7 research outputs found
Development and Back-Extrapolation of NO<sub>2</sub> Land Use Regression Models for Historic Exposure Assessment in Great Britain
Modeling
historic air pollution exposures is often restricted by
availability of monitored concentration data. We evaluated back-extrapolation
of land use regression (LUR) models for annual mean NO<sub>2</sub> concentrations in Great Britain for up to 18 years earlier. LUR
variables were created in a geographic information system (GIS) using
land cover and road network data summarized within buffers, site coordinates,
and altitude. Four models were developed for 2009 and 2001 using 75%
of monitoring sites (in different groupings) and evaluated on the
remaining 25%. Variables selected were generally stable between models.
Within year, hold-out validation yielded mean-squared-error-based <i>R</i><sup>2</sup> (MSE-<i>R</i><sup>2</sup>) (i.e.,
fit around the 1:1 line) values of 0.25ā0.63 and 0.51ā0.65
for 2001 and 2009, respectively. Back-extrapolation was conducted
for 2009 and 2001 models to 1991 and for 2009 models to 2001, adjusting
to the year using two background NO<sub>2</sub> monitoring sites.
Evaluation of back-extrapolated predictions used 100% of sites from
an historic national NO<sub>2</sub> diffusion tube network (<i>n</i> = 451) for 1991 and 70 independent sites from automatic
monitoring in 2001. Values of MSE-<i>R</i><sup>2</sup> for
back-extrapolation to 1991 were 0.42ā0.45 and 0.52ā0.55
for 2001 and 2009 models, respectively, but model performance varied
by region. Back-extrapolation of LUR models appears valid for exposure
assessment for NO<sub>2</sub> back to 1991 for Great Britain
Large Scale Air Pollution Estimation Method Combining Land Use Regression and Chemical Transport Modeling in a Geostatistical Framework
In recognition that intraurban exposure
gradients may be as large
as between-city variations, recent air pollution epidemiologic studies
have become increasingly interested in capturing within-city exposure
gradients. In addition, because of the rapidly accumulating health
data, recent studies also need to handle large study populations distributed
over large geographic domains. Even though several modeling approaches
have been introduced, a consistent modeling framework capturing within-city
exposure variability and applicable to large geographic domains is
still missing. To address these needs, we proposed a modeling framework
based on the Bayesian Maximum Entropy method that integrates monitoring
data and outputs from existing air quality models based on Land Use
Regression (LUR) and Chemical Transport Models (CTM). The framework
was applied to estimate the yearly average NO<sub>2</sub> concentrations
over the region of Catalunya in Spain. By jointly accounting for the
global scale variability in the concentration from the output of CTM
and the intraurban scale variability through LUR model output, the
proposed framework outperformed more conventional approaches
Western European Land Use Regression Incorporating Satellite- and Ground-Based Measurements of NO<sub>2</sub> and PM<sub>10</sub>
Land
use regression (LUR) models typically investigate within-urban variability
in air pollution. Recent improvements in data quality and availability,
including satellite-derived pollutant measurements, support fine-scale
LUR modeling for larger areas. Here, we describe NO<sub>2</sub> and
PM<sub>10</sub> LUR models for Western Europe (years: 2005ā2007)
based on >1500 EuroAirnet monitoring sites covering background,
industrial, and traffic environments. Predictor variables include
land use characteristics, population density, and length of major
and minor roads in zones from 0.1 km to 10 km, altitude, and distance
to sea. We explore models with and without satellite-based NO<sub>2</sub> and PM<sub>2.5</sub> as predictor variables, and we compare
two available land cover data sets (global; European). Model performance
(adjusted <i>R</i><sup>2</sup>) is 0.48ā0.58 for
NO<sub>2</sub> and 0.22ā0.50 for PM<sub>10</sub>. Inclusion
of satellite data improved model performance (adjusted <i>R</i><sup>2</sup>) by, on average, 0.05 for NO<sub>2</sub> and 0.11 for
PM<sub>10</sub>. Models were applied on a 100 m grid across Western
Europe; to support future research, these data sets are publicly available
Development of Land Use Regression Models for Elemental, Organic Carbon, PAH, and Hopanes/Steranes in 10 ESCAPE/TRANSPHORM European Study Areas
Land
use regression (LUR) models have been used to model concentrations
of mainly traffic-related air pollutants (nitrogen oxides (NO<sub><i>x</i></sub>), particulate matter (PM) mass or absorbance).
Few LUR models are published of PM composition, whereas the interest
in health effects related to particle composition is increasing. The
aim of our study was to evaluate LUR models of polycyclic aromatic
hydrocarbons (PAH), hopanes/steranes, and elemental and organic carbon
(EC/OC) content of PM<sub>2.5</sub>. In 10 European study areas, PAH,
hopanes/steranes, and EC/OC concentrations were measured at 16ā40 sites per study area. LUR models for each study area were developed on the basis of annual average concentrations and predictor variables including traffic, population, industry, natural land obtained from geographic information systems. The highest median model explained variance (<i>R</i><sup>2</sup>) was found for EC ā 84%. The median <i>R</i><sup>2</sup> was 51% for OC, 67% for benzoĀ[a]Āpyrene, and 38% for sum of hopanes/steranes, with large variability between study areas. Traffic predictors were included in most models. Population and natural land were included frequently as additional predictors. The moderate to high explained variance of LUR models and the overall moderate correlation with PM<sub>2.5</sub> model predictions support the application of especially the OC and PAH models in epidemiological studies
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