Evaluation of fast atmospheric dispersion models in a regular street network

The need to balance computational speed and simulation accuracy is a key challenge in designing atmospheric dispersion models that can be used in scenarios where near real-time hazard predictions are needed. This challenge is aggravated in cities, where models need to have some degree of building-awareness, alongside the ability to capture effects of dominant urban flow processes. We use a combination of high-resolution large-eddy simulation (LES) and wind-tunnel data of flow and dispersion in an idealised, equal-height urban canopy to highlight important dispersion processes and evaluate how these are reproduced by representatives of the most prevalent modelling approaches: (i) a Gaussian plume model, (ii) a Lagrangian stochastic model and (iii) street-network dispersion models. Concentration data from the LES, validated against the wind-tunnel data, were averaged over the volumes of streets in order to provide a high-fidelity reference suitable for evaluating the different models on the same footing. For the particular combination of forcing wind direction and source location studied here, the strongest deviations from the LES reference were associated with mean over-predictions of concentrations by approximately a factor of 2 and with a relative scatter larger than a factor of 4 of the mean, corresponding to cases where the mean plume centreline also deviated significantly from the LES. This was linked to low accuracy of the underlying flow models/parameters that resulted in a misrepresentation of pollutant channelling along streets and of the uneven plume branching observed in intersections. The agreement of model predictions with the LES (which explicitly resolves the turbulent flow and dispersion processes) greatly improved by increasing the accuracy of building-induced modifications of the driving flow field. When provided with a limited set of representative velocity parameters, the comparatively simple street-network models performed equally well or better compared to the Lagrangian model run on full 3D wind fields. The study showed that street-network models capture the dominant building-induced dispersion processes in the canopy layer through parametrisations of horizontal advection and vertical exchange processes at scales of practical interest. At the same time, computational costs and computing times associated with the network approach are ideally suited for emergency-response applications

Between-airport heterogeneity in air toxics emissions associated with individual cancer risk thresholds and population risks

Abstract Background Airports represent a complex source type of increasing importance contributing to air toxics risks. Comprehensive atmospheric dispersion models are beyond the scope of many applications, so it would be valuable to rapidly but accurately characterize the risk-relevant exposure implications of emissions at an airport. Methods In this study, we apply a high resolution atmospheric dispersion model (AERMOD) to 32 airports across the United States, focusing on benzene, 1,3-butadiene, and benzo [a]pyrene. We estimate the emission rates required at these airports to exceed a 10-6 lifetime cancer risk for the maximally exposed individual (emission thresholds) and estimate the total population risk at these emission rates. Results The emission thresholds vary by two orders of magnitude across airports, with variability predicted by proximity of populations to the airport and mixing height (R2 = 0.74–0.75 across pollutants). At these emission thresholds, the population risk within 50 km of the airport varies by two orders of magnitude across airports, driven by substantial heterogeneity in total population exposure per unit emissions that is related to population density and uncorrelated with emission thresholds. Conclusion Our findings indicate that site characteristics can be used to accurately predict maximum individual risk and total population risk at a given level of emissions, but that optimizing on one endpoint will be non-optimal for the other.</p

Vehicular pollution modeling using the operational street pollution model (OSPM) for Chembur, Mumbai (India)

Megacities in India such as Mumbai and Delhi are among the most polluted places in the world. In the present study, the widely used operational street pollution model (OSPM) is applied for assessing pollutant loads in the street canyons of Chembur, a suburban area just outside Mumbai city. Chembur is both industrialized and highly congested with vehicles. There are six major street canyons in this area, for which modeling has been carried out for NOx and particulate matter (PM). The vehicle emission factors for Indian cities have been developed by Automotive Research Association of India (ARAI) for PM, not specifically for PM10 or PM2.5. The model has been applied for 4 days of winter season and for the whole year to see the difference of effect of meteorology. The urban background concentrations have been obtained from an air quality monitoring station. Results have been compared with measured concentrations from the routine monitoring performed in Mumbai. NOx emissions originate mainly from vehicles which are ground-level sources and are emitting close to where people live. Therefore, those emissions are highly relevant. The modeled NOx concentration compared satisfactorily with observed data. However, this was not the case for PM, most likely because the emission inventory did not contain emission terms due to resuspended particulate matter

Evaluation of the performance of ADMS in predicting the dispersion of sulfur dioxide from a complex source in Southeast Asia: implications for health impact assessments

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