Bioconcentration and half-life of quinalphos pesticide in rice-fish integration system in the Mekong Delta,Vietnam

In order to determine the distribution and enable the elimination of quinalphos, a popular active pesticide compound used in the Mekong Delta, an experiment was set up in a rice-fish integration system in Can Tho City, Vietnam. Fish was stocked into the field when the rice was two-months old. Quinalphos was applied twice in doses of 42.5 g per 1000 m². Water, fish and sediment samples were collected at time intervals and analyzed by a Gas Chromatography Electron Capture Detector system. The results show that quinalphos residues in fish muscles were much higher than those of the water and the bioconcentration factor (logBCF) was above 2 for the fish. The half-life of first and second quinalphos applications were 12.2 and 11.1 days for sediment, 2.5 and 1.1 days for silver barb, 1.9 and 1.3 days for common carp, and 1.1 and 1.0 days for water, respectively.     

Dust Storms are an Indication of an Unhealthy Environment in East Asia

Dust storms frequently occur in Mongolia and in northern China. Each year there are 30 to 120 dusty days in source regions of Mongolia and 14–20 dusty days on the Korean Peninsula. Intense sand storms and associated dust falls produce environmental impacts in East Asia. This paper discusses the environmental degradation in Mongolia and the social, economic and atmospheric impacts of dust storms in the sink area. The impact of dust storms on environmental compartments as well as their direct and indirect consequences to basic resources like water and energy demand and supply is explained. Governments are encouraged to take appropriate action in specified regions. For monitoring dust storms there is a need for international cooperation to combat growing environmental and human security concerns.     

Chlorinated naphthalene formation from the oxidation of dichlorophenols

Polychlorinated naphthalenes (PCNs) formed along with dibenzo-p-dioxin and dibenzofuran products in the slow combustion of dichlorophenols (DCPs) at 600 degrees C were identified. Each DCP reactant produced a unique set of PCN products. Major PCN congeners observed in the experiments were consistent with products predicted from a mechanism involving an intermediate formed by ortho-ortho carbon coupling of phenoxy radicals; polychlorinated dibenzofurans (PCDFs) are formed from the same intermediate. Tautomerization of the intermediate and H2O elimination produces PCDFs; alternatively, CO elimination to form dihydrofulvalene and fusion produces naphthalenes. Only trace amounts of tetrachloronaphthalene congeners were formed, suggesting that the preferred PCN formation pathways from chlorinated phenols involve loss of chlorine. 3,4-DCP produced the largest yields of PCDF and PCN products with two or more chlorine substituents. 2,6-DCP did not produce tri- or tetra-chlorinated PCDF or PCN congeners. It did produce 1,8-DCN, however, which could not be explained.     

Effects of calibration on L-THIA GIS runoff and pollutant estimation

Urbanization can result in alteration of a watershed's hydrologic response and water quality. To simulate hydrologic and water quality impacts of land use changes, the Long-Term Hydrologic Impact Assessment (L-THIA) system has been used. The L-THIA system estimates pollutant loading based on direct runoff quantity and land use based pollutant coefficients. The accurate estimation of direct runoff is important in assessing water quality impacts of land use changes. An automated program was developed to calibrate the L-THIA model using the millions of curve number (CN) combinations associated with land uses and hydrologic soil groups. L-THIA calibration for the Little Eagle Creek (LEC) watershed near Indianapolis, Indiana was performed using land use data for 1991 and daily rainfall data for six months of 1991 (January 1-June 30) to minimize errors associated with use of different temporal land use data and rainfall data. For the calibration period, the Nash-Sutcliffe coefficient was 0.60 for estimated and observed direct runoff. The calibrated CN values were used for validation of the model for the same year (July 1-December 31), and the Nash-Sutcliffe coefficient was 0.60 for estimated and observed direct runoff. The Nash-Sutcliffe coefficient was 0.52 for January 1, 1991 to December 31, 1991 using uncalibrated CN values. As shown in this study, the use of better input parameters for the L-THIA model can improve accuracy. The effects on direct runoff and pollutant estimation of the calibrated CN values in the L-THIA model were investigated for the LEC. Following calibration, the estimated average annual direct runoff for the LEC watershed increased by 34%, total nitrogen by 24%, total phosphorus by 22%, and total lead by 43%. This study demonstrates that the L-THIA model should be calibrated and validated prior to application in a particular watershed to more accurately assess the effects of land use changes on hydrology and water quality.     

Gas chromatographic methods for monitoring of wastewater chlorophenol degradation in anaerobic reactors

Wastewater samples from an anaerobic reactor were extracted with hexane and derivatized with diazomethane (method 1) and with acetic anidride (method 2). Gas chromatography with electron-capture detection (ECD) was employed for separating the parent compound and intermediates trichlorophenols (TCP) and dichlorophenols (DCP) which originated from the penta chlorophenol (PCP) degradation process. The relations between concentrations of PCP, TCP and DCP areas were linear in the range of concentrations of 0.2 to 8 mg/L and 0.025 mg/L to 5 mg/L for methods 1 and 2, respectively. The repeatability of the extraction methods was satisfactory, with variation coefficients lower than 11%. For method 1, at the fortification level of 0.2 mg/L, recovery of PCP, TCP, and DCP was 112%, 74% and 45%, respectively. For method 2, the corresponding recovery values at the fortification level of 0.1 mg/L were 91%, 93% and 103%, respectively. Storage of the frozen samples did not alter their PCP determination properties. The chromatographic methods adapted for chlorophenol determination in wastewater were suitable with relatively simple manipulation techniques. The obtained results were reproducible and allowed identification of intermediates formed during the PCP degradation process.     

A directional passive air sampler for monitoring polycyclic aromatic hydrocarbons (PAHs) in air mass

A passive air sampler was developed for collecting polycyclic aromatic hydrocarbons (PAHs) in air mass from various directions. The airflow velocity within the sampler was assessed for its responses to ambient wind speed and direction. The sampler was examined for trapped particles, evaluated quantitatively for influence of airflow velocity and temperature on PAH uptake, examined for PAH uptake kinetics, calibrated against active sampling, and finally tested in the field. The airflow volume passing the sampler was linearly proportional to ambient wind speed and sensitive to wind direction. The uptake rate for an individual PAH was a function of airflow velocity, temperature and the octanol-air partitioning coefficient of the PAH. For all PAHs with more than two rings, the passive sampler operated in a linear uptake phase for three weeks. Different PAH concentrations were obtained in air masses from different directions in the field test.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Reduction of SO2 emissions by ammonia gas during unstaged combustion

The reduction of SO₂ by the addition of ammonia gas has been studied in a 2 m high fluidized bed combustor having a 30 cm static bed height and a freeboard height of 170 cm. Ammonia gas was injected at 52 cm above the distributor where the temperature is ca. 700° C by an uncooled stainless steel tube injector. Experiments were carried out to investigate the effects of amminia gas injection on sulphur dioxide emissions at unstaged conditions of: (i) excess air level, (ii) NH₃:SO₂ molar ratio, (iii) fluidizing velocity and (iv) bed height.A maximum reduction of 75% in SO₂ emissions was found at 40% excess air, at an NH₃:SO₂ molar ratio of 5.4. The onset of SO₂ reduction occurred at an NH₃:SO₂ ratio of 1.5 However, the most effective ratio was found to be between 3 and 5. Fluidizing velocity and bed height were also found to have significant influence on SO₂ reduction.It is difficult to determine how the SO₂ reduction varied with operating conditions. When ammonia is added in the main combustor zone, the temperature is much higher than that required for the occurrence of sulphur dioxide-ammonia and sulphur trioxide-ammonia reactions. However, this paper points out the significance of ammonia addition in the reduction of sulphur dioxide.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.