Search

A street canyon pollution dispersion model is described which accounts for a wide range of canyon geometries including deep and/or asymmetric canyons. The model uses up to six component sources to represent different effects of street canyons on the dispersion of road traffic emissions. The final concentration is a weighted sum of the component concentrations dependent on output point location; canyon geometry; and wind direction relative to canyon orientation. Conventional approaches to modeling pollution in street canyons, such as the "Operational Street Pollution Model" (OSPM), do not account for canyons with high aspect ratios, pavements, and building porosity, so are not applicable for all urban morphologies. The new model has been implemented within the widely used, street-level resolution ADMS-Urban air quality model, which is used for air quality assessment and forecasting in cities such as Hong Kong where high-rise buildings form deep and complex street canyons. The new model is evaluated in relation to measured pollutant concentration data from the "Optimisation of modelling methods for traffic pollution in streets" (TRAPOS) project and routine measurements from 42 monitoring sites in London. Comparisons have been made between modeling using the new canyon model; a simpler approach to canyon modeling based on the OSPM formulation; and without any inclusion of canyon effects. The TRAPOS dataset has been used to highlight the model's ability to replicate the dependence of concentration on wind speed and direction, and also to show improved model performance for the prediction of high concentration values, which is particularly important for model applications such as planning and assessment. The London dataset, in which the street canyons are less well defined, has also been used to demonstrate improved model performance for this advanced approach compared to the simpler methods, by categorizing the measurement locations according to site type (background, near-road, and strong canyon).

An explicit NOx chemistry method has been implemented in AERMOD version 15181, ADMSM. The scheme has been evaluated by comparison with the methodologies currently recommended by the U.S. EPA for Tier 3 NO2 calculations, that is, OLM and PVMRM2. Four data sets have been used for NO2 chemistry method evaluation. Overall, ADMSM-modeled NO2 concentrations show the most consistency with the AERMOD calculations of NOx and the highest Index of Agreement; they are also on average lower than those of both OLM and PVMRM2. OLM shows little consistency with modeled NOx concentrations and markedly overpredicts NO2. PVMRM2 shows performance closer to that of ADMSM than OLM; however, its behavior is inconsistent with modeled NOx in some cases and it has less good statistics for NO2. The trend in model performance can be explained by examining the features particular to each chemistry method: OLM can be considered as a screening model as it calculates the upper bound of conversion from NO to NO2 possible with the...

ADMS and AERMOD are the two most widely used dispersion models for regulatory purposes. It is, therefore, important to understand the differences in the predictions of the models and the causes of these differences. The treatment by the models of flat terrain has been discussed previously; in this paper the focus is on their treatment of complex terrain. The paper includes a discussion of the impacts of complex terrain on airflow and dispersion and how these are treated in ADMS and AERMOD, followed by calculations for two distinct cases: (i) sources above a deep valley within a relatively flat plateau area (Clifty Creek power station, USA); (ii) sources in a valley in hilly terrain where the terrain rises well above the stack tops (Ribblesdale cement works, England). In both cases the model predictions are markedly different. At Clifty Creek, ADMS suggests that the terrain markedly increases maximum surface concentrations, whereas the AERMOD complex terrain module has little impact. At Ribblesdale, AERMOD predicts very large increases (a factor of 18) in the maximum hourly average surface concentrations due to plume impaction onto the neighboring hill; although plume impaction is predicted by ADMS, the increases in concentration are much less marked as the airflow model in ADMS predicts some lateral deviation of the streamlines around the hill.

An optimisation scheme has been developed that applies a Bayesian inversion technique to a high resolution (street-level) atmospheric dispersion model to modify pollution emission rates based on sensor data. The scheme minimises a cost function using a non-negative least squares solver. For the required covariance matrices, assumptions are made regarding the magnitude of the uncertainties in source emissions and measurements and the correlation in uncertainties between different source emissions and different measurement sites. The scheme has been tested in an initial case study in Cambridge using monitored data from four reference monitors and 20 AQMesh sensor pods for the period 30 June 2016 to 30 September 2016. Hourly NOx concentrations from road sources modelled using ADMS-Urban and observed concentrations were processed using the optimisation scheme and the adjusted emissions were re-modelled. The optimisation scheme reduced average road emissions on average by 6.5% compared to the original estimates, changed the diurnal profile of emissions and improved model accuracy at four reference sites.

Two schemes for the determination of NO2 concentrations in the atmospheric dispersion model ADMS are evaluated using data from two sites in Alaska. Both schemes take account of the rate of oxidation of NO and photolysis of NO2 in the plume using identical chemical formulations. The differences lie in the approaches used for the entrainment and mixing of ambient ozone into the plume. In the standard scheme it is assumed that ozone is mixed instantaneously into the plume at source; in the entrainment limited scheme ozone is entrained into the plume at a rate determined by the rate of dilution of the instantaneous plume. A methodology comprising a scatter plot of the ratio of modelled to observed NO2 vs. modelled to observed NOx is used to distinguish errors in the chemistry schemes from errors in the prediction of NOx. Both schemes show good performance statistics with the standard scheme predicting higher NO2 concentrations.