Dispersion modelling of air pollution caused by road traffic using a Markov Chain-Monte Carlo model

Abstract

Although the strict legislation regarding vehicle emissions in Europe (EURO 4, EURO 5) will lead to a remarkable reduction of emissions in the near future, traffic related air pollution still can be problematic due to a large increase of traffic in certain areas. Many dispersion models for line-sources have been developed to assess the impact of traffic on the air pollution levels near roads, which are in most cases based on the Gaussian equation. Previous studies gave evidence, that such kind of models tend to overestimate concentrations in low wind speed conditions or when the wind direction is almost parallel to the street orientation. This is of particular interest, since such conditions lead generally to the highest observed concentrations in the vicinity of streets. As many air quality directives impose limits on high percentiles of concentrations, it is important to have good estimates of these quantities in environmental assessment studies. The objective of this study is to evaluate a methodology for the computation of especially those high percentiles required by e.g. the EU daughter directive 99/30/EC (for instance the 99.8 percentile for NO 2 ). The model used in this investigation is a Markov Chain -Monte Carlo model to predict pollutant concentrations, which performs well in low wind conditions as is shown here. While usual Lagrangian models use deterministic time steps for the calculation of the turbulent velocities, the model presented here, uses random time steps from a Monte Carlo simulation and a Markov Chain simulation for the sequence of the turbulent velocities. This results in a physically better approach when modelling the dispersion in low wind speed conditions. When Lagrangian dispersion models are used for regulatory purposes, a meteorological pre-processor is necessary to obtain required input quantities like MoninObukhov length and friction velocity from routinely observed data. The model and the meteorological pre-processor applied here, were tested against field data taken near a major motorway south of Vienna. The methodology used is based on input parameters, which are also available in usual environmental assess-

Point source identification using a simple permutation test: a case study of elevated PCDD/F levels in ambient air and soil and their relation to the distance to a local municipal solid waste incinerator

“…Furthermore, the underlying Gaussian distributional and stationary modeling assumptions are all difficult to justify simply based on approximately 10 observations. Dispersion modeling may also be used as an alternative for the assessment of the measured concentrations of the collected samples (Oettl et al 2003;Shih et al 2009). However, similar to most spatial methods, the underlying Gaussian distributional assumption could not be justified.…”

An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution

“…During the past decades, several stochastic methodologies were developed for assessing air pollution and the associated health impact. Oettl et al (2003) used a Markov Chain-Monte Carlo model to assess air pollution caused by road traffic. Morel et al (1999) proposed a distribution of air pollutant concentrations and applied it to Santiago de Chile.…”

Point source identification using a simple permutation test: a case study of elevated PCDD/F levels in ambient air and soil and their relation to the distance to a local municipal solid waste incinerator

“…Because of the limitation in sample size of the current study, geostatistical modeling of the spatial distribution of dioxins requiring relatively large scale of collected sample such as that of the industrial site study in Midland, Michigan (Goovaerts et al 2008a, b) is not applicable. Dispersion modeling may also be used as an alternative for the assessment of the measured concentrations of the collected samples (Oettl et al 2003;Shih et al 2009). However, similar to most spatial methods, the underlying Gaussian distributional assumption could not be justified.…”