Association between short-term exposure to atmospheric black carbon and acute exacerbations of childhood asthma.
Source: PubMed, NCBI / U.S. National Library of Medicine
This study aimed to investigate the association between short-term exposure to atmospheric black carbon and acute exacerbations of childhood asthma and provide a theoretical basis for identifying strategies to reduce air pollutant exposure and prevent acute asthma attacks in children. Data on chilhood asthma cases were obtained from the Xiamen Health and Medical Big Data Center between January 2020 and December 2023. A case-crossover design was applied, with the day of acute asthma exacerbation designated as the case day and the 7 days prior designated as control days. The study collected data on black carbon, PM₂.₅, and its components (sulfate, nitrate, ammonium, and organic matter) concentrations, as well as meteorological factors for the 0-6 day lag period. Generalized linear mixed-effects model (GLMM) were used to develop single- and two-pollutant models, while Bayesian kernel machine regression (BKMR) was employed to build multi-pollutant models. These models were used to evaluate the independent, interactive, and combined effects of short-term black carbon exposure. Stratified analyses were conducted by age, sex, and season. A total of 3,440 cases were ultimately included in this study. After adjusting for temperature, relative humidity, fever, and total PM₂.₅ mass, the single-pollutant model revealed that black carbon exposure was significantly associated with acute asthma exacerbations at a 3-day lag (aOR = 1.2089, 95% CI: 1.0
Abstract
This study aimed to investigate the association between short-term exposure to atmospheric black carbon and acute exacerbations of childhood asthma and provide a theoretical basis for identifying strategies to reduce air pollutant exposure and prevent acute asthma attacks in children. Data on chilhood asthma cases were obtained from the Xiamen Health and Medical Big Data Center between January 2020 and December 2023. A case-crossover design was applied, with the day of acute asthma exacerbation designated as the case day and the 7 days prior designated as control days. The study collected data on black carbon, PM₂.₅, and its components (sulfate, nitrate, ammonium, and organic matter) concentrations, as well as meteorological factors for the 0-6 day lag period. Generalized linear mixed-effects model (GLMM) were used to develop single- and two-pollutant models, while Bayesian kernel machine regression (BKMR) was employed to build multi-pollutant models. These models were used to evaluate the independent, interactive, and combined effects of short-term black carbon exposure. Stratified analyses were conducted by age, sex, and season. A total of 3,440 cases were ultimately included in this study. After adjusting for temperature, relative humidity, fever, and total PM₂.₅ mass, the single-pollutant model revealed that black carbon exposure was significantly associated with acute asthma exacerbations at a 3-day lag (aOR = 1.2089, 95% CI: 1.0348-1.4122), with the risk increasing as black carbon concentrations rose. In the two-pollutant model, black carbon exposure at a 3-day lag remained significantly associated with acute asthma exacerbations and demonstrated interaction effects with PMcomponents including sulfate, nitrate, and ammonium. In the multi-pollutant model, mixed exposure to pollutants was positively associated with acute asthma exacerbations at a 3-day lag, with black carbon emerging as a critical factor. Additionally, black carbon exhibited interaction effects with ammonium. Stratified analysis indicated that black carbon exposure in winter was more likely to trigger acute asthma exacerbations. Short-term exposure to black carbon is significantly associated with acute asthma exacerbations in children, particularly at a 3-day lag. The risk is higher in children exposed in winter. Furthermore, complex interaction effects exist between black carbon and other pollutants.