Modeling the fate of benzo[a]pyrene in the wastewater-irrigated areas of Tianjin with a fugacity model

A Level III fugacity model was applied to characterize the transfer processes and environmental fate of benzo[a]pyrene in wastewater-irrigated areas of Tianjin, China. The physical-chemical properties and transfer parameters of benzo[a]pyrene were used in the model and the concentration distribution of benzo[a]pyrene in sediment, soil, water, air, fish, and crop compartments, as well as transfer fluxes across the compartments, were then derived under steady-state assumptions. The calculated results were compared with monitoring data for air, soil, water, and sediment collected from the literature. The results indicate that there was generally good agreement and the differences were within an order of magnitude for air, soil, and sediment. The concentration of benzo[a]pyrene in the ambient air in the area was very low with a majority present sorbed to aerosol. In the water compartment, approximately 70% of benzo[a]pyrene dissolved in water phase. Relatively high concentrations of the compound were found in the soil and sediment, with the soil serving as the dominant sink in the area. Benzo[a]pyrene, with a slow metabolic rate, was found to accumulate in fish in the area.     

Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique

Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resources' variables. The feed-forward neural network modeling technique is the most widely used ANN type in water resources applications. The main purpose of the study is to investigate the abilities of an artificial neural networks' (ANNs) model to improve the accuracy of the biological oxygen demand (BOD) estimation. Many of the water quality variables (chemical oxygen demand, temperature, dissolved oxygen, water flow, chlorophyll a and nutrients, ammonia, nitrite, nitrate) that affect biological oxygen demand concentrations were collected at 11 sampling sites in the Melen River Basin during 2001-2002. To develop an ANN model for estimating BOD, the available data set was partitioned into a training set and a test set according to station. In order to reach an optimum amount of hidden layer nodes, nodes 2, 3, 5, 10 were tested. Within this range, the ANN architecture having 8 inputs and 1 hidden layer with 3 nodes gives the best choice. Comparison of results reveals that the ANN model gives reasonable estimates for the BOD prediction.     

Fractionation of Cd,Cr, Pb and Ni in roadside soils of Uyo,Niger Delta Region: Nigeria using the optimized BCR sequential extraction technique

The optimized BCR sequential extraction procedure was applied to nine roadside soil samples for the determination of Cd, Cr, Pb and Ni. The extractable metals were isolated into three operationally defined fractions viz: acid extractable, reducible and oxidizable. The residue was treated with aqua regia solution. Metal analysis was done using flame atomic absorption spectrophotometry with air–acetylene flame. Results obtained showed the concentrations of the metals as relative abundance in the mobile phases of the samples (based on the sum of the first three fractions) are in the following order: Cd(91.9) > Pb(82.8) > Ni(49.5) > Cr(39.0). The most non-mobilizable metals were Cr and Ni which are generally lithogenic, associated with the silicate matrix, and the order is as follows: Cr(61.0) > Ni(50.5) > Pb(17.2) > Cd(8.1). The recovery of all the metals expressed as the ratio of total metal concentration to fractional sum of the optimized BCR sequential extraction procedure was of the order: Cr(95.6) > Pb(95.0) > Ni(94.8) > Cd(92.4).     

Observations of relative humidity in the near-wake of a wind turbine using an instrumented unmanned aerial system

Simulation, modeling, and limited observations have shown that wind farms have an impact on the near-surface atmospheric boundary layer as turbulent wakes enhance vertical mixing of momentum, heat, and moisture. The few observational datasets that do exist lack high spatial resolution due to their use of a limited number of meteorological sensors or remote sensing techniques. This study utilizes an instrumented small unmanned aerial system to gather high-resolution in situ field measurements in order to differentially map near-wake changes to relative humidity. Observations show that downstream relative humidity is differentially altered in the vertical, spanwise and downstream directions.