Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model

Mobile and short-term monitoring campaigns are increasingly used to develop land-use regression (LUR) models for ultrafine particles (UFP) and black carbon (BC). It is not yet established whether LUR models based on mobile or short-term stationary measurements result in comparable models and concent...

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Published in:Environmental science & technology 2016-12, Vol.50 (23), p.12894-12902
Main Authors: Kerckhoffs, Jules, Hoek, Gerard, Messier, Kyle P, Brunekreef, Bert, Meliefste, Kees, Klompmaker, Jochem O, Vermeulen, Roel
Format: Article
Language:English
Subjects:
Air Pollutants
Air Pollution
Carbon
Carbon black
Comparative analysis
Environmental Monitoring
Environmental science
Land use
Particulate Matter
Regression analysis
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cited_by cdi_FETCH-LOGICAL-a394t-c12246653b27de5032fe19ea60625fc21f374df46c3b4bd00bba06f9e846b9873
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container_end_page 12902
container_issue 23
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container_title Environmental science & technology
container_volume 50
creator Kerckhoffs, Jules
Hoek, Gerard
Messier, Kyle P
Brunekreef, Bert
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Vermeulen, Roel
description Mobile and short-term monitoring campaigns are increasingly used to develop land-use regression (LUR) models for ultrafine particles (UFP) and black carbon (BC). It is not yet established whether LUR models based on mobile or short-term stationary measurements result in comparable models and concentration predictions. The goal of this paper is to compare LUR models based on stationary (30 min) and mobile UFP and BC measurements from a single campaign. An electric car collected both repeated stationary and mobile measurements in Amsterdam and Rotterdam, The Netherlands. A total of 2964 road segments and 161 stationary sites were sampled over two seasons. Our main comparison was based on predicted concentrations of the mobile and stationary monitoring LUR models at 12 682 residential addresses in Amsterdam. Predictor variables in the mobile and stationary LUR model were comparable, resulting in highly correlated predictions at external residential addresses (R 2 of 0.89 for UFP and 0.88 for BC). Mobile model predictions were, on average, 1.41 and 1.91 times higher than stationary model predictions for UFP and BC, respectively. LUR models based upon mobile and stationary monitoring predicted highly correlated UFP and BC concentration surfaces, but predicted concentrations based on mobile measurements were systematically higher.
doi_str_mv 10.1021/acs.est.6b03476
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source American Chemical Society:Jisc Collections:American Chemical Society Read & Publish Agreement 2022-2024 (Reading list)
subjects Air Pollutants
Air Pollution
Carbon
Carbon black
Comparative analysis
Environmental Monitoring
Environmental science
Land use
Particulate Matter
Regression analysis
title Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model
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