Using Building Heights and Street Configuration to Enhance

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Using building heights and street configuration to enhance intra-urban PM , NO and NO land use regression models 10

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Robert Tang, Marta Blangiardo, and John Gulliver Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 03 Sep 2013 Downloaded from http://pubs.acs.org on September 3, 2013

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Environmental Science & Technology

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Using building heights and street configuration to enhance intra-urban PM10, NOX and

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NO2 land use regression models

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Robert Tang1*, Marta Blangiardo1, and John Gulliver1

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1. Small Area Health Statistics Unit, MRC-PHE Centre for Environment and Health, School

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of Public Health, Imperial College London, St Mary’s campus, London, W2 1PG, UK.

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* Corresponding author:

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[email protected]

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Tel: +44 (0)20 7594 5027

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Fax: +44 (0)20 7594 0768

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Small Area Health Statistics Unit, MRC-PHE Centre for Environment and Health, School of

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Public Health, Imperial College London, St Mary’s campus, London, W2 1PG, UK.

1 ACS Paragon Plus Environment


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TOC/Abstract Art

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Abstract

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Land use regression (LUR) models have been widely used to provide long-term air pollution

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exposure assessment in epidemiological studies. However, models have rarely offered

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variables that account for the dispersion environment close to source (e.g. street canyons,

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position and dimensions of buildings, road width). This study used newly-available data on

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building heights and geometry to enhance the representation of land use and the dispersion

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field in LUR. Models were developed for PM10, NOX and NO2 for 2008-2011 for London,

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UK. A separate set of models using ‘traditional’ land use and traffic indicators (e.g. distance

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from road, area of housing within circular buffers) were also developed and their

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performance compared with ‘enhanced’ models. Models were evaluated using leave-one-out

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(n-1) (LOOCV) and grouped (n-25%) cross-validation (GCV). LOOCV R2 values were 0.71,

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0.50, 0.66 and 0.73, 0.79, 0.78 for traditional and enhanced PM10, NOX and NO2 models,

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respectively. GCV R2 values were 0.71, 0.53, 0.64 and 0.68, 0.77, 0.77 for traditional and

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enhanced PM10, NOX and NO2 models, respectively. Data on building volume within the area

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common to a 20 m road buffer within a 25 m circular buffer substantially improved the

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performance (R2 > 13%) of NOX and NO2 LUR models.

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Keywords

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GIS, land use regression, street canyon, street configurations, building heights



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1. Introduction

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Land use regression (LUR) models have been widely used for exposure assessment in

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epidemiological studies.1-3 The structure of LUR models varies between studies (i.e. number

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of variables, different distances and buffers sizes) due to location and size of area (i.e. city,

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national, continental), but typically models include information on traffic, road length, land

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use, population and altitude. Air pollution is quickly dispersed in open country but elevated

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levels of pollutants are often seen in densely built-up areas, especially in heavily trafficked

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streets canyons.4 Source data in LUR models are, however, often relatively coarse in spatial

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resolution (e.g. 100 m CORINE land cover used in the ESCAPE project5) and thus do not

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represent the physical characteristics of dispersion environments (i.e. buildings and street

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configuration). Some dispersion models simulate the physical properties of air pollution

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dispersion around buildings (e.g. ADMS-Urban), but are usually limited to 10-15 buildings

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(i.e. one street or road intersection) in any model run. Thus, dispersion modeling with

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buildings is not practical for city-wide exposure assessment. A few studies have developed

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‘hybrid’ approaches by combining outputs from dispersion models with other variables in

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LUR,6-8 but thus far these models have the same limitation of not including the effects of

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buildings on the dispersion of air pollution.

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Information on building heights together with streets widths has been used in previous LUR

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studies to calculate aspect ratio to act as an indicator of pollutant trapping.9,10 This has been

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limited to considering the height of buildings on either side of the street at the point of

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interest, thus it does not account for the effects of street canyon length or other buildings in

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the vicinity of sources. One constraint has been the availability of data on building heights,

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which in the past had to be measured in the field and/or imputed from aerial photography or

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satellite imagery.9,10


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During recent years, new datasets on building heights and geometry, and detailed urban land

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use data have been made available in several countries including the UK. The Landmap

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dataset (www.landmap.ac.uk) provides city-wide data on building heights and geometry

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(with horizontal and vertical accuracy of +/- 0.5m) for much of urban UK, whilst OS

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MasterMap (http://www.ordnancesurvey.co.uk/oswebsite/products/os-mastermap/index.html)

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provides highly detailed topography and land use data, including areas of roads (i.e. road

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width) and land use at fine spatial scale (i.e. 70% of daily

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mean concentration available for each year between 2008-2011 were selected. Supporting

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information about these sites, including site type (as defined by LAQN; i.e. kerbside,

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roadside, industrial, suburban and urban background), and British National Grid X-Y site

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coordinates were downloaded from the LAQN website. Site coordinates were checked using

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online Google Maps (maps.google.com). Daily mean concentrations of pollutants were

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downloaded then averaged over a four-year period (i.e. 2008-2011) to provide long-term

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average concentrations (i.e. avoid the influence of meteorology in individual years). There

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were 42, 57, and 56 monitoring sites for PM10, NOX and NO2, respectively, that passed the

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site selection criteria. Locations of these monitoring sites can be seen in Figures S1-S3

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(Supporting Information). The 4-year average concentrations of each pollutant are normally

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distributed.

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Development of predictor variables

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A range of predictor variables were derived from GIS datasets for developing the LUR

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models; a full list of variables can be found in Table S1 (Supporting Information). Traffic,

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land use and population variables extracted were similar to those used in the ESCAPE study.5

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The London Atmospheric Emissions Inventory 2008 (LAEI 2008) was used for road and

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traffic variables. It comprises a digital road geography, attributed with information on type of

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road (‘motorways’, ‘A-roads’ and some significantly trafficked ‘minor roads’), traffic flows

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and speeds, with separate categories for fleet type (e.g. buses, light and heavy vehicles),

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within M25 boundary in Greater London. For land use variables, we used a combination of


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CORINE land cover for Europe (CLC) and the Land Cover Map of Great Britain 2007

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(LCM2007) (Source: Centre of Ecology and Hydrology) on a 25m2 grid. The land cover

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datasets were aggregated into five groups: high density urban, low density urban, industry,

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ports and railways, and urban green space. Further details on this procedure can be found in

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Supporting Information. Population variables (i.e. number of inhabitants and households)

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were extracted from the UK 2001 Census (Source: Office for National Statistics) – the closest

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available year.

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Four variables on buildings and/or street configuration were offered to create enhanced

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models, including two on aspect ratio as used in other studies9, 10, 12, and two new variables

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designed as part of this study: (1) aspect ratio (i.e. average building height / road width); (2)

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maximum aspect ratio (i.e. maximum building height / road width); (3) building volume (i.e.

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sum of building area × height); and (4) building volume / road width. The new variables are

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used to account for building intensity and stature close to roads with traffic information; in

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other words, they aim to represent both the height and continuity of street canyons. The

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variable on both building volume and road width is thus an extension of aspect ratio to

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account for both the shape of the canyon and the density of buildings. Higher values of these

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variables are expected to have a positive effect (i.e. increase) on pollutant concentrations.

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Building areas and heights were extracted from Landmap. Road widths and areas of urban

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green space surfaces were extracted from OS MasterMap.

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The building and road width data were extracted only at locations where a road emission

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source from LAEI (i.e. with traffic information) is present (i.e. at road- and kerb-side sites),

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and limited to a distance of 100m to represent distance of influence of buildings and street

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configuration on pollution trapping in the wake of sources.4,11 Two types of buffers were



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used: (1) circular buffers as used in other studies, and (2) road buffers - buffers of road

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center-lines were created, intersected with circular buffers, and features within the area

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common to both buffers (e.g. buildings alongside roads) were clipped and extracted for each

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of the four building/street configuration variables. Distances for road buffers were 20, 30, 40

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and 50m from the center of roads within 25, 50 and 100m circular buffers. The road buffers

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thus focus on areas in the immediate wake of road sources. Figures S4 and S5 in Supporting

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Information illustrates how road and circular buffers were created.

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Model development

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Models were developed for PM10, NOx and NO2 using ArcGIS (v10.0; ESRI) to generate

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predictor variables and SPSS (v20.0; IBM) for regression analysis. Variables were regressed

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against monitored concentrations of each pollutant. For traditional models, the predictor

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variable showing the highest correlation with monitored concentrations was first entered in

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the model and entry of proceeding variables followed a supervised forward stepwise

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method.13-15

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Two approaches were used to develop enhanced models. Firstly, the four building and street

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configuration variables were regressed in turn against the residual from traditional models

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(i.e. coefficients of the variables in the traditional model were not allowed to change), similar

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to Eeftens et al..12 Secondly, models were developed from scratch with all variables being

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offered in the regression analysis. We ensured that building/street configuration variables

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were only entered into the model if a local traffic variable (i.e. traffic load within a 25 m

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buffer) was already present in the model. Initially, the best performing variable representing

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local traffic sources were identified, then stepwise coupled with building/street configuration

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variables. Once the best combination of a local traffic and building/street configuration


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variable was selected other variables reflecting background traffic (i.e. road and traffic

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variables beyond the extent of the local traffic variable) and non-traffic sources (e.g.

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population, housing, industry, green space) were offered.

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A variable is included in a final model if it provides: (1) greater than 1% increment in

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adjusted R2; (2) p-value 1%; and (2) p-value < 0.05 are

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highlighted. For PM10, no enhanced variables were found to be statistically significant (p
0.05, thus

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model residuals were deemed not to be spatially dependent.

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In order to compare the predictive powers of each variable and their contributions to absolute

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predicted values, regression coefficient × the (90th percentile – 10th percentile) were

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calculated.12 Predicted long-term average concentrations associated with each variable by site

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can be found in compound graphs presented in Figures S7-S9 (Supporting Information).

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Model evaluation

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Table 2 shows summary statistics from cross-validation. Figure 1 shows predicted

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concentrations from GCV plotted against monitored concentrations. Summary statistics for

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each evaluation group in GCV can be found in Table S5.

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For LOOCV, traditional models yielded R2 (MSE-R2 in brackets) values of 0.71 (0.70), 0.50

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(0.48), 0.66 (0.64), and enhanced models yielded R2 values of 0.73 (0.71), 0.79 (0.78), 0.78

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(0.77) for PM10, NOX and NO2, respectively (Table 2). For all pollutants, enhanced models

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outperformed traditional models, with 2%, 29% and 12% higher explained variance (i.e.,

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LOOCV R2) in monitored concentrations for PM10, NOX and NO2, respectively. The fall in

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R2 between model development (Table 1) and LOOCV R2 was below 10% (3~7% drop for all

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models), which is comparable to changes in R2 between model development and evaluation

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in the recent ESCAPE project (within 15%), indicating stable models.5

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The R2 values obtained from GCV for PM10, NOX and NO2, respectively, were 0.71 (0.69),

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0.53 (0.51), 0.64 (0.63) for traditional models and 0.68 (0.67), 0.77 (0.77), 0.77 (0.76) for



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enhanced models. Enhanced models yielded 24% and 13% increase in explained variation in

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NOX and NO2 concentrations, but the PM10 model was not improved.

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Values of RMSE calculated from LOOCV and GCV were found to be similar, with higher

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values of RMSE from traditional models, with the exception of PM10 in GCV. IOA ranged

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from 0.89~0.94 for LOOCV and 0.83~0.94 for GCV. IOAs are higher in enhanced models;

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with 11% and 5% more agreement between predicted and monitored values in GCV for NOX

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and NO2, respectively. Values of FB for all models are small (-0.002~0.004 for LOOCV; -

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0.015~0.009 for GCV), indicating that models are relatively free from bias (i.e. low levels of

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over- or under-prediction). The highest bias was under-predictions of enhanced PM10 model

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in GCV (FB = -0.015; i.e. under-prediction of about 1.5%). Values of beta (i.e. regression

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slopes) fell within 95% confidence interval in all cases.

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Discussion

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We developed enhanced intra-urban PM10, NOX and NO2 land use regression models for

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London, using data on building heights and street configuration alongside traditional LUR

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variables (i.e. land use, population, roads etc.), and compared their performance with

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traditional models. To our knowledge, this is the first study both to employ city-wide building

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geometry/heights and detailed land use datasets (with spatial accuracy of +/-1 meter in urban

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data) to represent the effects of the built environment on the pollutant dispersion in LUR

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models.

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We offered the enhanced variables in addition to the traditional models using a similar

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approach to the one applied in the Netherlands.12 We subsequently developed new models by

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offering enhanced and traditional variables together. Results showed that models with


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enhanced geographical variables provided substantial improvements in model performance

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for NOX (MSE-R2 = 26% in GCV) and NO2 (MSE-R2 = 13% in GCV). Although a higher

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value of R2 was achieved in model building for the enhanced PM10 model, its performance

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measured against the traditional model in evaluation was mixed.

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LUR model variables and model performance

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We developed a range of variables using circular and road buffers of varying size to represent

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building densities and street canyons based on canyon length, height, width and building

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areas. These variables represented both the immediate street-level dispersion environment

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and the dispersion field in the surrounding area around monitoring sites. This is an

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improvement in LUR models over simple street canyon (i.e. no canyon; partial/full canyon)

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classifications or aspect ratios which typically only consider the height of buildings on

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opposite sides of streets at the air pollution monitoring site.9,10,12

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In enhanced models, variables extracted with road buffers were retained in some models over

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traditional circular buffers. Smaller sized road buffers (

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