Land Use Regression Models for Ultrafine Particles and Black Carbon Based on Short-Term Monitoring Predict Past Spatial Variation.
Environ Sci Technol
; 49(14): 8712-20, 2015 Jul 21.
Article
em En
| MEDLINE
| ID: mdl-26079151
ABSTRACT
Health effects of long-term exposure to ultrafine particles (UFP) have not been investigated in epidemiological studies because of the lack of spatially resolved UFP exposure data. Short-term monitoring campaigns used to develop land use regression (LUR) models for UFP typically had moderate performance. The aim of this study was to develop and evaluate spatial and spatiotemporal LUR models for UFP and Black Carbon (BC), including their ability to predict past spatial contrasts. We measured 30 min at each of 81 sites in Amsterdam and 80 in Rotterdam, The Netherlands in three different seasons. Models were developed using traffic, land use, reference site measurements, routinely measured pollutants and weather data. The percentage explained variation (R(2)) was 0.35-0.40 for BC and 0.33-0.42 for UFP spatial models. Traffic variables were present in every model. The coefficients for the spatial predictors were similar in spatial and spatiotemporal models. The BC LUR model explained 61% of the spatial variation in a previous campaign with longer sampling duration, better than the model R(2). The UFP LUR model explained 36% of UFP spatial variation measured 10 years earlier, similar to the model R(2). Short-term monitoring campaigns may be an efficient tool to develop LUR models.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Tamanho da Partícula
/
Monitoramento Ambiental
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Material Particulado
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Fuligem
/
Modelos Teóricos
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
País/Região como assunto:
Europa
Idioma:
En
Revista:
Environ Sci Technol
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Holanda