4 research outputs found
Additional file 1: of Air pollution modelling for birth cohorts: a time-space regression model
Figure S1. Spatial distribution of the BECO (rural) and AFU (urban) measurement locations in the canton of Bern, displayed on background NO2 from the 2007 dispersion model. Figure S2. NO2 levels measured in a sample of urban monitoring sites during the year 2007. Figure S3. Internal validation Bland-Altman plot of predicted and measured values in the rural region (top) and urban area (bottom) in the log scale. Figure S4. Bland-Altman plot for external validation in Ī¼g/m3 (rural or urban model without intercept, corrected for backyard measurements). Table S1. Potential predictors of NO2. Table S2. Variance Inflation Factors (VIF) of main predictors in the rural and urban model. Table S3. Kappa statistics for External validation ā measured vs estimated concentration in quartiles. (DOCX 1047 kb
Evaluation of Land Use Regression Models for NO<sub>2</sub> and Particulate Matter in 20 European Study Areas: The ESCAPE Project
Land use regression models (LUR)
frequently use leave-one-out-cross-validation
(LOOCV) to assess model fit, but recent studies suggested that this
may overestimate predictive ability in independent data sets. Our
aim was to evaluate LUR models for nitrogen dioxide (NO<sub>2)</sub> and particulate matter (PM) components exploiting the high correlation
between concentrations of PM metrics and NO<sub>2</sub>. LUR models
have been developed for NO<sub>2</sub>, PM<sub>2.5</sub> absorbance,
and copper (Cu) in PM<sub>10</sub> based on 20 sites in each of the
20 study areas of the ESCAPE project. Models were evaluated with LOOCV
and āhold-out evaluation (HEV)ā using the correlation
of predicted NO<sub>2</sub> or PM concentrations with measured NO<sub>2</sub> concentrations at the 20 additional NO<sub>2</sub> sites
in each area. For NO<sub>2</sub>, PM<sub>2.5</sub> absorbance and
PM<sub>10</sub> Cu, the median LOOCV <i>R</i><sup>2</sup>s were 0.83, 0.81, and 0.76 whereas the median HEV <i>R</i><sup>2</sup> were 0.52, 0.44, and 0.40. There was a positive association
between the LOOCV <i>R</i><sup>2</sup> and HEV <i>R</i><sup>2</sup> for PM<sub>2.5</sub> absorbance and PM<sub>10</sub> Cu.
Our results confirm that the predictive ability of LUR models based
on relatively small training sets is overestimated by the LOOCV <i>R</i><sup>2</sup>s. Nevertheless, in most areas LUR models still
explained a substantial fraction of the variation of concentrations
measured at independent sites
Development of Land Use Regression Models for PM<sub>2.5</sub>, PM<sub>2.5</sub> Absorbance, PM<sub>10</sub> and PM<sub>coarse</sub> in 20 European Study Areas; Results of the ESCAPE Project
Land Use Regression (LUR) models have been used increasingly
for
modeling small-scale spatial variation in air pollution concentrations
and estimating individual exposure for participants of cohort studies.
Within the ESCAPE project, concentrations of PM<sub>2.5</sub>, PM<sub>2.5</sub> absorbance, PM<sub>10</sub>, and PM<sub>coarse</sub> were
measured in 20 European study areas at 20 sites per area. GIS-derived
predictor variables (e.g., traffic intensity, population, and land-use)
were evaluated to model spatial variation of annual average concentrations
for each study area. The median model explained variance (<i>R</i><sup>2</sup>) was 71% for PM<sub>2.5</sub> (range across
study areas 35ā94%). Model <i>R</i><sup>2</sup> was
higher for PM<sub>2.5</sub> absorbance (median 89%, range 56ā97%)
and lower for PM<sub>coarse</sub> (median 68%, range 32ā 81%).
Models included between two and five predictor variables, with various
traffic indicators as the most common predictors. Lower <i>R</i><sup>2</sup> was related to small concentration variability or limited
availability of predictor variables, especially traffic intensity.
Cross validation <i>R</i><sup>2</sup> results were on average
8ā11% lower than model <i>R</i><sup>2</sup>. Careful
selection of monitoring sites, examination of influential observations
and skewed variable distributions were essential for developing stable
LUR models. The final LUR models are used to estimate air pollution
concentrations at the home addresses of participants in the health
studies involved in ESCAPE
Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in Europe
Land Use Regression (LUR) models
have been used to describe and
model spatial variability of annual mean concentrations of traffic
related pollutants such as nitrogen dioxide (NO<sub>2</sub>), nitrogen
oxides (NO<sub><i>x</i></sub>) and particulate matter (PM).
No models have yet been published of elemental composition. As part
of the ESCAPE project, we measured the elemental composition in both
the PM<sub>10</sub> and PM<sub>2.5</sub> fraction sizes at 20 sites
in each of 20 study areas across Europe. LUR models for eight a priori
selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni),
sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed.
Good models were developed for Cu, Fe, and Zn in both fractions (PM<sub>10</sub> and PM<sub>2.5</sub>) explaining on average between 67 and
79% of the concentration variance (<i>R</i><sup>2</sup>)
with a large variability between areas. Traffic variables were the
dominant predictors, reflecting nontailpipe emissions. Models for
V and S in the PM<sub>10</sub> and PM<sub>2.5</sub> fractions and
Si, Ni, and K in the PM<sub>10</sub> fraction performed moderately
with <i>R</i><sup>2</sup> ranging from 50 to 61%. Si, NI,
and K models for PM<sub>2.5</sub> performed poorest with <i>R</i><sup>2</sup> under 50%. The LUR models are used to estimate exposures
to elemental composition in the health studies involved in ESCAPE