Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies

Abstract

LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.

Comparison of Land-Use Regression Modeling with Dispersion and Chemistry Transport Modeling to Assign Air Pollution Concentrations within the Ruhr Area

“…While the point-specific ESCAPE-LUR primarily aims to estimate temporally stable and spatial variable long-term exposure to locally-emitted (mostly traffic-related) air pollution with a very high spatial resolution, the EURAD-CTM aims to estimate a spatio-temporal average air pollutant concentration in a small area (i.e., 1 km 2 ), taking into account a range of major sources, e.g., traffic, industry, meteorological condition, and transport. While the observed weak to moderate overall agreement between the ESCAPE-LUR and the EURAD-CTM supports earlier findings [12], our analysis showed that the agreement between the two models improved considerably after restricting the EURAD-CTM to local traffic only. This finding was further supported by results comparing 14-day mean concentrations estimated by EURAD-CTM and measured at purpose-specific ESCAPE monitoring sites, yielding the highest correlations for traffic-specific EURAD-CTM estimates and measurements at traffic sites.…”

“…This rather weak correlation has been reported earlier [12] and is not unexpected due to the different spatial resolution but also due to the different spatial distribution of PM concentrations for the two modelling approaches within the study area (Figure 2 and Figure S2): while we observed a west-to-east gradient for EURAD-CTM with higher concentrations in the west, estimated concentrations of ESCAPE-LUR revealed only a slight west-to-east gradient, which was prominently overlapped by an additional decreasing north-to-south and local hot spots, e.g., in Essen at a motorway intersection. In our study area the decreasing west-to-east gradient mirrors the distribution of industrial locations, e.g., metallurgical-industry and Europe's largest inland harbour in Duisburg, located to the west of the study area (Figure 1), as well as transported emissions from other countries in the west of study area, e.g., the Netherlands or Great Britain.…”

“…Comparisons across 4-13 cohorts, including the Heinz Nixdorf Recall (Risk Factors, Evaluation of Coronary calcium and Lifestyle) (HNR) study, located in the Ruhr area in Germany, yielded moderate to good correlations between LUR and DM (or DCTM) for NO 2 (0.39-0.90) and for PM 10 and PM 2.5 (0.23-0.81). However, single correlation coefficients for the HNR study were below 0.4 for all three pollutants [12], raising the question of comparability of the two different exposure modelling approaches. So far, most studies on the comparison of different modeling strategies focused on the residential agreement of estimated exposure concentrations, disregarding the potential reasons for the disagreement between different modelling approaches, as well as respective strengths and limitations.…”

“…While some studies suggested that LUR models explained small-scale variations in air pollution concentrations as well or even better than various dispersion models [8][9][10], Beelen et al [11] showed that the dispersion models performed better than LUR models regarding monitored and modeled concentrations on several validation sites. Most recently, de Hoogh et al [12] investigated agreement between LUR and DM modeling approaches aiming to estimate residential exposure to NO 2 and particulate matter (PM) with an aerodynamic diameter ď10 µm and ď2.5 µm (PM 10 , PM 2.5 ) within the European Study of Cohorts for Air Pollution Effects (ESCAPE). Comparisons across 4-13 cohorts, including the Heinz Nixdorf Recall (Risk Factors, Evaluation of Coronary calcium and Lifestyle) (HNR) study, located in the Ruhr area in Germany, yielded moderate to good correlations between LUR and DM (or DCTM) for NO 2 (0.39-0.90) and for PM 10 and PM 2.5 (0.23-0.81).…”

Influence of Road Patterns on PM2.5 Concentrations and the Available Solutions: The Case of Beijing City, China

“…"Dispersion models use information on emissions, source characteristics, chemical and physical properties of the pollutants, topography, and meteorology to model the transport and transformation of gaseous or particulate pollutants through the atmosphere to predict, e.g., ground level concentrations" [34]. Also, it is expensive and difficult to obtain the high-precision data of pollutant sources and meteorology, which then has to be initialized and parameterized.…”