title: Land Use Regression Models for Ultrafine Particles and Black Carbon Based on Short-Term Monitoring Predict Past Spatial Variation.
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.
authors.affiliation: †Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands.
authors.forename: Denise R
authors.initials: DR
authors.lastname: Montagne
authors.affiliation: †Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands.
authors.forename: Gerard
authors.initials: G
authors.lastname: Hoek
authors.affiliation: †Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands.
authors.forename: Jochem O
authors.initials: JO
authors.lastname: Klompmaker
authors.affiliation: †Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands.
authors.forename: Meng
authors.initials: M
authors.lastname: Wang
authors.affiliation: †Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands.
authors.forename: Kees
authors.initials: K
authors.lastname: Meliefste
authors.affiliation: †Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands.|‡Julius Center for Health Sciences and Primary Care, University Medical Center, University of Utrecht, 3584 CK Utrecht, The Netherlands.
authors.forename: Bert
authors.initials: B
authors.lastname: Brunekreef
mesh_terms: D000393:Air Pollutants / Q000032:analysis; D000397:Air Pollution / Q000032:analysis; D002947:Cities; D004784:Environmental Monitoring; D008962:Models, Theoretical; D009426:Netherlands; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D012044:Regression Analysis; D012621:Seasons; D053260:Soot / Q000032:analysis*; D062211:Spatio-Temporal Analysis
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter; D053260:Soot