Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Nov 1;438:389-403.
doi: 10.1016/j.scitotenv.2012.08.049. Epub 2012 Sep 28.

Mobile Measurements and Regression Modeling of the Spatial Particulate Matter Variability in an Urban Area

Affiliations

Mobile Measurements and Regression Modeling of the Spatial Particulate Matter Variability in an Urban Area

Hendrik Merbitz et al. Sci Total Environ. .

Abstract

During 5 different periods between summer 2009 and spring 2011, mobile measurements were carried out in the city of Aachen, Germany, in order to capture the spatial variability of particulate matter concentrations in urban and suburban environments. Results show a large spatial variability on a scale of tens of meters, mainly depending on traffic density and building structure. Spatial coefficients of variation exhibit larger spatial variability for PM(10) than for PM(2.5) and larger variability in traffic influenced inner city environments than in suburban areas. Based on the results of an extensive campaign, a regression model is developed for the prediction of PM(10) and PM(2.5) distributions over the city area. The three predictors for the regression model are an exponential PM concentration profile simulated on the basis of PM(10) and PM(2.5) traffic emissions, building density and green area density within radii of 50 m and 100 m. The model shows good agreement between measured and modeled PM levels during the campaign used for the model training with R(2) values of 0.79 and 0.65, RMSE of 1.9 μg/m(3) and 1 μg/m(3) for PM(10) and PM(2.5), respectively. The model is further validated using data from the remaining measurement campaigns and modeling of PM levels at monitoring sites that were not used for the training of the regression model. For the total number of 59 monitoring sites, the regression model shows R(2) values of 0.77 (PM(10)) and 0.61 (PM(2.5)) with RMSE of 2.3 μg/m(3) and 1.2 μg/m(3). The modeled concentrations are generally in better accordance with measured concentrations for PM(10) than for PM(2.5) concentrations. We attribute this to higher spatial homogeneity of PM(2.5) levels compared to coarse particles. Inner city PM levels at traffic influenced sites are better reproduced by the model than suburban concentrations which exhibit the smallest spatial variability.

Similar articles

See all similar articles

Cited by 2 articles

Publication types

LinkOut - more resources

Feedback