Development and Transferability of Ultrafine Particle Land Use Regression Models in London
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Accepted version
Author(s)
Yang, Zhenchun
Freni Sterrantino, Anna
Fuller, Gary
Gulliver, John
Type
Journal Article
Abstract
Due to a lack of routine monitoring, bespoke measurements are required to develop ultrafine particle (UFP) land use regression (LUR) models, which is especially challenging in megacities due to their large area. As an alternative, for London, we developed separate models for three urban residential areas, models combining two areas, and models using all three areas. Models were developed against annual mean ultrafine particle count cm−3 estimated from repeated 30-min fixed-site measurements, in different seasons (2016–2018), at forty sites per area, that were subsequently temporally adjusted using continuous measurements from a single reference site within or close to each area. A single model and 10 models were developed for each individual area and combination of areas. Within each area, sites were split into 10 groups using stratified random sampling. Each of the 10 models were developed using 90% of sites. Hold-out validation was performed by pooling the 10% of sites held-out each time. The transferability of models was tested by applying individual and two-area models to external area(s). In model evaluation, within-area mean squared error (MSE) R2 ranged from 14% to 48%. Transferring individual- and combined-area models to external areas without calibration yielded MSE-R2 ranging from −18 to 0. MSE-R2 was in the range 21% to 41% when using particle number count (PNC) measurements in external areas to calibrate models. Our results suggest that the UFP models could be transferred to other areas without calibration in London to assess relative ranking in exposures but not for estimating absolute values of PNC.
Date Issued
2020-10-20
Date Acceptance
2020-06-05
Citation
Science of the Total Environment, 2020, 740
ISSN
0048-9697
Publisher
Elsevier
Journal / Book Title
Science of the Total Environment
Volume
740
Copyright Statement
© 2020 Elsevier B.V. All rights reserved. . This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Subjects
Environmental Sciences
Publication Status
Published
Article Number
ARTN 140059
Date Publish Online
2020-06-09