive (SRA) associated with BioProjects; PRJNA239251 (CDC / PulseNet surveillance), PRJNA287430 (FSIS surveillance), PRJNA292668 & PRJNA292664 (NARMS) and PRJNA258022 (FDA surveillance). Publicly available genomes, including reference genomes and isolates sampled worldwide from wild birds are associated with BioProject accessions: PRJNA176480, PRJNA177352, PRJNA342755, PRJNA345429, PRJNA312235, PRJNA415188, PRJNA524300, PRJNA528879, PRJNA529798, PRJNA575343, PRJNA524315 and PRJNA689604. Contiguous assemblies of all genome sequences compared are available at Mendeley data (assembled C. coli genomes doi: 10.17632/gxswjvxyh3.1; assembled C. jejuni genomes doi: 10.17632/6ngsz3dtbd.1) and individual project and accession numbers can be found in Supplementary tables S1 and S2, which also includes pubMLST identifiers for assembled genomes. Figshare (10.6084/m9.figshare.20279928). Interactive phylogenies are hosted on microreact separately for C. jejuni (https://microreact.org/project/pascoe-us-cjejuni) and C. coli (https://microreact.org/project/pascoe-us-ccoli).