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Atmospheric dispersion models use mathematical equations to calculate pollution concentrations in the receiving environment; however, there can be variations in the accuracy of a pollutant's calculated concentration among different models due to the nature of the modeling environment and limitations on computational complexities. In this study two types of popular Gaussian pollution dispersion models, AERMOD and ADMS, were investigated to analyze their behavior under specific conditions. Initially, both models were simulated for point and line sources of emission. Subsequently, their output results were compared with passive samplers placed for varying periods (2 weeks, 2 months, and 3 months) at modeled receptor locations. The results indicated that both models underestimate the passive sampler results. However, AERMOD's underestimation was found to be more than ADMS's, resulting in ADMS performing slightly better. Additionally, to study the effectiveness of input parameters such as surface characteristics and solar insolation, a relative sensitivity analysis was conducted under two different atmospheric conditions: convective mixing dominance (CMD) and mechanical mixing dominance (MMD). It was found that AERMOD was relatively more sensitive to surface roughness, especially in MMD conditions than to albedo, surface moisture, and solar insolation. Both models were somewhat insensitive to albedo and Bowen, and solar insolation; however, in CMD conditions, AERMOD was more sensitive to these sunlight-related parameters. Also, both models were more sensitive to surface roughness than other input parameters. The results emphasize the importance of selecting precise parameters, particularly surface roughness, with more care for AERMOD than for the ADMS model. [ABSTRACT FROM AUTHOR]

Local urban air quality models must be able to account for complex road geometries if they are to predict near-road concentrations accurately. This includes flyovers, which are often used to improve flow at busy junctions or to take traffic through urban greenspace. We present a new methodology for modelling elevated roads in which the plume is only allowed to grow downwards once it has left the downwind road edge, thus accounting for road shielding. This new approach has been implemented in the operational dispersion model ADMS-Urban. The updated model is validated against monitoring data from two sites located next to busy flyovers—one in London, UK, the other in Antwerp, Belgium. It is shown to perform very well compared with simulations in which the flyovers are modelled at ground level, and slightly better than simulations when the traditional approach to modelling elevated roads (no road shielding) is used. Near-ground concentrations are significantly reduced with road elevation due to (i) increased vertical source-receptor distance, (ii) greater dispersion from the source where wind speeds are higher, and (iii) reduced impact of ground-level plume reflections. Pollutant trapping in street canyons is also minimised in cases where a flyover is elevated above the local building level. A sensitivity analysis is also presented in which multiple road elevations are tested; these results can be used by urban planners when designing new flyovers or by modellers in deciding whether it is important to account for road elevation near sensitive receptors. [ABSTRACT FROM AUTHOR]

Gaussian dispersion modeling has implemented significant developments in the description of the planetary boundary layer characteristics along with research work, particularly on plume buoyancy and momentum, as well as airflow in complex terrain. Advanced Gaussian models show a remarkable performance in validation studies, providing sophisticated algorithms to take into account building downwash, deposition, chemical reactions, and so forth. This work aims to essentially validate the SYMOS model, currently referenced in the Czech Republic, and comparatively evaluate further modeling approaches applied to emissions from a stationary source in the suburban background of Prague. Sulfur dioxide was targeted as one of the criteria gaseous pollutants, relatively representing the sulfur content in the solid mineral fuel commonly used in local industrial facilities. Meteorological preprocessing, complex terrain, and plume rise were analyzed in-depth to determine the effect of the different algorithms in the estimation of ground-level concentrations. The obtained contour plots showed moderate similarities between SYMOS and the advanced models ADMS and AERMOD, but largely overestimated the concentrations when plume rise was considered. The descriptive statistical analysis revealed ADMS tended to predict higher hourly peak concentrations, while AERMOD estimated higher annual averages, attributed to differences in meteorological conditions and dispersion algorithms. Moreover, surface roughness was identified as a critical factor in the calculation of concentrations in complex terrain. Extensive assessment and validation of SYMOS algorithms are essential to investigate a different approach to characterize atmospheric stability and thermal lift. [ABSTRACT FROM AUTHOR]

This paper reports on the performance of Atmospheric Dispersion Modelling System (ADMS) 4.2 in predicting peak and mean ambient sulfur dioxide concentrations at two sites adjacent to the Map Ta Phut Industrial Estate in Eastern Thailand, the centre of the country's petrochemical industry. The model comprised 100 individual stacks and utilised four separate meteorological datasets from different points around the site. We show that model performance varies according to the location at which the meteorological data were obtained, with considerable differences in model outputs observed for meteorological stations that are relatively close to each other. The best performances were observed when there was co-location of the meteorological data and receptor. In such cases, acceptance criteria for the majority of performance parameters were satisfied across averaging periods ranging from 1 h to 7 days. We have also compared the results from this study with those obtained from a recent literature American Meteorological Society/United States Environmental Protection Agency Regulatory Model (AERMOD) study for the same site and time period; the comparison indicates that AERMOD is likely to be similarly influenced by the choice of meteorological dataset. Using ADMS model simulations for all four meteorological datasets and a breakdown of the local population by electoral ward, we were able to estimate exposure over 1 h, 24 h and yearly averaging periods and compare these to air quality standards and guidelines published by Thailand, the World Health Organisation (WHO) and the European Union (EU). The results of this analysis showed that despite the large variations in overall model performance, the impact of choice of meteorological dataset on prediction of compliance with the standards and guidelines is relatively small: the WHO 24-h guideline of 7.5 ppb (100th percentile) was predicted to be exceeded in all of the wards for all meteorological datasets, whilst compliance with Thai and EU standards was predicted for at least 86 % of the population, with relatively little variation between the different meteorological datasets. [ABSTRACT FROM AUTHOR]

Norwich is the eastern most city in the United Kingdom. Despite a population of only 100,000 and very little local industry, studies have shown that the city experiences levels of nitrogen dioxide, ozone, particulates and sulphur dioxide exceeding the UK Air Quality Standards. Because of Norwich's situation within a large, predominantly rural area a large non-resident workforce is one factor that contributes to large, often very congested traffic flows. The city's location close to the European mainland also exposes it to polluted airmasses transported from the continent, especially in the case of particulates and ozone. In order to assess the relative contributions of local and regional sources, data from rural and urban monitoring sites are to be used in conjunction with ADMS-Urban and the UK Meteorological Office's NAME model. [ABSTRACT FROM AUTHOR]