Large-eddy simulation of pollution dispersion in an urban street canyon—Part I: comparison with field data

Large-eddy simulations (LESs) are applied to the problem of pollution dispersion within the urban canopy layer, specifically street canyons. The objective is to study the turbulence structure and hence the physical dispersion mechanisms of pollutants. LESs are implemented by incorporating the dynamic sub-grid scale stress model into the commercial computational fluids dynamics code CFX. To gain confidence in the approach, simulations are performed for a canyon-like geometry (roof garden) for which experimental measurements were also made. The experimental campaign consisted of using sonic anemometers to measure mean flow and turbulence intensities at a high sample rate of 60 Hz. Good agreement between simulations and experimental data are obtained. Real geometric features, such as non-uniform wall heights, result in a very much three-dimensional flow distribution. Comparisons with the k–ε model show that LESs are able to predict more accurately the turbulence statistics of the flow.     

Large-eddy simulation of pollution dispersion in an urban street canyon—Part II: idealised canyon simulation

Three-dimensional large-eddy simulations are performed with the dynamic sub-grid scale model for an idealised urban canyon with pollution modelled as a passive scalar. In addition to concentration distributions, turbulence statistics for the canyon are presented. Higher turbulence intensities are predicted in the core of the vortex compared to the widely used k–ε model. This results in a more homogeneous distribution of pollutants, in agreement with experimental studies reported in the literature. Regions of enhanced turbulence are also observed near the walls leading to a lateral dispersion of pollutants along the canyon. The centre of the vortex is observed to precess around the canyon and also meanders along the length of the canyon. Puffs of pollution are ejected from the top of canyons intermittently rather than smoothly, with a characteristic time scale of the order of 30–60 s.     

Is an adjusted rhizosphere-based method valid for field assessment of metal phytoavailability? Application to non-contaminated soils

Previously recommended rhizosphere-based method (RHIZO) applied to moist rhizosphere soils was integrated with moist bulk soils, and termed adjusted-RHIZO method (A-RHIZO). The A-RHIZO and RHIZO methods were systematically compared with EDTA, DTPA, CaCl2 and the first step of the Community Bureau of Reference (BCR1) methods for assessing metal phytoavailability under field conditions. Results suggested that moist bulk soils are equally suited or even better than rhizosphere soils to estimate metal phytoavailability. The A-RHIZO method was preferred to other methods for predicting the phytoavailability of Ni, Cu, Zn, Cd, Pb and Mn to wheat roots with correlation coefficients of 0.730 (P<0.001), 0.854 (P<0.001), 0.887 (P<0.001), 0.739 (P<0.001), 0.725 (P<0.001) and 0.469 (P<0.05), respectively. When including soil properties, other extraction methods were also able to predict phytoavailability reasonably well for some metals. Soil pH, organic matter and Fe-Mn oxide contents, and cation-exchange capacity mostly influenced the extraction and phytoavailability of metals.     

Modeling urban air pollution in São Paulo,Brazil: sensitivity of model predicted concentrations to different turbulence parameterizations

Production and transport of urban air pollution were studied at São Paulo, Brazil, due to the importance of the megacity as source of pollutants and the flow pattern and topography of the region. An Eulerian air quality model was applied. An improved method for calculating vertical diffusivities was introduced in the model and the impact on the behavior of pollutants was analyzed. The approach includes both shear generated and buoyancy-driven turbulence in a continuous formulation that adequately represents turbulence evolution in the atmospheric boundary layer. Dispersion and transformation processes are well described by model simulations. The application of the proposed parameterization leads to increased predicted concentrations. Relative changes range from 1.2 to 2. Uncertainties in the emissions result in some disagreement between measured and simulated concentrations.     

Mathematical model for transport,interconversion, and removal of gaseous and particulate air pollutants—application to the urban plume

A steady state, three-dimensional solution of the atmospheric diffusion equation including settling, deposition, and first-order removal is presented. The solution is applied to the prediction of airborne concentrations of gaseous and particulate pollutants in the case in which gases are converted to secondary particulate matter. The conversion of sulfur dioxide, nitrogen oxides, and hydrocarbons to particulate sulfate, nitrate, and organics in the Los Angeles atmosphere and urban plume is analysed.     

Use of a food web model to evaluate the factors responsible for high PCB fish concentrations in Lake Ellasjøen, a high Arctic Lake

Background, aim, and scope

Lake Ellasjøen, located in the Norwegian high arctic, contains the highest concentrations of polychlorinated biphenyls (PCBs) ever recorded in fish and sediment from high arctic lakes, and concentrations are more than 10 times greater than in nearby Lake Øyangen. These elevated concentrations in Ellasjøen have been previously attributed, in part, to contaminant loadings from seabirds that use Ellasjøen, but not Øyangen, as a resting area. However, other factors, such as food web structure, organism growth rate, weight, lipid content, lake morphology, and nutrient inputs from the seabird guano, also differ between the two systems. The aim of this study is to evaluate the relative influence of these factors as explanatory variables for the higher PCB fish concentrations in Ellasjøen compared with Øyangen, using both a food web model and empirical data.

Methods

The model is based on previously developed models but parameterized for Lakes Ellasjøen and Øyangen using measured data wherever possible. The model was applied to five representative PCB congeners (PCB 105, 118, 138, 153, and 180) using measured sediment and water concentrations as input data and evaluated with previously collected food web data.

Results

Modeled concentrations are within a factor of two of measured concentrations in 60% and 40% of the cases in Lakes Ellasjøen and Øyangen, respectively, and within a factor of 10 in 100% of the cases in both lakes. In many cases, this is comparable to the variability associated with the data as well as the efficacy of the predictions of other food web model applications.

Discussion

We next used the model to quantify the relative importance of five major differences between Ellasjøen and Øyangen by replacing variables representing each of these factors in the Ellasjøen model with those from Øyangen, in separate simulations. The model predicts that the elevated PCB concentrations in Ellasjøen water and sediment account for 49%–58% of differences in modeled fish PCB concentrations between lakes. These elevated sediment and, to a lesser extent, water concentrations in Ellasjøen are due to PCB loadings from seabird guano. However, sediment–water fugacity ratios of PCBs are consistently greater in Ellasjøen compared with Øyangen, which suggests that internal lake processes also contribute to differences in sediment and water concentrations. We hypothesize that the nutrients associated with guano influence sediment–water fugacity ratios of PCBs by increasing the stock of pelagic algae. As both these algae and the guano settle, their organic carbon content is degraded faster than PCBs, which causes an extra magnification step in Ellasjøen before these detrital particles are consumed by benthic organisms, which are in turn consumed by fish. The model predicts that the remaining ～50% of the differences in PCB concentrations observed between the fish of these lakes are due to other subtle differences in their food web structures.

Conclusions

In conclusion, based on the results of a food web model, we found that the most dominant factors influencing the higher PCB fish concentrations in Lake Ellasjøen compared with Øyangen are the higher sediment and water concentrations in Ellasjøen, caused by seabird guano. Together, sediment and water are predicted to account for 49%–58% of differences in fish concentrations between lakes. Although seabird guano provides a source of nutrients to the lake, in addition to contaminants, empirical data and indirect model results suggest that nutrients are not leading to decreased bioaccumulation, in contrast to what has been observed in temperate, pelagic food webs.

Recommendations and perspectives

The results of this study emphasize the importance of considering even small differences in food web structure when comparing bioaccumulation in two lakes; although the food web structures of Ellasjøen and Øyangen differ only slightly, the model predicts that these differences account for most of the remaining ～50% of the differences in PCB fish concentrations between the two lakes. This study further demonstrates the utility of food web models as we were able to predict and tease apart the influence of various factors responsible for the elevated concentrations in the fish from Lake Ellasjøen, which would have been difficult using the field data alone.     

Will urban expansion lead to an increase in future water pollution loads?—a preliminary investigation of the Haihe River Basin in northeastern China

Urban expansion is a major driving force changing regional hydrology and nonpoint source pollution. The Haihe River Basin, the political, economic, and cultural center of northeastern China, has undergone rapid urbanization in recent decades. To investigate the consequences of future urban sprawl on nonpoint source water pollutant emissions in the river basin, the urban sprawl in 2030 was estimated, and the annual runoff and nonpoint source pollution in the Haihe River basin were simulated. The Integrated Model of Non-Point Sources Pollution Processes (IMPULSE) was used to simulate the effects of urban sprawl on nonpoint source pollution emissions. The outcomes indicated that the urban expansion through 2030 increased the nonpoint source total nitrogen (TN), total phosphorous (TP), and chemical oxygen demand (COD) emissions by 8.08, 0.14, and 149.57 kg/km², respectively. Compared to 2008, the total nonpoint emissions rose by 15.33, 0.57, and 12.39 %, respectively. Twelve percent of the 25 cities in the basin would increase by more than 50 % in nonpoint source TN and COD emissions in 2030. In particular, the nonpoint source TN emissions in Xinxiang, Jiaozuo, and Puyang would rise by 73.31, 67.25, and 58.61 %, and the nonpoint source COD emissions in these cities would rise by 74.02, 51.99, and 53.27 %, respectively. The point source pollution emissions in 2008 and 2030 were also estimated to explore the effects of urban sprawl on total water pollution loads. Urban sprawl through 2030 would bring significant structural changes of total TN, TP, and COD emissions for each city in the area. The results of this study could provide insights into the effects of urbanization in the study area and the methods could help to recognize the role that future urban sprawl plays in the total water pollution loads in the water quality management process.