RESUMEN

BACKGROUND: Polycyclic aromatic hydrocarbons and phthalate acid esters (PAHs & PAEs), known as endocrine disrupting chemicals (EDCs), widely exist in daily life and industrial production. Previous studies have suggested that PAHs & PAEs may modify the intrauterine homeostasis and have adverse effects on fetal development. However, epidemiological evidence on the associations between PAHs & PAEs and gestational diabetes mellitus (GDM) is still limited. OBJECTIVE: To investigate the effects of prenatal PAHs &PAEs exposure on the risk of GDM and hyperglycemia in pregnant women. METHODS: The study population was a total of 725 pregnant women from a prospective birth cohort study conducted from December 2019 to December 2021. Blood glucose levels were collected by the hospital information system. Urinary PAHs & PAEs concentrations were determined by gas chromatography tandem mass spectrometry. The Poisson regression in a generalized linear model (GLM), multiple linear regression, quantile-based g-computation method (qgcomp), and Bayesian kernel machine regression (BKMR) were applied to explore and verify the individual and overall effects of PAHs & PAEs on glucose homeostasis. Potential confounders were adjusted in all statistical models. RESULTS: A total of 179 (24.69%) women were diagnosed with GDM. The Poisson regression suggested that a ln-unit increment of 4-OHPHE (4-hydroxyphenanthrene) (adjusted Risk Ratio (aRR) = 1.13; 1.02-1.26) was associated with the increased GDM risk. Mixed-exposure models showed similar results. We additionally found that MBZP (mono-benzyl phthalate) (aRR = 1.19; 1.02-1.39) was positively related to GDM risk in qgcomp model. Although neither model demonstrated that 2-OHNAP (2-hydroxynaphthalene) and 9-OHFLU (9-hydroxyfluorene) increased the risk of GDM, 2-OHNAP and 9-OHFLU exposure significantly increased blood glucose levels. BKMR model further confirmed that overall effects of PAHs & PAEs were significantly associated with the gestational hyperglycemia and GDM risk. CONCLUSIONS: Our study presents that environmental exposure to PAHs & PAEs was positively associated with gestational glucose levels and the risks of developing GDM. In particular, 2-OHNAP, 9-OHFLU, 4-OHPHE and MBZP may serve as important surveillance markers to prevent the development of GDM.

Asunto(s)

RESUMEN

Residential biomass burning is an important source of black carbon (BC) exposure among rural communities in low- and middle-income countries. We collected 7165 personal BC samples and individual/household level information from 3103 pregnant women enrolled in the Household Air Pollution Intervention Network trial. Women in the intervention arm received free liquefied petroleum gas stoves and fuel throughout pregnancy; women in the control arm continued the use of biomass stoves. Median (IQR) postintervention BC exposures were 9.6 µg/m3 (5.2-14.0) for controls and 2.8 µg/m3 (1.6-4.8) for the intervention group. Using mixed models, we characterized predictors of BC exposure and assessed how exposure contrasts differed between arms by select predictors. Primary stove type was the strongest predictor (R2 = 0.42); the models including kerosene use, kitchen location, education, occupation, or stove use hours also provided additional explanatory power from the base model adjusted only for the study site. Our full, trial-wide, model explained 48% of the variation in BC exposures. We found evidence that the BC exposure contrast between arms differed by study site, adherence to the assigned study stove, and whether the participant cooked. Our findings highlight factors that may be addressed before and during studies to implement more impactful cookstove intervention trials.

Asunto(s)

RESUMEN

Pyrethroids (PYs) are widely applied pesticides whose residues pose potential health risks. This review describes current knowledge on PY chemical properties, usage patterns, environmental and food contamination, and human exposure models. It evaluates life cycle assessment (LCA), chemical alternatives assessment (CAA), and high-throughput screening (HTS) as tools for pesticide policy. Despite efforts to mitigate PY presence, their pervasive residues in the environment and food persist. And the highest concentrations ranged from 54,360 to 80,500 ng/L in water samples from agricultural fields. Food processing techniques variably reduce PY levels, yet no method guarantees complete elimination. This review provides insights into the fates and exposure pathways of PY residues in agriculture and food, and highlights the necessity for improved PY management and alternative practices to safeguard health and environment.

Asunto(s)

RESUMEN

Cyclists are particularly vulnerable to travel-related exposure to air pollution. Understanding the factors that increase exposure is crucial for promoting healthier urban environments. Machine learning models have successfully predicted air pollutant concentrations, but they tend to be less interpretable than classical statistical ones, such as linear models. This study aimed to develop a predictive model to assess cyclists' exposure to fine particulate matter (PM2.5) in urban environments. The model was generated using geo-temporally referenced data and machine learning techniques. We explored several models and found that the gradient boosting machine learning model best fitted the PM2.5 predictions, with a minimum root mean square error value of 5.62 µg m-3. The variables with greatest influence on cyclist exposure were the temporal ones (month, day of the week, and time of the day), followed by meteorological variables, such as temperature, relative humidity, wind speed, wind direction, and atmospheric pressure. Additionally, we considered relevant attributes, which are partially linked to spatial characteristics. These attributes encompass street typology, vegetation density, and the flow of vehicles on a particular street, which quantifies the number of vehicles passing a given point per minute. Mean PM2.5 concentration was lower in bicycle paths away from vehicular traffic than in bike lanes along streets. These outcomes underscore the need to thoughtfully design public transportation routes, including bus routes, concerning the network of bicycle pathways. Such strategic planning attempts to improve the air quality in urban landscapes.

RESUMEN

Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.

RESUMEN

The exposure of human lung and skin to carbon black (CB) is continuous due to its widespread applications. Current toxicological testing uses 'healthy' cellular systems; however, questions remain whether this mimics the everyday stresses that human cells are exposed to, including infection. Staphylococcus aureus lung and skin infections remain prevalent in society, and include pneumonia and atopic dermatitis, respectively, but current in vitro toxicological testing does not consider infection stress. Therefore, investigating the effects of CB co-exposure in 'stressed' infected epithelial cells in vitro may better approximate true toxicity. This work aims to study the impact of CB exposure during Staphylococcus aureus infection stress in A549 (lung) and HaCaT (skin) epithelial cells. Physicochemical characterisation of CB confirmed its dramatic polydispersity and potential to aggregate. CB significantly inhibited S. aureus growth in cell culture media. CB did not induce cytokines or antimicrobial peptides from lung and skin epithelial cells, when given alone, but did reduce HaCaT and A549 cell viability to 55% and 77%, respectively. In contrast, S. aureus induced a robust interleukin (IL)-8 response in both lung and skin epithelial cells. IL-6 and human beta defensin (hßD)-2 could only be detected when cells were stimulated with S. aureus with no decreases in cell viability. However, co-exposure to CB (100 µg/mL) and S. aureus resulted in significant inhibition of IL-8 (compared to S. aureus alone) without further reduction in cell viability. Furthermore, the same co-exposure induced significantly more hßD-2 (compared to S. aureus alone). This work confirms that toxicological testing in healthy versus stressed cells gives significantly different responses. This has significant implications for toxicological testing and suggests that cell stresses (including infection) should be included in current models to better represent the diversity of cell viabilities found in lung and skin within a general population. This model will have significant application when estimating CB exposure in at-risk groups, such as factory workers, the elderly, and the immunocompromised.

RESUMEN

The Quantitative Structure Use Relationship (QSUR) Summit, held on November 2-4, 2022, focused on advancing the development, refinement, and use of QSURs to support chemical substance prioritization and risk assessment and mitigation. QSURs utilize chemical structures to predict the function of a chemical within a formulated product or an industrial process. This presumed function can then be used to develop chemical use categories or other information necessary to refine exposure assessments. The invited expert meeting was attended by 38 scientists from Canada, Finland, France, the UK, and the USA, representing government, business, and academia, with expertise in exposure science, chemical engineering, risk assessment, formulation chemistry, and machine learning. Workshop discussions emphasized the importance of collection and sharing of data and quantification of relative chemical quantities to progress QSUR development. Participants proposed collaborative approaches to address key challenges, including mechanisms for aggregating information while still protecting proprietary product composition and other confidential business information. Discussions also led to proposals for applications beyond exposure and risk modeling, including sustainable formulation discovery. In addition, discussions continue to construct, conduct, and circulate case studies tied to various specific problem formulations in which QSURs supply or derive information on chemical functions, concentrations, and exposures.

Asunto(s)

RESUMEN

The health risks for dietary exposure after large-scale ethirimol application demand attention to protect the human population from hazardous effects. The occurrence, dissipation and terminal magnitude of ethirimol were revealed by original deposition of <0.005-28.1 mg kg-1, half-lives of 0.6-14.5 d, and terminal concentrations of  spinach > leaf lettuce > pepper > cucumber > papaya > pumpkin > stem lettuce > muskmelon > wax gourd > mango. The high-potential health risks of ethirimol should be continuously emphasized for susceptible children aged 2-13 years, especially those residing in rural areas and presenting increasing vegetable intake.

Asunto(s)

RESUMEN

Neurodevelopmental disorders (NDDs) are a widespread and growing public health challenge, affecting as many as 17% of children in the United States. Recent epidemiological studies have implicated ambient exposure to pyrethroid pesticides during pregnancy in the risk for NDDs in the unborn child. Using a litter-based, independent discovery-replication cohort design, we exposed mouse dams orally during pregnancy and lactation to the Environmental Protection Agency's reference pyrethroid, deltamethrin, at 3 mg/kg, a concentration well below the benchmark dose used for regulatory guidance. The resulting offspring were tested using behavioral and molecular methods targeting behavioral phenotypes relevant to autism and NDD, as well as changes to the striatal dopamine system. Low-dose developmental exposure to the pyrethroid deltamethrin (DPE) decreased pup vocalizations, increased repetitive behaviors, and impaired both fear conditioning and operant conditioning. Compared with control mice, DPE mice had greater total striatal dopamine, dopamine metabolites, and stimulated dopamine release, but no difference in vesicular dopamine capacity or protein markers of dopamine vesicles. Dopamine transporter protein levels were increased in DPE mice, but not temporal dopamine reuptake. Striatal medium spiny neurons showed changes in electrophysiological properties consistent with a compensatory decrease in neuronal excitability. Combined with previous findings, these results implicate DPE as a direct cause of an NDD-relevant behavioral phenotype and striatal dopamine dysfunction in mice and implicate the cytosolic compartment as the location of excess striatal dopamine.

RESUMEN

This retrospective cohort study examined associations of autism spectrum disorder (ASD) with prenatal exposure to major fine particulate matter (PM2.5) components estimated using two independent exposure models. The cohort included 318 750 mother-child pairs with singleton deliveries in Kaiser Permanente Southern California hospitals from 2001 to 2014 and followed until age five. ASD cases during follow-up (N = 4559) were identified by ICD codes. Prenatal exposures to PM2.5, elemental (EC) and black carbon (BC), organic matter (OM), nitrate (NO3-), and sulfate (SO42-) were constructed using (i) a source-oriented chemical transport model and (ii) a hybrid model. Exposures were assigned to each maternal address during the entire pregnancy, first, second, and third trimester. In single-pollutant models, ASD was associated with pregnancy-average PM2.5, EC/BC, OM, and SO42- exposures from both exposure models, after adjustment for covariates. The direction of effect estimates was consistent for EC/BC and OM and least consistent for NO3-. EC/BC, OM, and SO42- were generally robust to adjustment for other components and for PM2.5. EC/BC and OM effect estimates were generally larger and more consistent in the first and second trimester and SO42- in the third trimester. Future PM2.5 composition health effect studies might consider using multiple exposure models and a weight of evidence approach when interpreting effect estimates.

Asunto(s)

RESUMEN

Spraying is a common way to distribute occupational products, but it puts worker's health at risk by exposing them to potentially harmful particles and gases. The objective of this study is to use time-resolved measurements to gain an understanding of spray applications at the process level and to compare them to predictions of exposure models. We used proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) at 1-s time resolution to monitor the gas phase concentration of the solvents acetone, ethanol, butyl acetate, xylene and 1-methoxy-2-propy acetate during outdoor spraying and indoor drying of metal plate under various conditions of outdoor air supply. We found that during spraying, gas-phase exposure was dominated by the more volatile solvents acetone and ethanol, which exhibited strong concentration variations due to the outdoor winds. During drying, exposure strongly depended on the strength of ventilation. Under conditions with high supply of outdoor air, our measurements show a near-exponential decay of the solvent concentrations during drying. Conversely, under conditions without outdoor air supply, the drying process required hours, during which the less volatile solvents passed through a concentration maximum in the gas phase, so that the exposure during drying exceeded the exposure during spraying. The concentrations measured during spraying were then compared for each of the substances individually with the predictions of the exposure models ECETOC TRA, Stoffenmanager, and ART using TREXMO. For these conditions, ECETOC TRA and Stoffenmanager predicted exposures in the measured concentration range, albeit not conservative for all solvents and each application. In contrast, ART largely overestimated the exposure for the more volatile solvents acetone and ethanol and slightly underestimated exposure to 1M2PA for one spraying. ECETOC TRA and ART do not have options to predict exposure during drying. Stoffenmanager has the option to predict drying together with spraying, but not to predict the drying phase independently. Our study demonstrates the importance of considering both the spray cloud and solvent evaporation during the drying process. To improve workplace safety, there is a critical need for enhanced exposure models and comprehensive datasets for calibration and validation covering a broader range of exposure situations.

Asunto(s)

RESUMEN

Exposure models are essential in almost all relevant contexts for exposure science. To address the numerous challenges and gaps that exist, exposure modelling is one of the priority areas of the European Exposure Science Strategy developed by the European Chapter of the International Society of Exposure Science (ISES Europe). A strategy was developed for the priority area of exposure modelling in Europe with four strategic objectives. These objectives are (1) improvement of models and tools, (2) development of new methodologies and support for understudied fields, (3) improvement of model use and (4) regulatory needs for modelling. In a bottom-up approach, exposure modellers from different European countries and institutions who are active in the fields of occupational, population and environmental exposure science pooled their expertise under the umbrella of the ISES Europe Working Group on exposure models. This working group assessed the state-of-the-art of exposure modelling in Europe by developing an inventory of exposure models used in Europe and reviewing the existing literature on pitfalls for exposure modelling, in order to identify crucial modelling-related strategy elements. Decisive actions were defined for ISES Europe stakeholders, including collecting available models and accompanying information in a living document curated and published by ISES Europe, as well as a long-term goal of developing a best-practices handbook. Alongside these actions, recommendations were developed and addressed to stakeholders outside of ISES Europe. Four strategic objectives were identified with an associated action plan and roadmap for the implementation of the European Exposure Science Strategy for exposure modelling. This strategic plan will foster a common understanding of modelling-related methodology, terminology and future research in Europe, and have a broader impact on strategic considerations globally.

Asunto(s)

RESUMEN

Produced water (PW) is a hypersaline waste stream generated from the shale oil and gas industry, consisting of numerous anthropogenic and geogenic compounds. Despite prior geochemical characterization, the comprehensive toxicity assessment is lacking for evaluating treatment technologies and the beneficial use of PW. In this study, a suite of in vitro toxicity assays using various aquatic organisms (luminescent bacterium Vibrio fischeri, fish gill cell line RTgill-W1, and microalgae Scenedesmus obliquus) were developed to investigate the toxicological characterizations of PW from the Permian Basin. The exposure to PW, PW inorganic fraction (PW-IF), and PW salt control (PW-SC) at 30-50% dilutions caused significant toxicological effects in all model species, revealing the high salinity was the foremost toxicological driver in PW. In addition, the toxicity level of PW was usually higher than that of PW-IF, suggesting that organic contaminants might also play a critical role in PW toxicity. When comparing the observed toxicity with associated chemical characterizations in different PW samples, strong correlations were found between them since higher concentrations of contaminants could generally result in higher toxicity towards exposed organisms. Furthermore, the toxicity results from the pretreated PW indicated that those in vitro toxicity assays had different sensitives to the chemical components present in PW. As expected, the combination of multiple pretreatments could lead to a more significant decrease in toxicity compared to the single pretreatment since the mixture of contaminants in PW might exhibit synergistic toxicity. Overall, the current work is expected to enhance our understanding of the potential toxicological impacts of PW to aquatic ecosystems and the relationships between the chemical profiles and observed toxicity in PW, which might be conducive to the establishment of monitoring, remediation, treatment, and reuse protocols for PW.

Asunto(s)

RESUMEN

OBJECTIVES: The COLCHIC database contains workplace exposure results of chemical samples collected by the French prevention network since 1987. We aimed to investigate potential associations between exposure levels and ancillary variables in COLCHIC across a broad range of chemical agents in order to provide insight into how to best interpret and exploit the information in this national database. METHODS: We selected personal and area measurements in COLCHIC and collected outside respiratory personal protective equipment (PPE), restricted to chemical agents that had at least 1000 samples available. We used Tobit models to estimate associations between exposure concentrations and sample year, sampling duration, PPE, workforce size, collective protective equipment, origin of request, and reason for request for each chemical agent for the period 1987-2019. Models for period 2002-2019 also included type of process (open/closed) and exposure frequency. We used separate models for each combination of agent, period, and personal or area samples. We then applied Bayesian meta-analytical methods to assess average effects and effect heterogeneity of exposure factors across agents. RESULTS: COLCHIC contained 720 282 exposure results (62% personal and 38% area samples) to 77 agents, including 346 766 results for the more recent period 2002-2019 (67% personal and 33% area samples). Sample year and duration, PPE, and process type had the strongest and most consistent associations with exposure levels across agents. Personal and area exposure levels decreased yearly (6% for the entire period and 9% since 2002), and 30-min samples were approximately twice as high as 240-min samples. Workers wearing PPE were exposed to levels 1.7 times higher on average than those without PPE for both area and personal samples. Personal exposure levels associated with enclosed or semi-enclosed processes were approximately 20-30% lower compared with open processes. The associations for the other exposure variables were weaker and more inconsistent between agents. Between-agent heterogeneity of estimated effects, based on 80% prediction intervals, was lowest for sampling duration, time trends, and the presence of PPE. CONCLUSIONS: Sampling duration, time trends, and the presence of PPE are important factors to take into account when analyzing COLCHIC and had similar associations with exposure levels across agents. Other variables generally showed weaker associations or variable effects. These results will be used to adjust exposure estimates for the French working population from measurements stored in COLCHIC.

Asunto(s)

RESUMEN

Pathways for exposure and dissemination of antimicrobial-resistant (AMR) bacteria are major public health issues. Filter-feeding shellfish concentrate bacteria from the environment and thus can also harbor extended-spectrum ß-lactamase­producing Escherichia coli (ESBL E. coli) as an example of a resistant pathogen of concern. Is the short steaming procedure that blue mussels (Mytilus edulis) undergo before consumption enough for food safety in regard to such resistant pathogens? In this study, we performed experiments to assess the survival of ESBL E. coli in blue mussel. Consequently, a predictive model for the dose of ESBL E. coli that consumers would be exposed to, after preparing blue mussels or similar through the common practice of brief steaming until opening of the shells, was performed. The output of the model is the expected number of colony forming units per gram (cfu/g) of ESBL E. coli in a meal as a function of the duration and the temperature of steaming and the initial contamination. In these experiments, the heat tolerance of the ESBL-producing E. coli strain was indistinguishable from that of non-ESBL E. coli, and the heat treatments often practiced are likely to be insufficient to avoid exposure to viable ESBL E. coli. Steaming time (>3.5−4.0 min) is a better indicator than shell openness to avoid exposure to these ESBL or indicator E. coli strains.

RESUMEN

STOFFENMANAGER® and the Advanced REACH Tool (ART) are recommended tools by the European Chemical Agency for regulatory chemical safety assessment. The models are widely used and accepted within the scientific community. STOFFENMANAGER® alone has more than 37 000 users globally and more than 310 000 risk assessment have been carried out by 2020. Regardless of their widespread use, this is the first study evaluating the theoretical backgrounds of each model. STOFFENMANAGER® and ART are based on a modified multiplicative model where an exposure base level (mg m-3) is replaced with a dimensionless intrinsic emission score and the exposure modifying factors are replaced with multipliers that are mainly based on subjective categories that are selected by using exposure taxonomy. The intrinsic emission is a unit of concentration to the substance emission potential that represents the concentration generated in a standardized task without local ventilation. Further information or scientific justification for this selection is not provided. The multipliers have mainly discrete values given in natural logarithm steps (…, 0.3, 1, 3, …) that are allocated by expert judgements. The multipliers scientific reasoning or link to physical quantities is not reported. The models calculate a subjective exposure score, which is then translated to an exposure level (mg m-3) by using a calibration factor. The calibration factor is assigned by comparing the measured personal exposure levels with the exposure score that is calculated for the respective exposure scenarios. A mixed effect regression model was used to calculate correlation factors for four exposure group [e.g. dusts, vapors, mists (low-volatiles), and solid object/abrasion] by using ~1000 measurements for STOFFENMANAGER® and 3000 measurements for ART. The measurement data for calibration are collected from different exposure groups. For example, for dusts the calibration data were pooled from exposure measurements sampled from pharmacies, bakeries, construction industry, and so on, which violates the empirical model basic principles. The calibration databases are not publicly available and thus their quality or subjective selections cannot be evaluated. STOFFENMANAGER® and ART can be classified as subjective categorization tools providing qualitative values as their outputs. By definition, STOFFENMANAGER® and ART cannot be classified as mechanistic models or empirical models. This modeling algorithm does not reflect the physical concept originally presented for the STOFFENMANAGER® and ART. A literature review showed that the models have been validated only at the 'operational analysis' level that describes the model usability. This review revealed that the accuracy of STOFFENMANAGER® is in the range of 100 000 and for ART 100. Calibration and validation studies have shown that typical log-transformed predicted exposure concentration and measured exposure levels often exhibit weak Pearson's correlations (r is <0.6) for both STOFFENMANAGER® and ART. Based on these limitations and performance departure from regulatory criteria for risk assessment models, it is recommended that STOFFENMANAGER® and ART regulatory acceptance for chemical safety decision making should be explicitly qualified as to their current deficiencies.

Asunto(s)

RESUMEN

Spray applications enable a uniform distribution of substances on surfaces in a highly efficient manner, and thus can be found at workplaces as well as in consumer environments. A systematic literature review on modelling exposure by spraying activities has been conducted and status and further needs have been discussed with experts at a symposium. This review summarizes the current knowledge about models and their level of conservatism and accuracy. We found that extraction of relevant information on model performance for spraying from published studies and interpretation of model accuracy proved to be challenging, as the studies often accounted for only a small part of potential spray applications. To achieve a better quality of exposure estimates in the future, more systematic evaluation of models is beneficial, taking into account a representative variety of spray equipment and application patterns. Model predictions could be improved by more accurate consideration of variation in spray equipment. Inter-model harmonization with regard to spray input parameters and appropriate grouping of spray exposure situations is recommended. From a user perspective, a platform or database with information on different spraying equipment and techniques and agreed standard parameters for specific spraying scenarios from different regulations may be useful.

Asunto(s)

RESUMEN

PURPOSE OF REVIEW: Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models. RECENT FINDINGS: Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 µm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

Asunto(s)

RESUMEN

The impact of individuals' mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors-individuals' routine travel patterns and the local variations of air pollution fields. We investigated whether individuals' routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time-activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual's mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals' routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data ('a multi-sourced exposure model'). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data ('a single-sourced exposure model'). This study showed that there was a significant association between individuals' mobility and the long-term exposure measurement error. However, the effect could be modified by individuals' routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5.

Asunto(s)

RESUMEN

In the last years, the evaluation and validation of exposure modelling tools for inhalation exposure assessment at workplaces received new and highly increased attention by different stakeholders. One important study in this regard is the ETEAM (Evaluation of Tier 1 Exposure Assessment Models) project that evaluated exposure assessment tools under the European REACH regulation (Registration, Evaluation, Authorisation and Restriction of Chemicals), (but next to the ETEAM project-as a project publicly funded by the German Federal Institute for Occupational Safety and Health (BAuA)-it is a rather new development that research groups from universities in Europe, but also internationally, investigated this issue. These other studies focused not only on REACH tier 1 tools but also investigated other tools and aspects of tool validity. This paper tries to summarise the major findings of studies that explored the different issues of tool validity by focusing on the scientific outcomes and the exposure on the science community. On the other hand, this publication aims to provide guidance on the choice and use of tools, addressing the needs of tool users. The consequences of different stakeholders under REACH are discussed from the results of the validation studies. The major stakeholders are: (1) REACH registrants or applicants for REACH authorisations, meaning those companies, consortia or associations who are subject to REACH; (2) Evaluating authorities within the scope of REACH, meaning the ECHA (European Chemicals Agency) secretariat and committees, but also the competent authorities of the member states or the European Union; (3) Developers of the different models and tools; (4) Users of the different models and tools.

Asunto(s)