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A miniature multi-channel surface-enhanced Raman scattering (SERS) sensor based on polydimethylsiloxane (PDMS) is constructed to achieve rapid delivery of polluted water and specific identification of multiple components. Hg2+, organic pollutants, and sodium nitrite are successfully identified by the multi-channel SERS sensor using Cy5, cyclodextrin, and urea in the corresponding detection area. This multi-channel sensor exhibits excellent sensitivity and specificity, with detection limits of 3.2 × 10-10 M for Hg2+, 1.0 × 10-8 M for aniline, 6.9 × 10-9 M for diphenylamine, 9.1 × 10-8 M for PCB-77, and 7.5 × 10-9 M for pyrene, and 5.0 × 10-7 M for sodium nitrite. Compared with traditional analysis techniques, this method exhibited excellent recovery for the water pollutants ranging from 82.1 to 115.8%. The PDMS-based microchannel allows for simultaneous and rapid identification of multiple environmental pollutants, offering a portable detection method for emergency testing of environmental pollutants and routine determination of water pollutants.
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The long-term joint impacts of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) on mortality are inconclusive. To bridge this research gap, we included 283,568 adults from the Taiwan MJ cohort between 2005 and 2016 and linked with the mortality data until 31 May 2019. Participants' annual average exposures to PM2.5, NO2, and O3 were estimated using satellite-based spatial-temporal models. We applied elastic net-regularised Cox models to construct a weighted environmental risk score (WERS) for the joint effects of three pollutants on non-accidental, cardiovascular, and cancer mortality and evaluated the contribution of each pollutant. The three pollutants jointly raised non-accidental mortality risk with a WERS hazard ratio (HR) of 1.186 (95% CI: 1.118-1.259) per standard deviation increase in each pollutant and weights of 72.8%, 15.2%, and 12.0% for PM2.5, NO2, and O3, respectively. The WERS increased cardiovascular death risk [HR: 1.248 (1.042-1.496)], with PM2.5 as the first contributor and O3 as the second. The WERS also elevated the cancer death risk [HR: 1.173 (1.083-1.270)], where PM2.5 played the dominant role and NO2 ranked second. Coordinated control of these three pollutants can optimise the health benefits of air quality improvements.
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Contaminantes Atmosféricos , Enfermedades Cardiovasculares , Exposición a Riesgos Ambientales , Neoplasias , Dióxido de Nitrógeno , Ozono , Material Particulado , Humanos , Material Particulado/toxicidad , Material Particulado/análisis , Contaminantes Atmosféricos/toxicidad , Contaminantes Atmosféricos/análisis , Masculino , Taiwán/epidemiología , Persona de Mediana Edad , Femenino , Ozono/análisis , Dióxido de Nitrógeno/análisis , Dióxido de Nitrógeno/toxicidad , Estudios Longitudinales , Neoplasias/mortalidad , Enfermedades Cardiovasculares/mortalidad , Exposición a Riesgos Ambientales/efectos adversos , Adulto , Anciano , Estudios de Cohortes , Contaminación del Aire/efectos adversos , Contaminación del Aire/análisis , Causas de MuerteRESUMEN
Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.
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Exposición a Riesgos Ambientales , Contaminantes Ambientales , Humanos , Teorema de Bayes , Modelos EstadísticosRESUMEN
Single-pollutant methods to evaluate associations between endocrine disrupting chemicals (EDCs) and thyroid cancer risk may not reflect realistic human exposures. Therefore, we evaluated associations between exposure to a mixture of 18 EDCs, including polychlorinated biphenyls (PCBs), brominated flame retardants, and organochlorine pesticides, and risk of papillary thyroid cancer (PTC), the most common thyroid cancer histological subtype. We conducted a nested case-control study among U.S. military servicemembers of 652 histologically-confirmed PTC cases diagnosed between 2000 and 2013 and 652 controls, matched on birth year, sex, race/ethnicity, military component (active duty/reserve), and serum sample timing. We estimated mixture odds ratios (OR), 95% confidence intervals (95% CI), and standard errors (SE) for associations between pre-diagnostic serum EDC mixture concentrations, overall PTC risk, and risk of histological subtypes of PTC (classical, follicular), adjusted for body mass index and military branch, using quantile g-computation. Additionally, we identified relative contributions of individual mixture components to PTC risk, represented by positive and negative weights (w). A one-quartile increase in the serum mixture concentration was associated with a non-statistically significant increase in overall PTC risk (OR = 1.19; 95% CI = 0.91, 1.56; SE = 0.14). Stratified by histological subtype and race (White, Black), a one-quartile increase in the mixture was associated with increased classical PTC risk among those of White race (OR = 1.59; 95% CI = 1.06, 2.40; SE = 0.21), but not of Black race (OR = 0.95; 95% CI = 0.34, 2.68; SE = 0.53). PCBs 180, 199, and 118 had the greatest positive weights driving this association among those of White race (w = 0.312, 0.255, and 0.119, respectively). Findings suggest that exposure to an EDC mixture may be associated with increased classical PTC risk. These findings warrant further investigation in other study populations to better understand PTC risk by histological subtype and race.
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Disruptores Endocrinos , Contaminantes Ambientales , Personal Militar , Bifenilos Policlorados , Neoplasias de la Tiroides , Humanos , Cáncer Papilar Tiroideo/inducido químicamente , Cáncer Papilar Tiroideo/epidemiología , Disruptores Endocrinos/toxicidad , Estudios de Casos y Controles , Contaminantes Ambientales/análisis , Neoplasias de la Tiroides/inducido químicamente , Neoplasias de la Tiroides/epidemiologíaRESUMEN
Multi-pollutant removal (MPR) of NO and VOCs simultaneously is efficient of flue gas treatment in coal-fired power plants. But reducing the competition for active sites between NH3, NO, C6H6, and C7H8 remains challenging. Herein, Cr, Mn, and Fe were respectively doped to MoWTi catalyst via wet impregnation. The Fe3+ + Mo5+ â Fe2+ + Mo6+ redox cycle led to an increased proportion of low valence ions (Mo5+ and W5+) and facilitated the creation of active oxygen vacancies with several active sites. It also possessed plentiful mild to strong acid sites with ideal ratio. These factors enhanced catalytic activity of Fe-MoWTi. Remarkable MPR efficiencies of NO, C6H6, and C7H8 were achieved under industrial SCR condition, characterized by low oxygen but high SO2 levels at 340 °C, with removal rates reaching 89.85%, 97.57%, and 86.30% respectively. Theory calculations further revealed that Fe-MoWTi favor NH3 and O2 adsorptions. NO elimination was found to follow both Eley-Rideal (E-R) and Langmuir-Hinshelwood (L-H) processes, supported by in situ DRIFTS analysis. The reactions involving NO/NO2/nitrite/nitrate occurred with NH3(ads)/ NH4+(ads)/NH2 (ads). C6H6 and C7H8 underwent gradual oxidation, formatting alcohols, aldehydes, acids, and maleic acids, before eventually being mineralized to gaseous CO2 and H2O. Findings hold significant potential for application, providing guidance for the development of catalysts with improved resistance against SO2 poisoning and enhanced MPR capabilities.
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Contaminantes Ambientales , Dominio Catalítico , Amoníaco/química , Oxidación-Reducción , Oxígeno , CatálisisRESUMEN
Rapid industrialization has exacerbated the hazard to health and the environment. Wide spectrums of contaminants pose numerous risks, necessitating their disposal and treatment. There is a need for further remediation methods since pollutant residues cannot be entirely eradicated by traditional treatment techniques. Bio-adsorbents are gaining popularity due to their eco-friendly approach, broad applicability, and improved functional and surface characteristics. Adsorbents that have been modified have improved qualities that aid in their adsorptive nature. Adsorption, ion exchange, chelation, surface precipitation, microbial uptake, physical entrapment, biodegradation, redox reactions, and electrostatic interactions are some of the processes that participate in the removal mechanism of biosorbents. These processes can vary depending on the particular biosorbent and the type of pollutants being targeted. The systematic review focuses on the many modification approaches used to remove environmental contaminants. Different modification or activation strategies can be used depending on the type of bio-adsorbent and pollutant to be remediated. Physical activation procedures such as ultrasonication and pyrolysis are more commonly used to modify bio-adsorbents. Ultrasonication process improves the adsorption efficiency by 15-25%. Acid and alkali modified procedures are the most effective chemical activation strategies for adsorbent modification for pollution removal. Chemical modification increases the removal to around 95-99%. The biological technique involving microbial culture is an emerging field that needs to be investigated further for pollutant removal. A short evaluation of modified adsorbents with multi-pollutant adsorption capability that have been better eliminated throughout the adsorption process has been provided.
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Contaminantes Ambientales , Contaminantes Químicos del Agua , Purificación del Agua , Aguas Residuales , Contaminantes Químicos del Agua/análisis , Purificación del Agua/métodos , Biodegradación AmbientalRESUMEN
Early puberty has been found to be associated with adverse health outcomes such as metabolic and cardiovascular diseases and hormone-dependent cancers. The decrease in age at menarche observed during the past decades has been linked to an increased exposure to endocrine-disrupting compounds (EDCs). Evidence for the association between PFAS and phthalate exposure and menarche onset, however, is inconsistent. We studied the association between PFAS and phthalate/DINCH exposure and age at menarche using data of 514 teenagers (12 to 18 years) from four aligned studies of the Human Biomonitoring for Europe initiative (HBM4EU): Riksmaten Adolescents 2016-2017 (Sweden), PCB cohort (follow-up; Slovakia), GerES V-sub (Germany), and FLEHS IV (Belgium). PFAS concentrations were measured in blood, and phthalate/DINCH concentrations in urine. We assessed the role of each individual pollutant within the context of the others, by using different multi-pollutant approaches, adjusting for age, age- and sex-standardized body mass index z-score and household educational level. Exposure to di(2-ethylhexyl) phthalate (DEHP), especially mono(2-ethyl-5-hydroxyhexyl) phthalate (5OH-MEHP), was associated with an earlier age at menarche, with estimates per interquartile fold change in 5OH-MEHP ranging from -0.34 to -0.12 years in the different models. Findings from this study indicated associations between age at menarche and some specific EDCs at concentrations detected in the general European population, but due to the study design (menarche onset preceded the chemical measurements), caution is needed in the interpretation of causality.
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Metabolic syndrome is a chronic and complex disease characterized by environmental and genetic factors. However, the underlying mechanisms remain unclear. This study assessed the relationship between exposure to a mixture of environmental chemicals and metabolic syndrome (MetS) and further examined whether telomere length (TL) moderated these relationships. A total of 1265 adults aged > 20 years participated in the study. Data on multiple pollutants (polycyclic aromatic hydrocarbons, phthalates, and metals), MetS, leukocyte telomere length (LTL), and confounders were provided in the 2001-2002 National Health and Nutrition Examination Survey. The correlations between multi-pollutant exposure, TL, and MetS in the males and females were separately assessed using principal component analysis (PCA), logistic and extended linear regression models, Bayesian kernel machine regression (BKMR), and mediation analysis. Four factors were generated in PCA that accounted for 76.2% and 77.5% of the total environmental pollutants in males and females, respectively. The highest quantiles of PC2 and PC4 were associated with the risk of TL shortening (P < 0.05). We observed that the relationship between PC2, PC4, and MetS risk was significant in the participants with median TL levels (P for trend = 0.04 for PC2, and P for trend = 0.01 for PC4). Furthermore, mediation analysis revealed that TL could explain 26.1% and 17.1% of the effects of PC2 and PC4 associated with MetS in males, respectively. The results of BKMR model revealed that these associations were mainly driven by 1-PYE (cPIP = 0.65) and Cd (cPIP = 0.29) in PC2. Meanwhile, TL could explain 17.7% of the mediation effects of PC2 associated with MetS in the females. However, the relationships between pollutants and MetS were sparse and inconsistent in the females. Our findings suggest that the effects of the risk of MetS associated with mixed exposure to multiple pollutants are mediated by TL, and this mediating effect in the males is more pronounced than that in the females.
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Exposición a Riesgos Ambientales , Contaminantes Ambientales , Contaminación Ambiental , Síndrome Metabólico , Telómero , Síndrome Metabólico/epidemiología , Humanos , Masculino , Femenino , Hidrocarburos Policíclicos Aromáticos/análisis , Exposición a Riesgos Ambientales/estadística & datos numéricos , Encuestas Nutricionales , MetalesRESUMEN
Background: Air pollution, road traffic noise, and green space are correlated factors, associated with risk of stroke. We investigated their independent relationship with stroke in multi-exposure analyses and estimated their cumulative stroke burden. Methods: For all persons, ≥50 years of age and living in Denmark from 2005 to 2017, we established complete address histories and estimated running 5-year mean exposure to fine particles (PM2.5), ultrafine particles, elemental carbon, nitrogen dioxide (NO2), and road traffic noise at the most, and least exposed façade. For air pollutants, we estimated total, and non-traffic contributions. Green space around the residence was estimated from land use maps. Hazard ratios (HR) and 95% confidence limits (CL) were estimated with Cox proportional hazards models and used to calculate cumulative risk indices (CRI). We adjusted for the individual and sociodemographic covariates available in our dataset (which did not include information about individual life styles and medical conditions). Findings: The cohort accumulated 18,344,976 years of follow-up and 94,256 cases of stroke. All exposures were associated with risk of stroke in single pollutant models. In multi-pollutant analyses, only PM2.5 (HR: 1.058, 95% CI: 1.040-1.075) and noise at most exposed façade (HR: 1.033, 95% CI: 1.024-1.042) were independently associated with a higher risk of stroke. Both noise and air pollution contributed substantially to the CRI (1.103, 95% CI: 1.092-1.114) in the model with noise, green space, and total PM2.5 concentrations. Interpretation: Environmental exposure to air pollution and noise were both independently associated with risk of stroke. Funding: Health Effects Institute (HEI) (Assistance Award No. R-82811201).
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BACKGROUND: Exposure to environmental noise is associated with adverse health effects, but there is potential for confounding and interaction with air pollution, particularly where both exposures arise from the same source, such as transport. OBJECTIVES: To review evidence on confounding and interaction of air pollution in relation to associations between environmental noise and cardiovascular outcomes. METHODS: Papers were identified from similar reviews published in 2013 and 2015, from the systematic reviews supporting the WHO 2018 noise guidelines, and from a literature search covering the period 2016-2022 using Medline and PubMed databases. Additional papers were identified from colleagues. Study selection was according to PECO inclusion criteria. Studies were evaluated against the WHO checklist for risk of bias. RESULTS: 52 publications, 36 published after 2015, were identified that assessed associations between transportation noise and cardiovascular outcomes, that also considered potential confounding (49 studies) or interaction (23 studies) by air pollution. Most, but not all studies, suggested that the associations between traffic noise and cardiovascular outcomes are independent of air pollution. NO2 or PM2.5 were the most commonly included air pollutants and we observed no clear differences across air pollutants in terms of the potential confounding role. Most papers did not appear to suggest an interaction between noise and air pollution. Eight studies found the largest noise effect estimates occurring within the higher noise and air pollution exposure categories, but were not often statistically significant. CONCLUSION: Whilst air pollution does not appear to confound associations of noise and cardiovascular health, more studies on potential interactions are needed. Current methods to assess quality of evidence are not optimal when evaluating evidence on confounding or interaction.
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Contaminantes Atmosféricos , Contaminación del Aire , Ruido del Transporte , Exposición a Riesgos Ambientales/análisis , Contaminación del Aire/efectos adversos , Contaminantes Atmosféricos/análisis , Ruido del Transporte/efectos adversos , Bases de Datos Factuales , Material Particulado/análisisRESUMEN
INTRODUCTION: Air pollution health risk assessments have traditionally used single-pollutant effect estimates for one proxy ambient air pollutant such as PM2.5. Two-pollutant effect estimates, i.e. adjusted for another correlated pollutant, theoretically enable the aggregation of pollutant-specific health effects minimizing double-counting. Our study aimed at estimating the adult mortality in Switzerland in 2019 attributable to PM2.5 from a single-pollutant effect estimate and to the sum of PM2.5 and NO2 from two-pollutant estimates; comparing the results with those from alternative global, European and Swiss effect estimates. METHODS: For the single-pollutant approach, we used a PM2.5 summary estimate of European cohorts from the project ELAPSE, recommended by the European Respiratory Society and International Society for Environmental Epidemiology (ERS-ISEE). To derive the two-pollutant effect estimates, we applied ELAPSE-based conversion factors to ERS-ISEE PM2.5 and NO2 single-pollutant effect estimates. Additionally, we used World Health Organization 2021 Air Quality Guidelines as counterfactual scenario, exposure model data from 2019 and Swiss lifetables. RESULTS: The single-pollutant effect estimate for PM2.5 (1.118 [1.060; 1.179] per 10 µg/m3) resulted in 2240 deaths (21,593 years of life lost). Using our derived two-pollutant effect estimates (1.023 [1.012; 1.035] per 10 µg/m3 PM2.5 adjusted for NO2 and 1.040 [1.023; 1.058] per 10 µg/m3 NO2 adjusted for PM2.5), we found 1977 deaths (19,071 years of life lost) attributable to PM2.5 and NO2 together (23% from PM2.5). Deaths using alternative effect estimates ranged from 1042 to 5059. DISCUSSION: Estimated premature mortality attributable to PM2.5 alone was higher than to both PM2.5 and NO2 combined. Furthermore, the proportion of deaths from PM2.5 was lower than from NO2 in the two-pollutant approach. These seemingly paradoxical results, also found in some alternative estimates, are due to statistical imprecisions of underlying correction methods. Therefore, using two-pollutant effect estimates can lead to interpretation challenges in terms of causality.
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Contaminantes Ambientales , Material Particulado , Material Particulado/toxicidad , Material Particulado/análisis , Dióxido de Nitrógeno/toxicidad , Dióxido de Nitrógeno/análisis , Suiza/epidemiología , Exposición a Riesgos Ambientales/efectos adversos , Exposición a Riesgos Ambientales/análisisRESUMEN
Traditionally, studies that associate air pollution with health effects relate individual pollutants to outcomes such as mortality or hospital admissions. However, models capable of analyzing the effects resulting from the atmosphere mixture are demanded. In this study, multilayer perceptron neural networks were evaluated to associate PM10, NO2, and SO2 concentrations, temperature, wind speed, and relative air humidity with cardiorespiratory mortality among the elderly in São Paulo, Brazil. Daily data from 2007 to 2019 were considered and different numbers of neurons on the hidden layer, algorithms, and a combination of activation functions were tested. The best-fitted artificial neural network (ANN) resulted in a MAPE equal to 13.46%. When individual season data were analyzed, the MAPE decreased to 11%. The most influential variables in cardiorespiratory mortality among the elderly were PM10 and NO2 concentrations. The relative humidity variable is more important during the dry season, and temperature is more important during the rainy season. The models were not subjected to the multicollinearity issue as with classical regression models. The use of ANNs to relate air quality to health outcomes is still very incipient, and this work highlights that it is a powerful tool that should be further explored.
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Contaminantes Atmosféricos , Contaminación del Aire , Contaminantes Ambientales , Humanos , Anciano , Contaminantes Atmosféricos/análisis , Contaminantes Ambientales/análisis , Dióxido de Nitrógeno/análisis , Brasil/epidemiología , Contaminación del Aire/análisis , Redes Neurales de la Computación , Material Particulado/análisisRESUMEN
Research linking prenatal ambient air pollution with childhood lung function has largely considered one pollutant at a time. Real-life exposure is to mixtures of pollutants and their chemical components; not considering joint effects/effect modification by co-exposures contributes to misleading results. Methods: Analyses included 198 mother-child dyads recruited from two hospitals and affiliated community health centers in Boston, Massachusetts, USA. Daily prenatal pollutant exposures were estimated using satellite-based hybrid chemical-transport models, including nitrogen dioxide(NO2), ozone(O3), and fine particle constituents (elemental carbon [EC], organic carbon [OC], nitrate [NO3 -], sulfate [SO4 2-], and ammonium [NH4 +]). Spirometry was performed at age 6.99 ± 0.89 years; forced expiratory volume in 1s (FEV1), forced vital capacity (FVC), and forced mid-expiratory flow (FEF25-75) z-scores accounted for age, sex, height, and race/ethnicity. We examined associations between weekly-averaged prenatal pollution mixture levels and outcomes using Bayesian Kernel Machine Regression-Distributed Lag Models (BKMR-DLMs) to identify susceptibility windows for each component and estimate a potentially complex mixture exposure-response relationship including nonlinear effects and interactions among exposures. We also performed linear regression models using time-weighted-mixture component levels derived by BKMR-DLMs adjusting for maternal age, education, perinatal smoking, and temperature. Results: Most mothers were Hispanic (63%) or Black (21%) with ≤12 years of education (67%). BKMR-DLMs identified a significant effect for O3 exposure at 18-22 weeks gestation predicting lower FEV1/FVC. Linear regression identified significant associations for O3, NH4 +, and OC with decreased FEV1/FVC, FEV1, and FEF25-75, respectively. There was no evidence of interactions among pollutants. Conclusions: In this multi-pollutant model, prenatal O3, OC, and NH4 + were most strongly associated with reduced early childhood lung function.
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As wildfire risks have elevated due to climate change, the health risks that toxicants from fire smoke pose to wildland firefighters have been exacerbated. Recently, the International Agency for Research on Cancer (IARC) has reclassified wildland firefighters' occupational exposure as carcinogenic to humans (Group 1). Wildfire smoke contributes to an increased risk of cancer and cardiovascular disease, yet wildland firefighters have inadequate respiratory protection. The economic cost of wildland fires has risen concurrently, as illustrated by the appropriation of $45 billion for wildfire management over FYs 2011-2020 by the U.S. Congress. Occupational epidemiological studies of wildland firefighters are crucial for minimizing health risks; however, they must account for the mixture of exposures in wildfire smoke. This review focuses on four aspects of wildland firefighters' health risks at the wildland-urban interface: 1) economic costs and health impact, 2) respiratory protection, 3) multipollutant mixtures, and 4) proactive management of wildfires.
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BACKGROUND: Children are born with a burden of persistent organic pollutants (POPs) which may have endocrine disrupting properties and have been postulated to contribute to the rise in childhood obesity. The current evidence is equivocal, which may partly because many studies investigate the effects at one time point during childhood. We assessed associations between prenatal exposure to POPs and growth during infancy and childhood. METHODS: We used data from two Belgian cohorts with cord blood measurements of five organochlorines [(dichlorodiphenyldichloroethylene (p,p'-DDE), hexachlorobenzene (HCB), polychlorinated biphenyls (PCB-138, -150, -180)] (N = 1418) and two perfluoroalkyl substances [perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS)] (N = 346). We assessed infant growth, defined as body mass index (BMI) z-score change between birth and 2 years, and childhood growth, characterized as BMI trajectory from birth to 8 years. To evaluate associations between POP exposures and infant growth, we applied a multi-pollutant approach, using penalized elastic net regression with stability selection, controlling for covariates. To evaluate associations with childhood growth, we used single-pollutant linear mixed models with random effects for child individual, parametrized using a natural cubic spline formulation. RESULTS: PCB-153 was associated with increased and p,p'-DDE with decreased infant growth, although these results were imprecise. No clear association between any of the exposures and longer-term childhood growth trajectories was observed. We did not find evidence of effect modification by child sex. CONCLUSION: Our results suggest that prenatal exposure to PCB-153 and p,p'-DDE may affect infant growth in the first two years, with no evidence of more persistent effects.
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Contaminantes Ambientales , Obesidad Infantil , Bifenilos Policlorados , Efectos Tardíos de la Exposición Prenatal , Embarazo , Femenino , Humanos , Niño , Lactante , Contaminantes Orgánicos Persistentes , Diclorodifenil Dicloroetileno , Efectos Tardíos de la Exposición Prenatal/inducido químicamente , Bifenilos Policlorados/toxicidad , Contaminantes Ambientales/análisisRESUMEN
Numerous studies have reported adverse health effects of ambient air pollution on circulatory health outcomes mainly based on single-pollutant models. However, limited studies have focused on adjusted effect of multi-pollutant exposures on public health. This study aimed to examine short-term effects of three common air pollutants-ground-level ozone (ozone), nitrogen dioxide (NO2), and fine particulate matter (PM2.5)-through multi-pollutant models for mixed effect of adjustment. Daily data (circulatory hospitalization and mortality) and hourly data (air pollutants and temperature) were collected for 24 Canadian cities for 2001-2012. We applied generalized additive over-dispersion Poisson regression models with 1, 2, or 3 pollutants for city-specific risks, and Bayesian hierarchical models for national risks. This study found little mixed effect of adjustment through multi-pollutant models (ozone and/or NO2 and/or PM2.5) for circulatory hospitalization or mortality in Canada for 2001-2012, indicating that the 1-pollutant model did not result in considerable under- or over-estimates. It seemed weak-to-moderate correlations among air pollutants did not change the significant effect of one air pollutant after accounting for others. Inconsistent findings between other previous studies and this study indicate the need of comparable study design for multi-pollutant effect analysis.
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Contaminantes Atmosféricos , Contaminación del Aire , Contaminantes Ambientales , Ozono , Humanos , Contaminantes Atmosféricos/análisis , Contaminantes Ambientales/análisis , Dióxido de Nitrógeno/análisis , Teorema de Bayes , Canadá , Contaminación del Aire/análisis , Material Particulado/análisis , Ozono/análisis , Exposición a Riesgos Ambientales/análisisRESUMEN
Industrial development is an essential foundation of the national economy, but the industry is also the largest source of air pollution, of which power plants, iron and steel, building materials, and other industries emit large amounts of pollutants. Therefore, the Chinese government has promulgated a series of stringent emission regulations, and it is against this backdrop that research into air pollution control technologies for key industrial sectors is in full swing. In particular, during the 13th Five-Year Plan, breakthroughs have been made in pollution control technology for key industrial sectors. A multi-pollutant treatment technology system of desulfurization, denitrification, and dust collection, which applies to key industries such as power plants, steel, and building materials, has been developed. High-performance materials for the treatment of different pollutants, such as denitrification catalysts and desulfurization absorbers, were developed. At the same time, multi-pollutant synergistic removal technologies for flue gas in various industries have also become a hot research topic, with important breakthroughs in the synergistic removal of NOx, SOx, and Hg. Due to the increasingly stringent emission standards and regulations in China, there is still a need to work on the development of multi-pollutant synergistic technologies and further research and development of synergistic abatement technologies for CO2 to meet the requirements of ultra-low emissions in industrial sectors.
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Contaminantes Atmosféricos , Contaminación del Aire , Contaminantes Ambientales , Contaminantes Atmosféricos/análisis , Contaminación del Aire/prevención & control , Contaminación del Aire/análisis , China , AceroRESUMEN
Background Previous research indicated that isomers and alternatives of per- and polyfluoroalkyl substances (PFAS) probably disturb glucose metabolism; however, current epidemiological evidence on the associations of PFAS with fasting blood glucose is inconsistent. Besides, studies on the joint association of multiple components of PFAS and fasting blood glucose as well as the key component are scarce. Objective To evaluate the associations of PFAS isomers and alternatives with fasting blood glucose and their joint effects, as well as identify the key component among population without glucose metabolism problems. Methods We selected 923 adults without glucose metabolism problems or missing data from the Isomers of C8 Health Project in China (2015—2016). Serum PFAS isomers and alternatives and fasting blood glucose were measured using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) and automatic biochemical analyzer. We applied multiple linear regression to explore the associations of 16 pollutants which were detected among over 80% participants with fasting blood glucose. Meanwhile, we utilized qgcomp and Bayesian kernel machine regression (BKMR) models to explore the joint effects of PFAS isomers and alternatives mixture on target outcome indicators and identify the key component. Results The average age among the 923 participants in this study was (62.4±13.8) years old, including 472 men (51.1%) and 451 women (48.9%). Among selected PFAS isomers and alternatives, the highest serum concentration was ∑3+4+5m-PFOS (perfluoro-3/4/5-methylheptanesulfonate) with a median concentration of 10.20 ng·mL−1. The concentrations of linear perfluorooctane sulfonate (n-PFOS, 9.61 ng·mL−1), perfluorooctanoic acid (PFOA, 4.55 ng·mL−1), linear perfluorohexane sulfonic acid (n-PFHxS, 2.48 ng·mL−1), 6:2 chlorinated polyfluorinated ethersulfonic acid (6:2 Cl-PFESA, 1.90 ng·mL−1), perfluoro-6-methylheptanesulfonate (iso-PFOS, 1.85 ng·mL−1), perfluorobutanoic acid (PFBA, 1.81 ng·mL−1), perfluorinated n-nonanoic acid (PFNA, 1.39 ng·mL−1), and perfluoro-1-methylheptanesulfonate (1m-PFOS, 1.27 ng·mL−1) were higher than 1.00 ng·mL−1. After being adjusted for selected confounders, PFAS isomers and alternatives were positively associated with fasting blood glucose. With 1 ln unit concentration increment of 6:2 Cl-PFESA and PFNA, the estimated changes of fasting blood glucose were 0.18 (95%CI: 0.13, 0.23) mmol·L−1 and 0.24 (95%CI: 0.18, 0.30) mmol·L−1, respectively. The multi-pollutant models indicated a joint association of PFAS isomers and alternatives mixture with fasting blood glucose. The BKMR models reveals that as the quantiles of mixture elevated from the 50th to the 75th percentile, the values of fasting blood glucose increased 0.25 (95%CI: 0.21, 0.30) mmol·L−1, and the posterior inclusion probability of PFNA was 0.92, implying that PFNA was the key component. Conclusion PFAS isomers and alternatives are positively associated with fasting blood glucose. PFNA is the key component of the joint association.
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BACKGROUND: Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity. METHODS: We developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants. RESULTS: Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score>0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score>0.91). We found that the performance of the automated methods significantly surpassed those of manual logs. CONCLUSIONS: Automated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies.
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Contaminación del Aire , Inteligencia Artificial , Humanos , CiclismoRESUMEN
OBJECTIVE: Air pollution, road traffic noise and lack of greenness coexist in urban environments and have all been associated with type 2 diabetes. We aimed to investigate how these co-exposures were associated with type 2 diabetes in a multi-exposure perspective. METHODS: We estimated 5-year residential mean exposure to fine particles (PM2.5), ultrafine particles (UFP), elemental carbon (EC), nitrogen dioxide (NO2) and road traffic noise at the most (LdenMax) and least (LdenMin) exposed facade for all persons aged > 50 years living in Denmark in 2005 to 2017. For each air pollutant, we estimated total concentrations and traffic contributions. Based on land use maps, we estimated proportion of green and non-green space within 150 and 1000 m of all residences. In total, 1.9 million persons were included and 128,358 developed type 2 diabetes during follow-up. We performed analyses using Cox proportional hazards models, with adjustment for individual and neighborhood-level sociodemographic co-variates. RESULTS: In single-pollutant models, all air pollutants, noise and lack of green space were associated with higher risk of diabetes. In two-, three- and four-pollutant analyses of the air pollutants, only UFP and NO2 remained associated with higher diabetes risk in all models. LdenMax, LdenMin and the two proxies of green space remained associated with diabetes in two-pollutant models of, respectively, noise and green space. In a multi-pollutant analysis, we found hazard ratios (95 % confidence intervals) per interquartile range of 1.021 (1.005; 1.038) for UFP, 1.012 (0.996; 1.028) for NO2, 1.022 (1.012; 1.033) for LdenMin, 1.013 (1.004; 1.022) for LdenMax, and 1.038 (1.031; 1.044) and 1.018 (1.010; 1.025) for lack of green space within, respectively, 150 m and 1000 m, and a cumulative risk index of 1.131 (1.113; 1.149). CONCLUSIONS: Air pollution, road traffic noise and lack of green space were independently associated with higher risk of type 2 diabetes.