Development and application of a three dimensional numerical model for predicting pollutant and sediment transport using an Eulerian-Lagrangian marker particle technique

A computer coded Lagrangian marker particle in Eulerian finite difference cell solution to the three dimensional incompressible mass transport equation, Water Advective Particle in Cell Technique, WAPIC, was developed, verified against analytic solutions, and subsequently applied in the prediction of long term transport of a suspended sediment cloud resulting from an instantaneous dredge spoil release. Numerical results from WAPIC were verified against analytic solutions to the three dimensional incompressible mass transport equation for turbulent diffusion and advection of Gaussian dye releases in unbounded uniform and uniformly sheared uni-directional flow, and for steady-uniform plug channel flow. WAPIC was utilized to simulate an analytic solution for non-equilibrium sediment dropout from an initially vertically uniform particle distribution in one dimensional turbulent channel flow

The effect of Turbulence Models on Numerical Prediction of Air Flow within Street Canyons

November 15-17, Belgrad

Pollutant dispersion in a developing valley cold-air pool

Pollutants are trapped and accumulate within cold-air pools, thereby affecting air quality. A numerical model is used to quantify the role of cold-air-pooling processes in the dispersion of air pollution in a developing cold-air pool within an alpine valley under decoupled stable conditions. Results indicate that the negatively buoyant downslope flows transport and mix pollutants into the valley to depths that depend on the temperature deficit of the flow and the ambient temperature structure inside the valley. Along the slopes, pollutants are generally entrained above the cold-air pool and detrained within the cold-air pool, largely above the ground-based inversion layer. The ability of the cold-air pool to dilute pollutants is quantified. The analysis shows that the downslope flows fill the valley with air from above, which is then largely trapped within the cold-air pool, and that dilution depends on where the pollutants are emitted with respect to the positions of the top of the ground-based inversion layer and cold-air pool, and on the slope wind speeds. Over the lower part of the slopes, the cold-air-pool-averaged concentrations are proportional to the slope wind speeds where the pollutants are emitted, and diminish as the cold-air pool deepens. Pollutants emitted within the ground-based inversion layer are largely trapped there. Pollutants emitted farther up the slopes detrain within the cold-air pool above the ground-based inversion layer, although some fraction, increasing with distance from the top of the slopes, penetrates into the ground-based inversion layer.Peer reviewe

Submesoscale dispersion in the vicinity of the Deepwater Horizon spill

Reliable forecasts for the dispersion of oceanic contamination are important for coastal ecosystems, society and the economy as evidenced by the Deepwater Horizon oil spill in the Gulf of Mexico in 2010 and the Fukushima nuclear plant incident in the Pacific Ocean in 2011. Accurate prediction of pollutant pathways and concentrations at the ocean surface requires understanding ocean dynamics over a broad range of spatial scales. Fundamental questions concerning the structure of the velocity field at the submesoscales (100 meters to tens of kilometers, hours to days) remain unresolved due to a lack of synoptic measurements at these scales. \textcolor{black} {Using high-frequency position data provided by the near-simultaneous release of hundreds of accurately tracked surface drifters, we study the structure of submesoscale surface velocity fluctuations in the Northern Gulf Mexico. Observed two-point statistics confirm the accuracy of classic turbulence scaling laws at 200m$-$50km scales and clearly indicate that dispersion at the submesoscales is \textit{local}, driven predominantly by energetic submesoscale fluctuations.} The results demonstrate the feasibility and utility of deploying large clusters of drifting instruments to provide synoptic observations of spatial variability of the ocean surface velocity field. Our findings allow quantification of the submesoscale-driven dispersion missing in current operational circulation models and satellite altimeter-derived velocity fields.Comment: 9 pages, 6 figure

Predicting real-time roadside CO and NO2 concentrations using neural networks

The main aim of this paper is to develop a model based on neural network (NN) theory to estimate real-time roadside CO and $hbox{NO}_{2}$ concentrations using traffic and meteorological condition data. The location of the study site is at a road intersection in Melton Mowbray, which is a town in Leicestershire, U.K. Several NNs, which can be classified into three types, namely, the multilayer perceptron, the radial basis function, and the modular network, were developed to model the nonlinear relationships that exist in the pollutant concentrations. Their performances are analyzed and compared. The transferability of the developed models is studied using data collected from a road intersection in another city. It was concluded that all NNs provide reliable estimates of pollutant concentrations using limited information and noisy data

Atmospheric effects of the emerging mainland Chinese transportation system at and beyond the regional scale

Local surface travel needs in the People's Republic of China (mainland China) have traditionally been met largely by nonpolluting bicycles. A major automobile manufacturing/importing effort has begun in the country over the last decade, and planning documents indicate that the Chinese may strive to acquire more than 100 million vehicles early in the next century. By analogy with large automotive fleets already existing in the western world, both regional and global scale pollution effects are to be expected from the increase. The present work adopts the latest projections of Chinese automobile manufacture and performs some quantitative assessments of the extent of pollution generation. Focus for the investigation is placed upon the oxidant ozone. Emissions of the precursor species nitrogen oxides and volatile organics are constructed based on data for the current automotive sector in the eastern portion of the United States. Ozone production is first estimated from measured values for continental/oceanic scale yields relative to precursor oxidation. The estimates are then corroborated through idealized two dimensional modeling of the photochemistry taking place in springtime air flow off the Asian land mass and toward the Pacific Ocean. The projected fleet sizes could increase coastal and remote oceanic ozone concentrations by tens of parts per billion (ppb) in the lower troposphere. Influences on the tropospheric aerosol system and on the major greenhouse gas carbon dioxide are treated peripherally. Nitrogen oxides created during the vehicular internal combustion process will contribute to nitrate pollution levels measured in the open Pacific. The potential for soot and fugitive dust increases should be considered as the automotive infrastructure develops. Since the emerging Chinese automotive transportation system will represent a substantial addition to the global fleet and all the carbon in gasoline is eventually oxidized completely, a significant rise in global carbon dioxide inputs will ensue as well. Some policy issues are treated preliminary. The assumption is made that alterations to regional oxidant/aerosol systems and to terrestrial climate are conceivable. The likelihood that the Chinese can achieve the latest vehicle fleet goals is discussed, from the points of view of new production, positive pollution feedbacks from a growing automobile industry, and known petroleum reserves. Vehicular fuel and maintenance options lying before the Chinese are outlines and compared. To provide some perspective on the magnitude of the environmental changes associated with an Asian automotive buildup, recent estimates of the effects of future air traffic over the Pacific Rim are described

Predicting real-time roadside CO and NO2 concentrations using neural networks

The main aim of this paper is to develop a model based on neural network (NN) theory to estimate real-time roadside CO and $hbox{NO}_{2}$ concentrations using traffic and meteorological condition data. The location of the study site is at a road intersection in Melton Mowbray, which is a town in Leicestershire, U.K. Several NNs, which can be classified into three types, namely, the multilayer perceptron, the radial basis function, and the modular network, were developed to model the nonlinear relationships that exist in the pollutant concentrations. Their performances are analyzed and compared. The transferability of the developed models is studied using data collected from a road intersection in another city. It was concluded that all NNs provide reliable estimates of pollutant concentrations using limited information and noisy data