Predicting within-city spatiotemporal variations in daily median outdoor ultrafine particle number concentrations and size in Montreal and Toronto, Canada.

Background: Epidemiological evidence suggests that long-term exposure to outdoor ultrafine particles (UFPs, <0.1 µm) may have important human health impacts. However, less is known about the acute health impacts of these pollutants as few models are available to estimate daily within-city spatiotemporal variations in outdoor UFPs. Methods: Several machine learning approaches (i.e., generalized additive models, random forest models, and extreme gradient boosting) were used to predict daily spatiotemporal variations in outdoor UFPs (number concentration and size) across Montreal and Toronto, Canada using a large database of mobile monitoring measurements. Separate models were developed for each city and all models were evaluated using a 10-fold cross-validation procedure. Results: In total, our models were based on measurements from 12,705 road segments in Montreal and 10,929 road segments in Toronto. Daily median outdoor UFP number concentrations varied substantially across both cities with 1st-99th percentiles ranging from 1389 to 181,672 in Montreal and 2472 to 118,544 in Toronto. Outdoor UFP size tended to be smaller in Montreal (mean [SD]: 34 nm [15]) than in Toronto (mean [SD]: 44 nm [25]). Extreme gradient boosting models performed best and explained the majority of spatiotemporal variations in outdoor UFP number concentrations (Montreal, R 2: 0.727; Toronto, R 2: 0.723) and UFP size (Montreal, R 2: 0.823; Toronto, R 2: 0.898) with slopes close to one and intercepts close to zero for relationships between measured and predicted values. Conclusion: These new models will be applied in future epidemiological studies examining the acute health impacts of outdoor UFPs in Canada's two largest cities.

Linked Exposures Across Databases: an exposure common data elements aggregation framework to facilitate clinical exposure review.

Understanding the health outcomes of military exposures is of critical importance for Veterans, their health care team, and national leaders. Approximately 43% of Veterans report military exposure concerns to their VA providers. Understanding the causal influences of environmental exposures on health is a complex exposure science task and often requires interpreting multiple data sources; particularly when exposure pathways and multi-exposure interactions are ill-defined, as is the case for complex and emerging military service exposures. Thus, there is a need to standardize clinically meaningful exposure metrics from different data sources to guide clinicians and researchers with a consistent model for investigating and communicating exposure risk profiles. The Linked Exposures Across Databases (LEAD) framework provides a unifying model for characterizing exposures from different exposure databases with a focus on providing clinically relevant exposure metrics. Application of LEAD is demonstrated through comparison of different military exposure data sources: Veteran Military Occupational and Environmental Exposure Assessment Tool (VMOAT), Individual Longitudinal Exposure Record (ILER) database, and a military incident report database, the Explosive Ordnance Disposal Information Management System (EODIMS). This cohesive method for evaluating military exposures leverages established information with new sources of data and has the potential to influence how military exposure data is integrated into exposure health care and investigational models.

Reducing air pollution does not necessarily reduce related adults' mortality burden: Variations in 177 countries with different economic levels.

Prolonged exposure to PM2.5 is associated with increased mortality. However, reducing air pollution concentrations does not necessarily reduce the related burden of deaths. Here, we aim to estimate the variations in PM2.5-related mortality due to contributions from key factors - PM2.5 concentration, population exposure, and healthcare levels - for 177 countries from 2000 to 2018 at the 1-km grid scale according to the Global Mortality Exposure Model (GEMM) model. We find that global reductions in PM2.5-related deaths mainly come from high and upper-middle income countries, where lowered air pollutant concentration and better healthcare can offset mortality burdens caused by increasing exposed populations. Changes in population exposure to PM2.5 contribute the most (54 %) to change in global related deaths over the examined period, followed by changes in healthcare (-42 %) and pollution concentrations (4 %). The impacts vary across countries and regions within them due to other drivers, which are significantly influenced by development status. Policies aiming at reducing PM2.5 associated health risks need to account for country-specific balances of these key socioeconomic drivers.

Predicted 25-hydroxyvitamin D over the adult life and the risk of ovarian cancer.

The evidence from previous studies of serum 25-hydroxyvitamin D [25(OH)D] and ovarian cancer risk are not conclusive. However, 25(OH)D was generally only measured in late adulthood, which may not capture the etiologically relevant exposure periods. We investigated predicted 25(OH)D over the adult lifetime in relation to ovarian cancer risk in a population-based case-control study conducted from 2011 to 2016 in Montreal, Canada (490 cases, 896 controls). Predicted 25(OH)D was computed using previously validated regression models. Unconditional multivariable logistic regression models were used to estimate adjusted odds ratios (aOR) and 95% confidence intervals (CI) for average predicted 25(OH)D over the adult life and risk. In addition, the relative importance of different periods of past 25(OH)D exposure was explored using a weighted cumulative exposure (WCE) model. For each 20 nmol/L increase in average predicted 25(OH)D over the adult life, the aOR (95% CI) was 0.73 (0.55-0.96). In WCE analyses, the inverse association was strongest for exposures 5 to 20 years and 35 to 55 years prior to diagnosis, with aORs (95% CIs) of 0.82 (0.69-0.94) and 0.79 (0.66-1.02), respectively, for each 20 nmol/L increase in predicted 25(OH)D. These results support an inverse association between 25(OH)D in adulthood and ovarian cancer risk.

Mutational scanning of CRX classifies clinical variants and reveals biochemical properties of the transcriptional effector domain.

Cone-Rod Homeobox, encoded by CRX, is a transcription factor (TF) essential for the terminal differentiation and maintenance of mammalian photoreceptors. Structurally, CRX comprises an ordered DNA-binding homeodomain and an intrinsically disordered transcriptional effector domain. Although a handful of human variants in CRX have been shown to cause several different degenerative retinopathies with varying cone and rod predominance, as with most human disease genes the vast majority of observed CRX genetic variants are uncharacterized variants of uncertain significance (VUS). We performed a deep mutational scan (DMS) of nearly all possible single amino acid substitution variants in CRX, using an engineered cell-based transcriptional reporter assay. We measured the ability of each CRX missense variant to transactivate a synthetic fluorescent reporter construct in a pooled fluorescence-activated cell sorting assay and compared the activation strength of each variant to that of wild-type CRX to compute an activity score, identifying thousands of variants with altered transcriptional activity. We calculated a statistical confidence for each activity score derived from multiple independent measurements of each variant marked by unique sequence barcodes, curating a high-confidence list of nearly 2,000 variants with significantly altered transcriptional activity compared to wild-type CRX. We evaluated the performance of the DMS assay as a clinical variant classification tool using gold-standard classified human variants from ClinVar, and determined that activity scores could be used to identify pathogenic variants with high specificity. That this performance could be achieved using a synthetic reporter assay in a foreign cell type, even for a highly cell type-specific TF like CRX, suggests that this approach shows promise for DMS of other TFs that function in cell types that are not easily accessible. Per-position average activity scores closely aligned to a predicted structure of the ordered homeodomain and demonstrated position-specific residue requirements. The intrinsically disordered transcriptional effector domain, by contrast, displayed a qualitatively different pattern of substitution effects, following compositional constraints without specific residue position requirements in the peptide chain. The observed compositional constraints of the effector domain were consistent with the acidic exposure model of transcriptional activation. Together, the results of the CRX DMS identify molecular features of the CRX effector domain and demonstrate clinical utility for variant classification.

Pesticide exposure in greenspaces: Comparing field measurement of dermal contamination with values predicted by registration models.

Since 2014, the Agricultural Operator Exposure Model (AOEM) has been the harmonised European model used for estimating non-dietary operator exposure to pesticide. It is based on studies conducted by the pesticide companies and it features 13 different crops including non-agricultural areas such as amenity grasslands. The objective of this study was to compare the dermal exposure measured during a field study conducted in a non-agricultural area with the corresponding values estimated by the model AOEM. The non-controlled field study was conducted in France in 2011 and included 24 private and public gardeners who apply glyphosate with knapsack sprayers. Dermal exposure was measured using the whole-body method and cotton gloves. Each measured value had an estimated value given by AOEM and we tested their correlation using linear regression. The model overestimated body exposure for all observations and there was no correlation between values. However, it underestimated hand exposure by 42 times and it systematically underestimated the exposure when the operators were wearing gloves, especially during the application. The model failed at being conservative regarding hand exposure and highly overestimated the protection afforded by the gloves. At a time of glyphosate renewed approval in Europe, non-controlled field studies conducted by academics are needed to improve AOEM model, especially in the non-agricultural sector. Indeed, among the 34 studies included in the model, none were conducted on a non-agricultural area and only four assessed the exposure when using a knapsack sprayer. Moreover, knapsack sprayers being the main equipment used worldwide in both agricultural and non-agricultural settings, it is also crucial to integrate new data specific to this equipment in the model. Operator exposure should be estimated with accuracy in the registration process of pesticides to ensure proper safety as well as in epidemiological studies to improve exposure assessment.

Detailed aggregate exposure analysis shows that exposure to fragrance ingredients in consumer products is low: Many orders of magnitude below thresholds of concern.

The Research Institute for Fragrance Materials (RIFM) and Creme Global Cremeglobal.com partnered to develop an aggregate exposure model for fragrance ingredients. The model provides a realistic estimate of the total exposure of fragrance ingredients to individuals across a population. The Threshold of Toxicological Concern (TTC) and Dermal Sensitization Threshold (DST) were used to demonstrate the magnitude of low exposure to fragrance materials. The total chronic systemic, inhalation, and dermal 95th percentile exposures on approximately 3000 fragrance ingredients in RIFM's inventory were compared to their respective TTC or DST. Additionally, representative fragrance ingredients were randomly selected and analyzed for exposure distribution by product type (i.e., cosmetic/personal care, household care, oral care, and air care) and route of exposure. It was found that 76 % of fragrance ingredients fall below their respective TTC limits when compared to 95th percentile systemic exposure, while 99 % are below inhalation TTC limits. The lowest 95th percentile aggregate exposure by product type was from household care products, then air care, and oral care products. The highest exposure was from personal care/cosmetic products. The volume of use for most fragrance ingredients (63 %) was <1 metric ton, estimating that environmental exposure to fragrance ingredients is likely low.

Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach.

The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This article explores a modeling approach for integrating climate model projections into the assessment of near- and long-term ecological risks, developed in collaboration with climate scientists. State-of-the-art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate. Integr Environ Assess Manag 2024;20:367-383. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Attention Deficit-Hyperactivity Disorder (ADHD): From Abnormal Behavior to Impairment in Synaptic Plasticity.

Attention deficit-hyperactivity disorder (ADHD) is a neurodevelopmental disorder with high incidence in children and adolescents characterized by motor hyperactivity, impulsivity, and inattention. Magnetic resonance imaging (MRI) has revealed that neuroanatomical abnormalities such as the volume reduction in the neocortex and hippocampus are shared by several neuropsychiatric diseases such as schizophrenia, autism spectrum disorder and ADHD. Furthermore, the abnormal development and postnatal pruning of dendritic spines of neocortical neurons in schizophrenia, autism spectrum disorder and intellectual disability are well documented. Dendritic spines are dynamic structures exhibiting Hebbian and homeostatic plasticity that triggers intracellular cascades involving glutamate receptors, calcium influx and remodeling of the F-actin network. The long-term potentiation (LTP)-induced insertion of postsynaptic glutamate receptors is associated with the enlargement of spine heads and long-term depression (LTD) with spine shrinkage. Using a murine model of ADHD, a delay in dendritic spines' maturation in CA1 hippocampal neurons correlated with impaired working memory and hippocampal LTP has recently reported. The aim of this review is to summarize recent evidence that has emerged from studies focused on the neuroanatomical and genetic features found in ADHD patients as well as reports from animal models describing the molecular structure and remodeling of dendritic spines.

Validation and Recalibration of the Asbestos Removal Exposure Assessment Tool (AREAT).

OBJECTIVES: The Asbestos Removal Exposure Assessment Tool (AREAT) was previously developed to estimate exposure to respirable asbestos fibres during abatement processes. The current study describes the validation and recalibration of the AREAT model with external data. During model validation, the AREAT model was expanded to be able to estimate asbestos exposure from an additional source category: 'unspecified asbestos remnants'. METHODS: The validation dataset (n = 281) was derived from exposure measurement studies where for each exposure measurement the AREAT model parameters were coded and estimates were calculated. Pearson correlation coefficients (r) and intra class correlation coefficients (icc) were calculated as an indication of the agreement between the AREAT estimates and measured concentrations. In addition, the bias and the proportion of measurements with higher concentrations than model estimates were calculated. To expand and investigate model performance on exposure from 'unspecified asbestos remnants', a separate dataset was created with measurements collected during working with unspecified asbestos remnants, and similar validation comparisons were performed. Lastly, linear regression techniques were used to investigate possible improvements in model parameters. The model was recalibrated on a combined dataset consisting of the validation dataset and the original calibration dataset to increase model robustness. RESULTS: The validation comparisons showed good relative agreement (r) between AREAT estimates and measurements (r = 0.73) and a moderate absolute agreement (icc = 0.53). The overall relative bias was 108%, indicating an overall overestimation of exposure, and 4% of the estimated concentrations were higher than the actual measured concentrations. For the data subset concerning unspecified asbestos remnants, a moderate correlation between model estimates and measurement outcomes was found (r = 0.63). However, based on the low number of data in this subset, and moderate r, it was decided that cleaning of unspecified asbestos remnants is out of scope until more data are available. The results of this validation study suggested that two input parameters (product type friable material, efficacy of control measure foam) underestimated exposure. The effects of these parameters were updated to improve model performance. Compared to the original model, the recalibrated model resulted in slightly higher explained variance (62% compared to 56%) and lower uncertainty (15 compared to 17.3). CONCLUSION: The original AREAT model provided reliable asbestos exposure estimates with a sufficient level of conservatism taking into account the 90-percentile estimates. The model was further improved via the addition of a new feature and recalibration to predict asbestos exposure during the clean-up of unspecified asbestos remnants.

Respiratory Health Effects of In Vivo Sub-Chronic Diesel and Biodiesel Exhaust Exposure.

Biodiesel, which can be made from a variety of natural oils, is currently promoted as a sustainable, healthier replacement for commercial mineral diesel despite little experimental data supporting this. The aim of our research was to investigate the health impacts of exposure to exhaust generated by the combustion of diesel and two different biodiesels. Male BALB/c mice (n = 24 per group) were exposed for 2 h/day for 8 days to diluted exhaust from a diesel engine running on ultra-low sulfur diesel (ULSD) or Tallow or Canola biodiesel, with room air exposures used as control. A variety of respiratory-related end-point measurements were assessed, including lung function, responsiveness to methacholine, airway inflammation and cytokine response, and airway morphometry. Exposure to Tallow biodiesel exhaust resulted in the most significant health impacts compared to Air controls, including increased airway hyperresponsiveness and airway inflammation. In contrast, exposure to Canola biodiesel exhaust resulted in fewer negative health effects. Exposure to ULSD resulted in health impacts between those of the two biodiesels. The health effects of biodiesel exhaust exposure vary depending on the feedstock used to make the fuel.

Health Risks Posed by Dermal and Inhalation Exposure to High Concentrations of Chlorinated Paraffins Found in Soft Poly(vinyl chloride) Curtains.

Chlorinated paraffins (CPs) are used in many products, including soft poly(vinyl chloride) curtains, which are used in many indoor environments. Health hazards posed by CPs in curtains are poorly understood. Here, chamber tests and an indoor fugacity model were used to predict CP emissions from soft poly(vinyl chloride) curtains, and dermal uptake through direct contact was assessed using surface wipes. Short-chain and medium-chain CPs accounted for 30% by weight of the curtains. Evaporation drives CP migration, like for other semivolatile organic plasticizers, at room temperature. The CP emission rate to air was 7.09 ng/(cm2 h), and the estimated short-chain and medium-chain CP concentrations were 583 and 95.3 ng/m3 in indoor air 21.2 and 172 µg/g in dust, respectively. Curtains could be important indoor sources of CPs to dust and air. The calculated total daily CP intakes from air and dust were 165 ng/(kg day) for an adult and 514 ng/(kg day) for a toddler, and an assessment of dermal intake through direct contact indicated that touching just once could increase intake by 274 µg. The results indicated that curtains, which are common in houses, could pose considerable health risks through inhalation of and dermal contact with CPs.

ECETOC TRAv3: An In-depth Comparison of Publicly Available Measurement Data Sets With Modelled Estimates of Occupational Inhalation Exposure to Chemicals.

In this study, 129 exposure situations (ESs) with six or more measured inhalation exposures to dust from solids or vapour from liquids in occupational settings were compared with modelled European Centre of Ecotoxicology and Toxicology of Chemicals (ECETOC) targeted risk assessment tool, version 3 (TRAv3) estimates. The measurement data were extracted from previously published studies examining TRAv3 performance and pooled into a curated database. The comparison exercise focussed on the vapour exposure scenarios, as there were too few dust scenarios for a meaningful analysis of any required model corrections. A group of experts in the exposure modelling field retrieved and reviewed the input parameters used in these ESs. Where considered appropriate, modifications were applied to better match the input parameter definitions and the scope of applicability of the TRAv3. Differences and mean absolute error (MAE) were calculated between the log-transformed modelled exposure value and the 75th percentile of each measured data set and regression analysis was performed. The results indicated that the TRAv3 overestimated 80% of the measured data sets. Both over- and underestimations were mostly by factors 1-5. The calculated MAE for liquids was 0.7, indicating that on average the difference between the 75th percentile and the TRAv3 estimate was less than one order of magnitude. A multiple linear regression showed that some input parameters such as medium volatility, certain process categories (PROC), industrial setting, and the presence of local exhaust ventilation are associated with underestimations. The results of the regression analysis can be used by TRAv3 users to review the degree of over- or underestimation in their current exposure assessments, compared to the curated database. Although multiple linear regression is an appropriate methodology to characterize the TRAv3's performance, more data sets are still needed in view of some remaining data gaps. Nevertheless, the results of the current analysis are being used by ECETOC to further develop the tool as a suitably conservative screening tool for use in REACH chemical safety assessment of occupational exposure to chemicals.

Exposure to oxybenzone from sunscreens: daily transdermal uptake estimation.

BACKGROUND: Fugacity, the driving force for transdermal uptake of chemicals, can be difficult to predict based only on the composition of complex, non-ideal mixtures such as personal care products. OBJECTIVE: Compare the predicted transdermal uptake of benzophenone-3 (BP-3) from sunscreen lotions, based on direct measurements of BP-3 fugacity in those products, to results of human subject experiments. METHODS: We measured fugacity relative to pure BP-3, for commercial sunscreens and laboratory mixtures, using a previously developed/solid-phase microextraction (SPME) method. The measured fugacity was combined with a transdermal uptake model to simulate urinary excretion rates of BP-3 resulting from sunscreen use. The model simulations were based on the reported conditions of four previously published human subject studies, accounting for area applied, time applied, showering and other factors. RESULTS: The fugacities of commercial lotions containing 3-6% w/w BP-3 were ~20% of the supercooled liquid vapor pressure. Simulated dermal uptake, based on these fugacities, are within a factor of 3 of the mean results reported from two human-subject studies. However, the model significantly underpredicts total excreted mass from two other human-subject studies. This discrepancy may be due to limitations in model inputs, such as fugacity of BP-3 in lotions used in those studies. SIGNIFICANCE: The results suggest that combining measured fugacity with such a model may provide order-of-magnitude accurate predictions of transdermal uptake of BP-3 from daily application of sunscreen products.

Computational estimates of daily aggregate exposure to PFOA/PFOS from 2011 to 2017 using a basic intake model.

BACKGROUND: Human exposure to per- and polyfluoroalkyl substances has been modeled to estimate serum concentrations. Given that the production and use of these compounds have decreased in recent years, especially PFOA and PFOS, and that additional concentration data have become available from the US and other industrialized countries over the past decade, aggregate median intakes of these two compounds were estimated using more recent data. METHODS: Summary statistics from secondary sources were collected, averaged, and mapped for indoor and outdoor air, water, dust, and soil for PFOA and PFOS to estimate exposures for adults and children. European dietary intake estimates were used to estimate daily intake from food. RESULTS: In accordance with decreased concentrations in media, daily intake estimates among adults, i.e., 40 ng/day PFOA and 40 ng/day PFOS, are substantially lower than those reported previously, as are children's estimates of 14 ng/day PFOA and 17 ng/day PFOS. Using a first-order pharmacokinetic model, these results compare favorably to the National Health and Nutrition Examination Survey serum concentration measurements. CONCLUSION: Concomitant blood concentrations support this enhanced estimation approach that captures the decline of PFOA/PFOS serum concentration over a decade.

Occupation as a measure of life course socioeconomic position and the risk of oral cancers in India.

OBJECTIVES: Evidence suggests that different indicators of socioeconomic position (SEP) contribute to oral cancer risk. Occupational status, as a measure of SEP, may be able to capture aspects of social hierarchy in societies in which employment is highly correlated with other social structures such as caste systems. Often in such societies, the life course of an individual is also influenced by this hierarchy. However, the influence of life course occupational status on the risk of oral cancer is not well understood. This study aims to identify the life course model that is best supported by the data using life course SEP-as represented by occupation-on oral cancer risk in a population in the South of India. METHODS: Data from the HeNCe Life study, Indian site were used. Incident oral cancer cases (N = 350) were recruited from two major referral hospitals in Kozhikode, Kerala, South India, from 2008 to 2012. Controls (N = 371), frequency-matched by age (5 years) and sex were recruited from the outpatient clinics at the same hospitals as the cases. Life grid-based structured interviews collected information on an array of exposures throughout the life course of the participant. Occupation was coded with the 1988 International Standard Classification of Occupations, transformed to the simplified European Socioeconomic Classification, and further dichotomized into advantageous and disadvantageous SEP at three different life periods (childhood, early adulthood and late adulthood). The analysis was conducted using the Bayesian relevant life course exposure model with a Dirichlet noninformative prior and a weakly informative Cauchy prior to the overall lifetime effect and confounders. RESULTS: Participants in disadvantaged SEP throughout their life had 3.6 times higher risk of oral cancer than those in advantaged SEP (OR = 3.6; 95% CrI = 1.6-7.2), after adjusting for potential confounders. While the crude and sex- and age-adjusted models showed a clear childhood sensitive period for this risk, the model further adjusted for behavioural factors could not distinguish the specific life course period best explained by data. CONCLUSION: Occupation status alone could provide a similar overarching risk estimate for oral cancer to those obtained from more complex measures of SEP.

Biodiesel feedstock determines exhaust toxicity in 20% biodiesel: 80% mineral diesel blends.

To address climate change concerns, and reduce the carbon footprint caused by fossil fuel use, it is likely that blend ratios of renewable biodiesel with commercial mineral diesel fuel will steadily increase, resulting in biodiesel use becoming more widespread. Exhaust toxicity of unblended biodiesels changes depending on feedstock type, however the effect of feedstock on blended fuels is less well known. The aim of this study was to assess the impact of biodiesel feedstock on exhaust toxicity of 20% blended biodiesel fuels (B20). Primary human airway epithelial cells were exposed to exhaust diluted 1/15 with air from an engine running on conventional ultra-low sulfur diesel (ULSD) or 20% blends of soy, canola, waste cooking oil (WCO), tallow, palm or cottonseed biodiesel in diesel. Physico-chemical exhaust properties were compared between fuels and the post-exposure effect of exhaust on cellular viability and media release was assessed 24 h later. Exhaust properties changed significantly between all fuels with cottonseed B20 being the most different to both ULSD and its respective unblended biodiesel. Exposure to palm B20 resulted in significantly decreased cellular viability (96.3 ± 1.7%; p < 0.01) whereas exposure to soy B20 generated the greatest number of changes in mediator release (including IL-6, IL-8 and TNF-α, p < 0.05) when compared to air exposed controls, with palm B20 and tallow B20 closely following. In contrast, canola B20 and WCO B20 were the least toxic with only mediators G-CSF and TNF-α being significantly increased. Therefore, exposure to palm B20, soy B20 and tallow B20 were found to be the most toxic and exposure to canola B20 and WCO B20 the least. The top three most toxic and the bottom three least toxic B20 fuels are consistent with their unblended counterparts, suggesting that feedstock type greatly impacts exhaust toxicity, even when biodiesel only comprises 20% of the fuel.

PM2.5-Attributable Mortality Burden Variability in the Continental U.S.

Epidemiologic studies have consistently observed associations between fine particulate matter (PM2.5) exposure and premature mortality. These studies use air quality concentration information from a combination of sources to estimate pollutant exposures and then assess how mortality varies as a result of differing exposures. Health impact assessments then typically use a single log-linear hazard ratio (HR) per health outcome to estimate counts of avoided human health effects resulting from air quality improvements. This paper estimates the total PM2.5-attributable premature mortality burden using a variety of methods for estimating exposures and quantifying PM2.5-attributable deaths in 2011 and 2028. We use: 1) several exposure models that apply a wide range of methods, and 2) a variety of HRs from the epidemiologic literature that relate long-term PM2.5 exposures to mortality among the U.S. population. We then further evaluate the variability of aggregated national premature mortality estimates to stratification by race and/or ethnicity or exposure level (e.g., below the current annual PM2.5 National Ambient Air Quality Standards). We find that unstratified annual adult mortality burden incidence estimates vary more (e.g., ~3-fold) by HR than by exposure model (e.g., <10%). In addition, future mortality burden estimates stratified by race/ethnicity are larger than the unstratified estimates of the entire population, and studies that stratify PM2.5-attributable mortality HRs by an exposure concentration threshold led to substantially higher estimates. These results are intended to provide transparency regarding the sensitivity of mortality estimates to upstream input choices.

Development of a COVID-19 risk assessment model for participants at outdoor music festivals: evaluation of the validity and control measure effectiveness based on two actual events in Japan and Spain.

We developed an environmental exposure model to estimate the coronavirus disease 2019 (COVID-19) risk among participants at outdoor music festivals and validated the model using two real events-one in Japan (Event 1) and one in Spain (Event 2). Furthermore, we considered a hypothetical situation in which Event 1 was held but enhanced measures were implemented to evaluate the extent to which the risk could be reduced by additional infection control measures, such as negative antigen tests on the day of the event, wearing of masks, disinfection of environmental surfaces, and vaccination. Among 7,392 participants, the total number of already- and newly-infected individuals who participated in Event 1 according to the new model was 47.0 (95% uncertainty interval: 12.5-185.5), which is in good agreement with the reported value (45). The risk of infection at Event 2 (1.98 × 10-2; 95% uncertainty interval: 0.55 × 10-2-6.39 × 10-2), calculated by the model in this study, was also similar to the estimated value in the previous epidemiological study (1.25 × 10-2). These results for the two events in different countries highlighted the validity of the model. Among the additional control measures in the hypothetical Event 1, vaccination, mask-wearing, and disinfection of surfaces were determined to be effective. Based on the combination of all measures, a 94% risk reduction could be achieved. In addition to setting a benchmark for an acceptable number of newly-infected individuals at the time of an event, the application of this model will enable us to determine whether it is necessary to implement additional measures, limit the number of participants, or refrain from holding an event.

From single to multivariable exposure models to translate climatic and air pollution effects into mortality risk. A customized application to the city of Rome, Italy.

This study presents an approach developed to derive a Delayed-Multivariate Exposure-Response Model (D-MERF) useful to assess the short-term influence of temperature on mortality, accounting also for the effect of air pollution (O3 and PM10). By using Distributed, lag non-linear models (DLNM) we explain how city-specific exposure-response functions are derived for the municipality of Rome, which is taken as an example. The steps illustrated can be replicated to other cities while the statistical model presented here can be further extended to other exposure variables. We derive the mortality relative-risk (RR) curve averaged over the period 2004-2015, which accounts for city-specific climate and pollution conditions. Key aspects of customization are as follows: This study reports the steps followed to derive a combined, multivariate exposure-response model aimed at translating climatic and air pollution effects into mortality risk. Integration of climate and air pollution parameters to derive RR values. A specific interest is devoted to the investigation of delayed effects on mortality in the presence of different exposure factors.