Exploring the Benefits of 3D City Models in the Field of Urban Particles Distribution Modelling—A Comparison of Model Results
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We present a comparison of a particles distribution model using 3D parameters derived from a CityGML-based 3D city model with an already advanced but 2D-based Land Use Regression model. Particles, especially ultrafine particles have significant influence on the health status of the urban population. Next to emission by cars and others, its distribution is tightly coupled to the local wind field and, therefore, to urban morphology influencing this wind field. However, 3D city models, especially CityGML have been almost ignored when modelling urban particles distribution. We introduce 3D parameters derived from a CityGML-based 3D city model in an already tested Land Use Regression model and explore the benefits of 3D city models in the field of particles distribution modelling, especially, by minimizing the number of parameters entered to the model and the good results that it has shown and explore the enhancement by combining both models.
KeywordsLand use regression CityGML Ultrafine particle 3D city model Geostatistical model
1 Introduction and Problem Statement
Air quality in urban areas has a major influence on health of urban population. Health effects of ultrafine particles have been widely discussed and demonstrated in epidemiological and toxicological studies (WHO Panel 2013). The pollution effects of ultrafine particles (UFP) on the health in urban area have increased mortality and morbidity especially from cardiovascular and respiratory diseases (Brook et al. 2010). Ultrafine particles with diameter less than 0.1 microns often are the direct product of the combustion of fossil fuels by road transport (Geiser et al. 2005). In addition, Hoffmann et al. (2006) showed that long term exposure to fine particulate matter (PM2.5) may accelerate the development and progression of atherosclerosis. The UFP concentration can be estimated from measurements of the particle number size distribution. At street canyon or near-traffic sites, the number of UFP generally accounts for the majority of total particle number concentrations, i.e., greater than 80–90 % (Morawska et al. 2008; Weber et al. 2013).
Since high traffic intensity is considered as an essential source for ultrafine particles in many cities, the proximity of the residence to roads was identified as a major determinant of health effects by particulate air pollution (Jerrett 2011). Thus, accurate assessment of exposure to traffic-related air pollution is critical for both, epidemiologic studies as well as for the estimation of health impacts of current and future road networks (Gilbert et al. 2005).
Urban complexity, i.e., the road network with different traffic intensity on road sections in combination with the three-dimensional structure of buildings, which influence climate and dispersion, leads to a specific spatial distribution pattern of urban UFP concentrations. Therefore, detailed assessment of exposure by measurement and modelling of fine dust distribution is necessary in the field of urban planning, traffic management and city system modelling.
Urban morphology can be analysed concerning geometrical properties of street canyons (Vardoulakis et al. 2002), building density, alignment of streets towards the prevailing wind direction, and characteristics of crossing sections. However, the latter is only little noticed when considering the redistribution of air and exchange of fine particles between different street systems (Brand and Löwner 2014).
Land Use Regression (LUR) models were developed as an alternative to dispersion models. As a geostatistical method, they can be applied for predicting local variation in traffic pollution and to obtain accurate, small scale air pollutant concentrations without a detailed pollutant emission inventory (Briggs et al. 2000; Brauer et al. 2003). LUR predicts pollution concentrations at a given site based on surrounding spatial characteristics and other identified auxiliary data which may act as a pollution predictor (Jerrett 2011).
LUR models are multiple linear regressions that assume independent residuals and use GIS-based covariates to predict concentrations at locations without measurements (Mercer et al. 2011). They have been widely applied in cities of North America and Europe (Saraswat et al. 2013). Henderson et al. (2007) developed LUR models for UFP in the city of Amsterdam including traffic intensity of nearest road, inverse of distance squared, population density and land use. Arian et al. (2007) included wind flow effects in LUR models for predicting NO2 concentrations for health exposure studies in the greater Toronto-Hamilton region.
In comparison with meteorological data the main advantage of LUR is the incorporation of extensive data that can be measured with small errors (Jerrett et al. 2006). However, it neglects the process-oriented view of fine dust production, dilution or dispersion and accumulation or deposition. Only statistical measures are used to develop the prediction model without asking for causal linkage.
Recently, only few studies included 3D data of buildings and street canyons into LUR modelling. Tang et al. (2013) has used building height and geometry to enhance the representation of land use and the dispersion field in LUR. Eeftens et al. (2013) described an approach to calculate four indicators (the maximum aspect ratio, the mean buildings angle, median building angle, which is the angle between the horizontal street level and the line of sight to the top of surrounding buildings, and SkyView Factor) of urban canyon effect using 3D building data and evaluated the improvement on NO2 and NOx LUR models in the Netherlands. Brauer et al. (2003) added 3D parameters (sampling height, street type, and canyon) as additional variables into the regression models to determine the extent to which prediction could be improved.
Today, 3D city models are increasingly available due to the standardization processes like CityGML (Gröger et al. 2012) for semantically enriched virtual 3D city models. An advantage of CityGML is its scalability to the requirements of the user and the data available (Löwner et al. 2013b). Today, they already support a lots of application fields like urban planning and hazard assessment (Löwner et al. 2013a). If only geometrically models are needed, KML models may be sufficient.
Here, we present a comparison of a UFP model using 3D parameters derived from a CityGML-based 3D city model with an already advanced but 2D-based Land Use Regression model. A land use regression based geostatistical model (LUR) is developed to estimate UFP concentrations at given locations using 2D and 3D parameters. Conceptually, both models were built to depict particle concentrations in urban microenvironments as the result of a ‘process chain’ that can be subdivided into three parts: emission, dilution/dispersion and deposition. All these processes take place under certain boundary conditions which are variable due to anthropogenic behaviour, weather conditions and the surrounding built environment. Therefore, the models are implemented with respective input parameters that are related to the three processes of production, dilution and accumulation. Incorporating 3D attributes to further develop the aforementioned LUR model for the prediction of UFP distribution within a city quarter leads to a more precise UFP estimation.
1.1 Study Area and Measurements
Monitoring was conducted with a mobile platform consisting of a bicycle connected to a 2-wheel trailer which contained the instruments in a weather protected box. Particle number size distributions were measured with a mobile SMPS the NanoScan scanning mobility particle sizer (TSI 3910, TSI Inc.) which offers a size distribution in the range 11 < Dp <365 nm with a 1 min time resolution. The size fractioned ultrafine number concentration was calculated from the number size distribution as measured by the NanoScan device. Total number concentrations were measured with an Electrical Diffusion Size Classifier (DiSCmini, MatterAerosol). Black carbon concentrations were measured with an aethalometer AE51 (MageeScientific). To ensure a high accuracy and data quality, all instruments have been synchronized before each measurement campaign.
Furthermore, each campaign was limited to 2 h to reduce a trend of particle concentration due to weather changes over the diurnal course (cf. Ruths et al. 2014 for details).
2 Modelling Fine Dust Distribution with Land Use Regression (LUR) Models
2.1 Land Use Regression Using 2D Parameters
List of used spatial parameters and their source next to their extraction method in ArcGIS 10.0
Buffer radii (m)
Length of different types of streets
(residential, primary, tertiary, secondary
50,100 and 200
Using model builder: generate buffer, intersect with the street, and summarize according the type of the street
Width of the streets area of the buildings
−100, 200 and 300
Using model builder: generate buffer, intersect with the buildings, and summarize according the type of the building
Position of the sites (X, Y)
Recreational are (Residential_use500
Green_village500, Commercial500, Construction50, Meadow500, Cemetery500)
Using model builder: generate buffer, intersect with the buildings, and summarize according the type of the building
Emission category: Spatial parameters belonging to this group can be considered to be characteristic for the emission rate of UFP, i.e. the length and type of a street, reflecting traffic intensity. This category is not significantly affected by seasonal changes;
Dilution category: Spatial parameters that define proxies for urban ventilation capability such as the width of streets or the distribution of built up areas; and
Deposition category: Spatial parameters that define potential deposition surfaces such as different types of recreational areas with higher fractions of vegetated surfaces.
Zones of influence, i.e. buffer radii, were specified to reflect the scale of influence appropriate for each process related variable. However, zones representing the influence of a street should be chosen narrow because traffic produced emissions are quite local (buffer radii should be small). In contrast, effects of land use stretch out over a larger area and are more complex as dilution and emission. Thus, larger zones of influence are defined.
Buffers of radii of 50, 100, 200, 300 and 500 m (cf. Table 1) were generated for each measured site using ArcGIS 10.0. Using these buffer radii, all available data (streets, buildings and recreational area) were subsequently clipped to derive the street length, buildings area, and recreational area. Buffer radii of 50, 100 and 200 m were used to derive the length of streets, whereas buffers radii of 100, 200 and 300 m were used to derive the building area. Buffer radii of 500 m were used to derive the area of recreational area.
The algorithm as proposed by Henderson et al. (2007) was used and adopted to build the LUR model for UFP according to the following procedure: all variables were ranked by the absolute strength of their correlation with the measured pollutant. The highest-ranking variable in each sub-category was identified. Afterwards the variables in each sub-category that are correlated (i.e., Pearson’s r ≥ 0.6) with the most highly ranked variables were eliminated to avoid autocorrelation. All remaining variables were implemented into robust linear regression models and variables that were not significant at a 90 % confidence level or that had a coefficient with a counterintuitive sign were rejected. Finally, the last two steps were repeated until convergence.
By following the aforementioned procedures, the most significant variables were selected and implemented in the multi regression model using the open source software R in order to build two models for UFP reflecting the summer and winter campaign, respectively.
2.2 Land Use Regression Using 3D Parameters
3.1 Evaluation of 2D-LUR and 3D-LUR Models
Significant 2D variables of each process category (cf. Table 1) were identified and subjected to the multi linear regression process. Then, the best model for the summer and the winter campaign was built.
3D-LUR models used two 3D parameters in each model of summer and winter campaign (volumetric density and width in summer model and volumetric density and the ratio of height and width in winter).
2D-LUR models explained 74 and 85 % of the variance of UFP with root mean square errors (RMSE) of 668 and 1,639 pt. cm−3 in summer and winter, respectively.
3D-LUR models explained 41 and 54 % of the variance of UFP with root mean square errors (RMSE) of 868 and 2,541 pt. cm−3 in summer and winter, respectively.
A standardized deviation was defined by calculating the ratio of the RMSE to the average measured UFP concentrations across the study area during the summer and winter campaign. The deviation for 2D-LUR accounts to 8 % for summer and 13 % for winter concentrations, 11 % for summer and 19 % for winter concentrations for 3D-LUR, respectively.
The standard residuals of calculated UFP concentration do not exhibit any trend for the summer and winter model (data not shown here).
Leave-one-out cross validation was applied to validate the summer and winter model estimates. The model for each season was rerun on N − 1 data points (N = 27) and then used to predict the excluded measurement. Cross-validation standard error of estimate was calculated for both models. The deviation for 2D-LUR accounts to 9 % for summer and 14 % for winter and is very close to the original model results stating satisfactory model performance.
2D-LUR showed better results than 3D-LUR in BY, RO, and PA for the winter campaign and in BY, RO, and RE for the summer campaign. Both models showed close results in SL and RE for the winter campaign and in SL and SM for the summer campaign.
In spite of the low variance of UFP explained by the 3D-LUR models, the models showed good results comparison with 2D-LUR especially in summer with deviation of 27 % of RMSE between both models. Rather, 3D-LUR showed higher deviation about 38 % of RMSE in winter between both models.
3.2 Model Enhancement
The results indicate that 3D properties have significant impact on the spatial distribution pattern of urban UFP concentrations. We argue that more 3D parameters derived from a CityGML-based 3D city model should enhance the model represented UFP concentration and minimize the required number of 2D parameters needed to predict pollution concentrations quite well.
A new modelling approach of using 3D city models available in CityGML has been discussed in this paper to model the UFP distribution that incorporates processes of emission, dilution and deposition of UFP. First results indicate that the 3D parameter enriched model performs quite well in comparison to the 2D model. Although, exhibiting slightly greater deviations, the 3D model needs less parameters than the 2D model. Since ultrafine particle concentration is the result of a complex physical process chain, describing it using simple models with less parameters has many advantages. Fast calculation is a precondition for fast real time calculation and forecast systems.
Entering the significant 3D parameters into the 2D-LUR models has shown a slight enhancement in the final models. In our point of view, this account for the thesis that the local wind field and, therefore, the 3D built environment has a significant influence on the spatial distribution pattern of urban UFP concentrations. However, the research area and the measurement layout described in Sect. “ Introduction of Chap. Improving the Consistency of Multi-LOD CityGML Datasets by Removing Redundancy” were initially designed to develop 2D prediction models. That means that it has great variability of properties needed to test significant 2D parameters. Further studies will designate more suitable study areas concerning 3D properties discussed here and, therefore, will yield better results when defining correlations. The research area should have variety concerning 3D properties such as building porosity as an indicator of urban ventilation capacity.
We have shown that combining parameters describing the process of emission and deposition with 3D parameters describing the dilution process and with meteorological parameters can enhance the model. However, further research will focus not only on 3D geometric attributes but on semantic attributes represented in CityGML. This especially accounts for the possibility to incorporate surface properties and window surface proportions of buildings into the model. Since these properties act like a local and temporal sink of small particles, they are of great influence of the fine particle distribution but cannot be stored in a 2D data base.
Semantic attributes available in CityGML may even support the estimation of health situation because of information about the building’s usage and the localisation of its entrance that is more accurate than its address. However, addresses are needed (in addition to pure 3D geometry) because of linking modelling results to epidemiological studies by calculating the distances between residences and major roads and assigning annual fine particulate matter concentrations, derived from the models to each address. Semantics, therefor, may contribute to urban planning.
However, CityGML-based 3D city models still act as incomplete input models for the analysis of particle concentrations. Since no dynamic attributes can be represented in the CityGML2.0 standard, traffic intensities have to be added from other sources. This accounts for the representation of results, as well. Except modelling a specific Application Domain Extension (ADE), there is no way to store different raster based value data back to a CityGML model. Even more seriously, when modelling in different heights using voxel representation model output does not fit into the concept of boundary representation (BRep). The ongoing discussion within the Open Geospatial Consortium clearly should issue the weaving of field based and BRep representation amongst dynamic representation of complex attributes.
The authors would like to thank Matthias Ruths who conducted the mobile measurements of particle and pollutant concentrations.
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