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Exposure to air pollutants is associated with an increased risk of developing Alzheimer's disease (AD). AD pathological hallmarks and cognitive deficits are documented in children and young adults in polluted cities (e.g. Metropolitan Mexico City, MMC). Iron-rich combustion- and friction-derived nanoparticles (CFDNPs) that are abundantly present in airborne particulate matter pollution have been detected in abundance in the brains of young urbanites. Epigenetic gene regulation has emerged as a candidate mechanism linking exposure to air pollution and brain diseases. A global decrease of the repressive histone post-translational modifications (HPTMs) H3K9me2 and H3K9me3 (H3K9me2/me3) has been described both in AD patients and animal models. Here, we evaluated nuclear levels of H3K9me2/me3 and the DNA double-strand-break marker γ-H2AX by immunostaining in post-mortem prefrontal white matter samples from 23 young adults (age 29 ± 6 years) who resided in MMC (n = 13) versus low-pollution areas (n = 10). Lower H3K9me2/me3 and higher γ-H2A.X staining were present in MMC urbanites, who also displayed the presence of hyperphosphorylated tau and amyloid-β (Aβ) plaques. Transmission electron microscopy revealed abundant CFDNPs in neuronal, glial and endothelial nuclei in MMC residents' frontal samples. In addition, mice exposed to particulate air pollution (for 7 months) in urban Santiago (Chile) displayed similar brain impacts; reduced H3K9me2/me3 and increased γ-H2A.X staining, together with increased levels of AD-related tau phosphorylation. Together, these findings suggest that particulate air pollution, including metal-rich CFDNPs, impairs brain chromatin silencing and reduces DNA integrity, increasing the risk of developing AD in young individuals exposed to high levels of particulate air pollution., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Abstract Missing data from air quality datasets is a common problem, but is much more severe in small cities or localities. This poses a great challenge for environmental epidemiology as high exposures to pollutants worldwide occur in these settings and gaps in datasets hinder health studies that could later inform local and international policies. Here, we propose the use of imputation methods as a tool to reconstruct air quality datasets and have applied this approach to an air quality dataset in Temuco, a mid-size city in Chile as a case-study. We attempted to reconstruct the database comparing five approaches: mean imputation, conditional mean imputation, K-Nearest Neighbor imputation, multiple imputation and Bayesian Principal Component Analysis imputation. As a base for the imputation methods, linear regression models were fitted for PM 2.5 against other air quality and meteorological variables. Methods were challenged against validation sets where data was removed artificially. Imputation methods were able to reconstruct the dataset with good performance in terms of completeness, errors, and bias, even when challenged against the validations sets. The performance improved when including covariates from a second monitoring station in Temuco. K-Nearest Neighbor imputation showed slightly better performance than multiple imputation for error (25% vs. 27%) and bias (2.1% vs. 3.9%), but presented lower completeness (70% vs. 100%). In summary, our results show that the imputation methods can be a useful tool in reconstructing air quality datasets in a real-life situation. Graphical abstract Image 1 Highlights • An air quality dataset with high rate of data losses (>20%) was attempted to be reconstructed using imputations methods. • Regression models were successful in predicting PM 2.5 , with predictors in agreement with residential wood-burning source. • Imputation methods, particularly multiple imputation, the data-set was successfully reconstructed to a certain extent. • These methods seem promising to study health problems in cities whose datasets may be fragmented. [ABSTRACT FROM AUTHOR]

The objective of this study was to determine the association of respiratory symptoms and medication use and exposure to various air pollutants, PM 2.5 components, and source factors in a panel of asthmatic and nonasthmatic children in Santiago, Chile. To this end, 174 children (90 asthmatics and 84 nonasthmatics) were followed throughout the winter months of 2010 and 2011. During the study period, children filled out daily diaries to record respiratory symptoms and medication use. Air pollution data were obtained from government central site measurements and a PM 2.5 characterization campaign. PM 2.5 source factors were obtained using positive matrix factorization (PMF). Associations of symptoms and exposure to pollutants and source-factor daily scores were modeled separately for asthmatic and nonasthmatic children using mixed logistic regression models with random intercepts, controlling for weather, day of the week, year, and viral outbreaks. Overall, high concentrations of air pollutants and PM 2.5 components were observed. Six source factors were identified by PMF (motor vehicles, marine aerosol, copper smelter, secondary sulfates, wood burning, and soil dust). Overall, single pollutant models showed significant and strong associations between 7-day exposures for several criteria pollutants (PM 2.5 , NO 2 , O 3 ), PM 2.5 components (OC, K, S, Se, V), and source factors (secondary sulfate) and coughing, wheezing and three other respiratory symptoms in both in asthmatic and nonasthmatic children. No associations were found for use of rescue inhalers in asthmatics. Two-pollutant models showed that several associations remained significant after including PM 2.5 , and other criteria pollutants, in the models, particularly components and source factors associated with industrial sources. In conclusion, exposure to air pollutants, especially PM 2.5 , NO 2 , and O 3 , were found to exacerbate respiratory symptoms in both asthmatic and nonasthmatic children. Some of the results suggest that PM 2.5 components associated with a secondary sulfate source may have a greater impact on some symptoms than PM 2.5 . In general, the results of this study show important associations at concentrations close or below current air quality standards. [ABSTRACT FROM AUTHOR]

Temuco, like most Southern Chile cities, has a long-standing air pollution problem due to residential wood heating. Here, we study the temporal and spatial distribution of PM 2.5 and the biomass-burning tracers levoglucosan and water soluble potassium (K sol). PM 2.5 samples were collected at 40 sites throughout the city and sampled four times in four different sessions between 2017 and 2018. Large concentrations were observed for all species, particularly in winter. Signs of spatial and temporal correlation were observed from multilevel models for the different concentrations and ratios. Ratios of levoglucosan and K sol to PM 2.5 were lower in summer. Spatial clustering was observed both from spatial autocorrelation tests and kriging, with clusters of higher ratios for of all three species (comparing rotating to central site) observed in some neighborhoods. Clusters correlated with lower socioeconomic status (SES), which may reflect population density and lower quality stoves. Clusters of higher K sol to PM 2.5 ratios in some neighborhoods were moderately correlated with higher SES, which may reflect the use of better-quality stoves (e.g., pellets). The current work provides a case study and example of the investigation of wood burning as an air pollution source in a city where many inhabitants use wood as a fuel for heating and cooking. It reveals key temporal and spatial patterns which can assist the design of mitigation strategies. • High concentrations of PM 2.5 and wood burning tracer (levoglucosan and K sol) were observed. • Spatial and temporal correlation were observed from multilevel models for the different concentrations and ratios. • Clusters were located in lower socioeconomic status neighborhood, likely higher population density and lower quality stoves. • We provided key temporal and spatial patterns for undestanding of the wood burning problem. [ABSTRACT FROM AUTHOR]

Temuco, a medium-sized city in the south of Chile, is heavily impacted by residential wood-burning particularly in winter, which causes strong episodes of air pollution. We spatially characterized the distribution of particulate matter (PM) during winter nights in Temuco using mobile measurements. In winter 2016 (June 8-July 15th), continuous monitors for PM ≤ 2.5 μm (PM 2.5) and ultrafine particles <0.1 μm (UFP) were deployed in a vehicle along with a positioning system during 20 nights (20:00 to 2:00) in assigned routes that crossed the city and additional sampling was conducted at a central site. Measurements were expressed as concentrations and ratios to central site concentrations and collapsed by spatial grids of 50, 100, 200, and 400 m. Average PM 2.5 concentrations were ∼100 μg m−3, while UFP ∼30,000 counts cm−3. Some neighborhoods had concentrations of PM 2.5 nearly double those measured at a centrally located site, while other neighborhoods had less than half the measured level. We identified hotspots and cold spots of PM 2.5 and UFP throughout the urban area—and observed some neighborhoods in which over 20% of the area was categorized as a hotspot for one of the pollutants (14 for PM 2.5 and 11 for UFP out of 34). Using mobile monitoring—a relatively simple and inexpensive methodology— we characterized the spatial distribution of pollutants and likely clusters for the time period, which may guide future spatial campaigns and help targeting local interventions aimed at air pollution mitigation. [Display omitted] • Air pollution due to wood-burning is a growing concern around the world. • Cities in Southern Chile have massive emissions due to residential wood-burning. • Mapping of concentrations was conducted by an innovative mobile method. • Mobile monitoring identified spatial variations in particulate matter. • We identified clusters of spatially correlated hotspots of particulate matter. [ABSTRACT FROM AUTHOR]