Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 1 de 1
Filtrar
Mais filtros











Base de dados
Intervalo de ano de publicação
1.
Environ Sci Technol ; 55(18): 12483-12492, 2021 09 21.
Artigo em Inglês | MEDLINE | ID: mdl-34498865

RESUMO

Outdoor ultrafine particles (UFP, <0.1 µm) and black carbon (BC) vary greatly within cities and may have adverse impacts on human health. In this study, we used a hybrid approach to develop new models to estimate within-city spatial variations in outdoor UFP and BC concentrations across Bucaramanga, Colombia. We conducted a mobile monitoring campaign over 20 days in 2019. Regression models were trained on land use data and combined with predictions from convolutional neural networks (CNN) trained to predict UFP and BC concentrations using satellite and street-level images. The combined UFP model (R2 = 0.54) outperformed the CNN (R2 = 0.47) and land use regression (LUR) models (R2 = 0.47) on their own. Similarly, the combined BC model also outperformed the CNN and LUR BC models (R2 = 0.51 vs 0.43 and 0.45, respectively). Spatial variations in model performance were more stable for the CNN and combined models compared to the LUR models, suggesting that the combined approach may be less likely to contribute to differential exposure measurement error in epidemiological studies. In general, our findings demonstrated that satellite and street-level images can be combined with a traditional LUR modeling approach to improve predictions of within-city spatial variations in outdoor UFP and BC concentrations.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Carbono , Cidades , Colômbia , Monitoramento Ambiental , Humanos , Material Particulado/análise
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA