Showing papers by "Margaret Bell published in 2013"

Exploring the processes governing roadside pollutant concentrations in urban street canyon

Abstract: This paper describes an in-depth analysis to investigate the huge variation in the measured roadside air-pollutant concentrations of carbon monoxide and nitrogen dioxide in terms of the traffic flow levels, the orientation of the street to the prevailing wind, the wind speed, temperature and barometric pressure. The work has attempted to develop generic parameters that can be applied to other urban areas. However, in the absence of a measure of congestion at the site in Palermo (Italy), the methodological approach proposed used the simultaneous noise measurements, in units of decibels (B), to help parameterise a generic congestion indicator in terms of the traffic flow. The potential transferability of the approach was demonstrated for a site in Marylebone Road, London (UK), given the similarity of the two study sites, canyon shape, traffic characteristics and road orientation. The results showed that, within the range of data available, noise levels could be used as a proxy for flow change on the shoulders of the peak hour and hence congestion and a generic relationship with factors statistically significant at 99 % confidence allows roadside concentrations due to traffic to be estimated with a regression coefficient of R 2 = 0.73 (R = 0.85). The research demonstrates that whilst there are indeed underlying relationships that can explain the roadside concentrations based on traffic and meteorological conditions, evidence is presented that confirms the complexity of the physical and chemical processes that govern roadside concentrations.

Dispersion modelling for decision-making: combining spatial statistics with model parameterisation

Abstract: Often air quality is considered as part of a multi-criteria decision making process, requiring dispersion modelling to be conducted alongside a multitude of co-determinants of possible management outcomes. This typically involves diverse information layers, such as socio-demographics, land use, travel patterns, utilising spatial statistics to establish the causeeffect relationship over the study domain. The integrated approach presented in this paper combines spatially-resolved (raster) information on emission sources (road traffic and/or industrial) with the published statistics (socio-demographic, epidemiology, etc.) to gain a priori information on the hotspots. This enables iterative enhancement of dispersion model parameterisation, mainly emissions in specific areas, to refine the air quality predictions. A case study is presented demonstrating the application of spatial statistics, utilising a combination of weighted-overlay and ordinary least-squares regression analyses for the dissimilar datasets included in the multi-criteria decision problem. All the datasets are generated at the Lower Super Output Area-level for the local administration boundaries of Stockton (UK), spatially mapping the distribution of potential locations of priority areas for model refinement. The study demonstrates the need for robust dataset while associating dispersion modelling outcomes with multi-criteria decision problems.