Model sensitivity and uncertainty analysis using roadside air quality measurements

Most of the air quality modelling work has been so far oriented towards deterministic simulations of ambient pollutant concentrations. This traditional approach, which is based on the use of one selected model and one data set of discrete input values, does not reflect the uncertainties due to errors in model formulation and input data. Given the complexities of urban environments and the inherent limitations of mathematical modelling, it is unlikely that a single model based on routinely available meteorological and emission data will give satisfactory short-term predictions. In this study, different methods involving the use of more than one dispersion model, in association with different emission simulation methodologies and meteorological data sets, were explored for predicting best CO and benzene estimates, and related confidence bounds. The different approaches were tested using experimental data obtained during intensive monitoring campaigns in busy street canyons in Paris, France. Three relative simple dispersion models (STREET, OSPM and AEOLIUS) that are likely to be used for regulatory purposes were selected for this application. A sensitivity analysis was conducted in order to identify internal model parameters that might significantly affect results. Finally, a probabilistic methodology for assessing urban air quality was proposed.

Modelling air quality in street canyons: a review

High pollution levels have been often observed in urban street canyons due to the increased traffic emissions and reduced natural ventilation. Microscale dispersion models with different levels of complexity may be used to assess urban air qualityand support decision-making for pollution control strategies and traffic planning. Mathematical models calculate pollutant concentrations by solving either analytically a simplified set of parametric equations or numerically a set of differential equations that describe in detail wind flow and pollutant dispersion. Street canyon models, which might also include simplified photochemistry and particle deposition–resuspension algorithms, are often nested within larger-scale urban dispersion codes. Reduced-scale physical models in wind tunnels may also be used for investigating atmospheric processes within urban canyons and validating mathematical models. A range of monitoring techniques is used to measure pollutant concentrations in urban streets. Point measurement methods (continuous monitoring, passive and active pre-concentration sampling, grab sampling) are available for gaseous pollutants. A number of sampling techniques (mainlybased on filtration and impaction) can be used to obtain mass concentration, size distribution and chemical composition of particles. A combination of different sampling/monitoring techniques is often adopted in experimental studies. Relativelysimple mathematical models have usually been used in association with field measurements to obtain and interpret time series of pollutant concentrations at a limited number of receptor locations in street canyons. On the other hand, advanced numerical codes have often been applied in combination with wind tunnel and/or field data to simulate small-scale dispersion within the urban canopy.

Borehole Optimisation System (BOS) - a case study assessing options for abstraction of urban groundwater in Nottingham, UK

The recognition that urban groundwater is a potentially valuable resource for potable and industrial uses due to growing pressures on perceived less polluted rural groundwater has led to a requirement to assess the groundwater contamination risk in urban areas from industrial contaminants such as chlorinated solvents. The development of a probabilistic risk based management tool that predicts groundwater quality at potential new urban boreholes is beneficial in determining the best sites for future resource development. The Borehole Optimisation System (BOS) is a custom Geographic Information System (GIs) application that has been developed with the objective of identifying the optimum locations for new abstraction boreholes. BOS can be applied to any aquifer subject to variable contamination risk. The system is described in more detail by Tait et al. [Tait, N.G., Davison, J.J., Whittaker, J.J., Lehame, S.A. Lerner, D.N., 2004a. Borehole Optimisation System (BOS) - a GIs based risk analysis tool for optimising the use of urban groundwater. Environmental Modelling and Software 19, 1111-1124]. This paper applies the BOS model to an urban Permo-Triassic Sandstone aquifer in the city centre of Nottingham, UK. The risk of pollution in potential new boreholes from the industrial chlorinated solvent tetrachloroethene (PCE) was assessed for this region. The risk model was validated against contaminant concentrations from 6 actual field boreholes within the study area. In these studies the model generally underestimated contaminant concentrations. A sensitivity analysis showed that the most responsive model parameters were recharge, effective porosity and contaminant degradation rate. Multiple simulations were undertaken across the study area in order to create surface maps indicating areas of low PCE concentrations, thus indicating the best locations to place new boreholes. Results indicate that northeastern, eastern and central regions have the lowest potential PCE concentrations in abstraction groundwater and therefore are the best sites for locating new boreholes. These locations coincide with aquifer areas that are confined by low permeability Mercia Mudstone deposits. Conversely southern and northwestern areas are unconfined and have shallower depth to groundwater. These areas have the highest potential PCE concentrations. These studies demonstrate the applicability of BOS as a tool for informing decision makers on the development of urban groundwater resources. (c) 2007 Elsevier Ltd. All rights reserved.