Evaluation of a Gaussian and a Lagrangian model against a roadside data set, with emphasis on low wind speed conditions

Oettl, Dietmar; Kukkonen, Jaakko; Almbauer, Raimund; Sturm, Peter; Pohjola, Mia; Härkönen, Jari

doi:10.1016/s1352-2310(00)00492-1

Cited by 56 publications

(25 citation statements)

References 28 publications

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“…Oftedal et al applied EPISODE to assess the outdoor air pollution personal exposure for children [117]. Oettl et al used the Gaussian-based model CAR-FMI and the Lagrangian dispersion model (GRAL, Graz Lagrangian Model) together to estimate the pollutant concentrations near a major road on low wind speed conditions [118].…”

Section: Dispersion Modelsmentioning

confidence: 99%

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

Xie

Šemanjski

Gautama

et al. 2017

IJGI

147

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Abstract:The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens. To reduce the negative health impact, it is of great importance to measure the air pollution at high spatial resolution in a timely manner. Traditionally, air pollution is measured using dedicated instruments at fixed monitoring stations, which are placed sparsely in urban areas. With the development of low-cost micro-scale sensing technology in the last decade, portable sensing devices installed on mobile campaigns have been increasingly used for air pollution monitoring, especially for traffic-related pollution monitoring. In the past, some reviews have been done about air pollution exposure models using monitoring data obtained from fixed stations, but no review about mobile sensing for air pollution has been undertaken. This article is a comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods. Unlike the existing reviews on air pollution assessment, this paper not only introduces the models that researchers applied on the data collected from stationary stations, but also presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensing data.

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Section: Dispersion Modelsmentioning

confidence: 99%

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

Xie

Šemanjski

Gautama

et al. 2017

IJGI

147

View full text Add to dashboard Cite

show abstract

“…Karppinen et al, 2000b;Kousa et al, 2001;Hellén et al, 2005) and for PM 2.5 (Kauhaniemi et al, 2008;Sokhi et al, 2008;Singh et al, 2014). The performance of the CAR-FMI model has also been evaluated against the results of a field measurement campaign and other roadside dispersion models Öttl et al, 2001;Levitin et al, 2005). The UDM-FMI has been evaluated against the measured data of urban measurement networks in Helsinki Metropolitan Area (Karppinen et al, 2000b;Kousa et al, 2001) and the tracer experiments of Kincaid, Copenhagen and Lilleström.…”

Section: Dispersion Modellingmentioning

confidence: 99%

Refinement of a model for evaluating the population exposure in an urban area

et al. 2014

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Abstract. A mathematical model is presented for the determination of human exposure to ambient air pollution in an urban area; the model is a refined version of a previously developed mathematical model EXPAND (EXposure model for Particulate matter And Nitrogen oxiDes). The model combines predicted concentrations, information on people's activities and location of the population to evaluate the spatial and temporal variation of average exposure of the urban population to ambient air pollution in different microenvironments. The revisions of the modelling system containing the EXPAND model include improvements of the associated urban emission and dispersion modelling system, an improved treatment of the time use of population, and better treatment for the infiltration coefficients from outdoor to indoor air. The revised model version can also be used for estimating intake fractions for various pollutants, source categories and population subgroups. We present numerical results on annual spatial concentration, time activity and population exposures to PM 2.5 in the Helsinki Metropolitan Area and Helsinki for 2008 and 2009, respectively. Approximately 60 % of the total exposure occurred at home, 17 % at work, 4 % in traffic and 19 % in other microenvironments in the Helsinki Metropolitan Area. The population exposure originating from the long-range transported background concentrations was responsible for a major fraction, 86 %, of the total exposure in Helsinki. The largest local contributors were vehicular emissions (12 %) and shipping (2 %).

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“…For model evaluation, air quality data have to be combined with simultaneous measurements of traffic flow and meteorological parameters as well as information about local topography and buildings (Luhar and Patil, 1989;Benson, 1992;Kukkonen et al, 2001;Oettl et al, 2001;Tiitta et al, 2002;Levitin et al, 2005). Evaluation of roadside dispersion models is seriously hindered by the scarcity of appropriate good-quality experimental datasets (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and modeling of the size fractionated aerosol particle number concentration measurements nearby a major road in Helsinki – Part II: Aerosol measurements within the SAPPHIRE project

et al. 2007

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Abstract. This study presents an evaluation and modeling exercise of the size fractionated aerosol particle number concentrations measured nearby a major road in Helsinki during 23 August-19 September 2003 and 14 January-11 February 2004. The available information also included electronic traffic counts, on-site meteorological measurements, and urban background particle number size distribution measurement. The ultrafine particle (UFP, diameter<100 nm) number concentrations at the roadside site were approximately an order of magnitude higher than those at the urban background site during daytime and downwind conditions. Both the modal structure analysis of the particle number size distributions and the statistical correlation between the traffic density and the UFP number concentrations indicate that the UFP were evidently from traffic related emissions. The modeling exercise included the evolution of the particle number size distribution nearby the road during downwind conditions. The model simulation results revealed that the evaluation of the emission factors of aerosol particles might not be valid for the same site during different time.

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Evaluation of a Gaussian and a Lagrangian model against a roadside data set, with emphasis on low wind speed conditions

Cited by 56 publications

References 28 publications

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

Refinement of a model for evaluating the population exposure in an urban area

Evaluation and modeling of the size fractionated aerosol particle number concentration measurements nearby a major road in Helsinki – Part II: Aerosol measurements within the SAPPHIRE project

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