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

Abstract

Abstract:Two commonly used models to assess air pollution concentration for investigating health effects of air pollution in epidemiological studies are Land Use Regression (LUR) models and Dispersion and Chemistry Transport Models (DCTM). Both modeling approaches have been applied in the Ruhr area, Germany, a location where multiple cohort studies are being conducted. Application of these different modelling approaches leads to differences in exposure estimation and interpretation due to the specific characteristics of each model. We aimed to compare both model approaches by means of their respective aims, modeling characteristics, validation, temporal and spatial resolution, and agreement of residential exposure estimation, referring to the air pollutants PM 2.5 , PM 10 , and NO 2 . Residential exposure referred to air pollution exposure at residences of participants of the Heinz Nixdorf Recall Study, located in the Ruhr area. The point-specific ESCAPE (European Study of Cohorts on Air Pollution Effects)-LUR aims to temporally estimate stable long-term exposure to local, mostly traffic-related air pollution with respect to very small-scale spatial variations (ď100 m). In contrast, the EURAD (European Air Pollution Dispersion)-CTM aims to estimate a time-varying 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 conditions, and transport. Overall agreement between EURAD-CTM and ESCAPE-LUR was weak to moderate on a residential basis. Restricting EURAD-CTM to sources of local traffic only, respective agreement was good. The possibility of combining the strengths of both applications will be the next step to enhance exposure assessment.

Methods for Assessing Long-Term Exposures to Outdoor Air Pollutants

“…Dispersion/chemical transport models (DCTMs) have been applied in epidemiological studies frequently, especially in European studies [ 9 , 55 – 58 ]. Some studies have incorporated spatially detailed modelling down to points of individual addresses [ 55 , 56 ], while other models for PM 2.5 were at a larger spatial scale of 1 km 2 or above [ 9 , 59 ]. DCTMs are deterministic models, using physical and chemical knowledge to model the dispersion and chemical transformations of emitted pollutants from sources.…”

Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia

“…Comparison of LUR vs CTM NO 2 estimates in Germany reported better R 2 when the CTM method was restricted to local traffic areas only (Hennig et al 2016). A Dutch study reported good agreement at the mid-range of concentrations estimated by LUR versus dispersion modelling and versus validation sites, but larger differences at the ends of the concentration range, suggesting the differences might be due to the coarse categorisation for the industrial land use variable used in the LUR (Beelen et al 2010).…”

“…To date we have found no other studies of NO 2 estimation that have compared agreement, using appropriate agreement statistics, between LUR models and separately developed models using satellite data or Bayesian methods, that might indicate whether this finding is generalizable to other countries or areas. In Supplementary Table S1 we summarise results from studies (Beelen et al 2010;Buteau et al 2017;de Hoogh et al 2014;Hennig et al 2016;Marshall et al 2008;Sellier et al 2014;Wang et al 2015;Wu et al 2011) that have compared LUR estimates of NO 2 with other modelled NO 2 estimates (dispersion modelled including CTMs), or with measured concentrations (nearest monitor measurement; inverse distance weighted measurement). It reports correlations for the various comparisons which range from R=0.19 to 0.89, and includes at least two previous LUR comparison studies that underscore the influence of local characteristics in model development.…”

Epidemiologische Studien mit Umweltbezug in Deutschland

“…Darüber hinaus wurden die quellenspezifischen Belastungen (z. B. Belastung durch Verkehrs-und/oder Industrieemissionen) modelliert [40].…”

“…Im Rahmen des europaweiten ESCAPE-Projektes wurden die Expositionen gegenüber Feinstäuben (PM 10 , (Durchmesser≤10µm), PM 2.5 (Durchmesser≤2,5µm), PM 2.5-10 (grobe Partikel) und PM 2.5abs (die "Absorbance" von PM 2.5 ; ein Maß für den Rußgehalt im Feinstaub) und Stickoxiden (NO 2 , NO) nach einem standardisierten Protokoll mit Landnutzungsmodellen für alle Wohnadressen der StudienteilnehmerInnen modelliert. Diese Methodik ergänzt die Modellierung durch das EURAD-CTM in der Weise, dass insbesondere die verkehrsbezogenen Feinstäube und die Belastung gegenüber Stickoxiden mit einer sehr hohen räumlichen Auflösung erfasst werden [40]. [41][42][43][44][45] sowie der psychischen und allgemeinen Gesundheit zu untersuchen [45][46][47][48].…”