Risk assessment of particulate matter by considering time-activity-pattern and major microenvironments for preschool children living in Seoul,South Korea

Environmental Science and Pollution Research - Preschool children aged 3–6 years are vulnerable to exposed to particulate matter (“PM10” and “PM2.5”). It is required...     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

Removal of organic matter and nitrogen from river water in a model floodplain

A significant improvement in river water quality cannot be expected unless nonpoint-source contaminants are treated in addition to the further treatment of point-source contaminants. If river water is sprayed over a floodplain, the consequent water filtration through the sediment profile can simultaneously remove organic matter and nitrogen in the water through aerobic and denitrifying reactions. This hypothesis was tested using lysimeters constructed from polyvinyl chloride (PVC) pipe (150 cm long, 15 cm in diameter) packed with loamy sand floodplain sediment. Water was applied to the top of the lysimeters at three different flow rates (48, 54, and 68 mm d(-1)). Concentrations of NO3 and dissolved oxygen (DO), chemical oxygen demand (COD), and redox potential (Eh) in the water were measured as functions of depth after the system reached steady states for both water flow and reactions. At the rate of 68.0 mm d(-1), a reducing condition for denitrification developed below the 5-cm depth due to the depletion of O2 by organic matter degradation in the surface oxidizing layer; Eh and DO were below 205 mV and 0.4 mg L(-1), respectively. At a depth of 70 cm, COD and NO3-N concentration decreased to 5.2 and 3.8 mg L(-1) from the respective influent concentrations of 17.1 and 6.2 mg L(-1). Most biodegradable organic matter was removed during flow and further removal of NO3 was limited by the lack of an electron donor (i.e., organic matter). These results indicate that the floodplain filtration technique has great promise for treatment of contaminated river water.     

Polychlorinated dibenzo-p-dioxins and dibenzofurans from food salt samples processed by heat treatment in Korea

Eight samples of processed food salt collected from five plants in Korea were analyzed for 2,3,7,8-substituted polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) using liquid-liquid extraction, clean-up procedures, and high resolution gas chromatography-high resolution mass spectrometry. The study included the analyses of two kinds of salt product sample: bamboo-salt and parched salt. The levels of toxic PCDD/Fs found in the salt product samples were extremely low: the results revealed TEQ levels ranging between the sub pg TEQ/g and sub fg TEQ/g. The differences in the TEQ values of toxic PCDD/F were observed between the salt product samples, which were treated with different frequency of baking using four different fuels (firewood, pine wood, pine resin, and indirect heating by gas) at temperatures from 300 to 2000 degrees C. In bamboo-salt samples, the concentrations and TEQ values of toxic PCDD/Fs ranged between 0.57-66 pg/g and 5.7x10(-5)-0.64 pg TEQ/g, respectively. PCDD/Fs levels in bamboo-salts baked by firewood were found to be higher than those baked by pinewood or pine resin. In parched salt samples, the concentrations and TEQ values of toxic PCDD/Fs ranged between 0.97-3.7 pg/g and 0.0097-0.017 pg TEQ/g, respectively. The data was discussed regarding the concentration and the distribution pattern of congeners.     

Development of the Spatial Rainfall Generator (SRGEN) for the Agricultural Policy/Environmental Extender Model

Accurate spatial representation of climatic patterns is often a challenge in modeling biophysical processes at the watershed scale, especially where the representation of a spatial gradient in rainfall is not sufficiently captured by the number of weather stations. The spatial rainfall generator (SRGEN) is developed as an extension of the “weather generator” (WXGEN), a component of the Agricultural Policy/Environmental eXtender (APEX) model. SRGEN generates spatially distributed daily rainfall using monthly weather statistics available at multiple locations in a watershed. The spatial rainfall generator as incorporated in APEX is tested on the Cowhouse watershed (1,178 km²) in central Texas. The watershed presented a significant spatial rainfall gradient of 2.9 mm/km in the lateral (north‐south) directions based on four rainfall gages. A comparative analysis between SRGEN and WXGEN indicates that SRGEN performs well (PBIAS = 2.40%). Good results were obtained from APEX for streamflow (NSE = 0.99, PBIAS = 8.34%) and NO₃‐N and soluble P loads (PBIAS ≈ 6.00% for each, respectively). However, APEX underpredicted sediment yield and organic N and P loads (PBIAS: 24.75‐27.90%) with SRGEN, although its uncertainty in output was lower than WXGEN results (PBIAS: ?13.02 to ?46.13%). The overall improvement achieved in rainfall generation by SRGEN is demonstrated to be effective in the improving model performance on flow and water quality output.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Distribution characteristics of polychlorinated biphenyls in crucian carp (Carassius auratus) from major rivers in Korea

Polychlorinated biphenyls (PCBs) levels in crucian carp were determined at 20 locations along four major river systems, several small-scale rivers and a wetland in Korea. Twenty-eight congeners, ranging from tri- to hepta-CBs were detected. A gas chromatograph with a mass selective detector was used to quantify the individual PCB congeners. The objectives of this study were to investigate the levels of contamination of PCBs in freshwater fish and to observe the pattern of their distribution. The sampling locations were chosen among 31 sampling sites that are currently used as environmental residue checkpoints by the Korean Ministry of Environment. Concentrations of individual congeners ranged from not detectable (n.d.) to 0.75 ng g(-1) on a wet weight basis. The total concentrations of PCBs at each site ranged from n.d. to 5.41 ng g(-1) of wet weight. The most heavily contaminated site was the Nakdong estuary located near the Shinpyung-Janglim factory district. The PCB 153 and 138 were the principal congeners and penta- and hexa-chlorinated biphenyls comprised the main congener groups.