LUR Pollution Surfaces

Dataset

Description

LUR NO2 model surfaces for the Greater Glasgow conurbation supporting the publication in EST. We used a network of 135 NO2 passive diffusion tube sites to develop land use regression (LUR) models in a UK conurbation. Network sites were divided into 4 groups (32 – 35 sites per group) and models developed using combinations of 1 - 3 groups of ‘training’ sites to evaluate how the number of training sites influenced model performance and residential NO2 exposure estimates for a cohort of 13,679 participants. Dataset consists mainly of tif, ovr and xml files.
Date made available1 Sep 2016
PublisherUniversity of Strathclyde
Date of data production1 Jan 2014 - 1 Jan 2016
Geographical coverageGreater Glasgow

Cite this

Gillespie, J. (Creator), Beverland, I. (Contributor). (1 Sep 2016). LUR Pollution Surfaces. University of Strathclyde. Subset_2G_24(.tfw), Subset_2G_23(x.xml), Subset_2G_23(f.ovr), Subset_2G_23(.tif), Subset_2G_23(.tfw), Subset_1G_2(x.xml), Subset_2G_24(.tif), Subset_1G_2(f.ovr), Subset_1G_2(.tfw), Subset_2G_14(x.xml), Subset_2G_14(f.ovr), Subset_1G_2(.tif), Subset_2G_14(.tfw), Subset_2G_13(x.xml), Subset_2G_14(.tif), Subset_2G_13(f.ovr), Subset_2G_13(.tfw), Subset_2G_12(x.xml), Subset_2G_12(f.ovr), Subset_2G_13(.tif), Subset_2G_12(.tfw), Subset_1G_1(x.xml), Subset_1G_1(f.ovr), Subset_2G_12(.tif), Subset_1G_1(.tfw), Baseline_234(x.xml), Baseline_234(f.ovr), Baseline_234(.tfw), Subset_1G_1(.tif), Baseline_134(x.xml), Baseline_134(f.ovr), Baseline_234(.tif), Baseline_134(.tfw), Baseline_234_(x.xml), Baseline_234_(f.ovr), Baseline_234_(.tif), Baseline_124(x.xml), Baseline_124(f.ovr), Baseline_134(.tif), Baseline_124(.tfw), Baseline_123(x.xml), Baseline_123(f.ovr), Baseline_124(.tif), Baseline_123(.tfw), Subset_1G_4(x.xml), Subset_1G_4(f.ovr), Baseline_123(.tif), Subset_1G_4(.tfw), Subset_2G_34(x.xml), Subset_2G_34(f.ovr), Subset_2G_34(.tif), Subset_2G_34(.tfw), Subset_1G_3(x.xml), Subset_1G_3(f.ovr), Subset_1G_4(.tif), Subset_1G_3(.tfw), Subset_2G_24(x.xml), Subset_2G_24(f.ovr), Subset_1G_3(.tif), Read_Me_Sep16(.pdf). 10.15129/1692520b-deb7-4571-a401-3a870fe52a31