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Local attributable burden disease to PM _2.5 ambient air pollution in Medellin, Colombia, 2010-2016.

ABSTRACT: Background: Exposure to 2.5-micron diameter air pollutants (PM _2.5) has been associated with an increased risk of illness and death worldwide; however, in Latin American health impacts assessment of this risk factor is scarce. Medellín is one of the most polluted cities in the region, with a population growth rate that is twice as high as that of other Colombian cities, which implies a growing population at risk. Methods: A descriptive study of the disease burden was carried out using the city as the unit of observation. Health events were selected based on epidemiologic evidence and the availability of the population attributable fraction associated with PM _2.5. The mortality records were taken from the module of deceased of the Single Registry of Affiliates of the Health System; the morbidity records were taken from the Individual Health Services Registries. For the estimation of the burden of disease, the current Global Burden of Disease guidelines were followed. Results: Attributable disability-adjusted life years to exposure to ambient PM _2.5 pollution (DALYs _PM2.5) constituted 13.8% of total burden of the city. Males showed the greatest loss of DALYs _PM2.5 due to acute events, while in women the greatest loss was due to chronic events. Ischemic heart disease, chronic diseases of the lower respiratory tract, and influenza and pneumonia were the events that contributed the most to DALYs _PM2.5. 71.4% of the DALYs _PM2.5 corresponded to mortality, mainly in the population over 65 years of age. Regarding attributable morbidity, acute events were more prevalent in both sexes, especially due to respiratory diseases Conclusion: Premature death among the elderly population has the greatest weight on burden of disease attributable to ambient PM _2.5 pollution, mainly due to respiratory and cardiovascular diseases, without significant differences according to gender.

SUBMITTER: Grisales-Romero H

PROVIDER: S-EPMC8564742 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Elevated levels of ambient air pollution has been implicated as a major risk factor for morbidities and premature mortality in India, with particularly high concentrations of particulate matter in the Indo-Gangetic plain. High resolution spatiotemporal estimates of such exposures are critical to assess health effects at an individual level. This article retrospectively assesses daily average PM2.5 exposure at 1 km × 1 km grids in Delhi, India from 2010-2016, using multiple data sources and ensemble averaging approaches. We used a multi-stage modeling exercise involving satellite data, land use variables, reanalysis based meteorological variables and population density. A calibration regression was used to model PM2.5: PM10 to counter the sparsity of ground monitoring data. The relationship between PM2.5 and its spatiotemporal predictors was modeled using six learners; generalized additive models, elastic net, support vector regressions, random forests, neural networks and extreme gradient boosting. Subsequently, these predictions were combined under a generalized additive model framework using a tensor product based spatial smoothing. Overall cross-validated prediction accuracy of the model was 80% over the study period with high spatial model accuracy and predicted annual average concentrations ranging from 87 to 138 μg/m3. Annual average root mean squared errors for the ensemble averaged predictions were in the range 39.7-62.7 μg/m3 with prediction bias ranging between 4.6-11.2 μg/m3. In addition, tree based learners such as random forests and extreme gradient boosting outperformed other algorithms. Our findings indicate important seasonal and geographical differences in particulate matter concentrations within Delhi over a significant period of time, with meteorological and land use features that discriminate most and least polluted regions. This exposure assessment can be used to estimate dose response relationships more accurately over a wide range of particulate matter concentrations.

Project description:India is currently experiencing degraded air quality, and future economic development will lead to challenges for air quality management. Scenarios of sectoral emissions of fine particulate matter and its precursors were developed and evaluated for 2015-2050, under specific pathways of diffusion of cleaner and more energy-efficient technologies. The impacts of individual source sectors on PM2.5 concentrations were assessed through systematic simulations of spatially and temporally resolved particulate matter concentrations, using the GEOS-Chem model, followed by population-weighted aggregation to national and state levels. We find that PM2.5 pollution is a pan-India problem, with a regional character, and is not limited to urban areas or megacities. Under present-day emissions, levels in most states exceeded the national PM2.5 annual standard (40 μg m-3). Sources related to human activities were responsible for the largest proportion of the present-day population exposure to PM2.5 in India. About 60 % of India's mean population-weighted PM2.5 concentrations come from anthropogenic source sectors, while the remainder are from "other" sources, windblown dust and extra-regional sources. Leading contributors are residential biomass combustion, power plant and industrial coal combustion and anthropogenic dust (including coal fly ash, fugitive road dust and waste burning). Transportation, brick production and distributed diesel were other contributors to PM2.5. Future evolution of emissions under regulations set at current levels and promulgated levels caused further deterioration of air quality in 2030 and 2050. Under an ambitious prospective policy scenario, promoting very large shifts away from traditional biomass technologies and coal-based electricity generation, significant reductions in PM2.5 levels are achievable in 2030 and 2050. Effective mitigation of future air pollution in India requires adoption of aggressive prospective regulation, currently not formulated, for a three-pronged switch away from (i) biomass-fuelled traditional technologies, (ii) industrial coal-burning and (iii) open burning of agricultural residue. Future air pollution is dominated by industrial process emissions, reflecting larger expansion in industrial, rather than residential energy demand. However, even under the most active reductions envisioned, the 2050 mean exposure, excluding any impact from windblown mineral dust, is estimated to be nearly 3 times higher than the WHO Air Quality Guideline.

Project description:Background & aimsEmerging evidence supports ambient fine particulate matter (PM2.5) exposure is associated with insulin resistance (IR) and hepatic lipid accumulation. In this study, we aimed to evaluate the sex-dependent vulnerability in response to PM2.5 exposure and investigate the underlying mechanism by which PM2.5 modulates hepatic lipid metabolism.MethodsBoth male and female C57BL/6 mice were randomly assigned to ambient PM2.5 or filtered air for 24?weeks via a whole body exposure system. High-coverage quantitative lipidomics approaches and liquid chromatography-mass spectrometry techniques were performed to measure hepatic metabolites and hormones in plasma. Metabolic studies, histological analyses, as well as gene expression levels and molecular signal transduction analysis were applied to examine the effects and mechanisms by which PM2.5 exposure-induced metabolic disorder.ResultsFemale mice were more susceptible than their male counterparts to ambient PM2.5 exposure-induced IR and hepatic lipid accumulation. The hepatic lipid profile was changed in response to ambient PM2.5 exposure. Levels of hepatic triacylglycerols (TAGs), free fatty acids (FFAs) and cholesterol were only increased in female mice from PM group compared to control group. Plasmalogens were dysregulated in the liver from PM2.5-exposed mice as well. In addition, exposure to PM2.5 led to enhanced hepatic ApoB and microsomal triglyceride transport protein expression in female mice. Finally, PM2.5 exposure inhibited hypothalamus-pituitary-adrenal (HPA) axis and decreased glucocorticoids levels, which may contribute to the vulnerability in PM2.5-induced metabolic dysfunction.ConclusionsAmbient PM2.5 exposure inhibited HPA axis and demonstrated sex-associated differences in its effects on IR and disorder of hepatic lipid metabolism. These findings provide new mechanistic evidence of hormone regulation in air pollution-mediated metabolic abnormalities of lipids and more personalized care should be considered in terms of sex-specific risk factors.

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Local attributable burden disease to PM _2.5 ambient air pollution in Medellin, Colombia, 2010-2016.

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Dataset Information

Local attributable burden disease to PM 2.5 ambient air pollution in Medellin, Colombia, 2010-2016.

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OmicsDI is part of the ELIXIR infrastructure

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Local attributable burden disease to PM _2.5 ambient air pollution in Medellin, Colombia, 2010-2016.