Cargando…

Monitoring and modeling of household air quality related to use of different Cookfuels in Paraguay

In Paraguay, 49% of the population depends on biomass (wood and charcoal) for cooking. Residential biomass burning is a major source of fine particulate matter (PM (2.5)) and carbon monoxide (CO) in and around the household environment. In July 2016, cross‐sectional household air pollution sampling...

Descripción completa

Detalles Bibliográficos
Autores principales: Tagle, Matias, Pillarisetti, Ajay, Hernandez, Maria Teresa, Troncoso, Karin, Soares, Agnes, Torres, Ricardo, Galeano, Aida, Oyola, Pedro, Balmes, John, Smith, Kirk R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849814/
https://www.ncbi.nlm.nih.gov/pubmed/30339298
http://dx.doi.org/10.1111/ina.12513
Descripción
Sumario:In Paraguay, 49% of the population depends on biomass (wood and charcoal) for cooking. Residential biomass burning is a major source of fine particulate matter (PM (2.5)) and carbon monoxide (CO) in and around the household environment. In July 2016, cross‐sectional household air pollution sampling was conducted in 80 households in rural Paraguay. Time‐integrated samples (24 hours) of PM (2.5) and continuous CO concentrations were measured in kitchens that used wood, charcoal, liquefied petroleum gas (LPG), or electricity to cook. Qualitative and quantitative household‐level variables were captured using questionnaires. The average PM (2.5) concentration (μg/m(3)) was higher in kitchens that burned wood (741.7 ± 546.4) and charcoal (107.0 ± 68.6) than in kitchens where LPG (52.3 ± 18.9) or electricity (52.0 ± 14.8) was used. Likewise, the average CO concentration (ppm) was higher in kitchens that used wood (19.4 ± 12.6) and charcoal (7.6 ± 6.5) than in those that used LPG (0.5 ± 0.6) or electricity (0.4 ± 0.6). Multivariable linear regression was conducted to generate predictive models for indoor PM (2.5) and CO concentrations (predicted R (2) = 0.837 and 0.822, respectively). This study provides baseline indoor air quality data for Paraguay and presents a multivariate statistical approach that could be used in future research and intervention programs.