Systematic Evaluation of Land Use Regression Models for NO₂

Land use regression (LUR) models have become popular to explain the spatial variation of air pollution concentrations. Independent evaluation is important. We developed LUR models for nitrogen dioxide (NO₂) using measurements conducted at 144 sampling sites in The Netherlands. Sites were randomly divided into training data sets with a size of 24, 36, 48, 72, 96, 108, and 120 sites. LUR models were evaluated using (1) internal “leave-one-out-cross-validation (LOOCV)” within the training data sets and (2) external “hold-out” validation (HV) against independent test data sets. In addition, we calculated Mean Square Error based validation R²s. The mean adjusted model and LOOCV R² slightly decreased from 0.87 to 0.82 and 0.83 to 0.79, respectively, with an increasing number of training sites. In contrast, the mean HV R² was lowest (0.60) with the smallest training sets and increased to 0.74 with the largest training sets. Predicted concentrations were more accurate in sites with out of range values for prediction variables after changing these values to the minimum or maximum of the range observed in the corresponding training data set. LUR models for NO₂ perform less well, when evaluated against independent measurements, when they are based on relatively small training sets. In our specific application, models based on as few as 24 training sites, however, achieved acceptable hold out validation R²s of, on average, 0.60

Agreement of Land Use Regression Models with Personal Exposure Measurements of Particulate Matter and Nitrogen Oxides Air Pollution

Land use regression (LUR) models are often used to predict long-term average concentrations of air pollutants. Little is known how well LUR models predict personal exposure. In this study, the agreement of LUR models with measured personal exposure was assessed. The measured components were particulate matter with a diameter smaller than 2.5 μm (PM_2.5), soot (reflectance of PM_2.5), nitrogen oxides (NO_<i>x</i>), and nitrogen dioxide (NO₂). In Helsinki, Utrecht, and Barcelona, 15 volunteers (from semiurban, urban background, and traffic sites) followed prescribed time activity patterns. Per participant, six 96 h outdoor, indoor, and personal measurements spread over three seasons were conducted. Soot LUR models were significantly correlated with measured average outdoor and personal soot concentrations. Soot LUR models explained 39%, 44%, and 20% of personal exposure variability (<i>R</i>²) in Helsinki, Utrecht, and Barcelona. NO₂ LUR models significantly predicted outdoor concentrations and personal exposure in Utrecht and Helsinki, whereas NO_<i>x</i> and PM_2.5 LUR models did not predict personal exposure. PM_2.5, NO₂, and NO_<i>x</i> models were correlated with personal soot, the component least affected by indoor sources. LUR modeled and measured outdoor, indoor, and personal concentrations were highly correlated for all pollutants when data from the three cities were combined. This study supports the use of intraurban LUR models for especially soot in air pollution epidemiology

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_<i>x</i>), 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_2.5. In 10 European study areas, PAH, hopanes/steranes, and EC/OC concentrations were measured at 16–40 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>²) was found for EC – 84%. The median <i>R</i>² was 51% for OC, 67% for benzo­[a]­pyrene, 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_2.5 model predictions support the application of especially the OC and PAH models in epidemiological studies

Evaluation of Land Use Regression Models for NO₂ 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 (NO₂₎ and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO₂. LUR models have been developed for NO₂, PM_2.5 absorbance, and copper (Cu) in PM₁₀ based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and “hold-out evaluation (HEV)” using the correlation of predicted NO₂ or PM concentrations with measured NO₂ concentrations at the 20 additional NO₂ sites in each area. For NO₂, PM_2.5 absorbance and PM₁₀ Cu, the median LOOCV <i>R</i>²s were 0.83, 0.81, and 0.76 whereas the median HEV <i>R</i>² were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV <i>R</i>² and HEV <i>R</i>² for PM_2.5 absorbance and PM₁₀ 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>²s. 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_2.5, PM_2.5 Absorbance, PM₁₀ and PM_coarse 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_2.5, PM_2.5 absorbance, PM₁₀, and PM_coarse 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>²) was 71% for PM_2.5 (range across study areas 35–94%). Model <i>R</i>² was higher for PM_2.5 absorbance (median 89%, range 56–97%) and lower for PM_coarse (median 68%, range 32– 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower <i>R</i>² was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation <i>R</i>² results were on average 8–11% lower than model <i>R</i>². 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 (NO₂), nitrogen oxides (NO_<i>x</i>) 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 PM₁₀ and PM_2.5 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 (PM₁₀ and PM_2.5) explaining on average between 67 and 79% of the concentration variance (<i>R</i>²) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM₁₀ and PM_2.5 fractions and Si, Ni, and K in the PM₁₀ fraction performed moderately with <i>R</i>² ranging from 50 to 61%. Si, NI, and K models for PM_2.5 performed poorest with <i>R</i>² under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE