A review of dispersion modelling and its application to the dispersion of particles: An overview of different dispersion models available

Holmes, Nicholas S.; Morawska, Lidia

doi:10.1016/j.atmosenv.2006.06.003

Cited by 582 publications

(312 citation statements)

References 114 publications

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“…Yet, PM 2.5 pollution is understood to vary over space and time (Hoek et al 2002;Zhang et al 2013;Zhao et al 2009). To better measure the intra-urban heterogeneity in PM 2.5 concentrations and provide evidence for epidemiological studies, many approaches have been put forward such as exposure indicator variables, interpolation methods, dispersion models, and land use regression (LUR) models (Briggs 2005;Hoek et al 2008;Holmes and Morawska 2006;Jerrett et al 2005a;Ryan and LeMasters 2007) .…”

Section: Responsible Editor: Gerhard Lammelmentioning

confidence: 99%

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

133

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Fine particulate matter (PM 2.5 ) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM 2.5 by building separate LUR models in Beijing. Hourly routine PM 2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m 3 (SD=13.7). PM 2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R 2 of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m 3 . This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM 2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.

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Section: Responsible Editor: Gerhard Lammelmentioning

confidence: 99%

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

133

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“…As a drawback, these models need detailed information which is not always available. Holmes and Morawska (2006) reviewed different particle dispersion models and concluded that many factors influence the concentration of UFP, notably fluctuations in the wind flow and emissions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of ultrafine particle number concentrations in urban environments by means of Gaussian process regression based on measurements of oxides of nitrogen

Reggente

Peters

Theunis

et al. 2014

Environmental Modelling & Software

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“…The primary purpose of a dispersion model is to calculate the mean concentration of emitted material at a given sensor location. A plethora of dispersion models are in use today [13] to account for specific weather conditions, terrain, source height, etc. In this paper, we adopt a two-dimensional dispersion model of "particle encounters" in a turbulent flow, described in [14].…”

Section: Dispersion Modelmentioning

confidence: 99%

Localisation of a source of hazardous substance dispersion using binary measurements

Ristić

Gunatilaka

Gailis

2016

Atmospheric Environment

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Using the measurements collected at a number of known locations by a moving binary sensor, characterised by an unknown threshold, the problem is to estimate the parameters of a biochemical source, continuously releasing material into the atmosphere. The solution is formulated in the Bayesian framework using a dispersion model of Poisson distributed particle encounters in a turbulent flow. The method is implemented using the importance sampling technique and successfully validated with three experimental datasets under different wind conditions.

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A review of dispersion modelling and its application to the dispersion of particles: An overview of different dispersion models available

Cited by 582 publications

References 114 publications

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Prediction of ultrafine particle number concentrations in urban environments by means of Gaussian process regression based on measurements of oxides of nitrogen

Localisation of a source of hazardous substance dispersion using binary measurements

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