Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research.
Environ Sci Technol
; 51(21): 12473-12480, 2017 Nov 07.
Article
em En
| MEDLINE
| ID: mdl-28948787
ABSTRACT
Exposure to traffic related nitrogen dioxide (NO2) air pollution is associated with adverse health outcomes. Average pollutant concentrations for fixed monitoring sites are often used to estimate exposures for health studies, however these can be imprecise due to difficulty and cost of spatial modeling at the resolution of neighborhoods (e.g., a scale of tens of meters) rather than at a coarse scale (around several kilometers). The objective of this study was to derive improved estimates of neighborhood NO2 concentrations by blending measurements with modeled predictions in Sydney, Australia (a low pollution environment). We implemented the Bayesian maximum entropy approach to blend data with uncertainty defined using informative priors. We compiled NO2 data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighborhood annual average NO2. The spatial model produced a posterior probability density function of estimated annual average concentrations that spanned an order of magnitude from 3 to 35 ppb. Validation using independent data showed improvement, with root mean squared error improvement of 6% compared with the land use regression model and 16% over the chemical transport model. These estimates will be used in studies of health effects and should minimize misclassification bias.
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
2_ODS3
Base de dados:
MEDLINE
Assunto principal:
Poluentes Atmosféricos
/
Dióxido de Nitrogênio
Tipo de estudo:
Prognostic_studies
Aspecto:
Patient_preference
País/Região como assunto:
Oceania
Idioma:
En
Revista:
Environ Sci Technol
Ano de publicação:
2017
Tipo de documento:
Article