Development and transferability of ultrafine particle land use regression models in London

Zhenchun Yang*, Anna Freni-Sterrantino, Gary W. Fuller, John Gulliver

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


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.

Original languageEnglish
Article number140059
JournalScience of the Total Environment
Publication statusPublished - 20 Oct 2020


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