Simulating the impact of urban sprawl on air quality and population exposure in the German Ruhr area. Part I: Reproducing the base state

Ridder, Koen De; Lefebre, Filip; Adriaensen, Stefan; Arnold, Ute; Beckroege, Wolfgang; Bronner, Christine; Damsgaard, Ole; Dostál, Ivo; Dufek, J; Hirsch, Jacky; Panis, Luc Int; Kotek, Zdenek; Ramadier, Thierry; Thierry, Annette; Vermoote, Stijn; Wania, Annett; Weber, Christiane

doi:10.1016/j.atmosenv.2008.06.045

Cited by 51 publications

(26 citation statements)

References 26 publications

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“…Morton et al (2007) have also developed a land use-transportation-emissions model for Charlotte, North Carolina. What has arisen from the existing literature review is that relatively few studies have explored the connection to air quality (Marquez and Smith, 1999;Marshall et al, 2005;Lam and Niemeier, 2005;Borrego et al, 2006;EC, 2007;De Ridder et al, 2008a). Most of the work was performed for imaginary cities (Borrego et al, 2006), existing highly urbanized metropolitan regions (Civerolo et al, 2007;De Ridder et al, 2008a, b) or at a local scale (Borrego et al, 2003), but none for medium sized cities.…”

Section: Introductionmentioning

confidence: 99%

Impact of land use on urban mobility patterns, emissions and air quality in a Portuguese medium-sized city

Bandeira

Coelho

Sá

et al. 2011

Science of The Total Environment

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The main objective of this work was to evaluate the impact of urban development trends in mobility patterns of a medium sized Portuguese city and air quality consequences, using a sequential modeling process, comprising i) land use and transportation, TRANUS model; ii) road traffic air pollutants emissions, TREM model and; iii) air quality, TAPM model. This integrated methodology was applied to a medium sized Portuguese city. In order to evaluate the implementation of the methodology, a preliminary study was performed, which consisted on the comparison of modeled mobility patterns and CO and PM 10 concentrations with measured data used in the definition of the current scenario. The comparison between modeled and monitored mobility patterns at the morning peak hour for a weekday showed an RMSE of 31%. Regarding CO concentrations, an underestimation of the modeled results was observed. Nevertheless, the modeled PM 10 concentrations were consistent with the monitored data. Overall, the results showed a reasonable consistency of the modeled data, which allowed the use of the integrated modeling system for the study scenarios. The future scenarios consisted on the definition of different mobility patterns and vehicle technology characteristics, according to two main developing trends: (1) "car pooling" scenario, which imposes a mean occupancy rate of 3 passengers by vehicle and (2) the "Euro 6" scenario, which establishes that all vehicles accomplish at least the Euro 6 standard technology. Reductions of 54% and 83% for CO, 44% and 95% for PM 10 , 44% and 87% for VOC and 44% and 79% for NO x emissions were observed in scenarios 1 and 2, respectively. Concerning air quality, a reduction of about 100 μg m −3 of CO annual average concentration was observed in both scenarios. The results of PM 10 annual concentrations showed a reduction of 1.35 μg m −3 and 2.7 μg m −3 for scenarios 1 and 2 respectively.

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Section: Introductionmentioning

confidence: 99%

Impact of land use on urban mobility patterns, emissions and air quality in a Portuguese medium-sized city

Bandeira

Coelho

Sá

et al. 2011

Science of The Total Environment

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“…Lauwaet et al [39] evaluated the simulated UHI effect for the city of Brussels (Belgium) during the summer of 2008 and found that the model was able to reproduce the observed differences in time series of 2-m air temperatures from 3 different stations, with very small positive biases, root mean square errors around 1 °C and correlation coefficients up to 0.7. Furthermore, the land surface temperatures from the UrbClim land surface scheme have already been validated in the past with satellite data for the city of Paris and the German Ruhr area, yielding good comparisons between simulated and observed land surface temperatures from thermal infrared satellite imagery [16,22,23].…”

Section: Model Evaluationmentioning

confidence: 99%

“…Many models, also recent ones, rely on fairly ancient parameterizations, e.g., by Jürges [20] (used in, e.g., [17]) or Rowley et al [21] (used in, e.g., [18,19]). Whereas these wall transfer coefficients were established by means of scale experiments, the Stanton-based heat transfer coefficients in UrbClim are obtained from a series of real-world experiments that we conducted on actual cities, using remotely-sensed surface thermal infrared temperature [16,22,23]. While doing so entails disregarding certain physical processes occurring within the urban canopy, our approach is based on observations from actual urban areas, rather than it having to rely on scale model experiments.…”

Section: The Urbclim Modelmentioning

confidence: 99%

Detailed Urban Heat Island Projections for Cities Worldwide: Dynamical Downscaling CMIP5 Global Climate Models

Lauwaet

Hooyberghs

Maiheu

et al. 2015

Climate

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Abstract:A new dynamical downscaling methodology to analyze the impact of global climate change on the local climate of cities worldwide is presented. The urban boundary layer climate model UrbClim is coupled to 11 global climate models contained in the Coupled Model Intercomparison Project 5 archive, conducting 20-year simulations for present (1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) and future (2081-2100) climate conditions, considering the Representative Concentration Pathway 8.5 climate scenario. The evolution of the urban heat island of eight different cities, located on three continents, is quantified and assessed, with an unprecedented horizontal resolution of a few hundred meters. For all cities, urban and rural air temperatures are found to increase strongly, up to 7 °C. However, the urban heat island intensity in most cases increases only slightly, often even below the range of uncertainty. A potential explanation, focusing on the role of increased incoming longwave radiation, is put forth. Finally, an alternative method for generating urban climate projections is proposed, combining the ensemble temperature change statistics and the results of the present-day urban climate.

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“…In order to cope with these various elements MS have in the last decade developed and applied a wide range of different modelling methods to assess the effects of local and regional emission abatement policy options on air quality and human health (e.g. Cuvelier et al 2007;Thunis et al 2007;De Ridder et al 2008;Carnevale et al 2011;Lefebvre et al 2011;Borrego et al 2012; MediavillaSahagun and ApSimon 2013).…”

mentioning

confidence: 99%

Strengths and Weaknesses of the Current EU Situation

Belis

Blond

Bouland

et al. 2016

Air Quality Integrated Assessment

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Simulating the impact of urban sprawl on air quality and population exposure in the German Ruhr area. Part I: Reproducing the base state

Cited by 51 publications

References 26 publications

Impact of land use on urban mobility patterns, emissions and air quality in a Portuguese medium-sized city

Impact of land use on urban mobility patterns, emissions and air quality in a Portuguese medium-sized city

Detailed Urban Heat Island Projections for Cities Worldwide: Dynamical Downscaling CMIP5 Global Climate Models

Strengths and Weaknesses of the Current EU Situation

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