Exposure Datasets Research Articles

An introduction to Criteria for Reporting and Evaluating Exposure Datasets (CREED) for use in environmental assessments.

Risks posed by environmental exposure to chemicals are routinely assessed to inform activities ranging from environmental status reporting to authorization and registration of chemicals for commercial uses. Environmental risk assessment generally relies on two key values generated from exposure data and ecotoxicity data. Data sets of measured concentrations of chemicals in environmental matrices, referred to here as exposure data, are widely used to support environmental risk management, decision-making, and reporting, such as for chemical screening, ecological or human health risk assessments, and establishment of guidelines. Practitioners have developed schemes to determine the suitability of ecotoxicity data for specific purposes, focused on evaluating reliability and relevance, but analogous schemes are not available for exposure data. Moreover, regulatory guidance arguably provides less resolution on reporting and evaluating exposure data sets compared to ecotoxicity data. The evaluation of exposure data sets is subject to limitations from variable or unreported data quality objectives and/or from differences in expert judgments, potentially introducing bias and leading to decisions based on flawed and/or inconsistent information. Exposure data sets should be evaluated for reliability and relevance prior to use in environmental assessments. This paper is the first of a four-paper series detailing the outcomes of a Society of Environmental Toxicology and Chemistry technical workshop that has developed Criteria for Reporting and Evaluating Exposure Datasets (CREED). The workshop participants developed practical, systematic criteria for consistent and transparent evaluation of the reliability (quality) and relevance (fitness for purpose) of exposure data. This guidance should apply to many different (unspecified) purposes of assessment. CREED can be used to evaluate existing data sets, but can also inform data generators interested in improving their data collection and reporting to maximize data utility to other users. This first paper details existing frameworks for the evaluation of exposure data sets and demonstrates the need for CREED, drawing from different regulatory assessments, and describes the technical workshop. Integr Environ Assess Manag 2024;20:975-980. © 2024 SETAC.

Evaluating the reliability of environmental concentration data to characterize exposure in environmental risk assessments.

Environmental risk assessments often rely on measured concentrations in environmental matrices to characterize exposure of the population of interest-typically, humans, aquatic biota, or other wildlife. Yet, there is limited guidance available on how to report and evaluate exposure datasets for reliability and relevance, despite their importance to regulatory decision-making. This paper is the second of a four-paper series detailing the outcomes of a Society of Environmental Toxicology and Chemistry Technical Workshop that has developed Criteria for Reporting and Evaluating Exposure Datasets (CREED). It presents specific criteria to systematically evaluate the reliability of environmental exposure datasets. These criteria can help risk assessors understand and characterize uncertainties when existing data are used in various types of assessments and can serve as guidance on best practice for the reporting of data for data generators (to maximize utility of their datasets). Although most reliability criteria are universal, some practices may need to be evaluated considering the purpose of the assessment. Reliability refers to the inherent quality of the dataset and evaluation criteria address the identification of analytes, study sites, environmental matrices, sampling dates, sample collection methods, analytical method performance, data handling or aggregation, treatment of censored data, and generation of summary statistics. Each criterion is evaluated as "fully met," "partly met," "not met or inappropriate," "not reported," or "not applicable" for the dataset being reviewed. The evaluation concludes with a scheme for scoring the dataset as reliable with or without restrictions, not reliable, or not assignable, and is demonstrated with three case studies representing both organic and inorganic constituents, and different study designs and assessment purposes. Reliability evaluation can be used in conjunction with relevance evaluation (assessed separately) to determine the extent to which environmental monitoring datasets are "fit for purpose," that is, suitable for use in various types of assessments. Integr Environ Assess Manag 2024;20:981-1003. © 2024 Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Assessing the relevance of environmental exposure data sets.

Environmental exposure data are used by decision-makers to assess environmental risks and implement actions to mitigate risks from contaminants. The first article in this series summarized the available evaluation schemes for environmental exposure data, of which there are few compared to those available for environmental hazard data. The second article covered the assessment of the reliability of environmental exposure data sets under the Criteria for the Reporting and Evaluation of Exposure Data (CREED). The aim of this article is to provide an overview and practical guidance on the relevance assessment in the context of the CREED approach for evaluating exposure monitoring data sets. Systematically considering relevance is critical for both evaluating existing data sets and for optimizing the design of new monitoring studies. Relevance is defined here as the degree of suitability or appropriateness of a data set to address a specific purpose or to answer the questions that have been defined by the assessor or for those generating exposure data. The purpose definition will be the foundation for the relevance assessment, to clarify how the assessor should rate the assessment criteria (fully met, partly met, not met/inappropriate, not reported, not applicable). This will provide transparency for anyone reviewing the outcomes. An explicit gap analysis (i.e., an articulation of the data set limitations for the stated purpose) is an important outcome of the relevance assessment. The relevance evaluation approach is demonstrated with three case studies, all relating to the freshwater aquatic environment, where the data sets are scored as relevant with or without restrictions, not relevant, or not assignable. The case studies represent both organic and inorganic constituents, and have different data characteristics (e.g., percentage of censored data, sampling frequencies, relation to supporting parameters). Integr Environ Assess Manag 2024;20:1004-1018. © 2023 SETAC.

Protective effect of phospholipids in lipoproteins against diabetic kidney disease: A Mendelian randomization analysis.

The etiology of diabetic kidney disease is complex, and the role of lipoproteins and their lipid components in the development of the disease cannot be ignored. However, phospholipids are an essential component, and no Mendelian randomization studies have yet been conducted to examine potential causal associations between phospholipids and diabetic kidney disease. Relevant exposure and outcome datasets were obtained through the GWAS public database. The exposure datasets included various phospholipids, including those in LDL, IDL, VLDL, and HDL. IVW methods were the primary analytical approach. The accuracy of the results was validated by conducting heterogeneity, MR pleiotropy, and F-statistic tests. MR-PRESSO analysis was utilized to identify and exclude outliers. Phospholipids in intermediate-density lipoprotein (OR: 0.8439; 95% CI: 0.7268-0.9798), phospholipids in large low- density lipoprotein (OR: 0.7913; 95% CI: 0.6703-0.9341), phospholipids in low- density lipoprotein (after removing outliers, OR: 0.788; 95% CI: 0.6698-0.9271), phospholipids in medium low- density lipoprotein (OR: 0.7682; 95% CI: 0.634-0.931), and phospholipids in small low-density lipoprotein (after removing outliers, OR: 0.8044; 95% CI: 0.6952-0.9309) were found to be protective factors. This study found that a higher proportion of phospholipids in intermediate-density lipoprotein and the various subfractions of low-density lipoprotein, including large LDL, medium LDL, and small LDL, is associated with a lower risk of developing diabetic kidney disease.

A comparative analysis of potential aerosol exposure in a wide-body aircraft cabin using tracer gas and fluorescent particles

We compare two aerosol surrogate tracers in aircraft cabins for breathing and coughing sources: tracer gas collected in the ACER Boeing 767 mock-up and fluorescent particles collected in an actual Boeing 767 aircraft by the US Transportation Command (TRANSCOM). Each source was located individually in window and middle seats. Exposure generally decreased with source distance. A window seat breathing source resulted in good agreement between datasets for exposure (as percent of release) for the TRANSCOM hangar-AFT testing mode, which corresponds to the 11-row cabin ACER laboratory space. Average tracer gas exposure for a middle seat breathing source was higher in the ACER study than the fluorescent particle tracer exposure in the TRANSCOM study. Using a coughing source in a window seat, the exposure for the TRANSCOM data was higher within the first two rows from the source before decreasing to and tracking with the ACER levels, until increasing after about 5 m away. A similar trend was recorded for a middle seat coughing source with higher overall exposure for the TRANSCOM data. Sources of exposure variation between the studies include particle deposition. This work helps optimize aerosol dispersion research in aircraft cabins and provides some validation to the existing studies.

Sex‐specific effects of genetically predicted BMI and education on risk of AD

AbstractBackgroundExamining sex differences on the impact of modifiable risk factors in Alzheimer’s disease (AD) risk can help us better understand the mechanisms underlying sex differences in the prevalence and incidence of AD. Here, we used polygenic risk scores (PRS) and Mendelian randomization (MR) to investigate sex‐specific effects of sleep duration, insomnia, blood pressure, diabetes, alcohol intake, smoking, body mass index, high cholesterol, and education on AD risk.MethodWe obtained combined and sex‐stratified genome‐wide association study (GWAS) summary statistics for each risk factor from the UK Biobank and used them as the base dataset for constructing PRS and exposure datasets in the MR analysis. Linkage disequilibrium clumping was performed to identify independent genome‐wide significant single nucleotide polymorphisms (SNPs) across the sex‐combined, male‐specific, and female‐specific GWAS. The combined lead SNPs were then weighted by their strata‐specific effect sizes in the stratified PRS and MR analyses. PRS were constructed for each risk factor in participants from the Alzheimer’s Disease Genetics Consortium. Linear regression was used to investigate the association of each PRS with AD risk, adjusting for age, principal components, and cohort. MR was used to estimate sex‐stratified causal effects of each risk factor on AD. Sex differences in PRS associations and MR causal estimates were determined using Fisher’s Z score method.ResultsAssociation testing of the PRS with AD risk in sex stratified cohorts revealed that the BMI PRS was associated with differential effects in men and women (OR [95%CI]: males: 1.05 [1.00, 1.10] vs females: 0.96 [0.93, 0.99], p = 0.003). Furthermore, the university completion PRS was non‐significant in men but was associated with reduced risk in women (OR [95%CI]: males: 0.96 [0.92, 1.01] vs females: 0.92 [0.89, 0.96], p = 0.12). In the follow‐up MR analysis, university completion was also causally associated with reduced risk in women only (OR [95%CI]: males: 1.09 [0.77, 1.53] vs females: 0.55 [0.42, 0.72], p = 0.002) (Figure 1). No other risk factors showed evidence of sex‐differences.ConclusionOur study found sex‐specific effects of genetically predicted BMI and educational attainment on AD risk. These findings suggest the need for sex‐specific approaches to AD prevention and management.

Comparing the suitability of global gridded population datasets for local landslide risk assessments

AbstractLandslide risk assessments are increasingly crucial for meeting global disaster risk reduction strategies and mobilising knowledge for local governments to protect communities and infrastructure. These assessments are data intensive, requiring large amounts of spatial demographic and natural hazard information. There are a growing number of high-resolution gridded population datasets that have global coverage with significant potential to transform disaster risk modelling, however landslide research has not yet compared the suitability of these gridded datasets for local risk assessments. Combining social vulnerability indicators with high-resolution settlement layer (HRSL) and WorldPop gridded population datasets, as well as a local household survey, we layer landslide susceptibility maps to compare three landslide risk estimates, examining the case of the Municipality of Carigara located in the central Philippines. Using statistical t tests, we compare aggregated community landslide risk for 49 communities. Findings revealed that HRSL data resulted in similar landslide risk at community scales when compared to local surveys, however WorldPop data greatly overestimated risk. Our findings point to a high level of accuracy of HRSL when used as an exposure dataset for local landslide risk studies and recommend avoiding WorldPop for such purposes. This research advances understanding of the suitability of open population datasets for use in landslide risk assessments in resource-constrained communities.

Varicella‐zoster virus infection and primary membranous nephropathy: a Mendelian randomization study

Primary membranous nephropathy (MN) is a rare autoimmune cause of kidney failure. Observational studies have suggested some relationship between virus infection and primary MN, but the association remains unclear. The current study performed a two‑sample Mendelian randomization (MR) analysis to explore the causal association between varicella‐zoster virus (VZV) infection (chickenpox and shingles) and primary MN using genome‑wide association studies (GWASs) summary statistics. The exposure datasets containing chickenpox and shingles were obtained from the GWASs conducted by the 23andMe cohort. And summary‐level statistics for primary MN were used as the outcome dataset, comprising 2150 cases and 5829 controls from European Ancestry. The inverse variance weighted method was adopted as the main analysis. As a result, we found that both genetically determined chickenpox (odds ratio [95% confidential interval] = 3.61 [1.74–7.50], p = 5.59e−04) and shingles (p = 7.95e−03, odds ratio [95% confidential interval] = 2.49 [1.27–4.91]) were causally associated with an increased risk of developing primary MN. In conclusion, our MR findings provided novel genetic evidence supporting the causal effect of VZV infection on primary MN. Further studies are needed to elucidate the underlying mechanisms mediating the causal association.

Adverse Health Impacts of Outdoor Air Pollution, Including from Wildland Fires, in the United States: "Health of the Air," 2018-2020.

Rationale: Adverse health impacts from outdoor air pollution occur across the United States, but the magnitude of these impacts varies widely by geographic region. Ambient pollutant concentrations, emission sources, baseline health conditions, and population sizes and distributions are all important factors that need to be taken into account to quantify local health burdens. Objectives: To determine health impacts from ambient air pollution concentrations in the United States that exceed the levels recommended by the American Thoracic Society. Methods: Using a methodology that has been well established in previous "Health of the Air" reports, this study provides policy-relevant estimates for every monitored county and city in the United States for the adverse health impacts of outdoor pollution concentrations using U.S. Environmental Protection Agency design values for years 2018-2020. Additionally, for the first time, the report includes adverse birth outcomes as well as estimates of health impacts specifically attributable to wildland fires using an exposure dataset generated through Community Multiscale Air Quality simulations. Results: The adverse health burdens attributable to air pollution occur across the entire age spectrum, including adverse birth outcomes (10,660 preterm and/or low-weight births; 95% confidence interval [CI], 3,180-18,330), in addition to mortality impacts (21,300 avoidable deaths; 95% CI, 16,180-26,200), lung cancer incidence (3,000 new cases; 95% CI, 1,550-4,390), multiple types of cardiovascular and respiratory morbidity (748,660 events; 95% CI, 326,050-1,057,080), and adversely impacted days (52.4 million days; 95% CI, 7.9-92.4 million days). Two different estimates of mortality impacts from wildland fires were created based on assumptions regarding the underlying toxicity of particles from wildland fires (low estimate of 4,080 deaths, 95% CI, 240-7,890; middle estimate of 28,000 deaths, 95% CI, 27,300-28,700). Conclusions: This year's report identified sizable health benefits that would be expected to occur across the United States with compliance with more health-protective air quality standards such as those recommended by the American Thoracic Society. This study also indicates that a large number of excess deaths are attributable to emissions from wildland fires; air quality management strategies outside what is required by the Clean Air Act will be needed to best address this important source of air pollution and its associated health risks.

Unveiling the Association between HPV and Pan-Cancers: A Bidirectional Two-Sample Mendelian Randomization Study.

More and more studies have focused on the associations between human papillomavirus (HPV) infection and pan-cancers. However, current evidence is largely based on retrospective studies, which are susceptible to confounding factors and do not enable the establishment of causal relationships. A bidirectional two-sample Mendelian randomization (MR) design was employed to thoroughly evaluate the causal relationships between HPV and 12 site-specific cancers except cervical cancer. Single nucleoside polymers (SNPs) with strong evidence from genome-wide association studies (GWAS) were selected from HPV exposure datasets and used as instrumental variables (IVs) in this study. For the MR analysis results, MR-Egger's intercept P test, MR-PRESSO global test, Cochran's Q test and a leave-one-out test were applied for sensitivity analysis. Using HPVTIMER, we also performed immune infiltration analyses in head and neck squamous cell carcinoma (HNSCC), oropharyngeal squamous cell carcinoma (OPSCC) and vulval squamous cell carcinoma (VSCC) to evaluate the tumor-immune microenvironment. Based on the evidence of MR analysis, our study conclusively identified HPV16 as a risk factor implicated in the development of bladder cancer, colorectal cancer, and breast cancer, while HPV18 was identified as a risk factor for prostate cancer, ovarian cancer, lung cancer and breast cancer. The MR results also showed that HPV16 may be a protective factor for prostate cancer, anal cancer, lung cancer and oropharyngeal cancer, while HPV18 may be a protective factor for vaginal cancer. An HPV infection may modulate the immune microenvironment and therefore has a potential inhibitory effect on the development of certain cancers. These conclusions provided new insights into the potential mechanisms of carcinogenesis and needed further research for validation.

Comparison of earthquake risk for masonry and RC buildings in Barcelona using existing sets of vulnerability functions

Barcelona is in an area of low- to medium-seismicity and its earthquake vulnerability and risk has been assessed in different studies over the last 20 years. Most of these works have developed vulnerability models for the representative building classes of the city, trying to capture their main characteristics, and have been mostly represented through fragility curves. However, due to the lack of data for historical earthquake losses in the city, it has not been possible to validate, nor calibrate, any of these vulnerability models. In the absence of earthquake loss data, the objective of this paper is to compare the earthquake risk results of Barcelona, using common hazard and exposure datasets and different sets of vulnerability functions from previous studies to see how they rank compared with other locations with similar studies but more complete historical loss data. The results show large differences between the analysed vulnerability datasets, with some results that in our view tend to overestimate earthquake risk in Barcelona, and also allow identifying critical aspects that require future research.

RETRACTED ARTICLE: Dietary intake and incidence risk of idiopathic pulmonary fibrosis: a Mendelian randomization study

BackgroundDietary intake has been shown to have a causal relationship with various lung diseases, such as lung cancer and asthma. However, the causal relationship between dietary intake and idiopathic pulmonary fibrosis (IPF) remains unclear. We conducted a two-sample Mendelian Randomization (MR) study to investigate the causal relationship between dietary intake and IPF.MethodsThe exposure datasets included meat, fruit, vegetable, and beverage intake from the UK Biobank. IPF data came from the EBI database of 451,025 individuals. All data in this study were obtained from the IEU Open GWAS Project. The inverse variance weighted (IVW), MR-Egger, and weighted median methods were used as the primary methods. Sensitivity analyses were performed to ensure the validity of the results.ResultsOily fish intake [odds ratio (OR):0.995; 95% confidence interval (CI): 0.993–0.998; p = 6.458E-05] and Dried fruit intake (OR:0.995;95%CI:0.991–0.998; p = 0.001) were discovered as protective factors. There was also a suggestive correlation between Beef intake (OR:1.006;95%Cl:1.001–1.012; p = 0.023) and IPF. Sensitivity analysis did not reveal any contradictory results. No causal relationship was found between IPF and the rest of the dietary exposures.ConclusionsOur study found that Oily fish and Dried fruit intake were associated with the risk of IPF, while Beef intake was suggestively associated with the risk of IPF. Other studies are still needed to confirm the results in the future.

Two Residual Attention Convolution Models to Recover Underexposed and Overexposed Images

Inconsistent lighting phenomena in digital images, such as underexposure and overexposure, pose challenges in computer vision. Many studies have developed to address these issues. However, most of these techniques cannot remedy both exposure problems simultaneously. Meanwhile, existing methods that claim to be capable of handling these cases have not yielded optimal results, especially for images with blur and noise distortions. Therefore, this study proposes a system to improve underexposed and overexposed photos, consisting of two different residual attention convolution networks with the CIELab color space as the input. The first model working on the L-channel (luminance) is responsible for recovering degraded image illumination by using residual memory block networks with self-attention layers. The next model based on dense residual attention networks aims to restore degraded image colors using ab-channels (chromatic). A properly exposed image is produced by fusing the output of these models and converting them to RGB color space. Experiments on degraded synthetic images from two public datasets and one real-life exposure dataset demonstrate that the proposed system outperforms the state-of-the-art algorithms in optimal illumination and color correction outcomes for underexposed and overexposed images.

Causal relationship between dietary factors and breast cancer risk: A Mendelian randomization study

BackgroundPrevious studies have discovered an association between dietary factors and breast cancer. However, few studies have used Mendelian randomization (MR) to assess the potential causal relationship between dietary factors and breast cancer. MethodsThe exposure datasets for fresh fruit intake, dried fruit intake, salad/raw vegetable intake, cooked vegetable intake, oily fish intake, non-oily fish intake, cheese intake, and bread intake were obtained from the UK Biobank. The outcome dataset was extracted from the Breast Cancer Association Consortium (BCAC). We used the inverse variance weighted (IVW) method as the primary approach for the two-sample MR analysis. To ensure the accuracy of the results, we conducted heterogeneity and horizontal pleiotropy analyses. Additionally, multivariable MR analysis was conducted to ensure the stability of the results. ResultsDried fruit intake was found to be a protective factor for overall breast cancer (outliers excluded: OR: 0.549; 95 % CI: 0.429–0.702; p = 1.75 × 10−6). Subtype analyses showed that dried fruit intake was inversely associated with both estrogen receptor-positive (ER+) breast cancer (outliers excluded: OR: 0.669; 95 % CI: 0.512–0.875; p = 0.003) and ER-negative (ER−) breast cancer (OR: 0.559; 95 % CI: 0.379–0.827; p = 0.004), while fresh fruit intake was inversely associated with ER− breast cancer (excluded outliers: OR: 0.510; 95 % CI: 0.308–0.846; p = 0.009). No significant causal relationship was found between other dietary intakes and breast cancer. After adjusting for the effects of possible confounders, the causal relationships found by the two-sample MR analysis remained. ConclusionOur study provides evidence that dried fruit intake may reduce the risk of both ER+ and ER− breast cancer, and fresh fruit intake may reduce the risk of ER− breast cancer. Other factors included in this study were not linked to breast cancer.

Application of mendelian randomization to study the causal relationship between smoking and the risk of chronic obstructive pulmonary disease.

Smoking is a risk factor for chronic obstructive pulmonary disease (COPD). Few studies have assessed the causal relationship between smoking and COPD using Mendelian randomization. Exposure and outcome datasets were obtained from the IEU Open GWAS project (https://gwas.mrcieu.ac.uk/). The exposure data set includes smoking (ever smoke, smoking/smokers in household, exposure to tobacco smoke at home). The outcome data set includes COPD susceptibility and acute COPD admissions. The main methods of Mendelian randomization analysis are weighted median method and MR-Egger method. Heterogeneity and polymorphism analyses were performed to ensure the accuracy of the results. ever smoke increased the risk of COPD prevalence, and ever smoke and smoking/smokers in household increased the risk of acute COPD admission. Conclusion Therefore, we should enhance the management of nonpharmacological prescription of COPD to reduce the individual incidence.

VulANalyzeR: Explainable Binary Vulnerability Detection with Multi-task Learning and Attentional Graph Convolution

Software vulnerabilities have been posing tremendous reliability threats to the general public as well as critical infrastructures, and there have been many studies aiming to detect and mitigate software defects at the binary level. Most of the standard practices leverage both static and dynamic analysis, which have several drawbacks like heavy manual workload and high complexity. Existing deep learning-based solutions not only suffer to capture the complex relationships among different variables from raw binary code but also lack the explainability required for humans to verify, evaluate, and patch the detected bugs.We propose VulANalyzeR, a deep learning-based model, for automated binary vulnerability detection, Common Weakness Enumeration-type classification, and root cause analysis to enhance safety and security. VulANalyzeR features sequential and topological learning through recurrent units and graph convolution to simulate how a program is executed. The attention mechanism is integrated throughout the model, which shows how different instructions and the corresponding states contribute to the final classification. It also classifies the specific vulnerability type through multi-task learning as this not only provides further explanation but also allows faster patching for zero-day vulnerabilities. We show that VulANalyzeR achieves better performance for vulnerability detection over the state-of-the-art baselines. Additionally, a Common Vulnerability Exposure dataset is used to evaluate real complex vulnerabilities. We conduct case studies to show that VulANalyzeR is able to accurately identify the instructions and basic blocks that cause the vulnerability even without given any prior knowledge related to the locations during the training phase.

Investigating causal relationships between the gut microbiota and allergic diseases: A mendelian randomization study.

Observational studies revealed altered gut microbial composition in patients with allergic diseases, which illustrated a strong association between the gut microbiome and the risk of allergies. However, whether such associations reflect causality remains to be well-documented. Two-sample mendelian randomization (2SMR) was performed to estimate the potential causal effect between the gut microbiota and the risk of allergic diseases. 3, 12, and 16 SNPs at the species, genus, and family levels respectively of 15 microbiome features were obtained as the genetic instruments of the exposure dataset from a previous study. GWAS summary data of a total of 17 independent studies related to allergic diseases were collected from the IEU GWAS database for the outcome dataset. Significant causal relationships were obtained between gut microbiome features including Ruminococcaceae, Eggerthella, Bifidobacterium, Faecalibacterium, and Bacteroides and the risk of allergic diseases. Furthermore, our results also pointed out a number of putative associations between the gut microbiome and allergic diseases. Taken together, this study was the first study using the approach of 2SMR to elucidate the association between gut microbiome and allergic diseases.

Quantitative Self-Assessment of Exposure to Solvents among Formal and Informal Nail Technicians in Johannesburg, South Africa.

Participatory research, including self-assessment of exposure (SAE), can engage study participants and reduce costs. The objective of this study was to investigate the feasibility and reliability of a SAE regime among nail technicians. The study was nested in a larger study, which included exposure assessment supervised by experts, i.e., controlled assessment of exposure (CAE). In the SAE approach, ten formal and ten informal nail technicians were verbally instructed to use a passive sampler and complete an activity sheet. Each participant conducted measurements on three consecutive days, whereafter the expert collected the passive samplers. Sixty samples were, thus, analyzed for twenty-one volatile organic compounds (VOCs). The reported concentrations of 11 VOCs were converted into total VOC (TVOC) concentrations, adjusted for their respective emission rates (adj TVOC) to allow comparison within and between nail technician categories (formal vs informal), as well as assessment regimes (SAE versus CAE), using the data from the main study. In total, 57 SAE and 58 CAE results were compared, using a linear mixed-effects model. There were variations in individual VOC concentrations, especially for the informal sector participants. The major contributors to the adj TVOC concentrations were acetone and 2-propanol for the formal category, whereas ethyl- and methyl methacrylate contributed most to the informal nail technicians' total exposures. No significant differences in adj TVOC-concentrations were observed between the assessment regimes, but significantly higher exposures were recorded in the formal technicians. The results show that the SAE approach is feasible in the informal service sector and can extend an exposure dataset to enable reliable estimates for scenarios with substantial exposure variations.

Current exposure to phthalates and DINCH in European children and adolescents - Results from the HBM4EU Aligned Studies 2014 to 2021.

Phthalates are mainly used as plasticizers for polyvinyl chloride (PVC). Exposure to several phthalates is associated with different adverse effects most prominently on the development of reproductive functions. The HBM4EU Aligned Studies (2014-2021) have investigated current European exposure to ten phthalates (DEP, BBzP, DiBP, DnBP, DCHP, DnPeP, DEHP, DiNP, DiDP, DnOP) and the substitute DINCH to answer the open policy relevant questions which were defined by HBM4EU partner countries and EU institutions as the starting point of the programme. The exposure dataset includes ∼5,600 children (6-11 years) and adolescents (12-18 years) from up to 12 countries per age group and covering the North, East, South and West European regions. Study data from participating studies were harmonised with respect to sample size and selection of participants, selection of biomarkers, and quality and comparability of analytical results to provide a comparable perspective of European exposure. Phthalate and DINCH exposure were deduced from urinary excretions of metabolites, where concentrations were expressed as their key descriptor geometric mean (GM) and 95th percentile (P95). This study aims at reporting current exposure levels and differences in these between European studies and regions, as well as comparisons to human biomonitoring guidance values (HBM-GVs). GMs for children were highest for ∑DEHP metabolites (33.6μg/L), MiBP (26.6μg/L), and MEP (24.4μg/L) and lowest for∑DiDP metabolites (1.91μg/L) and ∑DINCH metabolites (3.57μg/L). In adolescents highest GMs were found for MEP (43.3μg/L), ∑DEHP metabolites (28.8μg/L), and MiBP (25.6μg/L) and lowest for ∑DiDP metabolites (= 2.02μg/L) and ∑DINCH metabolites (2.51μg/L). In addition, GMs and P95 stratified by European region, sex, household education level, and degree of urbanization are presented. Differences in average biomarker concentrations between sampling sites (data collections) ranged from factor 2 to 9. Compared to the European average, children in the sampling sites OCC (Denmark), InAirQ (Hungary), and SPECIMEn (The Netherlands) had the lowest concentrations across all metabolites and ESTEBAN (France), NAC II (Italy), and CROME (Greece) the highest. For adolescents, comparably higher metabolite concentrations were found in NEB II (Norway), PCB cohort (Slovakia), and ESTEBAN (France), and lower concentrations in POLAES (Poland), FLEHS IV (Belgium), and GerES V-sub (Germany). Multivariate analyses (Survey Generalized Linear Models) indicate compound-specific differences in average metabolite concentrations between the four European regions. Comparison of individual levels with HBM-GVs revealed highest rates of exceedances for DnBP and DiBP, with up to 3 and 5%, respectively, in children and adolescents. No exceedances were observed for DEP and DINCH. With our results we provide current, detailed, and comparable data on exposure to phthalates in children and - for the first time - in adolescents, and - for the first time - on DINCH in children and adolescents of all four regions of Europe which are particularly suited to inform exposure and risk assessment and answer open policy relevant questions.

Future exposure modelling for risk-informed decision making in urban planning

Population increases and related urban expansion are projected to occur in various parts of the world over the coming decades. These future changes to the urban fabric could fundamentally alter the exposure to natural hazards and the associated vulnerability of people and the built environment with which they interact. Thus, modelling, quantifying, and reducing future urban disaster risk require forward-looking insights that capture the dynamic form of cities. This paper specifically focuses on the exposure component of dynamic natural-hazard disaster risk, by considering urban planning as the centre of future exposure characterisation in a given region. We use the information provided by urban plans and propose an integrated data structure for capturing future exposure to hazards. The proposed data structure provides the necessary detailing for both future physical and socio-demographic exposure in disaster risk modelling. More specifically, it enables users to develop a comprehensive multi-level, multi-scale exposure dataset, characterising attributes of land use, buildings, households and individuals. We showcase the proposed data schema using the virtual urban testbed Tomorrowville. In this case study, we also demonstrate how simplified algorithmic procedures and disaggregation methods can be used to populate the required data. This implementation demonstrates how the proposed exposure data structure can effectively support the development of forward-looking urban visioning scenarios to support decision-making for risk-sensitive and pro-poor urban planning and design in tomorrow’s cities.