Impacts of Upstream Building Width and Upwind Building Arrangements on Airflow and Pollutant Dispersion in a Street Canyon

A two-dimensional numerical model for evaluating the wind flow and pollutant dispersion within a street canyon was first developed using the FLUENT code, which was then validated against a wind tunnel experiment. Then, the effects of the upstream building width and upwind building arrangement on the airflow and pollutant dispersion inside an isolated street canyon were investigated numerically. The numerical results revealed that: (1) the in-canyon vortex center shifts downwards as the upstream building width increases; (2) the recirculation zone covers the entire upstream building roof for the cases when W/H = 0.5, 1.0, 1.5, and 2.0 (W is the upstream building width and H is the building height), whereas the flow reattaches the upstream building roof for the cases when W/H = 2.5 and 3.0; (3) when the upstream building width is shorter than the critical width W_C (= 2H), an increase in the upstream building width leads to an increase in the pollution level on the leeward wall of the canyon and a decrease in the roof-level concentrations at the upstream building; (4) when the upstream building width is longer than the critical width, the roof-level concentrations at the upstream building are negligibly small and the pollution level on the leeward wall of the canyon is almost unaffected by a further increase in the upstream building width; (5) when the buildings are placed upwind of the canyon, the flow attaches the upstream building roof and, therefore, almost none of the pollutants are distributed on the upstream building roof; and (6) the pollution levels inside the canyon and on the downstream building roof increase significantly with the number of upwind buildings.     

Breakthrough Curves Characterization and Identification of an Unknown Pollution Source in Groundwater System Using an Artificial Neural Network (ANN)

Contamination of groundwater constrains its uses and poses a serious threat to the environment. Once groundwater is contaminated, the cleanup may be difficult and expensive. Identification of unknown pollution sources is the first step toward adopting any remediation strategy. The proposed methodology exploits the capability of a universal function approximation by a feed-forward multilayer artificial neural network (ANN) to identify the sources in terms of its location, magnitudes, and duration of activity. The back-propagation algorithm is utilized for training the ANN to identify the source characteristics based on simulated concentration data at specified observation locations in the aquifer. Uniform random generation and the Latin hypercube sampling method of random generation are used to generate temporal varying source fluxes. These source fluxes are used in groundwater flow and the transport simulation model to generate necessary data for the ANN model-building processes. Breakthrough curves obtained for the specified pollution scenario are characterized by different methods. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics are outputs for ANN model. Experimentation is also performed with different number of training and testing patterns. In addition, the effects of measurement errors in concentration measurements values are used to show the robustness of ANN based methodology for source identification in case of erroneous data.